Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits

Kevin J Gleason,Lin S Chen,Brandon L Pierce,Xin He,Fan Yang

doi:10.1186/s13059-020-02125-w

Abstract

To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

Highlights

In the post-genomic era, genome-wide association studies (GWAS) have identified tens of thousands of unique associations between single nucleotide polymorphisms (SNPs) and human complex traits [1, 2]
To assess whether a GWAS SNP is associated with omics traits not due to it being in linkage disequilibrium (LD) with other lead omics SNPs, we propose to conduct conditional association analysis within gene regions harboring susceptibility loci, with summary statistics of the GWAS SNP and other lead omics SNPs as input
We made a tailored development of Primo to comprehensively elucidate the molecular mechanisms of known complex trait-associated SNPs, where we assessed the omics or other trait associations of known complex trait-associated SNPs by conducting conditional association analysis in gene regions harboring known trait-associated SNPs to account for LD with other SNPs in the region

Summary

Introduction

In the post-genomic era, genome-wide association studies (GWAS) have identified tens of thousands of unique associations between single nucleotide polymorphisms (SNPs) and human complex traits [1, 2]. It is known that trait-associated SNPs are more likely to be expression quantitative trait loci (eQTLs) [5]; many of these SNPs likely affect complex traits through their effects on expression levels and/or other “omics” traits. Gleason et al Genome Biology (2020) 21:236 the genome These findings suggest that integrating data from omics and multi-omics QTL studies with GWAS would help to elucidate functional mechanisms that underlie trait/disease processes. The increasing availability of summary statistics for complex traits and omics QTL studies in many conditions and cellular contexts [6, 12,13,14] provides a valuable resource to conduct integrative analyses in a variety of settings and presents an unprecedented opportunity to gain a system-level perspective of the regulatory cascade, which may highlight targets for disease prevention and/or treatment strategies

Methods

Results

Conclusion