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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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MethodInfluentialVariance component model to account for sample structure in genome-wide association studies
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BackgroundMethodInfluential
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Genetic modifiers of Huntington disease differentially influence motor and cognitive domains
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“Applying the LRLMM-DF for association testing for HDL cholesterol in Europeans substantially boosts the signal from markers on chr8 between positions 19,852,309 and 19,869,675 compared to a standard linear model (Plink [60]) and three versions of the full rank LMM (EMMAX [4], GEMMA [9], FaST-LMM [6]), and FaST-LMM-Sel…”
InfluentialWang, B., Wu, Z., Li, Z., Zhang, Q., Hu, J., Xiao, Y., Cai, D., Wu, J., King, G. J. W., Li, H., et al. (2017) · Plant Biotechnology Journal · doi:10.1111/pbi.12873 · Cited by 114
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Prediction and association mapping of agronomic traits in maize using multiple omic data
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“We implement this model with multiple variance components in the current study by using the -vc argument within the GEMMA software.”
MethodInfluentialSelf-citationAristaless Controls Butterfly Wing Color Variation Used in Mimicry and Mate Choice
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Select and resequence reveals relative fitness of bacteria in symbiotic and free-living environments
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“1) (30) to measure the strength of the association between genotypes and strain fitness at each SNP/ multinucleotide polymorphism and PAV.”
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Awany, D., Allali, I., Dalvie, S., Hemmings, S. M. J., Mwaikono, K. S., Thomford, N. E., Gomez, A. M., Mulder, N., & Chimusa, E. R. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2018.00637 · Cited by 117
Whole-genome sequencing analysis of the cardiometabolic proteome
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A guide to genome‐wide association analysis and post‐analytic interrogation
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LIMIX: genetic analysis of multiple traitsPreprint
Lippert, C., Casale, F. P., Rakitsch, B., & Stegle, O. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/003905 · Cited by 107
“Instead, a number of rigid and highly specialized tools have been developed that speed up the specific computations that are needed for a particular task, such as GWAS testing [1, 2, 3, 4, 12], or variance component estimation [5, 6].”
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Stephan, J., Stegle, O., & Beyer, A. (2015) · Nature Communications · doi:10.1038/ncomms8432 · Cited by 115
“Using this notation, we can cast the split optimization (equation (3)) in a LM perspective”
BackgroundMethodInfluentialUnraveling a genetic roadmap for improved taste in the domesticated apple
Liao, L., Zhang, W., Zhang, B., Fang, T., Wang, X., Cai, Y., Ogutu, C. O., Gao, L., Chen, G., Nie, X., et al. (2021) · Molecular Plant · doi:10.1016/j.molp.2021.05.018 · Cited by 108
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Canine Brachycephaly Is Associated with a Retrotransposon-Mediated Missplicing of SMOC2
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“1) [64], which incorporates a kinship matrix in its implementation of univariate linear mixed models, was used to perform genome-wide association tests.”
MethodNavigating the Interface Between Landscape Genetics and Landscape Genomics
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“GWAS of 20 phenotypes identified totally 27 loci with 1215 unique candidate variations significantly associated with 13 phenotypes, including 20 reported loci (55), suggesting a high repeatability of these loci and reliability of the method (Supplementary Table S7).”
BackgroundMethodAdaptive, convergent origins of the pygmy phenotype in African rainforest hunter-gatherers
Perry, G. H., Foll, M., Grenier, J.-C., Patin, E., Nédélec, Y., Pacis, A., Barakatt, M., Gravel, S., Zhou, X., Nsobya, S. L., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402875111 · Cited by 105
“Most of these regions were supported by a second association method, implemented in the program GEMMA (21), that specifically accounts for interindividual relatedness.”
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“The genetic value of each individual was then used as the dependent trait in a univariate linear mixed model for SNP-trait association analyses performed with GEMMA [17].”
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“To analyze simulated and real data sets using a linear model (implemented using ‘lm’ in R) or the linear mixed model implemented in GEMMA [34], we estimated DNA methylation levels by dividing the number of methylated reads by the total read count for each individual and CpG site.”
