Journal
NATURE COMMUNICATIONS
Volume 9, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-018-05444-6
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Funding
- Wellcome Trust
- National Institutes of Health [R01 EB015611-01]
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB015611] Funding Source: NIH RePORTER
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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.
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