Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China; School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
Am J Hum Genet. 2024 Jul 11;111(7):1448-1461. doi: 10.1016/j.ajhg.2024.05.003. Epub 2024 May 30.
Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.
三重和群体设计都是全基因组关联研究(GWAS)中识别风险遗传变异的常用研究设计。三重设计作为一种基于家族的设计,由于群体结构而不易受到混杂因素的影响,而群体设计由于样本量较大通常更具效力。在这里,我们提出了一种基于 Knockoff 的统计方法 KnockoffHybrid,用于三重和群体设计的混合分析。KnockoffHybrid 提供了一个统一的框架,结合了两种设计的优势,并在存在连锁不平衡和群体结构的情况下控制假发现率(FDR),从而产生强大的混合分析。此外,KnockoffHybrid 具有灵活性,可以利用不同类型的汇总统计信息进行混合分析,包括表达数量性状基因座(eQTL)和 GWAS 汇总统计信息。我们在模拟中表明,KnockoffHybrid 在控制变体之间复杂相关性和受试者之间群体结构的 FDR 的同时,为具有相同病例数的三重和群体设计提供了比非混合方法更高的功效。在使用来自 iPSYCH 项目的 GWAS 汇总统计信息和来自 MetaBrain 项目的 eQTL 汇总统计信息对来自 Autism Speaks MSSNG、Autism Sequencing Consortium 和 Autism Genome Project 的三个三重自闭症谱系障碍(ASD)队列进行混合分析时,KnockoffHybrid 通过复制几个已知的 ASD 风险基因并识别其他基因中的变体的额外关联来超越传统方法,包括涉及轴突导向的 PRAME 家族基因,这些基因可能作为人类言语/语言进化和相关障碍的共同靶点。