Bayesian transcriptome-wide association study method leveraging both cis- and trans- eQTL information through summary statistics

Abstract

Transcriptome-wide association studies (TWAS) have been widely used to integrate gene expression and genetic data for studying complex traits. Due to the computational burden, existing TWAS methods do not assess distant trans- expression quantitative trait loci (eQTL) that are known to explain important expression variation for most genes. We propose a Bayesian Genome-wide TWAS (BGW-TWAS) method which leverages both cis- and trans- eQTL information for TWAS. Our BGW-TWAS method is based on Bayesian variable selection regression, which not only accounts for cis- and trans- eQTL of the target gene but also enables efficient computation by using summary statistics from standard eQTL analyses. Our simulation studies illustrated that BGW-TWAS achieved higher power compared to existing TWAS methods that do not assess trans-eQTL information. We further applied BWG-TWAS to individual-level GWAS data (N=[~]3.3K), which identified significant associations between the genetically regulated gene expression (GReX) of gene ZC3H12B and Alzheimers dementia (AD) (p-value= 5.42 x 10-13), neurofibrillary tangle density (p-value= 1.89 x10-6 ), and global measure of AD pathology (p-value=9.59 x 10-7). These associations for gene ZC3H12B were completely driven by trans-eQTL. Additionally, the GReX of gene KCTD12 was found to be significantly associated with {beta}-amyloid (p-value= 3.44 x10 -8) which was driven by both cis- and trans- eQTL. Four of the top driven trans-eQTL of ZC3H12B are located within gene APOC1, a known major risk gene of AD and blood lipids. Additionally, by applying BGW-TWAS with summary-level GWAS data of AD (N=[~]54K), we identified 13 significant genes including known GWAS risk genes HLA-DRB1 and APOC1, as well as ZC3H12B.