神经影像学全基因组关联研究的多重比较方法
Multiple Comparison Procedures for Neuroimaging Genomewide Association Studies
摘要：
最近的神经影像学研究集中在评估在全基因组范围内测量的遗传变异与脑成像表型之间的关联。该领域的大量工作在全基因组的基础上应用大规模单变量分析来发现影响大脑结构的单核苷酸多态性。在本文中，我们建议使用各种降维方法对大脑结构MRI扫描和基因组数据，由阿尔茨海默病神经影像学倡议（ADNI）的研究。我们还考虑了一种新的多重检验调整方法，并将其与现有的两种错误发现率（FDR）调整方法进行了比较。仿真结果表明，所提出的方法的功率增加。真实数据分析表明，所提出的程序能够找到遗传变异与脑容量差异之间的关联，从而提供潜在的新生物学见解。
英文摘要：
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.
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