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MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information |
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10.1093/bioinformatics/btw012 |
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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. |
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Bioinformatics, 32(9), p. 1420-1422 |
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