diff --git a/Disease_Longevity_UKBB.R b/Disease_Longevity_UKBB.R new file mode 100644 index 0000000..0eebd0b --- /dev/null +++ b/Disease_Longevity_UKBB.R @@ -0,0 +1,179 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,Rmd,R:light +# text_representation: +# extension: .R +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.11.4 +# kernelspec: +# display_name: R 4.2 +# language: R +# name: ir42 +# --- + +# + [markdown] tags=[] +# ##### Disease risks and longevity scores on UKBB +# - + +# ## Preprocessing UKBB phenotypic data (jan 2021) + +# ##### Initialize and load required packages + +# + tags=[] +source(here::here("code/init.R")) +source(here::here("code/ukbb_preprocessing.R")) +source(here::here("code/models.R")) +options(tgutil.cache=FALSE) +# - + +# ### loading full dataset + +ukbb_data <- load_data() + +# ### Date of birth (dob) and death (dod) +# #### extracting dob / dod /race info from full dataset + +# + tags=[] +ukbb_demog <- get_demog_data(ukbb_data) %cache_df% here('output/ukbb_demog.csv') %>% as_tibble() +# - + +# Show the column data in ukbb_demog + +colnames(ukbb_demog) + +# ### Extracting diagnosis from all sources +# hospitalizations, hesin followups, self reported questionnaires, first occurrences and general practice clinic followup. + +ukbb_diagnosis <- get_diagnosis_data(ukbb_data, ukbb_demog ) %cache_df% here('output/ukbb_diagnosis.csv') %>% as_tibble() + +# ### Loading lab data + +ukbb_visits <- get_visit_data(ukbb_demog) %cache_df% here('output/ukbb_visits.csv') %>% as_tibble() +ukbb_labs <- get_labs_data(ukbb_data, ukbb_visits) %cache_df% here('output/ukbb_labs.csv') %>% as_tibble() %>% + mutate(sex=c('male', 'female')[sex]) %>% + inner_join(ln_ukbb_labs() %>% mutate(field=as.numeric(ukbb_code)) %>% select(field), by = "field") + + +# + [markdown] tags=[] +# ### Normalize labs +# - + +ukbb_labs$q <- ln_normalize_multi_ukbb(ukbb_labs %>% select(id, lab_code=field, age, sex, value)) + +# + tags=[] +head(ukbb_labs %>% select(field, description, age, sex, value, q)) +# - + +# ### Computing diseases onset + +# + tags=[] +cancer_codes <- build_cancer_icd9_icd10_dictionary(ukbb_data) +ukbb_diseases <- get_diseases(ukbb_diagnosis, cancer_codes) %cache_df% here('output/ukbb_diseases.csv') %>% as_tibble() +# - + +# ### Computing parent survival data + +parents <- get_parents_survival(ukbb_data) %cache_df% here('output/ukbb_parents.csv') %>% as_tibble() + +# ### Free up memory + +rm(ukbb_data) +gc() + +# + [markdown] tags=[] +# ## computing Longevity and Diseases models scores +# We will use the `mldpEHR` package to run infer scores from the models that were generated using the Clalit database. +# We start by loading the models. +# ### Load prediction models + +# + +models_dir <- 'data/models/' +predictors <- c('longevity', 'diabetes', 'ckd', 'copd', 'cvd', 'liver') %>% + purrr::set_names() %>% + purrr::map(function(m) + { + readr::read_rds(paste0(models_dir, m, '.rds')) %>% + purrr::imap( ~ c(.x, age=as.numeric(.y), feature_names=list(unique(unlist(purrr::map(.x$model, ~ .x$feature_names)))))) + }) + + +# - + +# ### gathering all potential model features +# Each predictor had its own features used in the model. +# As the overlap is extensive between the different predictors, we will gather all features and compute them once. +# + +potential_features <- unique(unlist(purrr::map(predictors, function(predictor) { + purrr::map(predictor, function(p) { + p$feature_names + }) +}))) + +# ### computing all features for all patients + +#building features to be used by all predictors (longevity, diseases) +ukbb_to_clalit <- tgutil::fread('data/ukbb_lab_field_to_clalit_lab.csv') +features <- purrr::map2_df(predictors[[1]], names(predictors[[1]]), function(model, age_model) { + message(age_model) + age_model <- as.numeric(age_model) + labs_features <- ukbb_labs %>% filter(ageage_model-5, !is.na(q)) %>% + left_join(ukbb_to_clalit %>% select(field, track), by="field") %>% + mutate(feature=paste0(track, '.quantiles_1_years_minus1095')) %>% + filter(feature %in% potential_features) %>% + group_by(id, feature) %>% summarize(value=mean(q), .groups="drop") + + disease_features <- ukbb_diseases %>% filter(age <= age_model) %>% + mutate(feature=paste0('WZMN.', cohort, '_minus43800_0')) %>% + filter(feature %in% potential_features) %>% + distinct(id, feature) %>% + mutate(value=1) + + ids <- unique(c(labs_features$id, disease_features$id)) + + #adding female/male/age info + features_tidy <- data.frame(id=ids, feature="age", value=age_model) %>% + bind_rows(ukbb_demog %>% filter(id %in% ids) %>% mutate(feature="male", value= sex==1) %>% select(id, feature, value)) %>% + bind_rows(labs_features) %>% + bind_rows(disease_features) + + #moving from tidy format + features <- features_tidy %>% pivot_wider(id_cols='id', names_from='feature') %>% + mutate(sex=2-male) + + #setting missing diesease values to 0 + disease_feature_names <- grep('WZMN.disease', colnames(features), value=TRUE) + features[,disease_feature_names][is.na(features[,disease_feature_names])] <- 0 + + #adding missing features + missing_features <- setdiff(potential_features, colnames(features)) + features[,missing_features] <- NA + + #requiring RBC + features <- features %>% filter(!is.na(lab.101.quantiles_1_years_minus1095)) + return(features) +}) %cache_df% here('output/ukbb_mldp_features.csv') %>% as_tibble() + + +# #### compute scores + +predictor_scores <- purrr::map2_df(predictors, names(predictors), ~ mldp_predict_multi_age(features, .x) %>% mutate(predictor=.y)) + +#note: setting disease score for patients that are already sick to NA +pop <- predictor_scores %>% filter(predictor == "longevity") %>% + select(id, age, sex, longevity=score, longevity_q=quantile) %>% + mutate(sex=factor(c('male', 'female')[sex], levels=c('male', 'female'))) %>% + left_join(predictor_scores %>% filter(predictor != "longevity") %>% + select(id, age, predictor, score) %>% + left_join(ukbb_diseases %>% select(id, disease_age=age, predictor=cohort)) %>% + mutate(score = ifelse(!is.na(disease_age) & disease_age < age, NA, score)) %>% + pivot_wider(id_cols=c("id", "age"), names_from="predictor", values_from="score") +) %cache_df% here('output/pop_scores.csv') %>% as_tibble() +head(pop %>% select(-id)) + + + + + diff --git a/Disease_Longevity_UKBB.ipynb b/Disease_Longevity_UKBB.ipynb new file mode 100644 index 0000000..2f0b323 --- /dev/null +++ b/Disease_Longevity_UKBB.ipynb @@ -0,0 +1,739 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "##### Disease risks and longevity scores on UKBB " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing UKBB phenotypic data (jan 2021)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Initialize and load required packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "source(here::here(\"code/init.R\"))\n", + "source(here::here(\"code/ukbb_preprocessing.R\"))\n", + "source(here::here(\"code/models.R\"))\n", + "options(tgutil.cache=FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### loading full dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22m`data_frame()` was deprecated in tibble 1.1.0.\n", + "\u001b[36mi\u001b[39m Please use `tibble()` instead.\n", + "\u001b[36mi\u001b[39m The deprecated feature was likely used in the \u001b[34mukbtools\u001b[39m package.\n", + " Please report the issue to the authors.\"\n" + ] + } + ], + "source": [ + "ukbb_data <- load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Date of birth (dob) and death (dod)\n", + "#### extracting dob / dod /race info from full dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ukbb_demog <- get_demog_data(ukbb_data) %cache_df% here('output/ukbb_demog.csv') %>% as_tibble()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show the column data in ukbb_demog" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'id'
  2. 'sex'
  3. 'month_of_birth'
  4. 'year_of_birth'
  5. 'date_of_death_0'
  6. 'date_of_death_1'
  7. 'race_0'
  8. 'race_1'
  9. 'race_2'
  10. 'date_0'
  11. 'date_1'
  12. 'date_2'
  13. 'date_3'
  14. 'dob'
  15. 'age_0'
  16. 'age_1'
  17. 'age_2'
  18. 'age_3'
  19. 'dod'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'id'\n", + "\\item 'sex'\n", + "\\item 'month\\_of\\_birth'\n", + "\\item 'year\\_of\\_birth'\n", + "\\item 'date\\_of\\_death\\_0'\n", + "\\item 'date\\_of\\_death\\_1'\n", + "\\item 'race\\_0'\n", + "\\item 'race\\_1'\n", + "\\item 'race\\_2'\n", + "\\item 'date\\_0'\n", + "\\item 'date\\_1'\n", + "\\item 'date\\_2'\n", + "\\item 'date\\_3'\n", + "\\item 'dob'\n", + "\\item 'age\\_0'\n", + "\\item 'age\\_1'\n", + "\\item 'age\\_2'\n", + "\\item 'age\\_3'\n", + "\\item 'dod'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'id'\n", + "2. 'sex'\n", + "3. 'month_of_birth'\n", + "4. 'year_of_birth'\n", + "5. 'date_of_death_0'\n", + "6. 'date_of_death_1'\n", + "7. 'race_0'\n", + "8. 'race_1'\n", + "9. 'race_2'\n", + "10. 'date_0'\n", + "11. 'date_1'\n", + "12. 'date_2'\n", + "13. 'date_3'\n", + "14. 'dob'\n", + "15. 'age_0'\n", + "16. 'age_1'\n", + "17. 'age_2'\n", + "18. 'age_3'\n", + "19. 'dod'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"id\" \"sex\" \"month_of_birth\" \"year_of_birth\" \n", + " [5] \"date_of_death_0\" \"date_of_death_1\" \"race_0\" \"race_1\" \n", + " [9] \"race_2\" \"date_0\" \"date_1\" \"date_2\" \n", + "[13] \"date_3\" \"dob\" \"age_0\" \"age_1\" \n", + "[17] \"age_2\" \"age_3\" \"dod\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colnames(ukbb_demog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extracting diagnosis from all sources\n", + "hospitalizations, hesin followups, self reported questionnaires, first occurrences and general practice clinic followup." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ukbb_diagnosis <- get_diagnosis_data(ukbb_data, ukbb_demog ) %cache_df% here('output/ukbb_diagnosis.csv') %>% as_tibble()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading lab data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22mThere was 1 warning in `mutate()`.\n", + "\u001b[1m\u001b[22m\u001b[36mi\u001b[39m In argument: `value = as.numeric(value)`.\n", + "Caused by warning:\n", + "\u001b[33m!\u001b[39m NAs introduced by coercion\"\n" + ] + } + ], + "source": [ + "ukbb_visits <- get_visit_data(ukbb_demog) %cache_df% here('output/ukbb_visits.csv') %>% as_tibble()\n", + "ukbb_labs <- get_labs_data(ukbb_data, ukbb_visits) %cache_df% here('output/ukbb_labs.csv') %>% as_tibble() %>% \n", + " mutate(sex=c('male', 'female')[sex]) %>% \n", + " inner_join(ln_ukbb_labs() %>% mutate(field=as.numeric(ukbb_code)) %>% select(field), by = \"field\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Normalize labs" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "> Downloading to a temporary directory \u001b[34m\u001b[34m/tmp/6935891.1.all.q/RtmplmKP6K\u001b[34m\u001b[39m.\n", + "\n", + "> Extracting data to \u001b[34m\u001b[34m/tmp/6935891.1.all.q/RtmplmKP6K\u001b[34m\u001b[39m.\n", + "\n", + "> Extracting data to \u001b[34m\u001b[34m/tmp/6935891.1.all.q/RtmplmKP6K\u001b[34m\u001b[39m.\n", + "\n", + "\u001b[32mv\u001b[39m Data downloaded successfully.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mumol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mUrine Creatinine\u001b[32m\u001b[39m. Using the formula `0.011312 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mg/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mAlbumin\u001b[32m\u001b[39m. Using the formula `0.1 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mumol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mDirect Bilirubin\u001b[32m\u001b[39m. Using the formula `0.058467 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mUrea\u001b[32m\u001b[39m. Using the formula `6.006 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mCalcium\u001b[32m\u001b[39m. Using the formula `4.0078 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mTotal Cholesterol\u001b[32m\u001b[39m. Using the formula `38.665 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mumol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mCreatinine\u001b[32m\u001b[39m. Using the formula `0.011312 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmg/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mCRP\u001b[32m\u001b[39m. Using the formula `0.1 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mGlucose\u001b[32m\u001b[39m. Using the formula `18.016 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/mol\u001b[32m\u001b[39m to \u001b[32m\u001b[32m%\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mHemoglobin A1c\u001b[32m\u001b[39m. Using the formula `x/10.929 + 2.15`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mHDL Cholesterol\u001b[32m\u001b[39m. Using the formula `38.665 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mLDL Cholesterol\u001b[32m\u001b[39m. Using the formula `38.665 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mPhosphorus\u001b[32m\u001b[39m. Using the formula `3.1 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mumol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mTotal Bilirubin\u001b[32m\u001b[39m. Using the formula `0.058467 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmg/mL\u001b[32m\u001b[39m to \u001b[32m\u001b[32mg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mTotal Protein\u001b[32m\u001b[39m. Using the formula `0.1 * x`.\n", + "\n", + "\u001b[36mi\u001b[39m Converting \u001b[32m\u001b[32mmmol/L\u001b[32m\u001b[39m to \u001b[32m\u001b[32mmg/dL\u001b[32m\u001b[39m for lab \u001b[32m\u001b[32mTriglycerides\u001b[32m\u001b[39m. Using the formula `88.5 * x`.\n", + "\n", + "Warning message in ln_normalize(values = values, age = age, sex = sex, units = ln_ukbb_units(lab_code), :\n", + "\"\u001b[1m\u001b[22mAge must be at most \u001b[34m80\u001b[39m for \u001b[32mUKBB\u001b[39m.\"\n", + "Warning message in ln_normalize(values = values, age = age, sex = sex, units = ln_ukbb_units(lab_code), :\n", + "\"\u001b[1m\u001b[22mAge must be at most \u001b[34m80\u001b[39m for \u001b[32mUKBB\u001b[39m.\"\n", + "Warning message in ln_normalize(values = values, age = age, sex = sex, units = ln_ukbb_units(lab_code), :\n", + "\"\u001b[1m\u001b[22mAge must be at most \u001b[34m80\u001b[39m for \u001b[32mUKBB\u001b[39m.\"\n" + ] + } + ], + "source": [ + "ukbb_labs$q <- ln_normalize_multi_ukbb(ukbb_labs %>% select(id, lab_code=field, age, sex, value))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 6
fielddescriptionagesexvalueq
<dbl><chr><dbl><chr><dbl><dbl>
30000White blood cell (leukocyte) count57.70959female 6.100.4178591
30000White blood cell (leukocyte) count46.40000female11.350.9801270
30000White blood cell (leukocyte) count57.98356male 10.120.9570285
30000White blood cell (leukocyte) count67.73425female 5.400.1806039
30000White blood cell (leukocyte) count41.46849female 8.440.8015539
30000White blood cell (leukocyte) count63.21918male 5.600.2272401
\n" + ], + "text/latex": [ + "A tibble: 6 × 6\n", + "\\begin{tabular}{llllll}\n", + " field & description & age & sex & value & q\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t 30000 & White blood cell (leukocyte) count & 57.70959 & female & 6.10 & 0.4178591\\\\\n", + "\t 30000 & White blood cell (leukocyte) count & 46.40000 & female & 11.35 & 0.9801270\\\\\n", + "\t 30000 & White blood cell (leukocyte) count & 57.98356 & male & 10.12 & 0.9570285\\\\\n", + "\t 30000 & White blood cell (leukocyte) count & 67.73425 & female & 5.40 & 0.1806039\\\\\n", + "\t 30000 & White blood cell (leukocyte) count & 41.46849 & female & 8.44 & 0.8015539\\\\\n", + "\t 30000 & White blood cell (leukocyte) count & 63.21918 & male & 5.60 & 0.2272401\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 6\n", + "\n", + "| field <dbl> | description <chr> | age <dbl> | sex <chr> | value <dbl> | q <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| 30000 | White blood cell (leukocyte) count | 57.70959 | female | 6.10 | 0.4178591 |\n", + "| 30000 | White blood cell (leukocyte) count | 46.40000 | female | 11.35 | 0.9801270 |\n", + "| 30000 | White blood cell (leukocyte) count | 57.98356 | male | 10.12 | 0.9570285 |\n", + "| 30000 | White blood cell (leukocyte) count | 67.73425 | female | 5.40 | 0.1806039 |\n", + "| 30000 | White blood cell (leukocyte) count | 41.46849 | female | 8.44 | 0.8015539 |\n", + "| 30000 | White blood cell (leukocyte) count | 63.21918 | male | 5.60 | 0.2272401 |\n", + "\n" + ], + "text/plain": [ + " field description age sex value q \n", + "1 30000 White blood cell (leukocyte) count 57.70959 female 6.10 0.4178591\n", + "2 30000 White blood cell (leukocyte) count 46.40000 female 11.35 0.9801270\n", + "3 30000 White blood cell (leukocyte) count 57.98356 male 10.12 0.9570285\n", + "4 30000 White blood cell (leukocyte) count 67.73425 female 5.40 0.1806039\n", + "5 30000 White blood cell (leukocyte) count 41.46849 female 8.44 0.8015539\n", + "6 30000 White blood cell (leukocyte) count 63.21918 male 5.60 0.2272401" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(ukbb_labs %>% select(field, description, age, sex, value, q))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing diseases onset" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cancer_codes <- build_cancer_icd9_icd10_dictionary(ukbb_data)\n", + "ukbb_diseases <- get_diseases(ukbb_diagnosis, cancer_codes) %cache_df% here('output/ukbb_diseases.csv') %>% as_tibble()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing parent survival data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "parents <- get_parents_survival(ukbb_data) %cache_df% here('output/ukbb_parents.csv') %>% as_tibble()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Free up memory" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 2 × 6 of type dbl
used(Mb)gc trigger(Mb)max used(Mb)
Ncells 3859814 206.2 62787469 3353.3 78484336 4191.6
Vcells3984934953040.3862383345665794.71077979182082243.3
\n" + ], + "text/latex": [ + "A matrix: 2 × 6 of type dbl\n", + "\\begin{tabular}{r|llllll}\n", + " & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n", + "\\hline\n", + "\tNcells & 3859814 & 206.2 & 62787469 & 3353.3 & 78484336 & 4191.6\\\\\n", + "\tVcells & 398493495 & 3040.3 & 8623833456 & 65794.7 & 10779791820 & 82243.3\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 2 × 6 of type dbl\n", + "\n", + "| | used | (Mb) | gc trigger | (Mb) | max used | (Mb) |\n", + "|---|---|---|---|---|---|---|\n", + "| Ncells | 3859814 | 206.2 | 62787469 | 3353.3 | 78484336 | 4191.6 |\n", + "| Vcells | 398493495 | 3040.3 | 8623833456 | 65794.7 | 10779791820 | 82243.3 |\n", + "\n" + ], + "text/plain": [ + " used (Mb) gc trigger (Mb) max used (Mb) \n", + "Ncells 3859814 206.2 62787469 3353.3 78484336 4191.6\n", + "Vcells 398493495 3040.3 8623833456 65794.7 10779791820 82243.3" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rm(ukbb_data)\n", + "gc()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## computing Longevity and Diseases models scores\n", + "We will use the `mldpEHR` package to run infer scores from the models that were generated using the Clalit database.\n", + "We start by loading the models.\n", + "### Load prediction models" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "models_dir <- 'data/models/'\n", + "predictors <- c('longevity', 'diabetes', 'ckd', 'copd', 'cvd', 'liver') %>% \n", + " purrr::set_names() %>% \n", + " purrr::map(function(m) \n", + " {\n", + " readr::read_rds(paste0(models_dir, m, '.rds')) %>% \n", + " purrr::imap( ~ c(.x, age=as.numeric(.y), feature_names=list(unique(unlist(purrr::map(.x$model, ~ .x$feature_names))))))\n", + " })\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### gathering all potential model features\n", + "Each predictor had its own features used in the model.\n", + "As the overlap is extensive between the different predictors, we will gather all features and compute them once.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "potential_features <- unique(unlist(purrr::map(predictors, function(predictor) {\n", + " purrr::map(predictor, function(p) {\n", + " p$feature_names\n", + " })\n", + "})))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### computing all features for all patients" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "80\n", + "\n", + "75\n", + "\n", + "70\n", + "\n", + "65\n", + "\n", + "60\n", + "\n", + "55\n", + "\n", + "50\n", + "\n", + "45\n", + "\n", + "40\n", + "\n", + "35\n", + "\n", + "30\n", + "\n" + ] + } + ], + "source": [ + "#building features to be used by all predictors (longevity, diseases)\n", + "ukbb_to_clalit <- tgutil::fread('data/ukbb_lab_field_to_clalit_lab.csv')\n", + "features <- purrr::map2_df(predictors[[1]], names(predictors[[1]]), function(model, age_model) {\n", + " message(age_model)\n", + " age_model <- as.numeric(age_model)\n", + " labs_features <- ukbb_labs %>% filter(ageage_model-5, !is.na(q)) %>% \n", + " left_join(ukbb_to_clalit %>% select(field, track), by=\"field\") %>% \n", + " mutate(feature=paste0(track, '.quantiles_1_years_minus1095')) %>% \n", + " filter(feature %in% potential_features) %>% \n", + " group_by(id, feature) %>% summarize(value=mean(q), .groups=\"drop\")\n", + "\n", + " disease_features <- ukbb_diseases %>% filter(age <= age_model) %>% \n", + " mutate(feature=paste0('WZMN.', cohort, '_minus43800_0')) %>% \n", + " filter(feature %in% potential_features) %>% \n", + " distinct(id, feature) %>% \n", + " mutate(value=1)\n", + "\n", + " ids <- unique(c(labs_features$id, disease_features$id))\n", + "\n", + " #adding female/male/age info\n", + " features_tidy <- data.frame(id=ids, feature=\"age\", value=age_model) %>% \n", + " bind_rows(ukbb_demog %>% filter(id %in% ids) %>% mutate(feature=\"male\", value= sex==1) %>% select(id, feature, value)) %>% \n", + " bind_rows(labs_features) %>% \n", + " bind_rows(disease_features)\n", + "\n", + " #moving from tidy format\n", + " features <- features_tidy %>% pivot_wider(id_cols='id', names_from='feature') %>% \n", + " mutate(sex=2-male)\n", + "\n", + " #setting missing diesease values to 0\n", + " disease_feature_names <- grep('WZMN.disease', colnames(features), value=TRUE)\n", + " features[,disease_feature_names][is.na(features[,disease_feature_names])] <- 0\n", + "\n", + " #adding missing features\n", + " missing_features <- setdiff(potential_features, colnames(features))\n", + " features[,missing_features] <- NA\n", + " \n", + " #requiring RBC\n", + " features <- features %>% filter(!is.na(lab.101.quantiles_1_years_minus1095))\n", + " return(features)\n", + "}) %cache_df% here('output/ukbb_mldp_features.csv') %>% as_tibble()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### compute scores" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "predictor_scores <- purrr::map2_df(predictors, names(predictors), ~ mldp_predict_multi_age(features, .x) %>% mutate(predictor=.y))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(id, predictor)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id, age)`\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 9
agesexlongevitylongevity_qdiabetesckdcopdcvdliver
<dbl><fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
45female0.96758820.19257920.088155240.041675530.027986280.20603820.022592036
45male 0.91192810.10603480.159096920.039192490.090642470.29449000.008487539
45female0.99698310.45743610.209795140.138553650.128116470.63706790.022256322
45male 0.99452550.39842700.077918440.059655310.043355170.21177180.033393441
45female0.98381660.26512810.080082880.022186810.065933830.12077450.009952694
45male 0.93772170.13451660.031964070.049305710.012635090.11572290.014305011
\n" + ], + "text/latex": [ + "A tibble: 6 × 9\n", + "\\begin{tabular}{lllllllll}\n", + " age & sex & longevity & longevity\\_q & diabetes & ckd & copd & cvd & liver\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t 45 & female & 0.9675882 & 0.1925792 & 0.08815524 & 0.04167553 & 0.02798628 & 0.2060382 & 0.022592036\\\\\n", + "\t 45 & male & 0.9119281 & 0.1060348 & 0.15909692 & 0.03919249 & 0.09064247 & 0.2944900 & 0.008487539\\\\\n", + "\t 45 & female & 0.9969831 & 0.4574361 & 0.20979514 & 0.13855365 & 0.12811647 & 0.6370679 & 0.022256322\\\\\n", + "\t 45 & male & 0.9945255 & 0.3984270 & 0.07791844 & 0.05965531 & 0.04335517 & 0.2117718 & 0.033393441\\\\\n", + "\t 45 & female & 0.9838166 & 0.2651281 & 0.08008288 & 0.02218681 & 0.06593383 & 0.1207745 & 0.009952694\\\\\n", + "\t 45 & male & 0.9377217 & 0.1345166 & 0.03196407 & 0.04930571 & 0.01263509 & 0.1157229 & 0.014305011\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 9\n", + "\n", + "| age <dbl> | sex <fct> | longevity <dbl> | longevity_q <dbl> | diabetes <dbl> | ckd <dbl> | copd <dbl> | cvd <dbl> | liver <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| 45 | female | 0.9675882 | 0.1925792 | 0.08815524 | 0.04167553 | 0.02798628 | 0.2060382 | 0.022592036 |\n", + "| 45 | male | 0.9119281 | 0.1060348 | 0.15909692 | 0.03919249 | 0.09064247 | 0.2944900 | 0.008487539 |\n", + "| 45 | female | 0.9969831 | 0.4574361 | 0.20979514 | 0.13855365 | 0.12811647 | 0.6370679 | 0.022256322 |\n", + "| 45 | male | 0.9945255 | 0.3984270 | 0.07791844 | 0.05965531 | 0.04335517 | 0.2117718 | 0.033393441 |\n", + "| 45 | female | 0.9838166 | 0.2651281 | 0.08008288 | 0.02218681 | 0.06593383 | 0.1207745 | 0.009952694 |\n", + "| 45 | male | 0.9377217 | 0.1345166 | 0.03196407 | 0.04930571 | 0.01263509 | 0.1157229 | 0.014305011 |\n", + "\n" + ], + "text/plain": [ + " age sex longevity longevity_q diabetes ckd copd cvd \n", + "1 45 female 0.9675882 0.1925792 0.08815524 0.04167553 0.02798628 0.2060382\n", + "2 45 male 0.9119281 0.1060348 0.15909692 0.03919249 0.09064247 0.2944900\n", + "3 45 female 0.9969831 0.4574361 0.20979514 0.13855365 0.12811647 0.6370679\n", + "4 45 male 0.9945255 0.3984270 0.07791844 0.05965531 0.04335517 0.2117718\n", + "5 45 female 0.9838166 0.2651281 0.08008288 0.02218681 0.06593383 0.1207745\n", + "6 45 male 0.9377217 0.1345166 0.03196407 0.04930571 0.01263509 0.1157229\n", + " liver \n", + "1 0.022592036\n", + "2 0.008487539\n", + "3 0.022256322\n", + "4 0.033393441\n", + "5 0.009952694\n", + "6 0.014305011" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#note: setting disease score for patients that are already sick to NA\n", + "pop <- predictor_scores %>% filter(predictor == \"longevity\") %>% \n", + " select(id, age, sex, longevity=score, longevity_q=quantile) %>% \n", + " mutate(sex=factor(c('male', 'female')[sex], levels=c('male', 'female'))) %>% \n", + " left_join(predictor_scores %>% filter(predictor != \"longevity\") %>% \n", + " select(id, age, predictor, score) %>% \n", + " left_join(ukbb_diseases %>% select(id, disease_age=age, predictor=cohort)) %>% \n", + " mutate(score = ifelse(!is.na(disease_age) & disease_age < age, NA, score)) %>% \n", + " pivot_wider(id_cols=c(\"id\", \"age\"), names_from=\"predictor\", values_from=\"score\")\n", + ") %cache_df% here('output/pop_scores.csv') %>% as_tibble()\n", + "head(pop %>% select(-id))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,Rmd,R:light" + }, + "kernelspec": { + "display_name": "R 4.2", + "language": "R", + "name": "ir42" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.2.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000..4263ed0 --- /dev/null +++ b/Readme.md @@ -0,0 +1,15 @@ +# Longitudinal machine learning uncouples healthy ageing factors from chronic disease risks + +Files in this directory support Mendelson-Cohen et al. work on longitudinal machine learning for modeling healthy ageing. + +The code is supplied both as Jupyter notebooks and R scripts. + +The following notebooks are available: + +- build_longevity_models.ipynb +- Disease_Longevity_UKBB.ipynb +- score_clustering_UKBB.ipynb +- score_validation_UKBB.ipynb +- run_GWAS.ipynb +- snps.ipynb + diff --git a/build_longevity_models.R b/build_longevity_models.R new file mode 100644 index 0000000..0a15482 --- /dev/null +++ b/build_longevity_models.R @@ -0,0 +1,141 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,Rmd,R:light +# text_representation: +# extension: .R +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.11.4 +# kernelspec: +# display_name: R 4.0.3 +# language: R +# name: ir +# --- + +# + [markdown] tags=[] +# # Building age-dependent longevity models +# In this notebook we will describe the method for training a probablistic model for reaching the age of 85. +# The code below exemplifies the mldpEHR package which is used for training age-dependent multivariate prediciton models for longevity and disease. +# The examples will use datasets available in the mldpEHR.data package. +# +# +# +# - + +# ## Setting up simulation data +# The procedures demonstrated in this notebook will used a simulated dataset of patients stored mldpEHR.data dataset. +# The longevity simulated data consists of the following: +# * longevity.patients - a list of data frames, one for each age, containing the entire population of patients +# * longevity.features - a list of data frames, one for each age, containing the features to be used for training the prediction models. + +# + +# installing mldpEHR, and mdlrEHR.data +#remotes::install_github("tanaylab/mldpEHR") +#remotes::install_github("tanaylab/mldpEHR.data") +# - + +library(tidyverse) +library(mldpEHR) +library(mldpEHR.data) +names(longevity.patients) +head(longevity.patients[["80"]]) + +# ### defining model parameters + +SURVIVAL_YEARS <- 5 +STEP <- 5 +MAX_MISSING_PER_FEATURE <- 0.8 +FOLDS <- 5 + +# + [markdown] tags=[] +# ## Building longevity models +# For each age, we will build a classification model for patient survival to age>= 85. +# As our followup time is limited, we will use older age model score to define the target classification for the younger age model, basically stitching these models together. +# The data we need to provide consists of the entire popultation at each given age along with their sex, age, age at death and potential followup time (time until the end of the database). +# +# +# +# - + +longevity <- mldpEHR.mortality_multi_age_predictors(longevity.patients, longevity.features, step=5, nfolds=5, required_conditions='has_cbc') + +# + [markdown] tags=[] +# ### Looking at feature significance +# +# +# - + +features_sig <- purrr::map(longevity, ~ mldpEHR.prediction_model_features(.x)$summary %>% arrange(desc(mean_abs_shap))) +head(features_sig[[1]]) + +N_PATIENTS <- 10000 +shap_features_80 <- mldpEHR.prediction_model_features(longevity[["80"]])$shap_by_patient %>% + filter(feature %in% head(features_sig[["80"]] %>% pull(feature))) %>% + group_by(feature) %>% + sample_n(N_PATIENTS) %>% + ungroup %>% + mutate(feature=factor(feature, levels=head(features_sig[["80"]] %>% pull(feature)))) +options(repr.plot.width=14, repr.plot.height=2.5) +ggplot(shap_features_80, aes(x=value, y=shap)) + geom_point(size=0.01, alpha=0.3) + facet_wrap(~feature, nrow=1, scales="free_y") + theme_bw() + +# ## Computing Markovian probability model +# + +longevity_markov <- mldpEHR.mortality_markov(longevity, SURVIVAL_YEARS, STEP, seq(0, 1, by=0.1), required_conditions=glue::glue("time >= as.Date('2005-01-01') & time < as.Date('2016-01-01')")) + + +longevity_prob <- purrr::map2_df(longevity_markov, names(longevity_markov), ~ as_tibble(.x$model[[1]], rownames='sbin') %>% mutate(sex='male', age=.y) %>% bind_rows(as_tibble(.x$model[[2]], rownames='sbin') %>% mutate(sex='female', age=.y))) +options(repr.plot.width=14, repr.plot.height=2.5) +ggplot(longevity_prob %>% mutate(sbin=factor(sbin, levels=c(1:10, "death", "no_score"))), + aes(x=sbin, y=death, colour=factor(sex))) + geom_point() + facet_grid(.~age) + theme_bw() + + +# # Build a disease model for diabetes +# similar to longevity, will used simulated diabetes data, found in mldpEHR.data dataset: +# * diabetes.patients - a list of data frames, one for each age, containing the entire population of patients +# * diabetes.features - a list of data frames, one for each age, containing the features to be used for training the prediction models. +# + +diabetes <- mldpEHR.disease_multi_age_predictors(diabetes.patients, diabetes.features, step=5, nfolds=5, required_conditions='has_cbc') + +# ##Looking at feature significance + +features_sig <- purrr::map2(diabetes, names(diabetes), ~ mldpEHR.prediction_model_features(.x)$summary %>% mutate(age=.y) %>% + arrange(desc(mean_abs_shap))) +head(features_sig[[1]]) + + +N_PATIENTS <- 10000 +shap_features_50 <- mldpEHR.prediction_model_features(diabetes[["50"]])$shap_by_patient %>% + filter(feature %in% head(features_sig[["50"]] %>% pull(feature))) %>% + group_by(feature) %>% + sample_n(N_PATIENTS) %>% + ungroup %>% + mutate(feature=factor(feature, levels=head(features_sig[["50"]] %>% pull(feature)))) +options(repr.plot.width=14, repr.plot.height=2.5) +ggplot(shap_features_50, aes(x=value, y=shap)) + geom_point(size=0.01, alpha=0.3) + facet_wrap(~feature, nrow=1, scales="free_y") + theme_bw() + +# + +## Computing Markovian probability model +# - + +diabetes_markov <- mldpEHR.disease_markov(diabetes, 5, 5, seq(0, 1, by=0.1), required_conditions=glue::glue("time >= as.Date('2005-01-01') & time < as.Date('2016-01-01')")) + +diabetes_prob <- purrr::map2_df(diabetes_markov, names(diabetes_markov), ~ + as_tibble(.x$model[[1]], rownames='sbin') %>% mutate(sex='male', age=.y) %>% + bind_rows( + as_tibble(.x$model[[2]], rownames='sbin') %>% mutate(sex='female', age=.y)) + ) %>% + mutate(sbin=factor(sbin, levels=c(1:10, "disease", "disease_death", "death", "no_score")), + total_disease=disease+disease_death) +options(repr.plot.width=14, repr.plot.height=2.5) +ggplot(diabetes_prob %>% filter(as.numeric(sbin) <= 10), aes(x=sbin, y=total_disease, colour=factor(sex))) + geom_point() + facet_grid(.~age) + theme_bw() + +shap <- mldpEHR.prediction_model_features(diabetes[["50"]]) + +head(shap$shap_by_fold) + +nrow(shap$shap_by_fold) + + diff --git a/build_longevity_models.ipynb b/build_longevity_models.ipynb new file mode 100644 index 0000000..da42a12 --- /dev/null +++ b/build_longevity_models.ipynb @@ -0,0 +1,751 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5beccdb1-65fc-415f-9014-0cc4f9830f82", + "metadata": { + "tags": [] + }, + "source": [ + "# Building age-dependent longevity models\n", + "In this notebook we will describe the method for training a probablistic model for reaching the age of 85. \n", + "The code below exemplifies the mldpEHR package which is used for training age-dependent multivariate prediciton models for longevity and disease.\n", + "The examples will use datasets available in the mldpEHR.data package.\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "e45c4917-ea65-4c42-b189-027c737dca97", + "metadata": {}, + "source": [ + "## Setting up simulation data\n", + "The procedures demonstrated in this notebook will used a simulated dataset of patients stored mldpEHR.data dataset. \n", + "The longevity simulated data consists of the following:\n", + "* longevity.patients - a list of data frames, one for each age, containing the entire population of patients\n", + "* longevity.features - a list of data frames, one for each age, containing the features to be used for training the prediction models." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ff20503c-a387-4b7a-b308-0431a934e9c0", + "metadata": {}, + "outputs": [], + "source": [ + "# installing mldpEHR, and mdlrEHR.data\n", + "#remotes::install_github(\"tanaylab/mldpEHR\")\n", + "#remotes::install_github(\"tanaylab/mldpEHR.data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4209e563-d93d-41cb-bc59-640bade29ef7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.3.1 --\n", + "\n", + "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.4.0 \u001b[32mv\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.5 \n", + "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8 \u001b[32mv\u001b[39m \u001b[34mdplyr \u001b[39m 1.0.10\n", + "\u001b[32mv\u001b[39m \u001b[34mtidyr \u001b[39m 1.2.1 \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.1 \n", + "\u001b[32mv\u001b[39m \u001b[34mreadr \u001b[39m 2.1.3 \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1 \n", + "\n", + "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
  1. '80'
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  3. '70'
  4. '65'
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  6. '55'
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\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item '80'\n", + "\\item '75'\n", + "\\item '70'\n", + "\\item '65'\n", + "\\item '60'\n", + "\\item '55'\n", + "\\item '50'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. '80'\n", + "2. '75'\n", + "3. '70'\n", + "4. '65'\n", + "5. '60'\n", + "6. '55'\n", + "7. '50'\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] \"80\" \"75\" \"70\" \"65\" \"60\" \"55\" \"50\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 7
idtimeagesexdeathhas_cbcfollowup
<dbl><date><dbl><dbl><dbl><lgl><dbl>
19674672020-11-09801NATRUE0.1452055
29674682020-02-28801NATRUE0.8438356
39674962020-04-23801NATRUE0.6931507
49674992020-05-13801NATRUE0.6383562
59675042020-04-30801NATRUE0.6739726
69675132020-04-03801NATRUE0.7479452
\n" + ], + "text/latex": [ + "A data.frame: 6 × 7\n", + "\\begin{tabular}{r|lllllll}\n", + " & id & time & age & sex & death & has\\_cbc & followup\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 967467 & 2020-11-09 & 80 & 1 & NA & TRUE & 0.1452055\\\\\n", + "\t2 & 967468 & 2020-02-28 & 80 & 1 & NA & TRUE & 0.8438356\\\\\n", + "\t3 & 967496 & 2020-04-23 & 80 & 1 & NA & TRUE & 0.6931507\\\\\n", + "\t4 & 967499 & 2020-05-13 & 80 & 1 & NA & TRUE & 0.6383562\\\\\n", + "\t5 & 967504 & 2020-04-30 & 80 & 1 & NA & TRUE & 0.6739726\\\\\n", + "\t6 & 967513 & 2020-04-03 & 80 & 1 & NA & TRUE & 0.7479452\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 7\n", + "\n", + "| | id <dbl> | time <date> | age <dbl> | sex <dbl> | death <dbl> | has_cbc <lgl> | followup <dbl> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| 1 | 967467 | 2020-11-09 | 80 | 1 | NA | TRUE | 0.1452055 |\n", + "| 2 | 967468 | 2020-02-28 | 80 | 1 | NA | TRUE | 0.8438356 |\n", + "| 3 | 967496 | 2020-04-23 | 80 | 1 | NA | TRUE | 0.6931507 |\n", + "| 4 | 967499 | 2020-05-13 | 80 | 1 | NA | TRUE | 0.6383562 |\n", + "| 5 | 967504 | 2020-04-30 | 80 | 1 | NA | TRUE | 0.6739726 |\n", + "| 6 | 967513 | 2020-04-03 | 80 | 1 | NA | TRUE | 0.7479452 |\n", + "\n" + ], + "text/plain": [ + " id time age sex death has_cbc followup \n", + "1 967467 2020-11-09 80 1 NA TRUE 0.1452055\n", + "2 967468 2020-02-28 80 1 NA TRUE 0.8438356\n", + "3 967496 2020-04-23 80 1 NA TRUE 0.6931507\n", + "4 967499 2020-05-13 80 1 NA TRUE 0.6383562\n", + "5 967504 2020-04-30 80 1 NA TRUE 0.6739726\n", + "6 967513 2020-04-03 80 1 NA TRUE 0.7479452" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "library(tidyverse)\n", + "library(mldpEHR)\n", + "library(mldpEHR.data)\n", + "names(longevity.patients)\n", + "head(longevity.patients[[\"80\"]])" + ] + }, + { + "cell_type": "markdown", + "id": "f0c049d7-da67-4c95-93f9-e53a26be0422", + "metadata": {}, + "source": [ + "### defining model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "77408ea6-1448-4858-9d96-dd300dc4ecd1", + "metadata": {}, + "outputs": [], + "source": [ + "SURVIVAL_YEARS <- 5\n", + "STEP <- 5\n", + "MAX_MISSING_PER_FEATURE <- 0.8\n", + "FOLDS <- 5" + ] + }, + { + "cell_type": "markdown", + "id": "561c6981-c719-42cb-80a1-6d41118b1f9a", + "metadata": { + "tags": [] + }, + "source": [ + "## Building longevity models\n", + "For each age, we will build a classification model for patient survival to age>= 85. \n", + "As our followup time is limited, we will use older age model score to define the target classification for the younger age model, basically stitching these models together.\n", + "The data we need to provide consists of the entire popultation at each given age along with their sex, age, age at death and potential followup time (time until the end of the database).\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b557d61c-ec91-4bd4-a780-c0262144e477", + "metadata": {}, + "outputs": [], + "source": [ + "longevity <- mldpEHR.mortality_multi_age_predictors(longevity.patients, longevity.features, step=5, nfolds=5, required_conditions='has_cbc')" + ] + }, + { + "cell_type": "markdown", + "id": "5d7518a6-a2de-4fbc-9a77-4b60becb12d2", + "metadata": { + "tags": [] + }, + "source": [ + "### Looking at feature significance\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fb455dbe-61ec-4d57-b2eb-4db6d2bfcddb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 2
featuremean_abs_shap
<chr><dbl>
ALBUMINALBUMIN0.2641493
RDWRDW 0.1334548
UREAUREA 0.1217083
sexsex 0.1212199
ALTALT 0.1089143
CPKCPK 0.1055787
