add paper in readme

README.md

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-This is a package for analyzing summary statistics data from [*genome-wide association studies (GWAS)*](https://en.wikipedia.org/wiki/Genome-wide_association_study) under the statistical *knockoff framework*. Compared to marginal association testing which controls the FWER, the knockoff framework conducts conditional independence testing while controlling the FDR. As a consequence, `GhostKnockoffGWAS` can be both more precise and powerful than current state-of-the-art GWAS+fine-mapping methods. Its detailed evaluations can be found in our [companion paper]().
+This is a package for analyzing summary statistics data from [*genome-wide association studies (GWAS)*](https://en.wikipedia.org/wiki/Genome-wide_association_study) under the statistical *knockoff framework*. Compared to marginal association testing which controls the FWER, the knockoff framework conducts conditional independence testing while controlling the FDR. As a consequence, `GhostKnockoffGWAS` can be both more precise and powerful than current state-of-the-art GWAS+fine-mapping methods. Its detailed evaluations can be found in our [companion paper](https://www.biorxiv.org/content/10.1101/2024.02.28.582621v1).
 
 ## New users
 
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 If you use `GhostKnockoffGWAS` in your research, please cite the following references:
 
-> He Z, Chu BB, Yang J, Gu J, Chen Z, Liu L, Morrison T, Bellow M, Qi X, Hejazi N, Mathur M, Le Guen Y, Tang H, Hastie T, Ionita-laza, I, Sabatti C, Candes C. "In silico identification of putative causal genetic variants", bioRxiv 2024.
+> He Z, Chu BB, Yang J, Gu J, Chen Z, Liu L, Morrison T, Bellow M, Qi X, Hejazi N, Mathur M, Le Guen Y, Tang H, Hastie T, Ionita-laza, I, Sabatti C, Candes C. "In silico identification of putative causal genetic variants", bioRxiv, 2024.02.28.582621; doi: https://doi.org/10.1101/2024.02.28.582621.