{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T03:56:52Z","timestamp":1774324612418,"version":"3.50.1"},"reference-count":73,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Age-at-onset traits are of paramount importance in studies of age-related diseases and behavioral genetics. Cox mixed-effects models (CMEMs) are widely used when sample dependence is taken into account. However, the application of CMEMs in genome-wide association...<\/jats:p>\n                  <jats:p>Age-at-onset is one of the critical traits in cohort studies of age-related diseases. Large-scale genome-wide association studies (GWAS) of age-at-onset traits can provide more insights into genetic effects on disease progression and transitions between stages. Moreover, proportional hazards (or Cox) regression models can achieve higher statistical power in a cohort study than a case-control trait using logistic regression. Although mixed-effects models are widely used in GWAS to correct for sample dependence, application of Cox mixed-effects models (CMEMs) to large-scale GWAS is so far hindered by intractable computational cost. In this work, we propose COXMEG, an efficient R package for conducting GWAS of age-at-onset traits using CMEMs. COXMEG introduces fast estimation algorithms for general sparse relatedness matrices including, but not limited to, block-diagonal pedigree-based matrices. COXMEG also introduces a fast and powerful score test for dense relatedness matrices, accounting for both population stratification and family structure. In addition, COXMEG generalizes existing algorithms to support positive semidefinite relatedness matrices, which are common in twin and family studies. Our simulation studies suggest that COXMEG, depending on the structure of the relatedness matrix, is orders of magnitude computationally more efficient than coxme and coxph with frailty for GWAS. We found that using sparse approximation of relatedness matrices yielded highly comparable results in controlling false-positive rate and retaining statistical power for an ethnically homogeneous family-based sample. By applying COXMEG to a study of Alzheimer\u2019s disease (AD) with a Late-Onset Alzheimer\u2019s Disease Family Study from the National Institute on Aging sample comprising 3456 non-Hispanic whites and 287 African Americans, we identified the APOE \u03b54 variant with strong statistical power (P = 1e\u2212101), far more significant than that reported in a previous study using a transformed variable and a marginal Cox model. Furthermore, we identified novel SNP rs36051450 (P = 2e\u22129) near GRAMD1B, the minor allele of which significantly reduced the hazards of AD in both genders. These results demonstrated that COXMEG greatly facilitates the application of CMEMs in GWAS of age-at-onset traits.<\/jats:p>","DOI":"10.1534\/genetics.119.302940","type":"journal-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T21:19:20Z","timestamp":1583356760000},"page":"41-58","source":"Crossref","is-referenced-by-count":36,"title":["Fast Algorithms for Conducting Large-Scale GWAS of Age-at-Onset Traits Using Cox Mixed-Effects Models"],"prefix":"10.1093","volume":"215","author":[{"given":"Liang","family":"He","sequence":"first","affiliation":[{"name":"Biodemography of Aging Research Unit , Social Science Research Institute, Duke University, Durham, North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander M","family":"Kulminski","sequence":"additional","affiliation":[{"name":"Biodemography of Aging Research Unit , Social Science Research Institute, Duke University, Durham, North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,5,1]]},"reference":[{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/0377-0427(96)00018-0","article-title":"Some large-scale matrix computation problems.","volume":"74","author":"Bai","year":"1996","journal-title":"J. Comput. Appl. Math."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/S0024-3795(97)10009-X","article-title":"Monte Carlo estimates of the log determinant of large sparse matrices.","volume":"289","author":"Barry","year":"1999","journal-title":"Linear Algebra Appl."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1038\/ng.2652","article-title":"Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia.","volume":"45","author":"Berndt","year":"2013","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1214\/08-AOS600","article-title":"Covariance regularization by thresholding.","volume":"36","author":"Bickel","year":"2008","journal-title":"Ann. Stat."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"89","DOI":"10.2307\/2529620","article-title":"Covariance analysis of censored survival data.","volume":"30","author":"Breslow","year":"1974","journal-title":"Biometrics"},{"key":"2023031009165112200_","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/01621459.1993.10594284","article-title":"Approximate inference in generalized linear mixed models.","volume":"88","author":"Breslow","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"2023031009165112200_","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/(SICI)1097-0274(199801)33:1<33::AID-AJIM5>3.0.CO;2-X","article-title":"Empirical comparisons of proportional hazards, poisson, and logistic regression modeling of occupational cohort data.","volume":"33","author":"Callas","year":"1998","journal-title":"Am. J. Ind. Med."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.ajhg.2016.02.012","article-title":"Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models.","volume":"98","author":"Chen","year":"2016","journal-title":"Am. J. Hum. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.ajhg.2018.12.012","article-title":"Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies.","volume":"104","author":"Chen","year":"2019","journal-title":"Am. J. Hum. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1038\/ng.626","article-title":"Genome-wide association study of follicular lymphoma identifies a risk locus at 6p21.32.","volume":"42","author":"Conde","year":"2010","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","first-page":"847","article-title":"A version of the EM algorithm for proportional hazard model with random effects.","volume":"47","author":"Corti\u00f1as Abrahantes","year":"2005","journal-title":"Biom. J. J. Math. Methods Biosci."},{"key":"2023031009165112200_","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables.","volume":"34","author":"Cox","year":"1972","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1038\/ng.3656","article-title":"Next-generation genotype imputation service and methods.","volume":"48","author":"Das","year":"2016","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1137\/0719025","article-title":"Inexact Newton methods.","volume":"19","author":"Dembo","year":"1982","journal-title":"SIAM J. Numer. Anal."