MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information

Lee, S. H. and Van Der Werf, J. H. J. (2016) MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information. Bioinformatics, 32 9: 1420-1422. doi:10.1093/bioinformatics/btw012


Author Lee, S. H.
Van Der Werf, J. H. J.
Title MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1460-2059
1367-4803
Publication date 2016-05-01
Year available 2016
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btw012
Open Access Status DOI
Volume 32
Issue 9
Start page 1420
End page 1422
Total pages 3
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2017
Language eng
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.
Keyword Algorithm
Genetic analysis of complex traits
Genome-wide SNP
Genetic variance
MTG2
Genomic information
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
 
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