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & feature & mean\\_abs\\_shap\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\tALBUMIN & ALBUMIN & 0.2641493\\\\\n", + "\tRDW & RDW & 0.1334548\\\\\n", + "\tUREA & UREA & 0.1217083\\\\\n", + "\tsex & sex & 0.1212199\\\\\n", + "\tALT & ALT & 0.1089143\\\\\n", + "\tCPK & CPK & 0.1055787\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | feature <chr> | mean_abs_shap <dbl> |\n", + "|---|---|---|\n", + "| ALBUMIN | ALBUMIN | 0.2641493 |\n", + "| RDW | RDW | 0.1334548 |\n", + "| UREA | UREA | 0.1217083 |\n", + "| sex | sex | 0.1212199 |\n", + "| ALT | ALT | 0.1089143 |\n", + "| CPK | CPK | 0.1055787 |\n", + "\n" + ], + "text/plain": [ + " feature mean_abs_shap\n", + "ALBUMIN ALBUMIN 0.2641493 \n", + "RDW RDW 0.1334548 \n", + "UREA UREA 0.1217083 \n", + "sex sex 0.1212199 \n", + "ALT ALT 0.1089143 \n", + "CPK CPK 0.1055787 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "features_sig <- purrr::map(longevity, ~ mldpEHR.prediction_model_features(.x)$summary %>% arrange(desc(mean_abs_shap)))\n", + "head(features_sig[[1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "54fb0887-bf32-478a-8fd9-18ad1b46d202", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22mRemoved 5112 rows containing missing values (`geom_point()`).\"\n" + ] + }, + { + "data": { + "image/png": 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e8jgh5PsizLY4Ecx/FVGno86XQ6eZz6mffhLGazecAjy+N80F6RlpaWlpbm7VoA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\nlUK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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 150, + "width": 840 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "N_PATIENTS <- 10000\n", + "shap_features_80 <- mldpEHR.prediction_model_features(longevity[[\"80\"]])$shap_by_patient %>% \n", + " filter(feature %in% head(features_sig[[\"80\"]] %>% pull(feature))) %>% \n", + " group_by(feature) %>% \n", + " sample_n(N_PATIENTS) %>% \n", + " ungroup %>% \n", + " mutate(feature=factor(feature, levels=head(features_sig[[\"80\"]] %>% pull(feature))))\n", + "options(repr.plot.width=14, repr.plot.height=2.5)\n", + "ggplot(shap_features_80, aes(x=value, y=shap)) + geom_point(size=0.01, alpha=0.3) + facet_wrap(~feature, nrow=1, scales=\"free_y\") + theme_bw()" + ] + }, + { + "cell_type": "markdown", + "id": "debff637-78d5-4b07-b540-deabb74276b8", + "metadata": {}, + "source": [ + "## Computing Markovian probability model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dec6d3ac-4641-4bdd-9af3-eb9717338c2b", + "metadata": {}, + "outputs": [], + "source": [ + "longevity_markov <- mldpEHR.mortality_markov(longevity, SURVIVAL_YEARS, STEP, seq(0, 1, by=0.1), required_conditions=glue::glue(\"time >= as.Date('2005-01-01') & time < as.Date('2016-01-01')\"))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1bfd617d-f1e3-4e38-a31a-780caa18f2bc", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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7X5+eYrTb47Ta6Ghv04tx3bL4/oOE4vOHq+M4vlcBZomF4JJ69pDj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KLykNWOCYlR0I+mpTySmizmQTasWNXYVOHq4rRPgADA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XIo6HEVkQnxDgQP7APtJQ12UTJoAQt3Gd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\ntHDFlEajEQCio6OJRtNWISKEzhk+EosQQgghh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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 150, + "width": 840 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "longevity_prob <- purrr::map2_df(longevity_markov, names(longevity_markov), ~ as_tibble(.x$model[[1]], rownames='sbin') %>% mutate(sex='male', age=.y) %>% bind_rows(as_tibble(.x$model[[2]], rownames='sbin') %>% mutate(sex='female', age=.y)))\n", + "options(repr.plot.width=14, repr.plot.height=2.5)\n", + "ggplot(longevity_prob %>% mutate(sbin=factor(sbin, levels=c(1:10, \"death\", \"no_score\"))), \n", + " aes(x=sbin, y=death, colour=factor(sex))) + geom_point() + facet_grid(.~age) + theme_bw()" + ] + }, + { + "cell_type": "markdown", + "id": "b04610bc-86f4-4a07-b99a-bec7e4aa55c9", + "metadata": {}, + "source": [ + "# Build a disease model for diabetes\n", + "similar to longevity, will used simulated diabetes data, found in mldpEHR.data dataset:\n", + "* diabetes.patients - a list of data frames, one for each age, containing the entire population of patients\n", + "* diabetes.features - a list of data frames, one for each age, containing the features to be used for training the prediction models.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e299859f-ebda-427e-9ab7-c84a5a23e0cd", + "metadata": {}, + "outputs": [], + "source": [ + "diabetes <- mldpEHR.disease_multi_age_predictors(diabetes.patients, diabetes.features, step=5, nfolds=5, required_conditions='has_cbc')" + ] + }, + { + "cell_type": "markdown", + "id": "baf2a7fa-bb81-4950-98bf-9b6500ee4cfc", + "metadata": {}, + "source": [ + "##Looking at feature significance" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "98f6f12b-3f0a-4e06-b6de-ce2162b10115", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 3
featuremean_abs_shapage
<chr><dbl><chr>
GLUCOSEGLUCOSE 0.9654818480
HA1CHA1C 0.1598187780
NON-HDL_CHOLESTEROLNON-HDL_CHOLESTEROL0.0931728980
GLOBULINGLOBULIN 0.0912864780
BMIBMI 0.0888193780
CREATININECREATININE 0.0801290180
\n" + ], + "text/latex": [ + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & feature & mean\\_abs\\_shap & age\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\tGLUCOSE & GLUCOSE & 0.96548184 & 80\\\\\n", + "\tHA1C & HA1C & 0.15981877 & 80\\\\\n", + "\tNON-HDL\\_CHOLESTEROL & NON-HDL\\_CHOLESTEROL & 0.09317289 & 80\\\\\n", + "\tGLOBULIN & GLOBULIN & 0.09128647 & 80\\\\\n", + "\tBMI & BMI & 0.08881937 & 80\\\\\n", + "\tCREATININE & CREATININE & 0.08012901 & 80\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 3\n", + "\n", + "| | feature <chr> | mean_abs_shap <dbl> | age <chr> |\n", + "|---|---|---|---|\n", + "| GLUCOSE | GLUCOSE | 0.96548184 | 80 |\n", + "| HA1C | HA1C | 0.15981877 | 80 |\n", + "| NON-HDL_CHOLESTEROL | NON-HDL_CHOLESTEROL | 0.09317289 | 80 |\n", + "| GLOBULIN | GLOBULIN | 0.09128647 | 80 |\n", + "| BMI | BMI | 0.08881937 | 80 |\n", + "| CREATININE | CREATININE | 0.08012901 | 80 |\n", + "\n" + ], + "text/plain": [ + " feature mean_abs_shap age\n", + "GLUCOSE GLUCOSE 0.96548184 80 \n", + "HA1C HA1C 0.15981877 80 \n", + "NON-HDL_CHOLESTEROL NON-HDL_CHOLESTEROL 0.09317289 80 \n", + "GLOBULIN GLOBULIN 0.09128647 80 \n", + "BMI BMI 0.08881937 80 \n", + "CREATININE CREATININE 0.08012901 80 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "features_sig <- purrr::map2(diabetes, names(diabetes), ~ mldpEHR.prediction_model_features(.x)$summary %>% mutate(age=.y) %>% \n", + " arrange(desc(mean_abs_shap)))\n", + "head(features_sig[[1]])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "48b4d363-03fb-41f8-b38b-17c978fdd56b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22mRemoved 4653 rows containing missing values (`geom_point()`).\"\n" + ] + }, + { + "data": { + "image/png": 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+Pp7e3lR+ddLgzYITQkPp+vsbFRIpGUlpaObRr+HTt2bNu2jf+RpmmWZd1ud3/b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\ne7/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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 150, + "width": 840 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "N_PATIENTS <- 10000\n", + "shap_features_50 <- mldpEHR.prediction_model_features(diabetes[[\"50\"]])$shap_by_patient %>% \n", + " filter(feature %in% head(features_sig[[\"50\"]] %>% pull(feature))) %>% \n", + " group_by(feature) %>% \n", + " sample_n(N_PATIENTS) %>% \n", + " ungroup %>% \n", + " mutate(feature=factor(feature, levels=head(features_sig[[\"50\"]] %>% pull(feature))))\n", + "options(repr.plot.width=14, repr.plot.height=2.5)\n", + "ggplot(shap_features_50, aes(x=value, y=shap)) + geom_point(size=0.01, alpha=0.3) + facet_wrap(~feature, nrow=1, scales=\"free_y\") + theme_bw()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "663fb008-48e0-4346-b852-5bb598eee78f", + "metadata": {}, + "outputs": [], + "source": [ + "## Computing Markovian probability model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8581e6f5-573c-4697-98be-d11034e403b4", + "metadata": {}, + "outputs": [], + "source": [ + "diabetes_markov <- mldpEHR.disease_markov(diabetes, 5, 5, seq(0, 1, by=0.1), required_conditions=glue::glue(\"time >= as.Date('2005-01-01') & time < as.Date('2016-01-01')\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f244250f-c835-491b-9279-be154a47161b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2EJFcLv/ggw9KSkrGjh37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\nSIz+qHA0t4mXd93bB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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 150, + "width": 840 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "diabetes_prob <- purrr::map2_df(diabetes_markov, names(diabetes_markov), ~ \n", + " as_tibble(.x$model[[1]], rownames='sbin') %>% mutate(sex='male', age=.y) %>% \n", + " bind_rows(\n", + " as_tibble(.x$model[[2]], rownames='sbin') %>% mutate(sex='female', age=.y))\n", + " ) %>% \n", + " mutate(sbin=factor(sbin, levels=c(1:10, \"disease\", \"disease_death\", \"death\", \"no_score\")),\n", + " total_disease=disease+disease_death)\n", + "options(repr.plot.width=14, repr.plot.height=2.5)\n", + "ggplot(diabetes_prob %>% filter(as.numeric(sbin) <= 10), aes(x=sbin, y=total_disease, colour=factor(sex))) + geom_point() + facet_grid(.~age) + theme_bw()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2a3a5f40-d16c-4d8a-a156-abb5831e10be", + "metadata": {}, + "outputs": [], + "source": [ + "shap <- mldpEHR.prediction_model_features(diabetes[[\"50\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8f1bad18-2cad-464d-8e76-9c1cb86119b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 71
foldidsexWBCRBCHGBHCTPLTMCVMCH⋯FOLIC ACIDGLOBULINNON-HDL_CHOLESTEROLFERRITINTSHT4 FREEBMIBP SYSTOLICBP DIASTOLICBIAS
<fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1110.2523467-0.02487233 0.06180592 0.06173039 0.001842315-0.008975940-0.0274398495 0.0013276532⋯ 0.002132911 0.03006584 0.004185709 0.04380948-0.019279752 0.006839546-1.323644 0.13072027 0.035614271-0.6536388
2210.2772871-0.05033518 0.04164337 0.03500469-0.005127913-0.011284989-0.0213765651-0.0261953361⋯-0.003201672 0.03382161 0.005486948 0.03869656-0.018252173 0.013939313-1.355202 0.12744655 0.030342715-0.6511232
3310.2001412-0.01208779 0.05610102 0.03706371 0.006366817-0.003154173-0.0262789093-0.0045678602⋯-0.002863556 0.04699297 0.023726847 0.03983758-0.012896349 0.013132541-1.484894 0.09294418-0.007187250-0.6534403
4510.2484475-0.03974956 0.05934383 0.03510357-0.002895946-0.016887080-0.0361955725 0.0004802126⋯-0.001769888 0.02224203-0.009963578 0.03938646 0.002927497 0.012042018-1.220531 0.18412912 0.025309263-0.6487635
5120.2709689 0.14031480-0.11093173-0.02351592-0.003534155 0.009864728-0.0081585525 0.0030479573⋯-0.013154480-0.04122223-0.001037552-0.09508873-0.041288048-0.025651010-1.301617-0.23382096 0.010248748-0.6536388
6220.2869865 0.08529719-0.08143875-0.03048725-0.005592946 0.004758276-0.0007563605-0.0196335968⋯ 0.011931754-0.05018238 0.005825588-0.07780322-0.093282156-0.043640122-1.261442-0.29313573-0.003206228-0.6511232
\n" + ], + "text/latex": [ + "A data.frame: 6 × 71\n", + "\\begin{tabular}{r|lllllllllllllllllllll}\n", + " & fold & id & sex & WBC & RBC & HGB & HCT & PLT & MCV & MCH & ⋯ & FOLIC ACID & GLOBULIN & NON-HDL\\_CHOLESTEROL & FERRITIN & TSH & T4 FREE & BMI & BP SYSTOLIC & BP DIASTOLIC & BIAS\\\\\n", + " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & 1 & 0.2523467 & -0.02487233 & 0.06180592 & 0.06173039 & 0.001842315 & -0.008975940 & -0.0274398495 & 0.0013276532 & ⋯ & 0.002132911 & 0.03006584 & 0.004185709 & 0.04380948 & -0.019279752 & 0.006839546 & -1.323644 & 0.13072027 & 0.035614271 & -0.6536388\\\\\n", + "\t2 & 2 & 1 & 0.2772871 & -0.05033518 & 0.04164337 & 0.03500469 & -0.005127913 & -0.011284989 & -0.0213765651 & -0.0261953361 & ⋯ & -0.003201672 & 0.03382161 & 0.005486948 & 0.03869656 & -0.018252173 & 0.013939313 & -1.355202 & 0.12744655 & 0.030342715 & -0.6511232\\\\\n", + "\t3 & 3 & 1 & 0.2001412 & -0.01208779 & 0.05610102 & 0.03706371 & 0.006366817 & -0.003154173 & -0.0262789093 & -0.0045678602 & ⋯ & -0.002863556 & 0.04699297 & 0.023726847 & 0.03983758 & -0.012896349 & 0.013132541 & -1.484894 & 0.09294418 & -0.007187250 & -0.6534403\\\\\n", + "\t4 & 5 & 1 & 0.2484475 & -0.03974956 & 0.05934383 & 0.03510357 & -0.002895946 & -0.016887080 & -0.0361955725 & 0.0004802126 & ⋯ & -0.001769888 & 0.02224203 & -0.009963578 & 0.03938646 & 0.002927497 & 0.012042018 & -1.220531 & 0.18412912 & 0.025309263 & -0.6487635\\\\\n", + "\t5 & 1 & 2 & 0.2709689 & 0.14031480 & -0.11093173 & -0.02351592 & -0.003534155 & 0.009864728 & -0.0081585525 & 0.0030479573 & ⋯ & -0.013154480 & -0.04122223 & -0.001037552 & -0.09508873 & -0.041288048 & -0.025651010 & -1.301617 & -0.23382096 & 0.010248748 & -0.6536388\\\\\n", + "\t6 & 2 & 2 & 0.2869865 & 0.08529719 & -0.08143875 & -0.03048725 & -0.005592946 & 0.004758276 & -0.0007563605 & -0.0196335968 & ⋯ & 0.011931754 & -0.05018238 & 0.005825588 & -0.07780322 & -0.093282156 & -0.043640122 & -1.261442 & -0.29313573 & -0.003206228 & -0.6511232\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 71\n", + "\n", + "| | fold <fct> | id <dbl> | sex <dbl> | WBC <dbl> | RBC <dbl> | HGB <dbl> | HCT <dbl> | PLT <dbl> | MCV <dbl> | MCH <dbl> | ⋯ ⋯ | FOLIC ACID <dbl> | GLOBULIN <dbl> | NON-HDL_CHOLESTEROL <dbl> | FERRITIN <dbl> | TSH <dbl> | T4 FREE <dbl> | BMI <dbl> | BP SYSTOLIC <dbl> | BP DIASTOLIC <dbl> | BIAS <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 1 | 1 | 0.2523467 | -0.02487233 | 0.06180592 | 0.06173039 | 0.001842315 | -0.008975940 | -0.0274398495 | 0.0013276532 | ⋯ | 0.002132911 | 0.03006584 | 0.004185709 | 0.04380948 | -0.019279752 | 0.006839546 | -1.323644 | 0.13072027 | 0.035614271 | -0.6536388 |\n", + "| 2 | 2 | 1 | 0.2772871 | -0.05033518 | 0.04164337 | 0.03500469 | -0.005127913 | -0.011284989 | -0.0213765651 | -0.0261953361 | ⋯ | -0.003201672 | 0.03382161 | 0.005486948 | 0.03869656 | -0.018252173 | 0.013939313 | -1.355202 | 0.12744655 | 0.030342715 | -0.6511232 |\n", + "| 3 | 3 | 1 | 0.2001412 | -0.01208779 | 0.05610102 | 0.03706371 | 0.006366817 | -0.003154173 | -0.0262789093 | -0.0045678602 | ⋯ | -0.002863556 | 0.04699297 | 0.023726847 | 0.03983758 | -0.012896349 | 0.013132541 | -1.484894 | 0.09294418 | -0.007187250 | -0.6534403 |\n", + "| 4 | 5 | 1 | 0.2484475 | -0.03974956 | 0.05934383 | 0.03510357 | -0.002895946 | -0.016887080 | -0.0361955725 | 0.0004802126 | ⋯ | -0.001769888 | 0.02224203 | -0.009963578 | 0.03938646 | 0.002927497 | 0.012042018 | -1.220531 | 0.18412912 | 0.025309263 | -0.6487635 |\n", + "| 5 | 1 | 2 | 0.2709689 | 0.14031480 | -0.11093173 | -0.02351592 | -0.003534155 | 0.009864728 | -0.0081585525 | 0.0030479573 | ⋯ | -0.013154480 | -0.04122223 | -0.001037552 | -0.09508873 | -0.041288048 | -0.025651010 | -1.301617 | -0.23382096 | 0.010248748 | -0.6536388 |\n", + "| 6 | 2 | 2 | 0.2869865 | 0.08529719 | -0.08143875 | -0.03048725 | -0.005592946 | 0.004758276 | -0.0007563605 | -0.0196335968 | ⋯ | 0.011931754 | -0.05018238 | 0.005825588 | -0.07780322 | -0.093282156 | -0.043640122 | -1.261442 | -0.29313573 | -0.003206228 | -0.6511232 |\n", + "\n" + ], + "text/plain": [ + " fold id sex WBC RBC HGB HCT \n", + "1 1 1 0.2523467 -0.02487233 0.06180592 0.06173039 0.001842315\n", + "2 2 1 0.2772871 -0.05033518 0.04164337 0.03500469 -0.005127913\n", + "3 3 1 0.2001412 -0.01208779 0.05610102 0.03706371 0.006366817\n", + "4 5 1 0.2484475 -0.03974956 0.05934383 0.03510357 -0.002895946\n", + "5 1 2 0.2709689 0.14031480 -0.11093173 -0.02351592 -0.003534155\n", + "6 2 2 0.2869865 0.08529719 -0.08143875 -0.03048725 -0.005592946\n", + " PLT MCV MCH FOLIC ACID GLOBULIN \n", + "1 -0.008975940 -0.0274398495 0.0013276532 0.002132911 0.03006584\n", + "2 -0.011284989 -0.0213765651 -0.0261953361 -0.003201672 0.03382161\n", + "3 -0.003154173 -0.0262789093 -0.0045678602 -0.002863556 0.04699297\n", + "4 -0.016887080 -0.0361955725 0.0004802126 -0.001769888 0.02224203\n", + "5 0.009864728 -0.0081585525 0.0030479573 -0.013154480 -0.04122223\n", + "6 0.004758276 -0.0007563605 -0.0196335968 0.011931754 -0.05018238\n", + " NON-HDL_CHOLESTEROL FERRITIN TSH T4 FREE BMI \n", + "1 0.004185709 0.04380948 -0.019279752 0.006839546 -1.323644\n", + "2 0.005486948 0.03869656 -0.018252173 0.013939313 -1.355202\n", + "3 0.023726847 0.03983758 -0.012896349 0.013132541 -1.484894\n", + "4 -0.009963578 0.03938646 0.002927497 0.012042018 -1.220531\n", + "5 -0.001037552 -0.09508873 -0.041288048 -0.025651010 -1.301617\n", + "6 0.005825588 -0.07780322 -0.093282156 -0.043640122 -1.261442\n", + " BP SYSTOLIC BP DIASTOLIC BIAS \n", + "1 0.13072027 0.035614271 -0.6536388\n", + "2 0.12744655 0.030342715 -0.6511232\n", + "3 0.09294418 -0.007187250 -0.6534403\n", + "4 0.18412912 0.025309263 -0.6487635\n", + "5 -0.23382096 0.010248748 -0.6536388\n", + "6 -0.29313573 -0.003206228 -0.6511232" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(shap$shap_by_fold)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3b7f1b04-730e-4fe5-ab26-10a5d82927ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1104992" + ], + "text/latex": [ + "1104992" + ], + "text/markdown": [ + "1104992" + ], + "text/plain": [ + "[1] 1104992" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nrow(shap$shap_by_fold)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2886d853-f19b-48a2-bd9f-de8ff8a0417d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,Rmd,R:light" + }, + "kernelspec": { + "display_name": "R 4.0.3", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.0.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/.gitignore b/code/.gitignore new file mode 100644 index 0000000..f73806e --- /dev/null +++ b/code/.gitignore @@ -0,0 +1 @@ +.ipynb_checkpoints/ \ No newline at end of file diff --git a/code/finemapping.R b/code/finemapping.R new file mode 100755 index 0000000..f6fd606 --- /dev/null +++ b/code/finemapping.R @@ -0,0 +1,123 @@ +source('code/init.R') +source('code/gwas.R') +pvals <- get_gwas_pvals() %cache_df% here("output/all_pvals.tsv") +snps <- pvals %>% filter(longevity_disease_covar_pval <= log10(5e-8)) + +#remove chromosome X due to a lack of LD matrices in that chromosome. + +snps %>% + filter(chrom != "chrX") %>% + mutate(CHR = gsub("chr", "", chrom), Z = longevity_disease_covar_beta / longevity_disease_covar_stderr) %>% + select(CHR, BP = start, A1 = allele1, A2 = allele2, Z) %>% + fwrite(here("output/longevity_snps_sumstats.txt"), sep = " ", quote = FALSE) + + +## PolyFun + +# https://github.com/omerwe/polyfun + +# On the terminal: + +ml load ldstore finemap +cd utils/polyfun +conda activate polyfun + +python extract_snpvar.py --sumstats /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_sumstats.txt --out /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_with_var.gz --allow-missing +zcat /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_with_var.gz | wc -l +zcat /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_with_var.gz | head + +The column SNPVAR contains the per-SNP heritabilities, which are proportional to prior causal probabilities. These per-SNP heritabilities can be used directly as prior causal probabilities in fine-mapping. + +Note that a few SNPs did not exist in the metavar file: + +CHR BP A_eff A2 Z +1 21890632 G A -11.9912504858966 +1 21890587 G A -9.04853518903304 + +Get number of patients (from GWAS code): 328542 + +Run fine-mapping: + +Note that the following commands should be done from the terminal: + +Create jobs per region: + +mkdir /home/nettam/projects/emr/ukbiobank/notebook/output/polyfun + +python create_finemapper_jobs.py \ + --sumstats /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_with_var.gz \ + --n 322679 \ + --method susie \ + --max-num-causal 5 \ + --out-prefix /home/nettam/projects/emr/ukbiobank/notebook/output/polyfun/longevity_snps \ + --jobs-file /home/nettam/projects/emr/ukbiobank/notebook/temp_scripts/polyfun_all_jobs.txt +Change the links of the LD matrices to the ones already downloaded: + +sed -s 's/https:\/\/data.broadinstitute.org\/alkesgroup\/UKBB_LD\//\/net\/mraid14\/export\/tgdata\/users\/aviezerl\/proj\/ukbb\/data\/ukbb_LD\/broad\//' /home/nettam/projects/emr/ukbiobank/notebook/temp_scripts/polyfun_all_jobs.txt > /home/nettam/projects/emr/ukbiobank/notebook/temp_scripts/polyfun_all_jobs_fixed.txt + + +Run the scripts: + +cat /home/nettam/projects/emr/ukbiobank/notebook/temp_scripts/polyfun_all_jobs_fixed.txt | parallel -j 30 --gnu "{}" + +SNPs from the chromosome 6 region are discarded by default, this is due to (from https://github.com/omerwe/polyfun/wiki/7.-FAQ): + +"chr6_31000001_34000001 is the MHC region which has a very complex LD structure, and therefore it's results should be taken with a grain of salt." + +We will run it explicitly: + +python3 /net/mraid14/export/tgdata/users/aviezerl/proj/ukbb/utils/polyfun/finemapper.py --chr 6 --start 31000001 --end 34000001 --out /home/nettam/projects/emr/ukbiobank/notebook/output/polyfun/longevity_snps.chr6.31000001_34000001.gz --ld /net/mraid14/export/tgdata/users/aviezerl/proj/ukbb/data/ukbb_LD/broad/chr6_31000001_34000001 --method susie --sumstats /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_with_var.gz --n 322679 --memory 1 --max-num-causal 5 +Aggregate the results: + +python aggregate_finemapper_results.py \ + --out-prefix /home/nettam/projects/emr/ukbiobank/notebook/output/polyfun/longevity_snps \ + --sumstats /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_with_var.gz \ + --regions-file /net/mraid14/export/tgdata/users/aviezerl/proj/ukbb/utils/polyfun/ukb_regions_with_MHC.tsv.gz \ + --out /home/nettam/projects/emr/ukbiobank/notebook/output/finemap/polyfun_agg_longevity.txt.gz +Extract annotations for top SNPS: + +python extract_annotations.py \ + --pips /home/nettam/projects/emr/ukbiobank/notebook/output/finemap/polyfun_agg_longevity.txt.gz \ + --annot baselineLF2.2.UKB/baselineLF2.2.UKB.1.annot.parquet \ + --pip-cutoff 0.95 \ + --allow-missin \ + --out /home/nettam/projects/emr/ukbiobank/notebook/output/finemap/polyfun_agg_longevity_annot.txt.gz + +Collect the results: + +finemap_res <- tgutil::fread(here("/home/nettam/projects/emr/ukbiobank/notebook/output/finemap/polyfun_agg_longevity.txt.gz")) %>% + separate(CREDIBLE_SET, c("chrom_locus", "start_locus", "end_locus", "CREDIBLE_SET")) %>% + unite(chrom_locus:end_locus, col = "locus") %>% + as_tibble() +nrow(finemap_res) +nrow(snps) + +snps_annot <- snps %>% + left_join(finemap_res %>% mutate(chrom = paste0("chr", CHR), start = BP, allele1 = A1, allele2 = A2)) %cache_df% + here("output/longevity_snps_finemapped.tsv") +snps_annot %>% + filter(is.na(PIP)) %>% + count(chrom) +snps_annot %>% + filter(is.na(PIP)) %>% + nrow() + +#Choose at least one locus from each ~1M region + high PIP loci: +regs <- snps_annot %>% + gintervals.centers() %>% + gintervals.expand(5e5) %>% + gintervals.canonic() +snps_top_annot <- snps_annot %>% + add_count(locus, name = "n_locus") %>% + gintervals.neighbors1(regs) %>% + select(-dist) %>% + unite("region", chrom1, start1, end1) %>% + add_count(region, name = "n_region") %>% + arrange(region, desc(PIP)) %>% + group_by(region) %>% + mutate(i = 1:n()) %>% + ungroup() %>% + filter(i == 1 | PIP >= 0.5) %>% + select(-i) + +#Compute number of significant SNPs 1MB around each top snp: \ No newline at end of file diff --git a/code/gwas.R b/code/gwas.R new file mode 100755 index 0000000..5233e5c --- /dev/null +++ b/code/gwas.R @@ -0,0 +1,351 @@ +get_imputed_genes <- function() { + message("Loading preprocessed genetic data (imputed genotypes)") + # genes <- bigsnpr::snp_attach(here("output/ukbb_white.british.rds")) + # genes$genotypes <- readr::read_rds(here("output/ukbb_white.british.impute.rds")) + imputed_fam <- fread(here("data/ukbb_imp_fam.tsv")) %>% as_tibble() + genes <- snp_attach(here("data/ukbb_imp.rds")) + genes$fam <- imputed_fam %>% rename(sample.ID = id) + return(genes) +} + +get_ukbb_pca <- function() { + cli_alert_info("Loading precomputed PCA") + pca <- fread(here("output/ukbb_pca_dataframe.tsv")) + return(pca) +} + +#' Run GWAS on white british from UKBB +#' +#' Runs GWAS on white-british population from UKBB, adding the PCA as covariates +#' +#' @param covar additional covariates to add to the PCA +#' +#' @inheritDotParams gwiser::run_gwas +run_gwas_white_british <- function(score_df, covar = NULL, genes = get_imputed_genes(), pca = get_ukbb_pca(), ...) { + pca <- pca %>% + select(id, starts_with("PC")) + + if (!is.null(covar)) { + pca <- pca %>% left_join(covar) + } + + wb_patients <- fread(here("output/ukbb_white.british_patients.csv"))$id + score_df <- score_df %>% + filter(id %in% wb_patients) + + run_gwas(score_df, genes = genes, covar = pca, ...) +} + +get_all_gwas_files <- function(binary = FALSE) { + if (binary) { + pattern <- "^gwas_.+_(binary|good_health).*\\.rds$" + } else { + pattern <- "^gwas_.+\\.rds$" + } + res <- tibble(fn = list.files(here("output"), pattern = pattern)) %>% + mutate(name = gsub("\\.rds$", "", fn)) %>% + mutate(catalog_fn = gsub("\\.rds$", ".tsv", fn)) %>% + mutate(name = gsub("^gwas_", "", name)) %>% + mutate(color = chameleon::distinct_colors(nrow(.))$name) + return(res) +} + +merge_gwas_results <- function(binary = FALSE) { + df <- get_all_gwas_files(binary) + + parse_gwas <- function(fn, name) { + gw <- readr::read_rds(fs::path(here("output"), fn)) + gw[[glue("pval.{name}")]] <- gw$pval + gw[[glue("estim.{name}")]] <- gw$estim + gw %>% + select(chrom:end, marker.ID, allele1, allele2, ends_with(name)) + } + + merged <- map2(df$fn, df$name, parse_gwas) %>% reduce(left_join) + + summed_catalog <- merged %>% + select(marker.ID) %>% + annotate_gwas_catalog() %>% + data.table::as.data.table() %>% + group_by(marker.ID) %>% + summarise_at(vars(MAPPED_TRAIT, MAPPED_GENE), ~ paste(unique(.x), collapse = ",")) %>% + ungroup() %>% + as_tibble() %>% + mutate_at(vars(MAPPED_TRAIT, MAPPED_GENE), ~ ifelse(.x == "NA", NA, .x)) + + merged <- merged %>% + annotate_genome() %>% + left_join(summed_catalog) + + return(merged) +} + +create_bgi_symlinks <- function(data_dir) { + bgi_files <- glue("{data_dir}/ukb_imp_chr{chrom}_v3.bgen.bgi", chrom = c(1:22, "X")) + bgen_files <- glue("{data_dir}/ukb22828_c{chrom}_b0_v3.bgen", chrom = c(1:22, "X")) + tibble(bgi = bgi_files, bgen = bgen_files) %>% + mutate(new_bgi = gsub(".bgen$", ".bgen.bgi", bgen)) %>% + mutate(cmd = glue("ln -s {basename(bgi)} {basename(new_bgi)}")) %>% + pull(cmd) %>% + walk(system) +} + +get_all_snps <- function(data_dir, files = glue("{data_dir}/ukb22828_c{chrom}_b0_v3.bgen", chrom = 1:22)) { + stopifnot(all(file.exists(files))) + cli::cli_alert_info("Loading all SNPS") + all_snps <- plyr::ldply(files, bigsnpr::snp_readBGI, .parallel = TRUE) + return(all_snps) +} + +filter_chrX_patients <- function(data_dir = here("netta/data_45934")){ + patients <- fread(here(glue("{data_dir}/ukb22828_c1_b0_v3_s487205.sample"))) %>% + slice(-1) %>% + select(id = ID_1, sex) %>% + as_tibble() + + patients_chrX <- fread(here(glue("{data_dir}/ukb22828_cX_b0_v3_s486554.sample"))) %>% + slice(-1) %>% + select(id = ID_1, sex) %>% + as_tibble() + + patients_chrX %>% + filter(id %!in% patients$id) %>% + select(id) %>% + fwrite(here(glue("data/chrX_exclude_patients.csv")), col.names = FALSE) + + # bgen_file <- glue("{data_dir}/ukb22828_cX_b0_v3.bgen") + # cmd <- glue("{qctool} -g {bgen_file} -s {chrX_samples_file} -incl-samples {all_samples_file} -og {out_file}", + # qctool = "/home/aviezerl/modules/CO7/qctool/2.2.0/qctool_v2.2.0", + # bgen_File = bgen_file, + # chrX_samples_file = here(glue("{data_dir}/ukb22828_cX_b0_v3_s486554.sample")), + # all_samples_file = here("data/ukbb_imp_patients.csv"), + # out_file = here(glue("data/ukbb_imp_chrX_filtered.bgen")) + # ) + # cat(cmd) + # system(cmd) +} + +import_imputed_data <- function(data_dir, variants, backing_file = here("data/ukbb_imp")) { + bgen_files <- glue("{data_dir}/ukb22828_c{chrom}_b0_v3.bgen", chrom = c("X", 1:22)) + stopifnot(all(file.exists(bgen_files))) + bgi_files <- paste0(bgen_files, ".bgi") + stopifnot(all(file.exists(bgi_files))) + + cli_alert_info("Generating the list of SNPs") + list_snp_id <- plyr::llply(bgi_files, function(.x) { + cli::cli_li(.x) + df <- bigsnpr::snp_readBGI(.x) + df <- df %>% filter(rsid %in% variants$rsid) + with(df, paste(chromosome, position, allele1, allele2, sep = "_")) + }, .parallel = TRUE) + num_snps <- sum(map_int(list_snp_id, length)) + cli::cli_alert_info("Loaded {.field {scales::comma(num_snps)}} SNPS") + + patients <- fread(here(glue("{data_dir}/ukb22828_c1_b0_v3_s487205.sample"))) %>% + slice(-1) %>% + select(id = ID_1, sex) %>% + mutate(i = 1:n()) %>% + select(id, i) %>% + as_tibble() %>% + deframe() + + patients_chrX <- fread(here(glue("{data_dir}/ukb22828_cX_b0_v3_s486554.sample"))) %>% + slice(-1) %>% + select(id = ID_1, sex) %>% + as_tibble() + + new_patients <- patients[as.character(patients_chrX$id)] + + inds <- new_patients + names(inds) <- NULL + + ind_row <- c(list(1:nrow(patients_chrX)), map(1:22, ~inds)) + + cli_alert_info("Creating FBM matrix") + devtools::load_all("/home/aviezerl/src/bigsnpr") + rds <- bigsnpr::snp_readBGEN( + bgenfiles = bgen_files, + list_snp_id = list_snp_id, + backingfile = backing_file, + ind_row = ind_row, + # ind_row = ind_row[[2]], + ncores = 40 + ) + + cli::cli_alert_success("Loaded BGEN successfully") + return(rds) +} + +get_gwas_pvals <- function() { + longevity_snps <- fread(here("output/gwas_longevity_age_sex_covar_extended.tsv")) %>% as_tibble() + #longevity_low_disease_snps <- fread(here("output/gwas_longevity_age_sex_covar_no_disease_extended.tsv")) %>% as_tibble() + longevity_disease_covar_snps <- fread(here("output/gwas_longevity_age_sex_disease_covar_extended.tsv")) %>% as_tibble() + pvals <- longevity_snps %>% + select(chrom, start, end, marker.ID, rsid, allele1, allele2, freq, + longevity_pval = pval, longevity_beta = estim, longevity_score = score, longevity_stderr=std.err, + ) %>% + distinct(rsid, allele1, allele2, .keep_all = TRUE) %>% + # left_join(longevity_low_disease_snps %>% + # select(rsid, allele1, allele2, + # longevity_low_disease_pval = pval, + # longevity_low_disesae_beta = estim, + # longevity_low_disease_score = score + # ) %>% + # distinct(rsid, allele1, allele2, .keep_all = TRUE)) %>% + left_join(longevity_disease_covar_snps %>% + select(rsid, allele1, allele2, + longevity_disease_covar_pval = pval, + longevity_disease_covar_beta = estim, + longevity_disease_covar_score = score, + longevity_disease_covar_stderr = std.err, + ) %>% + distinct(rsid, allele1, allele2, .keep_all = TRUE)) + diseases <- c("diabetes", "ckd", "copd", "cvd", "liver") + for (d in diseases) { + pvals <- pvals %>% left_join( + fread(here(paste0("output/gwas_", d, "_age_sex_covar_extended.tsv"))) %>% + as_tibble() %>% + select( + rsid, allele1, allele2, + !!quo_name(paste0(d, "_pval")) := pval, + !!quo_name(paste0(d, "_beta")) := estim, + !!quo_name(paste0(d, "_score")) := score + ) %>% distinct() + ) + } + return(pvals) +} + + +get_neal_gwas_pvals <- function(labs = c("alk_phos", "glucose", "neut", "bmi", "creatinine", "ha1c", "alt", "hdl", "rdw", "mcv"), + lpval_thresh = -5) { + all_lab_files <- list.files(here("data/neale"), pattern = "_irnt", full.names = TRUE) + lab_files <- purrr::map_chr(labs, ~ grep(paste0("\\.", .x, "\\."), all_lab_files, , value = TRUE)) + neal_snps <- plyr::adply(tibble(lab = labs, fn = lab_files), 1, function(.x) { + tgutil::fread(cmd = paste0("cat ", .x$fn, " | awk '{ if ($11 != \"NaN\" && log($11)/log(10) < ", lpval_thresh, ") { print $1,$8,$11} }' ")) %>% + distinct(variant, .keep_all = TRUE) %>% + tidyr::separate(variant, into = c("chrom", "start", "allele1", "allele2"), sep = ":", remove = FALSE) %>% + mutate(chrom = paste0("chr", chrom), start = as.numeric(start)) + }, .parallel = TRUE) %>% + as_tibble() %>% + select(-fn) + + neal_snps <- neal_snps %>% + mutate(pval = ifelse(pval == 0, -400, log10(pval))) + + return(neal_snps) +} + + + + +run_gwas_cox <- function(genes, surv_df, null_fn, num_bins = nrow(genes$map) / 1e3, max.jobs = 400) { + cli_alert("Generating Cox NULL model") + cox_null <- SPACox::SPACox_Null_Model( + formula = survival::Surv(time, status) ~ age + gender + PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10 + PC11 + PC12 + PC13 + PC14 + PC15 + PC16 + PC17 + PC18 + PC19 + PC20 + ckd + copd + diabetes + liver + ncvd, + data = surv_df, + pIDs = surv_df$id, + gIDs = surv_df$id + ) %cache_rds% null_fn + + cli_alert("Running Cox GWAS") + all_snps <- genes$map %>% + mutate(bin = ntile(n = num_bins)) + + cmds <- glue("gwas_cox(genes, all_snps$marker.ID[all_snps$bin == {1:num_bins}], surv_df$id, cox_null)") + cli_alert("Running {.val {length(cmds)}} jobs") + res <- gcluster.run2(command_list = cmds, max.jobs = max.jobs, threads = 1, memory = 10, opt.flags = " -l 'h=!n7*' ", jobs_title = "gwas_cox") + res1 <- keep(res, ~ class(.x$retv) == "data.frame") + res_df <- map_dfr(res1, ~ as.data.frame(.x$retv)) + res_df <- res_df %>% + tibble::rownames_to_column("marker.ID") %>% + as_tibble() + return(res_df) +} + +gwas_cox <- function(genes, snps, ids, cox_null) { + devtools::load_all("/home/aviezerl/src/gwiser") + gene_tab <- gwiser::get_snp_matrix(genes, snps, sample.ID = ids) + res <- SPACox::SPACox( + cox_null, + gene_tab + ) + return(as.data.frame(res)) +} + +run_gwas_cox_both_parents <- function(genes, surv_df, null_fn, num_bins = nrow(genes$map) / 1e3, max.jobs = 400, use_sge = TRUE) { + cli_alert("Generating Cox NULL model") + cox_null <- SPACox::SPACox_Null_Model( + formula = survival::Surv(time, status) ~ age + gender + PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10 + PC11 + PC12 + PC13 + PC14 + PC15 + PC16 + PC17 + PC18 + PC19 + PC20 + ckd + copd + diabetes + liver + ncvd + parent, + data = surv_df, + pIDs = surv_df$id_both, + gIDs = surv_df$id_both + ) %cache_rds% null_fn + cli_alert("Running Cox GWAS") + all_snps <- genes$map %>% + mutate(bin = ntile(n = num_bins)) + + cmds <- glue("gwas_cox_both_parents(genes, all_snps$marker.ID[all_snps$bin == {1:num_bins}], surv_df, cox_null)") + cli_alert("Running {.val {length(cmds)}} jobs") + if (use_sge){ + res <- gcluster.run2(command_list = cmds, max.jobs = max.jobs, threads = 1, memory = 10, opt.flags = " -l 'h=!n7*' ", jobs_title = "gwas_cox") + res1 <- keep(res, ~ class(.x$retv) == "data.frame") + res_df <- map_dfr(res1, ~ as.data.frame(.x$retv)) + } else { + res <- purrr::map(cmds, ~ eval(parse(text = .x))) + res_df <- purrr::map_dfr(res, ~ as.data.frame(.x)) + } + + res_df <- res_df %>% + tibble::rownames_to_column("marker.ID") %>% + as_tibble() +} + +gwas_cox_both_parents <- function(genes, snps, surv_df, cox_null) { + devtools::load_all("/home/aviezerl/src/gwiser") + gene_tab_all <- gwiser::get_snp_matrix(genes, snps, sample.ID = unique(surv_df$id)) + gene_tab_father <- gene_tab_all[as.character(surv_df$id[surv_df$parent == "father"]), , drop = FALSE] + rownames(gene_tab_father) <- paste0(rownames(gene_tab_father), ".father") + gene_tab_mother <- gene_tab_all[as.character(surv_df$id[surv_df$parent == "mother"]), , drop = FALSE] + rownames(gene_tab_mother) <- paste0(rownames(gene_tab_mother), ".mother") + gene_tab <- rbind(gene_tab_father, gene_tab_mother) + gene_tab <- gene_tab[as.character(surv_df$id_both), , drop = FALSE] + res <- SPACox::SPACox( + cox_null, + gene_tab + ) + return(as.data.frame(res)) +} + +get_parents_survival_by_genotype <- function(snp, genes, parents, snp_field = "marker.ID", selected_patients = NULL) { + snp_idx <- which(snp == as.data.frame(genes$map)[, snp_field]) + snp_maf <- as.numeric(genes$map[snp_idx, "freq"]) + + d <- data.frame(id = genes$fam$sample.ID, geno = genes$genotypes[, snp_idx]) %>% + inner_join(parents) %>% + mutate(geno_red = ifelse(geno < 0.5, 0, ifelse(geno < 1.5, 1, 2)), maf = snp_maf) + + if (!is.null(selected_patients)) { + d <- d %>% filter(id %in% selected_patients) + } + dd <- d %>% + select(id, parent_follow_time = mfollow_time, parent_dead = mdead, geno_red) %>% + mutate(parent = "mother") %>% + bind_rows(d %>% select(id, parent_follow_time = ffollow_time, parent_dead = fdead, geno_red) %>% mutate(parent = "father")) %>% + filter(parent_follow_time > 0, !is.infinite(parent_follow_time)) + # checking to see if monozygote minor should be removed + genotype_count <- dd %>% + count(geno_red) %>% + arrange(n) + if (nrow(genotype_count) > 2) { + dd <- dd %>% filter(geno_red != genotype_count$geno_red[1]) + } + fparent <- survminer::surv_fit(survival::Surv(parent_follow_time, parent_dead) ~ geno_red, data = dd) + fstats <- survminer::surv_summary(fparent, d) + + return(list( + median = survminer::surv_median(fparent), + surv85 = fstats %>% filter(time == 85) %>% select(surv, upper, lower, geno_red), + pval = survminer::surv_pvalue(fparent)$pval + )) +} \ No newline at end of file diff --git a/code/init.R b/code/init.R new file mode 100755 index 0000000..d69ad8e --- /dev/null +++ b/code/init.R @@ -0,0 +1,22 @@ +suppressPackageStartupMessages(library(tidyverse)) +suppressPackageStartupMessages(library(here)) +suppressPackageStartupMessages(library(lubridate)) +suppressPackageStartupMessages(library(tgutil)) +suppressPackageStartupMessages(library(tgstat)) +suppressPackageStartupMessages(library(glue)) +suppressPackageStartupMessages(library(misha)) +suppressPackageStartupMessages(library(cli)) +# suppressPackageStartupMessages(library(patchwork)) +suppressPackageStartupMessages(library(misha.ext)) +suppressPackageStartupMessages(library(bigsnpr)) +suppressPackageStartupMessages(library(bigreadr)) +suppressPackageStartupMessages(library(labNorm)) +suppressPackageStartupMessages(library(mldpEHR)) + +doMC::registerDoMC(40) +gset_genome("hg19") +options(gmax.data.size = 1e9) + +CENSOR_DATE <- data.table::fread(here::here('data/censor_date.csv')) +DEATH_CENSOR_DATE <- lubridate::dmy(CENSOR_DATE %>% filter(type == "death") %>% pull(date)) +DISEASE_CENSOR_DATE <- lubridate::dmy(CENSOR_DATE %>% filter(type == "dx") %>% pull(date)) \ No newline at end of file diff --git a/code/mldpEHR_ukbb.R b/code/mldpEHR_ukbb.R new file mode 100644 index 0000000..64e8474 --- /dev/null +++ b/code/mldpEHR_ukbb.R @@ -0,0 +1,88 @@ +source(here::here("code/init.R")) +source(here::here("code/ukbb_preprocessing.R")) +source(here::here("code/models.R")) +library(labNorm) +library(mldpEHR) + +ukbb_data <- load_data() +ukbb_demog <- get_demog_data(ukbb_data) %cache_df% here('output/ukbb_demog.csv') %>% as_tibble() +ukbb_diagnosis <- get_diagnosis_data(ukbb_data, ukbb_demog) + +ukbb_visits <- get_visit_data(ukbb_demog) %cache_df% here('output/ukbb_visits.csv') %>% as_tibble() +ukbb_labs <- get_labs_data(ukbb_data, ukbb_visits) %cache_df% here('output/ukbb_labs.csv') %>% as_tibble() %>% + mutate(sex=c('male', 'female')[sex]) %>% + inner_join(ln_ukbb_labs() %>% mutate(field=as.numeric(ukbb_code)) %>% select(field)) + +ukbb_labs$q <- ln_normalize_multi_ukbb(ukbb_labs %>% select(id, lab_code=field, age, sex, value)) + +ukbb_diseases <- get_diseases(ukbb_diagnosis, build_cancer_icd9_icd10_dictionary(ukbb_data)) %cache_df% here('output/ukbb_diseases.csv') %>% as_tibble() +models_dir <- 'data/models/' +predictors <- c('longevity', 'diabetes', 'ckd', 'copd', 'cvd', 'liver') %>% + purrr::set_names() %>% + purrr::map(function(m) + { + readr::read_rds(paste0(models_dir, m, '.rds')) %>% + purrr::imap( ~ c(.x, age=as.numeric(.y), feature_names=list(unique(unlist(purrr::map(.x$model, ~ .x$feature_names)))))) + }) + +potential_features <- unique(unlist(purrr::map(predictors, function(predictor) { + purrr::map(predictor, function(p) { + p$feature_names + }) +}))) + + +#building features to be used by all predictors (longevity, diseases) +ukbb_to_clalit <- tgutil::fread('data/ukbb_lab_field_to_clalit_lab.csv') +features <- purrr::map2_df(predictors[[1]], names(predictors[[1]]), function(model, age_model) { + message(age_model) + age_model <- as.numeric(age_model) + labs_features <- ukbb_labs %>% filter(ageage_model-5, !is.na(q)) %>% + left_join(ukbb_to_clalit %>% select(field, track), by="field") %>% + mutate(feature=paste0(track, '.quantiles_1_years_minus1095')) %>% + filter(feature %in% potential_features) %>% + group_by(id, feature) %>% summarize(value=mean(q), .groups="drop") + + disease_features <- ukbb_diseases %>% filter(age <= age_model) %>% + mutate(feature=paste0('WZMN.', cohort, '_minus43800_0')) %>% + filter(feature %in% potential_features) %>% + distinct(id, feature) %>% + mutate(value=1) + + ids <- unique(c(labs_features$id, disease_features$id)) + + #adding female/male/age info + features_tidy <- data.frame(id=ids, feature="age", value=age_model) %>% + bind_rows(ukbb_demog %>% filter(id %in% ids) %>% mutate(feature="male", value= sex==1) %>% select(id, feature, value)) %>% + bind_rows(labs_features) %>% + bind_rows(disease_features) + + #moving from tidy format + features <- features_tidy %>% pivot_wider(id_cols='id', names_from='feature') %>% + mutate(sex=2-male) + + #setting missing diesease values to 0 + disease_feature_names <- grep('WZMN.disease', colnames(features), value=TRUE) + features[,disease_feature_names][is.na(features[,disease_feature_names])] <- 0 + + #adding missing features + missing_features <- setdiff(potential_features, colnames(features)) + features[,missing_features] <- NA + + #requiring RBC + features <- features %>% filter(!is.na(lab.101.quantiles_1_years_minus1095)) + return(features) +}) %cache_df% here('output/ukbb_mldp_features.csv') %>% as_tibble() + + +predictor_scores <- purrr::map2_df(predictors, names(predictors), ~ mldp_predict_multi_age(features, .x) %>% mutate(predictor=.y)) +pop <- predictor_scores %>% filter(predictor == "longevity") %>% select(id, age, sex, longevity=score, longevity_q=quantile) %>% + mutate(sex=factor(c('male', 'female')[sex], levels=c('male', 'female'))) %>% + left_join(predictor_scores %>% filter(predictor != "longevity") %>% + select(id, age, predictor, score) %>% + left_join(ukbb_diseases %>% select(id, disease_age=age, predictor=cohort)) %>% + mutate(score = ifelse(!is.na(disease_age) & disease_age < age, NA, score)) %>% + pivot_wider(id_cols=c("id", "age"), names_from="predictor", values_from="score") + ) + +#survival <- get_survival() diff --git a/code/models.R b/code/models.R new file mode 100755 index 0000000..5bdfb91 --- /dev/null +++ b/code/models.R @@ -0,0 +1,193 @@ +#' @param demog demographic data returned from demog_data function +#' @param labs contains normalized lab values on extended population +#' @param diseases contains onset of disease for all patients +compute_models_score <- function(demog, labs, diseases, disease_models=c('diabetes', 'ckd', 'copd', 'ncvd', 'liver')) +{ + longevity_score <- compute_longevity_score(demog, labs, diseases) + disease_scores <- compute_diseases_score(demog, labs, diseases, disease_models) + + pop_tidy <- longevity_score %>% mutate(disease="longevity") %>% + bind_rows(disease_scores %>% filter(!sick) %>% select(-disease_age, -sick)) %>% + mutate(disease=factor(disease, levels=c(disease_models, 'longevity'))) %>% + group_by(id, sex, age, disease) %>% + summarize_at(vars(score), mean, .groups="drop") %>% + ungroup + pop <- pop_tidy %>% pivot_wider(id_cols=c(id, sex, age), names_from=disease, values_from=score) + pop <- pop %>% group_by(age, sex) %>% mutate(longevity_q=ecdf(longevity)(longevity)) %>% ungroup + return(pop) +} + +compute_longevity_score <- function(demog, labs, diseases) { + message("computing longevity score") + multi_models <- readr::read_rds(here::here('data/models/longevity.rds')) + return(compute_model_score(multi_models, demog, labs, diseases)) +} + +compute_diseases_score <- function(demog, labs, diseases, disease_models) { + purrr::map_df(disease_models, function(disease) { + message("computing ", disease) + multi_models <- readr::read_rds(here::here(paste0('data/models/', disease, '.rds'))) + ds <- compute_model_score(multi_models, demog, labs, diseases) + return(ds %>% mutate(disease=disease)) + }) %>% left_join(diseases %>% select(id, disease=cohort, disease_age=age), by=c("id", "disease")) %>% + mutate(sick=ifelse(!is.na(disease_age) & age-disease_age > 0, 1, 0), + disease=factor(disease, levels=unique(disease))) +} + +compute_model_score <- function(multi_models, demog, labs, diseases) { + res <- purrr::map2_df(multi_models, names(multi_models), function(model, age_model) { + cat(age_model, "...") + age_model <- as.numeric(age_model) + labs_data <- labs %>% filter(age% filter(age <= age_model) + features <- build_features(model, age_model, demog, labs_data, disease_data) + #requiring RBC + features <- features %>% filter(!is.na(lab.101.quantiles_1_years_minus1095)) + if (nrow(features) == 0) { + return(data.frame()) + } + score <- purrr::map_df(1:length(model$model), function(cv) { + predictor_features <- features[,model$model[[cv]]$feature_names] + score = predict(model$model[[cv]], data.matrix(predictor_features)) + return(features %>% mutate(sex = c('male', 'female')[2-male]) %>% + select(id, sex) %>% + mutate(age=age_model, score=score, cv=cv)) + }) + return(score) + }) + return(res) +} + +build_features <- function(model, age, demog, labs_data, disease_data) +{ + feature_set <- model$feature.set + lab_features <- labs_data %>% mutate(dt = (age-!!age)*365) %>% + mutate(dt_breaks=cut(dt, feature_set$lab_time_breaks, + labels=gsub("-", "minus", head(feature_set$lab_time_breaks, -1)), right=FALSE)) %>% + filter(!is.na(dt_breaks)) %>% + mutate(feature_name=paste0(track, '.quantiles_1_years_', dt_breaks)) %>% + rename(feature_value=q) %>% + filter(feature_name %in% feature_set$feature_labels$feature) %>% + select(id, feature_name, feature_value) %>% + group_by(id, feature_name) %>% + summarize(feature_value = mean(feature_value), .groups="drop") + dx_features <- disease_data %>% mutate(track = paste0('WZMN.', cohort)) %>% + inner_join(feature_set$dx_tracks, by="track") %>% + mutate(feature_name = paste0(track, "_", gsub("-", "minus", dt1), "_", gsub("-", "minus", dt2))) %>% + distinct(id, feature_name) %>% + mutate(feature_value = 1) %>% + filter(id %in% lab_features$id) + + ids <- unique(c(lab_features$id, dx_features$id)) + + #adding female/male/age info + features_tidy <- data.frame(id=ids, feature_name="age", feature_value=age) %>% + bind_rows( + demog %>% + filter(id %in% ids) %>% + select(id, sex) %>% + mutate(feature_name="female", feature_value=sex == 2) %>% + select(id, feature_name, feature_value) + ) %>% + bind_rows( + demog %>% + filter(id %in% ids) %>% + select(id, sex) %>% + mutate(feature_name="male", feature_value=sex == 1) %>% + select(id, feature_name, feature_value) + ) %>% + bind_rows(lab_features) %>% + bind_rows(dx_features) + + #moving from tidy format + features <- features_tidy %>% pivot_wider(id_cols=id, names_from=feature_name, values_from=feature_value) + + #adding missing columns + all_features <- unique(unlist(purrr::map(model$model, ~ .x$feature_names))) + missing_features <- setdiff(all_features, colnames(features)) + missing_data <- data.frame(matrix(NA, ncol=length(missing_features), nrow=nrow(features))) + colnames(missing_data) <- missing_features + full_features <- cbind(features, missing_data) + + #setting missing diesease values to 0 + dx_feature_names <- feature_set$feature_labels %>% filter(type == "diagnosis") %>% pull(feature) + full_features[,dx_feature_names][is.na(full_features[,dx_feature_names])] <- 0 + + return(full_features) +} + + +#note that model predicts q longevity score from disease scores +compute_pop_residuals <- function(pop) +{ + #compute residuals of longevity score using diseases + residuals_model <- readr::read_rds(paste0(models_dir, 'residual_xgboost_model.rds')) + pre_res <- pop %>% pivot_longer(c("diabetes", "ckd", "copd", "ncvd", "liver"), names_to = "disease", values_to = "score") %>% + replace_na(replace = list(score = 1)) %>% + mutate(disease=factor(disease, levels=unique(disease))) %>% + pivot_wider(names_from=disease, values_from=score) %>% + mutate(g=ifelse(gender == 'male', 1, 2)) + pre_res$prediction <- predict(residuals_model, xgboost::xgb.DMatrix(data=pre_res %>% select(age, gender=g, diabetes, ckd, copd, cardio_hr=ncvd, liver) %>% as.matrix())) + return(pre_res %>% mutate(residual_q=q-prediction)) +} +######## +# Markovian Lifelong Disease Predisposition models +compute_lifelong_disease_risk <- function(disease_models=c('diabetes', 'ckd', 'copd', 'ncvd', 'liver')) { + return(purrr::map_df(disease_models, function(disease) { + mm <- data.table::fread(here::here(paste0('data/models/', disease, '.markov.lifelong.csv'))) + return(mm %>% mutate(disease=disease)) + }) %>% filter(sbin != "no_cbc") %>% mutate(qmax=1/20*as.numeric(sbin), qmin=qmax-1/20)) +} + + +######## +# Markovian longevity models +compute_lifelong_longevity_risk <- function(target_pop) { + mm <- tgutil::fread(here::here('data/models/longevity.markov.lifelong.csv')) %>% #reading in markov models + filter(sbin != "death", sbin != "no_cbc") %>% # removing death and no-cbc source bins + mutate(qmax=1/20*as.numeric(sbin), qmin=qmax-1/20) %>% + mutate(q=pmean(qmax, qmin)) %>% + rename(sex=gender) %>% + group_by(sex, age) %>% + summarize(model=list(approxfun(c(0, q, 1), c(min(death), death, max(death)))), .groups="drop") + target_risk <- plyr::ddply(target_pop, plyr::.(age, sex), function(x) { + f <- mm %>% filter(age == x$age[1], sex==x$sex[1]) %>% pull(model) %>% .[[1]] + return(x %>% mutate(risk=f(x %>% pull(longevity_q)))) + }, .parallel=T) +} + +######## +# umap projection +umap_projection <- function(pop) { + all_umap_models <- readr::read_rds(paste0(data_dir, 'longevity_models/umap_models.rds')) + names(all_umap_models) <- as.numeric(names(all_umap_models)) + umap_models <- all_umap_models[as.character(sort(pop %>% distinct(age) %>% pull(age)))] + projection <- runonce::save_run(purrr::map2_df(umap_models, as.numeric(names(umap_models)), function(u, a) { + message(a) + d <- pop %>% filter(age == a) %>% na.omit() + p <- predict(u, d %>% select(q, diabetes, ckd, copd, cardio_hr=ncvd, liver)) + d$x=p[,1] + d$y=p[,2] + return(d) + }), file=paste0(processed_dir, 'ukbb_clalit_umap_projection.rds')) + u <- projection %>% reshape2::melt(id.vars=c("id", "gender", "age", "x", "y", "longevity")) %>% + mutate(variable=factor(variable,levels=c('q', 'diabetes', 'ckd', 'copd', 'ncvd', 'liver'))) + + #fixing orientation: + u$x[u$age %in% c(45, 50, 55, 70)] <- -u$x[u$age %in% c(45, 50, 55, 70)] + u$y[u$age %in% c(55, 60, 70)] <- -u$y[u$age %in% c(55, 60, 70)] + g <- ggplot(u %>% filter(age<75), aes(x=x, y=y, colour=value)) + geom_point(size=0.005, alpha=0.3) + + scale_color_gradientn(colors=ocean.balance(20)) + facet_grid(variable~age) + theme_bw() + + theme(strip.background=element_blank()) + png(paste0(fig_dir, 'umap.png'), width=1200, height=1080) + print(g) + dev.off() + return(umap_projection) +} + + + + diff --git a/code/naryn_patients_data.R b/code/naryn_patients_data.R new file mode 100755 index 0000000..63849c2 --- /dev/null +++ b/code/naryn_patients_data.R @@ -0,0 +1,73 @@ +#this file contains methods for selecting patients with different criteria and extracts requested data for these patients. + + +#create global usefull filters to select patients that were already born, didn't die, were registered in the healthcare system and did not leave if for good. +emr_filter.create('born', 'patients.dob', time.shift=c(-120,0)*year()) +emr_filter.create('dead', 'patients.dod', time.shift=c(-120,0)*year()) +emr_filter.create('registered', 'patients.status.register', time.shift=c(-120,0)*year()) +emr_filter.create('left_for_good', 'patients.status.lfg', time.shift=c(-120,0)*year()) +emr_filter.create('absent', 'patients.status.absent', time.shift=c(-1,0)*month()) + +#create vtrack for sex +emr_vtrack.create('sex', 'patients.dob', time.shift=c(-120,0)*year(), func='earliest') #function doesn't matter, only a single entry per patient + +#create vtrack for age +emr_vtrack.create('age', 'patients.dob', time.shift=c(-120,0)*year(), func='dt2.earliest') #function computes the difference in time (in hours) between the iterator time and birth time + +#create vtrack for time_to_death +emr_vtrack.create('time_to_death', 'patients.dod', time.shift=c(0,120)*year(), func='dt1.earliest') #function computes the difference in time (in hours) between the iterator time and time of death + +#create vtrack for time_to_left_for_good +emr_vtrack.create('time_to_left_for_good', 'patients.status.lfg', time.shift=c(0,120)*year(), func='dt1.earliest') #function computes the difference in time (in hours) between the iterator time and time the patient left for good + + +#' Select all patients that are alive at a given age in the naryn database +#' @param target_age - the age of all patients to identify +#' @param start_time - the minimum time in which to start looking for patients +#' @param end_time - the maximum time in which to start looking for patients +#' @param filters - string containing any additional filters to apply to the list of patients +get_all_patients_at_age <- function(target_age, start_time, end_time, end_db, filters=NULL, additional_data=NULL, additional_names=NULL) +{ + age_filter <- emr_filter.create('f_age', 'patients.dob', time.shift=c(-target_age-1, -target_age+1)*year()) + patients_filter=paste(c(filters, '(born & !dead & registered & !left_for_good & !absent & f_age)'), collapse = " & ") + patients <- emr_extract(c('age/year()', 'sex', 'time_to_death/year()', 'time_to_left_for_good/year()', additional_data), + iterator=list(target_age*year(), 'patients.dob'), + filter=patients_filter, + stime=start_time, + etime=end_time, + names=c('age', 'sex', 'survival', 'left_for_good', additional_names) + ) %>% + mutate(time_in_system=ifelse(is.na(left_for_good), (end_db - time)/year(), left_for_good)) + ### BUG WORKAROUND ### + patients <- patients %>% inner_join(emr_extract('age', iterator=patients %>% select(id, time), filter=patients_filter) %>% select(id, time), by=c("id", "time")) + ### BUG WORKAROUND ### + return(patients) +} + +#' Extract EHR data for a set of patients (id/time) +#' @param patients - dataframe containing patient id and time reference point for lab and diesease extraction +#' @param labs - a vector of tracks representing lab data +#' @param labs_window - a vector of two numbers (e.g. c(-3*year(), 0) representing the time window relative to patient time, in which to look for lab values +#' @param diseases - a vector of tracks representing diseases +#' @param diseases_window - a vector of two numbers (e.g. c(-3*year(), 0) representing the time window relative to patient time, in which to look for diseases + +get_patients_features <- function(patients, labs, labs_window, diseases, diseases_window, max_missing_percent=1) { + #create a vtrack for each lab, representing the time window from which the lab value will be sampled + purrr::walk(labs, ~ emr_vtrack.create(paste0('v_', .x), .x, time.shift=labs_window, func='avg')) + + #create a vtrack for each disease, representing the time window from which the disease value will be sampled + purrr::walk(diseases, ~ emr_vtrack.create(paste0('v_', .x), .x, time.shift=diseases_window, func='size')) + + data <- emr_extract(c(paste0('v_', c(labs, diseases))), iterator=patients, names=c(labs, diseases)) %>% + as_tibble() %>% + mutate(across(all_of(diseases), ~+as.logical(.x))) + + missing_per_feature <- apply(t(data), 1, function(x) { sum(is.na(x)) }) + data <- data[, missing_per_feature/nrow(data) < max_missing_percent] #requiring common features, ignoring sporadic ones + return(patients %>% + select(id, time, age, sex) %>% + left_join(data %>% select(-ref), by=c("id", "time")) %>% + select(-time) %>% + mutate(sex=sex-1) + ) +} \ No newline at end of file diff --git a/code/snps.R b/code/snps.R new file mode 100644 index 0000000..e9a0a99 --- /dev/null +++ b/code/snps.R @@ -0,0 +1,37 @@ +get_gwas_pvals <- function() { + pvals <- + get_longevity_snps() %>% + select(chrom, start, end, marker.ID, rsid, allele1, allele2, freq, + longevity_pval=pval, + longevity_beta=estim, + longevity_score=score) %>% + distinct(rsid, allele1, allele2, .keep_all=T) %>% + left_join( + get_longevity_disease_covar_snps() %>% + select(chrom, start, end, marker.ID, rsid, allele1, allele2, freq, + longevity_disease_covar_pval=pval, + longevity_disease_covar_beta=estim, + longevity_disease_covar_score=score) %>% + distinct(rsid, allele1, allele2, .keep_all=T) + ) + + for (d in c('diabetes', 'ckd', 'copd', 'ncvd', 'liver')) { + pvals <- pvals %>% left_join( + tgutil::fread(here(paste0('notebook/output/gwas_', d, '_age_sex_covar', extention, '.tsv'))) %>% + as_tibble() %>% + select(rsid, allele1, allele2, + !!quo_name(paste0(d, "_pval")):=pval, + !!quo_name(paste0(d, "_beta")):=estim, + !!quo_name(paste0(d, "_score")) := score) %>% distinct + ) + } + for (cox in c('both', 'father', 'mother')) { + pvals <- pvals %>% left_join( + tgutil::fread(here(paste0('output/cox_parents_survival_', cox, '_gwas.tsv'))) %>% + as_tibble() %>% + select(rsid, allele1, allele2, + !!quo_name(paste0('cox.', cox, "_pval")):=pval, + !!quo_name(paste0('cox.', cox, "_z")):=z) %>% distinct + ) + } +} \ No newline at end of file diff --git a/code/ukbb_outcome.R b/code/ukbb_outcome.R new file mode 100644 index 0000000..061e213 --- /dev/null +++ b/code/ukbb_outcome.R @@ -0,0 +1,50 @@ +#computes the patient survival from time of experimental encounter. +get_patients_survival <- function(patients, demog, death_censor_date=DEATH_CENSOR_DATE) { + patients <- patients %>% select(id, sex, age) + patients_age <- patients %>% + left_join(demog %>% select(id, matches("age_.$"), dod), by="id") %>% + pivot_longer(cols = starts_with("age_"), names_to = "exp_id", values_to = "exp_age") %>% + mutate(exp_id = gsub("age_", "", exp_id)) %>% + filter(!is.na(exp_age)) + patients_date <- patients %>% + left_join(demog %>% select(id, matches("date_.$"), dod), by="id") %>% + pivot_longer(cols = starts_with("date_"), names_to = "exp_id", values_to = "exp_date") %>% + mutate(exp_id = gsub("date_", "", exp_id)) %>% + filter(!is.na(exp_date)) + patients_survival <- patients_age %>% + data.table::as.data.table() %>% + left_join(patients_date, by = c("id", "sex", "age", "dod", "exp_id")) %>% + mutate(diff = age - exp_age) %>% + filter(diff >= 0, diff <= 5) %>% #age model was applied to this experimental encounter (0-3) + group_by(id, sex, age, dod) %>% + summarise(exp_age = mean(exp_age), exp_date = mean(exp_date), .groups = "drop") %>% + as_tibble() %>% + mutate_at(vars(dod, exp_date), lubridate::as_datetime) %>% + mutate(dead = !is.na(dod), follow_time = if_else( + is.na(dod), + difftime(death_censor_date, exp_date, units = "days"), + difftime(dod, exp_date, units = "days") + )) + return(patients_survival) +} + +get_patients_disease_outcomes <- function(patients, + diseases, + selected_diseases=c('diabetes', 'ckd', 'copd', 'cvd', 'liver'), + censor_date=DISEASE_CENSOR_DATE) +{ + sdiseases <- diseases %>% filter(cohort %in% selected_diseases) + pd <- plyr::alply(selected_diseases, 1, function(selected_disease) { + patients_disease <- patients %>% + left_join(sdiseases %>% filter(cohort == selected_disease) %>% arrange(age) %>% distinct(id, .keep_all=TRUE) %>% + rename(disease_age=age, disease_date=date, disease=cohort), by="id") %>% + mutate_at(vars(exp_date, disease_date), lubridate::as_datetime) %>% + mutate(sick_at_exp=(!is.na(disease_date) & disease_date < exp_date) | (disease_age < exp_age), + sick=!is.na(disease_date), + disease_follow_time=if_else(is.na(disease_date), as.numeric(difftime(censor_date, exp_date, units='days')), + if_else(sick_at_exp, 0, as.numeric(difftime(disease_date, exp_date, units='days'))))) + return(patients_disease %>% mutate(disease=selected_disease)) + }, .parallel=TRUE) + names(pd) <- selected_diseases + return(pd) +} \ No newline at end of file diff --git a/code/ukbb_preprocessing.R b/code/ukbb_preprocessing.R new file mode 100755 index 0000000..6ac9f5e --- /dev/null +++ b/code/ukbb_preprocessing.R @@ -0,0 +1,456 @@ + +#' load entire annotation dataset of ukbiobank +load_data <- function() { + wd <- getwd() + setwd(here::here('data')) + data <- ukbtools::ukb_df('full') + exclusion <- data.table::fread('exclusion.csv', data.table=FALSE) %>% rename + setwd(wd) + ukbb_data <- data %>% filter(!(eid %in% exclusion[,1])) +} + +#' extract demographic data from dataset: sex, dob, race, date of interactions, dod +#' @param data full data returned from load_data function +get_demog_data <- function(data) { + demog <- data %>% + select( + id=eid, + sex=sex_f31_0_0, + month_of_birth=month_of_birth_f52_0_0, + year_of_birth=year_of_birth_f34_0_0, + date_of_death_0=date_of_death_f40000_0_0, + date_of_death_1=date_of_death_f40000_1_0, + race_0 = ethnic_background_f21000_0_0, + race_1 = ethnic_background_f21000_1_0, + race_2 = ethnic_background_f21000_2_0, + date_0=date_of_attending_assessment_centre_f53_0_0, + date_1=date_of_attending_assessment_centre_f53_1_0, + date_2=date_of_attending_assessment_centre_f53_2_0, + date_3=date_of_attending_assessment_centre_f53_3_0 + ) %>% + filter(!is.na(sex), !is.na(month_of_birth), !is.na(year_of_birth)) %>% + mutate( + dob=as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"), + sex=2-as.numeric(sex), + dod_showcase=pmin(date_of_death_0, date_of_death_1, na.rm=TRUE), + age_0 = as.numeric(date_0 - as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"))/365, + age_1 = as.numeric(date_1 - as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"))/365, + age_2 = as.numeric(date_2 - as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"))/365, + age_3 = as.numeric(date_3 - as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"))/365 + ) + + #adding death information from ukbb data portal (downloaded jan 2022, censor date 30/9/2021) + dod_portal <- data.table::fread(here::here('data/portal_death_jan_2022.txt')) %>% + mutate(dod_portal=dmy(date_of_death)) %>% + select(id=eid, dod_portal) %>% + distinct + demog <- demog %>% left_join(dod_portal, by="id") %>% + mutate(dod=if_else(is.na(dod_showcase), dod_portal, dod_showcase)) %>% + select(-dod_showcase, -dod_portal) + return(demog) +} + +#' extract icd diagnosis data (both from hospitals and self reported) +#' @param data full data returned from load_data function +#' @param demog demographic data returned from get_demog_data function +get_diagnosis_data <- function(data, demog) { + ########################### + # HOSPITAL + ########################### + #showcase fields + icd10_value <- data %>% select(all_of(c("eid", grep('f41270', colnames(data), value=TRUE)))) %>% + pivot_longer(cols=!eid, names_to="variable", values_to="value") + icd10_date <- data %>% select(all_of(c("eid", grep('f41280', colnames(data), value=TRUE)))) %>% + pivot_longer(cols=!eid, names_to="variable", values_to="value") + icd10_showcase <- cbind(icd10_value %>% select(id=eid, icd=value), icd10_date %>% select(date=value)) %>% filter(!is.na(icd)) + icd9_value <- data %>% select(all_of(c("eid", grep('f41271', colnames(data), value=TRUE)))) %>% + pivot_longer(cols=!eid, names_to="variable", values_to="value") + icd9_date <- data %>% select(all_of(c("eid", grep('f41281', colnames(data), value=TRUE)))) %>% + pivot_longer(cols=!eid, names_to="variable", values_to="value") + icd9_showcase <- cbind(icd9_value %>% select(id=eid, icd=value), icd9_date %>% select(date=value)) %>% filter(!is.na(icd)) + + #hesin followup period + hesin <- data.table::fread(here::here('data/portal_hesin_jan_2022.txt')) + hesin_dx <- data.table::fread(here::here('data/portal_hesin_diag_jan_2022.txt')) %>% + left_join(hesin, by = c("eid", "ins_index")) %>% + select(eid, diag_icd10, diag_icd9, epistart, admidate) %>% + mutate(date=ifelse(is.na(epistart) | epistart == "", admidate, epistart)) %>% + mutate(date=dmy(date)) + #icd 10 codings + icd10_hesin <- hesin_dx %>% + select(id=eid, icd=diag_icd10, date) %>% + filter(!is.na(icd), icd != "") %>% + arrange(id, date) %>% + distinct(id, icd, .keep_all=T) #looking at onset + #icd9 codings + icd9_hesin <- hesin_dx %>% + select(id=eid, icd=diag_icd9, date) %>% + filter(!is.na(icd), icd != "") %>% + arrange(id, date) %>% + distinct(id, icd, .keep_all=T) #looking at onset + + icd10_codes <- data.table::fread(here::here('data/icd10_coding19.tsv')) + icd9_codes <- data.table::fread(here::here('data/icd9_coding87.tsv')) + hospital_icd <- icd10_showcase %>% + bind_rows(icd10_hesin) %>% + arrange(id, date, icd) %>% + distinct(id, icd, .keep_all=T) %>% + filter(!is.na(icd)) %>% + left_join(icd10_codes %>% select(icd=coding, meaning), by="icd") %>% + mutate(icd_version=10) %>% + bind_rows( + icd9_showcase %>% + bind_rows(icd9_hesin) %>% + arrange(id, date, icd) %>% + distinct(id, icd, .keep_all=T) %>% + filter(!is.na(icd)) %>% + left_join(icd9_codes %>% select(icd=coding, meaning), by="icd") %>% + mutate(icd_version=9) + ) + + ########################### + # first occurrences + ########################### + major <- icd10_codes %>% + tidyr::separate(coding, into=c('major', 'minor'), 3) %>% + distinct(major) %>% + left_join(icd10_codes %>% select(major=coding, meaning), by="major") + + fields <- purrr::map_df(major$major, function(x) { + field=grep(paste0("^date_",tolower(x)), colnames(data), value=TRUE) + return(data.frame(major=x, field=ifelse(length(field)==0, NA, field))) + }) %>% filter(!is.na(field)) + columns <- c("eid", fields$field) + occurence_data <- data %>% select(all_of(columns)) %>% pivot_longer(cols=!eid, names_to="icd", values_to="date") + first_occurrence_icd <- plyr::adply(major, 1, function(x) { + field <- grep(paste0("^date_",tolower(x$major)), colnames(data), value=TRUE) + if (length(field) == 0) { + return() + } + return(data %>% select(all_of(c("eid", field))) %>% + rename(id=eid) %>% + rename(date = !!field) %>% + # rename_at(all_of(c("eid", field)), ~ c("id", "date")) %>% + # rename_with(~ c("id", "date"), where(all_of(c("eid", field)))) %>% + filter(!is.na(date)) %>% + mutate(icd=x$major, meaning=x$meaning, icd_version=10)) + }, .parallel=FALSE) + + ########################## + # self rep (nurse) + ########################## + self_rep_to_icd_codes <- data.table::fread(here::here('data/self_report_medical_coding_f20002_coding609.tsv')) %>% + rename(code=coding, icd=meaning) %>% mutate(code=as.character(code)) + #source for self rep is field 20002 + self_rep_data <- data %>% select(all_of(c("eid", grep('f20002', colnames(data), value=TRUE)))) %>% reshape2::melt(id.var='eid') + self_rep_age <- data %>% select(all_of(c("eid", grep('f20009', colnames(data), value=TRUE)))) %>% reshape2::melt(id.var='eid') + self_rep_showcase <- cbind(self_rep_data %>% select(id=eid, code=value), self_rep_age %>% select(age=value)) %>% + filter(!is.na(code), age > 0) %>% left_join(self_rep_to_icd_codes, by="code") %>% + filter(!is.na(icd)) %>% mutate(icd_version=10) %>% + select(-code) %>% + left_join(icd10_codes %>% select(icd=coding, meaning), by="icd") + + ########################## + # self rep (touchscreen) + ########################## + self_rep_touchscreen_to_icd_codes <- data.table::fread(here::here('data/self_rep_touchscreen_to_icd10_category100044.csv')) + #source for self rep is field category 100044 + self_rep_touch <- plyr::adply(self_rep_touchscreen_to_icd_codes, 1, function(rep) { + return( + data %>% select(all_of(c("eid", grep(rep$field, colnames(data), value=TRUE)))) %>% + reshape2::melt(id.var='eid') %>% + filter(!is.na(value), value > 1) %>% + select(id=eid, age=value) %>% + mutate(icd=rep$icd, icd_version=10, age=as.numeric(age)) %>% + left_join(icd10_codes %>% select(icd=coding, meaning), by="icd") + ) + }, .parallel=FALSE) + ########################## + # clinic + ########################## + gp_clinic_read2_icd10 <- data.table::fread(here::here('data/gp_clinic_read2_to_icd10_coding1834.tsv')) + gp_clinic_read3_icd10 <- data.table::fread(here::here('data/gp_clinic_read3_to_icd10_coding1835.tsv')) + + clinic <- data.table::fread(here::here('data/gp_clinic.txt')) + clinic <- clinic %>% + filter(!is.na(read_2), read_2 != "") %>% + select(id=eid, date=event_dt, coding=read_2) %>% + inner_join(gp_clinic_read2_icd10, by="coding") %>% + bind_rows( + clinic %>% filter(!is.na(read_3), read_3 != "") %>% + select(id=eid, date=event_dt, coding=read_3) %>% + inner_join(gp_clinic_read3_icd10, by="coding") + ) %>% + rename(icd=meaning) %>% + mutate(icd_version=10, date=as.Date(date, format="%d/%m/%Y")) %>% + filter(!is.na(date)) %>% + left_join(icd10_codes %>% select(icd=coding, meaning), by="icd") %>% + select(-coding) + + #combine it all together + diagnosis <- hospital_icd %>% mutate(rep="hospital") %>% + bind_rows(first_occurrence_icd %>% select(-major) %>% mutate(rep="first_occurrence")) %>% + bind_rows(clinic %>% mutate(rep="clinic")) %>% + left_join(demog %>% select(id, month_of_birth, year_of_birth), by="id") %>% + mutate(age = as.numeric(date - as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"))/365) %>% + select(-month_of_birth, -year_of_birth) %>% + bind_rows( + self_rep_showcase %>% mutate(rep="self_rep_nurse") %>% + bind_rows(self_rep_touch %>% mutate(rep="self_rep_touchscreen")) %>% + left_join(demog %>% select(id, month_of_birth, year_of_birth), by="id") %>% + mutate(date = as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y") + floor(age*365)) %>% + select(-month_of_birth, -year_of_birth) + ) %>% + arrange(id, age) %>% + filter(age>0) + return(diagnosis) +} + +#' associate the age of the participant at each visit to the center +#' @param demog demographic data returned from get_demog_data function +get_visit_data <- function(demog) { + demog_tidy <- demog %>% + select(id, sex, age_0, age_1, age_2, age_3) %>% + reshape2::melt(id.var= c("id", "sex")) + return(demog_tidy %>% + left_join( + demog_tidy %>% + count(variable) %>% + tidyr::separate(variable, into=c("age", "timepoint"), sep="_", remove=FALSE) %>% + select(-n), + by="variable" + ) %>% + select(id, sex, value, timepoint) %>% + rename(age=value) %>% + mutate(age = as.numeric(age), timepoint=as.numeric(timepoint)) + ) +} + + +#' extract all lab test measurements +#' @param data full data returned from load_data function +#' @param visits the age of the patients at each visit to the center, as returned from get_visit_data + +get_labs_data <- function(data, visits) { + lab_codes <- data.table::fread(here::here('data/blood_assays_count_and_biochemistry_category_100080.csv')) + labs_tidy <- plyr::adply(lab_codes, 1, function(lc) { + columns <- grep(paste0('_f', lc[1,1], '_'), colnames(data), value=TRUE) + return(data %>% select(all_of(c('eid', columns))) %>% + pivot_longer(cols=columns, names_to='feature_name', values_to='value') %>% + rename(id=eid) %>% + filter(!is.na(value))) + }, .parallel=TRUE) %>% mutate(feature_name=as.character(feature_name)) + feature_timepoint <- labs_tidy %>% + distinct(feature_name) %>% + mutate(timepoint = as.numeric(unlist(purrr::map(strsplit(feature_name, "_"), ~ .x[length(.x)-1])))) + labs_tidy <- labs_tidy %>% + left_join(feature_timepoint, by="feature_name") %>% + left_join(visits, by=c("id", "timepoint")) %>% + # filter(!is.na(as.numeric(value))) %>% #removing values that were textual (e.g. urin result flag) + filter(!is.na(readr::parse_number(value))) %>% #removing values that were textual (e.g. urin result flag) + mutate(value=as.numeric(value)) + return(labs_tidy) +} + +#' identify the onset of any of the chronic diseases from either diagnosis icd or cancer report +#' @param data full data returned from load_data function +#' @param diagnosis contains all reported diagnosis from various sources as returned from get_diagnosis +#' @param demog demographic data returned from get_demog_data function +get_chronic_onset <- function(data, diagnosis, demog) { + chronic_icd_codes <- data.table::fread(here::here('data/icd9_icd10_non_healthy_codes_longevity_S2.csv'), header=TRUE) + chronic_icd10 <- diagnosis %>% filter(icd_version == 10) %>% + filter(icd %in% chronic_icd_codes$icd10 | substring(icd, 1, 3) %in% chronic_icd_codes$icd10) %>% + left_join(demog %>% select(id, year_of_birth, month_of_birth), by="id") %>% + mutate(age = as.numeric(date - as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"))/365) + + chronic_icd9 <- diagnosis %>% filter(icd_version == 9) %>% + filter(icd %in% chronic_icd_codes$icd9 | substring(icd, 1, 3) %in% chronic_icd_codes$icd9) %>% + left_join(demog %>% select(id, year_of_birth, month_of_birth), by="id") %>% + mutate(age = as.numeric(date - as.Date(paste0("1-", month_of_birth, "-", year_of_birth), format="%d-%m-%Y"))/365) + + cancer <- data %>% select("eid", grep('interpolated_age_of_participant_when_cancer_first_diagnosed_f20007', colnames(data), value=TRUE)) %>% + rename(id=eid) %>% + reshape2::melt(id.var='id') %>% + filter(!is.na(value)) %>% + group_by(id) %>% summarize(age=min(value)) + + chronic <- chronic_icd10 %>% select(id, age) %>% + bind_rows(chronic_icd9) %>% + bind_rows(cancer) %>% + group_by(id) %>% + summarize(age = min(age)) + return(chronic) +} + +#' identify the diagnosis codes of each of the target diseases (diabetes, ckd, copd, ncvd, liver disease) +#' @param diagnosis contains all reported diagnosis from various sources as returned from get_diagnosis +get_diseases <- function(diagnosis, cancer_codes) { + disease_codes <- tgutil::fread(here::here('data/common_disease_inclusion_icd9_icd10.csv')) %>% + bind_rows(cancer_codes %>% mutate(subs=0, cohort='disease.CANCER')) + #adding exact matches of disease codes + disease_exact <- diagnosis %>% inner_join(disease_codes %>% filter(subs==0), by=c("icd", "icd_version"), multiple="all") + #searching the sub-codes + disease_subs <- plyr::adply(disease_codes %>% filter(subs==1), 1, function(dc) { + return(diagnosis %>% + filter(icd_version == dc$icd_version) %>% + filter(grepl(paste0("^", dc$icd), icd)) %>% + mutate(cohort=dc$cohort) + ) + }, .parallel=F) + return(disease_exact %>% bind_rows(disease_subs) %>% select(id, age, date, cohort) %>% + arrange(id, age) %>% distinct(id, cohort, .keep_all=TRUE)) +} + +#' normalize the raw data measurements (quantile normalization by age/sex) +#' @param labs all lab test measurements as returned from get_labs_data function +#' @param visits the age of the patients at each visit to the center, as returned from get_visit_data +#' @param chronic contains onset of chronic disease as returned from get_chronic_diseases function +#' @param age_breaks the age resolution used for computing quantile normalization +normalize_labs <- function(labs, visits, chronic, age_breaks=seq(35, 85, by=5)) { + labs_ukbb_to_clalit <- data.table::fread(here::here('data/ukbb_lab_field_to_clalit_lab.csv'), header=TRUE) + l <- labs %>% left_join(labs_ukbb_to_clalit, by="field") + + #need to fix units + labs_fix_units <- l %>% filter(action != "") + labs_fix_units$value <- sapply(paste(labs_fix_units$value, labs_fix_units$action), function(x) eval(parse(text=x))) + l <- l %>% filter(action == "") %>% bind_rows(labs_fix_units) + + message("finished fixing units") + age_centers <- zoo::rollmean(age_breaks, 2) + labs_chronic <- l %>% left_join(visits, by = c("id", "timepoint", "sex", "age")) %>% + mutate(age_group = cut(age, age_breaks, right=FALSE)) %>% + left_join(chronic %>% rename(dx_age=age), by="id") %>% + mutate(healthy= ifelse(is.na(dx_age) | dx_age > age, 1, 0)) + + message("computing lab quantiles") + normalizer <- .compute_lab_quantiles(labs_chronic %>% filter(healthy==1, !is.na(sex)), quantile_probs=NULL) + #going over all lab measurements and normalizing them + norm_labs <- plyr::ddply(labs_chronic %>% filter(!is.na(sex)), + plyr::.(track, lab, age_group, sex), + function(x) { + age_sex_lab_normalizer <- normalizer[[paste(x$track[1], x$lab[1], x$age_group[1], x$sex[1], sep=".")]] + if (is.null(age_sex_lab_normalizer)) { + return(x %>% mutate(q=NA)) + } + return(x %>% mutate(q=age_sex_lab_normalizer(value))) + }, .parallel=FALSE) + norm_labs$lab <- factor(norm_labs$lab, levels=labs_ukbb_to_clalit$lab) + return(norm_labs) +} + +#' computed ecdf for each lab test, age group and sex after unit conversion of lab tests +#' @param labs_chronic all lab test measurements after unit modification and annotation of chronic state +#' @param quantile_probs preset quantiles, to be used when stats are insufficient or predefined resolution is required +.compute_lab_quantiles <- function(healthy_labs, quantile_probs) { + normalizer <- plyr::dlply(healthy_labs, plyr::.(track, lab, age_group, sex), + function(x) { + if (nrow(x) < 10) { + return(NULL) + } + if (is.null(quantile_probs)) { + return(ecdf(x$value)) + } else { + return(quantile(x$value, quantile_probs)) + } + }, .parallel=TRUE) + return(normalizer) +} + + + +get_parents_survival <- function(data) { + #mothers + mothers_death_fields <- grep("f3526", colnames(ukbb_data), value=T) + mothers_age_fields <- grep("f1845", colnames(ukbb_data), value=T) + mothers_alive_fields <- grep("f1835", colnames(ukbb_data), value=T) + #fathers + fathers_death_fields <- grep("f1807", colnames(ukbb_data), value=T) + fathers_age_fields <- grep("f2946", colnames(ukbb_data), value=T) + fathers_alive_fields <- grep("f1797", colnames(ukbb_data), value=T) + + m_age_at_death <- matrixStats::rowMins(as.matrix(data[,mothers_death_fields]), na.rm=TRUE) + m_age_censoring <- matrixStats::rowMins(as.matrix(data[,mothers_age_fields]), na.rm=TRUE) + f_age_at_death <- matrixStats::rowMins(as.matrix(data[,fathers_death_fields]), na.rm=TRUE) + f_age_censoring <- matrixStats::rowMins(as.matrix(data[,fathers_age_fields]), na.rm=TRUE) + + parents <- data.frame(id=data$eid, mother_age_at_death=m_age_at_death, mother_last_alive=m_age_censoring, + father_age_at_death=f_age_at_death, father_last_alive=f_age_censoring) %>% + mutate(mdead=!is.infinite(mother_age_at_death), + mfollow_time=ifelse(is.infinite(mother_age_at_death), ifelse(is.infinite(mother_last_alive), NA, mother_last_alive), mother_age_at_death), + fdead=!is.infinite(father_age_at_death), + ffollow_time=ifelse(is.infinite(father_age_at_death), ifelse(is.infinite(father_last_alive), NA, father_last_alive), father_age_at_death)) + return(parents) +} + +get_socio_demog <- function(data, visits) { + socio_fields <- grep('f738', colnames(ukbb_data), value=T) + socio_tidy <- data %>% select(all_of(c('eid', socio_fields))) %>% + reshape2::melt(id.var="eid") %>% + rename(id=eid, feature_name=variable) %>% + filter(!is.na(value)) %>% + mutate(feature_name=as.character(feature_name)) %>% + mutate(timepoint = as.numeric(unlist(purrr::map(strsplit(feature_name, "_"), ~ .x[length(.x)-1])))) + + socio_tidy <- socio_tidy %>% left_join(visits) %>% mutate(value=as.numeric(value)) %>% + select(id, gender, age, socio=value) + return(socio_tidy) +} + + + +build_cancer_icd9_icd10_dictionary <- function(data) { + icd10_cancer_codes <- data %>% select("eid", + grep('type_of_cancer_icd10_f40006', colnames(data), value=TRUE)) %>% + reshape2::melt(id.var="eid") %>% filter(!is.na(value)) %>% distinct(value) + icd9_cancer_codes <- data %>% select("eid", + grep('type_of_cancer_icd9_f40013', colnames(data), value=TRUE)) %>% + reshape2::melt(id.var="eid") %>% filter(!is.na(value)) %>% distinct(value) + cancer_codes <- icd10_cancer_codes %>% mutate(icd_version = 10) %>% + bind_rows(icd9_cancer_codes %>% mutate(icd_version=9)) %>% + rename(icd=value) + return(cancer_codes) +} + + +#note that cancer entries are not updated with latest HESIN data +get_cancer <- function(data, demog) { + icd_cancer <- data %>% select("eid", + grep('type_of_cancer_icd10_f40006', colnames(data), value=TRUE), + grep('type_of_cancer_icd9_f40013', colnames(data), value=TRUE), + grep('age_at_cancer_diagnosis_f40008', colnames(data), value=TRUE)) + colnames(icd_cancer) <- gsub('age_at_cancer_diagnosis_f40008_', 'age_', + gsub('type_of_cancer_icd9_f40013_', 'cancer9_', + gsub('type_of_cancer_icd10_f40006_', 'cancer10_', colnames(icd_cancer)))) + + entries <- purrr::map_df(strsplit(grep("age", colnames(icd_cancer)[-1], invert=TRUE, value=TRUE), "_"), ~ data.frame(type=.x[1], tp=.x[2], idx=.x[3])) + icd_cancer <- plyr::adply(entries, 1, function(x) { + d <- icd_cancer[,c("eid", paste0(x$type, "_", x$tp, "_", x$idx), paste0("age_", x$tp, "_", x$idx))] + colnames(d) <- c("id", "cancer", "age") + d <- d %>% filter(!is.na(cancer)) %>% select(id, coding=cancer, age) + if(x$type == "cancer10") { + d <- d %>% left_join(icd10_codes %>% select(coding, meaning)) %>% mutate(coding_ref=19) + } else { + d <- d %>% left_join(icd9_codes %>% select(coding, meaning)) %>% mutate(coding_ref=87) + } + return(d) + }) %>% select(id, coding, age, meaning) + + + ########################### + # self_reported + ########################### + self_rep_cancer <- data %>% select("eid", + grep('cancer_code_selfreported_f20001', colnames(data), value=TRUE), + grep('interpolated_age_of_participant_when_cancer_first_diagnosed_f20007', colnames(data), value=TRUE)) + colnames(self_rep_cancer) <- gsub('interpolated_age_of_participant_when_cancer_first_diagnosed_f20007_', 'age_', gsub('cancer_code_selfreported_f20001_', 'cancer_', colnames(self_rep_cancer))) + + entries <- purrr::map_df(strsplit(colnames(self_rep_cancer)[-1], "_"), ~ data.frame(type=.x[1], tp=.x[2], idx=.x[3])) %>% distinct(tp, idx) + self_rep_cancer <- plyr::adply(entries, 1, function(x) { + d <- self_rep_cancer[,c("eid", paste0("cancer_", x$tp, "_", x$idx), paste0("age_", x$tp, "_", x$idx))] + colnames(d) <- c("id", "cancer", "age") + return(d %>% filter(!is.na(cancer))) + }) %>% select(id, coding=cancer, age) %>% mutate(coding=as.numeric(coding)) %>% + left_join(cancer_codes %>% select(coding, meaning)) %>% mutate(coding_ref=) + + return(cancer_merged) +} diff --git a/data/common_disease_inclusion_icd9_icd10.csv b/data/common_disease_inclusion_icd9_icd10.csv new file mode 100644 index 0000000..36d0902 --- /dev/null +++ b/data/common_disease_inclusion_icd9_icd10.csv @@ -0,0 +1,529 @@ +icd,subs,cohort,icd_version +4414,0,disease.ABDOMINAL_ANEURYSM_WITHOUT_MENTION_OF_RUPTURE,9 +I714,0,disease.ABDOMINAL_ANEURYSM_WITHOUT_MENTION_OF_RUPTURE,10 +7812,0,disease.ABNORMALITY_OF_GAIT,9 +R260,0,disease.ABNORMALITY_OF_GAIT,10 +R261,0,disease.ABNORMALITY_OF_GAIT,10 +R2689,0,disease.ABNORMALITY_OF_GAIT,10 +R269,0,disease.ABNORMALITY_OF_GAIT,10 +42,1,disease.AIDS_ACQUIRED_IMMUNE_DEFICIENCY_SYNDROME,9 +B20,1,disease.AIDS_ACQUIRED_IMMUNE_DEFICIENCY_SYNDROME,10 +3050,1,disease.ALCOHOL_ABUSE,9 +F1010,0,disease.ALCOHOL_ABUSE,10 +F102,1,disease.ALCOHOL_ABUSE,10 +5712,0,disease.ALCOHOLIC_CIRRHOSIS_OF_LIVER,9 +K7030,0,disease.ALCOHOLIC_CIRRHOSIS_OF_LIVER,10 +3310,0,disease.ALZHEIMERS_DISEASE,9 +G30,1,disease.ALZHEIMERS_DISEASE,10 +2050,1,disease.AML_LIRAN,9 +C920,1,disease.AML_LIRAN,10 +C924,1,disease.AML_LIRAN,10 +C925,1,disease.AML_LIRAN,10 +2710,0,disease.ANEMIAS_DISORDERS_OF_GLUTATHIONE_METABOLISM_G_6_P_D,9 +2822,0,disease.ANEMIAS_DISORDERS_OF_GLUTATHIONE_METABOLISM_G_6_P_D,9 +D550,0,disease.ANEMIAS_DISORDERS_OF_GLUTATHIONE_METABOLISM_G_6_P_D,10 +D550,0,disease.ANEMIAS_DISORDERS_OF_GLUTATHIONE_METABOLISM_G_6_P_D,10 +E740,1,disease.ANEMIAS_DISORDERS_OF_GLUTATHIONE_METABOLISM_G_6_P_D,10 +7200,0,disease.ANKYLOSING_SPONDYLITIS,9 +M459,0,disease.ANKYLOSING_SPONDYLITIS,10 +7430,1,disease.ANOPHTHALMOS,9 +Q110,0,disease.ANOPHTHALMOS,10 +Q111,0,disease.ANOPHTHALMOS,10 +Q112,0,disease.ANOPHTHALMOS,10 +4241,0,disease.AORTIC_VALVE_DISORDERS,9 +I35,1,disease.AORTIC_VALVE_DISORDERS,10 +2848,1,disease.APLASTIC_ANEMIA,9 +2849,0,disease.APLASTIC_ANEMIA,9 +D60,1,disease.APLASTIC_ANEMIA,10 +D61,1,disease.APLASTIC_ANEMIA,10 +4401,0,disease.ATHEROSCLEROSIS_OF_RENAL_ARTERY,9 +I701,0,disease.ATHEROSCLEROSIS_OF_RENAL_ARTERY,10 +4273,1,disease.ATRIAL_FIBRILLATION_AND_FLUTTER,9 +I489,1,disease.ATRIAL_FIBRILLATION_AND_FLUTTER,10 +2252,0,disease.BENIGN_NEOPLASM_OF_CEREBRAL_MENINGES,9 +D32,1,disease.BENIGN_NEOPLASM_OF_CEREBRAL_MENINGES,10 +2273,0,disease.BENIGN_NEOPLASM_OF_PITUITARY_GLAND_AND_CRANIOPHARYNGEAL_DUCT,9 +D352,0,disease.BENIGN_NEOPLASM_OF_PITUITARY_GLAND_AND_CRANIOPHARYNGEAL_DUCT,10 +D353,0,disease.BENIGN_NEOPLASM_OF_PITUITARY_GLAND_AND_CRANIOPHARYNGEAL_DUCT,10 +5716,0,disease.BILIARY_CIRRHOSIS,9 +K74,1,disease.BILIARY_CIRRHOSIS,10 +174,1,disease.BREAST_CANCER,9 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+1058629 +1080768 +1185136 +1221112 +1250240 +1366906 +1429864 +1655446 +1693218 +1751009 +1848386 +1980560 +2053598 +2110086 +2161911 +2257294 +2257507 +2595015 +2613498 +2730428 +2854803 +2908077 +2989041 +3035878 +3070110 +3214299 +3246011 +3348046 +3388383 +3389067 +3408919 +3446885 +3462805 +3489199 +3520811 +3586948 +3613028 +3633277 +3657600 +3734628 +3870691 +3912039 +3935141 +4193902 +4345711 +4347445 +4536096 +4635906 +4718762 +4813031 +4863191 +4942670 +5010375 +5110523 +5133664 +5187233 +5202400 +5208764 +5212723 +5343722 +5373847 +5380268 +5527630 +5579250 +5635590 +5759383 +5766979 +5771364 +5825337 +5830815 +5831047 +5867709 +5889303 +5902957 diff --git a/run_GWAS.R b/run_GWAS.R new file mode 100644 index 0000000..0d41691 --- /dev/null +++ b/run_GWAS.R @@ -0,0 +1,203 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,Rmd,R:light +# text_representation: +# extension: .R +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.11.4 +# kernelspec: +# display_name: R 4.2 +# language: R +# name: ir42 +# --- + +# # GWAS for longevity and disease models + +# ## Initialize definitions + +# + tags=[] +source(here::here("code/init.R")) +source(here::here("code/gwas.R")) +#options(gmax.data.size = 1e9) +options(tgutil.cache=FALSE) + +# + [markdown] tags=[] +# ### installing gwiser +# This package is used for running gwas +# + +# + +#remotes::install_github("tanaylab/gwiser") +# - + +library(gwiser) + +# ## Load scores + +# See definition of all scores at `import` notebook, and at Netta's scripts. + +pop <- data.table::fread(here::here('output/pop_scores.csv')) %>% as_tibble() +head(pop %>% select(-id)) + +# ### Longevity + +# * We are removing patients with age above 75 years old (age != 80) due to small numbers +# * When a patient appears twice (in multiple age groups), we are choosing the one closer to 60. +# * We are transforming the score to rank-based inverse normal (https://cran.r-project.org/web/packages/RNOmni/vignettes/RNOmni.html) + +longevity_score <- pop %>% + select(id, sex, age, longevity) %>% + filter(age != 80) %>% + group_by(age, sex) %>% + mutate(score = RNOmni::RankNorm(longevity)) %>% + ungroup() %>% + arrange(abs(age - 60)) %>% + distinct(id, .keep_all=TRUE) %>% + as_tibble() %cache_df% + here("output/longevity_score_inverse_rank.tsv") %>% + as_tibble() +head(longevity_score %>% select(-id)) + +longevity_score %>% + ggplot(aes(x=score, color=factor(age))) + geom_density() + +longevity_score %>% + gather("type", "val", -(id:age)) %>% + ggplot(aes(x=val, fill = type)) + geom_density(alpha = 0.3) + +# ### Disease score +# Patients who are already sick get a score of 1. + +# + +disease_score <- pop %>% + select(id:liver) %>% + pivot_longer(c("diabetes","ckd", "copd", "cvd", "liver"), names_to = "disease", values_to = "score") %>% filter(age != 80) %>% + arrange(abs(age - 60)) %>% + distinct(id, disease, .keep_all=TRUE) %>% + replace_na(replace = list(score = 1)) %>% # Patients who are already sick get a score of 1 + group_by(disease, age, sex) %>% + mutate(score_norm = RNOmni::RankNorm(score)) %>% + ungroup() %>% + as_tibble() %cache_df% + here("output/disease_score_inverse_rank.tsv") %>% + as_tibble() + +head(disease_score %>% select(-id)) +# - + +# ## Run GWAS + +library(bigsnpr) +library(bigreadr) +genes <- get_imputed_genes() + +# ### Longevity + +# number of patients: + +wb_patients <- fread(here("output/ukbb_white.british_patients.csv"))$id +sum(longevity_score$id %in% wb_patients & !is.na(longevity_score$score)) + +head(longevity_score %>% select(-id)) +longevity_score %>% filter(is.na(score)) %>% nrow() + +gwas_longevity <- run_gwas_white_british( + score_df = longevity_score %>% select(id, score), + covar = longevity_score %>% select(id, age, sex) %>% mutate(sex=as.numeric(factor(sex, levels=c('male', 'female')))), + genes = genes, ncores=70) %cache_rds% here("output/gwas_longevity_age_sex_covar_extended.rds") + +gwas_longevity_annot <- gwas_longevity %>% + mutate(chrom = gsub("chr0", "chr", chrom)) %>% + arrange(pval) %cache_df% here("output/gwas_longevity_age_sex_covar_extended.tsv") %>% as_tibble() + + +# ### Longevity with disease confounders + +covar_df <- longevity_score %>% + select(id, age, sex) %>% + mutate(sex=as.numeric(factor(sex, levels=c('male', 'female')))) %>% + left_join( + disease_score %>% + select(id, age, disease, score_norm) %>% spread(disease, score_norm), + by = c("id", "age")) %>% + as_tibble() %cache_df% + here::here("output/disease_covariance.tsv") %>% + as_tibble() +head(covar_df) +stopifnot(all(longevity_score$id == covar_df$id)) +#data.table::fwrite(covar_df, here::here("output/disease_covariance.csv")) + +bigparallelr::nb_cores() + +# + tags=[] +gwas_longevity_disease_covar <- run_gwas_white_british( + score_df = longevity_score %>% select(id, score), + covar = covar_df, + genes = genes, ncores=70) %cache_rds% here("output/gwas_longevity_age_sex_disease_covar_extended.rds") +# - + +options(repr.plot.width = 10, repr.plot.height = 8) +bigsnpr::snp_manhattan(gwas_longevity_disease_covar, genes$map$chromosome, genes$map$physical.pos, npoints = 50e3, coeff = 1) + +options(repr.plot.width = 10, repr.plot.height = 8) +bigsnpr::snp_qq(gwas_longevity_disease_covar) + +gwas_longevity_disease_covar_annot <- gwas_longevity_disease_covar %>% + mutate(chrom = gsub("chr0", "chr", chrom)) %>% + arrange(pval) %cache_df% here("output/gwas_longevity_age_sex_disease_covar_extended.tsv") %>% as_tibble() + +# ### Diseases + +diseases <- unique(disease_score$disease) +diseases + +# + jupyter={"outputs_hidden": true} tags=[] +library(glue) +walk(diseases, ~ { + cli_alert_info(.x) + df <- disease_score %>% + filter(disease == .x) %>% + select(-sex) %>% + left_join(genes$fam %>% select(id = sample.ID, sex)) %>% + select(id, age, score = score_norm, sex) + res <- run_gwas_white_british( + score_df = df %>% select(id, score), + covar = df %>% select(id, age, sex), + genes = genes, ncores=70) %cache_rds% here(glue("output/gwas_{.x}_age_sex_covar_extended.rds")) + res %>% + mutate(chrom = gsub("chr0", "chr", chrom)) %>% + arrange(pval) %cache_df% here(glue("output/gwas_{.x}_age_sex_covar_extended.tsv")) %>% as_tibble() + gc() +}) +# - + +gc() + +# ## H^2 SNP +# Create ldsc format sumstats: + +pvals <- get_gwas_pvals() %cache_df% here("output/all_pvals.tsv") %>% as_tibble() + +# adding std.err column from gwas of longevity with disease as covariance + +gwas_longevity_disease_covar <- readr::read_rds(here("output/gwas_longevity_age_sex_disease_covar.rds")) + +pvals <- pvals %>% left_join(gwas_longevity_disease_covar %>% select(marker.ID, allele1, allele2, std.err)) + +pvals %>% + filter(chrom != "chrX") %>% + mutate(N = 328542, CHR = gsub("chr", "", chrom), Z = longevity_disease_covar_beta / std.err) %>% + select(CHR, BP = start, A1 = allele1, A2 = allele2, N, Z, P = longevity_disease_covar_pval, SNP = rsid) %>% + fwrite(here("output/longevity_snps_ldsc.sumstats"), sep = " ", quote = FALSE) + +# at the terminal (polyfun conda): +# +# ./ldsc.py \ +# --out /home/aviezerl/proj/ukbb/output/longevity_snps_ldsc.h2 \ +# --h2 /home/aviezerl/proj/ukbb/output/longevity_snps_ldsc.sumstats \ +# --ref-ld-chr baselineLF2.2.UKB/baselineLF2.2.UKB. \ +# --w-ld-chr baselineLF2.2.UKB/weights.UKB. \ +# --not-M-5-50 +# Total Observed scale h2: 0.0637 (0.0084) diff --git a/run_GWAS.ipynb b/run_GWAS.ipynb new file mode 100644 index 0000000..92fade5 --- /dev/null +++ b/run_GWAS.ipynb @@ -0,0 +1,1199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "472286c5-bf38-4b46-bc7c-feaf10d83b1e", + "metadata": {}, + "source": [ + "# GWAS for longevity and disease models " + ] + }, + { + "cell_type": "markdown", + "id": "7a3bb391-c111-47d2-a845-61c4dfd9a2c7", + "metadata": {}, + "source": [ + "## Initialize definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "d4a3ab5f-2148-471a-a498-19fb3d8d652b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "source(here::here(\"code/init.R\"))\n", + "source(here::here(\"code/gwas.R\"))\n", + "#options(gmax.data.size = 1e9)\n", + "options(tgutil.cache=FALSE)" + ] + }, + { + "cell_type": "markdown", + "id": "fae35206-2b6f-481b-bb37-09ea13fe3912", + "metadata": { + "tags": [] + }, + "source": [ + "### installing gwiser\n", + "This package is used for running gwas \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e83f08dd-ae9d-436d-98eb-31256f3bf524", + "metadata": {}, + "outputs": [], + "source": [ + "#remotes::install_github(\"tanaylab/gwiser\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6e7cc3fd-a734-470a-8fc1-5106dc481ec2", + "metadata": {}, + "outputs": [], + "source": [ + "library(gwiser) " + ] + }, + { + "cell_type": "markdown", + "id": "19d2d0c6-3df7-439f-a3f7-3e7469122420", + "metadata": {}, + "source": [ + "## Load scores " + ] + }, + { + "cell_type": "markdown", + "id": "84b5c968-0d0b-4334-897c-29c4d77db57e", + "metadata": {}, + "source": [ + "See definition of all scores at `import` notebook, and at Netta's scripts. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "eaf96314-45c8-460b-b104-7c390ed8a8bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 9
agesexlongevitylongevity_qdiabetesckdcopdcvdliver
<int><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
45female0.96758820.19257920.088155240.041675530.027986280.20603820.022592036
45male 0.91192810.10603480.159096920.039192490.090642470.29449000.008487539
45female0.99698310.45743610.209795140.138553650.128116470.63706790.022256322
45male 0.99452550.39842700.077918440.059655310.043355170.21177180.033393441
45female0.98381660.26512810.080082880.022186810.065933830.12077450.009952694
45male 0.93772170.13451660.031964070.049305710.012635090.11572290.014305011
\n" + ], + "text/latex": [ + "A tibble: 6 × 9\n", + "\\begin{tabular}{lllllllll}\n", + " age & sex & longevity & longevity\\_q & diabetes & ckd & copd & cvd & liver\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t 45 & female & 0.9675882 & 0.1925792 & 0.08815524 & 0.04167553 & 0.02798628 & 0.2060382 & 0.022592036\\\\\n", + "\t 45 & male & 0.9119281 & 0.1060348 & 0.15909692 & 0.03919249 & 0.09064247 & 0.2944900 & 0.008487539\\\\\n", + "\t 45 & female & 0.9969831 & 0.4574361 & 0.20979514 & 0.13855365 & 0.12811647 & 0.6370679 & 0.022256322\\\\\n", + "\t 45 & male & 0.9945255 & 0.3984270 & 0.07791844 & 0.05965531 & 0.04335517 & 0.2117718 & 0.033393441\\\\\n", + "\t 45 & female & 0.9838166 & 0.2651281 & 0.08008288 & 0.02218681 & 0.06593383 & 0.1207745 & 0.009952694\\\\\n", + "\t 45 & male & 0.9377217 & 0.1345166 & 0.03196407 & 0.04930571 & 0.01263509 & 0.1157229 & 0.014305011\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 9\n", + "\n", + "| age <int> | sex <chr> | longevity <dbl> | longevity_q <dbl> | diabetes <dbl> | ckd <dbl> | copd <dbl> | cvd <dbl> | liver <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| 45 | female | 0.9675882 | 0.1925792 | 0.08815524 | 0.04167553 | 0.02798628 | 0.2060382 | 0.022592036 |\n", + "| 45 | male | 0.9119281 | 0.1060348 | 0.15909692 | 0.03919249 | 0.09064247 | 0.2944900 | 0.008487539 |\n", + "| 45 | female | 0.9969831 | 0.4574361 | 0.20979514 | 0.13855365 | 0.12811647 | 0.6370679 | 0.022256322 |\n", + "| 45 | male | 0.9945255 | 0.3984270 | 0.07791844 | 0.05965531 | 0.04335517 | 0.2117718 | 0.033393441 |\n", + "| 45 | female | 0.9838166 | 0.2651281 | 0.08008288 | 0.02218681 | 0.06593383 | 0.1207745 | 0.009952694 |\n", + "| 45 | male | 0.9377217 | 0.1345166 | 0.03196407 | 0.04930571 | 0.01263509 | 0.1157229 | 0.014305011 |\n", + "\n" + ], + "text/plain": [ + " age sex longevity longevity_q diabetes ckd copd cvd \n", + "1 45 female 0.9675882 0.1925792 0.08815524 0.04167553 0.02798628 0.2060382\n", + "2 45 male 0.9119281 0.1060348 0.15909692 0.03919249 0.09064247 0.2944900\n", + "3 45 female 0.9969831 0.4574361 0.20979514 0.13855365 0.12811647 0.6370679\n", + "4 45 male 0.9945255 0.3984270 0.07791844 0.05965531 0.04335517 0.2117718\n", + "5 45 female 0.9838166 0.2651281 0.08008288 0.02218681 0.06593383 0.1207745\n", + "6 45 male 0.9377217 0.1345166 0.03196407 0.04930571 0.01263509 0.1157229\n", + " liver \n", + "1 0.022592036\n", + "2 0.008487539\n", + "3 0.022256322\n", + "4 0.033393441\n", + "5 0.009952694\n", + "6 0.014305011" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop <- data.table::fread(here::here('output/pop_scores.csv')) %>% as_tibble()\n", + "head(pop %>% select(-id))" + ] + }, + { + "cell_type": "markdown", + "id": "cb3c74d6-72c5-4cea-b23b-4d11f284d033", + "metadata": {}, + "source": [ + "### Longevity" + ] + }, + { + "cell_type": "markdown", + "id": "23dd5108-0be3-47ea-8648-637752d3cff5", + "metadata": {}, + "source": [ + "* We are removing patients with age above 75 years old (age != 80) due to small numbers\n", + "* When a patient appears twice (in multiple age groups), we are choosing the one closer to 60. \n", + "* We are transforming the score to rank-based inverse normal (https://cran.r-project.org/web/packages/RNOmni/vignettes/RNOmni.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f1100ddd-f1c8-4519-8426-a7ade519c68e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 4
sexagelongevityscore
<chr><int><dbl><dbl>
female600.9549366-0.67789484
male 600.9999968 2.05605786
female600.5790967-2.22689019
female600.9999069 1.44644466
female600.9999707 1.74183198
male 600.9921567 0.02910422
\n" + ], + "text/latex": [ + "A tibble: 6 × 4\n", + "\\begin{tabular}{llll}\n", + " sex & age & longevity & score\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t female & 60 & 0.9549366 & -0.67789484\\\\\n", + "\t male & 60 & 0.9999968 & 2.05605786\\\\\n", + "\t female & 60 & 0.5790967 & -2.22689019\\\\\n", + "\t female & 60 & 0.9999069 & 1.44644466\\\\\n", + "\t female & 60 & 0.9999707 & 1.74183198\\\\\n", + "\t male & 60 & 0.9921567 & 0.02910422\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 4\n", + "\n", + "| sex <chr> | age <int> | longevity <dbl> | score <dbl> |\n", + "|---|---|---|---|\n", + "| female | 60 | 0.9549366 | -0.67789484 |\n", + "| male | 60 | 0.9999968 | 2.05605786 |\n", + "| female | 60 | 0.5790967 | -2.22689019 |\n", + "| female | 60 | 0.9999069 | 1.44644466 |\n", + "| female | 60 | 0.9999707 | 1.74183198 |\n", + "| male | 60 | 0.9921567 | 0.02910422 |\n", + "\n" + ], + "text/plain": [ + " sex age longevity score \n", + "1 female 60 0.9549366 -0.67789484\n", + "2 male 60 0.9999968 2.05605786\n", + "3 female 60 0.5790967 -2.22689019\n", + "4 female 60 0.9999069 1.44644466\n", + "5 female 60 0.9999707 1.74183198\n", + "6 male 60 0.9921567 0.02910422" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "longevity_score <- pop %>% \n", + " select(id, sex, age, longevity) %>% \n", + " filter(age != 80) %>% \n", + " group_by(age, sex) %>% \n", + " mutate(score = RNOmni::RankNorm(longevity)) %>% \n", + " ungroup() %>% \n", + " arrange(abs(age - 60)) %>% \n", + " distinct(id, .keep_all=TRUE) %>% \n", + " as_tibble() %cache_df%\n", + " here(\"output/longevity_score_inverse_rank.tsv\") %>% \n", + " as_tibble()\n", + "head(longevity_score %>% select(-id))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9545a7c1-e710-4086-ad16-b619c9d44625", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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y7/BRAReRGDHRFdtn3xoyNounmiCoDp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8WMgBgCSzfZTZETJOXn755RUrVixatOil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Pijjz5atWrVoUOH1q9f73a7n3jiCbfb/e67\n73788ccTbnfS19d3/vz5zMedOXPm/vvvTyQSra2tlZWV3d3dBw4cePbZZ3fs2JGd9QeAOxXB\nDsCtGBgYeO655w4cOOD3+0tKStauXbtt27bq6upMhd7e3ueee864s11DQ8OLL75YV1dnzDp1\n6tTWrVuPHDkSiURqa2ufeeaZpqamzBv/PdiJyODg4E9+8pPDhw9fvnx52bJl3/3udzdt2mSx\ncMwBAD6DYAcAAGAS/N4FAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4A\nAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJP4f9YmrR6qVG5OAAAA\nAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "longevity_score %>% \n", + " ggplot(aes(x=score, color=factor(age))) + geom_density()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2e721ed1-be51-4b59-839f-beb39e7da822", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dsSZ2QDyEHUA6jrEDouWs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\nLySdzs7OzTffvNqrYAP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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "longevity_score %>% \n", + " gather(\"type\", \"val\", -(id:age)) %>%\n", + " ggplot(aes(x=val, fill = type)) + geom_density(alpha = 0.3)" + ] + }, + { + "cell_type": "markdown", + "id": "a38d6cc9-593c-43fe-8fd2-34b245bb10bb", + "metadata": {}, + "source": [ + "### Disease score\n", + "Patients who are already sick get a score of 1." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "02eae8f2-6d95-4e29-a01f-d05a8d5c50ef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 7
agesexlongevitylongevity_qdiseasescorescore_norm
<int><chr><dbl><dbl><chr><dbl><dbl>
60female0.95493660.1554717diabetes0.28750988-0.03568444
60female0.95493660.1554717ckd 0.28626550 0.67358331
60female0.95493660.1554717copd 0.10343341 0.41308296
60female0.95493660.1554717cvd 0.66106439 0.31051869
60female0.95493660.1554717liver 0.08393564 0.38211641
60male 0.99999680.8883456diabetes1.00000000 1.76180221
\n" + ], + "text/latex": [ + "A tibble: 6 × 7\n", + "\\begin{tabular}{lllllll}\n", + " age & sex & longevity & longevity\\_q & disease & score & score\\_norm\\\\\n", + " & & & & & & \\\\\n", + "\\hline\n", + "\t 60 & female & 0.9549366 & 0.1554717 & diabetes & 0.28750988 & -0.03568444\\\\\n", + "\t 60 & female & 0.9549366 & 0.1554717 & ckd & 0.28626550 & 0.67358331\\\\\n", + "\t 60 & female & 0.9549366 & 0.1554717 & copd & 0.10343341 & 0.41308296\\\\\n", + "\t 60 & female & 0.9549366 & 0.1554717 & cvd & 0.66106439 & 0.31051869\\\\\n", + "\t 60 & female & 0.9549366 & 0.1554717 & liver & 0.08393564 & 0.38211641\\\\\n", + "\t 60 & male & 0.9999968 & 0.8883456 & diabetes & 1.00000000 & 1.76180221\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 7\n", + "\n", + "| age <int> | sex <chr> | longevity <dbl> | longevity_q <dbl> | disease <chr> | score <dbl> | score_norm <dbl> |\n", + "|---|---|---|---|---|---|---|\n", + "| 60 | female | 0.9549366 | 0.1554717 | diabetes | 0.28750988 | -0.03568444 |\n", + "| 60 | female | 0.9549366 | 0.1554717 | ckd | 0.28626550 | 0.67358331 |\n", + "| 60 | female | 0.9549366 | 0.1554717 | copd | 0.10343341 | 0.41308296 |\n", + "| 60 | female | 0.9549366 | 0.1554717 | cvd | 0.66106439 | 0.31051869 |\n", + "| 60 | female | 0.9549366 | 0.1554717 | liver | 0.08393564 | 0.38211641 |\n", + "| 60 | male | 0.9999968 | 0.8883456 | diabetes | 1.00000000 | 1.76180221 |\n", + "\n" + ], + "text/plain": [ + " age sex longevity longevity_q disease score score_norm \n", + "1 60 female 0.9549366 0.1554717 diabetes 0.28750988 -0.03568444\n", + "2 60 female 0.9549366 0.1554717 ckd 0.28626550 0.67358331\n", + "3 60 female 0.9549366 0.1554717 copd 0.10343341 0.41308296\n", + "4 60 female 0.9549366 0.1554717 cvd 0.66106439 0.31051869\n", + "5 60 female 0.9549366 0.1554717 liver 0.08393564 0.38211641\n", + "6 60 male 0.9999968 0.8883456 diabetes 1.00000000 1.76180221" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "disease_score <- pop %>%\n", + " select(id:liver) %>% \n", + " pivot_longer(c(\"diabetes\",\"ckd\", \"copd\", \"cvd\", \"liver\"), names_to = \"disease\", values_to = \"score\") %>% filter(age != 80) %>% \n", + " arrange(abs(age - 60)) %>% \n", + " distinct(id, disease, .keep_all=TRUE) %>% \n", + " replace_na(replace = list(score = 1)) %>% # Patients who are already sick get a score of 1 \n", + " group_by(disease, age, sex) %>% \n", + " mutate(score_norm = RNOmni::RankNorm(score)) %>% \n", + " ungroup() %>%\n", + " as_tibble() %cache_df%\n", + " here(\"output/disease_score_inverse_rank.tsv\") %>% \n", + " as_tibble()\n", + " \n", + "head(disease_score %>% select(-id))" + ] + }, + { + "cell_type": "markdown", + "id": "69722d52", + "metadata": {}, + "source": [ + "## Run GWAS" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "72245a91-f992-4b99-97b9-623a4dd00667", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading preprocessed genetic data (imputed genotypes)\n", + "\n" + ] + } + ], + "source": [ + "library(bigsnpr)\n", + "library(bigreadr)\n", + "genes <- get_imputed_genes()" + ] + }, + { + "cell_type": "markdown", + "id": "8e056f29-b1d4-4e91-8235-4f8a6e415ea8", + "metadata": {}, + "source": [ + "### Longevity" + ] + }, + { + "cell_type": "markdown", + "id": "b3143e8e-2c0e-4a4d-8f7d-2641abd34942", + "metadata": {}, + "source": [ + "number of patients:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "7fdaa747-0b7b-4369-ba63-f3b06a2a8b80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "328542" + ], + "text/latex": [ + "328542" + ], + "text/markdown": [ + "328542" + ], + "text/plain": [ + "[1] 328542" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wb_patients <- fread(here(\"output/ukbb_white.british_patients.csv\"))$id\n", + "sum(longevity_score$id %in% wb_patients & !is.na(longevity_score$score))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fe907e63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 4
sexagelongevityscore
<chr><int><dbl><dbl>
female600.9549366-0.67789484
male 600.9999968 2.05605786
female600.5790967-2.22689019
female600.9999069 1.44644466
female600.9999707 1.74183198
male 600.9921567 0.02910422
\n" + ], + "text/latex": [ + "A tibble: 6 × 4\n", + "\\begin{tabular}{llll}\n", + " sex & age & longevity & score\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t female & 60 & 0.9549366 & -0.67789484\\\\\n", + "\t male & 60 & 0.9999968 & 2.05605786\\\\\n", + "\t female & 60 & 0.5790967 & -2.22689019\\\\\n", + "\t female & 60 & 0.9999069 & 1.44644466\\\\\n", + "\t female & 60 & 0.9999707 & 1.74183198\\\\\n", + "\t male & 60 & 0.9921567 & 0.02910422\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 4\n", + "\n", + "| sex <chr> | age <int> | longevity <dbl> | score <dbl> |\n", + "|---|---|---|---|\n", + "| female | 60 | 0.9549366 | -0.67789484 |\n", + "| male | 60 | 0.9999968 | 2.05605786 |\n", + "| female | 60 | 0.5790967 | -2.22689019 |\n", + "| female | 60 | 0.9999069 | 1.44644466 |\n", + "| female | 60 | 0.9999707 | 1.74183198 |\n", + "| male | 60 | 0.9921567 | 0.02910422 |\n", + "\n" + ], + "text/plain": [ + " sex age longevity score \n", + "1 female 60 0.9549366 -0.67789484\n", + "2 male 60 0.9999968 2.05605786\n", + "3 female 60 0.5790967 -2.22689019\n", + "4 female 60 0.9999069 1.44644466\n", + "5 female 60 0.9999707 1.74183198\n", + "6 male 60 0.9921567 0.02910422" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "0" + ], + "text/latex": [ + "0" + ], + "text/markdown": [ + "0" + ], + "text/plain": [ + "[1] 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(longevity_score %>% select(-id))\n", + "longevity_score %>% filter(is.na(score)) %>% nrow()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "662c28cf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m seed: \u001b[34m\u001b[34m60427\u001b[34m\u001b[39m\n", + "\n", + "\u001b[36mi\u001b[39m using \u001b[34m\u001b[34m327411\u001b[34m\u001b[39m ids (out of \u001b[34m\u001b[34m328542\u001b[34m\u001b[39m in \u001b[32m\u001b[32mscore_df\u001b[32m\u001b[39m and out of \u001b[34m\u001b[34m486757\u001b[34m\u001b[39m at the full \u001b[32m\u001b[32mgenes\u001b[32m\u001b[39m object).\n", + "\n", + "\u001b[36mi\u001b[39m Running GWAS (linear regression)\n", + "\n", + "\u001b[36mi\u001b[39m Computing p-values\n", + "\n", + "\u001b[36mi\u001b[39m Formatting result\n", + "\n", + "\u001b[32mv\u001b[39m GWAS computed succesfully.\n", + "\n" + ] + } + ], + "source": [ + "gwas_longevity <- run_gwas_white_british(\n", + " score_df = longevity_score %>% select(id, score), \n", + " covar = longevity_score %>% select(id, age, sex) %>% mutate(sex=as.numeric(factor(sex, levels=c('male', 'female')))), \n", + " genes = genes, ncores=70) %cache_rds% here(\"output/gwas_longevity_age_sex_covar_extended.rds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "bb54fee3", + "metadata": {}, + "outputs": [], + "source": [ + "gwas_longevity_annot <- gwas_longevity %>%\n", + " mutate(chrom = gsub(\"chr0\", \"chr\", chrom)) %>% \n", + " arrange(pval) %cache_df% here(\"output/gwas_longevity_age_sex_covar_extended.tsv\") %>% as_tibble()\n" + ] + }, + { + "cell_type": "markdown", + "id": "7d23f278-b5c2-4bc1-a432-e9a63e36cfef", + "metadata": {}, + "source": [ + "### Longevity with disease confounders " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0d45c58d-21cd-412f-b250-4bde9eadbc61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 8
idagesexckdcopdcvddiabetesliver
<int><int><dbl><dbl><dbl><dbl><dbl><dbl>
1000019602 0.6735833 0.4130830 0.3105187-0.03568444 0.3821164
1000035601 1.1376099 1.5829298 1.6208614 1.76180221 0.6772193
1000078602-0.7250093-0.2529133-0.3637774-0.26826177-0.7730427
1000081602 0.1751377 0.7137014 1.2381123 1.02198105 2.0536824
1000287602 1.3299205 1.8791902 1.2179476 1.19609653 0.8294608
1000357601-0.1867358-0.4803372 0.2739957 0.90762588 0.9107958
\n" + ], + "text/latex": [ + "A tibble: 6 × 8\n", + "\\begin{tabular}{llllllll}\n", + " id & age & sex & ckd & copd & cvd & diabetes & liver\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t 1000019 & 60 & 2 & 0.6735833 & 0.4130830 & 0.3105187 & -0.03568444 & 0.3821164\\\\\n", + "\t 1000035 & 60 & 1 & 1.1376099 & 1.5829298 & 1.6208614 & 1.76180221 & 0.6772193\\\\\n", + "\t 1000078 & 60 & 2 & -0.7250093 & -0.2529133 & -0.3637774 & -0.26826177 & -0.7730427\\\\\n", + "\t 1000081 & 60 & 2 & 0.1751377 & 0.7137014 & 1.2381123 & 1.02198105 & 2.0536824\\\\\n", + "\t 1000287 & 60 & 2 & 1.3299205 & 1.8791902 & 1.2179476 & 1.19609653 & 0.8294608\\\\\n", + "\t 1000357 & 60 & 1 & -0.1867358 & -0.4803372 & 0.2739957 & 0.90762588 & 0.9107958\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 8\n", + "\n", + "| id <int> | age <int> | sex <dbl> | ckd <dbl> | copd <dbl> | cvd <dbl> | diabetes <dbl> | liver <dbl> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| 1000019 | 60 | 2 | 0.6735833 | 0.4130830 | 0.3105187 | -0.03568444 | 0.3821164 |\n", + "| 1000035 | 60 | 1 | 1.1376099 | 1.5829298 | 1.6208614 | 1.76180221 | 0.6772193 |\n", + "| 1000078 | 60 | 2 | -0.7250093 | -0.2529133 | -0.3637774 | -0.26826177 | -0.7730427 |\n", + "| 1000081 | 60 | 2 | 0.1751377 | 0.7137014 | 1.2381123 | 1.02198105 | 2.0536824 |\n", + "| 1000287 | 60 | 2 | 1.3299205 | 1.8791902 | 1.2179476 | 1.19609653 | 0.8294608 |\n", + "| 1000357 | 60 | 1 | -0.1867358 | -0.4803372 | 0.2739957 | 0.90762588 | 0.9107958 |\n", + "\n" + ], + "text/plain": [ + " id age sex ckd copd cvd diabetes liver \n", + "1 1000019 60 2 0.6735833 0.4130830 0.3105187 -0.03568444 0.3821164\n", + "2 1000035 60 1 1.1376099 1.5829298 1.6208614 1.76180221 0.6772193\n", + "3 1000078 60 2 -0.7250093 -0.2529133 -0.3637774 -0.26826177 -0.7730427\n", + "4 1000081 60 2 0.1751377 0.7137014 1.2381123 1.02198105 2.0536824\n", + "5 1000287 60 2 1.3299205 1.8791902 1.2179476 1.19609653 0.8294608\n", + "6 1000357 60 1 -0.1867358 -0.4803372 0.2739957 0.90762588 0.9107958" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covar_df <- longevity_score %>% \n", + " select(id, age, sex) %>% \n", + " mutate(sex=as.numeric(factor(sex, levels=c('male', 'female')))) %>% \n", + " left_join(\n", + " disease_score %>%\n", + " select(id, age, disease, score_norm) %>% spread(disease, score_norm),\n", + " by = c(\"id\", \"age\")) %>% \n", + " as_tibble() %cache_df%\n", + " here::here(\"output/disease_covariance.tsv\") %>% \n", + " as_tibble()\n", + "head(covar_df)\n", + "stopifnot(all(longevity_score$id == covar_df$id))\n", + "#data.table::fwrite(covar_df, here::here(\"output/disease_covariance.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d0124507-1a3e-49fa-b8a0-adb3bfaa7a57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1" + ], + "text/latex": [ + "1" + ], + "text/markdown": [ + "1" + ], + "text/plain": [ + "[1] 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bigparallelr::nb_cores()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "740504ae-f186-4f1c-a24b-c43b0cd6ab9f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m seed: \u001b[34m\u001b[34m60427\u001b[34m\u001b[39m\n", + "\n", + "\u001b[36mi\u001b[39m using \u001b[34m\u001b[34m327411\u001b[34m\u001b[39m ids (out of \u001b[34m\u001b[34m328542\u001b[34m\u001b[39m in \u001b[32m\u001b[32mscore_df\u001b[32m\u001b[39m and out of \u001b[34m\u001b[34m486757\u001b[34m\u001b[39m at the full \u001b[32m\u001b[32mgenes\u001b[32m\u001b[39m object).\n", + "\n", + "\u001b[36mi\u001b[39m Running GWAS (linear regression)\n", + "\n", + "\u001b[36mi\u001b[39m Computing p-values\n", + "\n", + "\u001b[36mi\u001b[39m Formatting result\n", + "\n", + "\u001b[32mv\u001b[39m GWAS computed succesfully.\n", + "\n" + ] + } + ], + "source": [ + "gwas_longevity_disease_covar <- run_gwas_white_british(\n", + " score_df = longevity_score %>% select(id, score), \n", + " covar = covar_df, \n", + " genes = genes, ncores=70) %cache_rds% here(\"output/gwas_longevity_age_sex_disease_covar_extended.rds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "465ef2d0-2511-4ca7-930c-da1ae6ac4128", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6UrTiGEtx7/FS1atOjZs6cQ4uOPP1YeHK38JKHyON+FCxcqlOfm5s6YMUP57PtW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\njgcPHlj8xHRkVBtRKBQs3zYHfUUQxJtvvmlL8DyT1NTUrFu3zpz+z8jIO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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 10, repr.plot.height = 8)\n", + "bigsnpr::snp_manhattan(gwas_longevity_disease_covar, genes$map$chromosome, genes$map$physical.pos, npoints = 50e3, coeff = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c2f5c606-2e72-453b-ad77-a5c0b8010670", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22mRemoved 4852 rows containing missing values (`geom_point()`).\"\n" + ] + }, + { + "data": { + "image/png": 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BmTaZwpU6YIIdRq9eXLl7N9ocTExOrV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\nrkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 10, repr.plot.height = 8)\n", + "bigsnpr::snp_qq(gwas_longevity_disease_covar)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "73e8e288-290a-4233-bfc6-fc3ea96f90e1", + "metadata": {}, + "outputs": [], + "source": [ + "gwas_longevity_disease_covar_annot <- gwas_longevity_disease_covar %>%\n", + " mutate(chrom = gsub(\"chr0\", \"chr\", chrom)) %>% \n", + " arrange(pval) %cache_df% here(\"output/gwas_longevity_age_sex_disease_covar_extended.tsv\") %>% as_tibble()" + ] + }, + { + "cell_type": "markdown", + "id": "90d73668-20a0-42c5-952f-7de2db52d7b4", + "metadata": {}, + "source": [ + "### Diseases" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fe860c7a-420d-40e5-9aae-9c0271b0bde9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'diabetes'
  2. 'ckd'
  3. 'copd'
  4. 'cvd'
  5. 'liver'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'diabetes'\n", + "\\item 'ckd'\n", + "\\item 'copd'\n", + "\\item 'cvd'\n", + "\\item 'liver'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'diabetes'\n", + "2. 'ckd'\n", + "3. 'copd'\n", + "4. 'cvd'\n", + "5. 'liver'\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] \"diabetes\" \"ckd\" \"copd\" \"cvd\" \"liver\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diseases <- unique(disease_score$disease)\n", + "diseases" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8f3d1581-17fe-41e8-a677-21f44ec6827f", + "metadata": { + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m diabetes\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m seed: \u001b[34m\u001b[34m60427\u001b[34m\u001b[39m\n", + "\n", + "\u001b[36mi\u001b[39m using \u001b[34m\u001b[34m327411\u001b[34m\u001b[39m ids (out of \u001b[34m\u001b[34m328542\u001b[34m\u001b[39m in \u001b[32m\u001b[32mscore_df\u001b[32m\u001b[39m and out of \u001b[34m\u001b[34m486757\u001b[34m\u001b[39m at the full \u001b[32m\u001b[32mgenes\u001b[32m\u001b[39m object).\n", + "\n", + "\u001b[36mi\u001b[39m Running GWAS (linear regression)\n", + "\n", + "\u001b[36mi\u001b[39m Computing p-values\n", + "\n", + "\u001b[36mi\u001b[39m Formatting result\n", + "\n", + "\u001b[32mv\u001b[39m GWAS computed succesfully.\n", + "\n", + "\u001b[36mi\u001b[39m ckd\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m seed: \u001b[34m\u001b[34m60427\u001b[34m\u001b[39m\n", + "\n", + "\u001b[36mi\u001b[39m using \u001b[34m\u001b[34m327411\u001b[34m\u001b[39m ids (out of \u001b[34m\u001b[34m328542\u001b[34m\u001b[39m in \u001b[32m\u001b[32mscore_df\u001b[32m\u001b[39m and out of \u001b[34m\u001b[34m486757\u001b[34m\u001b[39m at the full \u001b[32m\u001b[32mgenes\u001b[32m\u001b[39m object).\n", + "\n", + "\u001b[36mi\u001b[39m Running GWAS (linear regression)\n", + "\n", + "\u001b[36mi\u001b[39m Computing p-values\n", + "\n", + "\u001b[36mi\u001b[39m Formatting result\n", + "\n", + "\u001b[32mv\u001b[39m GWAS computed succesfully.\n", + "\n", + "\u001b[36mi\u001b[39m copd\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m seed: \u001b[34m\u001b[34m60427\u001b[34m\u001b[39m\n", + "\n", + "\u001b[36mi\u001b[39m using \u001b[34m\u001b[34m327411\u001b[34m\u001b[39m ids (out of \u001b[34m\u001b[34m328542\u001b[34m\u001b[39m in \u001b[32m\u001b[32mscore_df\u001b[32m\u001b[39m and out of \u001b[34m\u001b[34m486757\u001b[34m\u001b[39m at the full \u001b[32m\u001b[32mgenes\u001b[32m\u001b[39m object).\n", + "\n", + "\u001b[36mi\u001b[39m Running GWAS (linear regression)\n", + "\n", + "\u001b[36mi\u001b[39m Computing p-values\n", + "\n", + "\u001b[36mi\u001b[39m Formatting result\n", + "\n", + "\u001b[32mv\u001b[39m GWAS computed succesfully.\n", + "\n", + "\u001b[36mi\u001b[39m cvd\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m seed: \u001b[34m\u001b[34m60427\u001b[34m\u001b[39m\n", + "\n", + "\u001b[36mi\u001b[39m using \u001b[34m\u001b[34m327411\u001b[34m\u001b[39m ids (out of \u001b[34m\u001b[34m328542\u001b[34m\u001b[39m in \u001b[32m\u001b[32mscore_df\u001b[32m\u001b[39m and out of \u001b[34m\u001b[34m486757\u001b[34m\u001b[39m at the full \u001b[32m\u001b[32mgenes\u001b[32m\u001b[39m object).\n", + "\n", + "\u001b[36mi\u001b[39m Running GWAS (linear regression)\n", + "\n", + "\u001b[36mi\u001b[39m Computing p-values\n", + "\n", + "\u001b[36mi\u001b[39m Formatting result\n", + "\n", + "\u001b[32mv\u001b[39m GWAS computed succesfully.\n", + "\n", + "\u001b[36mi\u001b[39m liver\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(id)`\n", + "\u001b[36mi\u001b[39m seed: \u001b[34m\u001b[34m60427\u001b[34m\u001b[39m\n", + "\n", + "\u001b[36mi\u001b[39m using \u001b[34m\u001b[34m327411\u001b[34m\u001b[39m ids (out of \u001b[34m\u001b[34m328542\u001b[34m\u001b[39m in \u001b[32m\u001b[32mscore_df\u001b[32m\u001b[39m and out of \u001b[34m\u001b[34m486757\u001b[34m\u001b[39m at the full \u001b[32m\u001b[32mgenes\u001b[32m\u001b[39m object).\n", + "\n", + "\u001b[36mi\u001b[39m Running GWAS (linear regression)\n", + "\n", + "\u001b[36mi\u001b[39m Computing p-values\n", + "\n", + "\u001b[36mi\u001b[39m Formatting result\n", + "\n", + "\u001b[32mv\u001b[39m GWAS computed succesfully.\n", + "\n" + ] + } + ], + "source": [ + "library(glue)\n", + "walk(diseases, ~ {\n", + " cli_alert_info(.x) \n", + " df <- disease_score %>% \n", + " filter(disease == .x) %>% \n", + " select(-sex) %>% \n", + " left_join(genes$fam %>% select(id = sample.ID, sex)) %>% \n", + " select(id, age, score = score_norm, sex)\n", + " res <- run_gwas_white_british(\n", + " score_df = df %>% select(id, score), \n", + " covar = df %>% select(id, age, sex), \n", + " genes = genes, ncores=70) %cache_rds% here(glue(\"output/gwas_{.x}_age_sex_covar_extended.rds\"))\n", + " res %>%\n", + " mutate(chrom = gsub(\"chr0\", \"chr\", chrom)) %>% \n", + " arrange(pval) %cache_df% here(glue(\"output/gwas_{.x}_age_sex_covar_extended.tsv\")) %>% as_tibble() \n", + " gc()\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2c5f1f15-a9f9-4a26-a8e3-3c5bbef72258", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 2 × 6 of type dbl
used(Mb)gc trigger(Mb)max used(Mb)
Ncells 293405481567.0 59601695 3183.1 44553990 2379.5
Vcells7662720405846.2144084805010992.8143441482710943.8
\n" + ], + "text/latex": [ + "A matrix: 2 × 6 of type dbl\n", + "\\begin{tabular}{r|llllll}\n", + " & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n", + "\\hline\n", + "\tNcells & 29340548 & 1567.0 & 59601695 & 3183.1 & 44553990 & 2379.5\\\\\n", + "\tVcells & 766272040 & 5846.2 & 1440848050 & 10992.8 & 1434414827 & 10943.8\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 2 × 6 of type dbl\n", + "\n", + "| | used | (Mb) | gc trigger | (Mb) | max used | (Mb) |\n", + "|---|---|---|---|---|---|---|\n", + "| Ncells | 29340548 | 1567.0 | 59601695 | 3183.1 | 44553990 | 2379.5 |\n", + "| Vcells | 766272040 | 5846.2 | 1440848050 | 10992.8 | 1434414827 | 10943.8 |\n", + "\n" + ], + "text/plain": [ + " used (Mb) gc trigger (Mb) max used (Mb) \n", + "Ncells 29340548 1567.0 59601695 3183.1 44553990 2379.5\n", + "Vcells 766272040 5846.2 1440848050 10992.8 1434414827 10943.8" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gc()" + ] + }, + { + "cell_type": "markdown", + "id": "5aeb99fb", + "metadata": {}, + "source": [ + "## H^2 SNP\n", + "Create ldsc format sumstats:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "91e8434d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n" + ] + } + ], + "source": [ + "pvals <- get_gwas_pvals() %cache_df% here(\"output/all_pvals.tsv\") %>% as_tibble()" + ] + }, + { + "cell_type": "markdown", + "id": "ba87a8dc", + "metadata": {}, + "source": [ + "adding std.err column from gwas of longevity with disease as covariance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "592cc5db", + "metadata": {}, + "outputs": [], + "source": [ + "gwas_longevity_disease_covar <- readr::read_rds(here(\"output/gwas_longevity_age_sex_disease_covar.rds\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24710006", + "metadata": {}, + "outputs": [], + "source": [ + "pvals <- pvals %>% left_join(gwas_longevity_disease_covar %>% select(marker.ID, allele1, allele2, std.err))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96f37264", + "metadata": {}, + "outputs": [], + "source": [ + "pvals %>%\n", + " filter(chrom != \"chrX\") %>% \n", + " mutate(N = 328542, CHR = gsub(\"chr\", \"\", chrom), Z = longevity_disease_covar_beta / std.err) %>%\n", + " select(CHR, BP = start, A1 = allele1, A2 = allele2, N, Z, P = longevity_disease_covar_pval, SNP = rsid) %>%\n", + " fwrite(here(\"output/longevity_snps_ldsc.sumstats\"), sep = \" \", quote = FALSE)" + ] + }, + { + "cell_type": "markdown", + "id": "cce53e56", + "metadata": {}, + "source": [ + "at the terminal (polyfun conda):\n", + "\n", + "./ldsc.py \\\n", + "--out /home/aviezerl/proj/ukbb/output/longevity_snps_ldsc.h2 \\\n", + "--h2 /home/aviezerl/proj/ukbb/output/longevity_snps_ldsc.sumstats \\\n", + "--ref-ld-chr baselineLF2.2.UKB/baselineLF2.2.UKB. \\\n", + "--w-ld-chr baselineLF2.2.UKB/weights.UKB. \\\n", + "--not-M-5-50\n", + "Total Observed scale h2: 0.0637 (0.0084)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,Rmd,R:light" + }, + "kernelspec": { + "display_name": "R 4.2", + "language": "R", + "name": "ir42" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.2.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_GWAS_parents_survival.R b/run_GWAS_parents_survival.R new file mode 100644 index 0000000..0810046 --- /dev/null +++ b/run_GWAS_parents_survival.R @@ -0,0 +1,92 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,R:light +# text_representation: +# extension: .R +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.11.4 +# kernelspec: +# display_name: R 4.0.3 +# language: R +# name: ir +# --- + +# # GWAS for parents survival +# ## Initialize definitions + +# + tags=[] +source(here::here("code/init.R")) +source(here::here("code/gwas.R")) +#options(gmax.data.size = 1e9) +library(gwiser) +# - + +# ## Define parents survival phenotype + +parents_survival <- tgutil::fread(here::here("output/ukbb_parents.csv")) +head(parents_survival) + + +scores <- data.table::fread(here::here("output/disease_score_inverse_rank.tsv")) %>% + select(id, age, sex, disease, score_norm) %>% spread(disease, score_norm) +head(scores) + +# ### loading PCA and genes + +pca <- get_ukbb_pca() +genes <- get_imputed_genes() + +wb_patients <- fread(here("output/ukbb_white.british_patients.csv"))$id + +parents_survival <- parents_survival %>% + filter(id %in% wb_patients, id %in% scores$id, id %in% genes$fam$sample.ID) %>% + left_join(scores) %>% + left_join(pca) +head(parents_survival) + +father_survival <- parents_survival %>% + filter(!is.na(ffollow_time), ffollow_time > 0) %>% + select(id, time = ffollow_time, status = fdead, age:PC20) %>% + na.omit() + +mother_survival <- parents_survival %>% + filter(!is.na(mfollow_time), mfollow_time > 0) %>% + select(id, time = mfollow_time, status = mdead, age:PC20) %>% + na.omit() + +both_survival <- bind_rows( + father_survival %>% mutate(parent = "father"), + mother_survival %>% mutate(parent = "mother") + ) %>% + mutate(parent = factor(parent)) %>% + filter(!(status & time < 40)) %>% # remove parents who died before age 40 + mutate(id_both = paste0(id, ".", parent)) + +gwas_both <- { + df <- run_gwas_cox_both_parents(genes, both_survival %>% rename(gender=sex), null_fn = here("output/cox_parents_survival_both_null"), max.jobs=200, use_sge=TRUE) + df <- df %>% left_join(genes$map, by = "marker.ID") + df <- df %>% + rename(chrom = chromosome, start = physical.pos) %>% + mutate(chrom = paste0("chr", chrom), chrom = gsub("chr0", "chr", chrom), end = start + 1, pval = log10(p.value.spa)) %>% + select(chrom, start, end, pval, marker.ID, allele1, allele2, everything()) + } %cache_df% here("output/cox_parents_survival_both_gwas.tsv") %>% as_tibble() + +gwas_mother <- { + df <- run_gwas_cox(genes, mother_survival %>% rename(gender=sex), null_fn = here("output/cox_parents_survival_mother_null"), max.jobs=200) + df <- df %>% left_join(genes$map, by = "marker.ID") + df <- df %>% + rename(chrom = chromosome, start = physical.pos) %>% + mutate(chrom = paste0("chr", chrom), chrom = gsub("chr0", "chr", chrom), end = start + 1, pval = log10(p.value.spa)) %>% + select(chrom, start, end, pval, marker.ID, allele1, allele2, everything()) + } %cache_df% here("output/cox_parents_survival_mother_gwas.tsv") %>% as_tibble() + +gwas_father <- { + df <- run_gwas_cox(genes, father_survival %>% rename(gender=sex), null_fn = here("output/cox_parents_survival_father_null"), max.jobs=200) + df <- df %>% left_join(genes$map, by = "marker.ID") + df <- df %>% + rename(chrom = chromosome, start = physical.pos) %>% + mutate(chrom = paste0("chr", chrom), chrom = gsub("chr0", "chr", chrom), end = start + 1, pval = log10(p.value.spa)) %>% + select(chrom, start, end, pval, marker.ID, allele1, allele2, everything()) + } %cache_df% here("output/cox_parents_survival_father_gwas.tsv") %>% as_tibble() diff --git a/run_GWAS_parents_survival.ipynb b/run_GWAS_parents_survival.ipynb new file mode 100644 index 0000000..b97c586 --- /dev/null +++ b/run_GWAS_parents_survival.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "282536fb-b9fb-4c68-b1d6-8dd4f8435d57", + "metadata": {}, + "source": [ + "# GWAS for parents survival\n", + "## Initialize definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a8f5b635-9821-4897-8201-496b09bfcf84", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "source(here::here(\"code/init.R\"))\n", + "source(here::here(\"code/gwas.R\"))\n", + "#options(gmax.data.size = 1e9)\n", + "library(gwiser) " + ] + }, + { + "cell_type": "markdown", + "id": "9f43b5bf-0155-4293-b7c1-b25a391aadef", + "metadata": {}, + "source": [ + "## Define parents survival phenotype" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4325c6e6-df87-48e9-9bb1-4307e2bbaf80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 9
