Discovering Candidate Genes Regulated by GWAS Signals in Cis and Trans

TL;DR

This paper presents a new method to identify genes regulated by GWAS signals in both cis and trans, integrating functional genomic data with adaptive statistical metrics to better understand complex trait genetics.

Contribution

The study introduces a novel approach that jointly analyzes cis- and trans-eQTL effects using adaptive metrics, improving candidate gene prioritization beyond existing methods.

Findings

Efficient identification of candidate genes in adipose tissue linked to cardiometabolic traits.

Theoretical and numerical validation of the proposed statistical approach.

Application to METSIM data reveals key regulatory genes involved in complex traits.

Abstract

Understanding the genetic underpinnings of complex traits and diseases has been greatly advanced by genome-wide association studies (GWAS). However, a significant portion of trait heritability remains unexplained, known as ``missing heritability". Most GWAS loci reside in non-coding regions, posing challenges in understanding their functional impact. Integrating GWAS with functional genomic data, such as expression quantitative trait loci (eQTLs), can bridge this gap. This study introduces a novel approach to discover candidate genes regulated by GWAS signals in both cis and trans. Unlike existing eQTL studies that focus solely on cis-eQTLs or consider cis- and trans-QTLs separately, we utilize adaptive statistical metrics that can reflect both the strong, sparse effects of cis-eQTLs and the weak, dense effects of trans-eQTLs. Consequently, candidate genes regulated by the joint effects can be prioritized. We demonstrate the efficiency of our method through theoretical and numerical analyses and apply it to adipose eQTL data from the METabolic Syndrome in Men (METSIM) study, uncovering genes playing important roles in the regulatory networks influencing cardiometabolic traits. Our findings offer new insights into the genetic regulation of complex traits and present a practical framework for identifying key regulatory genes based on joint eQTL effects.