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

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    Author
    Ganjgahi, H.
    Winkler, A.M.
    Glahn, D.C.
    Date
    2018
    Journal
    Nature Communications
    Publisher
    Nature Publishing Group
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://dx.doi.org/10.1038/s41467-018-05444-6
    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. Copyright 2018, The Author(s).
    Keyword
    high dimensional imaging phenotypes
    Genome-Wide Association Study
    Neuroimaging
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051673084&doi=10.1038%2fs41467-018-05444-6&partnerID=40&md5=3f6b7d37586630333ac4328ddfb86953; http://hdl.handle.net/10713/9204
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41467-018-05444-6
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    UMB Open Access Articles 2018

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