idmother_age_at_deathmother_last_alivefather_age_at_deathfather_last_alivemdeadmfollow_timefdeadffollow_time
<int><dbl><dbl><dbl><dbl><lgl><int><lgl><int>
11000019Inf 87 49InfFALSE87 TRUE49
21000022Inf 75Inf 78FALSE75FALSE78
31000035InfInfInf 87FALSENAFALSE87
41000046 60Inf 60Inf TRUE60 TRUE60
51000054Inf 74 83InfFALSE74 TRUE83
61000063 52Inf 72Inf TRUE52 TRUE72
\n" + ], + "text/latex": [ + "A data.frame: 6 × 9\n", + "\\begin{tabular}{r|lllllllll}\n", + " & id & mother\\_age\\_at\\_death & mother\\_last\\_alive & father\\_age\\_at\\_death & father\\_last\\_alive & mdead & mfollow\\_time & fdead & ffollow\\_time\\\\\n", + " & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 1000019 & Inf & 87 & 49 & Inf & FALSE & 87 & TRUE & 49\\\\\n", + "\t2 & 1000022 & Inf & 75 & Inf & 78 & FALSE & 75 & FALSE & 78\\\\\n", + "\t3 & 1000035 & Inf & Inf & Inf & 87 & FALSE & NA & FALSE & 87\\\\\n", + "\t4 & 1000046 & 60 & Inf & 60 & Inf & TRUE & 60 & TRUE & 60\\\\\n", + "\t5 & 1000054 & Inf & 74 & 83 & Inf & FALSE & 74 & TRUE & 83\\\\\n", + "\t6 & 1000063 & 52 & Inf & 72 & Inf & TRUE & 52 & TRUE & 72\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 9\n", + "\n", + "| | id <int> | mother_age_at_death <dbl> | mother_last_alive <dbl> | father_age_at_death <dbl> | father_last_alive <dbl> | mdead <lgl> | mfollow_time <int> | fdead <lgl> | ffollow_time <int> |\n", + "|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 1000019 | Inf | 87 | 49 | Inf | FALSE | 87 | TRUE | 49 |\n", + "| 2 | 1000022 | Inf | 75 | Inf | 78 | FALSE | 75 | FALSE | 78 |\n", + "| 3 | 1000035 | Inf | Inf | Inf | 87 | FALSE | NA | FALSE | 87 |\n", + "| 4 | 1000046 | 60 | Inf | 60 | Inf | TRUE | 60 | TRUE | 60 |\n", + "| 5 | 1000054 | Inf | 74 | 83 | Inf | FALSE | 74 | TRUE | 83 |\n", + "| 6 | 1000063 | 52 | Inf | 72 | Inf | TRUE | 52 | TRUE | 72 |\n", + "\n" + ], + "text/plain": [ + " id mother_age_at_death mother_last_alive father_age_at_death\n", + "1 1000019 Inf 87 49 \n", + "2 1000022 Inf 75 Inf \n", + "3 1000035 Inf Inf Inf \n", + "4 1000046 60 Inf 60 \n", + "5 1000054 Inf 74 83 \n", + "6 1000063 52 Inf 72 \n", + " father_last_alive mdead mfollow_time fdead ffollow_time\n", + "1 Inf FALSE 87 TRUE 49 \n", + "2 78 FALSE 75 FALSE 78 \n", + "3 87 FALSE NA FALSE 87 \n", + "4 Inf TRUE 60 TRUE 60 \n", + "5 Inf FALSE 74 TRUE 83 \n", + "6 Inf TRUE 52 TRUE 72 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "parents_survival <- tgutil::fread(here::here(\"output/ukbb_parents.csv\"))\n", + "head(parents_survival)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3c364e88-a824-4f72-97b9-d5987e392244", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 8
idagesexckdcopddiabetesliverncvd
<int><int><chr><dbl><dbl><dbl><dbl><dbl>
100001960female 0.6888426 0.40155519-0.2422354 0.2831111 0.3710600
100002250female 1.6588384 1.29550119 1.5858586 1.7183820 1.6632586
100003560male 1.1588003 1.64335532 1.7567751 0.5568670 1.6163710
100004670female-0.5749957-0.01139192-1.2153981-0.6834640 1.5736620
100005445female-1.1543210-0.58443457-0.8761561-0.6001504-0.8822776
100006365male 1.4177173 2.05214399-0.2529519 0.6911006 1.3997413
\n" + ], + "text/latex": [ + "A data.table: 6 × 8\n", + "\\begin{tabular}{llllllll}\n", + " id & age & sex & ckd & copd & diabetes & liver & ncvd\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t 1000019 & 60 & female & 0.6888426 & 0.40155519 & -0.2422354 & 0.2831111 & 0.3710600\\\\\n", + "\t 1000022 & 50 & female & 1.6588384 & 1.29550119 & 1.5858586 & 1.7183820 & 1.6632586\\\\\n", + "\t 1000035 & 60 & male & 1.1588003 & 1.64335532 & 1.7567751 & 0.5568670 & 1.6163710\\\\\n", + "\t 1000046 & 70 & female & -0.5749957 & -0.01139192 & -1.2153981 & -0.6834640 & 1.5736620\\\\\n", + "\t 1000054 & 45 & female & -1.1543210 & -0.58443457 & -0.8761561 & -0.6001504 & -0.8822776\\\\\n", + "\t 1000063 & 65 & male & 1.4177173 & 2.05214399 & -0.2529519 & 0.6911006 & 1.3997413\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 8\n", + "\n", + "| id <int> | age <int> | sex <chr> | ckd <dbl> | copd <dbl> | diabetes <dbl> | liver <dbl> | ncvd <dbl> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| 1000019 | 60 | female | 0.6888426 | 0.40155519 | -0.2422354 | 0.2831111 | 0.3710600 |\n", + "| 1000022 | 50 | female | 1.6588384 | 1.29550119 | 1.5858586 | 1.7183820 | 1.6632586 |\n", + "| 1000035 | 60 | male | 1.1588003 | 1.64335532 | 1.7567751 | 0.5568670 | 1.6163710 |\n", + "| 1000046 | 70 | female | -0.5749957 | -0.01139192 | -1.2153981 | -0.6834640 | 1.5736620 |\n", + "| 1000054 | 45 | female | -1.1543210 | -0.58443457 | -0.8761561 | -0.6001504 | -0.8822776 |\n", + "| 1000063 | 65 | male | 1.4177173 | 2.05214399 | -0.2529519 | 0.6911006 | 1.3997413 |\n", + "\n" + ], + "text/plain": [ + " id age sex ckd copd diabetes liver ncvd \n", + "1 1000019 60 female 0.6888426 0.40155519 -0.2422354 0.2831111 0.3710600\n", + "2 1000022 50 female 1.6588384 1.29550119 1.5858586 1.7183820 1.6632586\n", + "3 1000035 60 male 1.1588003 1.64335532 1.7567751 0.5568670 1.6163710\n", + "4 1000046 70 female -0.5749957 -0.01139192 -1.2153981 -0.6834640 1.5736620\n", + "5 1000054 45 female -1.1543210 -0.58443457 -0.8761561 -0.6001504 -0.8822776\n", + "6 1000063 65 male 1.4177173 2.05214399 -0.2529519 0.6911006 1.3997413" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scores <- data.table::fread(here::here(\"output/disease_score_inverse_rank.tsv\")) %>% \n", + " select(id, age, sex, disease, score_norm) %>% spread(disease, score_norm)\n", + "head(scores)" + ] + }, + { + "cell_type": "markdown", + "id": "f9b0a18f-262e-440d-bb2b-85598022a6dc", + "metadata": {}, + "source": [ + "### loading PCA and genes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5a00c7f7-98fb-4555-86d1-ea2eaf165511", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading precomputed PCA\n", + "\n", + "Loading preprocessed genetic data (imputed genotypes)\n", + "\n" + ] + } + ], + "source": [ + "pca <- get_ukbb_pca()\n", + "genes <- get_imputed_genes()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ad1be286-4e86-4c3a-88cc-b6963239d8b2", + "metadata": {}, + "outputs": [], + "source": [ + "wb_patients <- fread(here(\"output/ukbb_white.british_patients.csv\"))$id" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "37472242-5ad0-4fa3-a666-d37d712da241", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining, by = \"id\"\n", + "\u001b[1m\u001b[22mJoining, by = \"id\"\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 37
idmother_age_at_deathmother_last_alivefather_age_at_deathfather_last_alivemdeadmfollow_timefdeadffollow_timeage⋯PC11PC12PC13PC14PC15PC16PC17PC18PC19PC20
<int><dbl><dbl><dbl><dbl><lgl><int><lgl><int><int>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11000022Inf 75Inf 78FALSE75FALSE7850⋯ 0.3170125 2.6328147 1.7163368-5.01615392-1.5772722-3.564473-2.2181418 1.5713741-1.9076169 2.055364
21000035InfInfInf 87FALSENAFALSE8760⋯-2.0972524 1.4066766-0.9012911 1.31854940 0.1942449-6.922619 0.3071414-2.0787710 1.5407592 1.184370
31000046 60Inf 60Inf TRUE60 TRUE6070⋯ 3.0222677-0.4236680-1.9314079-0.09110024-0.5955289-1.992061-6.1250101 0.2095399 0.2382121-2.225729
41000063 52Inf 72Inf TRUE52 TRUE7265⋯ 3.5168629-0.7654475 0.4362669 0.35964717-3.3958734-2.994583 4.4955826-1.0551477 3.6623219 4.815468
51000078 84Inf 63Inf TRUE84 TRUE6360⋯-2.4854311 4.0357319 2.6573689-3.91865870 0.9580225 1.568436-0.6186636 1.1179565-1.5180180-2.572448
61000081 78Inf 80Inf TRUE78 TRUE8060⋯-0.3018311 0.1101689-2.1468426 1.40032702-2.2095284 1.655876-4.4644750 0.3770999-2.8147615 3.609816
\n" + ], + "text/latex": [ + "A data.frame: 6 × 37\n", + "\\begin{tabular}{r|lllllllllllllllllllll}\n", + " & id & mother\\_age\\_at\\_death & mother\\_last\\_alive & father\\_age\\_at\\_death & father\\_last\\_alive & mdead & mfollow\\_time & fdead & ffollow\\_time & age & ⋯ & PC11 & PC12 & PC13 & PC14 & PC15 & PC16 & PC17 & PC18 & PC19 & PC20\\\\\n", + " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 1000022 & Inf & 75 & Inf & 78 & FALSE & 75 & FALSE & 78 & 50 & ⋯ & 0.3170125 & 2.6328147 & 1.7163368 & -5.01615392 & -1.5772722 & -3.564473 & -2.2181418 & 1.5713741 & -1.9076169 & 2.055364\\\\\n", + "\t2 & 1000035 & Inf & Inf & Inf & 87 & FALSE & NA & FALSE & 87 & 60 & ⋯ & -2.0972524 & 1.4066766 & -0.9012911 & 1.31854940 & 0.1942449 & -6.922619 & 0.3071414 & -2.0787710 & 1.5407592 & 1.184370\\\\\n", + "\t3 & 1000046 & 60 & Inf & 60 & Inf & TRUE & 60 & TRUE & 60 & 70 & ⋯ & 3.0222677 & -0.4236680 & -1.9314079 & -0.09110024 & -0.5955289 & -1.992061 & -6.1250101 & 0.2095399 & 0.2382121 & -2.225729\\\\\n", + "\t4 & 1000063 & 52 & Inf & 72 & Inf & TRUE & 52 & TRUE & 72 & 65 & ⋯ & 3.5168629 & -0.7654475 & 0.4362669 & 0.35964717 & -3.3958734 & -2.994583 & 4.4955826 & -1.0551477 & 3.6623219 & 4.815468\\\\\n", + "\t5 & 1000078 & 84 & Inf & 63 & Inf & TRUE & 84 & TRUE & 63 & 60 & ⋯ & -2.4854311 & 4.0357319 & 2.6573689 & -3.91865870 & 0.9580225 & 1.568436 & -0.6186636 & 1.1179565 & -1.5180180 & -2.572448\\\\\n", + "\t6 & 1000081 & 78 & Inf & 80 & Inf & TRUE & 78 & TRUE & 80 & 60 & ⋯ & -0.3018311 & 0.1101689 & -2.1468426 & 1.40032702 & -2.2095284 & 1.655876 & -4.4644750 & 0.3770999 & -2.8147615 & 3.609816\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 37\n", + "\n", + "| | id <int> | mother_age_at_death <dbl> | mother_last_alive <dbl> | father_age_at_death <dbl> | father_last_alive <dbl> | mdead <lgl> | mfollow_time <int> | fdead <lgl> | ffollow_time <int> | age <int> | ⋯ ⋯ | PC11 <dbl> | PC12 <dbl> | PC13 <dbl> | PC14 <dbl> | PC15 <dbl> | PC16 <dbl> | PC17 <dbl> | PC18 <dbl> | PC19 <dbl> | PC20 <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 1000022 | Inf | 75 | Inf | 78 | FALSE | 75 | FALSE | 78 | 50 | ⋯ | 0.3170125 | 2.6328147 | 1.7163368 | -5.01615392 | -1.5772722 | -3.564473 | -2.2181418 | 1.5713741 | -1.9076169 | 2.055364 |\n", + "| 2 | 1000035 | Inf | Inf | Inf | 87 | FALSE | NA | FALSE | 87 | 60 | ⋯ | -2.0972524 | 1.4066766 | -0.9012911 | 1.31854940 | 0.1942449 | -6.922619 | 0.3071414 | -2.0787710 | 1.5407592 | 1.184370 |\n", + "| 3 | 1000046 | 60 | Inf | 60 | Inf | TRUE | 60 | TRUE | 60 | 70 | ⋯ | 3.0222677 | -0.4236680 | -1.9314079 | -0.09110024 | -0.5955289 | -1.992061 | -6.1250101 | 0.2095399 | 0.2382121 | -2.225729 |\n", + "| 4 | 1000063 | 52 | Inf | 72 | Inf | TRUE | 52 | TRUE | 72 | 65 | ⋯ | 3.5168629 | -0.7654475 | 0.4362669 | 0.35964717 | -3.3958734 | -2.994583 | 4.4955826 | -1.0551477 | 3.6623219 | 4.815468 |\n", + "| 5 | 1000078 | 84 | Inf | 63 | Inf | TRUE | 84 | TRUE | 63 | 60 | ⋯ | -2.4854311 | 4.0357319 | 2.6573689 | -3.91865870 | 0.9580225 | 1.568436 | -0.6186636 | 1.1179565 | -1.5180180 | -2.572448 |\n", + "| 6 | 1000081 | 78 | Inf | 80 | Inf | TRUE | 78 | TRUE | 80 | 60 | ⋯ | -0.3018311 | 0.1101689 | -2.1468426 | 1.40032702 | -2.2095284 | 1.655876 | -4.4644750 | 0.3770999 | -2.8147615 | 3.609816 |\n", + "\n" + ], + "text/plain": [ + " id mother_age_at_death mother_last_alive father_age_at_death\n", + "1 1000022 Inf 75 Inf \n", + "2 1000035 Inf Inf Inf \n", + "3 1000046 60 Inf 60 \n", + "4 1000063 52 Inf 72 \n", + "5 1000078 84 Inf 63 \n", + "6 1000081 78 Inf 80 \n", + " father_last_alive mdead mfollow_time fdead ffollow_time age \n", + "1 78 FALSE 75 FALSE 78 50 \n", + "2 87 FALSE NA FALSE 87 60 \n", + "3 Inf TRUE 60 TRUE 60 70 \n", + "4 Inf TRUE 52 TRUE 72 65 \n", + "5 Inf TRUE 84 TRUE 63 60 \n", + "6 Inf TRUE 78 TRUE 80 60 \n", + " PC11 PC12 PC13 PC14 PC15 PC16 PC17 \n", + "1 0.3170125 2.6328147 1.7163368 -5.01615392 -1.5772722 -3.564473 -2.2181418\n", + "2 -2.0972524 1.4066766 -0.9012911 1.31854940 0.1942449 -6.922619 0.3071414\n", + "3 3.0222677 -0.4236680 -1.9314079 -0.09110024 -0.5955289 -1.992061 -6.1250101\n", + "4 3.5168629 -0.7654475 0.4362669 0.35964717 -3.3958734 -2.994583 4.4955826\n", + "5 -2.4854311 4.0357319 2.6573689 -3.91865870 0.9580225 1.568436 -0.6186636\n", + "6 -0.3018311 0.1101689 -2.1468426 1.40032702 -2.2095284 1.655876 -4.4644750\n", + " PC18 PC19 PC20 \n", + "1 1.5713741 -1.9076169 2.055364\n", + "2 -2.0787710 1.5407592 1.184370\n", + "3 0.2095399 0.2382121 -2.225729\n", + "4 -1.0551477 3.6623219 4.815468\n", + "5 1.1179565 -1.5180180 -2.572448\n", + "6 0.3770999 -2.8147615 3.609816" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "parents_survival <- parents_survival %>% \n", + " filter(id %in% wb_patients, id %in% scores$id, id %in% genes$fam$sample.ID) %>% \n", + " left_join(scores) %>% \n", + " left_join(pca)\n", + "head(parents_survival)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0948458a-0b83-45db-8e8b-80f8e47db788", + "metadata": {}, + "outputs": [], + "source": [ + "father_survival <- parents_survival %>% \n", + " filter(!is.na(ffollow_time), ffollow_time > 0) %>% \n", + " select(id, time = ffollow_time, status = fdead, age:PC20) %>% \n", + " na.omit()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0006bba1-522f-4df3-85c3-dc1dd49d3674", + "metadata": {}, + "outputs": [], + "source": [ + "mother_survival <- parents_survival %>% \n", + " filter(!is.na(mfollow_time), mfollow_time > 0) %>% \n", + " select(id, time = mfollow_time, status = mdead, age:PC20) %>% \n", + " na.omit()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "961d2343-94b7-4c15-a80f-854d9b4eba3f", + "metadata": {}, + "outputs": [], + "source": [ + "both_survival <- bind_rows(\n", + " father_survival %>% mutate(parent = \"father\"), \n", + " mother_survival %>% mutate(parent = \"mother\")\n", + " ) %>%\n", + " mutate(parent = factor(parent)) %>% \n", + " filter(!(status & time < 40)) %>% # remove parents who died before age 40\n", + " mutate(id_both = paste0(id, \".\", parent))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6e0b33d1-4f44-4653-bdf1-f9dd10246b5f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "> Generating Cox NULL model\n", + "\n", + "Using cached rds from '/net/mraid14/export/data/users/nettam/projects/emr/ukbiobank/notebook/output/cox_parents_survival_both_null'\n", + "\n", + "> Running Cox GWAS\n", + "\n", + "> Running \u001b[34m\u001b[34m13840\u001b[34m\u001b[39m jobs\n", + "\n", + "\u001b[36mi\u001b[39m Loading \u001b[34m\u001b[34mgwiser\u001b[34m\u001b[39m\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Sample size is 615506.\"\n", + "[1] \"Number of variants is 1001.\"\n", + "[1] \"Start Analyzing...\"\n", + "[1] \"2022-10-27 14:55:18 IDT\"\n", + "[1] \"Analysis Complete.\"\n", + "[1] \"2022-10-27 14:56:56 IDT\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading \u001b[34m\u001b[34mgwiser\u001b[34m\u001b[39m\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Sample size is 615506.\"\n", + "[1] \"Number of variants is 1001.\"\n", + "[1] \"Start Analyzing...\"\n", + "[1] \"2022-10-27 14:57:41 IDT\"\n", + "[1] \"Analysis Complete.\"\n", + "[1] \"2022-10-27 14:58:55 IDT\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading \u001b[34m\u001b[34mgwiser\u001b[34m\u001b[39m\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Sample size is 615506.\"\n", + "[1] \"Number of variants is 1001.\"\n", + "[1] \"Start Analyzing...\"\n", + "[1] \"2022-10-27 14:59:37 IDT\"\n", + "[1] \"Analysis Complete.\"\n", + "[1] \"2022-10-27 15:01:19 IDT\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading \u001b[34m\u001b[34mgwiser\u001b[34m\u001b[39m\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Sample size is 615506.\"\n", + "[1] \"Number of variants is 1001.\"\n", + "[1] \"Start Analyzing...\"\n", + "[1] \"2022-10-27 15:02:05 IDT\"\n", + "[1] \"Analysis Complete.\"\n", + "[1] \"2022-10-27 15:03:53 IDT\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading \u001b[34m\u001b[34mgwiser\u001b[34m\u001b[39m\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Sample size is 615506.\"\n", + "[1] \"Number of variants is 1001.\"\n", + "[1] \"Start Analyzing...\"\n", + "[1] \"2022-10-27 15:04:35 IDT\"\n", + "[1] \"Analysis Complete.\"\n", + "[1] \"2022-10-27 15:06:11 IDT\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading \u001b[34m\u001b[34mgwiser\u001b[34m\u001b[39m\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Sample size is 615506.\"\n", + "[1] \"Number of variants is 1001.\"\n", + "[1] \"Start Analyzing...\"\n", + "[1] \"2022-10-27 15:06:53 IDT\"\n", + "[1] \"Analysis Complete.\"\n", + "[1] \"2022-10-27 15:08:25 IDT\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36mi\u001b[39m Loading \u001b[34m\u001b[34mgwiser\u001b[34m\u001b[39m\n", + "\n" + ] + } + ], + "source": [ + "gwas_both <- {\n", + " df <- run_gwas_cox_both_parents(genes, both_survival %>% rename(gender=sex), null_fn = here(\"output/cox_parents_survival_both_null\"), max.jobs=200, use_sge=TRUE)\n", + " df <- df %>% left_join(genes$map, by = \"marker.ID\")\n", + " df <- df %>%\n", + " rename(chrom = chromosome, start = physical.pos) %>%\n", + " mutate(chrom = paste0(\"chr\", chrom), chrom = gsub(\"chr0\", \"chr\", chrom), end = start + 1, pval = log10(p.value.spa)) %>%\n", + " select(chrom, start, end, pval, marker.ID, allele1, allele2, everything()) \n", + " } %cache_df% here(\"output/cox_parents_survival_both_gwas.tsv\") %>% as_tibble()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7591936c-3a09-4273-b43a-13b46b4ac81b", + "metadata": {}, + "outputs": [], + "source": [ + "gwas_mother <- {\n", + " df <- run_gwas_cox(genes, mother_survival %>% rename(gender=sex), null_fn = here(\"output/cox_parents_survival_mother_null\"), max.jobs=200)\n", + " df <- df %>% left_join(genes$map, by = \"marker.ID\")\n", + " df <- df %>%\n", + " rename(chrom = chromosome, start = physical.pos) %>%\n", + " mutate(chrom = paste0(\"chr\", chrom), chrom = gsub(\"chr0\", \"chr\", chrom), end = start + 1, pval = log10(p.value.spa)) %>%\n", + " select(chrom, start, end, pval, marker.ID, allele1, allele2, everything()) \n", + " } %cache_df% here(\"output/cox_parents_survival_mother_gwas.tsv\") %>% as_tibble()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3eb3902a-9dcd-403c-adbd-1c8c28383ae4", + "metadata": {}, + "outputs": [], + "source": [ + "gwas_father <- {\n", + " df <- run_gwas_cox(genes, father_survival %>% rename(gender=sex), null_fn = here(\"output/cox_parents_survival_father_null\"), max.jobs=200)\n", + " df <- df %>% left_join(genes$map, by = \"marker.ID\")\n", + " df <- df %>%\n", + " rename(chrom = chromosome, start = physical.pos) %>%\n", + " mutate(chrom = paste0(\"chr\", chrom), chrom = gsub(\"chr0\", \"chr\", chrom), end = start + 1, pval = log10(p.value.spa)) %>%\n", + " select(chrom, start, end, pval, marker.ID, allele1, allele2, everything()) \n", + " } %cache_df% here(\"output/cox_parents_survival_father_gwas.tsv\") %>% as_tibble()" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,auto:light" + }, + "kernelspec": { + "display_name": "R 4.0.3", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.0.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/score_clustering_UKBB.ipynb b/score_clustering_UKBB.ipynb new file mode 100644 index 0000000..fedab2b --- /dev/null +++ b/score_clustering_UKBB.ipynb @@ -0,0 +1,1117 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "92d4277e-9dae-45a5-abe2-c0e18b5704b1", + "metadata": {}, + "source": [ + "# Clustering patients by longevity and disease scores\n", + "#### requires Disease_Longevity_UKBB notebook to be preprocessed (for model scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "13530f6b-9197-48b2-9013-1db46bd196cb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "========================================\n", + "circlize version 0.4.15\n", + "CRAN page: https://cran.r-project.org/package=circlize\n", + "Github page: https://github.com/jokergoo/circlize\n", + "Documentation: https://jokergoo.github.io/circlize_book/book/\n", + "\n", + "If you use it in published research, please cite:\n", + "Gu, Z. circlize implements and enhances circular visualization\n", + " in R. Bioinformatics 2014.\n", + "\n", + "This message can be suppressed by:\n", + " suppressPackageStartupMessages(library(circlize))\n", + "========================================\n", + "\n", + "\n" + ] + } + ], + "source": [ + "source(here::here(\"code/init.R\"))\n", + "source(here::here(\"code/models.R\"))\n", + "source(here::here(\"code/ukbb_outcome.R\"))\n", + "source(here::here(\"code/ukbb_preprocessing.R\"))\n", + "library(circlize)" + ] + }, + { + "cell_type": "markdown", + "id": "8c138b0d-889c-47ef-adce-93e7867c7916", + "metadata": { + "tags": [] + }, + "source": [ + "### Load scores" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d36bd73f-1ae0-4567-81d7-87fcdf023519", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 10
idagesexlongevitylongevity_qdiabetesckdcopdcvdliver
<int><int><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1100005445female0.96758820.19257920.088155240.041675530.027986280.20603820.022592036
2100011245male 0.91192810.10603480.159096920.039192490.090642470.29449000.008487539
3100018645female0.99698310.45743610.209795140.138553650.128116470.63706790.022256322
4100019845male 0.99452550.39842700.077918440.059655310.043355170.21177180.033393441
5100025545female0.98381660.26512810.080082880.022186810.065933830.12077450.009952694
6100037945male 0.93772170.13451660.031964070.049305710.012635090.11572290.014305011
\n" + ], + "text/latex": [ + "A data.frame: 6 × 10\n", + "\\begin{tabular}{r|llllllllll}\n", + " & id & age & sex & longevity & longevity\\_q & diabetes & ckd & copd & cvd & liver\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 1000054 & 45 & female & 0.9675882 & 0.1925792 & 0.08815524 & 0.04167553 & 0.02798628 & 0.2060382 & 0.022592036\\\\\n", + "\t2 & 1000112 & 45 & male & 0.9119281 & 0.1060348 & 0.15909692 & 0.03919249 & 0.09064247 & 0.2944900 & 0.008487539\\\\\n", + "\t3 & 1000186 & 45 & female & 0.9969831 & 0.4574361 & 0.20979514 & 0.13855365 & 0.12811647 & 0.6370679 & 0.022256322\\\\\n", + "\t4 & 1000198 & 45 & male & 0.9945255 & 0.3984270 & 0.07791844 & 0.05965531 & 0.04335517 & 0.2117718 & 0.033393441\\\\\n", + "\t5 & 1000255 & 45 & female & 0.9838166 & 0.2651281 & 0.08008288 & 0.02218681 & 0.06593383 & 0.1207745 & 0.009952694\\\\\n", + "\t6 & 1000379 & 45 & male & 0.9377217 & 0.1345166 & 0.03196407 & 0.04930571 & 0.01263509 & 0.1157229 & 0.014305011\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 10\n", + "\n", + "| | id <int> | age <int> | sex <chr> | longevity <dbl> | longevity_q <dbl> | diabetes <dbl> | ckd <dbl> | copd <dbl> | cvd <dbl> | liver <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 1000054 | 45 | female | 0.9675882 | 0.1925792 | 0.08815524 | 0.04167553 | 0.02798628 | 0.2060382 | 0.022592036 |\n", + "| 2 | 1000112 | 45 | male | 0.9119281 | 0.1060348 | 0.15909692 | 0.03919249 | 0.09064247 | 0.2944900 | 0.008487539 |\n", + "| 3 | 1000186 | 45 | female | 0.9969831 | 0.4574361 | 0.20979514 | 0.13855365 | 0.12811647 | 0.6370679 | 0.022256322 |\n", + "| 4 | 1000198 | 45 | male | 0.9945255 | 0.3984270 | 0.07791844 | 0.05965531 | 0.04335517 | 0.2117718 | 0.033393441 |\n", + "| 5 | 1000255 | 45 | female | 0.9838166 | 0.2651281 | 0.08008288 | 0.02218681 | 0.06593383 | 0.1207745 | 0.009952694 |\n", + "| 6 | 1000379 | 45 | male | 0.9377217 | 0.1345166 | 0.03196407 | 0.04930571 | 0.01263509 | 0.1157229 | 0.014305011 |\n", + "\n" + ], + "text/plain": [ + " id age sex longevity longevity_q diabetes ckd copd \n", + "1 1000054 45 female 0.9675882 0.1925792 0.08815524 0.04167553 0.02798628\n", + "2 1000112 45 male 0.9119281 0.1060348 0.15909692 0.03919249 0.09064247\n", + "3 1000186 45 female 0.9969831 0.4574361 0.20979514 0.13855365 0.12811647\n", + "4 1000198 45 male 0.9945255 0.3984270 0.07791844 0.05965531 0.04335517\n", + "5 1000255 45 female 0.9838166 0.2651281 0.08008288 0.02218681 0.06593383\n", + "6 1000379 45 male 0.9377217 0.1345166 0.03196407 0.04930571 0.01263509\n", + " cvd liver \n", + "1 0.2060382 0.022592036\n", + "2 0.2944900 0.008487539\n", + "3 0.6370679 0.022256322\n", + "4 0.2117718 0.033393441\n", + "5 0.1207745 0.009952694\n", + "6 0.1157229 0.014305011" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop <- tgutil::fread(here::here('output/pop_scores.csv')) #see Disease_Longevity_UKBB notebook for computation\n", + "head(pop)" + ] + }, + { + "cell_type": "markdown", + "id": "8552ea33-3802-4ca7-bae7-56f5f9437fb6", + "metadata": {}, + "source": [ + "### Computing probability for survival (to age 85) for all patients at age 50 using markovian lifelong risk models (precomputed on CHSDB)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6fdbeebb-a79f-403d-be05-22db7c20f4b6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 10
agesexlongevitylongevity_qdiabetesckdcopdcvdliverrisk
<int><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
150female0.99966380.669693140.96669810.86694170.642976490.99514630.631670190.5942129
250female0.99986480.743614930.96308110.70284270.167781950.98344350.415351000.6309899
350female0.99999930.965768550.91618180.79780590.937715300.99660760.751188000.8032003
450female0.99988990.758419870.84180770.57542850.372574280.97845670.162096240.6396717
550female0.96338080.169725890.80488850.21374870.030175820.85886060.196579830.4413530
650female0.82208110.049574330.03901710.12137600.017964210.14436990.011268790.3964383
\n" + ], + "text/latex": [ + "A data.frame: 6 × 10\n", + "\\begin{tabular}{r|llllllllll}\n", + " & age & sex & longevity & longevity\\_q & diabetes & ckd & copd & cvd & liver & risk\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 50 & female & 0.9996638 & 0.66969314 & 0.9666981 & 0.8669417 & 0.64297649 & 0.9951463 & 0.63167019 & 0.5942129\\\\\n", + "\t2 & 50 & female & 0.9998648 & 0.74361493 & 0.9630811 & 0.7028427 & 0.16778195 & 0.9834435 & 0.41535100 & 0.6309899\\\\\n", + "\t3 & 50 & female & 0.9999993 & 0.96576855 & 0.9161818 & 0.7978059 & 0.93771530 & 0.9966076 & 0.75118800 & 0.8032003\\\\\n", + "\t4 & 50 & female & 0.9998899 & 0.75841987 & 0.8418077 & 0.5754285 & 0.37257428 & 0.9784567 & 0.16209624 & 0.6396717\\\\\n", + "\t5 & 50 & female & 0.9633808 & 0.16972589 & 0.8048885 & 0.2137487 & 0.03017582 & 0.8588606 & 0.19657983 & 0.4413530\\\\\n", + "\t6 & 50 & female & 0.8220811 & 0.04957433 & 0.0390171 & 0.1213760 & 0.01796421 & 0.1443699 & 0.01126879 & 0.3964383\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 10\n", + "\n", + "| | age <int> | sex <chr> | longevity <dbl> | longevity_q <dbl> | diabetes <dbl> | ckd <dbl> | copd <dbl> | cvd <dbl> | liver <dbl> | risk <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 50 | female | 0.9996638 | 0.66969314 | 0.9666981 | 0.8669417 | 0.64297649 | 0.9951463 | 0.63167019 | 0.5942129 |\n", + "| 2 | 50 | female | 0.9998648 | 0.74361493 | 0.9630811 | 0.7028427 | 0.16778195 | 0.9834435 | 0.41535100 | 0.6309899 |\n", + "| 3 | 50 | female | 0.9999993 | 0.96576855 | 0.9161818 | 0.7978059 | 0.93771530 | 0.9966076 | 0.75118800 | 0.8032003 |\n", + "| 4 | 50 | female | 0.9998899 | 0.75841987 | 0.8418077 | 0.5754285 | 0.37257428 | 0.9784567 | 0.16209624 | 0.6396717 |\n", + "| 5 | 50 | female | 0.9633808 | 0.16972589 | 0.8048885 | 0.2137487 | 0.03017582 | 0.8588606 | 0.19657983 | 0.4413530 |\n", + "| 6 | 50 | female | 0.8220811 | 0.04957433 | 0.0390171 | 0.1213760 | 0.01796421 | 0.1443699 | 0.01126879 | 0.3964383 |\n", + "\n" + ], + "text/plain": [ + " age sex longevity longevity_q diabetes ckd copd cvd \n", + "1 50 female 0.9996638 0.66969314 0.9666981 0.8669417 0.64297649 0.9951463\n", + "2 50 female 0.9998648 0.74361493 0.9630811 0.7028427 0.16778195 0.9834435\n", + "3 50 female 0.9999993 0.96576855 0.9161818 0.7978059 0.93771530 0.9966076\n", + "4 50 female 0.9998899 0.75841987 0.8418077 0.5754285 0.37257428 0.9784567\n", + "5 50 female 0.9633808 0.16972589 0.8048885 0.2137487 0.03017582 0.8588606\n", + "6 50 female 0.8220811 0.04957433 0.0390171 0.1213760 0.01796421 0.1443699\n", + " liver risk \n", + "1 0.63167019 0.5942129\n", + "2 0.41535100 0.6309899\n", + "3 0.75118800 0.8032003\n", + "4 0.16209624 0.6396717\n", + "5 0.19657983 0.4413530\n", + "6 0.01126879 0.3964383" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "raw_data_50 <- compute_lifelong_longevity_risk(\n", + " pop %>% filter(age == 50, !is.na(longevity_q)) %>% distinct(id, .keep_all=TRUE)\n", + ")\n", + "head(raw_data_50 %>% select(-id))" + ] + }, + { + "cell_type": "markdown", + "id": "c731dde3-3ec9-4246-a888-a9a8a3031d94", + "metadata": { + "tags": [] + }, + "source": [ + "##### loading precomputed models\n", + "Models were generated on CHSDB by clustering (kmeans) all scores of patients of age 50. UKBB scores are projected to these clusters. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f2add2e7-8eaf-43c7-bee9-6d3cba126500", + "metadata": {}, + "outputs": [], + "source": [ + "kmeans_centers_50 <- data.table::fread(here::here('data/models/age_50_clustering_centers.csv')) %>% \n", + " arrange(clust) %>% rename(longevity_q=q, cvd=ncvd)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8fc4a5eb-27f3-4cab-b8e1-f22230ae7dee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'longevity_q'
  2. 'diabetes'
  3. 'ckd'
  4. 'copd'
  5. 'cvd'
  6. 'liver'
  7. 'clust'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'longevity\\_q'\n", + "\\item 'diabetes'\n", + "\\item 'ckd'\n", + "\\item 'copd'\n", + "\\item 'cvd'\n", + "\\item 'liver'\n", + "\\item 'clust'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'longevity_q'\n", + "2. 'diabetes'\n", + "3. 'ckd'\n", + "4. 'copd'\n", + "5. 'cvd'\n", + "6. 'liver'\n", + "7. 'clust'\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] \"longevity_q\" \"diabetes\" \"ckd\" \"copd\" \"cvd\" \n", + "[6] \"liver\" \"clust\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colnames(kmeans_centers_50)\n", + "cluster_data_50 <- raw_data_50 %>% select(c('id', 'age', colnames(kmeans_centers_50 %>% select(-clust))))\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf844d8e-8e76-44c9-9625-08576039e2de", + "metadata": { + "tags": [] + }, + "source": [ + "##### divide work into jobs" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "14b076f0-97e8-412a-a489-0ec31abf0e01", + "metadata": {}, + "outputs": [], + "source": [ + "#divide work into jobs\n", + "cluster_data_50 <- cluster_data_50 %>% \n", + " mutate(i=cut(1:n(), c(seq(1, n(), by=10000), n()), include.lowest=TRUE, right=FALSE))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8b9c06b1-2681-473e-b92e-cb3be945eede", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "working on 7 batches\n", + "\n", + "1\n", + "\n", + "2\n", + "\n", + "3\n", + "\n", + "4\n", + "\n", + "5\n", + "\n", + "6\n", + "\n", + "7\n", + "\n" + ] + } + ], + "source": [ + "message(\"working on \", length(unique(cluster_data_50$i)), \" batches\")\n", + "clusters_50 <- plyr::ddply(cluster_data_50, plyr::.(i), function(batch) {\n", + " message(as.numeric(batch$i[1]))\n", + " m <- as.matrix(kmeans_centers_50 %>% \n", + " select(-clust) %>% \n", + " bind_rows(batch %>% select(-id, -age, -i))\n", + " )\n", + " mdist <- tgs_dist(m, tidy=TRUE)\n", + " mdist_clust <- mdist %>% filter(row1 <= max(kmeans_centers_50$clust), \n", + " row2 > max(kmeans_centers_50$clust)) %>% \n", + " arrange(row2, dist) %>% \n", + " distinct(row2, .keep_all=TRUE) %>% \n", + " rename(clust=row1)\n", + " return(batch %>% mutate(clust = mdist_clust$clust))\n", + "}) %>% select(-i) %>% \n", + " rename(longevity=longevity_q) %>% \n", + " reshape2::melt(id.vars=c(\"id\", \"age\", \"clust\"))%>% \n", + " mutate(variable=factor(variable, levels=c('longevity', 'diabetes', 'ckd', 'copd', 'cvd', 'liver'))) %>% \n", + " mutate(clust=factor(paste0('C', clust), levels=paste0('C', 1:16)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "81719294-9ad1-4592-91f6-25bba643d821", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
ageclustvariablevalue
<int><fct><fct><dbl>
150C15longevity0.66969314
250C13longevity0.74361493
350C15longevity0.96576855
450C13longevity0.75841987
550C9 longevity0.16972589
650C1 longevity0.04957433
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & age & clust & variable & value\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 50 & C15 & longevity & 0.66969314\\\\\n", + "\t2 & 50 & C13 & longevity & 0.74361493\\\\\n", + "\t3 & 50 & C15 & longevity & 0.96576855\\\\\n", + "\t4 & 50 & C13 & longevity & 0.75841987\\\\\n", + "\t5 & 50 & C9 & longevity & 0.16972589\\\\\n", + "\t6 & 50 & C1 & longevity & 0.04957433\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | age <int> | clust <fct> | variable <fct> | value <dbl> |\n", + "|---|---|---|---|---|\n", + "| 1 | 50 | C15 | longevity | 0.66969314 |\n", + "| 2 | 50 | C13 | longevity | 0.74361493 |\n", + "| 3 | 50 | C15 | longevity | 0.96576855 |\n", + "| 4 | 50 | C13 | longevity | 0.75841987 |\n", + "| 5 | 50 | C9 | longevity | 0.16972589 |\n", + "| 6 | 50 | C1 | longevity | 0.04957433 |\n", + "\n" + ], + "text/plain": [ + " age clust variable value \n", + "1 50 C15 longevity 0.66969314\n", + "2 50 C13 longevity 0.74361493\n", + "3 50 C15 longevity 0.96576855\n", + "4 50 C13 longevity 0.75841987\n", + "5 50 C9 longevity 0.16972589\n", + "6 50 C1 longevity 0.04957433" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "head(clusters_50 %>% select(-id))\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "1db420a3-fc60-485c-b834-bdca085bf442", + "metadata": {}, + "source": [ + "##### compute average survival probability per cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f2ae9fda-5130-4551-b369-f5e9e72c4b6f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(id, age)`\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 15 × 2
clustrisk
<fct><dbl>
C1 51
C2 48
C3 42
C4 44
C5 46
C6 38
C7 31
C8 29
C9 42
C1039
C1130
C1230
C1335
C1435
C1525
\n" + ], + "text/latex": [ + "A tibble: 15 × 2\n", + "\\begin{tabular}{ll}\n", + " clust & risk\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t C1 & 51\\\\\n", + "\t C2 & 48\\\\\n", + "\t C3 & 42\\\\\n", + "\t C4 & 44\\\\\n", + "\t C5 & 46\\\\\n", + "\t C6 & 38\\\\\n", + "\t C7 & 31\\\\\n", + "\t C8 & 29\\\\\n", + "\t C9 & 42\\\\\n", + "\t C10 & 39\\\\\n", + "\t C11 & 30\\\\\n", + "\t C12 & 30\\\\\n", + "\t C13 & 35\\\\\n", + "\t C14 & 35\\\\\n", + "\t C15 & 25\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 15 × 2\n", + "\n", + "| clust <fct> | risk <dbl> |\n", + "|---|---|\n", + "| C1 | 51 |\n", + "| C2 | 48 |\n", + "| C3 | 42 |\n", + "| C4 | 44 |\n", + "| C5 | 46 |\n", + "| C6 | 38 |\n", + "| C7 | 31 |\n", + "| C8 | 29 |\n", + "| C9 | 42 |\n", + "| C10 | 39 |\n", + "| C11 | 30 |\n", + "| C12 | 30 |\n", + "| C13 | 35 |\n", + "| C14 | 35 |\n", + "| C15 | 25 |\n", + "\n" + ], + "text/plain": [ + " clust risk\n", + "1 C1 51 \n", + "2 C2 48 \n", + "3 C3 42 \n", + "4 C4 44 \n", + "5 C5 46 \n", + "6 C6 38 \n", + "7 C7 31 \n", + "8 C8 29 \n", + "9 C9 42 \n", + "10 C10 39 \n", + "11 C11 30 \n", + "12 C12 30 \n", + "13 C13 35 \n", + "14 C14 35 \n", + "15 C15 25 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "risk <- clusters_50 %>% select(id, age, clust) %>% distinct %>% full_join(raw_data_50 %>% select(id, age, sex, risk)) %>%\n", + " group_by(clust) %>% summarize(risk=round(mean(1-risk)*100))\n", + "risk" + ] + }, + { + "cell_type": "markdown", + "id": "456647a9", + "metadata": {}, + "source": [ + "##### compute average score per cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b074d11b-d0fa-4945-b9d4-2678a9df4e9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 3
clustvariablemean_value
<fct><fct><dbl>
C1longevity0.14745159
C1diabetes 0.08932806
C1ckd 0.06456448
C1copd 0.03653607
C1cvd 0.16699662
C1liver 0.02177502
\n" + ], + "text/latex": [ + "A tibble: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " clust & variable & mean\\_value\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t C1 & longevity & 0.14745159\\\\\n", + "\t C1 & diabetes & 0.08932806\\\\\n", + "\t C1 & ckd & 0.06456448\\\\\n", + "\t C1 & copd & 0.03653607\\\\\n", + "\t C1 & cvd & 0.16699662\\\\\n", + "\t C1 & liver & 0.02177502\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 3\n", + "\n", + "| clust <fct> | variable <fct> | mean_value <dbl> |\n", + "|---|---|---|\n", + "| C1 | longevity | 0.14745159 |\n", + "| C1 | diabetes | 0.08932806 |\n", + "| C1 | ckd | 0.06456448 |\n", + "| C1 | copd | 0.03653607 |\n", + "| C1 | cvd | 0.16699662 |\n", + "| C1 | liver | 0.02177502 |\n", + "\n" + ], + "text/plain": [ + " clust variable mean_value\n", + "1 C1 longevity 0.14745159\n", + "2 C1 diabetes 0.08932806\n", + "3 C1 ckd 0.06456448\n", + "4 C1 copd 0.03653607\n", + "5 C1 cvd 0.16699662\n", + "6 C1 liver 0.02177502" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmean <- clusters_50 %>% group_by(clust, variable) %>% \n", + " summarize(mean_value=mean(value, na.rm=T), .groups=\"drop\")\n", + "head(cmean)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d4cafeec-4169-49a5-a62c-857a91b2d830", + "metadata": {}, + "outputs": [], + "source": [ + "#this is used for cluster annotation\n", + "examples <- nrow(cluster_data_50)\n", + "cmtab <- clusters_50 %>% distinct(id, clust) %>% count(clust) %>% \n", + " left_join(cmean %>% pivot_wider(names_from=variable, values_from=mean_value), by=\"clust\") %>% \n", + " mutate(p=n/sum(n)*100, title=paste0(ifelse(n>10000, paste0(round(n/1000), 'k'), n), '\\n(', round(p), '%)')) %>% \n", + " bind_rows(data.frame(clust='C16', n=examples/0.92*0.08)) %>% #adding a fictitious spacer cluster\n", + " mutate(clust=factor(clust,levels=paste0('C', 1:16))) %>% \n", + " arrange(clust)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9b496061-5a68-4016-8202-3dd2f87017c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 10
clustnlongevitydiabetesckdcopdcvdliverptitle
<fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><chr>
1C1134980.14745160.089328060.064564480.036536070.16699660.0217750221.02918013k\n", + "(21%)
2C2117550.21659670.368808640.149490440.057296910.58183300.0620250818.31367712k\n", + "(18%)
3C3 45050.48688220.098518430.095165260.113729890.25126150.02947602 7.0185554505\n", + "(7%)
4C4 16510.34550710.375139250.497703680.144541560.79996940.10791873 2.5721721651\n", + "(3%)
5C5 960.36767180.262652140.216611240.169900920.59370810.63017959 0.14956396\n", + "(0%)
6C6 49220.55313170.341913030.234654620.171722580.77477840.08949527 7.6682194922\n", + "(8%)
