Wen Canhong, Mehta Chintan M, Tan Haizhu, Zhang Heping
Department of Biostatistics, Yale University School of Public Health, Connecticut, United States of America.
Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, China.
Genet Epidemiol. 2018 Apr;42(3):265-275. doi: 10.1002/gepi.22111. Epub 2018 Feb 7.
Neuropsychological disorders have a biological basis rooted in brain function, and neuroimaging data are expected to better illuminate the complex genetic basis of neuropsychological disorders. Because they are biological measures, neuroimaging data avoid biases arising from clinical diagnostic criteria that are subject to human understanding and interpretation. A challenge with analyzing neuroimaging data is their high dimensionality and complex spatial relationships. To tackle this challenge, we introduced a novel distance covariance tests that can assess the association between genetic markers and multivariate diffusion tensor imaging measurements, and analyzed a genome-wide association study (GWAS) dataset collected by the Pediatric Imaging, Neurocognition, and Genetics (PING) study. We also considered existing approaches as comparisons. Our results showed that, after correcting for multiplicity, distance covariance tests of the multivariate phenotype yield significantly greater power at detecting genetic markers affecting brain structure than standard mass univariate GWAS of individual neuroimaging biomarkers. Our results underscore the usefulness of utilizing the distance covariance to incorporate neuroimaging data in GWAS.
神经心理障碍有植根于脑功能的生物学基础，并且神经影像数据有望更好地阐明神经心理障碍复杂的遗传基础。由于神经影像数据是生物学测量手段，它们避免了因受人类理解和解释影响的临床诊断标准而产生的偏差。分析神经影像数据面临的一个挑战是其高维度和复杂的空间关系。为应对这一挑战，我们引入了一种新型距离协方差检验，该检验可评估遗传标记与多变量扩散张量成像测量之间的关联，并分析了由儿科影像、神经认知与遗传学（PING）研究收集的全基因组关联研究（GWAS）数据集。我们还将现有方法作为比较对象。我们的结果表明，在校正多重性之后，多变量表型的距离协方差检验在检测影响脑结构的遗传标记方面，比单个神经影像生物标志物的标准大规模单变量GWAS具有显著更高的效能。我们的结果强调了利用距离协方差将神经影像数据纳入GWAS的有用性。
