RT Journal Article
SR Electronic
T1 Fast and Powerful Genome Wide Association Analysis of Dense Genetic Data with High Dimensional Imaging Phenotypes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 179150
DO 10.1101/179150
A1 Habib Ganjgahi
A1 Anderson M. Winkler
A1 David C. Glahn
A1 John Blangero
A1 Brian Donohue
A1 Peter Kochunov
A1 Thomas E. Nichols
YR 2017
UL http://biorxiv.org/content/early/2017/08/21/179150.abstract
AB Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.