Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10⁻⁵ for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.

中文翻译：

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通过 K-sample Ball Divergence 方法识别与脑容量表型相关的遗传风险变异

包括总面积、平均厚度和总体积在内的区域人脑体积是可遗传的，并且与神经系统疾病有关。然而，大脑结构和功能的遗传结构在很大程度上仍然是未知的，值得探索。儿科影像、神经认知和遗传学 (PING) 数据集为全基因组遗传数据和相关神经影像数据提供了极好的资源。在这项研究中，我们对来自 PING 数据集的 315 个脑容量表型进行了全基因组关联研究 (GWAS)，其中包括 1036 个具有 539,865 个单核苷酸多态性 (SNP) 的样本。我们引入了一种基于 K 样本球散度 (KBD) 的非参数检验，以识别影响区域脑容量的遗传风险变异。我们进行模拟以证明 KBD 是一种强大的测试，用于识别与多变量表型相关的重要 SNP，尽管控制 I 型错误率。我们成功识别出低于 5 × 10 显着性水平的 9 个 SNP^-5用于 PING 数据。在已识别的九个遗传变异中，两个 SNP rs486179 和 rs562110 位于 ADRA1A 基因中，这是众所周知的精神疾病风险因素，如精神分裂症和注意力缺陷多动障碍。我们的研究表明，非参数检验 KBD 是在多种表型的大规模 GWAS 中识别与复杂疾病相关的遗传变异的有效方法。