revert single-variant and single-gene to single variant and sigle gene

content/02.introduction.md

@@ -23,5 +23,5 @@ Methods such as non-negative matrix factorization and variational autoencoders (
 In this review, we explore current approaches based on gene modules to integrate genetic studies with other data types.
 We discuss how integrating machine learning-derived gene modules with genetic and multi-omics data enhances our understanding of complex traits and diseases.
 We highlight the PhenoPLIER framework [@doi:10.1038/s41467-023-41057-4], which integrates gene modules derived from transcriptome data with TWAS and drug-induced transcriptional profiles to uncover disease-relevant molecular mechanisms.
-This approach moves beyond single-gene analyses by capturing the broader gene networks that contribute to phenotypic outcomes, offering a more nuanced understanding of the molecular basis of human complex traits and paving the way for more effective, personalized therapeutic strategies.
+This approach moves beyond single gene analyses by capturing the broader gene networks that contribute to phenotypic outcomes, offering a more nuanced understanding of the molecular basis of human complex traits and paving the way for more effective, personalized therapeutic strategies.
 

content/03.single-variant-single-gene-approaches.md

@@ -78,7 +78,7 @@ Del Rosario et al. (2022) conducted a HAWAS identifying over 2,000 differentiall
 haQTL analysis further revealed variants associated with immune phenotypes, complementing GWAS findings and enhancing understanding of disease mechanisms [@doi:10.1038/s41564-021-01049-w].
 The tissue-specific nature of epigenetic modifications, alongside the capacity to capture cellular plasticity and environmental influences, enhances our insight into the effects of genetic variants across distinct tissues, temporal cellular states, and gene-environment interactions [@doi:10.3390/cells9112424; @doi:10.1016/j.placenta.2007.09.011].
 
-Despite the advancements facilitated by single-variant and single-gene methodologies, a common thread persists: the focus remains on one gene at a time.
+Despite the advancements facilitated by single variant and single gene methodologies, a common thread persists: the focus remains on one gene at a time.
 The underlying expectation is that identifying a gene linked to a trait will directly unveil the biological mechanisms driving disease processes.
 While this has been successful in certain monogenic disorders, complex traits often involve intricate interactions among multiple genes and environmental factors.
 Consequently, the one-gene-at-a-time paradigm may oversimplify the multifaceted nature of these traits.

content/04.from-single-genes-to-gene-networks.md

@@ -13,7 +13,7 @@ Phenotypes are linked to their relevant biological networks, highlighting the in
 ](images/fig2.svg "Omnigenic model"){#fig:fig2 width="100%"}
 
 In the early 20th century, a significant debate arose between Mendelian geneticists and biometricians concerning the inheritance of complex traits.
-Mendelians emphasized single-gene traits with discrete inheritance patterns, while biometricians argued that such models could not account for the continuous phenotypic variation observed in populations.
+Mendelians emphasized single gene traits with discrete inheritance patterns, while biometricians argued that such models could not account for the continuous phenotypic variation observed in populations.
 This conflict highlighted a fundamental question: how can discrete genetic factors produce continuous phenotypic variation? [@doi:10.1016/j.cell.2017.05.038; @doi:10.1073/pnas.2005634117].
 The disagreement was resolved by R.A. Fisher in 1918, who introduced the infinitesimal model, demonstrating that if many genes influence a trait, their combined effects can generate continuous phenotypic distributions [@doi:10.1017/S0080456800012163].
 Despite the success of Fisher’s infinitesimal model, the actual number of genes involved in complex traits and the magnitude of their effects remained uncertain for much of the 20th century.
@@ -82,5 +82,5 @@ Additionally, while the model provides a conceptual framework, it remains unclea
 
 As George E. P. Box remarked, "all models are wrong, but some are useful." While we acknowledge that the omnigenic model simplifies the inherent complexity of biological systems and may not universally apply to all traits, we maintain that it remains a valuable framework for elucidating genetic architectures.
 The model effectively bridges quantitative and molecular genetics, offering comprehensive mechanistic insights with predictions on quantitative variation.
-Moreover, the omnigenic model has been instrumental in shifting the focus from single-gene analyses to network-based models.
+Moreover, the omnigenic model has been instrumental in shifting the focus from single gene analyses to network-based models.
 Recognizing the necessity of translating this model into practical applications, machine learning-derived gene modules, which use this concept to infer gene-gene networks, offer a promising way to ground these theoretical ideas into practical approaches.

