title: MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.
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.
  
  
  
  MTG2 is available in https://sites.google.com/site/honglee0707/mtg2 CONTACT: hong.lee@une.edu.au
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia and Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
authors.forename: S H
authors.initials: SH
authors.lastname: Lee
authors.affiliation: School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia and.
authors.forename: J H J
authors.initials: JH
authors.lastname: van der Werf
mesh_terms: D000465:Algorithms; D000818:Animals; D016678:Genome; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D016014:Linear Models; D051379:Mice