Multiple Comparison Procedures for Neuroimaging Genomewide Association Studies

Recent researches in neuroimaging have 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. Based on the results from both simulation studies and the real data analysis, our proposed procedure is able to find the association between genetic variants and brain volume difference with increased power.