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biona001 / GhostKnockoffGWAS

Knockoff-based analysis of GWAS summary statistics data
MIT License
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conditional-independence false-discovery-rate fdr genomics gwas knockoffs summary-statistics variable-selection

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GhostKnockoffGWAS

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This is a package for analyzing summary statistics data from genome-wide association studies (GWAS) 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.

New users

To get started, please refer to the documentation.

In GhostKnockoffGWAS, the main working assumption is that we do not have access to individual level genotype or phenotype data. Rather, for each SNP, we have its Z-scores with respect to some phenotype from a GWAS, and access to LD (linkage disequilibrium) data. The user is expected supply the Z-scores, while we supply pre-processed LD files freely downloadable from the cloud.

Advantages/disadvantages of GhostKnockoffGWAS

Compared to existing knockoff methods for GWAS, the main advantages of GhostKnockoffGWAS is (1) its ease of use and (2) its computational efficiency. The only user-provided input is marginal Z-scores. Computationally, running a knockoff-based GWAS pipeline took approximately 15 minutes on 650,000 SNPs. The main limitation of GhostKnockoffGWAS is that it relies on the availability of pre-processed LD files suitable for the user's target samples.

Bug fixes and user support

If you encounter a bug or need user support, please open a new issue on Github. Please provide as much detail as possible for bug reports, ideally a sequence of reproducible code that lead to the error.

PRs and feature requests are welcomed!

Citation

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, 2024.02.28.582621; doi: https://doi.org/10.1101/2024.02.28.582621.