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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 enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.

Original publication

DOI

10.1038/s41467-018-05444-6

Type

Journal article

Journal

Nature communications

Publication Date

14/08/2018

Volume

9

Addresses

Department of Statistics, University of Oxford, Oxford, UK.

Keywords

Brain, Humans, Linear Models, Phenotype, Anisotropy, Models, Genetic, Computer Simulation, Software, Databases, Genetic, Genome-Wide Association Study