Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

TLDR

GWAS have identified thousands of variants linked to complex traits, yet their biological roles remain unclear, though many overlap with eQTLs suggesting regulatory functions. The study proposes a transcriptome‑wide summary statistics‑based Mendelian Randomization (TWMR) approach that simultaneously uses multiple SNPs as instruments and multiple gene expression traits as exposures. TWMR employs multiple SNP instruments and multiple gene expression traits as exposures in a simultaneous analysis. Applied to 43 human phenotypes, TWMR uncovered 3,913 putatively causal gene–trait associations, 36 % of which lack nearby genome‑wide significant SNPs and were missed by GWAS due to power limitations; notable links include BSCL2 with educational attainment and pleiotropic effects suggesting mechanistic connections, demonstrating TWMR’s superior pleiotropy handling and potential to reveal biological mechanisms.

Abstract

Abstract Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene–trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2 , known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.