https://github.com/biona001/citlasso
Knockoff-based analysis of GWAS summary statistics data
https://github.com/biona001/citlasso
conditional-independence false-discovery-rate fdr genomics gwas knockoffs summary-statistics variable-selection
Last synced: 13 days ago
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Knockoff-based analysis of GWAS summary statistics data
- Host: GitHub
- URL: https://github.com/biona001/citlasso
- Owner: biona001
- License: mit
- Created: 2023-05-20T21:43:57.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2026-06-24T07:02:48.000Z (20 days ago)
- Last Synced: 2026-06-24T07:28:26.845Z (20 days ago)
- Topics: conditional-independence, false-discovery-rate, fdr, genomics, gwas, knockoffs, summary-statistics, variable-selection
- Language: Julia
- Homepage:
- Size: 14.1 MB
- Stars: 9
- Watchers: 1
- Forks: 1
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CIT-Lasso
| **Documentation** | **Build Status** | **Code Coverage** |
|-------------------|------------------|--------------------|
| [](https://biona001.github.io/CITLasso/dev/)| [](https://github.com/biona001/CITLasso/actions) [](https://github.com/biona001/CITLasso.jl/actions/workflows/JuliaNightly.yml) | [](https://codecov.io/gh/biona001/CITLasso) |
CIT-Lasso is software 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*. CIT stands for conditional independence testing: compared to marginal association testing, which controls the FWER, the knockoff framework conducts conditional independence testing while controlling the FDR. As a consequence, CIT-Lasso 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).
## Installation
CIT-Lasso (and the accompanying software `solveblock`) are available:
+ as a **standalone binary**
+ as a regular Julia package named `CITLasso`
For more extended instructions on installation, please refer to the [documentation](https://biona001.github.io/CITLasso/dev/man/examples/).
## New users
To get started, please refer to the [documentation](https://biona001.github.io/CITLasso/dev).
In CIT-Lasso, 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 CIT-Lasso
Compared to existing knockoff methods for GWAS, the main advantages of CIT-Lasso are (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 CIT-Lasso 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 CIT-Lasso 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.
If you use `solveblock` to generate LD files, please cite the following instead:
> Chu BB, He Z, Sabatti C. "It's a wrap: deriving distinct discoveries with FDR control after a GWAS pipeline", bioRxiv, 2025.06.05.658138; doi: https://doi.org/10.1101/2025.06.05.658138.