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Activity Number: 249
Type: Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #315249
Title: Fast Integrated Genetic Effect Investigation on Imaging Data Using Weighted Score Test via Functional Mixed Effects Model
Author(s): Ja-An Lin* and Joseph Ibrahim and Hongtu Zhu and Hongtu Zhu
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Keywords: score test ; adaptive method ; mixed model ; imaging ; genetics ; likelihood

Traditional statistical methods analyzing imaging genetics data often have low statistical power. We previously proposed a method called Functional Mixed Effect Model (FMEM) with a likelihood ratio test, which provides better power to identify the genetic effect to the imaging responses while incorporating the linkage disequilibrium (LD) in genomic information, and spatial correlation in imaging responses. However, due to high dimensional feature in imaging data, the method is computationally intensive. Thus we further proposed fast algorithm with a weighted score test statistic, which pertains the correlation in both genomic variation and imaging responses with better statistical power. Our method is evaluated by simulation studies and the result shows it outperforms voxel-wise based approach by greater statistical power with great type I error control with efficient computational time. To our up-to-date knowledge, our method is the first one to directly weight the original test statistic instead of weighting likelihood or estimators.

Authors who are presenting talks have a * after their name.

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