Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization.

ABSTRACT: A transcriptome-wide association study (TWAS) integrates data from genome-wide association studies and gene expression mapping studies for investigating the gene regulatory mechanisms underlying diseases. Existing TWAS methods are primarily univariate in nature, focusing on analyzing one outcome trait at a time. However, many complex traits are correlated with each other and share a common genetic basis. Consequently, analyzing multiple traits jointly through multivariate analysis can potentially improve the power of TWASs. Here, we develop a method, moPMR-Egger (multiple outcome probabilistic Mendelian randomization with Egger assumption), for analyzing multiple outcome traits in TWAS applications. moPMR-Egger examines one gene at a time, relies on its cis-SNPs that are in potential linka

SUBMITTER: Liu L 

PROVIDER: S-EPMC7895847 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature