STATISTICAL METHODS FOR BRAIN IMAGING GENOMICS

Abstract

Brain Imaging genetic studies examine genetic basis of brain images to better understand the genetic impact on behavior and disease phenotypes. Methods for identifying genetic associations with voxelwise brain imaging data have evolved from parallel analysis on each voxel to incorporating spatial smoothness and correlation to increase statistical detection power. Challenges still exist on the joint analysis of imaging data and genetic data, including imperfect alignment of affected regions and registration error, low signal to noise ratio in high-dimensional data, complex relationships, high computation complexity, and between-study heterogeneity. To address these issues, the following methods are proposed.First, to deal with imperfect alignment and registration error in brain imaging data, we proposed a region-based functional genome-wide association detection method, which also reduces computation burden as compared to standard voxelwise methods. The method summarizes regional voxelwise measurements into density curves. The non-parametric ball covariance test is then used to detect association between the log-quantile transformed regional densities and genetic markers. We compared the ball covariance test with other state-of-the-art methods on simulated datasets and demonstrate good sensitivity and specificity of our method. Second, we combined functional partial least squares with distance correlation to reduce computation burden of high dimensional data and allow flexible characterization of the imaging-genetic relationship. Third, given imaging-genetic data from more than one studies, we theoretically compared the ensembled learner and merged learner in the prediction problem, where learners are trained using the multivariate varying coefficient model and multi-study data are assumed to come from a mixed model, where the mixed effect represents inter-study heterogeneity.Doctor of Philosoph