Department of Biostatistics, Columbia University, New York, NY 10032.
Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305.
Proc Natl Acad Sci U S A. 2021 Nov 23;118(47). doi: 10.1073/pnas.2105191118.
Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.
基于基因的测试是鉴定复杂性状中遗传因素的有效技术。在这里,我们提出了一种基于基因的测试框架,该框架结合了长程染色质相互作用的数据、最近用于区域测试的几项技术进展,并利用合成基因型生成的置换框架来提高基因发现的能力。通过模拟和对多种疾病和性状的全基因组关联研究 (GWAS) 和全基因组测序数据的应用,我们表明,与文献中的基于基因的最新测试相比,该测试提高了检测能力,识别了在更大规模研究中可重复的基因,并通过减少连锁不平衡的混杂效应,为基因座上的可能因果基因提供了更集中的关注。此外,我们的结果表明,纳入远端调控元件中的遗传变异往往会提高传统测试的功效。英国生物银行和日本生物银行性状的结果也可在一个公开访问的数据库中获得,允许研究人员以简单的方式查询基于基因的结果。