Hua Wen-Yu, Nichols Thomas E, Ghosh Debashis
Department of Statistics, Penn State University, State College, PA 16802, USA
Department of Statistics, University of Warwick, Conventry, CV4 7AL, UK.
Biostatistics. 2015 Jan;16(1):17-30. doi: 10.1093/biostatistics/kxu026. Epub 2014 Jun 23.
Recent research in neuroimaging has focused on assessing associations between genetic variants that are measured on a genomewide scale and brain imaging phenotypes. A large number of works in the area apply massively univariate analyses on a genomewide basis to find single nucleotide polymorphisms that influence brain structure. In this paper, we propose using various dimensionality reduction methods on both brain structural MRI scans and genomic data, motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustment method and compare it with two existing false discovery rate (FDR) adjustment methods. The simulation results suggest an increase in power for the proposed method. The real-data analysis suggests that the proposed procedure is able to find associations between genetic variants and brain volume differences that offer potentially new biological insights.
近期神经影像学研究聚焦于评估全基因组范围内测量的基因变异与脑成像表型之间的关联。该领域的大量研究在全基因组基础上应用大规模单变量分析，以寻找影响脑结构的单核苷酸多态性。在本文中，受阿尔茨海默病神经影像学计划（ADNI）研究的启发，我们提议对脑结构MRI扫描数据和基因组数据都使用各种降维方法。我们还考虑了一种新的多重检验校正方法，并将其与两种现有的错误发现率（FDR）校正方法进行比较。模拟结果表明所提方法的功效有所提高。实际数据分析表明，所提程序能够找到基因变异与脑容量差异之间的关联，这可能提供新的生物学见解。
