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CIT-Lasso is software for analyzing summary statistics data from genome-wide association studies (GWAS) 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.
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.
To get started, please refer to the documentation.
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.
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.
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!
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.