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

Abstract

Summary 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.

Omnibus proteome-wide association study (PWAS-O) identified 43 risk genes for Alzheimer’s disease dementia

“…1 and Fig. 2) 23 , protein abundance imputation models are first trained in Stage I to obtain effect size estimates of genetic variants/predictors . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

“…CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint (i.e., pQTL weights), by TIGAR 23 (nonparametric Bayesian DPR), PrediXcan 13 (Elastic-Net), and FUSION 4 (Elastic-Net, LASSO, BLUP, Top1). Genetic variants within ±1MB around the transcription starting and termination sites of the corresponding coding gene were considered as predictors in the imputation models.…”

“…A reference panel of the same ancestry as the GWAS data can be used to provide LD information for gene-based association tests in Stage II. The TIGAR 23 tool provides functions to calculate LD covariance matrices and conduct gene-based association tests.…”

OTTERS: A powerful TWAS framework leveraging summary-level reference data

“…In particular, and V can be approximated from a reference panel with genotype data of samples of the same ancestry such as those available from the 1000 Genomes Project 54 . If are standardized effect sizes estimated assuming standardized genotype X and gene expression E g in Equation 1, FUSION and S-PrediXcan Z-score statistics are equivalent 13 . Otherwise, the S-PrediXcan Z-score should be applied to avoid false positive inflation.…”

“…A transcriptome-wide association study (TWAS) is a valuable analysis strategy for identifying genes that influence complex traits and diseases through genetic regulation of gene expression [1][2][3][4][5] . Researchers have successfully deployed TWAS analyses to identify risk genes for complex human diseases, including Alzheimer's disease [6][7][8] , breast cancer [9][10][11] , ovarian cancer 12,13 , and cardiovascular disease 14,15 . A typical TWAS consists of two separate stages.…”