Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes

• Objective: The new method, OPERA, was developed to integrate GWAS and multi-omics QTL (xQTL) summary statistics to quantify the proportion of complex trait genetic signals mediated by molecular phenotypes.
• Key Finding: The study found that approximately 50% of genetic signals identified in GWAS are shared with (and likely mediated by) at least one molecular phenotype, with eQTLs (gene expression QTLs) being the most dominant mediators.
• Impact: OPERA led to the discovery of 89 novel genes for 11 complex traits, confirming the approach’s ability to significantly enhance gene discovery and fine-mapping by linking genetic variants to their underlying molecular regulatory mechanisms.

PubMed: 37601976 DOI: 10.1016/j.xgen.2023.100344 Overview generated by: Gemini 2.5 Flash, 27/11/2025

Background and Objective

Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex human traits and diseases. However, the precise molecular mechanisms by which these non-coding genetic variants exert their effects—often by regulating gene expression or other molecular phenotypes—remain largely unknown. Quantitative Trait Loci (QTL) studies provide molecular data (e.g., gene expression, methylation) but are often limited by sample size.

The objective of this study was to develop a new method, OPERA (Overlap-Based Partitioned Estimation and Regression Analysis), to integrate summary statistics from GWAS and various multi-omics QTL (xQTL) studies to: 1. Quantify the proportion of GWAS signals that are shared with, and likely mediated by, specific molecular phenotypes. 2. Improve the power for fine-mapping and gene discovery for complex traits.

Methods: The OPERA Framework

Data Integration

OPERA is a computational method that jointly analyzes GWAS summary statistics and multi-omics xQTL summary statistics (including eQTLs for gene expression, pQTLs for protein levels, sQTLs for splicing, etc.).

Key Innovation: Shared Genetic Variance

The core of OPERA is its ability to partition the heritability of a complex trait into components explained by genetic variants that are shared with different molecular phenotypes (i.e., those that are QTLs for specific molecular traits). This partitioning allows the study to estimate the proportion of GWAS signals (or heritability) that is mediated through each molecular phenotype. OPERA is robust to issues like linkage disequilibrium (LD) and confounding.

Application

The method was applied to 11 complex human traits from GWAS, integrated with xQTL data across 13 different molecular phenotypes (e.g., gene expression, DNA methylation, histone modifications) from various human tissues and cell types.

Key Findings

Large Fraction of Shared Signals

The primary and most significant finding was that, on average, approximately 50% of the genetic signals identified in GWAS are shared with at least one molecular phenotype (xQTL). This provides strong statistical evidence that a substantial portion of complex trait heritability is mediated by genetic regulatory effects on molecular traits.

• eQTLs are Major Mediators: Among the molecular phenotypes studied, expression QTLs (eQTLs), which regulate gene expression, were the most significant molecular mediators, accounting for the largest shared fraction of GWAS signals.

Enhanced Discovery Power

By jointly analyzing the data, OPERA achieved: * Identification of novel genes/variants: The joint analysis led to the discovery of 89 novel genes for the 11 complex traits studied, primarily through more effective fine-mapping in previously identified GWAS loci. * Improved fine-mapping: The ability to integrate the xQTL data dramatically enhanced the precision of identifying the likely causal variant within a GWAS locus.

Conclusions and Significance

The OPERA framework successfully demonstrated that a large fraction of the genetic basis of complex traits is shared with molecular phenotypes, confirming that these molecular traits (particularly gene expression) are critical intermediate steps between genetic variants and disease risk.

This integrated approach provides a powerful tool for converting GWAS signals from abstract associations into biologically actionable regulatory mechanisms, aiding in the discovery of novel therapeutic targets.