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Massively expedited genome-wide heritability analysis (MEGHA)
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Ge, Tian, Nichols, Thomas E., Lee, Phil H., Holmes, Avram J., Roffman, Joshua L., Buckner, Randy L., Sabuncu , Mert R. and Smoller, Jordan W. (2015) Massively expedited genome-wide heritability analysis (MEGHA). Proceedings of the National Academy of Sciences of the United States of America, Volume 112 (Number 8). pp. 2479-2484. doi:10.1073/pnas.1415603112 ISSN 0027-8424 .
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Official URL: http://dx.doi.org/10.1073/pnas.1415603112
Abstract
The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide Complex Trait Analysis (GCTA) is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that is increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a novel, fast and accurate statistical method for high-dimensional heritability analysis using genome-wide single nucleotide polymorphism (SNP) data from unrelated individuals, termed Massively Expedited Genome-wide Heritability Analysis (MEGHA), and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural magnetic resonance imaging (MRI) scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.
Item Type: | Journal Article | ||||||||||
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Subjects: | Q Science > QH Natural history > QH426 Genetics | ||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Genomes -- Mathematical models, Imaging systems in genetics | ||||||||||
Journal or Publication Title: | Proceedings of the National Academy of Sciences of the United States of America | ||||||||||
Publisher: | National Academy of Sciences | ||||||||||
ISSN: | 0027-8424 | ||||||||||
Official Date: | 24 February 2015 | ||||||||||
Dates: |
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Volume: | Volume 112 | ||||||||||
Number: | Number 8 | ||||||||||
Page Range: | pp. 2479-2484 | ||||||||||
DOI: | 10.1073/pnas.1415603112 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||
Date of first compliant deposit: | 28 December 2015 | ||||||||||
Funder: | Massachusetts General Hospital. Center for Functional Neuroimaging Technologies, National Institute of Biomedical Imaging and Bioengineering (U.S.) (NIBIB), National Institutes of Health (U.S.) (NIH), Wellcome Trust (London, England), BrightFocus Foundation | ||||||||||
Grant number: | P41EB015896 (MGH), R01 EB015611-01 (NIH), U54 MH091657-03 (NIH), K99MH101367 (NIH), K01MH099232 (NIH), R01 NS083534 (NIH), R01 NS070963 (NIH), 1K25EB013649-01 (NIBIB), K24MH094614 (NIH), R01 MH101486 (NIH), 100309/Z/12/Z (WT), 098369/Z/12/Z (WT) |
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