美国芝加哥大学Xin He等研究人员合作发现，在全转录组关联研究中调整遗传混杂因素有助于发现复杂性状的风险基因。相关论文于2024年1月26日在线发表在《自然—遗传学》杂志上。

研究人员表示，目前已开发出许多方法来利用表达量性状位点（eQTL）数据，提名全基因组关联研究的候选基因。这些方法包括共定位、转录组关联研究（TWAS）和基于孟德尔随机化的方法；然而，所有这些方法都存在一个关键问题——当利用基因的eQTL评估基因在性状中的作用时，附近的变异和其他基因表达的遗传成分可能与这些eQTL相关，并对性状产生直接影响，成为潜在的混杂因素。

研究人员的大量模拟结果表明，现有的方法未能考虑到这些“遗传混杂因素”，导致假阳性结果严重增加。这个新方法”因果-TWAS（cTWAS）”借鉴了统计精细图谱的思想，允许研究人员调整所有遗传混杂因素。cTWAS在模拟中显示了校准的误发现率，它在几个常见性状上的应用发现了新的候选基因。总之，cTWAS为基因发现提供了一个稳健的统计框架。

附：英文原文

Title: Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits

Author: Zhao, Siming, Crouse, Wesley, Qian, Sheng, Luo, Kaixuan, Stephens, Matthew, He, Xin

Issue&Volume: 2024-01-26

Abstract: Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem—when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes’ expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these ‘genetic confounders’, resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.

DOI: 10.1038/s41588-023-01648-9

Source: https://www.nature.com/articles/s41588-023-01648-9