Habib Ganjgahi, University of Warwick
Computationally Efficient Mixed Effect Model for Genetic Analysis of High Dimensional Neuroimaging Data

A new research direction in the neuroimaging discipline, so called imaging genetic, has emerged recently concerns describing individual differences in imaging phenotypes using genetic and environmental factors. The large number of voxel- and vertex-wise measurements in imaging genetics studies present a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot perform essential genetic analyses including heritability and association estimations and testings, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available genetic tools rely on P-values that can be inaccurate with usual parametric inference methods. In this talk computationally efficient linear mixed effect model for voxel-wise genetic analyses of high-dimensional imaging phenotypes are presented. Specifically, fast estimation and inference procedures for heritability and association analyses are introduced using orthogonal transformations that dramatically simplify the likelihood and restricted likelihood functions of mixed effect model.

Date & Time: 
Tuesday, January 3, 2017, 16:00 - 17:00
Room 221, Dept. Math. Sci., Sharif University of Technology