InfluentialSelf-citationGenome‐wide association of volatiles reveals candidate loci for blueberry flavor
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“SNP-trait association analyses were based on a univariate linear mixed model (LMM), as described by Yu et al. (2006) and implemented in the GEMMA package (option - lmm 4) (Zhou & Stephens, 2012).”
MethodStatistical analysis for genome-wide association study
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“The structured association is carried out by the software STRUCTURE.”
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“A linear mixed model was used for marker association analysis with phenotype with GEMMA software (Zhou and Stephens, 2012).”
A super-pangenome of the North American wild grape species
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Whole-genome sequence-based analysis of thyroid function
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TraitCapture: genomic and environment modelling of plant phenomic data
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Genome-Wide Association Mapping of Flowering and Ripening Periods in Apple
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“GEMMA software (Zhou and Stephens, 2012) was used to estimate the standardized relatedness matrix ( K ) between the genotypes at the whole population level and within each collection.”
Association between the pig genome and its gut microbiota composition
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InfluentialVisconti, A., Duffy, D. L., Liu, F., Zhu, G., Wu, W., Chen, Y., Hysi, P. G., Zeng, C., Sanna, M., Iles, M. M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04086-y · Cited by 103
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Genomic variation at the tips of the adaptive radiation of Darwin's finches
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“Genomewide associations analysis and beak-size prediction We scanned for SNPs associated with PC1 of beak size using the Bayesian sparse linear mixed model (BSLMM) (Zhou & Stephens 2012, 2014) implemented in the software package GEMMA (Zhou et al. 2013).”
MethodAdaptive diversification of growth allometry in the plant Arabidopsis thaliana
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“For polygenic GWA, we used the Bayesian Sparse Linear Mixed model (BSLMM) implemented in GEMMA (30).”
InfluentialGenomics of sorghum local adaptation to a parasitic plant
Bellis, E. S., Kelly, E. A., Lorts, C. M., Gao, H., DeLeo, V. L., Rouhan, G., Budden, A., Bhaskara, G. B., Hu, Z., Muscarella, R., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908707117 · Cited by 103
A multiple-phenotype imputation method for genetic studies
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“We simulated 100,000 unlinked loci on which we performed GWAS, for each of the P = 15 phenotypes, with gemma using the default QC filters (top row of Supplementary Figure 9) [32].”
Genetic Characterization of Dog Personality Traits
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“Genome-wide association analyses of the SetA traits with significant heritabilities were performed using GEMMA (Zhou and Stephens 2012), accounting for population stratification by fitting the genomic relationship matrix (GRM,G).”
MethodGenetic diversity and association mapping of seed vigor in rice (Oryza sativa L.)
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“2008), therefore it has become a new method to mine elite alleles in humans and plants (Yu et al. 2005; Zhang et al. 2005c, 2010; lü et al. 2011; Segura et al. 2012; Zhou and Stephens 2012).”
MethodNovel genetic loci affecting facial shape variation in humans
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“, 2007), GEMMA (Zhou and Stephens, 2012), and Rare Metal Worker (Feng et al.”
BackgroundFingerlin, T. E., Zhang, W. E., Yang, I. V., Ainsworth, H. C., Russell, P. H., Blumhagen, R. Z., Schwarz, M. I., Brown, K. M., Steele, M. P., Loyd, J. E., et al. (2016) · BMC Genetics · doi:10.1186/s12863-016-0377-2 · Cited by 105
“Figure S5: Comparison of SNPTest p-values after genomic control (see manuscript statistical methods) and GEMMA dosage-values.”
MethodInfluentialLloyd‐Jones, L. R., Robinson, M. R., Yang, J., & Visscher, P. M. (2018) · Genetics · doi:10.1534/genetics.117.300360 · Cited by 97
“…biobank-baseddata sets forGWAS,whichmay include related individuals and population stratification, the application of LMM methods (Yang et al. 2011; Zhou and Stephens 2012; Loh et al. 2015) is likely to remain a reasonable practical choice for most researchers, especially given the promise of…”
MethodOn the Extent of Linkage Disequilibrium in the Genome of Farm Animals
Qanbari, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01304 · Cited by 108
Zeng, B., Bendl, J. M., Kosoy, R., Fullard, J. F., Hoffman, G. E., & Roussos, P. (2022) · Nature Genetics · doi:10.1038/s41588-021-00987-9 · Cited by 92
“For the eQTL analysis we considered a linear regression model including five genotype principal components (PCs) and a linear mixed model that accounts for the genetic similarity between all pairs of samples 39–41 .”
Genome-Wide Association Study of Loneliness Demonstrates a Role for Common Variation
Gao, J., Davis, L. K., Hart, A. B., Sanchez‐Roige, S., Han, L., Cacioppo, J. T., & Palmer, A. A. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.197 · Cited by 111
“For the case/control studies, controls were coded as 0 and cases were coded as 1, as suggested in the GEMMA documentation (Zhou and Stephens, 2012).”
BackgroundMethodInfluentialGenome-wide association studies for feed intake and efficiency in two laying periods of chickens
Yuan, J., Wang, K., Yi, G., Ma, M., Dou, T., Sun, C., Qu, L., Shen, M., Qu, L., & Yang, N. (2015) · Genetics Selection Evolution · doi:10.1186/s12711-015-0161-1 · Cited by 99
“To account for population structure, a stratification approach was used in a GWAS using a mixed model [16] that included fixed effects (overall mean and covariates) and random effects (SNP effect, individual effect and residual errors), as implemented in GEMMA software [17].”
MethodCrespo‐Piazuelo, D., Criado‐Mesas, L., Revilla, M., Castelló, A., Noguera, J. L., Fernández, A. I., Ballester, M., & Folch, J. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-70894-2 · Cited by 95
Polygenic Scores for Major Depressive Disorder and Risk of Alcohol Dependence
Andersen, A. M., Pietrzak, R. H., Kranzler, H. R., Ma, L., Zhou, H., Liu, X., Kramer, J., Kuperman, S., Edenberg, H. J., Nurnberger, J. I., et al. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.2269 · Cited by 90
Tachmazidou, I., Dedoussis, G. V., Southam, L., Farmaki, A., Ritchie, G. R. S., Xifara, D. K., Matchan, A., Hatzikotoulas, K., Rayner, N. W., Chen, Y., et al. (2013) · Nature Communications · doi:10.1038/ncomms3872 · Cited by 96
The transferability of lipid loci across African, Asian and European cohorts
Kuchenbaecker, K. B., Telkar, N., Reiker, T., Walters, R. G., Lin, K., Eriksson, A., Gurdasani, D., Gilly, A., Southam, L., Tsafantakis, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12026-7 · Cited by 91
Hicks, A. L., Wheeler, N. E., Sánchez-Busó, L., Rakeman, J. L., Harris, S. R., & Grad, Y. H. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007349 · Cited by 96
“362 Further, as the focus with statistical classifiers is building models from feature sets that 363 can accurately predict an outcome, rather than understanding the association between 364 each of the features and the outcome, potential confounding effects from factors such as 365 population structure (36-38) or corr…”
BackgroundBourgeois, Y., & Warren, B. H. (2021) · Molecular Ecology · doi:10.1111/mec.15989 · Cited by 103
“These methods apply a dimension reduction procedure to matrices of individual genotypes, projecting genotypic variability along several axes of variation (Jombart et al., 2009).”
MethodXue, L., Gao, Y., Wu, M., Tian, T., Fan, H., Huang, Y., Huang, Z. J., Li, D., & Xu, L. (2021) · Genome biology · doi:10.1186/s13059-021-02430-y · Cited by 96
“22) [80] pipeline to identify the SNPs that are associated with sex.”
