TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8.

ABSTRACT: Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian D

SUBMITTER: Parrish RL 

PROVIDER: S-EPMC8756507 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature