Efficient multivariate linear mixed model algorithms for genome-wide association studies
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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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In TwinsUK (females only), GWASs used a linear mixed model with an empirical genetic relatedness matrix to account for cryptic pedigree and population structure.
“GWASs in TwinsUK (females only) were performed using GEMMA [55], which implements an LME model with an empirical genetic relatedness matrix to account for cryptic pedigree and population structure.”
From Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci (2023) · cites this paper as [55]
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“We employed a linear mixed model (LMM) in gemma (Zhou & Stephens, 2014) and included a kinship relatedness matrix estimated for 391 individuals in related (see above section for more details).”
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“PLOS Genetics | https://doi.org/10.1371/journal.pgen.1010786 July 17, 2023 16 / 25 Genetic correlation, heritability, GWASs, meta-analyses Genetic correlations were estimated from all SNPs using GEMMA [55] based on a multivariate linear mixed model.”
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- Authors
- Zhou, X., & Stephens, M.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Zhou, Xiang
- Stephens, Matthew
- Venue
- Nature Methods
- Published
- 2014-02-16
- Volume
- 11
- Issue
- 4
- Pages
- 407-409
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4211878
- OpenAlex
- W1952660301
- Cited by
- 841
- References
- 37
- Citations by year
- 2026: 63 · 2025: 63 · 2024: 77 · 2023: 76 · 2022: 83 · 2021: 70 · 2020: 74 · 2019: 80 · 2018: 85 · 2017: 58 · 2016: 57 · 2015: 40 · 2014: 14
- FWCI
- 37.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Broad Institute
- Terveyden ja hyvinvoinnin laitos
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL092206, HL092206)
- National Human Genome Research Institute (R56HG002585, HG02585, R01HG002585)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- multivariate linear mixed model, genome-wide association study, Gemma, likelihood ratio test, single nucleotide polymorphisms, correlated phenotypes, population stratification, computational efficiency, p-value calibration
- MeSH terms
- Algorithms, Software, Multivariate Analysis, Likelihood Functions, Linear Models, Genome, Polymorphism, Single Nucleotide
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