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Title: MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
Contributor(s): Lee, Sang Hong  (author); Van Der Werf, Julius H  (author)orcid 
Publication Date: 2016
Open Access: Yes
DOI: 10.1093/bioinformatics/btw012Open Access Link
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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.
Publication Type: Journal Article
Grant Details: NHMRC/APP1080157
Source of Publication: Bioinformatics, 32(9), p. 1420-1422
Publisher: Oxford University Press
Place of Publication: United Kingdom
ISSN: 1367-4803
Field of Research (FOR): 070201 Animal Breeding
060408 Genomics
060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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