Quantifying genetic effects on disease mediated by assayed gene expression levels

Genome-wide association studies (GWAS) frequently identify disease-associated variants in noncoding regions, leading to the hypothesis that these variants influence disease risk by regulating gene expression. While many variants overlap with expression quantitative trait loci (eQTLs), it has remained difficult to determine whether this overlap reflects a causal mediation mechanism or simply pleiotropy and linkage. This paper addresses this gap by quantifying the proportion of disease heritability mediated by cis-genetic components of gene expression.

The authors developed Mediated Expression Score Regression (MESC), a statistical framework that regresses squared GWAS summary statistics on squared cis-eQTL effect sizes. By conditioning on linkage disequilibrium (LD) scores and stratifying by functional annotations, MESC distinguishes directional mediated effects from nondirectional pleiotropic and linkage effects. The authors validated this method through extensive simulations and applied it to GWAS summary statistics for 42 complex traits and eQTL data from 48 tissues in the GTEx consortium.

The study reveals that, on average, only 11% of complex trait heritability is mediated by the cis-genetic component of assayed gene expression levels. This modest proportion suggests that bulk tissue eQTLs capture only a fraction of the regulatory mechanisms underlying disease. Furthermore, the researchers identified an inverse relationship between a gene's expression cis-heritability and its contribution to mediated disease heritability, indicating that genes with weaker eQTLs often have larger causal effects on complex traits. The method also successfully identified trait-specific enrichment in biologically relevant pathways, such as FMRP-interacting genes in schizophrenia and Wnt signaling in bone density.

This work provides a rigorous, genome-wide quantification of the role of gene expression in disease architecture. By demonstrating that current bulk tissue eQTL data cannot explain the majority of disease heritability, the study highlights the need for larger, context-specific, and single-cell expression assays to better characterize the molecular mechanisms of disease. MESC serves as a valuable tool for researchers to prioritize disease-relevant gene sets and evaluate the utility of new molecular QTL studies.