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

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 acco...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1038/s41467-018-05444-6

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
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ORCID:
0000-0002-4169-9781
Blangero, J More by this author
Donohue, B More by this author
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Wellcome Trust More from this funder
Publisher:
Nature Publishing Group Publisher's website
Journal:
Nature Communications Journal website
Volume:
9
Issue:
1
Pages:
3254
Publication date:
2018-08-14
Acceptance date:
2018-07-09
DOI:
EISSN:
2041-1723
Pubs id:
pubs:908884
URN:
uri:58ad1789-edb3-4fed-a496-c4f4127c41ad
UUID:
uuid:58ad1789-edb3-4fed-a496-c4f4127c41ad
Local pid:
pubs:908884
Language:
English
Keywords:

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