PT  - JOURNAL ARTICLE
AU  - S.H. Lee
AU  - J.H.J. van der Werf
TI  - MTG2: An efficient algorithm for multivariate linear mixed model analysis based on genomic information
AID  - 10.1101/027201
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 027201
4099  - http://biorxiv.org/content/early/2015/12/02/027201.short
4100  - http://biorxiv.org/content/early/2015/12/02/027201.full
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.