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

Title
MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
Publication Date
2016
Author(s)
Lee, Sang Hong
Van Der Werf, Julius H
( author )
OrcID: https://orcid.org/0000-0003-2512-1696
Email: jvanderw@une.edu.au
UNE Id une-id:jvanderw
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Oxford University Press
Place of publication
United Kingdom
DOI
10.1093/bioinformatics/btw012
UNE publication id
une:19178
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.
Link
Citation
Bioinformatics, 32(9), p. 1420-1422
ISSN
1367-4811
1367-4803
Start page
1420
End page
1422

Files:

NameSizeformatDescriptionLink