Efficient multivariate linear mixed model algorithms for genome-wide association studies
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Updated: Jan 24, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Xiang Zhou1, Matthew Stephens1
11] Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. [2] Department of Statistics, University of Chicago, Chicago, Illinois, USA.
Abstract:
Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.
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