Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we exploit the distributed nature of genetic effects across the brain and apply the Multivariate Omnibus Statistical Test (MOSTest) to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology. This reflects an approximate 10-fold increase compared to the commonly applied univariate GWAS methods. Power analysis indicates that applying MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. Functional follow up including gene-based analyses implicate 10% of all protein-coding genes and point towards pathways involved in neurogenesis and cell differentiation.

中文翻译：

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多变量全基因组关联研究确定了780个与皮层形态相关的独特基因位点。

脑形态学已显示出高度可遗传性，但是迄今为止发现的遗传变异仅解释了遗传性的一小部分。在这里，我们利用遗传效应在整个大脑中的分布性质，将多变量综合统计测试（MOSTest）应用于来自N = 35,657位参与者的顶点结构磁共振成像（MRI）皮质测量的全基因组关联研究（GWAS）。英国生物银行。我们在总共780个与皮层形态相关的独特遗传基因座中，确定了695个基因座的皮质表面积和539个皮质厚度。与常用的单变量GWAS方法相比，这反映了大约10倍的增长。功效分析表明，与传统的单变量GWAS方法相比，将MOSTest应用于顶点结构MRI数据可使有效样本大小增加三倍。功能性后续研究（包括基于基因的分析）意味着所有蛋白质编码基因的10％涉及到神经发生和细胞分化的相关途径。