https://github.com/biona001/ghostknockoffgwas

Knockoff-based analysis of GWAS summary statistics data
https://github.com/biona001/ghostknockoffgwas

conditional-independence false-discovery-rate fdr genomics gwas knockoffs summary-statistics variable-selection

Last synced: 4 days ago
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Knockoff-based analysis of GWAS summary statistics data

• Host: GitHub
• URL: https://github.com/biona001/ghostknockoffgwas
• Owner: biona001
• License: mit
• Created: 2023-05-20T21:43:57.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-29T22:45:12.000Z (18 days ago)
• Last Synced: 2024-10-29T22:50:34.258Z (18 days ago)
• Topics: conditional-independence, false-discovery-rate, fdr, genomics, gwas, knockoffs, summary-statistics, variable-selection
• Language: Julia
• Homepage:
• Size: 12.1 MB
• Stars: 7
• Watchers: 1
• Forks: 1
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GhostKnockoffGWAS

| **Documentation** | **Build Status** | **Code Coverage**  |

|-------------------|------------------|--------------------|

| [![](https://img.shields.io/badge/docs-latest-blue.svg)](https://biona001.github.io/GhostKnockoffGWAS/dev/)| [![build Actions Status](https://github.com/biona001/GhostKnockoffGWAS/actions/workflows/CI.yml/badge.svg)](https://github.com/biona001/GhostKnockoffGWAS/actions) [![CI (Julia nightly)](https://github.com/biona001/GhostKnockoffGWAS/actions/workflows/JuliaNightly.yml/badge.svg)](https://github.com/biona001/GhostKnockoffGWAS.jl/actions/workflows/JuliaNightly.yml) | [![codecov](https://codecov.io/gh/biona001/GhostKnockoffGWAS/branch/main/graph/badge.svg)](https://codecov.io/gh/biona001/GhostKnockoffGWAS) |

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

To get started, please refer to the [documentation](https://biona001.github.io/GhostKnockoffGWAS/dev). 

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.