\n" + ], + "text/latex": [ + "A data.frame: 6 × 10\n", + "\\begin{tabular}{r|llllllllll}\n", + " & clust & n & longevity & diabetes & ckd & copd & cvd & liver & p & title\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & C1 & 13498 & 0.1474516 & 0.08932806 & 0.06456448 & 0.03653607 & 0.1669966 & 0.02177502 & 21.029180 & 13k\n", + "(21\\%)\\\\\n", + "\t2 & C2 & 11755 & 0.2165967 & 0.36880864 & 0.14949044 & 0.05729691 & 0.5818330 & 0.06202508 & 18.313677 & 12k\n", + "(18\\%)\\\\\n", + "\t3 & C3 & 4505 & 0.4868822 & 0.09851843 & 0.09516526 & 0.11372989 & 0.2512615 & 0.02947602 & 7.018555 & 4505\n", + "(7\\%)\\\\\n", + "\t4 & C4 & 1651 & 0.3455071 & 0.37513925 & 0.49770368 & 0.14454156 & 0.7999694 & 0.10791873 & 2.572172 & 1651\n", + "(3\\%)\\\\\n", + "\t5 & C5 & 96 & 0.3676718 & 0.26265214 & 0.21661124 & 0.16990092 & 0.5937081 & 0.63017959 & 0.149563 & 96\n", + "(0\\%) \\\\\n", + "\t6 & C6 & 4922 & 0.5531317 & 0.34191303 & 0.23465462 & 0.17172258 & 0.7747784 & 0.08949527 & 7.668219 & 4922\n", + "(8\\%)\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 10\n", + "\n", + "| | clust <fct> | n <dbl> | longevity <dbl> | diabetes <dbl> | ckd <dbl> | copd <dbl> | cvd <dbl> | liver <dbl> | p <dbl> | title <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | C1 | 13498 | 0.1474516 | 0.08932806 | 0.06456448 | 0.03653607 | 0.1669966 | 0.02177502 | 21.029180 | 13k\n", + "(21%) |\n", + "| 2 | C2 | 11755 | 0.2165967 | 0.36880864 | 0.14949044 | 0.05729691 | 0.5818330 | 0.06202508 | 18.313677 | 12k\n", + "(18%) |\n", + "| 3 | C3 | 4505 | 0.4868822 | 0.09851843 | 0.09516526 | 0.11372989 | 0.2512615 | 0.02947602 | 7.018555 | 4505\n", + "(7%) |\n", + "| 4 | C4 | 1651 | 0.3455071 | 0.37513925 | 0.49770368 | 0.14454156 | 0.7999694 | 0.10791873 | 2.572172 | 1651\n", + "(3%) |\n", + "| 5 | C5 | 96 | 0.3676718 | 0.26265214 | 0.21661124 | 0.16990092 | 0.5937081 | 0.63017959 | 0.149563 | 96\n", + "(0%) |\n", + "| 6 | C6 | 4922 | 0.5531317 | 0.34191303 | 0.23465462 | 0.17172258 | 0.7747784 | 0.08949527 | 7.668219 | 4922\n", + "(8%) |\n", + "\n" + ], + "text/plain": [ + " clust n longevity diabetes ckd copd cvd liver \n", + "1 C1 13498 0.1474516 0.08932806 0.06456448 0.03653607 0.1669966 0.02177502\n", + "2 C2 11755 0.2165967 0.36880864 0.14949044 0.05729691 0.5818330 0.06202508\n", + "3 C3 4505 0.4868822 0.09851843 0.09516526 0.11372989 0.2512615 0.02947602\n", + "4 C4 1651 0.3455071 0.37513925 0.49770368 0.14454156 0.7999694 0.10791873\n", + "5 C5 96 0.3676718 0.26265214 0.21661124 0.16990092 0.5937081 0.63017959\n", + "6 C6 4922 0.5531317 0.34191303 0.23465462 0.17172258 0.7747784 0.08949527\n", + " p title \n", + "1 21.029180 13k\\n(21%)\n", + "2 18.313677 12k\\n(18%)\n", + "3 7.018555 4505\\n(7%)\n", + "4 2.572172 1651\\n(3%)\n", + "5 0.149563 96\\n(0%) \n", + "6 7.668219 4922\\n(8%)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(cmtab)" + ] + }, + { + "cell_type": "markdown", + "id": "4802e257-bf72-4e61-9778-86a091a46ef2", + "metadata": {}, + "source": [ + "### Drawing the circular plot of clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "541aa8c9-7104-40f3-8ef1-b27de57254cd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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maeMWPGxIkTs7KysCFIS0vL0NBQLi6u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\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t+4nHapNNDiCAAiEUqbOuyBwnS0yQ4H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\nDHTBX2ZmZmpqqsoUCxEJ+XH2oulsKiW31wQGBqA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CQECgtbVVQUHBy8vr+fPnX8yoqampPX78GP2Hyaavr8/ExOTRo0fz58+XlpZetWrV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\nJCT4di0Wi0EhYAAAoJAwAINGo+DIRejKO6G/v7+urq6SkhIlJeXZs2fv3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PQMDDQ0NLS0tNTU1DjRoqX/4EAgEAsLCwAAwOFwGAwGhUIX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\nRK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "circ_longevity_pal <- colorRampPalette(c(\"#316f2f\", \"white\", \"#603a64\"))(100)\n", + "circ_disease_pal <- colorRampPalette(c(\"#23478D\", \"white\", \"#93260C\"))(100)\n", + "\n", + "options(warn=-1)\n", + "circos.par(points.overflow.warning=FALSE)\n", + "#initialize circular plot\n", + "circos.par(cell.padding=c(0,0,0,0), start.degree=90-0.08*360/2, canvas.xlim=c(-1.2, 1.2), canvas.ylim=c(-1.2, 1.2))\n", + "circos.initialize(cmtab$clust, xlim=as.matrix(cmtab %>% mutate(m=1) %>% select(m, n)))\n", + "\n", + "#longevity score ring\n", + "circos.track(cmean$clust, ylim=c(0,1), track.height=0.1, track.margin=c(0.05, 0.01), \n", + " bg.col= colorRampPalette(circ_longevity_pal)(100)[floor(cmtab[,levels(cmean$variable)[1]]*100)],\n", + " bg.border=c(rep(\"black\", nrow(cmtab)-1), 'NA'), \n", + " panel.fun=function(x,y) {\n", + " if (CELL_META$sector.index != \"C16\") {\n", + " circos.text(CELL_META$xcenter, CELL_META$cell.ylim[2] + mm_y(15), CELL_META$sector.index, cex=1.2, niceFacing=TRUE)\n", + " circos.text(CELL_META$xcenter, CELL_META$cell.ylim[2] + mm_y(6), cmtab %>% filter(clust == CELL_META$sector.index) %>% pull(title), cex=1, niceFacing=TRUE)\n", + " }\n", + " }\n", + ")\n", + "\n", + "#disease score rings\n", + "purrr::walk2(levels(cmean$variable)[-1], 2:length(levels(cmean$variable)), function(var, ti) {\n", + " circos.track(cmean$clust, ylim=c(0,1), track.height=0.05, track.margin=c(0.02, 0),\n", + " bg.col= colorRampPalette(circ_disease_pal)(100)[floor(cmtab[,var]*100)],\n", + " bg.border=c(rep(\"black\", nrow(cmtab)-1), 'NA'))\n", + "})\n", + "\n", + "#text ring\n", + "circos.track(cmean$clust, ylim=c(0,1), track.height=0.05, track.margin=c(0.02,0), \n", + " bg.col= rep('NA', nrow(cmtab)),\n", + " bg.border=rep('NA', nrow(cmtab)),\n", + " panel.fun=function(x,y) {\n", + " if (CELL_META$sector.index != tail(levels(cmean$clust), 1)) { #dont want to print the spacer\n", + " circos.text(CELL_META$xcenter, CELL_META$cell.ylim[2]-mm_y(3), \n", + " risk %>% filter(clust == CELL_META$sector.index) %>% mutate(risk=paste0(risk, \"\")) %>% pull(risk), cex=0.75, niceFacing=TRUE)\n", + " } else {\n", + " circos.text(CELL_META$xcenter, CELL_META$cell.ylim[2]-mm_y(3), \"P85 (%)\", cex=0.75, niceFacing=TRUE)\n", + " }\n", + "\n", + "})\n", + "\n", + "purrr::walk(1:length(levels(cmean$variable)), ~ circos.update('C16', .x, bg.border=NA))\n", + " circos.text(x=get.cell.meta.data(\"xcenter\", sector.index=\"C16\"), y=0.5, labels='longevity', sector.index=\"C16\", track.index=1, cex=1.1)\n", + " purrr::walk2(levels(cmean$variable)[-1], 2:length(levels(cmean$variable)), ~\n", + " circos.text(x=get.cell.meta.data(\"xcenter\", sector.index=\"C16\"), y=0.5, labels=.x, sector.index=\"C16\", track.index=.y, cex=1.1))\n", + "\n", + "text(0,0, paste0('UKBB', ':\\nage=50\\nN=', round(examples/1000), 'K'), cex=1.3)\n", + "options(warn=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "344b0c37-6e30-4733-8aa6-cfc41fefcc3a", + "metadata": {}, + "outputs": [], + "source": [ + "child_clusters <- clusters_50 %>% distinct(id, clust) %>% mutate(clust=as.numeric(gsub('C', '', clust))) %>% \n", + " filter(clust==1 | clust == 3 | clust >= 10) %>% \n", + " mutate(clust=ifelse( clust >= 10 & clust < 15, '10-14', clust)) %>% \n", + " mutate(clust = factor(clust, levels=c('1', '3', '10-14', '15')))" + ] + }, + { + "cell_type": "markdown", + "id": "809bb5d2", + "metadata": {}, + "source": [ + "### child survival by cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8e2a7290-a579-40d4-aabd-bf45980f2324", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(id, age, sex)`\n" + ] + } + ], + "source": [ + "ukbb_demog <- data.table::fread(here::here('output/ukbb_demog.csv'))\n", + "\n", + "clust_survival_data <- pop %>% filter(age == 50) %>% \n", + " inner_join(child_clusters, by=\"id\") %>% \n", + " left_join(get_patients_survival(pop,ukbb_demog)) %>% mutate(follow_time=follow_time/365)\n", + " \n", + "fit <- survminer::surv_fit(survival::Surv(follow_time, dead)~clust+sex, data=clust_survival_data)\n", + "stats <- survminer::surv_summary(fit, clust_survival_data)\n", + "clust_survival_10y <- stats %>% mutate(t=floor(time)) %>% \n", + " distinct(t, strata, .keep_all=T) %>% \n", + " filter(t == 10) %>% \n", + " mutate(cluster = factor(paste0(\"C\", clust), levels=paste0('C', unique(clust))), sex=factor(sex, levels=c('male', 'female')))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "aeb4377a-6270-4e1c-8fe1-6c6c9c5dff4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ObVm69v/+FSaQVfZFlud/aFGdu+N5hp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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 216, + "width": 360 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width=6, repr.plot.height=3.6)\n", + "g <- ggplot(clust_survival_10y, aes(x=cluster, y=surv, ymax=upper, ymin=lower, color=cluster)) + \n", + " geom_errorbar(width=0.2) + geom_point() + theme_bw() + ylab(\"surv\") + \n", + " facet_wrap(~sex) + \n", + " scale_colour_manual(values=c('#649146', '#408da1', '#8870ad', '#A1405D' )) + \n", + " theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), strip.background=element_blank())\n", + "g" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "d9a4649f-afff-476b-acfd-b75d046d13ad", + "metadata": {}, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in dyn.load(file, DLLpath = DLLpath, ...): unable to load shared object '/home/nettam/r_packs/openssl/libs/openssl.so':\n libssl.so.1.0.0: cannot open shared object file: No such file or directory\n", + "output_type": "error", + "traceback": [ + "Error in dyn.load(file, DLLpath = DLLpath, ...): unable to load shared object '/home/nettam/r_packs/openssl/libs/openssl.so':\n libssl.so.1.0.0: cannot open shared object file: No such file or directory\nTraceback:\n", + "1. loadNamespace(x)", + "2. namespaceImport(ns, loadNamespace(i, c(lib.loc, .libPaths()), \n . versionCheck = vI[[i]]), from = package)", + "3. loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]])", + "4. namespaceImportFrom(ns, loadNamespace(j <- i[[1L]], c(lib.loc, \n . .libPaths()), versionCheck = vI[[j]]), i[[2L]], from = package)", + "5. asNamespace(ns)", + "6. loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])", + "7. library.dynam(lib, package, package.lib)", + "8. dyn.load(file, DLLpath = DLLpath, ...)" + ] + } + ], + "source": [ + "#tgppt::plot_gg_ppt(g, out_ppt=here('figures/child_survival_by_child_clusters.pptx'), \n", + "# rasterize_plot=FALSE, top=11, left=9.2, width=9, height=6, overwrite=TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94443342-4e5f-4d39-a49c-fc196cd33618", + "metadata": {}, + "outputs": [], + "source": [ + "### parent survival by cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e55be685-89ab-4515-b47d-a2c08849220b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "parents <- data.table::fread(here::here('output/ukbb_parents.csv'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "647f5e2c-6273-4652-bf56-f76ec66fe484", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22m`select_()` was deprecated in dplyr 0.7.0.\n", + "\u001b[36mi\u001b[39m Please use `select()` instead.\n", + "\u001b[36mi\u001b[39m The deprecated feature was likely used in the \u001b[34msurvminer\u001b[39m package.\n", + " Please report the issue at \u001b[3m\u001b[34m\u001b[39m\u001b[23m.\"\n" + ] + } + ], + "source": [ + "cluster_parents <- child_clusters %>% left_join(parents, by=\"id\") %>% left_join(pop %>% select(id, sex) %>% distinct, by=\"id\") %>% rename(child=clust)\n", + "d <- cluster_parents %>% \n", + " select(id, sex, parent_follow_time=mfollow_time, parent_dead=mdead, child) %>% \n", + " mutate(parent=\"mother\") %>% \n", + " bind_rows(\n", + " cluster_parents %>% \n", + " select(id, sex, parent_follow_time=ffollow_time, parent_dead=fdead, child) %>% \n", + " mutate(parent=\"father\")\n", + " ) %>% \n", + " filter(parent_follow_time > 0, !is.infinite(parent_follow_time))\n", + " #fitting parent survival model by child clusters\n", + " fparent <- survminer::surv_fit(survival::Surv(parent_follow_time, parent_dead) ~ child, data=d)\n", + " age85 <- survminer::surv_summary(fparent, d) %>% filter(time == 85) %>% \n", + " mutate(cluster=gsub(\"child=\", \"\", strata)) %>% \n", + " mutate(cluster = factor(cluster, levels=unique(cluster)))\n", + " median_survival <- survminer::surv_median(fparent) %>% \n", + " mutate(cluster=gsub(\"child=\", \"\", strata)) %>% \n", + " mutate(cluster = factor(cluster, levels=unique(cluster)))\n", + " pvals <- survminer::pairwise_survdiff(survival::Surv(parent_follow_time, parent_dead) ~ child, data=d)\n", + " \n", + " gmedian <- ggplot(median_survival, aes(x=cluster, y=median, ymin=lower, ymax=upper, color=cluster)) + \n", + " geom_errorbar(width=0.2) + geom_point() + theme_bw() + ylab(\"median age of death\") + \n", + " scale_colour_manual(values=c('#649146', '#408da1', '#8870ad', '#A1405D' )) + \n", + " theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "15b8f1d0-4b99-4e2f-9630-35a50ea2e17f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tPairwise comparisons using Log-Rank test \n", + "\n", + "data: d and child \n", + "\n", + " 1 3 10-14 \n", + "3 6.2e-08 - - \n", + "10-14 < 2e-16 < 2e-16 - \n", + "15 < 2e-16 < 2e-16 2.1e-05\n", + "\n", + "P value adjustment method: BH " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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PCYMWMyMzP37t178+bNa9eurV+/Pjk5\nefPmzYmJif7+/oSQBw8e9O7dWyqVTp482dra+tSpUx988MG1a9e2b9/ObKq2tnbs2LE2Njar\nVq0yMNDSdNX1qNvrZ5991r9/fxcXl0ePHvXt27ewsHDSpEmenp7fffddWFgYqyWqKBQKmUxW\nXl7OdkdKpVILvWhKGxphUCKRtJW7MhUKBdufAZlMpvFtKpXKTz75xNbW9tq1azY2NoSQjz76\nyMfH5/PPP//Pf/7Tok1VV1f/5z//effdd/fs2cO0rFix4uuvv87NzQ0ODk5PTyeE9OvXz8vL\nixASGRlJ03RGRoaLiwshZNWqVbNnz962bdu7777br18/Qsj58+ejo6M/++wzzX6/LaJu2IWG\nhh46dCgpKYmiKH9//zVr1kRFRdXW1vr4+MTFxbFaooqBgQFN09bW1ux1IRaLa2pqeDxevaeA\n9ZBCoaioqCCEmJmZCQQCXZfTjOfPn8vlciMjI2NjY13X0gzmM8Dn8y0sLFjtiI0re7du3crI\nyPj888+ZpCOEuLm5ffPNN63YS+XxeBRFXbx48fbt28zuW0xMTExMTMM1xWLx4cOH582bxyQd\nY968edu2bTt69CgTdoSQuXPntuZb0pwW7E+OHj169OjRzOvFixd//PHHT5486dSpk5YnG6Mo\nSgsbZ7UXjahbqv5Xq4JSWcWcngsICKjbOHHixFZsysjIKCEhITIyMiAgwNfXt2/fvkOHDh0+\nfHi9gXuZTpVKZXx8fHx8fL23SkpKmBe2trZmZmatKEODWnbwfP78+aNHj5aUlMyZM8fMzMzA\nwADTKgLoD+bcf6tPitXbAZw1a9aYMWOOHDly+vTp3377LTEx0cPD4/Tp087OznVXYw4sZs2a\nNWrUqHobdHR0ZF6YmJi0riQNakFUzZw5s3///uvWrdu5c2dBQUFaWpqHh8eCBQtUJ/UBQLc6\nd+5MCMnMzKzbGBcX98knnzS6fr0/3rrzK5SWlqalpQkEgunTp3/33Xe5ubk7d+7MyclJSEio\ntxEPDw8ejycUCsPq6NGjR0VFhc735upSN+y2b9+emJg4e/Zs1WXsQYMGTZs2LT4+Pikpia3q\nAKAlunTp4u7unpCQwJzPJYTk5+dHR0c3+qiDsbFxTk6O6gHQ3NzcuoOT37t3r3fv3qtWrWIW\nKYoaMGAA+d9+HIPJSkNDw1GjRiUlJd24cUP11qJFiyZOnKhXe0Lq7u5+/fXX/fr127Jli6rF\n2tp69+7dubm5W7dujYiIYKc8AGgBoVC4adOmMWPGBAUFjR8/XiAQJCUlyeXy6OjohisPGTLk\n1KlTgwcPfvfdd4uLi7du3crn81XvBgcH+/v7b968+cmTJ127ds3Ozv7tt9/MzMymTJlC/nd1\n5csvvxwxYsSoUaM+//zzkJCQ/v37jx8/3t3d/fTp0ydPnpw/f75ePTiv7p5dVlbWwIEDG7YP\nHDjw/v37Gi0JAFpv5MiRqampXl5e33zzTWJiYpcuXS5dusRcTq1nwYIFy5cvz8/P//jjj1eu\nXNm5c+d169ap3hUKhUePHp06deoff/wRExNz+vTpgQMHXrx40dvbmxAyfPjwsLCw77//nrmT\nztPT8+bNm2+++ebZs2djY2PLysoSExPXr1+vte9aHeru2bVv3760tLRhe05OjoODg0ZLAoCX\n0rNnz6NHjzb61okTJ1Sv+Xw+czeJRCIpKytr3749IWT27NmqFTp06LBr165Gt2Nvb//bb7/V\nbXFycvruu+8aXfnw4cMt/RbYoO6eXd++fffu3ZuXl1e38caNGwcPHqz3xAkAtC0ikYhJuleb\numEXGxtrYGAQFBS0cOFCQkhKSsrcuXNfe+01oVAYGxvLZoUAABqgbtg5ODikpaX16dNnw4YN\nhJBt27Zt3rz59ddfv3TpEhf+JwBAW9eCmw/d3d1TUlLEYnFWVpZQKHR3dzc0NGSvMgAADWrx\nndZGRkZdunRhoxQAAPY0FXYpKSlqbqXhYyIAAHqlqbBTPfbfLF0NPQoAoKamwu7MmTOq1wqF\n4qOPPnr69OmMGTOCg4PbtWuXkZGRkJDg5+e3d+9e1ssEAHg5TYUd8ygcY8WKFSUlJenp6R4e\nHkzLiBEjpk6dGhQUtHHjxro3XgMA6CF1bz358ccfJ0yYoEo6hqOj44QJE37++WcWCgMA0CR1\nwy4/P7/uQ8J1FRQUaK4eAABWqBt2Xbt2PXTokGrcUcazZ88OHjzYrVs3FgoDANAkdcNu8eLF\nT58+7d27d2Ji4vXr169fv75r167evXs/efKEeYAMAECfqXtT8bBhw3bu3Llo0aKZM2eqGm1t\nbXfu3Dl06FB2agMA0JgWPEERERExduzYc+fOZWVlGRgYeHh4DBgwoF27duwVBwCgKS17XMzc\n3Dw8PJylUgAA2IO5wQCAE1o55VpLSSSS5OTkS5cuicVib2/viIiIerOxffrpp++9956vr692\n6gEArtHSnt2OHTsuXLgQERGxdOlSuVweFRUlFouZt2iaPnHixJ07d/RqIiIAeMVoI+xomk5N\nTZ04cWJoaGhAQMC8efOePXt29+5dQsjJkycnT568efNmLZQBACpyhZwmGh6/Y8CAAampqZrd\npgY1dRjbo0ePRYsWvf3224QQHx+fxMTE0NDQ1nXD4/FUs5Qz805SFEUICQoKcnV1ra6uXrFi\nReu2DADqowmddu/U0av/KazIN+AJ3B28x/SN6Gjv9bKbpendu3efO3dOoVBopE42NBV2Dx8+\n/Prrr+3t7UUi0b17965evVp3fty6evXq1cR2KIoKCws7cOCAvb29mZnZ/v37XV1dfXx8CCGW\nlpaWlpZVVVWNfmFxcXHdSZKqqqoEAoHq+JcNMpmMEELTNKu9aITqqL+2tlYul+u2mGYx1crl\ncv3/wTI/TC18BnSSC3t+/zI18xhF8WhaKVUqsp5krDvwyfQhi3t5NTJRqpqSkpIiIyPLy8s1\nWCcbmgq7jz76iJkvklmcN2/eP63Z7Hh2EyZMuHDhwrJlywghFEWtXbtWJBI1W1xhYWHdI1w/\nPz8LC4vq6upmv/Al0TSthV40RSKR6LoEdclkMubfif5TKpVsfwa0H3ZZTzJSM48RQmj6r/+U\nSlpJEWrv6YSu7r0NBUat2+zQoUMDAgIqKioGDRqksVpZ0FTYrVq1avTo0X/++SdN0xMnTlyy\nZEnXrl1b0YdEIomMjPT19Y2NjTU2Nk5NTY2Ojo6KivLz82v6C4VCoZOT09+1GhhQFPVP4xFo\nBE3TSqWSoigeT99vymFKJYTweDzmnIA+UyqVNE23oR+sFkpl9bd2NfvctT/P12t8XJLTcE2a\n0GJpzaaU5eYmVvXe6t6pXw/P/s325eDg4ODg0Oi80nqlmVtPgoKCgoKCCCE//PDDhAkTWhd2\n6enpRUVFCQkJQqGQEDJkyJCrV68eO3as2bDz9vauOzT80qVLZTKZpaVlK2pQk1gsrq6upiiK\n1V40QqFQMAcO7dq1+6fTC/qjoqJCLpeLRCITExNd19KMmpqampoaHo/H9meA1d/a07Lcq9nn\n1F8/++ntho2OVi6aq0j31L3P7r///W+r+6AoSqlU1tbWMmFHCKmurjY2Nm71BgGgae2tXBvu\nlOWVPCiqyG90fc/2/g337NpbubJSnI607KbiwsLC48eP//nnnzKZzMvLa8iQIepMGtutWzdH\nR8fVq1dPnDjRyMjo/PnzmZmZmFobgD09PPs3DLs/C+58/t/Ieo0UxTM2NJk7eq3Q4BWfGbUF\nYRcXF7dy5cq6Z22NjY1XrVo1f/78pr9QJBKtXr16z549mzZtkkgk7u7uMTExXl4ve7UbAFqk\nk6PfwMA3T2f8TFEUc1GRoihCkSlvzHvlk46oH3YpKSkLFy4MCQlZunRpYGAgn8/PyMhYvXr1\nggULvLy8Ro4c2fSX29jYREbW/5ei0q5dO4ztDqAF77z2kbdz19+uHnhS+tDAQNi5vf/okH85\nWXfUdV3aoG7Ybdy40dfX9+TJk0ZGf12fdnJyeu2113r06LFx48Zmww4A9ESQR98gj766rkIH\n1L24npGRER4erko6hpGR0ejRo2/cuMFCYQDQllhbW9M0XXdKQn2jbtgZGxu/ePGiYfuLFy/0\n/2YCAAB1w65Hjx779+9/9OhR3cbc3Nzvv/++e/fumq8LAECj1D1nt27duu7du3fp0mXGjBmB\ngYGEkIyMjF27dkml0jVr1rBZIQCABqgbdt7e3r///vvcuXM3bNigagwODt64cSPzSD8AgD5r\nwX12ISEhaWlp+fn52dnZhJBOnTrVG20YAEBvtXhY9g4dOnTo0IGNUgAA2KPvQ1AAAGgEwg4A\nOAFhBwCcgLADAE5A2AEAJ7Tsauz58+ePHj1aUlIyZ84cMzMzpVLp7u7OUmUAABrUgj27mTNn\n9u/ff926dTt37iwoKEhLS/Pw8FiwYAEmtwYA/adu2G3fvj0xMXH27NlZWVlMy6BBg6ZNmxYf\nH5+UlMRWdQAAGqJu2H399df9+vXbsmWLp6cn02Jtbb179+7XX39969atrJUHAKAZ6oZdVlbW\nwIGNTKM7cODA+/fva7QkAADNUzfs2rdv3+i8kDk5OQ4ODhotCQBA89QNu759++7duzcvL69u\n440bNw4ePBgSEsJCYQDAllq5PKuo9HHZc4WSfvmtFRQUTJo0ycHBwcrKavjw4Xfu3Hn5bbJB\n3VtPYmNjf/nll6CgoKlTpxJCUlJSjh49mpSUJBQKMSkiQFtRI5V9c/HGjzfuK5RKQkg7kfD9\n0KCRgZ2p1m6Qpulx48ZVVVXt2LHDwsIiJiZm6NChd+/eNTU11WDZGqFu2Dk4OKSlpc2bN48Z\nz27btm08Hm/UqFGff/65OlPHAoDOKWl68aGTd54Wq3bnXtRKN/x+ubRaPDWkS+u2mZOTc/Hi\nxcuXL/fq1YsQsnv3bhcXlz/++KPRU/y61YKbit3d3VNSUsRicVZWllAodHd3NzR89eeaBHhl\nnMt+fPtpcd0WmiaEkO/Sbo3q4mVpLGrFNoVCYVxcXJcuf2WlVColhFhYWLxsrSxQN+zqzo3d\nqVMnQohcLpfL5SKRiM/ns1IaALTWmazcs1mP6jVmFZURipAGp+kUSuWKlNO27YzrtQ/o7PZa\nZ9emO3JxcZk/fz4h5Pz58xcvXty7d+/bb7/dtWvXlymeJeqG3T8dgfN4PCsrKzs7u5EjR374\n4YcdO7I4265CoZDL5VVVVax2QQihaZrVXjSCmdGdEFJTU8Pj6fszzsxjNlKpVP+ft2E+A0ql\nku3PgFwuZ2/jj0orzmTlqr/+nYISUlC/0dXagpBmwk7l5MmThw4dysrKeuutt2iapqhWnwZk\ni7pht23btrVr1+bl5YWEhPj6+vJ4vHv37p0/f75Xr16+vr75+fkbN2786quvbt68yez3sYGi\nKIqiWP3DVv0p6n98qMKOx+Ppf7UMtn99GsF8BrRQKqtx4GZt0XCn7FFpxaPS542u362DvXmD\nw1g36xYcja5cuXLlypU3btzo06ePg4PDrFmzWlSwFqgbdhKJpLS09Ny5c/369VM1Xrx4cdSo\nUTExMW+88UZBQUHfvn0//fTT//73v+yUSng8Hp/PZ3WaWrFYLJPJKIrS/8lwFQqFRCIhhIhE\nIoFAoOtymiGTyZRKpUAg0P8fbE1NjXY+A6ye/3mts2vDsMsrr5yW9LOS/n/3m/Ao4mje7otx\nQ/i81oTvtWvXbt++/a9//YtZ7Nq1a3Bw8B9//KGHYafu/66kpKQpU6bUTTpCSJ8+fSZMmBAd\nHU0IcXR0/Pjjj9PT0zVfIwBogrOl2dxBvXg8HkUoilA8iiKEmBmJokYOaF3SEUJKSko+/PBD\n1SG/QqF4/Pixq6u6B7/apO6eXW5u7tChQxu2m5ub37p1i3ltb29fVFSksdIAQNNGBnh27WB/\n8PrdB88qDA34/u3txgX5GAtbf2TQr18/MzOzt99+e/HixQYGBl9//XVpaemUKVM0WLOmqBt2\nvXr1+u9//7tw4UJra2tVY3l5+YEDB7p3784s/vrrr6phAgBAP3WwNPvk9V6a2pqJicmJEyei\noqImT55cW1vbo0ePs2fPsnqhstXUDbt169aFhoYGBgZOnz6dmRX73r17u9KqXx0AACAASURB\nVHfvLi0tPXDgQG5u7oQJE9LS0nbv3s1mtQCgdwIDA3/88UddV9E8dcOuW7duJ0+eXLRoUUxM\njKqxe/fuBw8e7Nat27Vr18rKyrZs2TJt2jR26gQAeCkteIKid+/e586dy8vLy87OlkqlnTt3\n7tixI3PjW/fu3VWDegIA6KGWzUFBCHF2dnZ2dlYt7tq1a/ny5c+ePdNoVQAAGqZu2Eml0qVL\nlx49erSmpkbVSNN0fn4+5twBAP2n7n12a9asiY+PNzU1FYlEjx49CggI8Pf3r6qq8vT03Ldv\nH6slAgC8PHX37Pbv3x8SEnLx4kWJRGJpabl+/Xpvb++cnJzg4GChUMhqiQAAL0/dPbu8vLzQ\n0FBCiEgkCg4OvnbtGiHEw8Nj8uTJy5cvZ7FAAABNUDfsLC0tKysrmdeBgYGpqanMa19f3ytX\nrrBSGgCA5qgbdn5+fsePH2fyLiAg4PDhw0z7zZs3WR2pBgBAI9Q9Z/fZZ5/179/fxcXl0aNH\nffv2LSwsnDRpkqen53fffRcWFsZqiQAAL0/dsAsNDT106FBSUhJFUf7+/mvWrImKiqqtrfXx\n8YmLi2O1RACAl9eC4QlHjx79008/mZubE0IWL15cWlp6//7927dv6+dDvwAAdbX4CQoVExOT\nzp07a7AUAAD26PsY2QAAGoGwAwBOQNgBACcg7ACgbXj27FltbW2rvxxhB8AxNHmaW3bj0sPM\na3nlJS80uOEBAwaonq1S+fLLL318fOzs7KZMmfL8eeMTOTa9BUZmZqazs/OZM2daXV7Lrsbm\n5+dXV1c3bPfy8mp1BQCgNeUlL47uT8978NcAlBRFBfZ2fWN0F4HwpeZ1pGl69+7d586dY6YY\nV9m8efOyZcs2bdrUvn37Tz/9dMyYMSdPnmzRFhhSqfSdd95h5g5tNXXD7sGDB2+++eadO3ca\nfVc1YTMA6C2ZVL5/2/mqir8jg6bpm5cfyWoV4e8Ft3qzSUlJkZGR5eXl9doVCsWGDRuWLl36\n/vvvE0JcXV0DAgLS09ODgoLU3ILKsmXLzMzMWl0hQ92wmzdv3t27d6dPn969e/dWzJQukUiS\nk5MvXbokFou9vb0jIiJUwx2npKQcO3asqqoqKCjogw8+MDY2bunGQc/JZYriJ5VVFTV27S1N\n3ExIK2cohZd1+2peZbm4fitNMtPzQof5WNqYtm6zQ4cODQgIqKioGDRoUN32nJycR48ehYeH\nM4v+/v5ubm4nTpxoGHb/tAXG6dOnv/322wsXLrzkjb3qht358+dnzJixffv21nWzY8eO9PT0\nGTNmmJubHzhwICoqasuWLUZGRkeOHElOTn7//fetra2//fbbtWvXrl69unVdgH7KTM879VNG\nddVf55UdXayGvt3Nzslct1W98u7deHL/Zn69xoLH5RRFGj0MO/LdVTNLo3qNXl06eHd1arYv\nBwcHBweH0tLSeu1PnjwhhLi4uKhanJ2dCwoK1N8CIaS8vHzKlCnbtm1r3759s5U0Ta2wq62t\nLS8vDwwMbF0fNE2npqZGREQwI+I5OTlNnTr17t27Xbt2/emnn8aPH88MJWBraztnzpycnBwP\nD4/WdQT6JjM973DyH7w6s80X5pXv/epsxMJB5lbYhWfRs8LKezeeqL/+09yyp7n1G63tzQhp\nPuz+sYZnzwgh7dq1U7WYmZmVlJS0aCMffvjhoEGDxo4d2+jVghZRK+yEQqGLi0tqaupHH33U\num54PJ6BwV99CQQCQghFUQUFBcXFxT179mTaXV1d7ezsbty4gbB7RdDk7JHbFEWUyr/3JWia\nltYqLp+8Hza+mw5Le+XZOJg13CkrK35R/LTx66EdvewMjQQNN/IyNVhZWRFCXrx4YWFhwbRU\nVla6ubmlpKSMHj2aaTly5MiIESP+aQt79+69cuVKRkbGy5SholbYURS1e/fu0aNHR0VFLViw\noG5Uq/nlYWFhBw4csLe3NzMz279/v6urq4+PT3Z2NiHE1tZWtaatrW1ZWZlqMSMjIyIiQrXo\n5+dnYWGhhZnMlEplG5ovrdnL+TqRnVF07vD9xt+j6RsXH964+HDSJ72NTfV0TH+FQsH2Z0Aq\nlbK3ce+uTg3DruZF7fY1x+S18jr/fQhFqA4eNm9/GKrxGhwcHAgh+fn5qrB7+vRpWFjY0KFD\nCwsLmRbVW426fPnyo0eP6l6aGDp0qJub28OHD1tRj7rn7FauXGllZbVq1aqYmBg7Oztm70wl\nLy+v6S+fMGHChQsXli1bRgihKGrt2rUikYgZCtTI6O8zBUZGRqrxkAFAs4xNDce/3+fn5D+q\nKsSEIoQmhJAO7taj/tX6S7FN8PHxcXZ2/u233/z9/QkhOTk5OTk5YWFhhoaG9vb26mxh4cKF\nU6dOZV7X1NT0798/ISHhtddea1096oadjY2NjY1Nw8so6pBIJJGRkb6+vrGxscbGxqmpqdHR\n0VFRUaampsy7JiYmzJpisbjuT6FDhw5Lly5VLZ49e5bP5zNfxRKZTFZbW0tRlKokvaVUKplp\nLY2MjPj8l7pJig0dO1O8N/nnfrmrVNQ/H05RlJWdSZc+bpZW5i95excbpFKpVCrl8Xhs3xig\nk99aB3eb9z8dcu9GfsnT5wZCA6eOVh7eDixdH+fxeHPnzl21apWPj4+Dg8O///3vfv369ejR\nQ/0tuLi4qK5vMOfsOnfuHBAQ0Lp61A27H3/8sdF2mUzW7J1+6enpRUVFCQkJzDxkQ4YMuXr1\n6rFjx8aNG0cIefbsmSpZSktLu3X7+1SOlZXVmDFjVItXr16VyWQikUjNmluBpmkm7FjtRSMU\nCgUTdkKhsN6Otj5w6CBy6GD9/Jn4+qWH5P/HHU3TA0YGePo76qi0ZiiVSqlUqoXPgK7+RQmE\n/ICertrpKzIyUiqVMvfQDR48eNu2bdrpt1GtH8+OsWvXruXLlzd9doOiKKVSWVtbq5p0sbq6\n2tjY2NnZ2cbGJj093dXVlRBSWFhYWFjYup1H0E+vvRlQXlrz6H6RqoVHUX3CvPU26aDVrK2t\nG324YMmSJUuWLHmZLTBMTExe8uEFdcNOKpUuXbr06NGjzN4Eg6bp/Px8d3f3pr+2W7dujo6O\nq1evnjhxopGR0fnz5zMzM2NjYymKevPNN/fv3+/s7GxhYbFjxw4/P79OnTq1/rsBPSM0NJjw\nYd8/bxf88v0fErHcvoP5iHeCbR1f9lZ4gFZQN+zWrFkTHx/fs2dPpVJ579698PBwmqYvXrzI\nzLnT9NeKRKLVq1fv2bNn06ZNEonE3d09JiaGeZx29OjRcrl8165dL1686Nq166xZs172GwL9\n08nf0dhMJBG/aO9miaQDXVE37Pbv3x8SEnLx4kWJRGJpabl+/Xpvb++cnJzg4GDVwWkTbGxs\nIiMjG31r3LhxzMk7AAD2qPuUa15eHvP8g0gkCg4OvnbtGiHEw8Nj8uTJy5cvZ7FAAABNUDfs\nLC0tVXfABQYGqsac8vX1vXLlCiulAQBojrph5+fnd/z4cSbvAgICDh8+zLTfvHlTLpezVR0A\ngIaoe87us88+69+/v4uLy6NHj/r27VtYWDhp0iTm6gTzGD8AgD5TN+xCQ0MPHTqUlJREUZS/\nv/+aNWuioqJqa2t9fHzi4uJYLREA4OW1YBjO0aNH//TTT+bm5oSQxYsXl5aW3r9///bt2x07\ndmStPAAAzWj9ExQmJiYvOXAoAIDWYHYxAOCEpvbsunXrxufzr169yrxuYs3r169ruC4AAI1q\nKuxMTU1VAzM0PcYeAICeayrszp8/r3p9+vRp9osBAGBLU2GXkpKi5lZGjRqliWIAANjSVNip\nJsVoFibJBgA919TV2DN1nDx50tvb28zMLDIyct++fUeOHFm7dq2Dg8Mbb7yhmjsDALhswIAB\nqqfmGdu2baP+v7S0NF2V19Se3YABA1SvV6xYUVJSkp6erprncMSIEVOnTg0KCtq4ceO6devY\nLRMANERWI35w/EJ5Tp6BkaGNr4frgJ4U9bKTUNA0vXv37nPnzikUirrtDx48CA4OXrx4sarF\n09PzJftqtRbMQTFhwoR6M7o6OjpOmDDh559/RtgBtAkFV2+fX7NNUlFFURRNE0Joq86/vL56\nrrGtVau3mZSUxMwy0fCtBw8e9OrVa+zYsa2vWHPUvak4Pz//n+YHKSgo0Fw9AMAWcWnF6c82\n1T6vJn+dZ6cJIeXZuWdXfkVe4rT70KFDT5w48fvvvzd8Kycnx8PDo6qq6vHjxzo/s6/unl3X\nrl0PHTq0YsWKunNaP3v27ODBg03fbwwA2pd75sqjs/UHmnz++KlcXFuvkabpksw/Tyz8Qtiu\n/tSRbgN6ur7Ws9m+HBwcHBwcSktLG7714MGDPXv2LFiwQKFQWFlZffHFF3WnvdcydcNu8eLF\nI0aM6N2795IlS5iZH9PT09etW/fkyZPExEQ2KwSAFqt49CT3TAtG1S24drtho4Wr08tMuVha\nWkrTdPfu3VNSUoyMjL766qvp06d37Nhx4MCBL7HV1lM37IYNG7Zz585FixbNnDlT1Whra7tz\n586hQ4eyUxsAtJKFm1PDnbLSew9eFJU2esRq4+thYmfdcCMvU4O1tXVVVZVqceXKlb/++mty\ncrK+hx0hJCIiYuzYsefOncvKyjIwMPDw8BgwYEC7du3YKw4AWsf1tUaOQLN/OXMpbncja1NU\n/+WzTR1tG3lLo7y9vYuKippfjx0tG+LJ3Nw8PDycpVIAgFXug/rc/v6XqsJiovx/O3edhvZj\nI+lOnDgxc+bMU6dOMUNeKpXK69evjxgxQuMdqallYXf+/PmjR4+WlJTMmTPHzMxMqVQ2O0M2\nAOgJvqFwyIbFl+O/efLHLaaFx+d5vTU4aOYENrobOHAgj8ebOHHi/PnzHRwcduzYkZ+fP3fu\nXDb6UkcLwm7mzJmqaxHjxo27e/cu822sX7+ex9PGuHg0TdM0Xe+uRc1SKpXMC1Z70QhVhUql\nUv+rVdH/UlV3SLBdqk5uxTCxt3lj/cLyB3nlD/IMDIU2Ph7GNpYs9WVgYHD58uXIyMgFCxbU\n1NT06dMnLS3NwcGBpe6ar0fN9bZv356YmDh79uy5c+cyAxQPGjRo2rRp8fHxvr6+2rmcrFAo\nZDJZo/cuapZSqdRCL5pS9xyw3mL+sOVyeVv5wSoUCrZLlclkrG6/CZbuzpbuzprdprW1dcP4\ntrW1TU5O1mxHraZu2H399df9+vXbsmWLqsXa2nr37t25ublbt27VTtgZGBjQNG1jY8NeF2Kx\nuLq6msfjWVm1/oZy7VD9NZqbmwsEAl2X0wzmgSQDAwNWf30aUVNTU1NTw+fzLS3Z2uVhCIVC\nVrcP9ah7+JmVldXoBeOBAwfev39foyUBAGieumHXvn37Ru+QzsnJ0eFBOACAmtQNu759++7d\nuzcvL69u440bNw4ePBgSEsJCYQAAmqRu2MXGxhoYGAQFBS1cuJAQkpKSMnfu3Ndee00oFMbG\nxrJZIQCABqgbdg4ODmlpaX369NmwYQMhZNu2bZs3b3799dcvXbrUvn17NisEANCAFtxn5+7u\nnpKSIhaLs7KyhEKhu7u7oaEhe5UBAGhQC8KuoKDg2rVr1dXVzGJGRobqrQkTWLkDGwBAU9QN\nu5SUlIkTJ0okkkbfRdgBgJ5TN+w+/fRToVAYFxcXEBDwT0MWAwDoLXXDLjc3d8mSJR999BGr\n1QAAsETdq7E+Pj76/0wSAMA/UTfsPv744y1bttS7qRgAoK1Q9zB26tSp+/bt8/Pze+ONNxo+\ny41pKABAz6kbdvHx8cePHyeE/Prrrw1Hr0PYAYCeU/cwdufOnX5+fllZWbW1teIGWC0RAODl\nqRV2Uqn0/v37M2fO9PT0ZLsgAAA2qBV2PB6vXbt2jx8/ZrsaAACWqBV2BgYGcXFxW7duTUlJ\nYbsgAAA2qHuBYs+ePUKhcPTo0ebm5g3nisUtKQCg59QNOxsbG13N4w0A8PLUDbsff/yR1ToA\nAFiljfleAQB0DmEHAJyAsAMATmjBSMWtlpaWtmbNmnqNdnZ2O3furKmp+fbbb//44w+FQtGl\nS5cPPvjAxMRECyUBANdoI+w8PT2XLFlSt+WHH35wc3MjhKxZs6aiomLWrFlCofD7779fsWJF\nfHw8M3s8gPY9Scu4f/hU+aMnxtYWzr26+IwdwjcU6roo0AxthJ2VlVWfPn1Uizk5ORUVFdOn\nT7979+6tW7c2b97s6upKCHF3d58+ffqNGze6deumhaoA/h+avvjFrj+PniM8HlEqq58Wl2Tc\nzzp8KmzTchM7K10XBxqg7XN2NE1v376dOVzNy8szMDBgko4Q0q5duw4dOtSdxwdAax6euvzn\n0XOEEKJUEkIITRNCqotK075M0mVZoDna2LOr6+zZs4SQXr16EUJsbGzkcnlBQYGjoyMhRCKR\nFBYWuri4qFYuKCj44YcfVIuVlZVCoVA1vRkb5HI5IYSmaVZ70QiappkXEolEKpXqtpiGSp5W\n/nm7ULUoflFLCHnysOz3H6+rGnu85iEQ6n4+E4VMfnfvkfzUaxRF/vdD/QtN0/mXb1zc8E2H\nAT2svd013K9CodkNQtO0GnYSiSQpKWn+/PnMYmBgoLOz8xdffDFlyhQej/fDDz+IxeK6E5iV\nlJR8++23qkU/Pz8LCwstjChF03QbGreqtrZW1yU0oiCv7Nq5h/Uai59UFj+pVC127mZvbKr7\nM2Jyce39H441scKfh08b2lsZuzpqtl+EnZZpNeyOHDliYWEREBDwV98GBtHR0YmJiV988YWx\nsXFYWJhCobCwsFCtLxQKnZyc/q7VwICiKFbnNqNpWqlUUhTVcIBSfcOUSgjh8Xh6eEnH0Ehg\nZmmkWlTth9Yt1cCArxcz1QkMTOxtJGXPFTJZo+8b2VgKTYw1Xqoe/tZebdoLO5qmjx8/Hh4e\nXrfRxsbm008/VS0eP368a9euqkVvb++646wsXbpUJpNZWlqyV6RYLK6urqYoitVeNEKhUJSX\nlxNC2rVrp4dzIVmGWAaFdFYtVlRUyOVyIyMj/by1aOz+DTeTfrz5bf1nIikeZepg99beL9jo\nVA9/a6827e2/ZGZmFhYW9uvXT9Xy4sWLlStX3r17l1n8888/i4qK6l63BdAan3Fhpg42hPy9\nt0XxeISmev77PR1WBRqkvbBLT093cnKqe5RqamoqlUq/+uqrS5cupaWlrV+//vXXX6973Aqg\nNUJT4+FbozoN60f+d3Rp6eEctvFTp16Bui0MNEV7h7E3b9708fGp17hw4cLt27dv2rTJyspq\nwIAB77zzjtbqAahHZGneZ9EMgbnp3f2/GttZj9wRo+uKQJO0F3ZxcXENGy0tLes9XAGgWxSP\nR3D14FWk79ccAQA0AmEHAJyAsAMATkDYAQAnIOwAgBMQdgDACQg7AOAEhB0AcALCDgA4AWEH\nAJyAsAMATkDYAQAnIOwAgBMQdgDACQg7AOAEhB0AcALCDgA4AWEHAJyAsAMATkDYAQAnIOwA\ngBMQdgDACQg7AOAEhB0AcIL2Jsl+eQqFQi6XV1VVsdoFIYSmaVZ70QiappkXNTU1PJ6+/9NS\nKpWEEKlUyrzQZ1r7DMjlcla3D/W0pbCjKIqiKFb/sFV/ivofH6qw4/F4+l8tg+1fn0ZQFEW0\nUirTEWhNWwo7Ho/H5/NNTEzY60IsFstkMoqiWO1FIxQKhUQiIYSIRCKBQKDrcpohk8mUSqVA\nIND/H6wq49gulc/ns7p9qEff/80CAGgEwg4AOAFhBwCcgLADAE5A2AEAJyDsAIATEHYAwAkI\nOwDgBIQdAHACwg4AOAFhBwCcgLADAE5A2AEAJyDsAIATEHYAwAkIOwDgBIQdAHACwg4AOAFh\nBwCcgLADAE5A2AEAJyDsAIATEHYAwAkIOwDgBG1Mkp2WlrZmzZp6jXZ2djt37pRIJMnJyZcu\nXRKLxd7e3hEREc7OzlooCQC4Rhth5+npuWTJkrotP/zwg5ubGyFkx44d6enpM2bMMDc3P3Dg\nQFRU1JYtW4yMjLRQFQBwijbCzsrKqk+fPqrFnJycioqK6dOn0zSdmpoaERERGhpKCHFycpo6\nderdu3eDgoK0UBUAcIo2wq4umqa3b9/+wQcfmJiY0DTN4/EMDP6qQSAQEEIoilKtrFAoqqur\nVYtKpZLZAqvl1Xuht+qWqv/VquhjqTQtfVGjWlJIZYQQolTWVr5QNfJFhnyBtv9YQLO0/fs7\ne/YsIaRXr16EEIqiwsLCDhw4YG9vb2Zmtn//fldXVx8fH9XKd+7ciYiIUC36+flZWFiUlpay\nXaRSqdRCL5pSWVmp6xLUJRaLxWKxrquoTy6uPT7l03qN1SVl/xk1W7XoN2Osa1hfzfYrlUo1\nu0FomlbDTiKRJCUlzZ8/X9UyYcKECxcuLFu2jBBCUdTatWtFIpE2SwIAjtBq2B05csTCwiIg\nIIBZlEgkkZGRvr6+sbG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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 216, + "width": 210 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width=3.5, repr.plot.height=3.6)\n", + "pvals\n", + "gmedian\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b68d4466-90f3-492e-95e6-2e0d0fc3148c", + "metadata": {}, + "outputs": [], + "source": [ + "#tgppt::plot_gg_ppt(gmedian, out_ppt=here('figures/parents_survival_by_child_clusters.pptx'), \n", + "# rasterize_plot=FALSE, top=11, left=7, width=5, height=7, overwrite=TRUE, sep_legend=TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e559cd79-e82c-413c-8d4f-195f97653091", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R 4.2", + "language": "R", + "name": "ir42" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.2.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/score_validation_UKBB.ipynb b/score_validation_UKBB.ipynb new file mode 100644 index 0000000..cea45bc --- /dev/null +++ b/score_validation_UKBB.ipynb @@ -0,0 +1,410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "54906659-5255-48b7-a4d4-3b324d8f3f71", + "metadata": {}, + "source": [ + "# Longevity and Disease model validation on UKBB data\n", + "##### requires Disease_Longevity_UKBB notebook to be preprocessed (for model scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "fa9337aa-7c8a-445a-9070-fe170acbf0d7", + "metadata": {}, + "outputs": [], + "source": [ + "source(here::here(\"code/init.R\"))\n", + "source(here::here(\"code/ukbb_preprocessing.R\"))\n", + "source(here::here(\"code/ukbb_outcome.R\"))" + ] + }, + { + "cell_type": "markdown", + "id": "fa85e6cf-bb68-4e63-9a00-910e3c9e21ae", + "metadata": {}, + "source": [ + "### Loading scores" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "55608603-717f-40f8-b0cc-99b5cc492bbd", + "metadata": {}, + "outputs": [], + "source": [ + "pop <- tgutil::fread(here::here('output/pop_scores.csv')) #see Disease_Longevity_UKBB notebook for computation" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "30b3dcf1-db54-40eb-89e2-34d01b300c45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 9
agesexlongevitylongevity_qdiabetesckdcopdcvdliver
<int><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
145female0.96758820.19257920.088155240.041675530.027986280.20603820.022592036
245male 0.91192810.10603480.159096920.039192490.090642470.29449000.008487539
345female0.99698310.45743610.209795140.138553650.128116470.63706790.022256322
445male 0.99452550.39842700.077918440.059655310.043355170.21177180.033393441
545female0.98381660.26512810.080082880.022186810.065933830.12077450.009952694
645male 0.93772170.13451660.031964070.049305710.012635090.11572290.014305011
\n" + ], + "text/latex": [ + "A data.frame: 6 × 9\n", + "\\begin{tabular}{r|lllllllll}\n", + " & age & sex & longevity & longevity\\_q & diabetes & ckd & copd & cvd & liver\\\\\n", + " & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 45 & female & 0.9675882 & 0.1925792 & 0.08815524 & 0.04167553 & 0.02798628 & 0.2060382 & 0.022592036\\\\\n", + "\t2 & 45 & male & 0.9119281 & 0.1060348 & 0.15909692 & 0.03919249 & 0.09064247 & 0.2944900 & 0.008487539\\\\\n", + "\t3 & 45 & female & 0.9969831 & 0.4574361 & 0.20979514 & 0.13855365 & 0.12811647 & 0.6370679 & 0.022256322\\\\\n", + "\t4 & 45 & male & 0.9945255 & 0.3984270 & 0.07791844 & 0.05965531 & 0.04335517 & 0.2117718 & 0.033393441\\\\\n", + "\t5 & 45 & female & 0.9838166 & 0.2651281 & 0.08008288 & 0.02218681 & 0.06593383 & 0.1207745 & 0.009952694\\\\\n", + "\t6 & 45 & male & 0.9377217 & 0.1345166 & 0.03196407 & 0.04930571 & 0.01263509 & 0.1157229 & 0.014305011\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 9\n", + "\n", + "| | age <int> | sex <chr> | longevity <dbl> | longevity_q <dbl> | diabetes <dbl> | ckd <dbl> | copd <dbl> | cvd <dbl> | liver <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 45 | female | 0.9675882 | 0.1925792 | 0.08815524 | 0.04167553 | 0.02798628 | 0.2060382 | 0.022592036 |\n", + "| 2 | 45 | male | 0.9119281 | 0.1060348 | 0.15909692 | 0.03919249 | 0.09064247 | 0.2944900 | 0.008487539 |\n", + "| 3 | 45 | female | 0.9969831 | 0.4574361 | 0.20979514 | 0.13855365 | 0.12811647 | 0.6370679 | 0.022256322 |\n", + "| 4 | 45 | male | 0.9945255 | 0.3984270 | 0.07791844 | 0.05965531 | 0.04335517 | 0.2117718 | 0.033393441 |\n", + "| 5 | 45 | female | 0.9838166 | 0.2651281 | 0.08008288 | 0.02218681 | 0.06593383 | 0.1207745 | 0.009952694 |\n", + "| 6 | 45 | male | 0.9377217 | 0.1345166 | 0.03196407 | 0.04930571 | 0.01263509 | 0.1157229 | 0.014305011 |\n", + "\n" + ], + "text/plain": [ + " age sex longevity longevity_q diabetes ckd copd cvd \n", + "1 45 female 0.9675882 0.1925792 0.08815524 0.04167553 0.02798628 0.2060382\n", + "2 45 male 0.9119281 0.1060348 0.15909692 0.03919249 0.09064247 0.2944900\n", + "3 45 female 0.9969831 0.4574361 0.20979514 0.13855365 0.12811647 0.6370679\n", + "4 45 male 0.9945255 0.3984270 0.07791844 0.05965531 0.04335517 0.2117718\n", + "5 45 female 0.9838166 0.2651281 0.08008288 0.02218681 0.06593383 0.1207745\n", + "6 45 male 0.9377217 0.1345166 0.03196407 0.04930571 0.01263509 0.1157229\n", + " liver \n", + "1 0.022592036\n", + "2 0.008487539\n", + "3 0.022256322\n", + "4 0.033393441\n", + "5 0.009952694\n", + "6 0.014305011" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(pop %>% select(-id))" + ] + }, + { + "cell_type": "markdown", + "id": "efe66af5-cb31-47f4-b392-059b92ad3a8d", + "metadata": {}, + "source": [ + "## 10-year survival by longevity score" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b48b12b1-05c7-435a-bb58-aab57e69c622", + "metadata": {}, + "outputs": [], + "source": [ + "demog <- tgutil::fread(here::here('output/ukbb_demog.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3564403c-265f-4cfe-a514-dcb76cc58dfa", + "metadata": {}, + "outputs": [], + "source": [ + "survival <- get_patients_survival(pop, demog)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "12b43c5b-2bbd-4b23-9650-b5e9234dbe23", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "pop_survival <- pop %>% left_join(survival, by=c(\"id\", \"sex\", \"age\")) %>% \n", + " mutate(qbin=cut(longevity_q, seq(0, 1, by=0.1), right=FALSE, include.lowest=T))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "390be939-25cb-46a9-a6dd-7130a6f00070", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "km_10y_age_60 <- plyr::ddply(pop_survival %>% filter(age == 60), plyr::.(sex), function(x) \n", + "{\n", + " fit <- survminer::surv_fit(survival::Surv(follow_time, dead)~qbin, data=x)\n", + " stats <- survminer::surv_summary(fit, x)\n", + " stats_10y <- stats %>% mutate(t=floor(time/365)) %>% \n", + " distinct(t, strata, .keep_all=T) %>% \n", + " filter(t == 10) %>% \n", + " mutate(age=x$age[1])\n", + " return(stats_10y)\n", + "}) %>% mutate(x=1-(as.numeric(qbin)-1)/length(levels(qbin)), \n", + " sex=factor(sex, levels=c('male', 'female')))\n", + "g <- ggplot(km_10y_age_60, aes(x=x, y=surv, ymax=upper, ymin=lower, colour=sex, group=sex)) + \n", + " geom_line() + \n", + " scale_colour_manual(values=c('#586c82', '#f6bfcb')) + \n", + " geom_errorbar(width=0.005) + \n", + " theme_bw() + \n", + " ylab('10 year survival') + \n", + " scale_x_continuous(breaks=seq(0, 1, by=0.2)) + \n", + " xlab('longevity score') + \n", + " theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "7b3dcd1f-5e3c-47fc-8def-5d19d0a4cbce", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Y15ff5hr2/U5/f6/F7fmHfUPx6OR8dG\nRscuNpHkYjgiRuSw29ZfvrSkIKcwz12Ul+2w2yL0EgCiAwEaACAOGP7A+EZdc2D67RYTS1tw\nNpmzyWQb34jZcdBcikMsK/D7/Xa7XXClWl1OohnzB4dGRsf8wbFAYGjEZ24Pe8c3fP7AyKhv\npsu9SJLoSktxpTqTHUnJDrsrzZmempLsSHI6klxpKempzsefe+PN/UdLCnL++pNbIvOyACyA\nAA0AEMMY6YMjWkePMTQ6fuD89Dy+roV8XlDmbBJnk+OxKZlPtnOOJHVgwOly8fif/kxMGY7H\nAsHhkdGhEZ/PHxgZHZvydr1LkCQx2Z7kSktxpTmnDMepKcnCR60ziFZmSEgI0AAAMUk3tN4B\nraOX+YPnHuZzM8WMtPGULMvEI50kPkXVzBw8Fgj4/MFhMx8HgmP+8azs9Y0ZxpzCsbmdnuZ0\nOpKS7XZXmjPTlSbE4d9gANGBAA0AEFuYompdHr2zj03cicXZbYI7Q2vrJiIuK13Ael4JRNP1\nvv6hS4TjUZ9fN6bVtDPJDMdO85pxWsqHw3GGKxU37QHMBQI0AECsMHx+vcuj9QzQRGDi05xi\nYY6Qla4HQtTWbW15EBa+scDJhpaDx+rN3fvuf3xGH26TpVRnclpqcqrTkZLsSE1JTktJTnE6\nUp2ONHM72SHFzAyaujOtJxtaiKizp3/foZNrVi22uiKA8IiV7zEAgPmLkTHs1Tp69YGR8SM8\nJ2Smi0W5fGqypZVBeARDyqnGthOnW2rrm1s7ey/WiyxLYorTkZaSnJqSnJrsSHE60lKdaU5H\nitNhhuaUZIdNvnB2YyzzDAz3DQwTkdfn7+jxWF0OQNggQAMAWOdDjc6cJAp5WWJBNhdvywPD\nBVRNP9PSYYbmM62dFwwQycxIGxgcIaLbb960dFFFmtORmpIcd2tCfyR3ZvqalYtDoZAsy4W5\nbqvLAQgbBGgAAAtM1eicJBa4xTz3lKtnGBMXp41BL6EHOlYZBmvt7DnV2H66qf1oXWMgGDr3\nrakpyYsXlCypKlu6qJyImetALygpWFhaYFG9EVe9oGRRRfHAwIDL5RLQdQ0JBAEaACCqjFG/\n1tGr9w1OTjzh05xica6QmX6Jj9I7es0N1uWhypKIVwkz0dc/dLy+uba+uba+2TexYrcpySYv\nLC1YUlW+dFFZaWHe5JJufQNDFhQKAGGCAA0AEBWM9MFhraPPGPKOH+E5wZ0hFufyyfaP/nAs\nphtjhkZG65vaa+ubD584MzjsPfdNNlmqLCtcUlVeVV60oLQAi8EBJB4EaACACPtwo7MsCrlZ\nYkEOZ5vuDWHy6prBwUEiSktLi1Sd8FG8o2Mnz7TW1jcfP9Vk3hs3SeD54oKcpYvKllSVL6oo\nlmJ11iMAhAUCNABApEzR6OxIEvPdYr6bPmp+G8SIYEhpaOmsrW86fqq5paP73PUzOI4ryMmq\nqihaUlW2bFGFw26b/sO+9s4hc2PvviNLF1XgHwwA8QUBGgAg/KZodHalioXZl250hhgRUtTT\nzR219U31je0fXkAjO8u1tKpsSVXZkqoyp2Ma7TdTeff9WnPjvUMn7/nL7XOtGACiCwEaACB8\n5tjoDNbRDaOts/f4qeba+qZTjW2qpp/71vRU56KK4iVVZSsWL8h0pc796dyZ6eafVjz+FwEQ\nhxCgAQDCQde17n6to49NrFzGyZKQ7xYLsrmYGQsX+9q7+w4db/D7/Xa7vXpBSVVFUUSfjjHW\n2dNv3gt47FSjP3D+qnNOx+KFpUuqyqrKiwrzwryG8T/e89nJpnb0bwDEHfxYBwCYExYMaV0e\nvcvDJq5Z8k6HkO8WczPR6DxTzW3djz33urm9fcvGCAXoWaw6BwBwLgRoAIBZurDRmSM+HY3O\ncyKKoigImq4LAi+F9cr9sNd3qrGttr75yMkzA0PnrTonS2JZUV5VRdGSqvLFC0qw6hwAfCQE\naACAGTIbndt6jBHf+BGeF9wuqTiXQ6Pz3Ky7rOb4qaa9+45UlhVuu379HB/N6/OfbGiprW+u\nb2zv6PGc+yae50oKcsdXnSsvCm9YB4CEhx8ZAADTpulaT7/W0cuCinkAjc6x5hKrztE5C2jM\ndNU5AIBz4Sc+AMBHY4GQ1o1G5xilqFpzW3d9U3ttfdPJM60XXXWussyJfxEAQDggQAMAXIox\n4tM6evX+4clGZyEjTSzM4cOxlhnMmmGw1s6e8VXnmtrViVE1pslV55ZXV2RlYHYjAIQZAjQA\nwFQY0/uHtfYewzs2fkTgxZxMsTCHcyRZWtm8NrmAxsVWnassL1pUUVRWlGdVhQAwHyBAAwCc\nh2m63tOvtfeyEBqdY8LZVedON/vGzlt1ziZLlWWFE6vO5XJYdg4AogK/DAAgjjFFNXx+wRdg\nnMgcdm5ut4WxQEjr7NW6+2miiZZPcYgFOUJOBiGZRZem6/sOn6ytbz5a19g/OHLumwSeLy7I\nMRfQqF5QLAqCVUUCwLyFAA0AccwY9CqnmpOIVOplpflSaf4sHweNzjGAMTrT0tHY2klEDc2d\nP2t+avJNAs+Xl+QvqSytqSyrLCvEqnMAYC38DAKAecxg+sAFjc6CmJOBRudoMgxW39S2/0jd\nwaP1g8NnR5xwHBXn59RUltZUllUvLLHbZAuLBAA4FwI0AMQxITuDHxoxege51GSpOHf6HzhF\no7NNEvLcYmEOJ6IlIBp0wzjZ0HrgSN3Bo6dGRscmj5uTCAtys7731b9IdTosrBAA4GIQoAEg\nnvEcZy7DzHHTXI8Zjc7WMgzW0Nyx7/DJ9w6dODc3O5PtK2sWrllZvf9w3VsHjqU6HUjPABCz\nEKABYL4wRnxaW7c+MHFHmtnoXJzHpzktrWteUFXteH3TvsN1Hxw/7Q8EJ4+nOB0rFi9Ys7J6\nefUCQeCJ6ODReuvKBACYFgRoAEh0BtP7BrX2HmNyBTRBEPMyxcJcLglttZGlqFptfdO+w3Xv\nH6sPBM8u25zpSr1i2aJVSytrFpbyPC78A0CcQYAGgITFVE3v7tc6+842OifZxHy3kO9Go3NE\nhRT1xOnmfYfrDh49FZz45BOROyP9sqWVa1YuriwvQr8MAMQvBGgASEDMH9S6+rSufjLQ6Bw9\nY/7gB7WnD9c2HD7REFLUyePZWa5VNQuRmwEgYSBAA0BC+VCjMydkpIoleXwqGp0jxTcWOHSi\nYf/hk8fqmjRdnzxemOu+cmX1ZUsrMVgbABIMAjQAJAi9Z+C8RmdREHPR6BxBoz7/4ZNn9h8+\nebSuUZ9Y0oQmcvO6y2ryc7Jm+pjDXt/giI+IxgLBvv6h7CxXOCsGAAgTBGgAiG9MN4iIeceU\nEZ95hLMniYXZYm4WCdNa2A5mZGDIe+DoqcO1p082tOrGhbl5wxVLc90Zs37wY3WNx+rOEFFb\nZ99bB47tuGlTGCoGAAg3BGgAiFeGL6B19BqeQSIyR3Dz6SliYY6QmU5otA03z+Dw+8dO7z98\n8nRzuznvnIg4jqssK7xy5eIrV1RnpKdYWiAAQPQgQANA/NEHRrSOXmPo7NhnkkTb8koeozfC\nra9/6IPahgtyM89zC0sLr1y5eO2qxelhbS6/YkV1VXnR8PBwampqagra1gEgRiFAA0D8+NCK\nzpwokE1iY0HOkZTY6fnffvtE/+AwEQmC8MNvfD7Sa1l0dHv2HT55qLahub178uBkbl53WU1a\nSnIkntduk5OyXAJnuFwuQcBSgwAQoxCgASAOMEXTuvr0zj6mauYRzm4TC7LFPLdypk0fC176\nwxNAe3df/+DIR7/f3Ji5+b1DJ7t6+ycPSpK4tKrsypWLL19a5bDbIl0DAEDsQ4AGgJg2xYrO\naU6xMEfISp9XKzqvu6zm2VfeJaK1qxaH/cHN3PzO+7U9ZkM5ERHJkrikquzKlYuvWL7IbsNK\nJgAAZyFAA0CMmmJF56x0sSiXT41I80CM+9j6VWaAvnrNirD84cAYO93Use/wyQNH6waHRyeP\n22SpprL0ypWLVy9flITcDAAwFQRoAIgxUzU6C7mZYlEuhzw3Z4bBGpo79h0+ue/wyWGvb/J4\nsiNp1ZLKNSurly6qkDDnHADgkhCgASBWMEXTe/q1zl4WGp8CPdnojBWd52gyN7936MTI6Njk\ncWeyfWXNwjUrq5dVV4i4aQ8AYHoQoAHAemh0jhBV1Y7XN+07XPfB8Xp/IDR5PNXpWL54wZqV\n1csXLxB4/HECADAzCNAAYCU0OkeComq19U37Dte9f6w+EDybm7NcaZcvq1qzcnFleSGHv0wA\nAGYLARoArBCmRmfD62PmBO9ASB8YFjLTI1FsvAgp6onTzfsO1x08eioYUiaPuzPSL1tauWbl\n4sryIsRmAIC5Q4AGgKgKb6Mz84cMf5CImKIao/75GaDH/MEPak/vP3zy+KkmVdMnj2dnuVbV\nLFyzcnFVRZGF5QEAJB4EaACIkkg0OnM2iXelqqoqiiKfNL9mfPjGAodONOw/fPJYXZOmn83N\nhbnuK1dWr1m1uDDXbWF5AAAJDAEaACIuco3OvCtVTk8ZHRiYP5Of/YHQWweO7T988ujJRn3i\nTxGayM3rLl+Sn51pYXkAAPMBAjQARMyHGp1JFESs6Dwr2kRvxs9/9zRjzNzmOK6yrHD1iurV\nKxZludKsqw4AYH5BgAaA8MOKzmGkqNpr7xza9crb5i5jjOe56oqS1Suqr1he5UpLsbY8AIB5\nKF4DNGOMMaaf0/YX0ecioug8l4WMif8FG4aR2C8WJzSimD9o9PTr3QOTjc5carJQkM1nphHH\n6cQo3MUk8AkNKepr7xx68Y39544MvHHT6q3Xr0t1OszdxHvhCXxCz4UfuQkm+id08j9RYIl4\nDdCapqmqOjQ0FLVn9Hq9UXsua42OjlpdQjTghIad4A9JnmFhdKJbg+O0FLualWY4bESMhocj\n+uwJdkIVVXv3UN0rbx/y+vzmkYri3Ma2HiKqLM3T1dDQUOiSDxD3EuyEXgJ+5CaYqJ1QVVWj\n80QwpXgN0KIoyrKcmRmNe2UYY4ODg+np6Yl9i5JhGOYfJKmpqZIkWV1OBOGEhvuZmN43qHf0\nXtDoLBTmJEWl0TnBTmgwpLzx3pHnXn138qrz0qqy22++OjXF8bV//g8iSk5Ojs6PPqsk2Am9\nGPzITTDRP6GyjDtJrBSvAdqcoRXNSVocxyX24K7JV5fwr9SU8C9TO37G7g8Qkc7z8pplkXiK\nmGp0ToATakbnXXveHhkdM48srSq745ZrK0ryiahv4Ow/3OL9lU5HApzQS8OP3AQz304oxGuA\nBoBLYwGFV7RIPXgEVnSezwIh5ZW33n/u1Xd9/gARcRytrFm4fcum8uI8q0sDAIApIEADJCYh\n26W19xAR73aF8WEjt6Lz/GRG52dffWfMH6SJ6Lzjpk1lRYjOAACxCwEaIDHxeVnU3kNEXE5G\nGB4OKzqHm88feGnvgd17D/gDZnTmVtYsuP3mq0sLc60uDQAAPgICNABcSkw1OicG31jgpTfP\ni85XrqjecdOmgtwsq0sDAIBpQYAGgKmh0TnsRn3+l986uHvvfn8gRBPR+fabN+XnIDoDAMQT\nBGgAuBAancPO6/Pveevgi2/sDwTPRudPfvzqvOxEXpAOACBRIUADwAQ0OkeAd3TshTf2vfzm\nwZCiEpEg8OsuW3LbjVflumfWm36o9rS5cfjEmWXVFeEvFAAApg0BGgDQ6BwRZnR+ae8BRdVo\nDtHZ9MJr+8yNl948cNf2zWiiAQCwEAI0wLyGRudIGBjyPv/ae6+/e8iMzqIgrL2sZvuWjTlZ\ns19SMDnZ4Z9o/whboQAAMCsI0ADzFBqdI6F/aOSF1/a99u4hdSI6b1qz/LYbr8pIT53jI//w\n3s8NDg4SUVpaGiI0AIC1EKAB5hk0OkdG/+DIC6/ve+2dD1RNJyJJFDZeufy2GzdmpKdYXRoA\nAIQZAjTAfIFG5wjxDA6/+Pr+C6Lz9i0bXWmIzgAAiQkBGiDxodE5QvoGhp995Z297x3RDYOI\nbLJ0zdqV225Yn57qtLo0AACIIARogATH2nqC3rHxHTQ6h0lf/9Czr747GZ2TbPLVa1YgOgMA\nzBMI0ACJyQgExze8Y0TEiYKQ7xYLstHoPEcdPZ5nX3n3nfePGwajieh862T0i3YAACAASURB\nVOYNaSn4mwQAYL5AgAZIOIy0jl6tqWN8V5akkjwxNwuNznPU0e159tWz0dluk6/fePkt161z\nOuxWlwYAAFGFAA2QUFhQUU41G8Ojk0eEymJxDssPx7j27r5Dxxv8fr/dbq9eUFJVURShZ3nu\n1fcuiM5br1uf7EiKxNMBAECMQ4AGSBy6Z0itb2GaTkRcqpN5fVZXFHHNbd2PPfe6ub19y8aw\nB+j2rr7nXnvv7YPHGWNE5HTYN2+64qZrrnTYEZ0BAOYvBGiARMA0XW1o03sHiIh4XirN5zLT\nlYO1VtcVcUlJtiSbHAwpsiQmh7WVorWz95mX395/5CRjRETOZPvmjYjOAABAhAANkACMQa9S\n38JCChHxyXapupx32nV/0Oq6omH18kWHaxv27jtSUZK/5erVYXnM1o6eZ/a8MxmdU5yOG666\n/KZr1jjstrA8PgAAxDsEaIB4ZhhqU4fW0UdExHFiUY5UWkA8lnaepYbmjmf2vH34RIMZnVOd\njuuvuvzma9fYkxCdAQDgLARogHhleMeUU83MHyQiLskmV5fxaViEeJZON3fs2vP2odoGczc1\nJfnma9Zs3nSFTZasLQwAAGIQAjRAHGJMa+9VmzuJMSIScjLlyhKsUjc79Y3tz776zgXR+car\nV8sSfjwCAMDU8BsCIM6wQEg51WyM+IiIk0WpslTISre6qLhU39j+1O43j9c3m7uZrtSbr137\nsfWrEJ0BAODS8HsCIJ5oXR61sZ10g4iEjDRpUSmHHoOZq29sf/LFvSdOt5i7Wa60m65dc936\nVRKiMwAATAN+WwDEB6aoan2LPjBCRCQIUkWhmO+2uqj4U9/Y/uQLe080tJi7WRlpN12z5roN\nl0miYGldAAAQTxCgAeKA7hlST7cyVSMiPtUpV5dxWFJtho7XNz/x/BtnWjrNXXdG+pZrrkR0\nBgCAWUCABohtmq42dWhdHiIijhNL8qSSPOKwUN0MHK9vfvy51xtbu8zd7Mz0rdevv3rtCoHH\nbZcAADAbCNAAscsYGlVONZ8zIaWMdzqm+bEsEJzYCEWqvtjGGB0+cfqp3W81tXWbR7KzXFuv\nW4foDAAAc4QADRCTDENt6dLae4gREYn5bqmiaEYL1Wmn28YfqbGDinIjUWPMMqPzH198q7l9\nPDoX5rpvuX7dhsuX8pgyAwAAc4YADRBzjLGAWtds+PxExCXJ8qIyPj1lxo8yL9s8GGOHTzSc\nF53z3Ldch+gMAADhhAANEEsYaZ29alMHGYyIBLdLqirlZnWXm7Rs4cjwMBGlpMw8fMenQ7Wn\nn3jhzdaOHnO3KC/749etRXQGAICwQ4AGiBUsGFJOtRjDo0TEiYK0sFjIyZz1o3FJsiGLRERJ\ncrgqjE2MMSJqauu67/7HzSOlhbnbt2y8bGnVvLwKDwAAEYcADRAT9J4BpaGNdJ2I+IxUuaqU\nsyV48A2LYEiprW8mopCiEVF5cd72LRtX1lQiOgMAQOQgQANYjKmaWt+i9w8TEfG8VF4gFuZY\nXVR8GPb6fvybxwaGvURkT5K/8pfbV9QssLooAABIfAjQAFbSB0fUUy1MUYmIT02WF5VxjiSr\ni4oPHd2eH/3m0f7BEXO3tDAX6RkAAKIDARrAIrqhNndoHX1ERBwnFuVIZQXzc+mMWaitb/7p\ng0/6AyGe58qL8yfnCwIAAEQBAjSABQzvmFLXbM464ew2eVEZn+a0uqi48eb+ow88+ryuGzZZ\n+spf3XbwaD0CNAAARBMCNEB0Maa2dmut3cQYzWpCynzGGD21+82ndr9FROmpznu/eGd5cd7B\no/VW1wUAAPMLAjRA9LCxgHKq2Rj1ExEnS1JVqZCZZnVRcUPT9d8+8vyfDxwjosI89ze/tDMr\nA589AACwAAI0QJRoXR61sZ10g8wJKZUlnIRvwOka8wf//T+fONnQSkQ1laVf/5vbHXbcbQkA\nANbA72+AiGMhRTnVbAyNEhGJglReKOa7rS4qnvQNDP/4N4929vQT0cYrl39+582iMJvpjAAA\nAGGBAA0QWbpnSD3dylSNiHhXiryoDBNSZqSxteu++x8bGR3jOLrtxo3bt2zCUiUAAGAtBGiA\nSGGarja06b0DREQ8L5Xmi0W5hPA3E+8fq//l7/8UUlRREL7wqY9ftXqZ1RUBAAAgQANEhjHk\nVU61sJBCRHyyXaou450Oq4uKM7v3Hnj4T3sMgznsSV///O01C0utrggAAIAIARog/AxDbenS\n2nuIEXEkFmRL5UXE48rzDBgGe+jpPS+9eYCI3Bnp37j7zsJcdI0DAECsQICG+cUYHtU6+2yh\nkNYzTNkZQnZGmB9/dEypa2b+IBFxSTZ5USmfnhLep0h4qqr96uFd+w6dJKKSgpxvfGlnBj6H\nAAAQSxCgYX5hQUX3DIlEBvkNpyOcSzkwprX3qi2dZDAiEnIy5cpiwmIRMzTq8//kgSfqm9qJ\naPniiq/+9Q477rkEAIAYgwAN8wuXJHNJNhYMkSTyjrAtJMyCIaWu2RjxEREniVJVqZCVHq4H\nnz96PIM/+vWjPZ5BIvrY+lV/9cktAv8RMxoDISUYDBGRqmpj/mBy+M4pAADAxWCAMMwvfHoK\n70ohIs6RFK7+Db1nIHjwhJmehYw02xU1SM+zUN/U/r1//+8ezyDH0fYtG//mzps/Mj0T0cEj\ndfuO1BHRmdau3Xv3R75MAAAAXIEGmAOmqGp9qz4wTEQk8FJZgViYY3VRcWnfoZO/eniXqmqS\nJN79ma1rV9VYXREAAMBFIUADzNJ5E1JSk+XqMg7DpWdl994DDz39MmPkTLb//RfuqCovmv7H\nrlpS+YN7PzcyMpKampqRnhq5IgEAACYhQAPMnKarTR1al4eIiOPEkjypJI8wH2/mdMP47yd2\nv/bOISLKdWd88+6due6Z9dU4k+3JjqQBh+xyuQTcsgkAAFGBAA0wM8aITznVzAIhIuKS7fKi\nMj4FE1JmIxBSfv5fTx05eYaIKssK//4Ld6Rg1gwAAMQDBGiAaTOY2tKptfcSY0Qk5ruliiIS\ncCfubAwOj/74/sdaO3qIaM3KxV/+7DZJwo8jAACID/iNBTAtxlhArWs2fH4i4myyvKjMXM0D\nZqGts/dHv3lscNhLRDduWn3X9hs4NMAAAED8QIAG+GhaR6/a1DE+IcXtkipLOFwuna1jdY3/\n97+eCgRDAs//5e03XrfhMqsrAgAAmBmEAIBLYUFFOdVsDI8SEScK0sJiISfT6qLi2BvvHn7w\n8Rd1w0iyyV/96+0rFi+wuiIAAIAZQ4AGuCjdM6TWtzBNJyLelSovKuUwVnq2GKOndr/51O63\niMiVlvLNL91ZUphrdVEAAACzgQANMAWmamp9q94/RETE81JpvliUS2jTnS1V03/z8K53PzhB\nRMUFOd/44p2ZLqzZDAAA8QoBGuBC+qBXrW9mIZWI+JRkubqMc2BCyuz5xgI/eeCJU41tRLRs\nUfnXPrfDnmSzuigAAIDZQ4AGOIdhqE0dWkcfERHHiUU5UmkB8bjyPHu9/UM/+vWj3X0DRHTN\nupWfu+MmgcfCfwAAEN8QoAHGGd4x5VQz8weJiEuyydVlfJrT6qLiW0Nzx7/99nGvz89xdNuN\nG3fctMnqigAAAMIAARqAiDGtvVdt7jQnpAg5mXJlCSakzNH+I3W/+sMziqpJovClz2xdd9kS\nqysCAAAIDwRomO+YP6jUNRmjfiLiZEmqKhEy060uKu7t3nvgoaf3MMacDvvffeGTiyqKra4I\nAAAgbBCgYV7TujxqYzvpBmFCSpgYBvvdH3e/8ucPiCgny/WNu3fmZ2PlbAAASCjICjBfMaYc\nb9AHRoiIREEqLxTz3VbXFPeCIeXn//304RMNRLSwtODvv3hnqtNhdVEAAABhhgAN8xQb9euM\nERGf5pQXlXF2LKw2V0Mjo/fd/3hzezcRrV5R/bd33Srjcj4AACQi/HqD+YWpmjmXmxgjnpPK\nCsXCHExImbv2rr4f/+ax/qERIrpx0+q7tt/Acfi0AgBAYkKAhnlEHxxRT7UwRSUiEnjbqmo+\n2W51UYng+Kmm//tff/QHQjzP/eWOG6+/6nKrKwIAAIggBGiYH3RDbZ6ckELEiHM6kJ7DYu++\nI//52Au6biTZ5Hv+8rZVSxZaXREAAEBkIUBD4jO8PqWuhQXGJ6RwjiRjcMTqohIBY/TU7jef\n2v0WEaWnOr/xpTvLivKsLgoAACDiEKAhoTGmtnZrrd3mhBQx3y1VFCln2qwuKxGomn7//zz7\nzvu1RFSUl/2Nu+/McqVZXRQAAEA0IEBDwjLGAmpds+GbnJBSKmQi4YWHzx/49weeqDvTRkRL\nq8q+9rnbHVjGBAAA5g0EaEhE5mjulk4yGGFCSrj19Q/96DePdfX2E9HVa1b8zZ03Cxh7DgAA\n8wkiBSQaFggpp5qNER8RcaIgLSwWcjAJL2zOtHTe99vHvaNjHEe33bhxx02brK4IAAAg2hCg\nIaGcN5o7I1WqKuVsstVFJY4DR0/9x+//pKiaJApf+PQtGy5fanVFAAAAFkCAhgTBFFWtbxkf\nzS3wUlmBWJhjdVFW+l//+z/6BobN7Ud+/t25TzXZvffAQ0/vYYwlO5L+7vOfrF5QMtdHBAAA\niE8I0JAIdM+QerqVqRoR8alOuRqjuckwjPA9FPv9Uy/teet9IsrOTP/m3Tvzc7LC9eAAAABx\nB7f+QHxjqqacaFRONDJVI56XygttK6uQnolosjv5E5s3zOXyc0hRf/LAE2Z6XlBa8P//3V8j\nPQMAwDyHK9AQx/TBEbW+hYVUIuKT7VJ1Ge90WF1UrKheON5isaiieNYPMuz1/fg3jzW3dxPR\nFcsX/e1dt9pkKTz1AQAAxC0EaIhPmq42dWhdHiIijhOLcqTSAuLn3OcL5+jo9vzoN4/2D44Q\n0Y2bVt+1/QZu7p3UAAAA8Q8BGuKPMeJTTjWzQIiIOLtNXlTGpzmtLirR1NY3//TBP/oDQZ7n\n7tq+efPGK6yuCAAAIFYgQENcMQy1pUtr7yFGNDGamzDFI9ze3H/0gUef13XDJktf+avbVi2p\ntLoiAACAGIIADXHD8AXUU02GL0BEXJIsV5XyrlSri0o0jNFTu998+qW3GKP0VOe9X7yzvDjP\n6qIAAABiCwI0xAOM5o4KTdd/+8jzfz5wjIgKc93fvHtnVkaa1UUBAADEHEQQiHXMH1Tqmo3R\nMSLiJFGqKhGyXFYXlYDG/MF//88nTja0ElFNZenX/+Z2hz3J6qIAAABiEQI0jFMOnEgOhohI\nI5KuvtzqcsadP5o7Taoq5WxYRi38+gaGf/ybRzt7+olo45XLP7/zZlEQrC4KAAAgRiFAwwTG\nrK7gPCyoKKeajeFRIiJRkMoLxXy31UUlpsbWrvvuf2xkdIzj6LYbN27fsgmr1QEAAFxC9AL0\nrl27Xn755dHR0VWrVn3xi190OC4ceOH3+3//+98fPHhQ1/Xly5d/8YtfTE5Ojlp5IBTlaGfa\niYiPgZyqe4bU+ham6UTEpznlRWEbza17hnTPEBGxUb/W0SsW5oTlYePX+8fqf/n7P4UUVRSE\nz3/q4xtXL7O6IgAAgFgXpfW/nn/++Yceemjbtm1f/epXm5qafvjDH374fX7wgx/U1tbefffd\nX//613t7e7/73e+yGLsmmtj4idvFuAwrl7ZgiqbUnlFONDJNHx/NvWJROEdz6wZpOhGRYZgB\nfT7bvffATx98MqSoDnvSt/72U0jPAAAA0xGNK9CGYTzzzDO333775s2bicjtdt9zzz2NjY0V\nFRWT71NXV3f8+PFf/OIXJSUlRFReXv65z33uyJEjK1eujEKFECN0z5B6upWpGhHxKclydRnn\nCPN9bFyyXSzODQQCSUlJwjwev2IY7KGn97z05gEicmekf+PuOwtzrf/PAwAAQFyIRoDu7u7u\n6+tbvXq1uVtSUpKdnX3kyJFzA3R7e7soimZ6JqKUlJTCwsJjx44hQM8TTNO1C0ZzlxVQBFpx\n+RQH57QrAwPJLhc/X++TU1XtVw/v2nfoJBGVFOR840s7M9JTrC4KAAAgbkQjQA8ODhKR2332\n+pbb7TYPTsrKytI0rbu7Oy8vj4iCwWBPT09xcfHkOxiGcd11103uVlVVybI8MDAQ8eonDA8P\nR+25LMEpmp2IiPx+/+iAEc2nFnwBuaOfUzUiMpJkpTDLsNvo/K+QsEv4EzoyMmpu+P3+c79T\nfGOBBx5/qbGtm4gWLyz+69s3M12J5rdShCT8CZ3k9XqtLiEacEITDE5o2CmKEp0ngilFI0Cb\nX0x2u33yiN1uv+ArbNmyZUVFRffdd99dd93F8/wf//jHQCAQDAY//DgmVVVlWY5mk3TiN2RP\nvEDGWNReLMeY1Dcs9Y+Yo7m1jBQlN4PxXBSWBEn4E8qmOqGewZFf/88LnsERIlp/2eLbb7pK\n4PnE+FQkxquYjnnySufJy6R580rnycuk+fRK57loBGin00lEwWBwclWNQCCQk3Pe6geiKP7z\nP//zAw88cN999zkcjs2bN+u6np6ePvkOHMfdc889k7tdXV0ejyc6y3Qwxvx+v91u5/ko3XNp\nCSaM/y0ryzIfnfVPfAFq6qRAiIjIJlF5gZiaHIWvyHlyQgOKZm7Ismx+pzS0dP7i98/4xgIc\nR1uvW7ft+nWWFhg28+SEmi+TiJKSkoSE7j7CCU0wOKERkthfNrEvGgHa5XIRUX9//2TeHRgY\n+HBzc1ZW1re//e3J3T179qxYsWJyl+O4v/iLv5jcfeSRRwYHB8+9qh055ndFwv+M0xlnJmhJ\nkuRIf2LN0dzNneaVZiEnU64spmh9eufJCbX5x/+BI0mS3W7fd+jkrx7epaqaJIl3f2br2lU1\n1pYXRvPkhBqGYf56ttlskpTI44RwQhMMTmiEJPbnM/ZF48/BoqKirKysQ4cOmbs9PT09PT2r\nVq069318Pt/3v//9uro6c/fMmTO9vb3r1iXIFTI4lzEWCB2qU5s6iDFOFuUlC+Tqsqil5/lp\n994DP//dU6qqOZPt//D/fSaR0jMAAED0ReMKNMdxW7dufeyxx4qKitLT03/729/W1NQsWLCA\niF599VWPx7Nz506n06koyi9/+cvPfOYzPM8/+OCD1157bUFBQRTKg2jSOnrVpg4yGBEJbpdU\nWcJJGIcZWS+9eeDwiTNElOvO+ObdO3PdGVZXBAAAEN+ilF1uvfVWTdMefPBBn8+3YsWKu+++\n2zx+4MCBM2fO7Ny5k4juvffe+++//2c/+1lGRsamTZs+9alPRac2iA4WDCmnWszR3JwoiBjN\nHS1meq4sK/z7L9yR4rxwAigAAADMVPQu/u3YsWPHjh0XHPzOd74zue1yub71rW9FrR6IJr1n\nQGloJd0gIt6VKi8q5Wyy1UUlMsbYkRNnJnc3XL70C5++RRLRJwMAABAG+O85RBZTVLW+VR8Y\nJiLieak0XyzKpfAPSIFxjNGh2tNPvrC3tbPXPLL+8iVfvuvWCAylAQAAmKcQoCGCzhvNnZos\nLwr/aG4417G6xide2NvY2nXuwY2rlyE9AwAAhBECNEQE03S1oU3vHSAi4jixJE8qyYvEaG4w\n1Te1P/n83hMNLeZuQW7W1WtX/s+fXrG0KAAAgMSEAA3hZwx6lfoWFlKIiE+2S4vK+BTcuxYp\nZ1o6//Tynw/VNpi77oz0bTesv2btyv6hYQRoAACASECAhrDSDbW5Q+voIyLiSCzIlsqLiMeF\n54ho7+57evef9x85aQ6OzXSl3nzt2us2XIabBQEAACIKARrCxvD6lLoWFggSEZdkkxeV8ukp\nVheVmDp7+ne98s7bB48zxogo1em4+dq1W65eLWFRbQAAgMjDr1sIB8bU1m6ttdsczS3mu6WK\nIhKiMedyvvEMDu/a884b7x02DEZEzmT7LR9bt3nTFTY5kUcBAwAAxBQEaJgrYyyg1jUbPj8R\ncbIkVZUKmWlWF5WABoa8f3r5z3vfO6IbBhHZbfL1Gy/fdv0Gh91mdWkAAADzCwI0zAFjWnuv\n2tKJ0dwR5R0de+GNfbv3HlBVjYiSbPINGy/fet36ZKwJCAAAYAVkHZglFggpp5qNER8RcaIg\nLSwWcjKtLuqjDXt9bV19Xq83JWUkz52RneWyuqJLGfX5n3/9vZf2HlBUjYhssnTN2pW3bt6Q\nlpJsdWkAAADzFwI0zIbW5VEb283R3EJGqlQVN6O5j9U1/vrhZ83t7Vs27rhpk7X1XEwgpLzy\n1vvP7Hk7EAwRkSgIm9Ys375loysN92UCAABYDAEaZoYpqlrfog+MEBEJvFRWIBbmWF1UQgmG\nlD1vvb/rlXf8gSARCQK/7rIlO7ZsjPGL5QAAAPMHAjTMwPmjuZ1ydRkXb3ewrbt8ybFTze+8\nf7yyvHDr9eutLuc8IUV9/d3Du/a8PTI6RkQcx125ovqOW67JdWdYXRoAAACchQAN08JUTT3d\nqnuGiIh4XirNF4ty4nE0tygI5pwRgeflmLnfUdP1N/cd/eOLbw57fUTEcbSyZuEdH7+muABX\n9wEAAGJOrAQIiGX64Iha38JCKpmjuavLeCdGc4eHrhvvfFD71Itv9g0Mm0eWVpXt3PaxsqI8\nawsDAACAi0GAhkvSdLWpQ+vyEBFxnFiUI5UWYDR3WDDG9h+pe/y5N3o8g+aRpVVld9xybUVJ\nvrWFAQAAwKUhQMNFGSM+5VQzC4SIiLPb5EVlfJrT6qISAWO0/8jJJ5/f29U3YB6pKi+6/eNX\n1ywstbQuAAAAmBYEaJiKYagtXVp7DzEijOYOq+P1zY/ueq25vdvcXVBa8InNG1YtqbS2KgAA\nAJg+BGj4kKASOlRn+AJExCXJclUp70q1uqZEcLy++fHnXm9s7TJ3i/Kyb9ty1ZUrFsfhrZgA\nAADzGgI0XEhv6sBo7vCqb2p/8vm9JxpazN2C3Kyt16+/6oqlHLIzAABAHEI2gnFMN8a3DMbJ\nolRZImByx5w1tHQ+8/KfD9U2mLvujPRtN6y/Zu1KHjdiAgAAxC0EaCAiIt3Q61vNTS412bZk\nISfja2NO2rv6nn7pz/uPnGSMiCjLlXbr5g1Xr10h8FFqJW9qG2+zbu7oWVZdEZ0nBQAAmA8Q\nkoDIMEK1DYZvzNzji3ORnueio8fz1ItvTUbn1JTkm69Zs+Xq1VJ0m2Ee/tMr5sZjz76+9br1\n6BYBAAAIF+SkeY8x5USjMTRqdR2JwDM4vGvPO2+8d9gwGBGlOB0fv3btjVevtmTkYdQudQMA\nAMw3CNDzG2NKXbM+MEJEfJbL6B+yuqB41T808szLb+9974huGETkdNg3b7ri5mvX2JNsVpX0\ng3v/ZnBwkIhSU1Nx+RkAACCMEKDnNfV0m943SERCTqZQnKcgQM+cd3TshTf27d57QFU1Ikqy\nyTdsvHzb9esd9iRrC3PYbUG7jYiSHRZXAgAAkGAQoOcvtbFd6/YQkZCVLi8q1QMhqyuKM6M+\n//Ovv/fS3gOKqhGRTZauWbvy1s0b0lKSrS4NAAAAIggBep5Smzq19l4i4l2p8uIKwv/4Z8Ln\nD7y098CLb+wPBENEJArCpjXLd9y0KT0Vo84BAAASHwL0fKS1dmtt3UTEpzltSxYQ1iSetmBI\n2fPW+7teeccfCBKRIPDrLluy46ZN2ZnpVpcGAAAAUYIAPe9onX1qcycR8U6HvHQhCVirYVpC\nivr6u4ef2fO2d3SMiDiOu3JF9R23XJPrzrC6NAAAAIgqBOj5Re8ZUM+0ERGfbJeXV3KiYHVF\ncUDT9Tf3Hf3ji28Oe31ExHF05YrFt3/86vzsTKtLAwAAAAsgQM8jumdIqW8hRpzdJi+v5KxY\nnDi+6Lrxzge1T734Zt/AsHlkaVXZp269rrQw19rCAAAAwEKIUPOFPuhV6pqIMc4m25ZXcrJk\ndUUxjTG2/0jd48+90eMZNI8srSq745ZrK0ryrS0MAAAALIcAPS8YQ6NK7RkyGCeLtuWVnHXT\nPWKfGZ2ffH5vV9+AeaSqvOiTH79m8cISawsDAACAGIEAnfgM71iotoEMg5NEeXkVh7EaF3e8\nvvmRZ15t6egxdxeWFty6ecOqJZXWVgUAAAAxBQE6wRk+v3LsNOkGiYK8bCGfbLe6ohh1vL75\nsWdfa2rrNneL8rNvu/GqNSsXW1sVAAAAxCAE6ETG/EHlWAPTdBJ425IFPCbkTaW+sf2JF944\n2dBq7hbmurfftPHKFYsxWwYAAACmhACdsFggFDpazxSVeE6uqeDTU6yuKOY0tHQ++fwbx+ub\nzd3szPSt16+/Zu1KHpNlAAAA4OIQoBMTCymho6dZSCWOk6vLhYw0qyuKOZ09/d/7yX+Z21mu\ntFs3b7h67QqBx1gZAAAA+AgI0AmIqVro6GkWDBFHcnWZ4HZZXVFs6ejxEJHX5yeijPSUW2+4\n6pp1K0QBM2UAAABgWhCgEw3TdOXYaeYPEpG0sETIxqDp87R19ja1dhGRKPCf2nbddRsukzBQ\nBgAAAGYC0SGx6IZyvMEY9RORVFEo5rutLii2qJr+Hw/tMhgjorKivC3XXGl1RQAAABB/0PGZ\nQAwjdLzBGPERkVRWIBZh3PSFnnz+jbbOXnNbFNGzAQAAALOBK9AfQe8ZUE41JxMp1CKW5kul\nsTrJmTHlRKMxPEpEYmGOWJJndUExp76p/YU39hGROyPNMzhidTkAAAAQr3AFOiEwptQ16wMj\nRCQWZEsLiqwuKOaEFPU3Dz9rGMyVllJZhs8PAAAAzB4C9EfgU5O5NCcRkd0mZKZbXc7UlNOt\net8gEQm5mdKCYqvLiUW//+PLPZ5BjqMv7Pw47hoEAACAuUCA/gicI4l3JBERJ0t8isPqcqag\nnm7Vu/uJSMhyyVWlhBkgH3Ko9vQb7x0mous2XL6iZoHV5QAAAEB8u9SluIULF07zURoaGsJR\nDMyY2tShdXmIiM9IlReXE8ZPf8ioz//Aoy8QUU6W61PbPmZ1OQAA5T60YAAAIABJREFUABD3\nLhWgS0tLo1UGzIbW0qW19RARn+a01SwgDKCeyoOPvzjs9Qk8/7d/8Ykkm2x1OQAAABD3LhWg\nX3nllajVATOldfSpLV1ExKcm25YtJAHdOFN4c//R/UfqiGjbDesXlhZYXQ4AAAAkgrmmLlVV\nR0dHw1IKTJ/e06+eaSMi3mmXly4kjKGeyuCw96Gn9xBRaWHuJ268yupyAAAAIEHMNUA/+OCD\nZWVlYSkFpkn3DCn1rUTE2ZPkZZUc1pSYCmPsV3/YNeYPSqLw5bu2ifgbAwAAAMJkutlLUZTv\nfOc7u3fv9vv9kwcZYx0dHeXl5ZGpDaag9w8rJ5uIMS5Jti2v5GTJ6opi1Itv7D/R0EJEO7dd\nV5SXbXU5AAAAkDimewX6Bz/4wU9+8hOn05mUlNTS0rJ06dIlS5aMjo4uXLjw0UcfjWiJMMkY\n8o6nZ5tsW17FJYXzlji9Y3zGNevyhPFhLdHR43ni+TeIqKay9MZNV1hdDgAAACSU6V6Bfuyx\nx9auXfvuu+8Gg0GXy/XjH/940aJFjY2NV1xxhSxjZYNoMLy+UO0ZMgxOEm3LFnJ2W5gff2B8\nurUx6A3vI0eZrhu/fmiXomoOu+1Ln9nKYWk/AAAACKvpXoFub2/fsGEDESUlJV1xxRUffPAB\nEVVUVHz605/+x3/8xwgWCEREZPj8yrEG0g1OFORllVyyPexPwSXbDbts2GXOGf4Hj6YnX9zb\n1NZNRH91+5YsV5rV5QAAAECimW6AdrlcXu/4hclly5a9/fbb5vbixYsPHDgQkdJggjEWUI6e\nZppOAi8vXRihgYjSkopARX6gIl9YXhmJx4+OhuaO5159l4guX1a14YqlVpcDAAAACWi6Abqm\npmbPnj1mhl66dOlzzz1nHj969KimaZGqDohYIKQcPc1UjXjetnQhn+a0uqLYFVLUXz20yzBY\nakry5++82epyAAAAIDFNtwf6e9/73saNG4uLi1taWtavX9/T07Nz586FCxc+/PDDmzdvjmiJ\n8xkLKaGjp5miEsfJNRV8eorVFcW0h57e0+MZJKIvffqW1JRkq8sBAACAxDTdAL1hw4ann376\nd7/7HcdxS5Ys+cEPfvBP//RPoVCourr63/7t3yJa4rzFFC109DQLhojj5OoyIRPtvJdy7FTT\n6+8eIqKPrV+1smah1eUAAABAwppuC4eiKLfeeuszzzyTlpZGRN/85jcHBgbq6+tra2sxSCUS\nmKYrx04zf5CIpMoSITvD6opi2qjP/+uHdjFG2VmuT3/ieqvLAQAAgEQ23QCdn5//la985fDh\nw5NHkpOTKysreX6uswxhCpquHD1t+PxEJFUUiXlZVhcU6/7ryd3DXh/HcV/69Fa7DesqAgAA\nQARNN/6WlZX94he/WLVq1YoVK372s5/19/dHtKx5TTdCtWeM0TEiksoLxaIcqwuKdX8+cGzf\noZNEtO369dULiq0uBwAAABLcdAP0wYMHGxoa/uVf/sUwjK997Wv5+fm33Xbbc889hyU4wsxg\nyolGY3iUiMSSPLE41+qCYt3g8Ojvn3qZiEoKc7fftNHqcgAAACDxzaABY8GCBf/wD/9w7Nix\nEydOfOtb3zpx4sTWrVsLCwvvvffeyNU3vzCm1DXpgyNEJBZkS2UFVhcU6xij+x95bswflETh\nbz+7TRQEqysC+H/s3XlclPXe//HPNQMDww4C4oLggoqKAu67KWmbZaZHPWqLWZad011ax06d\n2zwuLaey1Uytjh3NrF9aLh3T3FKgUmQTV8RdQRBE9nWu3x9zbm5vK51BZi5meD3/OI+Za66Z\n6z18q/Pm4ntdXwCA86vPDOYuXbrMnz8/ISFh5syZubm53IWjYahSdfRUbd4VEdGHNHNlKoIF\nvt/9S/qRLBGZMHp4aMtgreMAAIAmwdLb2NXJycn55ptv1q1bt3v37tra2vDw8AkTJtgiWVNT\nnXmm9lKBiOiD/A2dwkXROlCjdyHn8tpNO0WkU/vQO4f11ToOAABoKiwt0GfPnl2/fv26desS\nExNNJlNISMhTTz01ceLE/v372zRfE1Gddb7mYp6I6AP9DF3aiUJ9volak2np6g1V1TVGd7en\nHhyj01n6E8vOzb94KV9ErlwtOXUuu21oC1vGBAAATsjSAh0WFiYiAQEBjz766MSJE4cNG8YN\n7BpK9akLNedyRETn70N7ttC6f+/JOnNRRB4aNyoowM/yN2aeOn/81DkRyckrOHDwOAUaAABY\ny9ICPWXKlIkTJ44cOdLV1dWmgZqamvOXas5ki4jOx9OtWwfh1xILnDqXvXF7goj06t5paN8e\nWscBAABNi6UFetWqVTbN0TTVZl+uPnFORHReHobuHUVPe765yqrq91d+U1tr8vHymD7xbmvf\nPqRvj8F9uufn5/v7++u5awcAALDejQp0TEyMXq9PSkoyP77BnteuUAgL1V7Krzp+WkQUD3dD\n9wjFhTJnkc+/3Z6dmy8ij064y9fbU+s4AACgyblRgfby8qo7RefnZ8U0U9xU7eXCqqOnRRXF\n6ObWo5NiYGKMRQ4ePbk9PklEbusf0yc6Uus4AACgKbpRgd67d2/d461btxoMBtvnaRJMV4qq\nDmeJqipuBrceHRU32rNFysorlq3ZpKoS3Mxv6tjbtY4DAACaKEsn3bZs2fLpp59mqsatM10t\nqcw4ISZVcXVx69FRcXfTOpHD+OTLf+dfKVIU5Ykp9xr5uQEAAI1YWqDbtm37/vvvx8bGRkdH\nv/vuu5cvX7ZpLGdlKiqtTM+UWpPiojf06Kh4uGudyGEkHshIPHBIRO4Z0T+yQ5jWcQAAcELl\n5eULFy7s0qWLh4dHmzZtHnnkkYsXL167w6VLl6ZNm9apUycvL6+ePXsuX77cvD01NdVgMEyb\nNq1uz++++05RlLfeesuuX8BeLC3Q+/fvz8zMXLhwoclkeuaZZ1q2bDl27NhNmzbV1NTYNJ8z\nMZWWVx3MlNpacdEbenTUeXlonchhXLla/M+vvheR1iFB4+8aqnUcAACc0/Tp0+fOnRsaGjpr\n1qxevXp9/vnn99xzj6qq5ldPnjwZFRW1fv36uLi4WbNmGY3GGTNmzJgxQ0Sio6P/+7//+5//\n/Oe2bdtE5OrVqzNmzBg8ePCzzz6r5fexGSvum9ahQ4eXXnopPT390KFDL7zwwqFDh+69997W\nrVs///zztsvnNNTyiqq042p1jeh0bt066Lh9hMVUVZav2VxSVu7qov/Tw/e7ulq9/jwAALip\n0tLSL7/8csqUKVu3bl24cOH69evnzJlz7ty5M2fOmHeYNWuWqqrp6elLliyZP39+fHz8k08+\nuXz5cvNVc3/9619jY2Mff/zxkpKS55577urVqytXrnTWdffq8626dOkyf/78hISEmTNn5ubm\nvvnmmw0ey8molVWVacfVqmrRKYau7XV+3lonciTb9u5PPXxCRMbfPSysVXOt4wAA4Jx0Op2i\nKImJiRkZGeYtCxYsyMvLCw8PF5Hy8vJNmzY99NBDbdq0qXuL+QTzli1bRMTFxeWzzz7Lzs4e\nPXr0xx9//NZbb7Vr106Dr2EXVhfonJycpUuXxsXFhYSEfPjhh2FhYXPmzLFFMqehVlVXph1X\nK6pEUQyd2+qb+WqdyJFcunxl7cadItKpXejdw/trHQcAAKdlNBrfe++9CxcuREVFde3a9fHH\nH1+/fn1FRYX51ePHj5tMprfeeku5RseOHUUkLy/PvE+3bt3mzp27e/fuuLi4xx9/XLNvYnuW\n/jX87Nmz69evX7duXWJioslkCgkJeeqppyZOnNi/P53mRtTqmsq042pZhYgYOobpgwO0TuRI\nak2mDz77pqKyys3g+sSUe3U6RetEAAA4syeffHLs2LGbN2/etWvX999/v2LFivbt2+/atSs0\nNNTV1dW8w3333Xfdu1q0aFH3+NSpUyJy4sSJkpISLy8ve4a3J0sLdFhYmIgEBAQ8+uijEydO\nHDZsmLNOamlAak1tVfpxtbRcRFw7hulbBGqdyMF8uzX+xOkLIvLQuFEhQfzuAQCADeXn5584\ncSIiIuLRRx999NFHVVX99NNPp0+f/t57773xxhvt27fX6XQGg2HUqFHXvmX79u2dOnUyP/3u\nu+8++eSTp59+esmSJS+88MIHH3yg0VexOUtL8JQpUzZv3pyTk7N8+fLhw4fTnm+u1lR1MNNU\nXCYiru1au7QM0jqQgzl1LvubrXtFJLZbx9v632gleQAAcOuOHj3ar1+/+fPnm58qijJ06FAR\nMZ97dnNzu++++1auXJmamlr3lr/85S8TJ040mUwikp+fP3369Li4uHfffXf27Nkffvjh7t27\nNfgadmHRGWhVVT/++GMXF5e6lb1xEya16lCW6WqJiLiGt3RpE6J1IAdTXV3z4aoNtbUmby+P\nxybdrXUcAACcX+/evbt16/b+++9fuHAhOjo6MzPz+++/9/HxefDBB807vP766/379x8yZMj4\n8ePbtWu3a9euHTt2zJ4923yx4MyZM0tKSlasWCEi8+bNW79+/aOPPpqenu7p6YR3HrPoRHJR\nUZGfn98777xj6zROQlWrDmXVFlwVEZfWwS7hLbUO5Hg+37D9fHaeiDw64S4/H6edQQUAQONh\nMBi2bNny8MMP79+/f8GCBbt27brtttsSExM7d+5s3iEiIiItLe3ee+/98ccfX3vttYKCghUr\nVvzjH/8QkbVr13711Vevvfaa+ZYdRqNxxYoVp06d+utf/6rhN7Idi85A+/r63nPPPYmJibNn\nz7Z1IIenqlVHTtXmF4qIPiTQtUObm74D18k4dmrbnv0iMrRvj77RkVrHAQCgqWjduvUnn3xy\ngx1atWq1evXqX2+fOHHixIkTr90ybNgw89QOp2TpVOYlS5bk5uYuWLCguLjYpoEcXfXxs7W5\nBSKib97M0Clc6ziOp6y8ctnnm1RVAvx8po4dqXUcAACA61l6F47JkycrijJ37ty5c+cGBQVd\nN53FfMsSVGedq8nOExF9oJ+hc7hw1zXrffrVvy9fuaooyswH7/P0cNc6DgAAwPUsLdDu7u7u\n7u733HOPTdM4tOqTF2rOXRIRnb+PoUt7UajPVtufdjQhKUNE7rqtb9eIcK3jAAAA/AZLC/Sm\nTZtsmsNatbW1tbW1paWl9jhYdbVOxGQy3eBwyqUC5ewlEVG9jDVtW9SUl9kjWINSVdX8oKKi\noqqqyv4BCotKlq3ZJCItg5vdfVsf2w2u+ZuWl5crTv1LjuYDajcMqJNhQJ0MA2ojtbW1djgK\nfo+lBboRUlXVPpPTdTc7nJJ7RXcuV0TEw722Q2tRRBx51rwmU/5VVVau21ZaVqHX6x4ZP8pF\nr7NdDPN/5kwmk3P/17yOE1/DYcaAOhkG1MkwoDZSV9mhCUsL9NWrV2/wqq+vb0OEsYJer3dx\ncfH29rbDsapc82tF9Hq9528drjYnv+p8rojoPI2G6E6Kq6P+TmIymSorK0XEw8PDfMt0e/ph\n74GMY6dFZPxdw7p1bm/TY6mqmp+f7+np6dz3Ndd2QO2JAXUyDKiTYUBtxMXFUfuGc7D0p+/n\n53eDV5vsr0G1eVeqjp0WVRSjm6FHR8dtz9rKvXxlzYbtIhLRtvXouAFaxwEAALgRSwvfvHnz\nrn1aW1t78uTJb775xsXF5bXXXmv4XI7AVFBUdeSkqKriZnDr0VExOPMpBNtRVXXp6o0VlVVu\nBteZU+/T6ZrE3/gAAIDjsrRAv/zyy7/eePr06T59+qxdu/bJJ59s0FQOwHSluDLjhJhUxeDi\n1qOj4u6mdSJH9e22+KNZZ0Vk6tiRIUEBWscBAAC4CUsXUvlN4eHhM2fO3LNnT05OTkMFcgim\notLKjEwxmRRXF0OPTgq3K66v0+dz1m/ZKyLdO7cbPiBW6zgAAAA3d0sFWkSCg4N1Ot2NZ0g7\nGVNJWdXBTKk1iYve0D1C52nUOpGjqq6p/XDVhpraWi9P45NT72sa12cDAACHd0sFuqysbPXq\n1W3atHF3byqnYNWyiqr0TLW6RvQ6t24ddN6eN38PfscXG3ecu5grIo/+4S4/Hy+t4wAAAFjE\n0jnQ/fv3v26LyWQ6ceJEQUHB3LlzGzpVI6WWV1amHVOrqkWnGLq21/nZ4yZ6zupo1tnvd+8T\nkUG9o/rFdtE6DgAAgKXqf9s1nU4XHR09cuTI5557rgEDNV4mtTLtuFpZLYpiiGynD7D3ra+d\nSVl55ZJ/fauqaoCf98Pj7tA6DgAAgBUsLdA//fSTTXM0fmppuZhMooihc1t9kL/WcRzbZ19/\nf7ngqqLI45NGe3IJJgAAcChWzIFWVfXUqVPmx+fOnXv++edfeumlY8eO2SZYI6Kal+U0mUTE\ntWO4vjm3WrslSenH9uxLF5E7hvbp0cW2iw4CAAA0OEvPQF+4cGH06NHnzp3Ly8urqKgYNmzY\nyZMnRWTJkiWJiYldujjzHFblf24P4dq+tUuLQG3DOLqikrKP134nIi2bB068d4TWcQAAAKxm\n6RnoF198MSMjw7xgyubNm0+ePPnJJ58cP37czc1t0aJFtkzYCCiKiCie7i6hIVpHcXjLPt94\ntbhUr9PNnHqfgZXPAQCAA7K0wezYsWP06NHz588Xka1bt4aGhj7yyCOKotxxxx179uyxZcJG\nw4W2d6t2JiYnZ2SKyNg7h7QPa6l1HAAAgPqw9Ax0QUFBZGSk+XFCQsKQIUPMExs6duzY1JYh\nRP3k5heuXv+DiLQNbXHf7QO1jgMAAFBPlhbosLCw/fv3i8jBgwePHDkSFxdn3p6SktKiRQtb\npYOzUFX1o9Ubyyur3Ayuf3r4fr3+VpfABAAAMBqN3333nf2Pa2mP+eMf//jDDz88+OCDY8aM\nMRqNd999d0FBwTPPPPPNN9+MGMGlYLiJjdsTj5w4IyKTx8S1DG6mdRwAAID6s3Re73PPPXfk\nyJE1a9YoivL+++8HBQX98ssv7777blRU1Lx582yZEA7vfHbeui17RCSqc7u4Qb20jgMAAHBL\nLD0DbTQa16xZc/Xq1aKioieeeEJEOnToEB8fn5SU1KpVK1smhJ18uGrDklWblqza9I+P1qpq\ng31sdU3t+599U11d42F0n/HH0f9zS0AAAADx9vZeunRpRESE0Wjs2bNnamrqypUrIyMjfXx8\nHnjggbKyMvNu2dnZkydPbt26tZeXV8+ePTdv3vzrjyovL3/++ecjIiK8vLyGDRsWHx9vu9jW\n3VnC09Oz7nGzZs0GDuRSMOdx/OS5vIKrDf6xX23edfbCJRF5dMJdzfx9GvzzAQBAw1q1ftsv\nKUdu8UP6xkROHTvSkj3nzZu3bNmywMDAp59+evDgwQMHDvziiy+SkpIee+yxESNGzJw5U0TG\njh1bUVHx/vvv+/n5LVu2bNy4cQUFBR4eHtd+zsSJE8+ePbt48eLAwMD169cPHz48MTGxVy+b\n/OmbW7PhPzp3aJO376CIdG7fpqE+89jJc//e9bOIDOjZbUDPrg31sQAAwHZOncvJLyy69Q+x\ncM85c+aMGTNGRGbMmPHEE0+sWrUqKCgoOjp62bJldWtgjx079o477oiKihKRwMDAL7/88ty5\nc506dar7kLS0tI0bN545c6ZNmzYi0r9///T09K+++ooCDdsad9fQvfsOisj9owY1yESLisqq\nj1ZvNJlUf1/vR/5wRwN8IgAAsL22oSG5l6/c+odYuGdYWJj5QWBgoK+vb1BQUN3Tun2effbZ\n3bt379y5My0tbceOHb/+kIyMjGs/yuy6U9QNiAINW/ns6605eQWKIo//8R4vD6PWcQAAgEWm\njh1p4eyLBqf81jm88vLyoUOHFhcXP/DAAxMmTHj66adjYmKu28fHx8fNze3y5cvXfoJOZ6vb\n5lpUoFVVraqqcnFx0ev1NsoBJ3Pg4PHdP6eKyMjBvaO7dNA6DgAAcFQ//vhjUlJSUVGRl5eX\niBw8ePDX+0RFRVVXV6empg4aNEhEqqqqxowZM2HChIceesgWkSwq5kVFRX5+fu+8844tEsD5\nFJWUrfhis4g0D/SfeO9wreMAAAAHFhgYqKrqe++9l5mZuXnzZnMnzszMVK+5a1h4ePiUKVPG\njRv3xRdf7N69e+rUqYmJicOGDbNRJIsKtK+v7z333JOYmGijEHAyn3z576vFpXqd7k8P3e/u\nZtA6DgAAcGC9evV6++23ly5d2rt37yVLlqxevfr++++fNGnSpUuXrt1txYoVU6dOfemll0aP\nHp2bm7tt27brpkQ3IEvnQC9ZsuSBBx5YsGDBM8884+3tbaM0cAK7f07dl3pERMaMGtQhnHuE\nAwCA31VcXFz3+IEHHnjggQfqnm7ZsqXu8TPPPPPMM8/UPV2/fr35QXl5ed1Gg8HwxhtvvPHG\nGzaM+z8sLdCTJ09WFGXu3Llz584NCgq69obQIlJ3kxE0cXkFhf9at01E2oa2uH/UYK3jAAAA\nNDxLC7S7u7u7u/s999xj0zRwaKqqfrR6Y3lFpaury8yp9+n1trr0FQAAQEOWFuhNmzbZNAec\nwOadPx/OPCMik++La90iSOs4AAAANnGr5wirq6uvnbyCJut8Tt7/+263iHTr1HbkkN5axwEA\nALCVWy3Qn3zySdu2bRskChxXba3pw39tqK6u8TC6zZg8ukEWMgQAAGicLJ3CUVVV9eKLL27Z\nsqWsrKxuo6qq58+fb9eunW2ywWF89d2uU+eyReSRP9wZ6O+rdRwAAAAbsvQM9KJFi9566y0v\nLy93d/fTp09HRUV169atuLg4IiLiiy++sGlENHLHT53fvOMnEendo/OgXlFaxwEAALAtS89A\nr127tn///omJiRUVFf7+/v/4xz86d+6clZXVu3dvg4GVMpquyqrqpas2mEyqn4/X45O4SQsA\nAHB+lp6BPnfunHltcXd39969ex84cEBE2rdvP3ny5L/97W82DIjG7V/rtuXkFSiKPP7He7w8\njVrHAQAAsDlLC7S/v39RUZH5cffu3ePj482Pu3Tpsm/fPptEQ6OXfiRr10/JIjJiYM+YrhFa\nxwEAALAHSwt0165dt23bZu7QUVFRdbeFTktLq6mpsVU6NGLFJWVLV29UVQkO9P/jmDit4wAA\nANiJpXOg586dO2TIkDZt2pw+fXrgwIE5OTmTJk2KiIhYvXr1qFGjbBoRjdOnX20pLCpRFOXJ\nKfca3ZgHDwAAmgpLC/SgQYPWr1+/cuVKRVG6deu2aNGil19+ubKyMjIy8s0337RpRDRCe/al\n/5xyWETGjBzYuX0breMAAADYjxULqYwZM+bbb7/19fUVkTlz5uTn5x87diwjI4OFVJqagsKi\nf63bKiLhrUPG3jlE6zgAAAB2ZekZaLO9e/du2bIlLy/vz3/+s4+Pj4uLi053q2sZwrGoqvrh\nqg2lZRWuLvqZU+9z0eu1TgQAAGBXVtTfxx9/fMiQIa+++urHH3+cnZ39yy+/tG/f/rnnnjOZ\nTLbLh8Zmy+59h46fFpGJ944IbRmsdRwAAAB7s7RAL1u2bMWKFTNnzjx+/Lh5S1xc3COPPPLW\nW2+tXLnSVunQyFzIufzlpp0i0rl9mzuH9dE6DgAAcGwpKSkdO3YMDw+36VGMRuN3333XgB9o\naYH+6KOPBg8evGTJkoiI/9zut1mzZp9++unw4cM//PDDBgyERqvWZFq6ekNVdY2H0e2pB8co\niqJ1IgAA4Njee++9kJCQnTt3ah3EOpYW6OPHj992222/3n7bbbcdO3asQSOhkVr37x+zzlwU\nkYfG3REY4Kt1HAAA4PBKSkp69OjRrl07rYNYx9IC3bJly/z8/F9vz8rKCgkJadBIaIwyT53f\n8EOCiPTq3mlIn+5axwEAAA7v9ttv//rrrz/44IMWLVqISHl5+fPPPx8REeHl5TVs2LC6da9F\nxNvbe+nSpREREUajsWfPnqmpqStXroyMjPTx8XnggQfKyspEJDs7e/Lkya1bt/by8urZs+fm\nzZt/fcQbHMIqlt6FY+DAgZ9//vmcOXNCQ0PrNqampq5bt27MmDH1OzYcRWVV9dLVG00m1cfL\nY/rEu7WOAwAAbKg663xt7m+cNrWKPriZa/vWN95n06ZNkyZNCg4Ofvvtt0Vk4sSJZ8+eXbx4\ncWBg4Pr164cPH56YmNirVy/zzvPmzVu2bFlgYODTTz89ePDggQMHfvHFF0lJSY899tiIESNm\nzpw5duzYioqK999/38/Pb9myZePGjSsoKPDw8Lj2iDc+hOUsLdCvvfbad999Fxsb+/DDD4vI\nhg0btmzZsnLlSoPB8Nprr1l7VDiW1d/8kJ2bLyIzJt/r6+2pdRwAAGBDpuJStbL61j/kpvu4\nu7u7uLgYDAYPD4+0tLSNGzeeOXOmTZs2ItK/f//09PSvvvqqrt3OmTPHfNJ2xowZTzzxxKpV\nq4KCgqKjo5ctW3bq1CkRGTt27B133BEVFSUigYGBX3755blz5zp16lR3uJsewnKWFuiQkJBf\nfvnl2WefXbx4sYgsXbpUp9Pdd999r7/+esuWLa09KhzIwaMndyQcEJHhA2Jju0VoHQcAANiW\nzttTLa+49Q+xav+MjAwRCQsLu3bjteeP614KDAz09fUNCgqqe2p+8Oyzz+7evXvnzp1paWk7\nduyoxyEsZ8VCKu3atduwYUN5efnx48cNBkO7du3c3NzqcUg4kLLyio8+36SqEtzMb8rY27WO\nAwAAbM61feubzr5ocD4+Pm5ubpcvX772Nl+/t2Dfr28FVl5ePnTo0OLi4gceeGDChAlPP/10\nTEzMrRzixix9T3R09OLFi3NycoxGY48ePSIjI2nPTcHHa/9dUFikKMoTU+41uhm0jgMAAJxT\nVFRUdXV1amqqp6enp6enq6vr+PHjv/rqKwvf/uOPPyYlJe3fv3/hwoWjRo3S/9ZKybd4iGtZ\nWqALCwtnz57dunXru+66a+3ateXl5fU4GBxLfNLBn5IPichglw5hAAAgAElEQVS9cQMiO4Td\ndH8AAID6CQ8PnzJlyrhx47744ovdu3dPnTo1MTFx2LBhFr49MDBQVdX33nsvMzNz8+bNDz30\nkIhkZmaqqtpQh7iWpQX61KlTe/fufeyxx/bt2zdp0qSQkJDp06fv2bPn2lhwJgWFxZ/9v60i\n0rpF0AN3DtE6DgAAcHIrVqyYOnXqSy+9NHr06Nzc3G3btl03X/kGevXq9fbbby9durR3795L\nlixZvXr1/fffP2nSpEuXLjXUIa6lWNuAq6urv//++88//3zjxo3l5eXh4eFTp06dOnVq3QqF\n9rFmzZqUlJQ33njDDseqOna6Nvuy4uvlHtPZDofTSm7+lf+a94GI/HXmH6M6t3/9ozVph7Nc\nXfQLn3u0TavmWqdrSKqq5ufn+/v7/+bfd5yGyWQqKCgQEV9fX1dXV63j2BAD6mQYUCfDgNrI\n888/v2vXrh07dvj6srSZBqyeN+3q6jp69Oi1a9fu3LmzS5cup0+fXrBgQceOHfv371+/SSRo\nhLbu2Zd2OEtE/nDPbU7WngEAAG6RFXfhMEtLS/v666+//vrro0ePKorSr1+/8ePHFxQUfPrp\npxMmTDh9+vRf/vIXWwSF3RQUFn2xYYeIdGoXetdt/bSOAwAA0LhYWqD379+/bt26r7/+Oisr\nS0T69u371ltvjRs3znwnahF58cUX4+LiVqxYQYF2dBu3/1RVXeNmcH1iyr063fW3iQEAAGji\nLC3Qffr0Mf/vE088MX78+F9PuPbw8IiNjf3+++8bOCDszrzo4MPj7wgJCtA6CwAAQKNjaYH+\nxz/+MX78+PDw8Bvs88EHHzRAIjQCPaM6DusXrXUKAACAxsjSAv3888/bNAc0ZzKZzA88jO6P\nTbpH2zAAAACNVn1WL4RTOnjslPnBncP6+Fq5fj0AAEDTQYHGf8TvP2h+0LFta22TAAAANGYU\naIiIXLp8JfPUea1TAAAAOAAKNEREftibxKLsAAAAlqBAQ6pravfuS9c6BQAAgGOw6C4c2dnZ\n6enpOTk5OTk5lZWVLVq0aNGiRa9evUJCQmydD3bwU/KhopIyrVMAAAA4hpsU6JSUlNdff33d\nunU1NTXXv9PF5e677/7b3/7Wq1cvm8XTXm1ugSknX0TUopKaszkubZzwd4Yd8QdEJCw05My5\nHK2zAAAANHY3KtAZGRlDhw4VkUmTJt1+++2tWrUKCAhQVfXKlSs5OTk7d+785ptvBg0atGvX\nrv79+9srsN2ZVNU8O1gV9X/ulOxMzl64dPzUeREZ2LMrBRoAAOCmblSg//rXv3p6eu7atatz\n586/fvWPf/zjyy+/HBcX9+abb65bt85mCTWmeHu4tG1VVlZmNBr1/j5ax2l42xOSRcTD6B7T\nNWLNtzu0jgMAANDY3egiwp9//nn8+PG/2Z7NQkNDH3nkkfj4eBsEayx0nkaXNiHVQb760OY6\nXy+t4zSwisoq8+2fh/brYXC1dFlKAACApuxGBbpZs2a5ubk3fn9eXl6zZs0aNBLsJyEpo7yi\nUkSG94/ROgsAAIBjuFGBvvPOO9etW/f2229XV1f/+tXa2tqVK1d+9NFHw4cPt1k82NaOhGQR\n6RIR1rpFkNZZAAAAHMON/mr/yiuvJCQkzJo16+WXXx4yZEirVq38/f0VRbly5Up2dvaePXsK\nCwujoqLmzZtnr7RoSCdOXzh1LltERgzsqXUWAAAAh3GjAm00GhMTE9evX//BBx/s3r27tLS0\n7iU3N7cBAwZMmzZt3Lhx7u7uts+Jhmc+/ezj5dG7x+9OcwcAAMB1bnLdmIuLyx/+8Ic//OEP\nIlJcXHzx4kVVVZs3b+7v72+XeLCVsvKKn5IPichtA2JcXfRaxwEAAHAYVtx4wdvbu1OnTraL\nAnv68ee0yqpqRZFh/aK1zgIAAOBIbnQRIZzYzp9SRKRHZIeQoACtswAAADgSCnRTdDjzzPns\nPBEZMTBW6ywAAAAO5kZTONavX79//35LPuXVV19toDywhx0JB0QkwM8npluE1lkAAAAczI0K\ndG5u7rJly65cuXLTT6FAO5CikrL9aUdFZPiAGL2OP0EAAABY50YF+oknnpg8efLYsWO3b9++\nfPnykSNH2i0WbGdXYkp1Ta1ep7uN1QcBAACsd5O7cHh7ey9cuHD79u3BwcFhYWH2yQTbUVXZ\n/XOqiPSM6hjg5611HAAAAMdz87/gx8bGurq62iEK7CDtyImcvAIRGTGI1QcBAADq4+b3gXZ1\ndb1w4YKPj48d0sDWzKsPNg/0j+rUVussAAAADsmihVSCgoJsnQN2UFBYlJKRKSIjBvVUFEXr\nOAAAAA6JmzA0ITsTU2pNJhe9fmif7lpnAQAAcFQU6Kai1mTa9VOKiPSL6eLj7al1HAAAAEdF\ngW4qDhw8XlBYLCIjBrH6IAAAQP1RoJuK7fEHRKRVSGCndm20zgIAAODALLqIsEFs2LBh69at\nxcXFsbGxM2bM8PDwuG6HioqKVatW/fTTT+Xl5Z07d542bVpoaKjd4jm3S5evZBw7JSK3D+7F\n1YMAAAC3wk5noDdv3rxq1ar77rvvv/7rv06ePPnKK6/8ep/ly5cnJCRMmzbtxRdfrKmpefnl\nl8vLy+0Tz+ntiD+gqqqbwXVQryitswAAADg2e5yBNplM33777fjx40eNGiUiQUFBf/7zn7Oy\nstq3b1+3j6qq8fHx06ZNGzRokIi0atXq4YcfPnLkSGwsE3ZvVU1t7Y/70kVkQK9unh7uWscB\nAABwbPY4A52dnZ2bm9unTx/z07CwsODg4NTU1Ouj6HQuLv8p9Oa1D7lXcYP4OeVwUXGpiMQN\nZPVBAACAW2WPM9AFBQXyf1djCQoKMm+soyjKqFGjvvrqq+bNm/v4+KxduzYsLCwyMrJuB1VV\nd+zYUff00qVLJpOpsrLS9vFFVVURqaqq0ukc8prLbXv2i0i7Ni1aNQ+4wU+surra/KC2ttY+\nP1itOPqAWsj8NUWkurraZDJpG8amGFAnw4A6GQbURpz7H5vGzx4FuqioSESMRmPdFqPRaN54\nrQkTJiQkJLz00ksioijKK6+84u7+v/MNVFV94YUX6p5GR0d7enoWFxfbNvo1SktL7XasBpST\nd+XE6Qsi0i+6041/XHVfsKKiwp4/WK046IDWQ1lZmdYR7IEBdTIMqJNhQBtcTU2NfQ6E32SP\nXwe9vLxEpKKiom5LeXm5eWOdioqKWbNmdenS5dNPP127du1TTz3197///dChQ3aI59zikw6p\nqhjdDT27RWidBQAAwBnY4wy0v7+/iFy+fNnT8z8L4OXn58fExFy7T3Jy8qVLl9577z2DwSAi\nI0eOTEpK2rp1a9euXc076HS6pKSkuv3XrFmTkpISGBhoh/yqqubn5/v7++v1ejscrgFVVlUn\nZWSKyJC+PVq2CLnxziblP9/O09PTPj9YrTjugFrFZDKZJ0r5+vqaLypwVgyok2FAnQwDaiPm\nvgSt2OMMdGhoaGBgYHJysvlpTk5OTk7OdbfXUBTlujnNpaWlzj1fyg4SkzJKyypEZMRAbmYC\nAADQMOxxBlpRlHvvvXft2rWhoaF+fn7Lly/v2rVrhw4dRGT79u15eXmTJk2KiYlp0aLFwoUL\nJ06caDQa9+7de/jw4ddee80O8ZzY9oQDIhLZoU1oi2CtswAAADgJO61EOGbMmJqamk8++aSk\npCQ6OvrJJ580b9+3b9+JEycmTZrk7u6+cOHCf/3rX++++25FRUW7du0WLFjQqVMn+8RzSlln\nLp48my0iIyy7e515ZxE5dT6ne2T7G+8MAADQZNlvKe9x48aNGzfuuo0vvvhi3ePAwMBZs2bZ\nLY/T25GQLCLeXh59enS2ZP/Pv/nB/GDtxp33xg3kHtwAAAC/iUnGzqmsvDLxQIaIDOsX7epq\n0a9JzDgHAACwhP3OQMOe9u5Lr6yqVhQZPiDm5nuLiMii56ebryD28fHh9DMAAMDvoUA7J/P8\njajO7UOCAix8i4fRrcLoJiKeHu433RkAAKDJ4q/2TujIibPnsnNFJI671wEAADQ0CrQT2pFw\nQET8fLxiu3XUOgsAAICzoUA7m+KSsn1pR0VkxMBYvZ7xBQAAaGAULGez++fU6uoanU4Z1i9a\n6ywAAABOiALtVFRVdiamiEhst46BAb5axwEAAHBCFGincvBoVk5egYjEWbb6IAAAAKxFgXYq\n2xOSRSS4mV/3yHZaZwEAAHBOFGjnUVhUkpxxXESGD4xVWAoFAADANijQzmNHQnJtrclFr+fy\nQQAAANuhQDsJk0nd/XOqiPSJjvT19tQ6DgAAgNOiQDuJ5IzjlwuuikjcIFYfBAAAsCEKtJPY\nnnBARFo2D+zcPkzrLAAAAM6MAu0McvML04+cFJG4QT25ehAAAMCmKNDOYEfCAVVVDa4ug/tE\naZ0FAADAyVGgHV5Nbe3un9NEZEDPbl4eRq3jAAAAODkKtMPbl3qkqLhUROIGsfogAACAzVGg\nHd72+GQRCWvVvH1YS62zAAAAOD8KtGO7eOny0awzIjJySC+tswAAADQJFGjH9sPeA6oqRjdD\n/57dtM4CAADQJFCgHVhVdc3e/ekiMrhvd6ObQes4AAAATQIF2oElHsgoLasQkeH9Y7TOAgAA\n0FRQoB3Y9vgDItKpXWhY6xCtswAAADQVFGhHdebCpawzF4W71wEAANgXBdpRbduTJCJensa+\n0ZFaZwEAAGhCKNAOqbyy6qcDGSIyrF+0q6uL1nEAAACaEAq0Q9r7S3p5ZZWiyPABXD4IAABg\nVxRoh7TzpxQR6daxbYvgZlpnAQAAaFoo0I7n2MlzZ87nCJcPAgAAaIEC7XjMd6/z8/HqGdVJ\n6ywAAABNDgXawZSUlv+SekREhg+I0esZPgAAAHujgTmY3T+nVlfXKIpyG6sPAgAAaIEC7UhU\nVXYmpohITNeIwABfreMAAAA0RRRoR5Jx/FR2br6I3M7lgwAAABqhQDsS8+WDgf6+3SPba50F\nAACgiaJAO4zCopIDB4+JyIhBsTqdonUcAACAJooC7TB2JqbU1pr0et3Qvj20zgIAANB0UaAd\ng6qqu35KEZHePTr7+3prHQcAAKDpokA7hpRDmZcLrgqrDwIAAGiNAu0Yfog/ICItg5t16RCu\ndRYAAIAmjQLtAC5fuZp+JEtERgzqqXD1IAAAgKYo0A5gR3yyyaQaXF2G9O2udRYAAICmjgLd\n2NXWmn78JU1E+sV29fIwah0HAACgqaNAN3b7045euVosXD4IAADQOFCgGzvz6oNtWjWPCG+l\ndRYAAABQoBu3i7n5h0+cFpHbOf0MAADQOFCgG7Ud8QdUVdzdDAN7ddM6CwAAAEQo0I1ZVXXN\nnl/SRWRQ7yiju5vWcQAAACBCgW7Mfk4+VFJWLiIjBsZqnQUAAAD/QYFuvMyXD0a0bR3eOkTr\nLAAAAPgPCnQjdfbCpczTF0QkjtPPAAAAjQkFupH6If6AiHgY3fvGdNE6CwAAAP4XBboxqqis\nSkjKEJFh/Xq4GVy1jgMAAID/RYFujOL3HyyvqBSR2wbEaJ0FAAAA/wcFujHakZAsIl07hrcO\nCdI6CwAAAP4PCnSjk3nq/OnzOSISN5DVBwEAABodCnSjsz0hWUR8vT179eikdRYAAABcjwLd\nuJSVV/ySclhEbusf46LXax0HAAAA16NANy67f06rrKpWFGX4QC4fBAAAaIwo0I3LrsQUEYnu\n0iEowE/rLAAAAPgNFOhG5NDx0+dz8kQkbhCrDwIAADRSFOhGZHvCARFp5u8T3SVC6ywAAAD4\nbRToxuJqcWlS2jERGTEwVqdTtI4DAACA30aBbix2/ZRSU1ur1+mG9YvWOgsAAAB+FwW6UVBV\ndWdCioj06t7J39db6zgAAAD4XRToRiH18Im8gkIRGTGI1QcBAAAaNQp0o7A9PllEmgf6d+sY\nrnUWAAAA3AgFWnv5V4pSD2eKyO2DeykKlw8CAAA0ahRo7e1ISDaZVFcX/eA+3bXOAgAAgJug\nQGus1mTa/XOqiPSL7erj5aF1HAAAANwEBVpjSenHrlwtFpERA1l9EAAAwAFQoDW2I/6AiLQO\nCerULlTrLAAAALg5CrSWLl2+knH8tIiMHNJL6ywAAACwCAVaSz/sTVJV1d3NMKh3lNZZAAAA\nYBEKtGaqa2r37ksXkYG9uhnd3bSOAwAAAItQoDXzc/KhopIy4fJBAAAAh0KB1syOhGQRaR/W\nsm1oC62zAAAAwFIUaG2cz8k7dvKciMQN6ql1FgAAAFiBAq2NbXuSRMTD6N4/tqvWWQAAAGAF\nF60DNHZ7fklbunqj+fEDdw4Zd9fQW//Misqq+P0HRWRo3+5uBtdb/0AAAADYDWegNZCQlFFe\nUSkiwwdw+SAAAICDoUDfRGREWJeIMBFpFRLYL6ZLg3ym+fLByA5hrVsENcgHAgAAwG4o0DcR\nFOAX3MxfRHy8PBqk72aduXjqXLZw+SAAAIBjokDb2/b4AyLi7eXRu0dnrbMAAADAahRouyor\nr/gp+ZCIDO8f4+qi1zoOAAAArEaBtqsff0mvrKpWFBnWP1rrLAAAAKgPCrRd7UxMFpHuke1D\nggK0zgIAAID6oEDbz5ETZ85n54lI3EAuHwQAAHBUjrqQSnV1dVVV1eXLl+1wrIqKChGpqam5\nxcN9tyNRRPx8vEJD/O2TvH6uXr2qdQR7uHLlitYR7IQBdTIMqJNhQJ2M3Qa0qqrKPgfCb3LU\nAu3i4uLq6urn52eHYxkMBhHR6/W3crjikrL0o6dEZPiAmGYBjXH+hslkKioqEhEvLy8XF0f9\nB8MSqqpevXrVx8dHp3Pmv8AwoE6GAXUyDKiTsf+AurqykrGWHPVfWkVRFEWxzz+j5n/nb/Fw\ne/YfrK6p1et0Iwb2bJz/rTSZTOYHer2+cSZsKKqqioher9frnflGKAyok2FAnQwD6mTsP6CK\notjhKPg9zvzrYOOhqrL7p1QRiY3qGODnrXUcAAAA1B8F2h7Sj2Tl5BUIqw8CAAA4Pgq0PWxP\nOCAiwYH+UZ3aap0FAAAAt4QCbXMFhUUpGZkiEjcwlhlLAAAAjo4CbXM7E1NqTSYXvX5o3x5a\nZwEAAMCtokDbVq3JtOunFBHpGxPp4+2pdRwAAADcKgq0bSUfPF5QWCxcPggAAOAsKNC2tT3+\ngIi0Cgns1K6N1lkAAADQACjQNpR7+crBY6dE5PZBvbh6EAAAwDlQoG1oe0KyqqoGV5dBvaO0\nzgIAAICGQYG2lZra2h9/SRORgb2iPD3ctY4DAACAhkGBtpWfUw4XFZcKlw8CAAA4Fwq0reyI\nTxaRsNYh7dq00DoLAAAAGgwF2iYu5Fw+dvKsiIwa3EvrLAAAAGhIFGib+CE+SVXFw+g2oFc3\nrbMAAACgIVGgG15lVXX8/oMiMrhPdzeDq9ZxAAAA0JAo0A0vMSmjtKxCREYMjNU6CwAAABoY\nBbrhbU84ICKd27cJbRGsdRYAAAA0MAp0AztzPufk2WwRGTGI088AAABOiALdwLbuTRIRby+P\nvj0itc4CAACAhkeBbkhl5ZWJSRkiMqxftKuri9ZxAAAA0PAo0A1p7770yqpqRZHhA2K0zgIA\nAACboEA3pB0JySIS1aldSFCA1lkAAABgExToBnM06+y57FwRGTGop9ZZAAAAYCsU6AazIz5Z\nRPx8vHp266h1FgAAANgKBbphFJeU/ZJ2RERGDIzV6/mpAgAAOC2qXsPY/XNqdXWNTqcM6xet\ndRYAAADYEAW6Aaiq7ExMEZHYbh0DA3y1jgMAAAAbokA3gIPHTubkFYjIiIGsPggAAODkKNAN\nYEf8AREJDPDt3rm91lkAAABgWxToW1VYVHIg47iIxA3qqdMpWscBAACAbVGgb9WOhOTaWpOL\nXs/lgwAAAE0BBfqWmEzq7p9TRaRPdGdfb0+t4wAAAMDmKNC3JDnj+OWCqyIyYiCrDwIAADQJ\nFOhbsiMhWURaNg+M7BCmdRYAAADYAwW6/i4XXE0/miUicYN6Klw9CAAA0DRQoOtve/wBk0k1\nuLoM7hOldRYAAADYCQW6nmpqa82XDw7o2c3Lw6h1HAAAANgJBbqe9qUevVpcKqw+CAAA0MRQ\noOtpe/wBEQlr1bxDeCutswAAAMB+KND1cfHS5aNZZ0Tk9sG9tM4CAAAAu6JA18f2+AOqKkY3\nw4Be3bTOAgAAALuiQFutqrpm776DIjKoT3ejm0HrOAAAALArCrTVEg9klJSVi8jwATFaZwEA\nAIC9UaCtZr58sFO70PDWIVpnAQAAgL1RoK1z5sKlrDMXhbvXAQAANFUUaOv8sDdJRLw8jf1i\numidBQAAABqgQFuhvLIqMSlDRIb27eHq6qJ1HAAAAGiAAm2F+H3p5ZVVisL8DQAAgKaLAm2F\nnYkpItKtY9sWwc20zgIAAABtMA/BUuUVlafPXxKREYN6ap0FAAAAmuEMtKWuXC0RET8fr15R\nnbTOAgAAAM1QoC1VVFImIrf1j9Hr+aEBAAA0XXRBS6mqqijKbQOitQ4CAAAALVGgb05V//Mg\npmtEUICfplkAAACgMQr0zV0pKjY/iBvE3esAAACaOgr0zRndDCLi5ubaI7KD1lkAAACgMQr0\nzRnd3USkXWgLnU7ROgsAAAA0RoEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxA\ngQYAAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAA\nrECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxAgQYA\nAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAArECB\nBgAAAKxAgQYAAACsQIEGAAAArOBityNt2LBh69atxcXFsbGxM2bM8PDwuPbVX375ZdGiRde9\nJTg4+OOPP7ZbQgAAAOCm7FSgN2/evGrVqscee6xZs2afffbZK6+8snDhwmt3iIiIeOGFF67d\n8vXXX4eHh9snHgAAAGAhexRok8n07bffjh8/ftSoUSISFBT05z//OSsrq3379nX7BAQEDBgw\noO5pVlZWYWHho48+aod4AAAAgOXsMQc6Ozs7Nze3T58+5qdhYWHBwcGpqam/t7+qqsuWLZsx\nY4anp6cd4gEAAACWs8cZ6IKCAhEJCgqq2xIUFGTe+Jt+/PFHEenbt++1G00m00MPPVT3NDAw\nUFXVwsLCho/7K1VVVSJSW1trn8NprqSkRFEUrVPYXFFRUVP4msKAOh0G1MkwoE7GbgNaXV1t\nh6Pg99ijQBcVFYmI0Wis22I0Gs0bf62iomLlypWzZ8/+9UtHjhypexwdHe3p6VlTU9PQYX+D\nyWQSEVVV7XM4zdXW1modwR6ayNeUJvNNm8jXlCbzTZvI15Qm802byNcUO35TVVXtcyD8JnsU\naC8vLxGpqKiom5JRXl7evHnz39x58+bNfn5+UVFR121XFGXs2LF1TysqKoqLi93d3W0T+f/Q\n6/UiotPp7HM4raiqWllZKSIGg0Gnc/L7G1ZUVLi5uTn36RAG1MkwoE6GAXUy9h9Qp//HppGz\nR4H29/cXkcuXL9cV6Pz8/JiYmF/vqarqtm3bRo8e/euXFEV58cUX656uWbMmJSXFXM1tzdXV\nVUR0Op19DqcVk8lk/pffaDSav7KzUlW1oqLCw8PD/KuRs2JAnQwD6mQYUCdj/wF1cbHfnYjx\na/b49SU0NDQwMDA5Odn8NCcnJycnJzY29td7Hj58OCcnZ/DgwXZIBQAAANSDPX59URTl3nvv\nXbt2bWhoqJ+f3/Lly7t27dqhQwcR2b59e15e3qRJk8x7Jicnt2rVys/Pzw6pAAAAgHqw0/n/\nMWPG1NTUfPLJJyUlJdHR0U8++aR5+759+06cOFFXoNPS0iIjI+0TCQAAAKgH+02gGTdu3Lhx\n467beO20ZhF588037ZYHAAAAqAcu4QQAAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAA\nrECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxAgQYA\nAACsQIEGAAAArECBBgAAAKxAgQYAAACsQIEGAAAArECBBgAAAKxAgb6JwqKSgqslIlJaXpF7\n+YrWcQAAAKAxCvRNpB/JSj9yQkTOXsjdsy9d6zgAAADQGAUaAAAAsIKL1gEau97RkZ3ahRYW\nFvr4+Ph4e2kdBwAAABqjQN+E0c3gHuivV0z+/v56vV7rOAAAANAYUzgAAAAAK1CgAQAAACtQ\noAEAAAArUKABAAAAK1CgAQAAACtQoAEAAAArUKABAAAAK1CgAQAAACtQoAEAAAArUKABAAAA\nK1CgAQAAACtQoAEAAAArUKABAAAAK1CgAQAAACtQoAEAAAArUKABAAAAK1CgAQAAACtQoAEA\nAAArUKABAAAAK1CgAQAAACtQoAEAAAArUKABAAAAK1CgAQAAACtQoAEAAAArUKABAAAAK7ho\nHaD+Dh069MILL9jnWJWVlQaDQVEU+xxOE6qqVlVViYirq6tO5+S/WTGgToYBdTIMqJNhQG3h\n0KFDdjgKfo8DF+jc3Nzt27drnQIAAABNi6MW6Ojo6D//+c9ap3AqxcXFK1euFJGxY8e2atVK\n6zi4VQyok2FAnQwD6mS0GlB3d3e7HQvXUlRV1ToDGoWcnJx77rlHRJYtW9azZ0+t4+BWMaBO\nhgF1Mgyok2FAmxonn3cFAAAANCwKNAAAAGAFpnAAAAAAVuAMNAAAAGAFCjQAAABgBQo0AAAA\nYAVHvQ80GtCJEye2bt165cqVqKiou+66y9XVtW77unXr5syZo2083Lrp06f/93//d1hYmNZB\nUB+5ublZWVn9+/cXkaysrO+//z4vLy80NHT06NHBwcmSPwAAABAvSURBVMFap4PVysvLDx8+\nrChKly5d3N3dMzIytm3bptfr77zzzo4dO2qdDvWRmZmZlpZWUFBQWFjo7e0dEBAQExPDaDo3\nCnRTl5KSMn/+/LCwMD8/v1WrVu3bt2/+/Pl6vV5ECgsLExIStA4IK1RXVx88ePDX23Nzcw8e\nPJifny8isbGxds+F+jt48OCCBQu6du3av3//5OTk+fPnh4eHh4WFpaSkbN269fXXX2/btq3W\nGWGFc+fOvfzyy5cvX1YUpU2bNjNmzJg/f350dHRpaemcOXMWLlzYtWtXrTPCCqWlpYsWLcrI\nyAgODg4KCjIajVeuXElJSfn888+7dOkyd+5cDw8PrTPCJrgLR1M3e/bs8PDwP/3pT4qiXLhw\nYc6cOXfeeefkyZNFJCkpaf78+Rs3btQ6IyxVVFQ0bdq0qqqqG+zDgDqWZ599tkWLFs8884zB\nYJg1a1ZERMSTTz4pIqqqvvPOO1euXJk/f77WGWGFefPmqao6a9YsFxeXDz74IDEx8amnnho5\ncqSIrFix4vTp04sWLdI6I6ywePHiM2fOzJ49u02bNtduz8nJeeedd4KDg2fNmqVVNtgUc6Cb\nunPnzsXFxSmKIiKtWrV6+umn169fn5ubq3Uu1IePj8/ixYvbtm3btWvXjz766Kv/ISKvvvpq\n3WM4kIsXL44aNcpgMIjI+fPnhwwZYt6uKMqoUaMyMzM1TQerHT16dNy4cb6+vp6enhMnTlRV\ndfDgweaXBg0alJWVpW08WCslJeXBBx+8rj2LSEhIyPTp01NSUjRJBTugQDd1QUFBFy9erHva\np0+fHj16vPvuuyaTScNUqLc2bdq89dZbHTt2nDNnTkpKiru7u7u7u4i4ubnVPYYDad++fV1L\nDg0NvXTpUt1L2dnZzZo10ygX6sloNBYVFZkfFxYWikhZWZn5aVlZGX/udzheXl7m2XG/VlBQ\n4O3tbec8sBv9vHnztM4ALeXl5X399dd+fn6enp5eXl4i0q1btzVr1pw/f95oNCYlJU2aNEnr\njLCOTqeLiYnp0KHDhx9+ePr06R49enz99dejRo0KCAjQOhqs5u/v/+GHH5aWlnp7e4eHh3/2\n2WctWrRwdXVNTk7++OOP77rrrsjISK0zwgp5eXkbNmxwd3c/ffr0ypUrPTw8srOze/bsWVpa\nunTp0g4dOvTr10/rjLBCTU3NP//5T5PJ5OHhodPpdDpdaWlpdnb2zp07P/7449GjR/NvqLNi\nDnRTZzKZ3nvvvV27drVt2/add94xbzx58uQrr7xinsjBlFnHVVJSsmTJkszMzNzc3MWLF3fo\n0EHrRKiPpKSkNWvWnDhx4tqNvr6+991337hx47RKhfqpqKhYtmxZQkKCTqcbNWrUhAkTZs2a\ndenSJZPJ1LJly0WLFvGLrsPZvHnzt99+e93Ux6CgoNGjR48ZM0arVLA1CjRERIqKiq5evRoa\nGlq3xWQypaenX7hw4e6779YwGG7d7t27jx07Nnbs2KCgIK2zoP5ycnLy8/MLCwvd3Nz8/f3b\ntGlTd8dJOBzz//OaLz6pqKg4cOCA+Q9HTLJyUCaTKTs7u6CgoKioyMvLy9/fPzQ01Dy+cFYU\naAAAAMAKXESI3/XFF19MmzZN6xRoMAyok2FAnQwD6mQYUOdGgcbvCgoKYtasM2FAnQwD6mQY\nUCfDgDo3pnAAAAAAVmApb4iIZGZmpqWlFRQUFBYWent7BwQExMTEdOzYUetcqCcG1MkwoE6G\nAQUcHWegm7rS0tJFixZlZGQEBwcHBQUZjcby8vL8/PycnJwuXbrMnTuXG/s7FgbUyTCgToYB\nBZwDBbqpW7x48ZkzZ2bPnn3dSqQ5OTnvvPNOcHDwrFmztMqGemBAnQwD6mQYUCdz+PDhGy/c\n261bN7uFgT0xhaOpS0lJeeaZZ677T7mIhISETJ8+/e9//7smqVBvDKiTYUCdDAPqZFatWnXo\n0KEb7MBiZM6KAt3UeXl55efn/+ZLBQUF3t7eds6DW8SAOhkG1MkwoE7m1Vdf3bp165IlSxYs\nWNCuXTut48B+9PPmzdM6A7RUU1Pzz3/+02QyeXh46HQ6nU5XWlqanZ29c+fOjz/+ePTo0ZGR\nkVpnhBUYUCfDgDoZBtT5tG/ffsOGDXfccUeLFi3cfkXrdLAV5kBDNm/e/O233+bm5l67MSgo\naPTo0WPGjNEqFeqNAXUyDKiTYUCdz86dO2NjY/38/LQOAvuhQENExGQyZWdnFxQUFBUVeXl5\n+fv7h4aGKoqidS7UEwPqZBhQJ8OAAo6OAg0AAABYgaW8AQAAACtQoAEAAAArUKABAAAAK1Cg\nAQAAACtQoAFo7Pbbb4+IiNA6xU0MHjw4JiZG6xQAgEaBAg0AN6fX6/V6vfnxli1bRo8eff78\neW0jAQC0QoEGgJvbvXt3UlKS+fHZs2c3b95cWlqqbSQAgFYo0AAcUlVVldYRGq+ioiLu8Q8A\ntkOBBtC4HD16dOzYsWFhYYGBgSNGjNi0aVPdS/fff//gwYN37twZHh7u5uYWEBAwZsyYkydP\nXvv21NTUu+66KyAgICYm5p133vnoo48URak7W3zp0qVp06Z16tTJy8urZ8+ey5cvN2+fMmWK\noihnz5699qN69eoVGBj4/9u795Am3zYO4NfMaSsxsJWd3KYrO3oo12ygZZFltpyos6WY6Doo\nFdqBIjrhoaJSrAUVaOUfCUlmB4MMCksrXeAyNZnmAXMeSCUDUTvY8/7xvO/D0Po5/SGF7/fz\n13Mfruc+CHLxcHOPzdTXrl3LnoFeu3ZtXFwcES1atEihUBDRhQsXeDwe932aiBiGkUgkHh4e\nw1fX39+fmpq6ZMmSKVOmiESimJiYtrY28w5VVVUqlWr27NlOTk5hYWF1dXWW7AwRbdmyxd/f\n/9OnT2q12tHRkZ3279YLAAD/BhJoAPiLlJaWenl5vXjxQqlUarXatra2oKCg9PR0rkNLS4tK\npfL29r58+XJYWFhBQYFKpeJa9Xq9j4+P0WjcuXOnXC4/duzYmTNnuNbGxkY3N7f8/Pz169cf\nOHBAIBDs3r179+7dRLRt2zYiys/P5zrX19eXl5dHRETY2NiYz/D8+fP79u0joszMzIyMDCJS\nq9VElJeXx/V59epVc3NzdHT08AXu2LHj5MmTTk5OBw4ckMlkOTk5SqWS+1r8/Plzb2/vioqK\nyMhIjUbz8uVLuVxuMBgs2Rki+vr1a2ho6I8fP5KTk62trf9hvQAA8K8wAAB/1Pr16+fPn88+\ny+Vye3v7xsZGttjX16dQKOzs7Do6OhiGCQ4OJqITJ05wsVqtlohMJhNb9PX1nTNnTldXF1ss\nLi5m/9H19vYyDKNSqYRCYXNzMxceHx9PRMXFxd++fXNwcPDx8eGaUlNTichgMLBFPz8/T09P\n9vnatWtEZDQauc5yuVwqlXLFuLi4SZMmtbe3D1lpb2/vpEmToqKiuJrjx48LhcKmpiaGYQYH\nB93d3UUiUWdnJ9va1NQ0efLk8PDwEXeGYRilUklESUlJ3Mv/Yb2/+jsAAICl8AUaAP4WJpPp\nzZs3Wq3W2dmZrREIBEePHu3t7X3y5Albw+PxDh06xIV4eXkRUW9vLxG1tLSUlJRotdrp06ez\nrb6+vuwpCyLq7+8vKCiIjo4WiURc+P79+4no8ePHfD4/NDT09evXHR0dbFNubq6Hh4eFV9ep\n1eqGhoZ3794R0ffv3+/cubNhw4ZZs2YN6WZlZcXj8V6/fl1dXc3WpKSkdHZ2SiQSIqqqqqqs\nrNyzZ49QKGRbJRLJzZs3g4KCLNkZVmJioiXrtWRRAADwO0igAeBv8eHDByJyd3c3r2SL9fX1\nbHHmzJn29vZcq5WV1ZDwxYsXm4cvWbKEfairq/v582d6ejrPjKurKxF1dnYSkUaj+fnz5717\n94iopqamqqoqJibGwpmbn+IoLCzs7u7evn378G4CgUCn07W2trq5uS1dunTXrl35+fkDAwPc\nDInIzc3NPESj0URGRlqyM0Q0Y8YMbnNGXC8AAIyZ9Z+eAADAfzEMQ0Q8Hs+80tramoi+f//O\nFvl8/u/Cf3kvB5dhs4Hx8fHmZ6ZZs2fPJiI/P79Zs2bdvXs3Pj7+9u3bfD4/MjLSwpmLxWJv\nb++8vLyUlJScnBx7e/vho7Di4+NDQkIePXpUVFRUWFiYmZkplUqLioqcnJzY+bPrHcKSnSGi\nqVOncs8jrhcAAMYMX6AB4G/B/h5hVVWVeWVlZSURLVy4cMRw9vNqbW2teaXRaGQfpFKplZWV\njY3NRjMymaynp4f9amtlZRUeHv78+fOurq7c3FylUskdpbBEeHi40WjU6/UPHz5Uq9UCgWB4\nn+7ubr1ez+fztVrtrVu3mpubs7KyGhoadDodN/+amhrzkLS0tISEhDHszIjrBQCAMUMCDQB/\nCycnJ5lMlpWVxV0nNzAwkJqaOmXKFH9//xHDnZ2dvby8rl+//vnzZ7amtLS0pKSEfba1tVWp\nVNnZ2RUVFVzI4cOH2ZMbbFGj0QwODp46daqurm7E8xtcFCssLIzH48XGxvb39//y/AYRGY3G\nVatWJScns0Uej7dmzRr639diDw8PFxcXnU7X09PDdjCZTElJSSaTaQw7Y8l6AQBgbHCEAwD+\nIhcvXvT395fJZBEREXZ2dvfv33///n1aWtrcuXNHjOXxeFeuXPHz85PL5Vu3bv3y5Utubq5C\nodDr9ZMnTyaic+fOKRSK1atXq9VqFxeXoqKiZ8+eHTx40MXFhX2DQqEQi8VXr151dHTctGnT\n7wZiL7bLyMjYvHkzd0BCJBJ5e3uXlZWJxWJfX99fBq5cuXLZs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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "library(repr)\n", + "options(repr.plot.width=8, repr.plot.height=6)\n", + "g\n", + "#tgppt::plot_gg_ppt(g, out_ppt=here('figures/ukbb_longevity_10y_survival.pptx'), \n", + "# rasterize_plot=FALSE, top=1, left=1, width=6.4, height=4, overwrite=TRUE)" + ] + }, + { + "cell_type": "markdown", + "id": "bd31f0ae-037b-41e9-bb6d-baa2b2e712f4", + "metadata": {}, + "source": [ + "## 10-year disease outcome by disease score" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "575564c6-35e0-49e6-a7f3-f8ee1c7c1088", + "metadata": {}, + "outputs": [], + "source": [ + "diseases <- data.table::fread(here::here('output/ukbb_diseases.csv')) #see Disease_Longevity_UKBB notebook for computation" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9a0de4aa-0959-47d0-a09c-c437ed3a6190", + "metadata": {}, + "outputs": [], + "source": [ + "disease_outcomes <- get_patients_disease_outcomes(survival, diseases)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "85cf61c4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'diabetes'
  2. 'ckd'
  3. 'copd'
  4. 'cvd'
