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使用协作混合模型来解释转录组关联研究中插补不确定性。

Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies.

机构信息

Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China.

Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore, Singapore.

出版信息

Methods Mol Biol. 2021;2212:93-103. doi: 10.1007/978-1-0716-0947-7_7.

Abstract

Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually straightforward, some steps are required to perform the TWAS properly. Here we provide a step-by-step guide to integrate eQTL data with both GWAS individual-level data and GWAS summary statistics from complex traits.

摘要

转录组关联研究 (TWAS) 将表达数量性状基因座 (eQTL) 研究与全基因组关联研究 (GWAS) 相结合,优先考虑复杂性状的候选靶基因。TWAS 越来越受欢迎。它们已被用于分析来自不同组织的表达谱的许多复杂性状,成功地增强了对复杂性状遗传风险位点的发现。尽管概念上很简单,但要正确执行 TWAS 需要一些步骤。在这里,我们提供了一个逐步指南,将 eQTL 数据与 GWAS 个体水平数据和复杂性状的 GWAS 汇总统计数据相结合。

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