content/05.from-gene-networks-to-machine-learning-derived-gene-modules.md

@@ -18,7 +18,7 @@ The third row highlights interpretable VAEs, which incorporate prior knowledge i
 
 High-throughput technologies, particularly genome-wide gene expression profiling tools such as RNA sequencing (RNA-seq), have fundamentally transformed the landscape of molecular biology.
 Unlike traditional gene-by-gene approaches, RNA-seq offers a comprehensive view of the transcriptomic landscape [@doi:10.1038/s41598-022-23985-1].
-These advancements, alongside conceptual frameworks like the omnigenic model, have facilitated a paradigm shift from a single-gene perspective to a module-based approach, wherein groups of genes, rather than individual genes, are essential for elucidating the complexities of biological networks [@doi:10.1016/j.copbio.2008.07.011].
+These advancements, alongside conceptual frameworks like the omnigenic model, have facilitated a paradigm shift from a single gene perspective to a module-based approach, wherein groups of genes, rather than individual genes, are essential for elucidating the complexities of biological networks [@doi:10.1016/j.copbio.2008.07.011].
 
 Gene modules, a key type of biological network, consist of nodes (genes) and edges that reflect coexpression relationships between them (**Figure {@fig:fig3}*a***).
 These modules, formed by genes with coordinated expression patterns under specific biological conditions, not only reveal coexpression but could also provide valuable insights into regulatory mechanisms, gene functions, and the pathways involved in traits and diseases [@doi:10.1371/journal.pone.0247671].

content/06.a-gene-module-perspective-for-genetic-studies.md

@@ -48,7 +48,7 @@ The gene module-based approach outperformed the single gene-based one with an ar
 Although the performance difference in this task was not large, the authors noted that the gene module-based approach represents a compressed version of the entire set of single gene-based results, and the higher performance implied that the low-dimensional latent space used (which necessarily misses some information) captured biologically meaningful gene-gene patterns.
 Additionally, since gene modules represent interpretable features, the authors found that lipid-related gene modules expressed in adipose tissue and liver were among the top modules contributing to the prediction of high cholesterol and Nicotinic acid (Niacin, which can treat lipid disorders), potentially resembling known mechanisms of action of Niacin, such as decreasing the production of low-density lipoproteins (LDL) either by modulating triglyceride synthesis in hepatocytes or by inhibiting adipocyte triglyceride lipolysis [@doi:10.1016/j.amjcard.2008.02.029].
 
-The PhenoPLIER framework is also able to compute an association between a gene module and a trait by integrating single-gene TWAS results (**Figure {@fig:fig4}*d***).
+The PhenoPLIER framework is also able to compute an association between a gene module and a trait by integrating single gene TWAS results (**Figure {@fig:fig4}*d***).
 The association is computed using a regression model that tests whether genes that strongly belong to a module (using a column in matrix Z in **Figure {@fig:fig4}*a***) are also strongly associated with the trait (using a row in the yellow TWAS matrix in **Figure {@fig:fig4}*b***).
 For validation, the study conducted a CRISPR-Cas9 screen in the HepG2 (liver) cell line to identify genes associated with lipid regulation and found a high-confidence lipid-increasing gene set that included genes *DGAT2* and *ACACA*, which fit the definition of core genes.
 The gene module identified as LV246 (**Figure {@fig:fig4}*e***) was the module most strongly enriched with this lipid-increasing gene set, containing *DGAT2* and *ACACA* among the top 15 genes most strongly connected with the module.
@@ -64,7 +64,7 @@ The association with AD is relevant since this module was also found to be expre
 From TWAS alone, it can be seen that *APOE* is strongly associated and colocalized with the same traits as LV246 (**Figure {@fig:fig4}*e***, bottom).
 However, a recent systematic survey across clinical trials that evaluate ApoE-targeted drugs for AD found modest to no efficacy in the treatment for this disease [@doi:10.1212/WNL.0000000000204861].
 Although there is strong evidence that *APOE* is a causal gene for AD, it still remains to be determined whether it represents a core gene or plays a more peripheral role.
-A gene module-based approach might help prioritize core genes that remain elusive when using standard single-gene strategies.
+A gene module-based approach might help prioritize core genes that remain elusive when using standard single gene strategies.
 
 Another approach to compute an association between gene modules and traits is MAGMA gene-set analysis [@doi:10.1371/journal.pcbi.1004219].
 In fact, the regression model employed by PhenoPLIER is based on MAGMA.