RT Journal Article
SR Electronic
T1 MTG2: An efficient algorithm for multivariate linear mixed model analysis based on genomic information
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 027201
DO 10.1101/027201
A1 S.H. Lee
A1 J.H.J. van der Werf
YR 2015
UL http://biorxiv.org/content/early/2015/12/02/027201.abstract
AB 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 could be more than 1000 times faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multitrait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss advantages and limitations.Availability: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2.Contact: hong.lee{at}une.edu.auSupplementary information: Supplementary data are available.