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School of Medicine Publications and Presentations
Document Type
Article
Publication Date
8-18-2018
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.
Recommended Citation
Ganjgahi, H., Winkler, A.M., Glahn, D.C. et al. Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes. Nat Commun 9, 3254 (2018). https://doi.org/10.1038/s41467-018-05444-6
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Nature Communications
DOI
10.1038/s41467-018-05444-6
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
Copyright © 2018, The Author(s)