Advantages and pitfalls in the application of mixed-model association methods
Open accessAbstract
Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.
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Analytical techniques including PCA and linear mixed models reduce population stratification confounding but often leave residual confounding in GWAS results.
“Analytical techniques including principal component analysis (PCA) and linear mixed models have been developed to reduce population stratification confounding12, but these techniques often leave residual confounding in GWAS results5,8,10.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [12]
The “Mixed model association methods prevent false-positive associations and increase power” section says mixed linear models address confounding from sample structure. The “Pitfall: using a small subset of markers in GRM” section shows that particular implementations can leave stratification insufficiently corrected. But the paper also describes correction as nearly perfect for common variants, so it does not support the broad claim that PCA and linear mixed models often leave residual confounding in GWAS results. (LLM, conf=0.86)
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Self-citation
- Authors
- Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L.
- Affiliations
- The University of Queensland (AU)
- University of California, San Francisco (US)
- The University of Melbourne (AU)
- Broad Institute (US)
- Corresponding author
- Visscher, Peter M
- Price, Alkes L
- Venue
- Nature Genetics
- Published
- 2014-01-29
- Volume
- 46
- Issue
- 2
- Pages
- 100-106
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3989144
- OpenAlex
- W2090075367
- Cited by
- 981
- References
- 42
- Citations by year
- 2026: 66 · 2025: 85 · 2024: 83 · 2023: 84 · 2022: 108 · 2021: 93 · 2020: 85 · 2019: 85 · 2018: 88 · 2017: 78 · 2016: 63 · 2015: 38 · 2014: 24 · 2013: 1
- FWCI
- 44.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust
- National Institutes of Health
- Medical Research Council
- National Human Genome Research Institute (R01HG006399)
- National Institute of General Medical Sciences (P01GM099568, R01GM075091)
- National Institute of Environmental Health Sciences (T32ES007142)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- genetic association studies, population structure, false positive association, statistical power, conditional analysis, research design, computational cost
- MeSH terms
- Colitis, Ulcerative, Computer Simulation, Humans, Multiple Sclerosis, Research Design, Linear Models, Genetic Association Studies
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