PT - JOURNAL ARTICLE AU - Ganjgahi, Habib AU - Winkler, Anderson M. AU - Glahn, David C. AU - Blangero, John AU - Donohue, Brian AU - Kochunov, Peter AU - Nichols, Thomas E. TI - Fast and Powerful Genome Wide Association Analysis of Dense Genetic Data with High Dimensional Imaging Phenotypes AID - 10.1101/179150 DP - 2017 Jan 01 TA - bioRxiv PG - 179150 4099 - http://biorxiv.org/content/early/2017/08/21/179150.short 4100 - http://biorxiv.org/content/early/2017/08/21/179150.full 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.