Cited by 1,001Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic diversity and population structure

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Advantages and pitfalls in the application of mixed-model association methods

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Yang, Jian, Zaitlen, Noah A, Goddard, Michael E, Visscher, Peter M, Price, Alkes L·2014·Nature Genetics

DOI 10.1038/ng.2876PMC PMC3989144

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Abstract

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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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
ORCID: Yang, Jian
Zaitlen, Noah A
Goddard, Michael E
Visscher, Peter M
Price, Alkes L
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2014-01-29
Volume: 46
Issue: 2
Pages: 100-106
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/ng.2876
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
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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