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.

翻译：全基因组关联研究（GWAS）极大地增进了我们对复杂性状和疾病遗传基础的理解。然而，仍有相当一部分性状遗传力未能得到解释，即所谓的"缺失遗传力"。大多数GWAS位点位于非编码区域，这为理解其功能影响带来了挑战。将GWAS与功能基因组数据（如表达数量性状位点eQTL）相结合，可以弥合这一鸿沟。本研究提出了一种新方法，用于发现受顺式和反式GWAS信号调控的候选基因。与现有仅关注顺式eQTL或分别考虑顺式和反式QTL的eQTL研究不同，我们采用能够同时反映顺式eQTL强而稀疏的效应以及反式eQTL弱而密集效应的自适应统计度量。因此，可以优先筛选出受联合效应调控的候选基因。我们通过理论和数值分析证明了该方法的有效性，并将其应用于男性代谢综合征（METSIM）研究中的脂肪组织eQTL数据，发现了在影响心脏代谢性状的调控网络中起重要作用的关键基因。我们的研究结果为复杂性状的遗传调控提供了新的见解，并为基于联合eQTL效应识别关键调控基因提供了实用框架。