The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Genome-wide efficient mixed-model analysis for association studies
Open accessAbstract
Linear mixed models have attracted considerable attention recently as a powerful and effective tool for accounting for population stratification and relatedness in genetic association tests. However, existing methods for exact computation of standard test statistics are computationally impractical for even moderate-sized genome-wide association studies. To address this issue, several approximate methods have been proposed. Here, we present an efficient exact method, which we refer to as genome-wide efficient mixed-model association (GEMMA), that makes approximations unnecessary in many contexts. This method is approximately n times faster than the widely used exact method known as efficient mixed-model association (EMMA), where n is the sample size, making exact genome-wide association analysis computationally practical for large numbers of individuals.
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“We employed GEMMA, a package that performs a likelihood ratio test using a linear mixed model to adjust for relatedness between individuals.”
From Genetic associations with age at dementia onset in the PSEN1 E280A Colombian kindred (2023) · cites this paper as [13]
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Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Yu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M., Doebley, J., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2005) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,155
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928 · cited 4×
“In particular, because individuals in the data set are closely related, and the strongly associated SNPs contribute to a significant proportion of phenotypic variation in HDL-C 13 , using estimates of variance components or fitted residuals from the null model for testing may be expected to yield conservative p values…”
MethodInfluentialVariance component model to account for sample structure in genome-wide association studies
Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S.-Y., Freimer, N. B., Sabatti, C., & Eskin, E. (2010) · Nature Genetics · doi:10.1038/ng.548 · Cited by 2,817 · cited 3×
“Kang et al 1 also avoid repeatedly estimating variance components by a slightly different strategy, which keeps the heritability estimated from the null model fixed when testing individual SNPs.”
BackgroundMethodMixed linear model approach adapted for genome-wide association studies
Zhang, Z., Ersoz, E. S., Lai, C., Todhunter, R. J., Tiwari, H. K., Gore, M. A., Bradbury, P. J., Yu, J., Arnett, D. K., Ordovás, J. M., et al. (2010) · Nature Genetics · doi:10.1038/ng.546 · Cited by 2,132 · cited 5×
“…models (LMMs, also known as mixed linear models, or MLMs) to test for association in genome-wide association studies (GWAS), because of their demonstrated effectiveness in accounting for relatedness among samples and in controlling for population stratification and other confounding factors 1–7 .”
BackgroundMethodInfluentialEfficient Control of Population Structure in Model Organism Association Mapping
Kang, H. M., Zaitlen, N., Wade, C. M., Kirby, A. W., Heckerman, D. E., Daly, M. J., & Eskin, E. (2008) · Genetics · doi:10.1534/genetics.107.080101 · Cited by 1,869 · cited 1×
“…algorithm for computing (effectively) exact association test statistics (either the Wald test or the likelihood ratio test), implemented in the Efficient Mixed Model Association (EMMA) software 3 , had a per-SNP computational time that increases with the cube of the number of individuals ( n ).”
MethodGenABEL: an R library for genome-wide association analysis
Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm108 · Cited by 1,836 · cited 2×
“Probably the simplest and fastest of these approximations, GRAMMAR (Genome-wide Rapid Association using Mixed Model And Regression), implemented in the software GenABEL 9 , first estimates the residuals from the LMM under the null model, and then treats these residuals as phenotypes for further genome-wide analysis by…”
MethodApplications of linear models in animal breeding
Henderson, C. R. (1984) · University of Guelph eBooks · Cited by 1,700
New approaches to population stratification in genome-wide association studies
Price, A. L., Zaitlen, N., Reich, D., & Patterson, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2813 · Cited by 1,226 · cited 1×
“For the derivatives of the log-likelihood and log-restricted likelihood functions (5)–(8), we need to evaluate two types of quantities: trace terms (trace(H−1G), trace(H−1GH−1G), trace(PxG) and trace(PxGPxG)), and vector-matrix-vector (5)”
FaST linear mixed models for genome-wide association studies
Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209 · cited 5×
“However, as noted by Lippert et al 8 , if a lower-rank relatedness matrix is used then this reduces computing time (computational complexity of the singular value decomposition can scale with nr 2 ) and in some cases memory requirements (e.g. Lippert et al. 8 suggest using a relatedness matrix…”
BackgroundMethodInfluential
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Research works printed but not linked: 1
- Searle, S.R., Casella, G. & McCulloch, C.E. Variance Components. (Wiley, New York, 2006).
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The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547
Self-citation10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719
Genome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849
Genome-Wide Regression and Prediction with the BGLR Statistical Package
Pérez‐Rodríguez, P., & Campos, G. D. L. (2014) · Genetics · doi:10.1534/genetics.114.164442 · Cited by 1,632
Liu, X., Huang, M., Fan, B., Buckler, E. S., & Zhang, Z. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005767 · Cited by 1,635
“Similar to previous comparisons[6,18,20], we decomposed the total computational time into three components: building the K, attaining optimization on variance components, and performing association tests.”
Method
Metadata
- Authors
- Zhou, X., & Stephens, M.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Zhou, Xiang
- Stephens, Matthew
- Venue
- Nature Genetics
- Published
- 2012-06-17
- Volume
- 44
- Issue
- 7
- Pages
- 821-824
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3386377
- OpenAlex
- W1980168725
- Cited by
- 3,627
- References
- 19
- Citations by year
- 2026: 390 · 2025: 424 · 2024: 469 · 2023: 378 · 2022: 396 · 2021: 370 · 2020: 257 · 2019: 238 · 2018: 199 · 2017: 150 · 2016: 135 · 2015: 101 · 2014: 76 · 2013: 38 · 2012: 5
- FWCI
- 42.40
- 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)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust (085475)
- 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 Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genome-wide association study, linear mixed models, population stratification, relatedness, Gemma, Emma, computational efficiency, genetic association testing
- MeSH terms
- Computer Simulation, Humans, Models, Genetic, Software, Genome, Human, Linear Models, Polymorphism, Single Nucleotide, Population Groups, Genome-Wide Association Study
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