Efficient Bayesian mixed-model analysis increases association power in large cohorts
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Abstract
Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts and may not optimize power. All existing methods require time cost O(MN(2)) (where N is the number of samples and M is the number of SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here we present a far more efficient mixed-model association method, BOLT-LMM, which requires only a small number of O(MN) time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to 9 quantitative traits in 23,294 samples from the Women's Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for genome-wide association studies in large cohorts.
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All traits have modest heritability, ranging from 10% for moderate intensity activity to 21% for overall activity.
“Using heritability estimates from BOLT-LMM15 (Methods), we found all traits to have modest heritability, ranging from 10% for moderate intensity activity to 21% for overall activity (Supplementary Fig. 3).”
From GWAS identifies 14 loci for device-measured physical activity and sleep duration (2018) · cites this paper as [15]
Using BOLT-LMM, which adjusts for population structure and relatedness, allowed inclusion of 13,320 additional individuals compared with analysing a reduced set of unrelated white British individuals using linear regression.
“To improve our power to detect associations, we used BOLT-LMM15 to perform linear mixed models analysis. As this adjusts for population structure and relatedness, we could include many additional individuals (n = 13,320) than if following the common practice of analysing a reduced set of unrelated white British individuals using linear regression60.”
From GWAS identifies 14 loci for device-measured physical activity and sleep duration (2018) · cites this paper as [15]
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- Authors
- Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., Patterson, N., & Price, A. L.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- Brigham and Women's Hospital (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Loh, Po-Ru
- Price, Alkes L
- ORCID
- Venue
- Nature Genetics
- Published
- 2015-02-02
- Volume
- 47
- Issue
- 3
- Pages
- 284-290
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC4342297
- OpenAlex
- W1966775465
- Cited by
- 1,507
- References
- 60
- Citations by year
- 2026: 81 · 2025: 153 · 2024: 173 · 2023: 161 · 2022: 204 · 2021: 217 · 2020: 136 · 2019: 153 · 2018: 114 · 2017: 56 · 2016: 42 · 2015: 15 · 2014: 2
- FWCI
- 51.93
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- Donald W. Reynolds Foundation
- Amgen
- Fondation Leducq
- National Institutes of Health (5r01hl043851-10, HL-043851)
- National Heart, Lung, and Blood Institute (R01HL080467, HL043851, R01‐HL043851, K99HL122515, HL080467, CA047988)
- National Human Genome Research Institute (R01HG006399, F32HG007805)
- National Cancer Institute (HL-080467, HL-043851, CA 047988, UM1CA182913, R01CA047988)
- National Institute of General Medical Sciences (R01GM105857)
- 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 and phenotypic traits in livestock
- Keywords
- linear mixed models, genome-wide association study, statistical power, computational efficiency, large cohorts, quantitative traits
- MeSH terms
- Algorithms, Bayes Theorem, Female, Humans, Genome, Human, Linear Models, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genetic Association Studies, Genotyping Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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