美国密歇根大学Xiang Zhou等研究人员合作，开发出用于精细绘制候选因果基因图谱的条件性转录组关联研究方法。该研究于2024年1月26日在线发表于国际一流学术期刊《自然—遗传学》。

研究人员开发了一种名为GIFT（通过条件性转录组关联研究（TWAS）进行基于基因的整合精细图谱绘制）的方法，该方法通过明确控制局部区域内所有其他基因的遗传预测表达（GReX）来执行条件性TWAS分析，从而精细绘制推测的因果基因图谱。GIFT本质上是频谱分析法，明确地对多基因的表达相关性和顺式单核苷酸多态性连锁不平衡进行建模，并使用似然法框架来考虑表达预测的不确定性。因此，GIFT能产生校准的P值，并能有效地进行精细图谱绘制。

研究人员应用GIFT分析了英国生物库中的六个性状，与现有的TWAS精细作图方法相比，GIFT将推定因果基因集的规模缩小了32.16%-91.32%。GIFT发现的基因突显了血管调节对决定血压和脂质代谢对调节血脂水平的重要性。

据了解，TWAS旨在整合全基因组关联研究和表达图谱研究，以确定与复杂性状相关的GReX基因。

附：英文原文

Title: Conditional transcriptome-wide association study for fine-mapping candidate causal genes

Author: Liu, Lu, Yan, Ran, Guo, Ping, Ji, Jiadong, Gong, Weiming, Xue, Fuzhong, Yuan, Zhongshang, Zhou, Xiang

Issue&Volume: 2024-01-26

Abstract: Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies with expression-mapping studies to identify genes with genetically predicted expression (GReX) associated with a complex trait. In the present report, we develop a method, GIFT (gene-based integrative fine-mapping through conditional TWAS), that performs conditional TWAS analysis by explicitly controlling for GReX of all other genes residing in a local region to fine-map putatively causal genes. GIFT is frequentist in nature, explicitly models both expression correlation and cis-single nucleotide polymorphism linkage disequilibrium across multiple genes and uses a likelihood framework to account for expression prediction uncertainty. As a result, GIFT produces calibrated P values and is effective for fine-mapping. We apply GIFT to analyze six traits in the UK Biobank, where GIFT narrows down the set size of putatively causal genes by 32.16–91.32% compared with existing TWAS fine-mapping approaches. The genes identified by GIFT highlight the importance of vessel regulation in determining blood pressures and lipid metabolism for regulating lipid levels.

DOI: 10.1038/s41588-023-01645-y

Source: https://www.nature.com/articles/s41588-023-01645-y