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

• Type of Material: Article
• Authors: Randy L. Parrish, Emory University; Greg C. Gibson, Georgia Institute of Technology; Michael Epstein, Emory University; Jingjing Yang, Emory University
• Language: English
• Date: 2022-01-13
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Author(s)
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.xhgg.2021.100068
• Title of Journal or Parent Work: Human Genetics and Genomics Advances
• Volume: 3
• Issue: 1
• Start Page: 100068
• End Page: 100068
• Grant/Funding Information: R.L.P. and J.Y. are supported by National Institutes of Health (NIH/NIGMS) grant award R35GM138313. M.P.E. was supported by NIH/NIGMS grant award R01GM117946 and NIH/NIA grant award RF1AG071170.
• Keywords: GWAS; transcriptome-wide association study; cis-eQTL; Elastic-Net; nonparametric Bayesian DPR; GTEx V8; TWAS; TIGAR
• Research Categories: Biology, Genetics

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 Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.