Expression Quantitative Trait Loci (eQTLs) have often been used to nominate candidate genes from Genome-wide association studies (GWAS). However, commonly used methods are susceptible to false positives largely due to Linkage Disequilibrium of eQTLs with causal variants acting on the phenotype directly. Our method, causal-TWAS (cTWAS), addressed this challenge by borrowing ideas from statistical fine-mapping. It is a generalization of Transcriptome-wide association studies (TWAS), but when analyzing any gene, it adjusts for other nearby genes and all nearby genetic variants.
Running cTWAS involves four main steps: preparing input data, imputing gene z-scores, estimating parameters, and fine-mapping genes and variants. The output of cTWAS are posterior inclusion probabilities (PIPs) for all variants and genes with expression models. We have included a tutorial of how to use the ctwas software. You can browse source code and report a bug here.
Install ctwas:
remotes::install_github("xinhe-lab/ctwas",ref = "main")
Currently, ctwas has only been tested on linux systems.
We recommend running ctwas on a High-Performance Computing system.
If you find the ctwas package or any of the source code in this
repository useful for your work, please cite:
Zhao S, Crouse W, Qian S, Luo K, Stephens M, He X. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Nat Genet (2024). https://doi.org/10.1038/s41588-023-01648-9
We have pre-computed the LD matrices of European samples from UK Biobank. They can be downloaded here.
cTWAS requires the expression prediction models, or weights, of genes. The pre-computed weights of GTEx expression and splicing traits can be downloaded from PredictDB.
We acknowledge the authors of susieR package for using their codes.
Original susieR code obtained by:
git clone git@github.com:stephenslab/susieR.git
git checkout c7934c0
Minor edits to make it accept different prior variances for each variable.
