发现由顺式和反式GWAS信号调控的候选基因
Discovering Candidate Genes Regulated by GWAS Signals in Cis and Trans
AI总结：
针对GWAS非编码区功能解读难题，提出利用自适应统计指标整合顺式强稀疏与反式弱密集eQTL效应，以优先发现受联合调控的候选基因，并在METSIM脂肪数据中验证其有效性。
摘要：
全基因组关联研究（GWAS）极大地推进了人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无法解释，被称为“缺失遗传力”。大多数GWAS位点位于非编码区，这给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）整合可以弥合这一差距。本研究引入了一种新方法，用于发现由顺式和反式GWAS信号共同调控的候选基因。与现有仅关注顺式eQTL或将顺式和反式QTL分开考虑的eQTL研究不同，我们采用自适应统计指标，能够同时反映顺式eQTL的强稀疏效应和反式eQTL的弱密集效应。因此，可以优先考虑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效率，并将其应用于男性代谢综合征（METSIM）研究的脂肪eQTL数据，揭示了在影响心脏代谢性状的调控网络中发挥重要作用的基因。我们的发现为复杂性状的遗传调控提供了新见解，并提出了一个基于联合eQTL效应识别关键调控基因的实用框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