BackgroundA thousand-genome panel retraces the global spread and adaptation of a major fungal crop pathogen
Feurtey, A., Lorrain, C., McDonald, M. C., Milgate, A., Solomon, P. S., Warren, R., Puccetti, G., Scalliet, G., Torriani, S. F. F., Gout, L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36674-y · Cited by 93
“Based on these environmental estimates and the genomic variants, we identi fi ed genotype-to-environment associations with the softwareGEMMA 0.98.3 66 .”
InfluentialAssociation mapping for seed size and shape traits in soybean cultivars
Niu, Y., Xu, Y., Liu, X., Yang, S.-X., Wei, S., Xie, F.-T., & Zhang, Y. (2013) · Molecular Breeding · doi:10.1007/s11032-012-9833-5 · Cited by 95
“K matrices for background control and multi-QTL analysis of both Zhou and Stephens (2012) and Segura et al. (2012).”
MethodGenome-wide association study of type 2 diabetes in Africa
Ji, C. F., Sun, M., Adeyemo, A., Pirie, F. J., Carstensen, T., Pomilla, C., Doumatey, A. P., Chen, G., Young, E. H., Sandhu, M. S., et al. (2019) · Diabetologia · doi:10.1007/s00125-019-4880-7 · Cited by 93
Liang, X., Liu, S., Wang, T., Li, F., Cheng, J., Lai, J., Qin, F., Li, Z., Wang, X., & Jiang, C. (2021) · New Phytologist · doi:10.1111/nph.17323 · Cited by 97
Using population isolates in genetic association studies
Hatzikotoulas, K., Gilly, A., & Zeggini, E. (2014) · Briefings in Functional Genomics · doi:10.1093/bfgp/elu022 · Cited by 106
The genomic basis of adaptation in plants
Flood, P. J., & Hancock, A. M. (2017) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2017.02.003 · Cited by 96
Widespread Allelic Heterogeneity in Complex Traits
Hormozdiari, F., Zhu, A., Kichaev, G., Ju, C. J. ‐., Segrè, A. V., Joo, J. W. J., Won, H., Sankararaman, S., Paşaniuc, B., Shifman, S., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.005 · Cited by 90
Statistical methods for SNP heritability estimation and partition: A review
Zhu, H., & Zhou, X. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.011 · Cited by 103
“The variance components r 2 g and r 2 e can be estimated using software such as GCTA [36] and GEMMA [37].”
MethodSelf-citationStride-level analysis of mouse open field behavior using deep-learning-based pose estimation
Sheppard, K., Gardin, J., Sabnis, G. S., Peer, A., Darrell, M., Deats, S., Geuther, B. Q., Lutz, C. M., & Kumar, V. (2022) · Cell Reports · doi:10.1016/j.celrep.2021.110231 · Cited by 109
Liu, Z., Sun, C., Yan, Y., Li, G., Shi, F., Wu, G., Liu, A., & Yang, N. (2018) · Scientific Reports · doi:10.1038/s41598-018-29162-7 · Cited by 93
Association of modifiers and other genetic factors explain Marfan syndrome clinical variability
Aubart, M., Gazal, S., Arnaud, P., Benarroch, L., Gross, M., Buratti, J., Boland, A., Meyer, V., Zouali, H., Hanna, N., et al. (2018) · European Journal of Human Genetics · doi:10.1038/s41431-018-0164-9 · Cited by 97
Genome-wide association study in essential tremor identifies three new loci
Müller, S. H., Girard, S. L., Hopfner, F., Merner, N. D., Bourassa, C. V., Lorenz, D., Clark, L. N., Tittmann, L., Soto-Ortolaza, A. I., Klebe, S., et al. (2016) · Brain · doi:10.1093/brain/aww242 · Cited by 88
“Genotyping of variants associated with essential tremor in previous genome-wide association studies We genotyped all available samples using TaqMan probes for SNPs rs9652490 in LINGO1 and rs3794087 in SLC1A2 and called alleles using QuantStudioTM Real-Time PCR software v1.”
