Author(s) |
Lee, Sang Hong
Van Der Werf, Julius H
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Publication Date |
2016
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Abstract |
We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations.
|
Citation |
Bioinformatics, 32(9), p. 1420-1422
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ISSN |
1367-4811
1367-4803
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Link | |
Publisher |
Oxford University Press
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Title |
MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
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Type of document |
Journal Article
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Entity Type |
Publication
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