NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Nat Commun. 2020 Jul 14;11(1):3512. doi: 10.1038/s41467-020-17368-1.
Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.
区域脑形态具有复杂的遗传结构，由许多具有个体效应小的常见多态性组成。这对全基因组关联研究（GWAS）来说极具挑战性。由于遗传信号在脑区之间呈分布式，对区域指标进行多元分析可能会增强遗传变异的发现。目前用于 GWAS 的多元分析方法不适合这种复杂的、大规模的数据。在这里，我们引入了多元综合统计检验（MOSTest），其具有高效的计算设计，能够实现快速可靠的推断，并将其应用于 26502 名英国生物库参与者的 171 个区域脑形态测量值。在传统的全基因组显著性阈值α=5×10 下，MOSTest 确定了 347 个与区域脑形态相关的基因组位点，比以往任何研究都多，比已建立的 GWAS 方法的发现提高了三倍以上。我们的研究结果表明，超过 5%的所有蛋白编码基因都受到影响，并为涉及神经元发育和分化的基因集提供了证据。
