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dc.contributor.authorGanjgahi, H.
dc.contributor.authorWinkler, A.M.
dc.contributor.authorGlahn, D.C.
dc.date.accessioned2019-05-17T13:21:17Z
dc.date.available2019-05-17T13:21:17Z
dc.date.issued2018
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85051673084&doi=10.1038%2fs41467-018-05444-6&partnerID=40&md5=3f6b7d37586630333ac4328ddfb86953
dc.identifier.urihttp://hdl.handle.net/10713/9204
dc.description.abstractGenome 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. Copyright 2018, The Author(s).en_US
dc.description.urihttps://dx.doi.org/10.1038/s41467-018-05444-6en_US
dc.language.isoen_USen_US
dc.publisherNature Publishing Groupen_US
dc.relation.ispartofNature Communications
dc.subjecthigh dimensional imaging phenotypesen_US
dc.subject.meshGenome-Wide Association Studyen_US
dc.subject.meshNeuroimagingen_US
dc.titleFast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypesen_US
dc.typeArticleen_US
dc.identifier.doi10.1038/s41467-018-05444-6
dc.identifier.pmid30108209


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