Efficient multivariate linear mixed model algorithms for genome-wide association studies

• Published in: Nature methods Vol. 11; no. 4; pp. 407 - 409
• Main Authors: Zhou, Xiang, Stephens, Matthew
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2014; Nature Publishing Group
• Subjects: 45/43; 49/23; 631/114/2415; 631/208/480; Algorithms; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; brief-communication; Calibration; Genome; Genomes; Life Sciences; Likelihood Functions; Linear Models; Mathematical models; Multivariate Analysis; Polymorphism, Single Nucleotide - genetics; Proteomics; Software
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.2848

Multivariate linear mixed models implemented in the GEMMA software package add speed, power and the ability to test for genome-wide associations between genetic polymorphisms and multiple correlated phenotypes. 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.