Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes.

ABSTRACT: 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 a 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 enhanceme

SUBMITTER: Ganjgahi H 

PROVIDER: S-EPMC6092439 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature