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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The main association analysis used GEMMA, a linear mixed model likelihood ratio test that adjusts for relatedness between individuals.
“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 <i>PSEN1 E280A</i> Colombian kindred (2023) · cites this paper as [13]
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MethodInfluentialModel-free Estimation of Recent Genetic Relatedness
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Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels
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Moser, G., Lee, S., Hayes, B. J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1004969 · Cited by 381 · see citing works
“We compare BayesR with traditional single-SNP GWAS analyses [13], a linear mixed-effects modeling approach (LMM) [5,10–12] and a Bayesian sparse linear mixed model (BSLM) [9,14].”
MethodMethodological implementation of mixed linear models in multi-locus genome-wide association studies
Wen, Y.-J., Zhang, H., Ni, Y.-L., Huang, B., Zhang, J., Feng, J.-Y., Wang, S.-B., Dunwell, J. M., Zhang, Y., & Wu, R. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw145 · Cited by 396 · see citing works
“[3] proposed an efficient mixed model association (EMMA), and other authors suggested alternatives, such as EMMA eXpedited (EMMAX) [20], FaST-LMM [21], FaST-LMM-Select [22], genome-wide EMMA [4] and genome-wide rapid association using mixed model and regression-Gamma (GRAMMAR-Gamma) [23].”
MethodInfluentialGenome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis
Coll, F., Phelan, J. E., Hill-Cawthorne, G. A., Nair, M. B., Mallard, K., Ali, S., Abdallah, A. M., Alghamdi, S. A., Alsomali, M., Ahmed, A. O., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0029-0 · Cited by 364 · see citing works
Salt stress under the scalpel – dissecting the genetics of salt tolerance
Morton, M. J. L., Awlia, M., Al‐Tamimi, N., Saadé, S., Pailles, Y., Negrão, S., & Tester, M. A. (2018) · The Plant Journal · doi:10.1111/tpj.14189 · Cited by 375 · see citing works
A super pan-genomic landscape of rice
Shang, L., Li, X., He, H., Yuan, Q., Song, Y., Wei, Z., Lin, H., Hu, M., Zhao, F., Zhang, C., et al. (2022) · Cell Research · doi:10.1038/s41422-022-00685-z · Cited by 375 · see citing works
Plassais, J., Kim, J., Davis, B. W., Karyadi, D. M., Hogan, A. N., Harris, A. C., Decker, B. J., Parker, H. G., & Ostrander, E. A. (2019) · Nature Communications · doi:10.1038/s41467-019-09373-w · Cited by 363 · see citing works
Spindel, J. E., Begum, H., Akdemir, D., Collard, B. C. Y., Redoña, E. D., Jannink, J., & McCouch, S. R. (2016) · Heredity · doi:10.1038/hdy.2015.113 · Cited by 351 · see citing works
“Run GWAS using Genome-wide Efficient Mixed Model Association (GEMMA) five times, once for each validation fold (Zhou and Stephens, 2012).”
MethodLi, X., Yang, J. H., Shen, M., Xie, X.-L., Liu, G., Xu, Y., Lv, F., Yang, H., Yang, Y., Liu, C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16485-1 · Cited by 352 · see citing works
Conomos, M. P., Laurie, C. A., Stilp, A. M., Gogarten, S. M., McHugh, C. P., Nelson, S. C., Sofer, T., Fernández-Rhodes, L., Justice, A. E., Graff, M., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.12.001 · Cited by 336 · see citing works
Strope, P. K., Skelly, D. A., Kozmin, S. G., Mahadevan, G., Stone, E. A., Magwene, P. M., Dietrich, F. S., & McCusker, J. H. (2015) · Genome Research · doi:10.1101/gr.185538.114 · Cited by 337 · see citing works
“To conduct marker-trait association tests, we tested for association at all variation with a minor allele frequency of at least 5%, using the program GEMMA version 0.94beta, which is a mixed model approach that corrects for sample relatedness and population stratification (Zhou and Stephens 2012).”
Loci associated with skin pigmentation identified in African populations
Crawford, N. G., Kelly, D., Hansen, M. E. B., Beltrame, M., Fan, S.-H., Bowman, S. L., Jewett, E., Ranciaro, A., Thompson, S., Lo, Y., et al. (2017) · Science · doi:10.1126/science.aan8433 · Cited by 345 · see citing works
Social status alters immune regulation and response to infection in macaques
Snyder-Mackler, N., Sanz, J., Kohn, J. N., Brinkworth, J. F., Morrow, S., Shaver, A. O., Grenier, J.-C., Pique-Regi, R., Johnson, Z. P., Wilson, M. E., et al. (2016) · Science · doi:10.1126/science.aah3580 · Cited by 318 · see citing works
A genome-wide association study of anorexia nervosa
Perica, V. B., Franklin, C. S., Floyd, J. A. B., Thornton, L. M., Huckins, L. M., Southam, L., Rayner, N. W., Tachmazidou, I., Klump, K. L., Treasure, J. L., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.187 · Cited by 314 · see citing works
Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms
CARDIoGRAMplusC4D, Howson, J. M. M., EPIC-CVD, Zhao, W., Barnes, D. R., Ho, W.-K., Young, R., Paul, D. S., Waite, L. L., Freitag, D. F., et al. (2017) · Nature Genetics · doi:10.1038/ng.3874 · Cited by 304 · see citing works
Population genetics of the coral Acropora millepora : Toward genomic prediction of bleaching
Fuller, Z. L., Mocellin, V. J. L., Morris, L. A., Cantin, N. E., Shepherd, J., Sarre, L. A., Peng, J., Liao, Y., Pickrell, J. K., Andolfatto, P., et al. (2020) · Science · doi:10.1126/science.aba4674 · Cited by 287 · see citing works
“Given these data from corals and their symbionts, we used a linear mixed model (LMM) to test for additive effects of SNPs (with minor allele frequencies >5%) on the quantilenormalized visual score (67, 68), including as covariates the top four principal components of environmental variables (which together account for…”
MethodSoftware for Genome‐Wide Association Studies in Autopolyploids and Its Application to Potato
Rosyara, U. R., Jong, W. S. D., Douches, D. S., & Endelman, J. B. (2016) · The Plant Genome · doi:10.3835/plantgenome2015.08.0073 · Cited by 294 · see citing works
“, 2010) and the availability of several software packages (Bradbury et al., 2007; Endelman, 2011; Zhou and Stephens, 2012; Lipka et al., 2012), the Q + K method is now the most widely used single-marker test for GWAS in diploid species.”
MethodA SUPER Powerful Method for Genome Wide Association Study
Wang, Q., Tian, F., Pan, Y., Buckler, E. S., & Zhang, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107684 · Cited by 287 · see citing works
“FaST-LMM-Select performs better than EMMAX and GLM. SUPER performs better than FaST-LMM-Select (Figure 2b).”
MethodInfluentialEarle, S. G., Wu, C., Charlesworth, J., Stoesser, N. E., Gordon, N. C., Walker, T. M., Spencer, C. C. A., Iqbal, Z., Clifton, D. A., Hopkins, K. L., et al. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.41 · Cited by 279 · see citing works
Tamba, C. L., Ni, Y.-L., & Zhang, Y. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005357 · Cited by 287 · see citing works
Genetic mapping of quantitative trait loci in crops
XU, Y. C., Li, P., Yang, Z., & Xu, C. (2016) · The Crop Journal · doi:10.1016/j.cj.2016.06.003 · Cited by 283 · see citing works
“[65] X. Zhou, P. Carbonetto, M. Stephens, Polygenic modeling with Bayesian sparse linear mixed models, PLoS Genet.”
MethodInfluentialThe gut microbiota is largely independent of host genetics in regulating fat deposition in chickens
Wen, C., Yan, W., Sun, C., Ji, C., Zhou, Q., Zhang, D., Zheng, J., & Yang, N. (2019) · The ISME Journal · doi:10.1038/s41396-019-0367-2 · Cited by 279 · see citing works
A single gene underlies the dynamic evolution of poplar sex determination
Müller, N. A., Kersten, B., Montalvão, A. P. L., Mähler, N., Bernhardsson, C., Bräutigam, K., Lorenzo, Z. C., Hoenicka, H., Kumar, V., Mäder, M., et al. (2020) · Nature Plants · doi:10.1038/s41477-020-0672-9 · Cited by 263 · see citing works
Complex disease and phenotype mapping in the domestic dog
Hayward, J. J., Castelhano, M. G., Oliveira, K. C., Corey, E. E., Balkman, C. E., Baxter, T. L., Casal, M. L., Fang, M., Garrison, S. J., Kalla, S. E., et al. (2016) · Nature Communications · doi:10.1038/ncomms10460 · Cited by 267 · see citing works
Hao, C., Jiao, C., Hou, J., Tian, L., Liu, H., Wang, Y., Zheng, J., Liu, H., Bi, Z., Xu, F., et al. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.09.001 · Cited by 268 · see citing works
Population structure in genetic studies: Confounding factors and mixed models
Sul, J. H., Martin, L. S., & Eskin, E. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007309 · Cited by 264 · see citing works
“For example, many approaches have been developed to improve the efficiency of mixed models, including the methods Fast-LMM [17] and GEMMA [7].”
MethodThe variant call format provides efficient and robust storage of GWAS summary statistics
Lyon, M., Andrews, S. J., Elsworth, B., Gaunt, T. R., Hemani, G., & Marcora, E. M. (2021) · Genome biology · doi:10.1186/s13059-020-02248-0 · Cited by 260 · see citing works
Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa
Gurdasani, D., Carstensen, T., Fatumo, S., Chen, G., Franklin, C. S., Prado-Martinez, J., Bouman, H. J., Abascal, F., Haber, M., Tachmazidou, I., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.004 · Cited by 253 · see citing works
Våge, D. I., Husdal, M., Kent, M., Klemetsdal, G., & Boman, I. A. (2013) · BMC Genetics · doi:10.1186/1471-2156-14-1 · Cited by 253 · see citing works
“Association testing was performed by a linear mixed model (GEMMA) [12], using estimated breeding values (EBVs) for daughter litter size as phenotypes.”
MethodGenome-Wide Association Studies of the Human Gut Microbiota
Davenport, E., Cusanovich, D. A., Michelini, K., Barreiro, L. B., Ober, C., & Gilad, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140301 · Cited by 253 · see citing works
The Genetic Architecture of Gene Expression in Peripheral Blood
Lloyd‐Jones, L. R., Holloway, A., McRae, A. F., Yang, J., Small, K. S., Zhao, J., Zeng, B., Bakshi, A., Metspalu, A., Dermitzakis, M., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.12.008 · Cited by 243 · see citing works
Zhang, J., Feng, J.-Y., Ni, Y.-L., Wen, Y.-J., Niu, Y., Tamba, C. L., Yue, C., Song, Q., & Zhang, Y.-M. (2017) · Heredity · doi:10.1038/hdy.2017.8 · Cited by 249 · see citing works
“We also conducted GEMMA (Zhou and Stephens, 2012) and its power is same as that of EMMA (results not shown). pLARmEB works better than all the other methods considered.”
MethodHeritable components of the human fecal microbiome are associated with visceral fat
Beaumont, M., Goodrich, J. K., Jackson, M. A., Yet, İ., Davenport, E., Vieira‐Silva, S., Debelius, J. W., Pallister, T., Mangino, M., Raes, J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1052-7 · Cited by 239 · see citing works
Deng, S., Caddell, D. F., Xu, G., Dahlen, L., Washington, L. J., Yang, J., & Coleman‐Derr, D. (2021) · The ISME Journal · doi:10.1038/s41396-021-00993-z · Cited by 243 · see citing works
“the MLM-based GWAS pipeline for each OTU using GEMMA [40].”