MethodInfluentialWang, Z., Cabrera, M., Yang, J., Yuan, L., Gupta, B., Liang, X., Kemirembe, K., Shrestha, S., Brashear, A., Li, X., et al. (2016) · Scientific Reports · doi:10.1038/srep33891 · Cited by 93
Single-Locus and Multi-Locus Genome-Wide Association Studies for Intramuscular Fat in Duroc Pigs
Ding, R., Yang, M. J., Quan, J., Li, S., Zhuang, Z., Zhou, S., Zheng, E., Hong, L., Li, Z., Cai, G., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00619 · Cited by 92
“The MLM was performed by using GEMMA (Zhou and Stephens, 2012; Zhou and Stephens, 2014).”
MethodFerguson, J. N., Fernandes, S. B., Monier, B., Miller, N. D., Allen, D. P., Dmitrieva, A., Schmuker, P., Lozano, R. D., Valluru, R., Buckler, E. S., et al. (2021) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiab346 · Cited by 91
“The association analysis was conducted using the unified MLM (Yu et al., 2006) implemented in the software GEMMA (Zhou and Stephens, 2012).”
MethodLiu, C., Wang, Y., Peng, J., Fan, B., Xu, D., Wu, J., Cao, Z., Gao, Y., Wang, X., Li, S., et al. (2022) · Plant Communications · doi:10.1016/j.xplc.2022.100352 · Cited by 92
“GWASs based on SNP and gene PAV data were performed using a mixed linear model in the genome-wide efficient mixed-model association algorithm (GEMMA, version 0.98.1) (Zhou and Stephens, 2012).”
MethodInfluentialIso‐Touru, T., Sahana, G., Guldbrandtsen, B., Lund, M. S., & Vilkki, J. (2016) · BMC Genetics · doi:10.1186/s12863-016-0363-8 · Cited by 91
“This leads to systematic underestimation of the most significant p values [43], but makes EMMAX one of the fastest LMM based programs [42]; an argument that has to be considered when having whole genome sequence level data from substantially large amount of individuals.”
BackgroundXU, Y. C., Yang, T., Zhou, Y., Yin, S., Li, P., Liu, J., Xu, S., Yang, Z., & Xu, C. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01311 · Cited by 91
“, 2011), and genome-wide efficient mixed model association (GEMMA) (Zhou and Stephens, 2012).”
BackgroundKumar, J., Saripalli, G., Gahlaut, V., Goel, N., Meher, P. K., Mishra, K. K., Mishra, P. C., Sehgal, D., Vikram, P., Sansaloni, C., et al. (2018) · Euphytica · doi:10.1007/s10681-018-2284-2 · Cited by 91
“…GWAS, which overcome the above limitations of genetic analysis (Segura et al. 2012; Korte et al. 2012; Jaiswal et al. 2016; Thoen et al. 2017). mvLMM has also been used so that more than two correlated traits may be examined in multi-trait analysis (Zhou and Stephens 2012; Furlotte and Eskin 2015).”
BackgroundHui, K. F., Chan, T. F., Yang, W., Shen, J. J., Lam, K. P., Kwok, H. F., Sham, P. C., Tsao, S. W., Kwong, D. L. �., Lung, M. L., et al. (2018) · International Journal of Cancer · doi:10.1002/ijc.32049 · Cited by 91
“In order to address this potential problem and identify the most robust variations that are associated with NPC, we carried out a genome-wide association study (GWAS) using a linear mixed model (LMM) implemented in GEMMA.31 We tested the significance of 2919 common SNPs/indels (minor allele frequency > 5”
MethodKertho, A., Mamidi, S., Bonman, J. M., McClean, P. E., & Acevedo, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129580 · Cited by 91
“92 [38] was used to estimate population relatedness.”
MethodImpacts of human activity modes and climate on heavy metal “spread” in groundwater are biased
Chen, M., Qin, X., Zeng, G., & Li, J. (2016) · Chemosphere · doi:10.1016/j.chemosphere.2016.03.046 · Cited by 91
“Fortunately, an effective approach, named mixed-effect model, has been proposed to account for data dependence, data stratification and relatedness (Korte et al., 2012; Whitehorn et al., 2012; Zhou and Stephens, 2012).”
