Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
Department of Preventive Medicine, Shantou University Medical College, Shantou, Guangdong, China.
Genet Epidemiol. 2021 Oct;45(7):710-720. doi: 10.1002/gepi.22423. Epub 2021 Jun 29.
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 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.
区域人脑体积包括总面积、平均厚度和总体积是可遗传的，并与神经紊乱有关。然而，大脑结构和功能的遗传结构在很大程度上仍然未知，值得探索。儿科成像、神经认知和遗传学 (PING) 数据集提供了一个极好的资源，其中包含全基因组遗传数据和相关的神经影像学数据。在这项研究中，我们对 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 个样本，涉及 539865 个单核苷酸多态性 (SNP)。我们引入了一种基于 K 样本球距离 (KBD) 的非参数检验方法，用于识别影响区域脑容量的遗传风险变异。我们进行了模拟，以证明 KBD 是一种强大的检验方法，用于识别与多变量表型相关的显著 SNP，尽管控制了第一类错误率。我们成功地在 PING 数据中确定了九个显著性水平低于 5 × 10 的 SNP。在确定的九个遗传变异中，两个 SNP(rs486179 和 rs562110)位于 ADRA1A 基因中，该基因是精神疾病（如精神分裂症和注意力缺陷多动障碍）的一个已知风险因素。我们的研究表明，非参数检验 KBD 是一种有效的方法，可用于识别与多种表型的大规模 GWAS 相关的复杂疾病的遗传变异。
