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, 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 random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss advantages and limitations.