MethodThe allotetraploid origin and asymmetrical genome evolution of the common carp Cyprinus carpio
Xu, P., Xu, J. H., Liu, G., Chen, L., Zhou, Z., Peng, W., Jiang, Y., Zhao, Z., Jia, Z., Sun, Y., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12644-1 · Cited by 240 · see citing works
Durrant, M. G., Li, M. M., Siranosian, B. A., Montgomery, S. B., & Bhatt, A. S. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.10.022 · Cited by 239 · see citing works
Admixture facilitates genetic adaptations to high altitude in Tibet
Jeong, C., Alkorta‐Aranburu, G., Basnyat, B., Neupane, M., Witonsky, D., Pritchard, J. K., Beall, C. M., & Rienzo, A. D. (2014) · Nature Communications · doi:10.1038/ncomms4281 · Cited by 237 · see citing works
mrMLM v4.0.2: An R Platform for Multi-Locus Genome-Wide Association Studies
Zhang, Y., Tamba, C. L., Wen, Y.-J., Li, P., Ren, W., Ni, Y.-L., Gao, J., & Zhang, Y. (2020) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.06.006 · Cited by 232 · see citing works
“the aforementioned six methods and three current methods (GEMMA [5], FarmCPU [14], and EMMAX [4]).”
MethodInfluentialGPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation
Chung, D., Yang, C., Li, C., Gelernter, J. E., & Zhao, H. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004787 · Cited by 224 · see citing works
Lee, S., & van der Werf, J. H. J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw012 · Cited by 229 · see citing works
“This technique has been used in a number of seminal papers in the context of GWAS (Lippert, et al., 2011; Zhou and Stephens, 2012; Zhou and Stephens, 2014).”
MethodZhang, Y., Jia, Z., & Dunwell, J. M. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00100 · Cited by 227 · see citing works
Jaillard, M., Lima, L., Tournoud, M., Mahé, P., van Belkum, A., Lacroix, V., & Jacob, L. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007758 · Cited by 222 · see citing works
“We thus rely on the bugwas method [5], which uses the linear mixed model (LMM) implemented in the GEMMA library [58], to test for association with phenotypes while correcting for the population structure.”
MethodThe single-cell eQTLGen consortium
van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 215 · see citing works
Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes
Grarup, N., Moltke, I., Andersen, M. K., Dalby, M., Vitting-Seerup, K., Kern, T., Mahendran, Y., Jørsboe, E., Larsen, C. V. L., Dahl-Petersen, I. K., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0022-7 · Cited by 216 · see citing works
Population Stratification in Genetic Association Studies
Hellwege, J. N., Keaton, J. M., Giri, A., Gao, X., Edwards, D. R. V., & Edwards, T. L. (2017) · Current Protocols in Human Genetics · doi:10.1002/cphg.48 · Cited by 211 · see citing works
“…for quantitative traits are the linear mixed models, which can mitigate both the issues that arise when there is cryptic relatedness (Voight & Pritchard, 2005) and PS in association studies in one procedure (Kang et al., 2010; Kang et al., 2008; Listgarten et al., 2012; Zhou and Stephens 2012).”
BackgroundGenomic Evidence for Complex Domestication History of the Cultivated Tomato in Latin America
Razifard, H., Ramos, A., Valle, A. L. D., Bodary, C., Goetz, E., Manser, E. J., Li, X., Zhang, L., Visa, S., Tieman, D. M., et al. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz297 · Cited by 223 · see citing works
Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice
Gatti, D. M., Svenson, K. L., Shabalin, A. A., Wu, L.-Y., Valdar, W. S. J., Šimeček, P., Goodwin, N. C., Cheng, R., Pomp, D., Palmer, A. A., et al. (2014) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.013748 · Cited by 212 · see citing works
“Methods for kin-ship correction remain an active area of research (Zhou and Stephens 2012; Yang et al. 2014).”
Nievergelt, C. M., Maihofer, A. X., Atkinson, E. G., Chen, C., Choi, K. W., Coleman, J. R. I., Daskalakis, N. P., Duncan, L. E., Polimanti, R. A., Aaronson, C. J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01707-9 · Cited by 222 · see citing works
BnIR: A multi-omics database with various tools for Brassica napus research and breeding
Yang, Z., Wang, S., Wei, L., Huang, Y., Liu, D., Jia, Y., Luo, C., Lin, Y.-C., Liang, C., Hu, Y. H., et al. (2023) · Molecular Plant · doi:10.1016/j.molp.2023.03.007 · Cited by 205 · see citing works
Zhou, H., Rentsch, C. T., Cheng, Z., Kember, R. L., Nunez, Y. Z., Sherva, R. M., Tate, J. P., Dao, C., Xu, K., Polimanti, R., et al. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.1206 · Cited by 186 · see citing works
- 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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