BackgroundChen, H., Cade, B. E., Gleason, K. J., Bjonnes, A. C., Stilp, A. M., Sofer, T., Conomos, M. P., Ancoli-Israel, S., Arens, R., Azarbarzin, A., et al. (2018) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2017-0237oc · Cited by 82
“Statistical Analysis Rank-normalized age and sex-adjusted residuals were analyzed using linear mixed models with a genetic relatedness matrix in GEMMA (27) to control for population stratification and relatedness, adjusting for BMI and BMI(2).”
MethodVery low-depth whole-genome sequencing in complex trait association studies
Gilly, A., Southam, L., Suveges, D., Kuchenbaecker, K., Moore, R., Melloni, G. E. M., Hatzikotoulas, K., Farmaki, A.-E., Ritchie, G., Schwartzentruber, J., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1032 · Cited by 93
Chen, C., Huang, X., Fang, S., Yang, H., He, M., Zhao, Y., & Huang, L. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02626 · Cited by 95
“The formula: Kinship = 1 p ∑p i = 1(xi − 1nxl)(xi − 1nxl) was used for calculating the kinship matrix based on the host genotyping data and the microbial composition data (OTU compositions and relative abundances), respectively (Zhou and Stephens, 2012); where x represents the n× p matrix of genotypes or OTUs, xi and…”
MethodNegro, S. S., Millet, E., Madur, D., Bauland, C., Combes, V., Welcker, C., Tardieu, F., Charcosset, A., & Nicolas, S. (2019) · BMC Plant Biology · doi:10.1186/s12870-019-1926-4 · Cited by 96
“approximate methods by avoiding re-estimating variance components for each SNP [5] or exact methods”
MethodSan, J. E., Baichoo, S., Kanzi, A. M., Moosa, Y., Lessells, R. J., Fonseca, V., Mogaka, J., Power, R. A., & de Oliveira, T. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.03119 · Cited by 84
“The LMM’s are implemented using Genome-wide Efficient Mixed Model Association (GEMMA) (Zhou and Stephens, 2012), a fast software toolkit for the application of LMM’s to GWAS.”
MethodLv, Q., Li, W., Sun, Z., Ouyang, N., Jing, X., He, Q., Wu, J., Zheng, J., Zheng, J., Tang, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18608-0 · Cited by 90
Hwang, L., Lin, C., Gharahkhani, P., Cuéllar-Partida, G., Ong, J., An, J., Gordon, S. D., Zhu, G., MacGregor, S., Lawlor, D. A., et al. (2019) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqz043 · Cited by 91
Gozalo‐Marcilla, M., Buntjer, J. B., Johnsson, M., Batista, L. G., Diez, F., Werner, C. R., Chen, C.-Y., Gorjanc, G., Mellanby, R. J., Hickey, J. M., et al. (2021) · Genetics Selection Evolution · doi:10.1186/s12711-021-00671-w · Cited by 91
Tan, X., Liu, R., Zhao, D., He, Z., Liu, R., Zheng, M., Li, Q., Wang, Q., Liu, D., Feng, F., et al. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.02.016 · Cited by 89
Dynamics of sex chromosome evolution in a rapid radiation of cichlid fishes
Taher, A. E., Ronco, F., Matschiner, M., Salzburger, W., & Böhne, A. (2021) · Science Advances · doi:10.1126/sciadv.abe8215 · Cited by 87
Tan, Z., Xie, Z., Dai, L., Zhang, Y., Zhao, H., Tang, S., Wan, L., Yao, X., Guo, L., & Hong, D. (2021) · Plant Biotechnology Journal · doi:10.1111/pbi.13707 · Cited by 88
“We used the GEMMA software package (Zhou and Stephens, 2012) to correlate the 3-year WH SGC trait data using a mixed linear model.”
