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

Author(s)
Lee, Sang Hong
Van Der Werf, Julius H
Publication Date
2016
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
ISSN
1367-4811
1367-4803
Link
Publisher
Oxford University Press
Title
MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
Type of document
Journal Article
Entity Type
Publication

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