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

Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Brian Donohue, Peter Kochunov and Thomas E. Nichols ()
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Habib Ganjgahi: University of Oxford
Anderson M. Winkler: University of Oxford
David C. Glahn: Yale University School of Medicine
John Blangero: University of Texas Rio Grande Valley School of Medicine
Brian Donohue: University of Maryland School of Medicine
Peter Kochunov: University of Maryland School of Medicine
Thomas E. Nichols: University of Oxford

Nature Communications, 2018, vol. 9, issue 1, 1-13

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

Date: 2018
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DOI: 10.1038/s41467-018-05444-6

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