  5. 'liver'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'diabetes'\n", + "\\item 'ckd'\n", + "\\item 'copd'\n", + "\\item 'cvd'\n", + "\\item 'liver'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'diabetes'\n", + "2. 'ckd'\n", + "3. 'copd'\n", + "4. 'cvd'\n", + "5. 'liver'\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] \"diabetes\" \"ckd\" \"copd\" \"cvd\" \"liver\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "names(disease_outcomes)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "b40b73e2-a371-467e-aa00-069766b1ef7b", + "metadata": {}, + "outputs": [], + "source": [ + "km_disease_10y_60 <- purrr::map2_df(disease_outcomes, names(disease_outcomes), function(d, name) {\n", + " data <- pop %>% filter(age == 60) %>% \n", + " select(id, age, sex, value=!!name) %>% left_join(d, by=c(\"id\", \"age\", \"sex\")) %>% \n", + " mutate(obsT = ifelse(follow_time < disease_follow_time, follow_time, disease_follow_time), \n", + " status=ifelse(sick, 1, ifelse(dead, 2, 0)),\n", + " qbin=cut(value, seq(0, 1, by=0.1), right=FALSE, include.lowest=T)) %>% \n", + " filter(!is.na(qbin)) # removing patients that were sick before lab tests, will have a missing score\n", + " return(purrr::map_df(c('male', 'female'), function(s) {\n", + " fit <- cmprsk::cuminc(data %>% filter(sex == s) %>% pull(obsT), \n", + " data %>% filter(sex == s) %>% pull(status), \n", + " data %>% filter(sex == s) %>% pull(qbin))\n", + " fit_data <- purrr::map2_df(fit, names(fit), ~ as.data.frame(.x) %>% mutate(name=.y)) %>% \n", + " select(time, est, var, name) %>% filter(time>= 3650) %>% \n", + " distinct(name, .keep_all=TRUE) %>% \n", + " mutate(std=sqrt(var),\n", + " lower=est ^ exp(-qnorm(0.975) * sqrt(var)/(est*log(est))),\n", + " upper=est ^ exp(qnorm(0.975) * sqrt(var)/(est*log(est)))\n", + " ) %>% \n", + " tidyr::separate(name, into=c('qbin', 'status'), sep=\" \") %>% \n", + " filter(status == 1)\n", + " return(fit_data %>% mutate(sex = s))\n", + " }) %>% mutate(disease=name))\n", + " }) %>% mutate(\n", + " disease=factor(disease, levels=names(disease_outcomes)), \n", + " qbin=factor(qbin), \n", + " x=(as.numeric(qbin)-1)/length(levels(qbin)), \n", + " sex=factor(sex, levels=c('male', 'female')))\n" + ] + 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TB41RAABSwp6aehEp6qIx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k2eJwcoDfYnCdbmGn06kmhmAw2MlfJUkK\nBoPqt2Ic31FopFuR9EC/Dl7T9in4U0mCBIOO57ijlTUAMlIS3I3NRgB6nd3ngS88+yWVwXe+\nIQuC0K0NORAIBAIazhr2+XzaNe73+/1+v3bta3esh8bBB4NBjYJXrjEkSdJiW3O5XDnxcQFR\ntNvtXpPp4y+/3bBtF2NMr9ctmT198tgih6Pr/bah1WlscQAIpsR7mICfxMkAyLLcWfAmnh+U\naapq4gVRrGoItLYFclOZijzCerfXBACw2+2sqxSlnW/FoWXUv8kqTwN6pq2tTbvGAbS3t2vX\nuMPh0K5xaBa8IAjKz9B3gDHm8vjanJ52l6fN6XG6PY52d7vb63R5210ej0/VzkTH87ExFqvF\nVNdoB3DjglnJcd24fOsWTc+yoPHXUmXwnW/Ioih2ayvWaL8aomnjoihS8GeiaeMn7iXCyNHW\ncV3c3u7ctPUrAINzM8K4u1DyNzLGetAgJzNLs50DgnFW9+k2VT4gKDlB2tvbZaHrfaOaI3KP\n8bE2pm7/3Hkj3Vr++++/B5Cf/5NpqSeO9wz9KSUlJT4+PtTpl5LSMSL47rvv3r59+7Zt2/bv\n379169YerEI9tR2jF1xwwZtvvvnAAw/k5uaGnty3b9+6deuUXt4zUc6QLJYfM8VYLBb1p029\nfDkhpHOllbUACvMy1SQY1bl8nHItGksF0AiJGMZYfZMDQGZqos4XACBZjJEOipAB5Z6pxUda\n7EFBXPX+9n0HjwFIjI/51eLLcjNTu3wtAN4XMDY6AEhWk5DWw8wzssXoK8w017To3D6dx28p\nq/fnpshdzd8nJIyUMZ41DS0vv/2hy+11ON0ut086oRREJ6wWU3ysLT7WFh9jTYiLiY9Vftri\nY61xMVaO46rrm59+aS0AHRUZI4R0qrK2sbaxFcCM88dFOpYOunYPJzMAYkI/SGZlKMzpchZ8\n2MXFxZlMppaWFu6EKS9nKtvOnTItxufzTZ8+3eVyzZ8/f/HixXfeeef48eN7s4rOqe0YffLJ\nJzdt2jRhwoRf/OIXAN57773Nmze/9tprRqPxySef7OSFMTExAPx+v83W8Y3x+Xzp6ekq19vl\ny0eNGnViAKtWrdLr9bGxsV227Ha7GWMmk8lo1OR60uPxGI1Gg0GTRDkDIHiz2axR+x6Px2Qy\n6fVqv9vdonXwbrdbGX+uRePKWFSLxRJqX5LliupGAKOGDVKz1bCGNgZwVrMtJenUv7rdbovF\nolPRwdr5P8jzvMqtWBRFZRRYTEzMqTvT3pMkyev1qomkBwRBUEZp9evgNWpfFEW/36/s/8Ou\nXwcf2r7aXW4Ag/Oz9b4gA/QJcWH5d5RhnqEDbuc635B1Op3KDdnn84miaDAYzGZNenx8Ph/P\n8yaTSYvGvV6vJEkU/GlpGryC53ktNuR0vTHejEk5mU+8s6WspgFA0eCc31w/NzZG1U1BJkqs\ntA4yg15nGDHIcErORBc4ADzPqQo+MYHVNrPqRk4QLeWNXG46l91Z5ywLysqEvZiYGOi6uDZQ\nc76hZkOWZVmZSWO1WtWcBnQXY8ztdttstp5d7XQuFLxG7SvBa3qshwbvvCTJ//7sm8PlNQBa\nHM4Wx2lGpRj0uoS4mIT4mKT4uPg4W1J8bHysLTkxLj7WlhgfazR08e0yOzrGglmtVi025L55\n5zX9WqoMPlyH40AgEAwGeZ5XeRrQXcFgUBTFng3m6pISvE6n0659WZZPHK0VRn6/XxAE7d75\nPgheo3c+KHbMQFfuUKanJJ5fPDqMW3SoqR7sguTqZgBcrPW018UAoA8oN5FsNhtiun7zNeoB\niKDRo0cLgrBv376pU6cCCAaDc+fOXbx48Q033KDm5Z9++unu3budTqdyVfXdd9+FfRUnUvvu\nZ2Rk7Nq16+67737uuecALF++nOf5q6+++qmnnsrK6qzIZmJiIoCWlpbQdt7a2npqX2+PX56W\nljZz5szQr2vWrFF5Fu7xeBhjer1eo1N2n89nMBg0alzr4L1er3bBK/O4tQ5eoy5jrYP3eDza\nBa90jJ4YfGlFrS8QBDBmRGHX/5HMfG0uALrURMPpFlaCV9Nl3PkZJMdxKrdijuOUjlGTyaTF\nWa8gCBzHaXdVr3TP9ffgtWic47hAIKBR46EsP/0xeO74sBrlnxiakcbK6wAYkxP4MK1REASV\nwXe+IfM8r3JDVmbQa9eDplzmadS43++XJEmn02kXvHaNax18IBDQ7nCp7DY12svpdHogCKCu\nsRXAFRefe+3cmTrVfR/Bo2VSIAjAOGKQLraTeySqgx+cIyfFB0uOsaDAqhp4b8AwvIA7w7R6\nyWBQJuOZTKYuO0bVdOio2XwkSVL6FlWeBnSX0sNlNBq16HU9MXgtrktDwWvRfRaqtWA0GsMY\n/A9HKl5ds7m2oUX51WYxFxXmJsbHJsbHJiXEJsbFJifGxcfFxKm7VXAmoa+KRhcdyiRZ7c6y\nlAeafi1VBh+uw7EoitBsvwpAlmVZljVqXOvgJUkSRVGjxgVBUDpGKfiThC6KK2ubAMyZeX54\nb+X2+FyCBYJ+pxeAPiNFf4bXMokpc9cNBoOas3QtjhGRVVBQ8POf/3zBggXLli3LzMxcvnz5\njh07li9frvLlKSkpjLEXXnhh4cKFhw8ffuSRRwCUlpYOGzYstGPs5SpO1I0j6ODBg9977z2f\nz3fkyBGj0Th48GA1X6Dc3NyUlJS9e/cqM/8bGhoaGhomTJigcqW9fDkhpBMlpRUAzCbj4LzO\nbm8opDYnRAmgevSERAW9Tpes10sAOK67SX8IIaeqaWg+WFpZUlrJ7M5bi0cDMBj0N15zxbTJ\nY9Q3ItY0Ss0OAPq8TF1y2A6XfEKsaeKIYMkxud0ttTjkvT7jqELepsnwH3I2a3O6V7239Ytv\nDii332JjrC63d+TQ/N//alGkQyOEnNXiYm3dOhxrSmpoBWPgOV3aGYaLEgDAyy+//NBDDz30\n0EPNzc3FxcVbtmw5KR9oJ4qLi5ctW/bss88+/fTTU6ZMeeONNx5++OElS5aUlpaeWOWoN6s4\nkdqO0XHjxl1//fXXXnttRkbG2LFj1a+A47irrrpq9erVubm5CQkJL7300qhRo4YMGQLg448/\nbm5uXrJkSc9eTgjppZLSSgDDBufquhpUguP16DmTkbpgCIkGWRkpcHkB8DZLl+PCCCGnVdvQ\nUlJaWXK04mBpZfvxcqvjMtOUB3fftGB4UYH61mSXRzhWA4CPsxkGdX3HsVs4k9E0rkgorxWr\nGpjXH9hz0DgsT5eREt61kLOWJMtbPtu9ZtN2nz8AICkhbvGci7/ed3DPd0ciHRohhGDWtOIu\nE3T0GbGxFYAuKYGLmpCix5o1a0KPjUbjM88888wzz5y62IkVp+fPnz9//vzQr5s3b1Ye3HXX\nXXfddVfo+fXr14ceh6qqdrKKblH7Qba1td1zzz3333//rFmzrr/++quvvlp9loq5c+eKorhy\n5Uq32z1u3Ljbb79def7rr78+evRo5x2jnbycENIbkiyXHqsGMHKoipsqDFJLG2i4KCFRIy8z\nTXa6AfDxmuQzJWSgqmtsOXi0qqS0oqS0ss3pPvFPOp4vzM8altWRyz4vO019s0yUgiXHIDPO\noDeOLMSZJ8Cy41Xpux06xxkG5/BxMcKhciZKwUMVeqfHMCQPVLuG9M7Bo1WvrtlcXdcEQKfj\nL51avHjOxWaT8et9ByMdGiGEwGjQz5pWHOkoOshOD/P6AegykiMdCwkbtR2j5eXlX3755Ztv\nvrlmzZrNmzfHxcUtXLjw+uuvnzZtmprUJwsWLFiwYMFJTz744IOnLhkbG/v++++reTkhpDeO\nVdYpCUZHDi3ocmHZ6WZBAYAuJVHrwAghnVOSpBZkpymnZXwcDeImpAtNLY5Dx6qPHKveX1LW\n4mg/8U88z+VnZxQNzi0qzB0zvNBqMR3ZU6IMx+4W4Ugl8wUAGIoKOHNnucJ5jgOgP0OS0C7p\nUhL4CSOCP5TJHp9Y1yy7PMZRhZxZwyJXJJr5gwKO587ugZPmzo8aVnDjwsuzaSQyISQKeHxK\nlk6MGVGosv5hH5AaWwFwBr0uKT7SsZCwUdsxynHc1KlTp06d+sILL3z44YdvvvnmqlWrVq5c\nWVBQcN1111133XVDhw7VNFBCSHgp8+jNJuPg3MwuF1aGi3IGPZ9AY9MIiQpDkxPR7gHAx9FW\nSchp/NgZerCsxd5JZ+hgq6W3xRzE2iapyQ5An5fR5dQKm9UMIDu9531PnNVsmjgiWFol1bfI\nLm9gz0HDiEF0hXZ2+usbGyVJeuK+m7v7QmXu/NoPtnt9ytz52MVzLrkwalL4EULIF98cUB6c\nN36EFu1nmM1/n3/5+xVV3XiNzJTDvS4tiaZrDCTdzolgMBjmzJkzZ86cnTt3/vKXvywpKXn8\n8ccff/zx88477+677160iDJzE9I/KJWXigpVJRiVWtsA8MnxncwNJIQAYA6XsakV8Qla5/1M\nN5vQ7uGMes5CI8UI6eBodx0+Vv394fI+6AwNkd0+oex4atGC7C6Xt1nM8AWsvdxyed5YVCDF\nxwaPVDJBDB4o1edlGAZ1vXbSxw4erXz25Xd+c8N/jB8V/kEk9c326voWgNU2tuRnZ3T9guPK\nKuteXbO5rLIOx+fOL5pzscXU2UhnQgjpS4IgfrbrO+VxUkKcFqsoiosBMCapGxMipdY2Joig\nefQDTrc7Rvfv37927dq1a9ceOnSI47jzzjtv4cKFdrv973//++LFiysqKu6//34tAiWEhJEk\ny6XlNQBGDinocmHZ4+tIpEIJRgnpCmto0TlcUrtbl6TJOZzCajGZg4JMw0UJOaEz9MDBY832\nthP/xPNcVlpKUWHuOUWDRhcNVoZqhpkkBUvKIMucXpyB5PYAACAASURBVGccObiPx4/oMpJN\nNkvwhzLmD4hVDbLTo0unS7Xo8umuA15f4NOdB7ToGJVESRRFAKIoqXyJ2+tb98Fn//7sG8YY\ngJFD829cdHlORmrYYyOEkN745Kt9TrdH01XoOQ6AQdeNzDYd8+itZipHPMCo7Rj95ptv1q1b\nt3bt2rKyMgDnnnvus88+u2DBgry8PGWBBx98cObMmS+//DJ1jBIS/cp+TDDadeUlpR49eF6X\nSNP0COkCc/kAyE6Xph2j+ZlpspPm0ZOzlywzAAePVv32kRdOmzN05ND8kUMLhhfm9XZgZleC\nRyqVe4eG4YMikuiTj7WaJo4QDpZL9na5zcU8vr6PgXTC5fGEfkYWY+zzb757ff0Wt8cHIDE+\n9pqraO48ISQayTL74JOdWq/FL4oA/IKocnkmiJK9HYCeEjEPOGo7RidPnqz8vO222xYuXJif\nf3JnitVqnTBhwocffhjmAAkhGlDm0ZtNxkGqEow6AOiS4rSeGkzIQMBzADhe241lVF4mJBlU\nkp6clRqa7XVNrQDqm+3KMxzH5WenjxyaP2qY0hmqwcjQ0xFrm6RGOwB9bnoEJ1VwBr1x9FCx\nql6oqGOqL/BI3zAZjQBMRkNkwyivrl/59gc0d54Q0i98tfeHxhaH1mtJiLXheOJvNaRGO2QG\nDrr0JC3jIhGgtmP06aefXrhwYUFBQSfLvPjii2GIiBCiPaXy0vDCvC4TjLJAUHZ5QfPoCYkC\njDFwADAsJRkM4Dg+amp0EtI3Dh+rfvbld5SJw7E2y9TJY0YNLRhemKfJNPlO/SS16KCcPl77\nyTjo8zP5+Jjg90eZ6lnV5Gxw0tz5EUPyb1x0WW5mWqTjQl5WenZ6ikGvy0yj/A+EkJ/YuPUr\nAHlZaVV1TdqtJTE+Fi5fvOpJ8VJjCwA+IY6ju0oDjtqO0fvuu0/TOAghfUaS5CPHqqFuHr3U\n3AYAHMcnU8coIRHmDwRhtgDIsJjh9fOxVhrHTc4qu/Yd/Os//xUURI4DY5g0puj6ebMiE4oo\nBX8ogyxzBr1xZGGUlKblE2L1g3OEI5WRDqSfWfHWB15/4L/uvjHSgYSZMnf+jXc/crm9ABLi\nYpZcPWPapDHq62gqdyBESZOudp7nrpl9Ic/zfHRsPoSQKLGv5GhFTQOAS6cVr3z7A+1WZDIa\nAJ/KEf3M41NGC+mp7NJA1O3iS4SQ/u5oZW0gKAAYObSgy4WlVgcAPj6GM9DugpAIc7q9Sseo\nTZlHTwlGydlk8/avX1+/hTEWY7XE2MwNzZpPsutEsLSK+fwADEUFnDmKRo7Qwbq7WhztpRV1\nssxq6psLVOQX6i/Kq+tffWdzaUUtAB3PXzqteNHsiyzdToPLADAwDQIEgIKcdI1a7u+Yx29s\ndDBbDNdXiUEIiR7vf7QDQHpK4pjhhZGO5UdiYysA6HhdSjeq2JP+gk6eCDlb/PvzvT+UVj76\nu+uVefQWk7EgN6PzlzBBlNvcAOgAQEjEVdU2VtU2ISWZA7hAEAAfRwUxSfTSH6nmrGaMDMNV\njSTLr6358OMv9gBIT0m8//Ylf351Xe+b7TGxrlmpSxvZ1KIkLARBVO4W+4NCpGMJD4/Xv/aD\nT7d8/o1So2x4Yd5Niy7PzerJ3PlZF07af/DYrGmTwh0j6YLc4jA0t8uJCTx1jJKzTFll3cGj\nlQCuuvT8KBpOzqCkFNelJtFsrQGJOkYJOVvs3HfQ7nAdPlZ9sLQCQNGQPF1X9WHk1nYwBkCX\nQvXoCYkkt9f33CtrRsTFnvgkjRglUUt2eeD1wy8wUeL0ut405QsEX/j7un0lRwEMHZRz7y2L\n4yKaWlf2+ISj1QD42ChILUrICRjD598cePPdj5w9nTt/ktFFg5+6/6bMDBrX2ddkuxOA1Nqm\nz0qNdCyE9Kn3PvoSQHysbdqkMe0uT6TD6SA7nCwQBM2jH7ioY5SQs0Wrw8kYGlscR8proHIe\nfYsDAB9r5bo994oQEjaSLD//ytrGFsfI+LjQk5zJEFUTeEm/w/mDBrsTGZqcCjJB4mQGWYQs\nAz3vGG1qbXvmb6trGpoBnDd+5O3XXW2M7FRxSe5ILarXGUcNjpLUooQAqKxpWPnO5tLyGgAc\nx02dNPr6ebNibJbetKnT8VaLqctCnST89DpQZgxy9qlrat194DCAKy4+zxBN339lHj1nNvLx\nsV0uTPqjKPq2EUL6QHNre0eC0SFdVV6SZcnhBM2jJyTS/rnu3z+UVgDIyUgJPUnDRUkv8W1u\nXaNDjrEgMUong5dV1j2zYrUyYOSy6ZOvn/+zHg98C5fgkUrm9QMwDC+gW4YkemzatnPXvoPK\n3PnBeZk3LbqiMD8r0kERQkj3bPhoB2PMYjbNnDoh0rGcQJKV0UK69GRE+jyEaERVx2h9ff2B\nAwcaGhoaGhoCgUBmZmZmZmZxcXFGRhcJCgkh0aa+qQWA1WIa1FWFAcnuhFLghRKoERI5n+3a\nv+Wz3QAmjx2efWLHaDx1jJJe4e1OAJzdhdxIh3I63+w/9Jd//isQFHQ8f+Oiy2dcEPlrpB9T\ni+ak9+aWISdKAKBNpW/Sv3h9AcYYgKAgCIIIQGbM5w8of/X5g5IsA5BEyR8MKk96vH7lQTAo\nNLW2KY+/2lsCIC7Wdu3VMy6cPDbitxAIIaS77G2uL775DsCl0yZaoym7rtRsVy6K9ek0j37A\n6qJj9Ntvv33qqafWrVsniuLJr9Trr7zyyocffri4uFiz8AghYVbfZAcwvDCvy2zWyp0xzmLi\nezcPixDSY6XlNa+s3gQgPzv9juvnHtrxbehPNGKU9BLT67iAgN4lAD2T6vomJSuh2+OLNRq6\n+/JQAXqb1Xz3zQtHqcj9ojXZ4xPKjqcWHdyr1KJSYqyuycElUfLuAUiS5NY2Z7O9rcXe3tza\n1mxvLymtAFBSWnnbg8+JkgRAlmRfIBje9fI8N3PqxEVXXmyzRlFvAiGEqPfBJztFSTLodZdN\nnxzpWH5CbGgFwMfZONrBDlyddYx+//3306dPB7BkyZJLL700Ozs7KSmJMeZwOBoaGrZt2/bu\nu+9OnTr1k08+mTJlSl8FTAjpFWVwwYgu59EzJre2g+bRExI5bU73spVrBVGKsVl+f/NC04m9\nSzzHR7T+DCGdE4WO4ZAyY916oSyzf6z7UBklnZaccP9tS04cKB1i0vGpNivfZ1PalNSikszp\ndcaRvU0tKiXESBz0dH3Vnwmi1Opob7a3t9jbmlvbWxztTa2OFnu7o92lzGc/SSAoBMJX9V7H\n82azEQCTmdcfAHDb0jnTJo8NV/uEENLHvL7AJ199C+DCc8cmRlMeT+YPyu0uALrTnY2QAaOz\njtE//OEPNpvtk08+GT58+Kl/vfbaax999NGZM2f+6U9/WrdunWYREkLCSRAlqKi8JLe7mSAC\n0NE8ekIiQRDEP730tqPdpdPxd/9yYdpPb1HwsTaq+kIGHn8g+OfX1u/9vhTAkILse29ZHB9r\nO+2SI5MT5xSP29jU0jeB/ZhatKiAs/Q2tSizmkSjTq/NWF0SXkFBbG5ta7G3tzjam5U+UHtb\ns729zenqvM+f57nE+NiUpPhWh7PF3p6Vnjz93HEArBYTx3EADAa9UkmM5zjL8UmjFpNRqXSk\n1+lCN8Ns1o6JO0ajwfDTr01Fdf0fnn4FQFY6XbEPIIyx4+kUCDlL/Puzb7y+AMdxs2dE15A7\nqbEVDOA5XSqNFhrIOusY3blz55IlS07bK6rIzc298cYbly1bpkFghBCtWC2mgpwuEgR3zKM3\n6vm401+UEkI09fd3NpdV1gG4Yf5lI4eePMSb5tGTgcfR7npmxdvl1fUAJo8dfsf1c01nnoM/\nLSsDwDmxfbEhiPUtHalFs9PoumgAc7S7vjtc3tTiaGp1ONrdbe2uxta25tY21tWoZ5vVnJac\nmJ6SmJaSkJacmJaSmBgXk5qcoHyB/+/VdS329vSUxKsuPb9P/g/Sz8ksWFIGf1B5HOloCOkL\ngiBu+ewbAOeOG5GRmhTpcH5CqUevS07gDFS3fCDr7NNNTk5uamrq/PXNzc3JyZSDlpD+ZHhh\nvooEo+0AdMmJoAT+hPS5DR/v2L5zH4CLzht36bSJP/5BZuABgO5YkAGmqrbx6RWrWx1OdBSg\nn8V1evTxSxIAQZa1Dkz2+ISjVQD4GKuhsFepRUkUYgzfHTqmPH7h1fWdLKnX6VKS4lOS4lMS\n41OTE1KT4lOTE1KS4pPi47o8pyJELZkFS8qklo6aWlT/mpwltu/c3+Z0A7jykvMiHctPyE63\nMl9ER2WXBrrOOkYvv/zyF198cdmyZb/5zW8MhpNv2kuS9Prrr//tb3+78cYbtYyQEBJmp44+\nO4ns8ipTeKgePSF977tDx1Zv2AZg2KCcX15zBRNE2eWRnR7mdMPhRHIiaMQoGVgOHCx7/u/r\nfP4Az3O/WHDZpdO6ruopMQZAhsbDqSQ5eKQSkgydklqU13Z1pG99d7j87Q3blLH5IQaDPjUp\nISUpPjUpPiUpXnmclpyQEBfTeWc9Ib0lyYHvj8oOJwAYDQgKNDqBnA1kmX3wyU4A5xQNGlKQ\nHelwfkJqaAXAGfS6ZKqXOMB11jH6xz/+8csvv/z973//6KOPXnjhhdnZ2YmJiRzHORyO+vr6\nzz77rK2tbfTo0Y899lhfRUsICYMuO0aVefTQ8brEKEp9TcjZoL6p9YVX1+fExY7Nybz6/PHi\nnoOC1//jn4/P6ORM3S7zTUh02rZj79/f3izJstlkvPPGeeNHDY10RD8KVtSYPD4AxmH5VIt2\nICmrrHt7w7bvDpef+OTN11w5cfSwBLrtRCKBiVLwu1K53Q1An5Uqef0sfNW6CIlmO78taWi2\nA7hqZpTlG5GZ1OyAMlyU7lIMdJ11jFoslh07dqxfv/7FF1/cvn27x+MJ/clkMp1//vk33XTT\nggULzGY6UySk3zAa9PnZXSQYlVvaAOiSE2h0DCF9gwUE2eUO2p1th8qfmTXNpNMBQGt7aDgc\nZ9DzcTZ4fZGLkZAwYwzrNn+6bvNnABLjY++/7Zou81/3jbzsdHaoHIDR4QI4fXaaLj26Up6R\nHqtrbFmz6dNd+0qU20wpifHTzxurfAmzM1KoV5REBBOl4IEjstMDQJ+dZhiaJ+07HOmgCOkj\nG7d+BSA/O/2cosGRjuUnpNa2jlrENI/+LNBFBlm9Xr9o0aJFixYBcLlcdXV1jLH09PTERMo9\nT0g/o/SwZKQmdZ4Mi/kCsscHqkdPiKYYk90+ud0lu7yy28s8HT2eBaHLco7jrGY+xsrHx/Dx\nMbzNAgCtjgiFS0iYBYLCi/94d/eBwwDystPvv/Wa5MS4SAfVwaDXBQEAHDg+xkKpRQcGe5tz\n/Yefb/9qnyTLAOJirFdeMuXyi89tbG5VOkYJiQgWFIMHjshuLwB9XoZhMO1wyFnkwKFjStHF\nubOmRtugzI559DYLH2uNdCxEc90orRUbG1tUVKRdKGHBGJNlWRC6nnqglJiUJEnNwj2LRLvG\nFRT8afX34EVRDHsSK8bYu//+QukZzUhN7Dx4ubEFADhOirXK3fw3RVFUs5jcabkM9VuxJEnK\nA0EQtMj8pfw7Gn3WAyZ4Ldrv18GH1sJObT8oMo+XOT3M6WFuH07ZENr9gQpHuzEp7pwJI7lY\nG3Q8AAZIgCQIANjx4kvavTmMMZWNh2tDVg7HKhfuASVO7Xba0Dh4juM0/a6q/8S7RZI7NjRR\nFE5tv83pXrZybXl1A4AxIwb/+vq5FpOxJ2EwTT5ZFspfodfxRQWCJOH4fiM87Wv5tWEn7uLk\nLqZ9dL4Vh5bpMs5QOypPA7pLecdEUVQT8KncHt8H23dt+Wx3UBABWEzGGVMnzJl5vsVkBJND\nBwWNzu6UmDXa0EJvuKbBC4LAazCFKPTO9/iT7ZzytdH0LAu9Dp4FBfmHY8o+h89NR2668jkq\n8+jlQFDNxxquw7Gm31V08xyjuyj4ztvX+iyrx8G/t+ULAKnJCeNGFZ7awokbmiZHzOMpqk5t\nnAVFyd4OgEtJ6OGqTwie6/WGTLTWjY7RfkGSJFEUnU5nl0sqm0EgEAgEAhoF4/P5fD4Npz36\n/X6NgmeMaRe88s77/X6/39/lwj3j9Xo1alnr4LV4533+wGvrPv6htFL5NS8rtfMNxNzs4AE5\nxuL1ejpZ7FSMMZXvfOcXTrIsq9yKQ1wul/qF1VM+7m5F0t3GoVnwyio0Cj5E0zdH63deo/Yt\njHFAIBAQnE5OZpwvwPsCOm+A9/g48ZQzHo6TzQbZZq5obf/HR1/UtrtHDMm7feZkF8/gcZ/a\nuCRL4HXaBd+td77zDVkUxW5tyIIgaPpPBYNB7RoXhNP0/YWLKIoaHeuVREiyLGvxzgf8HTF7\nPF7oftIrUd9kX75qk73NBeCCiSMXXXmhEPALgZ4cVRnCvJfjBNHQ1Ka3d2x9gfREb9CPYJiP\n+KFTUC2+ljqv1wQAcLlcrKtq6Wr6MdVsyKH9qnYnYADc7tPsFTsXFMRPd3235Ys9Pn8QgF6n\nO3dc0ZxLzo2xWULfulDMPp9Pi21BeZO7e2KjUuiMUaPgFT1456OnfU1PEdG74LmgaCqv54Mi\ngGBGkphgxfEPUZ8YY/T6/fE2ScXH2vmG3N2rY40OCqFVaHpqKklSNJwg9axx7YKH9u98z3Zx\nlbVNJaWVAGZMGes53aYU2n69Xq8W8RsZwxneHH1Lu5ExcPBY9KxHq+YDgnKi5fF4ZKbqaNuD\ntZBwGWgdo3q93mg0Jid3nQbCbrfLsmy1Wi0WixaRtLW1WSwWk8mkReN2u50xZrPZNAre4XBY\nrVaNgm9tbVWC1yg7rcPhsNlsRqNRi8a1Dt5ut8fExIQx+Lqm1udfe6+2oQUAx4ExJMbHdbKB\nsKDg91YAMGWm2lRsRyey/3/27ju6jus8FP2398yc3tFB9E4SYBWLxCKqUs1UtWTHLXbs2E7i\nl1wnuVkrK+vlZV07uevaTuIkTpZ7bMdXiiTLqhYlURQlFrE3gEQHiF5P7zOz935/DAgCIAgc\nAGdQiP1bWhTOOXP22TiY+s3e3+fz2e12SZq9JszMv6AgCCluxbIsa4cxj8ejx3AALVKTSk/m\nIZlMagf7Fd15ndqXZTkSiXg8uiT1SyQS2mWMTp2Po14AMMaSxo5BGovfXDQbmU3YYR37z2YB\nhLp6B7/381eSspKfnfEXf/icxXzL3UuvOHbU1u8vG4vFUsyWM/OGLElSihtyKBSSZdloNNrt\nulR7C4fDGGOr1apH48FgUFEUo9Fos+mSmjAcDguCYLHoMocr2jEAAIIg6LE6+S2jIIcBwOl0\nOj03arnWN3f+889/G4snMUafe+rBh+7ePs/2x/6P0tV5phK1e0DtGwZy4waGs7QQiUJa2p8o\nEAioqmo2m/VYLQnFMgwDgMfj0UadzyCV841UNmRCiN/vBwCHw5HKacBcdfcN/fzF3z3z6D3r\nq0pSfItKyIcnL738uw8DoQgACALet3PT0w/vdTun7mfi8ti4RZvNpse2QAgDAEWlejQejo3F\n1nXqPKXU5/O53W49RoyqqhoIBADA5XIJQvo3NK3z+p1lBYNBWEDnWSKZbG1hsgoAUmWReU32\nxFdVsyUiYEd+DjYs9Lw69avjaDQaj8cFQdApaV48Hpdl2enUpbq31nlRFF0uXVKBxWIxVVUd\nDl1SvkQikUQisaI7L0nSPP6yv3z1AwBw2K0P33uXQZomMMXQ2JMOx0wXsPMW7fcCAELTnEsk\nO4cogOB2eHJz5tc4iyUS0AsATqcTO2Y/3OsUvuBSNFNg9JVXXjlz5kwqrfzDP/xDmvrDcVza\nnG9o+cEvX43FkwjBJ+6/641DJ2Z9CxkNAGOAeIJRjpsPlpCprGAAEooiGLsSQ6KA7Fok1IYd\nVjT5zC8Sjf/jT15KyorZZPwfX/nkDFFRjlvpjpy8+JMX3iKEGg3SN37/qa11VfNuihIKAKqa\njhnulKmDo2pnn1ZjATASXHbi03e4PTcnx842NHX0fnjyYiqBUcbYqYuNz79+eHjUDwAIwY5N\n6577xD25WUtTQau4IPdSY7tOhcVcTluG28EY9dwU8OWWMxZLJC+1sKQMCBmqi4XczCkLIKNB\ndVjQdKEijrs9DI36z15uAoBH9u2YNiq6hGg0rqX9FXKmbpvc7WqmVXB4ePiHP/yhdgd4Zjww\nynHLCmPwxqETL7xxmDFmMhq+9tkDOzatTSUwqtWjx3YbSuEGNcdxE7GkkrzULKvEJAoqxuac\nTOywIocVW8xwi9EqhNB/+ulLw94AQugbv/9kQW7W4naZ4xbJxAL0LoftL7/6qbKivIU06LBb\nAaAwb2FXLAzIqF9p72XaxH8EQqZbKiug0TgPjC4rA8NeABgY8c26ZH1z53/99r3uviHtYV11\n6acfv6+0cEEr2wLt2FTT0t61c8s6PRq3Wcy7tqxjwOw2XhtkxWDRePJSC5MVQMhQU8ILXnOr\n02vvHaeUmY2GB/ZsXeq+TKWVXQJR4EOFVo+ZAqNf+9rXPvOZzzz11FOHDh360Y9+9OCDDy5a\ntziOm7d4Ivnvv3pNK/Wbm+X55lc+WZiXPeu7AAAIJYEw8Hr0HDd3TFGTl1tYPJlQVZMotCcS\nm6uLZ33Xf758UEuu9OkD925eX6l/NzluCSgq+eGvXz9+tgEACvOy/+fXPpXpWehUSqvZBAnZ\nap1/QiHqDyltPTQ6lqIRux1SeQHWoktRHRPEc/NgNhkBwGScaZphc0fPC68fbmrv1h5WlKz5\n1IF711eWLEL3ZlZamPfHn/uETslhAOCB3Zt1apnTAw3H5MstTFEBI8PaMiFLl0nrHLfMBcPR\nY2fqAeD+3VuX3WQpBmTYBwBClnvWjDTcbWOWQct2u/1b3/rWoUOHsrOzi4tnv8bjOG5p9Q97\nv/ejF/uHRgFg07qKP/nCk1ZLqgcb4g1oZbJ5YJTj5oSpRL7cwqJxANBymeEUTqTeO3ru0LFz\nALBj09rH7rtL705y3JKIxZP//MtXtXBVXU3Zn33pGYs5DRnMBQEDgDCvvIc0GFE6emlwrM4D\ndlilsgLs4jORV6qe/uFXDh49eeGq9rAgN+vpR/bu2LQuxcSSdpslw+VQCMng6wCnMxqMJOtb\nQSWAkWFdOT/f5lattw6fVBRVEPD++aYa1w/xB1lSBgCRj+ZeTWbP5rBlyxY90qhzHJd25xta\n/u0Xr8YTY0lFP/WJe+eUb56MBgAAWc0o5Vgqx3GgEvlSCw3HAEAqL4CrHQCAbjV5/rrmjp5f\n/uYdACgpyP365x7XoTIEx80iGU+aAZL+kNTaDQIGhJAgAEIgYIQQiAIguP7MhJ9FAQBSL0n0\ng1+82j4wBAD33Ln5D557RFjSwRc0ElM7+ogvqD3EVrNYViBk6FIJhFsE/cPel948curiVa1C\neKbb+cT+3ffcuRnjOexSbRbz9o1VsqLeXJeJ49KIBsLJ+jYgBARsrK3Abl0q4XDc8hdPJN8/\nfg4A9u7YmLH8NgRtHj0yGTA/KKwmswdGJUnq6+vTqYoZx3FpMSWp6Nc/e2D7prVzbYL6gsCH\ni3LcnBCabGij4SgASKVrxMJcLTA6s1F/8B9/8pJKiNNu/Ys/fM7IU/pyi07tHYqEo2arJeAP\nm1Q6+xtuNk2o9MbPZNgHBgkAJKJghD914J5P3L+Uw6JZPKl09pERHzAAAGQySCVrhJyM2W5h\ncMuULxB65eDRIx9fJJQCgN1meezeOx++Z4eUcsh+okf2bUt3BzluEuILyg3tQCkSBUNdJXba\nlrpHHLdk3j16NhZPIoQevWfnUvflJioh3gAA8DOE1Sal+l9ZWbwcBMctX/FE8ge/fPVcfQsA\n5GZ5/vwrzxbkzXmbJf4QUwnwwCjHpY7SZH0rDYQBQCzJF4tTqu8hK+o//eSlUDgqCPhPv/T0\nnG6VMwb8LI1bKErl5i4y5I0pCgD4ksn8/CygjFEKlAEhjDEgKYRKGWhHDVBUAC3eOOFDojEw\nOAHg69s3W0xGQ5abDHqxx7H4lf2YrKhdA2r/CDAGAEgSxeI8MT8b5jKokFs+IrH4G4dOHDxy\nWlZUADAZDQ/uveOJB3dreUg5bhki3qB85XpUdEMVdliXukcct2QUlRw8choAtm2oXpO77Gq+\nkxG/dgrE59GvNikFRjmOW7b6h0a/9+OXxpKKrq/4xheenF8Ga60ePTIasJ2frnFcCiiTr7SP\nRUULcqSS/FTexBj88Nevd3QPAMAXP/nw2oq5Je82GQ0gqzw2ys0bS8jylTYt8wPGGABEs9G4\ndbp62RNCpcAYIxTYhLApY2NRUVV7lQADoihDw96RYX8imVQZlLjHpqgLjJFhn1bKANvM2O0U\nPA7stOsemlSJ0jOo9g6NxXkFLBbmSgU5MK9BhdySo4y9/t6J1947HosnAEAUhLt3bnz20X0O\nft7CLWNk2Cc3dgJjyCAaNlSNVXjjuNXqo1OXAqEIADx2351L3ZdpqENeAMBOG88st9rwwCjH\nrWDn6lt+8Mv5JxW9gY0lGOXDRTkuJYzJV9uJNwgA4ppsqaIwxfe99t6xE+euAMD+vdvu27Vl\nrh9blJ8N1/ox4iUyufmggbB8tYPJCgCI+VkQicy0NEYIawFEEWYbqTzqD7770dkPTlyIxMbq\nuW8vKdxRkAsAhnXlIiHUG6CRGDCgkTiNxNWeQcAYO22C24HdDmxPd6SAMnVwVL3Wr/2ygJCY\nlymW5C/+eFUuLShlANDS0XO15RoACBjvu3PTUw/t9fBySdzyRoZ8cpMWFZUMG6uw1bzUPeK4\npUQpe/P9jwFgfWVJZWnBUndnKpZIaiMeBD5cdPXhgVGOWy4URT1/pa2ipLCkcPYJuZSy/37z\n8BuHTjAGZqPha597fPvGmpnfYjQYkknZaDRM01oool098sAox82OMbmxc+xeQm6mVFGU4vsu\nN7a/9NYRAKguL/zsUw/M45O1Gx9LW76GW6HUMLrpPwAAIABJREFU/hGltRsYA4ykymIxLxNa\nOhfebGfPwNtHTp8426ClegSA0sK8h/ZtzzUYIBABAGQ1SR4nlOQzRaWBMPWHiDfIkjJQSv0h\n6g8BADJI2O0QMl3Y7Ui9rNP0GJBRv9LRy+JJ7Qkhyy2VFSAzn2e9IhFKj56+fLa+GQBUlSAE\nOzevf/axfblZnqXuGsfNQu0fUVq7gAEyGYwbq/leiONOX2ocHPEBwIEHljLt+K1oZZcAYyGb\nH2JWHR4Y5bjlYmDE99MX33lg95YvPffozEtGYvF//fkrl5s6YC5JRbfWVpy62HRHXdXNL43V\noxcFzAdfcNxs5JYubVKwkOU2VBenOLG9f9j7/Z+/QinL9Di/+eVnRYFP5uUWC6VySzcZHAUA\nZJQM6ysWnuFOUcm5+ubfHT7Zeq1Pe0YSha111Y/eu7OiZA0AtF1snvIWJIlCllvIcksALJ4k\n3gDxBmkwDJQxWSFDXjLkBYSwzYzdDsHtwC47zHEOBPWHlPZeGolpD7HbIZUVpH84KrcoGINT\nF66+9NaR/mGv9ozNav7rP/5MaQo3jzluyal9w0prNwAgk9G4sYpHRTkOAN44dAIAitbk1NWU\nL3VfpqFqp/eZroXeo+VWIB4Y5bjl4vjZegA4fvbKzIHR7r6h7/3kpeFRPwBsXl/5J194IsWk\nok/t33Xgvulr/2nV93CGa65XoRy32igtXWRgFLSo6LqyFDeZeCL5Tz9+KRZPGCTxm1/+pIOn\nGOMWC0vKckM7DUcBADtthvXlC5xOHghFPjp1+eCHp/3BsPaMy2G7b9eWB/duS33FRmajWJAj\nFuQAoTQUIf4Q9YdoOAaM0XCMhmNq9yAIWHDZcYZL8DiRaZq5DhPRUFTp6NVmwAEAdlil0gLs\n5rf6Vqr65s7nX3u/s2dAe+iwWUKRWElBLo+KcsuH5A0xjMDtvvkltXtQ6egFAGQxGTdWoelm\na3Hc7UFW1J6BkZxMj9PpnHnJ+uZOLcn+4w/ctQyvOGkwwmIJ4PPoVyseGOW45cJkNADAzGVV\nPz5/5Ye/fiMpK/NIKmqQRIM0zSZPo/GxwwCfR8+tDj/4rzdrq4qffuSeub5Rae9V+0cAQPA4\nDWtTjYoyxv71P3/bOziCEHztswf4hT23aGggLF9tZ7IKAGJ+llRRtJB6R2Oz5s81EDJp1vyu\nrbXzT+8gYOx2YLcDAFgiSXwhbX49UwkQSrxB4g0qAMhs1LKRCh4HTB5tzWIJpbOPjPi1h8hi\nkkrXCFnThCq4FaGls/e/3zh8tbVLe1iQl/X0w3vPN7QePX15aTvGcRMxRZW8IYYxFNMpOyXl\nWr96rR8AkNVs3FjFUxtzt7e+wdH/86OXf//pB/LzcmZe8o33jgNAdoZrx+bpSj4uNTLkBQBk\nEAWPY6n7wi0BHhjluJVhSlLRr3/u8W2zJRVNteXRAAAAxoJnlht9HHcbuNY71NzRG43F5xoY\nVTp61Z5BAMBuh6G2PPUA0/OvvX/hSisAPLl/z51b1s+1wxw3P9MkFZ1fO4Scvdx88Mjp5o4e\n7RlREO7YUP3wPTuq0lo5AZmMYn4W5GcBYzQSp/4Q8YdoIAyMsXhSjY9A/8h4ySat0DwKxxJn\nrgBjAICMBrE4T8zL5FMflsSRk5djieSnH79/3i309A+/cvDoyQtXtYdZHtfjD+66587NGKPz\nDa1p6ibHpUk8iWQVATBZgQmhT6W9R+0ZAgBstxg2VKHpRiRw3O3krSOnAeDgh2f275t+YqKm\nq3ewoaUTAB67704BL79c+ZSOpcnKzuBnEasT31lz3AowMaloXnbGN7/yyYLc2ZOKpmishozH\nAbyiC7cK9AwMM8b6Br1zepdyrV/tHgQA7LQZaysg5VO6Y2fr33j/YwDYWlf1zCN3z7W3HDcf\nhMot18iQDwCQ0WBYXz6/pKKhcPTIyUvvfHTGFwhpzzjt1rt3bty/d5vHped4CoSw3YLtFrEo\nlykq9YeIL0R9QSYr4yWbxq5a4kkAQJIoFuWKa7JT3zC59IrFkx+duRJPJPffvSNn7mWR+odG\nX3rrw1MXrzIGAJDhdjy5f8++Ozctx4tnjrs1pa1b7R0GAGy3GjZU8qgotxpYzSYAsFpmSez2\n6rvHGQOH3Xr3zk2L0q+5IaMBphIAEHL5PPpViu+vOW656+ob+sfrSUW31Fb+8eeftKQvgztL\nylruOT6PnltVUs9BAQBq75A2LQ47rMYNlanfQrjWO/iT598CgPyczD/63BNz+lCOmx+WSMoN\n7VoBonknFb3WO3jo2Lmjpy/Liqo9U1qYd9+uLXu2b5g2JYt+kCQK2R6tPiyNxKk/SHwhrWQT\nAABCYmGOWJTH6yQsrWg8PuILAMCwNzCnwOioP/jqO8eOfHyRUAoAdpvlsXvvfHjfdolHlLiV\nhTH5egpy7LIb6yqAl1jkVgeMEQDgGe9jDY/6T19qBICH7t4+p7MIhJCAMWNs5vYXTptHj61m\nzMsArFb8tIPjlrUT56786P/OM6loKrThooAQzuCBUY6bhto3rLT1AAC2WQx1lalf6gTD0e/+\n6L+TsmK1mP7yq8+l8X4G0kZVcasAvjYABgNUl6S4PPWH5KsdTFEBQFyTLVUUzmlGGGPswpXW\ng0dO1zd3jnUAo03rKh7at6OuunSOfU8/bDNjm1kszAVCY2fqUUJhHodUls7p/NyiCUVibx3+\n+O0jpxVFBQCz0fDA3jueeHD3zJnWOW45YkxuuqYFVgSP01Bbzkevc9xErx86QSkzGqQHdm+d\n0xsz3I6a8kKvP1RdVqhT3wAAGCO+EAAIufNMOsTdBnhglOOWqSlJRf/o80/csaE67Z9CRv0A\ngJ02Pt+HWz2+/9h9H/f0p7IkGRxVWrsBAFvNho1zSBbGGPv+z172+kMYoz/5wpO5c59bOi1k\nlBgANfD6tqsCiydRIIrEOFNJKiMiF5JUNBZPfnjq0u8Onxz1B7VnLGbT3u0bHr1vZ6Z7+aWf\nFjAIAoDCM8DcDDusgBFYLQsptKWrSCx+8Mjp3x0+GU/KAGA0SPfcufnJ/bsd9lsmfEgkZQBI\nJuXF6yUHAABMJTguM5WAgW9rt8BAvtqh1X8TMpyG9TwqynGTBMNRrXre/bu32qzmub79S88+\npFyfvKITpBBgDBASctJzus6tRDwUwnHLUSQa/5f/fKW+qQMA8rMzvvmVZ9focAuLqYQGIsDn\n0XOriYeB3SjdWZg/65JkyCs3XwMAZDbNKSoKAP1D3sa2bgD4vcfv37SuYr6dvYnTRg0icc0n\nXyS34rCkjAgB7b+ZA6OUyi1dZNALc08qqhLyi5ff+eDjC0lZ0Z4pLsh9YPfW3dvqjLyY8gqE\njAaS7RZslmVYPiIpK+98eOa1947H4gkAEAS8b+emZx652+WwzfzGDJcDADLcvFLwYmOhqLm9\nn1kssALnFbFEUvSFmc2BTDreTSTtPTQQBgAh22NYW7oMtzuOW1pvf3BKVlRBwA/t2z6Ptzvt\nVkpp2ns1iaICgOB2zCP1EHfb4IFRjlt2uvqG/vHHLw57A6BDUtGJ6GhAK+bLA6Pc6mFgAICs\ns536kBG/3HQNGCCTwbixKvVTJQwIAPzBEADs2b7h0XtnqtE5ZwjFqwp4rlJuIpaU5YZ2LVv0\nnJKKUsoAYMQbPHi+AQAwRnfUVT+0b/vaimJdO8zpjeRlCMtsXLlKyIcnL738uw8DoQgAIIR2\nbFr7qQP35mS6U3n7rjtqz1xu2rN9g87d5KYi3QMAQLoHxRUYGKXegLHfS202nJ+2gqXTfIoW\nFc3xGGp4VJTjpoonku8dOwcAe7ZtWI4TUAAAAFEKvOzSqscDoxy3vMiK8rf/+HP9kopOpCUY\nxTYL4im9uFVm5iSdxBeUGzuAMWQ0GDdVpz7YJBKNayVEZELLi/O/8qlHF9xTjpsJDYTlq+1M\nVgFAzM+SKopSmUA9NOr/r9++t9/jAQCMkNViuvfOzQ/u3ZbpWaYXLdzKxRg7dbHx+dcPazUk\nEYLN6yufe+yeojU5qTdSXpz///0/n3G7U4qicumkjVVfmcXN6LAfAMiwX0xvYJQxGomzEf/4\nE2J+llRZDDwoynE3OXTsXCyeQAjmPVAAdfYLsgpb1qa3Y1OJgrACb/9wacQDoxy3XGjxlHA0\nDgAWs+kbX3hy0/r0zcC9GaXEHwQ+XJTjJqP+kNzQDpQhg2TcWDXrbYP+odGWzt7m9p6Wzt6B\n4dEDNRWPr6s8NzDyd//zy7ywMqeriUlFDZXFQgpJReNJ+dV3jr79wSlFJU8/eDcA5LjsP/hf\nf8ZnzXNpxxicunj1xTePDAx7tWfqqks/deC+sqK8pe0YNwfavfkVOhBSEgEAGdJxIGZAozEa\nCGv/MZWMvyLkZEhVfJQ9x01DUcnbR04DwNa66oK8+dyfYIqKghEgjEXjaO75SVMnZrl51vJV\njl+zcdzSI4QeOXnx7Q9OaQ8L8rL+/CvPpqtayy0/1BcCQgEAZ/EhGBw3hgYjyYY2oBRJonFj\nFbKYbl5GUdSO7oHmjp6Wzp6Wzt5wJDbx1d+1dLzX3rVr+0aPy75YveZWn7knFWUMjp65/Pxr\n72tzmY0G6XjfwFM15e1yciOPinLpVt/c+fxr73f2DGgPK0sLnvvEPesrS5a0U9yyYxgJMgBY\nrmOBaTROA2HqD9NgmE0p/4KQlo0KF85h7DPHrSrHTl/2B8MA8In775pnE4SCQkCLkKaxZ9eh\n69s1r0fPLVJg9LXXXnvnnXfC4fCWLVu++tWvWiyW1Bd7++23/+M//mPiYt/97nerqqoWo98c\npzNK2bGz9b95+yNtihkAGCXpf/35l0xG3bODafPokcmI9bz/xnErCA1FkpdbgVAkCoYNVRNv\nTQfD0fauvo7ugZaOnqaOnpvrY2ZnuqvLCqvLCl9664NgOMaLhHD6mUdS0fau/l/85p3Wzl4A\nQAh2bFr3mSfv/+EvXvm790+UV+sy1sntskMgDAAm48qLugqZLpBEsFvQypxBvLRaOntfeP1w\nY1uX9rAwL/uph/fs3LxuaXvFLUNMVkRfCAAxRQVhuWxrLJ4k/hANRmggzJLypNcQwjYzdjsE\nt0MlhDS0L1EfOW4FYIy99cFJAFhbUVRVWrDU3ZkGDUZQJA4ADCPsnKUG4HxIIjNKiDCk/3U9\nt3CLERh98803f/WrX33lK1/JyMj4xS9+8fd///ff+ta3Ul9scHCwsrLy6aefHl8yL4/PweFW\nPMbgwpWW/37zSHffkPZMptsx6g/ZrOZFiIoCY9QbAAAhi8+j55adwycuvPXBqW//xR847ItX\nfp1GYvLlVq38t2FjFbZbhkf9TR09LR09ze09fUMjbHJeUqNBKinIrS4vrC4rrCortFnGoqi/\nefvDResztwrNNamoLxB+4Y3Dx85c1lbg8uL8Lzy9v7K0AADiKukKBIvZzBl358nlsGkRBYO0\n8gKjAKBWFIh8IO3cvXLwWFP7WEg0Pzvj8f2792yr4/XiuOnJKlIIAICiwpImu2fxJA1GaChC\nvMEZgqHYaQN8fbKtN7D4/eS4FeTMpaa+wVEAOHD/rqXuyzTIwKjc2gWUAQDoc+mNJBEyXVRV\n0Qq8Q7wK6R4YpZS++uqrn/zkJ/fv3w8AWVlZ3/jGN9rb28vLy1NcbHBwsKqq6q675jsAm+OW\nnylTzCpK1jy5f3dX39CLbx5ZnA7QYESbEySkVhCW4xbT8bNXRn3BM5eb7tu1dXE+kUbj8qUW\nphKK0WVVOf3SwYaWzkg0PmUxl8NWVpRXXVZUXVZYXpIvLpsRLtwqMaekoklZeefDM79952gi\nKQOAx2V/7hP37tm2YXGCVNgogYBBFBBekUm7mNkAKzOku7S0qGimx/n0w3v3bt+IUygFxnFL\ngiUVGopQf4j4giwxfTAUO22C075Ca09x3NJ68/2PAaAwP3vjOj1rZswDY0pnn9o9CACAEVDG\ndNvGWX4mpVSnxrn00j0wOjAwMDw8vH37du1hcXFxdnb2xYsXpwRGZ1hscHBw/fr18Xg8Eolk\nZmby287citbc3vPCG4eb2ru1h9oUsx2b1iEEXdeHji4CohWHNYizpqXjuMU3GggBwIgvuAif\nFQhFejt7C3xhI0Iyod//6GzjiHf8VYxRfnZmdXlhVVlhWWHe/DLHc1wazCWpqFb05tevHhr1\nBQHAIIkP7dv+5P49izEd4TpkNTObGVlMPKawqpjNpice2PXwvu289By3DDFZoUEtGBpiieSk\n17RgqNOGnXbsdvA0Ghy3EFdarrVe6wOAAw/ctayCN0xR5asd1B8CAGwzE0lA/shSd4pbFnQ/\na/H5fACQlXXjYjIrK0t7MsXFBgcHDx8+/LOf/YxSarfbv/jFL95///3ji7W0tLz88svjD+Px\nuMViiURmX78ZYwAgyzIhZNaF54FSmkgkFEXRo3Gt88lkckV3XlWnJulLC63zsizPvujcfXS6\n4XJTx5996al5dL69e+DVd483tfdoD/OyPQ/dve3OzWsRQtFoBAC0PjPGUlmB5037y+JhPwKg\nDmskGk1Xy4yxeDyeTCZnXXLmb49SqqpqKl/C+C24aPp+iynt6/fnGN94I5GIHvd7Fq3zerQv\nChgAKCF6tK8SClgAgO//9OXWrj5RIX9193ajyaQy+u8nLzSOeE1GQ3lxfnlRXkVxfllR3sRq\n3an3R5ZlPTqv9zdPCKGUptj4zBsyISTFDVn7pVJceB5UVUUI6botKIqiR/tKOKwV/wp5fYa+\nUYgmAIDZzKR8jYoZ3OITu/qGXnjzSNu1fu3hhprS33v83gyXQ1XkiDLNkTH1v/hcycU5GGOi\nT+OIMaRn51VVpZTqd5YFuq02AEAISfEUMZWTmVS2zUQiof3wh596uLaqJJlMpHAuMGexWEyP\nwyW7nk0iHo/rN/wiGo3qdKzXfojFYliH0dmYEARA9Nk/s0RCuxCNx+MIpT+nh9Z5VZaVrj4I\nx1A4hpKTL20QgNnEHBZmtzKbmYzXpE5MnS9yM5ZMap1PJBIA6R8Rpq2WKZ7fputwrF366b1f\n1a9xWLGd1755os95L+jc+fG90MT2f3vwIwDwuOx1lcUL+tykot2jiMfjTFzoLhQlZNTWixIy\nADC3XS3NZ9f6BT2vvlVVTb1xnWIjXIp0D4yGQiEAMJtvlLAwm83ak6ksFg6HGWMVFRV/8zd/\nYzAY3nrrrX/5l3/Jycmpq6vTFuvr63vllVfG31VeXq7FxVLsnqIoOoX/AIBSql/jAKCqqn7b\nj67fjN7t6xQVBYCTFxu7+0farvUVr8lO/V1dfcMHPzrb0DKWdcvjsj+4e8udm9dijCaGEcf/\nmqmvwPOgKAoJRc2yAgBJq4mk9bNS/OZnnlPAGJvTVgw6f2O6Ng4AqYSS503vzuvUvnY9oKpq\nGtsPR+NN7T1NHb22aLJm41oAOHmx0WMx/fndO10mE6HsvcHhmi01jxbty8v2jF++MkoSiblF\nRrTOK4qi35fPGFsO6/zMGzKldE4bMiFEpyCURqfDJU4qUiimuqwJHTo/0D+sTT+LXunUMnWq\nHnsyzwNEBTLNrxOKxH535MyJ843aSliYl/X0Q7vKi/LgFn9TbTFdVyftRpceLWvni4yx5Ars\nvEbXs7gUO5/KFL9UNuQbR389VyddD5d6t69f44beUQSQLEhnSWWkUjESw6EYCscAAAUiwsVW\nahCpQWIGkRokahCpli5jAbCiaBeisiyzdAR1EWOQVLBCsKKipCJEE1rnUWBSVIIaJWIzEauZ\nWE03fgVFhrlclwiqOt55qkNgVLMkh+Nlco4xP3O9gpgrXRvX75v/v28cGfEG/vT3n9Cj8ZvP\nJfqGvFfbugDg3js3Koq8kCt+JKtazW5ZlkliQbsJIRw39Y4AoQCgZDnlbDfISeP1G2O6/mVT\nPL/lk+6Xlu6BUZvNBgCJRMJqHZvzFY/Hc3JyUlzMbre/+OKL44t9+tOfPnv27AcffDAeGPV4\nPOMT8AEgGAwihKQU0kJpUTlBEPS4vwoAqqpijPVrnDGmX+cVRdG1cdDzm9e181oO6YERX0XJ\nmlSWHxj2vXn41Pkrrdpe1+20PbB7655ttdJ0M3TG+5zKCjwP2jcviqIYDYFWgM9lS+MXpSiK\nKIqpDIiYeRmEUIpbMWNMu/DT6RvT2tep8fGxSLzzN9PWEEEQFtg+obRvcPRyU+flps6egRHt\n1G1P8VhlzJqC3K9trXOIIiDEynLv21q18J6PW3jnp6X3N6+1n2LjM2/IGOMUN2RtmCrGWNAn\nZ6v2jenUOBoNSUN+MIiQaZ596TkSYex8HakUjJgUZlOPfdovVCXko9MNrx/6WEsn6rRbH7t3\nx66t62fO8Kj9BVP8M82DNlZXp29+nH6d1/ssTr91PvXOp3jInvVLHv9FdNr1wVzOMeZq/FxC\np/ZBz86DrEiROABQBrDgcmEolsTBCA7HUCwBU0ZwqgSrBMcmh3dFgRklZjQwg8hMBu3nOURL\n5bFggSRJbE6rDWNIIUhWUFIBWUGyipIKkhVQbnkzgJmNzGamNjO1mbX8HhhgIZs3MzEmCgwj\nbDQIOsy7n9NZXLrOq7XDMeh5jkEpFUVd4g9a5xFC+rXPGFuJnY/Fk1dbuxMJedgbXJObzjso\nmvHVb3y1ef/ERcbAajHt3b5hgWlVEBtrXBRFvIDVUhjyCwOjwIBhRIpzmcs2pS2d1vk5nYLy\njJFLS/fAqNvtBoDR0dHxiKfX6928efP8FgOAgoKCQOBGHcCNGzf++7//+/jDr371q5IkOZ3O\nWTvm8/kopSaTaeIw1TQKBAJms9lo1KXGos/nY4zp13m/32+xWHTqvNfrZYyZzWaTyaRH+36/\n32q1Ggz6VJdDCIClso71D42++u7x42frKWUA4LBZHr33zpmzbmlfCEIolRV4HrzDI4wxs90M\n4TgFEDPdFnc6Ky/5fD6r1ZrKcWXmZTDGKW7Fsixrg80dDocexxJFUUKhkE5/jmQyGQ6HYYV3\nXqf2tS/EaDTOr/3+odHLTR2XGtsbW7uS8qT71IV52Wuu16v5q3vvYtE4AEhVxeYZi9jMidZ5\nk8mkx5eTSCS03Av6/WVjsViKjc+8IWvxkVSaCoVCsixLkmS321Pt6FyEw2GM8fjZRXpFm7oA\nQAzFreXFaWmQJWXqD5NAiPrDeNgPTgcAIINo3FyN7dP/CucbWn7xm3eHR/0AIAj4gd13PPvY\nPnPKJZ4xxjqtTqFQSBRFi8WiR+Ox67dP7Lp1XpIk/U4RVVU1Go06rZbBYNBgMKTS+RQP2bOu\nIXF5LCCl066PMeb1eu12ux6hZEKI3+8HAJvNpkdUQuu8w+HQI86uhCKqSgDAYjBKznntQgkh\nvhDxBak3yCYfMbHdwghlsQR2WIVMN4snaDzJ4skbddtVglSCopNGWiFJRGYjMpuwxYTMRu3n\naVN2Kgxr643FYhFvsX9jisoSSRZPsoTMEkkaT7JEkiVkYDNOvccYm41MUZisYofVUFuBFhw1\nntp5RYm7/ICx3eXUb7VM8RRx5g1ZFMUUD8fRaDQejwuCoNNBIR6Py7KsU+N6dz4Wi6mq6nA4\n9Gg8EokkEgmdOs9QJBiOAgBhupw9anu28VPTYW/gfEMbADy8b0dWZsYCG2cJWdu/WK1WPL9d\nHKVycxcZ8gIAMhuNtRXYeuPgGMVDoOfVdzQa1bJBprKwTsFZLkW6B0YLCwszMzPPnz9fXFwM\nAIODg4ODg1u2bElxsYsXL/7bv/3bt7/9bW2QKWOso6Pjjjvu0LvbHDdvo77gq+8eO/LxRUIp\nANis5k/cd9f+u7cZ031ONleGQZ/gD8OmahqNA4CQ6Vra/nBcGiWS8tXWa+cbWi81to9Ortpk\ns5prq0prq0s3ravIcDvqPzyjDYQZj4qK6YuKcquOdrW/sOFCTFZpIEQDYeIPs/g0k7mktaXT\nRkX7Bkd/+cq7lxvbtYdbais///T+nMx03vHiOO62xOJJ4g9Rb4D4Q0AnBBkFjB02IcMpZLmR\n0ZCsb2WxBBgksSj3xjKUsaTMEkkajbNY4nqwMjl2bFVUpqgQik6cO4pEAZmNyGTU/sVWM7JO\nDtyPt6m1Nv6vOssU1Kktaz+bjIAgebmF+ULIZEx7VFQj53oAgNcw5biJ3nz/BKHUaJAe3LP0\nERuWlOWGdhqOAgB22gzrK5CBFwbkpqf7moEQOnDgwAsvvFBYWOhyuX70ox+tX7++oqICAA4d\nOjQyMvLpT396hsUIIRjj73znO0888YTb7X7nnXdGR0cPHDigd7c5bh68/tCb73/8/vFzikoA\nwGw0PLD3jscf2G0xpzRyR1EIAMi3nge0QEIwigBY7xAAAELYo8udMY5bNJSyrr7B+qbOhuaO\nq21dhNxIzYMxKl6TW1dTumV9VVVZwaQBF4oK4tg1klRRJObzQvPcUiCUhiLEH6L+EA3HpryI\nzEbB7RDMEsQVAEA3jWKLROO/efujd4+e0SYlrMnN/NyTD25cVz6nLmh5v/Q76HDcSoHjMjAG\nt/39YspoMEy8QeINTrkHg8wmIcMpZDix0w4zpuAAAMBIGwqK3RNGzxFK4wkWT7J48voPCXa9\n2BFTCQvHYPK+bnzPplxuVWbbESFJvB79NEwIgBqATz7luGUjFI5+ePISANy7a4vdpst8kdTR\nYES+0q4NhBfzs6TKIr674GawGCHzJ554QlXVn/70p5FIZNOmTV//+te150+fPt3W1qYFRm+1\nmCAI3/nOd37605/+7Gc/SyaTa9eu/e53v+tO6/xfjlu4UCT21uGP3z5yWjuxMxqk/XdvO3D/\nLqtlDukCSgpyAaC4YA5lneYEEQoALBwHAMFtn3ZaE8ctBzvzc3Zv23jxFq8GQpHLTR0XGlrr\nmzuisUmXdtmZ7rrq0trq0g01ZRbzpK2PyQoZ9KoDoywYgQw3AEhlBaJumxvHTUMLhgYjNBih\ngfCUqaBaMBQ7bNhtR0YDAOAGGeLBqW1NnGymAAAgAElEQVQQ+u7Rsy//7kgsngQAm8X81MN7\n9+/dNnM60Wl5nPae/lGPU5eJgRy3YjBm7PcCIZB7ex4RmKxQb5D4gsQXgok1QDDCTrsWD0Xm\nBae3EjC2WWBKKOTmaGksMT5hn43XB5sYFcUYmY3YZBgfBIpMBmw2gs7ZijmOW7i3j5yWFVUQ\n8CP7dixtT9T+EaWtGygDjKRKPjmMm90ijSV+5plnnnnmmSlP/vVf/3Uqizmdzm9+85s6do7j\nFiASjR/88PTvDp+MJ2UAEAXh7p0bn3nkbpfDNtemaqtLt6yv2LWtToduTpCQAQDzuZbcMrY9\nPyfLalkzXuYYICkrLZ29Dc0d9U2dnT0DExc2GqSq0oLa6rItdZUFuTcN/2SM+IJkYJR4g1Pi\nUJMmBnKcThijkTj1h4g/RIPhSVNWAZBBwk4bdjsEjxOZZk+NXd/c+cvfvNM7MAIAgoD37dz0\n3GP3zHtQxs7N63oHvds3prPs2OJRCQCg1Cq9ctwMWDyJ40nQ8qvoMOeaBsLWxm66TsQZizgk\nlQENR4kvSL2BKWPSkUHCGU7B4xQ8Dt2jjTNGS1VfkA6MAgDOzxKdtrFI6FInnuI4bn7iSfm9\nY2cBYNcddZlLODGRMaWzT+0eBAAkiYb15dilSwp77jbDkyxw3DwlkvK7H5197b1j2rAd7Rr1\nqYf2eua787WYjX/w7H6bbc4R1TliACBk8Hn03PJlN0gAYECod2DkfENrQ3NHU3u3MiHRGEKo\npCC3rqa0trpsbUWRON2lHUvKZMin9g+zhDz+NuyyI0W+eWGOS6frwVAaDJNgBCbnyEOSiF12\n7LRhpx3bU41p9g97/+uV9y5cadUe1lWXfv7p/QV5C0oEsXldeW1lkU6FEPVGHRZhJEjmV4qB\n4xYRGfICoWTQKy5CYJRQEghRb5B4A+PT2DXIahYynEKGCztssLTTSa9HS6kkaoFRIS9TuEXx\nJY7jVor3j52LxhIIwWP37VyqPjBFla+2U38YALDNYqitSOWuM8cBD4xy3DyohLz+3onXDx3X\npvEihHZsWvupA/eulJIX2GHV5mly3DKkKKpCKAA0dfa88vqhiS95XI6Na8s31JTVVpfarLco\nuEwZ8QbU/hHqD40/h8wmIccj5mYikwE+DMKMxWw5bn7Gipn4Q9QfmlowRKtn4nZgtyP1YOi4\nd4+eeevoOZUQAMjLzvjskw9sqa1MV7dXKOKyUwBs16VqPDdXAsaSJFLKDBK/spiKafPEFWW2\nBReEjgbk7sGbKykJLjvOcAkZLmScz0hMxtj4vyuP1usV2nmOW2kOfngaALbUVhXmLU1aEhqJ\nyw1tLJEEACHbY6guAQEvSU+4lYifvnC3FULo+SttZUVrSovy9fuUl393NJGUAQAhdNfW9c88\ncndulke/j0s7YYUEcLlVhTHW2NZ99PTl05ca//bunQCgFVMyGqSaiqKNNeV1a8ummSk/sYVo\nXB3ykoFRNp6tDGMhwynmZ00qEKEzbecgy/peA3PLhCwrJgDFH04cv8imFA8RsOC0Y7cdu+zY\nZplryn/GIBCK5AEAwNHTl1VCrBbT0w/f/eCeOwR+og/ArCbFKJp4GG558Lgc5UV5/mCkrChv\nqfuy7CBtTgNO02bLgCWSNJ5gsQSLJdRwVHua9gzd+ESLSchwCRlO7LQttNiIQgAA1BVZoo2p\nCsD1X4HjOD2FIjGvPwQAn7j/riXpAPEG5cYObY6OWJIvlegYCuBuS/yEkrutBELhn/z3wQd3\nb01vYDQUiV282nahoVWL1GiBj7rq0s88cX9xwcpLUyhk3vaFVzndXWzs8DjtmZlpyGXe0z98\n7Gz98bMN3gljPAEgw+X46z/5bE1ZoTRz7EMl6rCPDHlpMDL+HLKaxZwMIS8TLXrc5I4N1UdP\n12/bWL3In8stNsbU/pGwP2SyWPzBSK42DhQj7LAJLjt2O7DDOteQBKG0q3eosa2rqb27qb27\n0GJZu3c7AGCEHtiz9ZOP7FvyGq8cdytffnb/Sh1XuJypRIuB0thYJJTGE1NSFY/BWHDZcIZL\n8DiR2ZiuzxeL85SGNnFlxrvFghyl8ZpQkLPUHeG4218gFAGA6vLC6rLCxf90tXtQ6ewFBiAI\nhrWl/FKXmwceGOVuKx+evAQAH52p/+JzjyywKcZYR/fA+YaWC1farvUOTDzbL8jL/KPPPVFa\nuKJOE8d/AUlClhWZUY5bPhJJ+ZevHKqrKtmyYe28G/EHwycvNJ66cLW5o2f8SbfTvmPTWowx\nXC8xP0MLNBwjAyPqkBcI1Z5BoiBke4S8rHnMVk6Xvds3eBy2/BkHt3IrHfWHlbZuGo2Hk3KW\nxeJLJApqywWXHTttcx0Xpqiko6u/sb2rqb27pb1Hq+M3xjK2Gv/R559cu648jf3nuLQzr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tsCRoxqgx2G/WTUDxOTuVjNyCBRfwgkUaoo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\nJz00kmGIiMmlTCMraiajZiQ1I2b/0cAXkpoRRyx6Doyzl+S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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 216, + "width": 900 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "g <- ggplot(km_disease_10y_60, aes(x=x, y=est, ymax=upper, ymin=lower, colour=sex, group=sex)) + \n", + " geom_line() + \n", + " facet_grid(.~disease) + \n", + " scale_colour_manual(values=c('#586c82', '#f6bfcb')) + \n", + " geom_errorbar(width=0.005) + \n", + " theme_bw() + ylab('10 year outcome') + \n", + " scale_x_continuous(breaks=seq(0, 1, by=0.2)) + \n", + " xlab('disease score') + \n", + " coord_cartesian(ylim=c(0,0.25)) + \n", + " theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5), strip.background = element_blank())\n", + "options(repr.plot.width=15, repr.plot.height=3.6)\n", + "g" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "923d62a1-26e5-46ae-b29e-f1d96ff28cdc", + "metadata": {}, + "outputs": [], + "source": [ + "#tgppt::plot_gg_ppt(g, out_ppt=here('figures/ukbb_disease_10y_outcome.pptx'), \n", + "# rasterize_plot=FALSE, top=1, left=1, width=13.3, height=4, overwrite=TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce1f2b7c-a872-45a7-b27d-8d4f86f7f7a2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R 4.2", + "language": "R", + "name": "ir42" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.2.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/snps.ipynb b/snps.ipynb new file mode 100644 index 0000000..3633594 --- /dev/null +++ b/snps.ipynb @@ -0,0 +1,463 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b414298a", + "metadata": {}, + "source": [ + "# SNP analysis\n", + "After running GWAS on longevity, longevity with disease score covariances and all diseases, we run finemapping of the longevity with disease covariance. \n", + "Finemapping is performed via PolyFun. \n", + "Follow insuctions at: https://github.com/omerwe/polyfun \n", + "Finemapping results file was saved in output directory: output/finemap/polyfun_agg_longevity.txt.gz\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2a4f6ac7-d2df-4ff3-983b-847ebed6feb2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(rsid, allele1, allele2)`\n" + ] + } + ], + "source": [ + "source(here::here(\"code/init.R\"))\n", + "source(here::here(\"code/gwas.R\"))\n", + "options(tgutil.cache=FALSE)\n", + "pvals <- get_gwas_pvals() %cache_df% here(\"output/all_pvals.tsv\")\n", + "snps <- pvals %>% filter(longevity_disease_covar_pval <= log10(5e-8)) \n", + "finemap_res <- tgutil::fread(here(\"output/finemap/polyfun_agg_longevity.txt.gz\")) %>% \n", + " separate(CREDIBLE_SET, c(\"chrom_locus\", \"start_locus\", \"end_locus\", \"CREDIBLE_SET\")) %>% \n", + " unite(chrom_locus:end_locus, col = \"locus\") %>% \n", + " as_tibble()\n" + ] + }, + { + "cell_type": "markdown", + "id": "125bef89", + "metadata": {}, + "source": [ + "annotate finemapped snps" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5c7f8a45", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, allele1, allele2)`\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 11 × 2
chromn
<chr><int>
chr1 2
chr141
chr161
chr181
chr2 1
chr3 1
chr5 2
chr7 1
chr8 1
chr9 1
chrX 7
\n" + ], + "text/latex": [ + "A tibble: 11 × 2\n", + "\\begin{tabular}{ll}\n", + " chrom & n\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t chr1 & 2\\\\\n", + "\t chr14 & 1\\\\\n", + "\t chr16 & 1\\\\\n", + "\t chr18 & 1\\\\\n", + "\t chr2 & 1\\\\\n", + "\t chr3 & 1\\\\\n", + "\t chr5 & 2\\\\\n", + "\t chr7 & 1\\\\\n", + "\t chr8 & 1\\\\\n", + "\t chr9 & 1\\\\\n", + "\t chrX & 7\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 11 × 2\n", + "\n", + "| chrom <chr> | n <int> |\n", + "|---|---|\n", + "| chr1 | 2 |\n", + "| chr14 | 1 |\n", + "| chr16 | 1 |\n", + "| chr18 | 1 |\n", + "| chr2 | 1 |\n", + "| chr3 | 1 |\n", + "| chr5 | 2 |\n", + "| chr7 | 1 |\n", + "| chr8 | 1 |\n", + "| chr9 | 1 |\n", + "| chrX | 7 |\n", + "\n" + ], + "text/plain": [ + " chrom n\n", + "1 chr1 2\n", + "2 chr14 1\n", + "3 chr16 1\n", + "4 chr18 1\n", + "5 chr2 1\n", + "6 chr3 1\n", + "7 chr5 2\n", + "8 chr7 1\n", + "9 chr8 1\n", + "10 chr9 1\n", + "11 chrX 7" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "19" + ], + "text/latex": [ + "19" + ], + "text/markdown": [ + "19" + ], + "text/plain": [ + "[1] 19" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "snps_annot <- snps %>%\n", + " left_join(finemap_res %>% mutate(chrom = paste0(\"chr\", CHR), start = BP, allele1 = A1, allele2 = A2)) %cache_df%\n", + " here(\"output/longevity_snps_finemapped.tsv\")\n", + "snps_annot %>% \n", + " filter(is.na(PIP)) %>% \n", + " count(chrom)\n", + "snps_annot %>% \n", + " filter(is.na(PIP)) %>% \n", + " nrow()" + ] + }, + { + "cell_type": "markdown", + "id": "1006e1fd", + "metadata": {}, + "source": [ + "Choose at least one locus from each ~1M region + high PIP loci:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "14245d98", + "metadata": {}, + "outputs": [], + "source": [ + "regs <- snps_annot %>%\n", + " gintervals.centers() %>%\n", + " gintervals.expand(5e5) %>%\n", + " gintervals.canonic()\n", + "snps_top_annot <- snps_annot %>% \n", + " add_count(locus, name = \"n_locus\") %>% \n", + " gintervals.neighbors1(regs) %>% \n", + " select(-dist) %>% \n", + " unite(\"region\", chrom1, start1, end1) %>% \n", + " add_count(region, name = \"n_region\") %>% \n", + " arrange(region, desc(PIP)) %>% \n", + " group_by(region) %>% \n", + " mutate(i = 1:n()) %>% \n", + " ungroup() %>% \n", + " filter(i == 1 | PIP >= 0.5) %>% \n", + " select(-i) " + ] + }, + { + "cell_type": "markdown", + "id": "65bf6445", + "metadata": {}, + "source": [ + "Compute number of significant SNPs 1MB around each top snp:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "315f3b8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, end, marker.ID, allele1, allele2)`\n" + ] + } + ], + "source": [ + "snps_1M <- snps_top_annot %>%\n", + " mutate(orig_chrom = chrom, orig_start = start, orig_end = end) %>% \n", + " gintervals.centers() %>%\n", + " gintervals.expand(5e5) %>%\n", + " select(chrom, start, end, marker.ID, allele1, allele2, orig_chrom, orig_start, orig_end) %>%\n", + " gintervals.neighbors1(snps_annot %>% select(chrom, start, end), maxneighbors = 1e6) %>%\n", + " as_tibble() %>%\n", + " filter(dist == 0) %>%\n", + " count(chrom, start, end, marker.ID, allele1, allele2, orig_chrom, orig_start, orig_end, name = \"n_1M\") %>%\n", + " mutate(n_1M = n_1M - 1) %>% # remove the SNP itself\n", + " select(-(chrom:end)) %>% \n", + " rename(chrom = orig_chrom, start = orig_start, end = orig_end)\n", + " \n", + "snps_top_annot <- snps_top_annot %>% left_join(snps_1M)" + ] + }, + { + "cell_type": "markdown", + "id": "9a0ba92e", + "metadata": {}, + "source": [ + "Add results of COX regression (see GWAS_parents_survival notebook):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bba3a275", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, end, marker.ID, allele1, allele2,\n", + "rsid)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, end, marker.ID, allele1, allele2,\n", + "rsid)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, end, marker.ID, allele1, allele2,\n", + "rsid)`\n" + ] + } + ], + "source": [ + "parents_mother <- fread(here(\"output/cox_parents_survival_mother_gwas.tsv\")) %>% \n", + " inner_join(snps_top_annot %>% select(chrom, start, end, rsid, marker.ID, allele1, allele2)) %>% \n", + " as_tibble()\n", + "parents_father <- fread(here(\"output/cox_parents_survival_father_gwas.tsv\")) %>% \n", + " inner_join(snps_top_annot %>% select(chrom, start, end, rsid, marker.ID, allele1, allele2)) %>% \n", + " as_tibble()\n", + "parents_both <- fread(here(\"output/cox_parents_survival_both_gwas.tsv\")) %>% \n", + " inner_join(snps_top_annot %>% select(chrom, start, end, rsid, marker.ID, allele1, allele2)) %>% \n", + " as_tibble()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c09d650f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, end, marker.ID, rsid, allele1,\n", + "allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, end, marker.ID, rsid, allele1,\n", + "allele2)`\n", + "\u001b[1m\u001b[22mJoining with `by = join_by(chrom, start, end, marker.ID, rsid, allele1,\n", + "allele2)`\n" + ] + } + ], + "source": [ + "snps_top_annot <- snps_top_annot %>% \n", + " left_join(parents_mother %>% \n", + " select(chrom, start, end, rsid, marker.ID, allele1, allele2, cox_mother_surv_pval = pval, cox_mother_surv_z = z, cox_mother_surv_stat = Stat) \n", + " ) %>% \n", + " left_join(parents_father %>% \n", + " select(chrom, start, end, rsid, marker.ID, allele1, allele2, cox_father_surv_pval = pval, cox_father_surv_z = z, cox_father_surv_stat = Stat) \n", + " ) %>% \n", + " left_join(parents_both %>% \n", + " select(chrom, start, end, rsid, marker.ID, allele1, allele2, cox_both_surv_pval = pval, cox_both_surv_z = z, cox_both_surv_stat = Stat) \n", + " ) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cb9a4862", + "metadata": {}, + "outputs": [], + "source": [ + "fwrite(snps_top_annot, here(\"output/longevity_top_finemapped.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fc96e2a", + "metadata": {}, + "outputs": [], + "source": [ + "### H^2 SNP\n", + "### With Diseases as covariates\n", + "Create ldsc format sumstats:" + ] + }, + { + "cell_type": "markdown", + "id": "65d9df49", + "metadata": {}, + "source": [ + "adding std.err column from gwas of longevity with disease as covariance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "039dafef", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(marker.ID, allele1, allele2)`\n" + ] + } + ], + "source": [ + "gwas_longevity_disease_covar <- readr::read_rds(here(\"output/gwas_longevity_age_sex_disease_covar_extended.rds\"))\n", + "pvals <- pvals %>% left_join(gwas_longevity_disease_covar %>% select(marker.ID, allele1, allele2, std.err))\n", + "pvals %>%\n", + " filter(chrom != \"chrX\") %>% \n", + " mutate(N = 328542, CHR = gsub(\"chr\", \"\", chrom), Z = longevity_disease_covar_beta / std.err) %>%\n", + " select(CHR, BP = start, A1 = allele1, A2 = allele2, N, Z, P = longevity_disease_covar_pval, SNP = rsid) %>%\n", + " fwrite(here(\"output/longevity_snps_ldsc.sumstats\"), sep = \" \", quote = FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98662463", + "metadata": {}, + "outputs": [], + "source": [ + "at the terminal (polyfun conda):\n", + "​\n", + "./ldsc.py \\\n", + "--out /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_ldsc.h2 \\\n", + "--h2 /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_ldsc.sumstats \\\n", + "--ref-ld-chr baselineLF2.2.UKB/baselineLF2.2.UKB. \\\n", + "--w-ld-chr baselineLF2.2.UKB/weights.UKB. \\\n", + "--not-M-5-50\n", + "Total Observed scale h2: 0.1008 (0.0143)" + ] + }, + { + "cell_type": "markdown", + "id": "02913c6c", + "metadata": {}, + "source": [ + "### Without Diseases as covariates\n", + "Create ldsc format sumstats: " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "72898630", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22mJoining with `by = join_by(marker.ID, allele1, allele2)`\n" + ] + } + ], + "source": [ + "gwas_longevity <- readr::read_rds(here(\"output/gwas_longevity_age_sex_covar_extended.rds\"))\n", + "pvals <- pvals %>% left_join(gwas_longevity %>% select(marker.ID, allele1, allele2, std.err_longevity = std.err))\n", + "pvals %>%\n", + " filter(chrom != \"chrX\") %>% \n", + " mutate(N = 328542, CHR = gsub(\"chr\", \"\", chrom), Z = longevity_beta / std.err_longevity) %>%\n", + " select(CHR, BP = start, A1 = allele1, A2 = allele2, N, Z, P = longevity_pval, SNP = rsid) %>%\n", + " fwrite(here(\"output/longevity_snps_no_covar_ldsc.sumstats\"), sep = \" \", quote = FALSE)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "656fd052", + "metadata": {}, + "outputs": [], + "source": [ + "at the terminal (polyfun conda):\n", + "\n", + "./ldsc.py \\\n", + "--out /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_no_covar_ldsc.h2 \\\n", + "--h2 /home/nettam/projects/emr/ukbiobank/notebook/output/longevity_snps_no_covar_ldsc.sumstats \\\n", + "--ref-ld-chr baselineLF2.2.UKB/baselineLF2.2.UKB. \\\n", + "--w-ld-chr baselineLF2.2.UKB/weights.UKB. \\\n", + "--not-M-5-50\n", + "Total Observed scale h2: 0.1641 (0.0139)" + ] + } + ], + 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