MethodRapid adaptive radiation of Darwin’s finches depends on ancestral genetic modules
Rubin, C., Enbody, E. D., Dobreva, M. P., Abzhanov, A., Davis, B. W., Lamichhaney, S., Pettersson, M. E., Sendell‐Price, A. T., Sprehn, C. G., Valle, C. A., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm5982 · Cited by 91
“Ad-mixture mapping ( 37 ) revealed 28 loci that exceeded the significance threshold set by permutation (Fig.”
Pampouille, E., Berri, C., Boitard, S., Hennequet‐Antier, C., Beauclercq, S. A., Godet, E., Praud, C., Jégo, Y., & Bihan-Duval, É. L. (2018) · BMC Genomics · doi:10.1186/s12864-018-4598-9 · Cited by 89
“Genome-wide association studies were performed on classical and molecular phenotypes using an univariate linear mixed model (LMM) implemented in GEMMA (Genome-wide Efficient Mixed Model Association) software [54].”
MethodWu, J., Yu, R., Wang, H., Zhou, C., Huang, S., Jiao, H., Yu, S., Nie, X., Wang, Q., Liu, S., et al. (2020) · Plant Biotechnology Journal · doi:10.1111/pbi.13452 · Cited by 83
“GWAS was conducted using a univariate linear mixed model with GEMMA software (Zhou and Stephens, 2012).”
MethodEstimating FST and kinship for arbitrary population structures
Ochoa, A., & Storey, J. D. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009241 · Cited by 102
Jeong, C., Witonsky, D., Basnyat, B., Neupane, M., Beall, C. M., Childs, G. H., Craig, S. R., Novembre, J., & Rienzo, A. D. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007650 · Cited by 88
RAINBOW: Haplotype-based genome-wide association study using a novel SNP-set method
Hamazaki, K., & Iwata, H. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007663 · Cited by 88
“Solving the following single-kernel linear mixed model (LMM) by using EMMA (efficient mixed model association) or GEMMA (genome-wide efficient mixed model association) [40, 41].”
MethodBoatwright, J. L., Sapkota, S., Jin, H., Schnable, J. C., Brenton, Z. W., Boyles, R. E., & Kresovich, S. (2022) · The Plant Journal · doi:10.1111/tpj.15853 · Cited by 90
“Using the 25 traits collected by Boyles et al. (Boyles et al., 2016) and Sapkota et al. (Sapkota, Boyles, et al., 2020), we first performed GWAS for all traits using LMMs in GEMMA (Zhou & Stephens, 2012).”
InfluentialEnhancing genomic prediction with genome-wide association studies in multiparental maize populations
Bian, Y., & Holland, J. B. (2017) · Heredity · doi:10.1038/hdy.2017.4 · Cited by 86
“Although computationally efficient ‘exact’ linear mixed-effect model GWAS methods, such as FaSTLMM (Lippert et al., 2011) and GEMMA (Zhou and Stephens, 2012), have been developed, we used our own implementation of EMMAX to incorporate test statistics extended to marker effects nested within families with more than one…”
MethodStudy, B. M. D. I. C., Mishra, R., Chesi, A., Cousminer, D. L., Hawa, M. I., Bradfield, J. P., Hodge, K. M., Guy, V. C., Hakonarson, H. H., Mauricio, D., et al. (2017) · BMC Medicine · doi:10.1186/s12916-017-0846-0 · Cited by 89
“were calculated using μ (intercept) and β (effect size) estimates from the linear mixed model, with the formula: OR 1⁄4 β eμ 1−μ ð Þ [18].”
MethodInfluentialAn association between chronic widespread pain and the gut microbiome
Freidin, M. B., Stalteri, M. A., Wells, P. M., Lachance, G., Baleanu, A.-F., Bowyer, R. C. E., Kurilshikov, A., Zhernakova, A., Steves, C. J., & Williams, F. M. K. (2020) · Rheumatology · doi:10.1093/rheumatology/keaa847 · Cited by 88
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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