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1038\/ng.219","article-title":"A genome-wide association study identifies six susceptibility loci for chronic lymphocytic leukemia.","volume":"40","author":"Di Bernardo","year":"2008","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1137\/0917003","article-title":"Choosing the forcing terms in an inexact Newton method.","volume":"17","author":"Eisenstat","year":"1996","journal-title":"SIAM J. Sci. Comput."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1016\/j.jalz.2019.06.3914","article-title":"The Alzheimer\u2019s disease exposome.","volume":"15","author":"Finch","year":"2019","journal-title":"Alzheimers Dement."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","article-title":"Sparse inverse covariance estimation with the graphical lasso.","volume":"9","author":"Friedman","year":"2008","journal-title":"Biostatistics"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"3241","DOI":"10.1534\/g3.116.034256","article-title":"Genome-wide association studies with a genomic relationship matrix: a case study with wheat and Arabidopsis.","volume-title":"G3 (Bethesda)","author":"Gianola","year":"2016"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","DOI":"10.1515\/9781400833887","volume-title":"Matrices, Moments and Quadrature with Applications.","author":"Golub","year":"2009"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1016\/0021-9681(83)90165-0","article-title":"A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies.","volume":"36","author":"Green","year":"1983","journal-title":"J. Chronic Dis."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1038\/nature24277","article-title":"Genetic effects on gene expression across human tissues.","volume":"550","author":"GTEx Consortium","year":"2017","journal-title":"Nature"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1093\/biomet\/88.1.233","article-title":"Hierarchical likelihood approach for frailty models.","volume":"88","author":"Ha","year":"2001","journal-title":"Biometrika"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"2144","DOI":"10.1002\/sim.4250","article-title":"Frailty modelling for survival data from multi-centre clinical trials.","volume":"30","author":"Ha","year":"2011","journal-title":"Stat. Med."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1137\/1031049","article-title":"Updating the inverse of a matrix.","volume":"31","author":"Hager","year":"1989","journal-title":"SIAM Rev."},{"key":"2023031009165112200_","first-page":"A1558","article-title":"Approximating the spectral sums of large-scale matrices using Chebyshev approximations.","volume-title":"SIAM J. Sci. Comput.","author":"Han","year":"2016"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.462","article-title":"Genome-wide time-to-event analysis on smoking progression stages in a family-based study.","volume":"6","author":"He","year":"2016","journal-title":"Brain Behav."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1534\/genetics.115.183905","article-title":"Estimating modifying effect of age on genetic and environmental variance components in twin models.","volume":"202","author":"He","year":"2016","journal-title":"Genetics"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"179","DOI":"10.3389\/fgene.2016.00179","article-title":"Pleiotropic meta-analyses of longitudinal studies discover novel genetic variants associated with age-related diseases.","volume":"7","author":"He","year":"2016","journal-title":"Front. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1038\/s41588-018-0311-9","article-title":"Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer\u2019s disease risk.","volume":"51","author":"Jansen","year":"2019","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1038\/ng.548","article-title":"Variance component model to account for sample structure in genome-wide association studies.","volume":"42","author":"Kang","year":"2010","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s11222-009-9156-5","article-title":"Functional variance estimation using penalized splines with principal component analysis.","volume":"21","author":"Kauermann","year":"2011","journal-title":"Stat. Comput."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970944","volume-title":"Iterative Methods for Linear and Nonlinear Equations.","author":"Kelley","year":"1995"},{"key":"2023031009165112200_","first-page":"31","article-title":"Discussion of heritability of survival traits.","volume-title":"Interbull Bull.","author":"Korsgaard","year":"1999"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1006314","article-title":"Pleiotropic associations of allelic variants in a 2q22 region with risks of major human diseases and mortality.","volume":"12","author":"Kulminski","year":"2016","journal-title":"PLoS Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1001\/archneur.65.11.1518","article-title":"Analyses of the National Institute on Aging Late-Onset Alzheimer\u2019s Disease Family Study: implication of additional loci.","volume":"65","author":"Lee","year":"2008","journal-title":"Arch. Neurol."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","DOI":"10.1201\/9781420011340","volume-title":"Generalized Linear Models with Random Effects: Unified Analysis via H-Likelihood.","author":"Lee","year":"2006"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1093\/biomet\/73.1.13","article-title":"Longitudinal data analysis using generalized linear models.","volume":"73","author":"Liang","year":"1986","journal-title":"Biometrika"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"2788","DOI":"10.3168\/jds.S0022-0302(99)75536-0","article-title":"Solving large test-day models by iteration on data and preconditioned conjugate gradient.","volume":"82","author":"Lidauer","year":"1999","journal-title":"J. Dairy Sci."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1038\/nmeth.1681","article-title":"FaST linear mixed models for genome-wide association studies.","volume":"8","author":"Lippert","year":"2011","journal-title":"Nat. Methods"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/ng.3190","article-title":"Efficient Bayesian mixed-model analysis increases association power in large cohorts.","volume":"47","author":"Loh","year":"2015","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1080\/03610929308831013","article-title":"Approximations to the determinant term in gaussian maximum likelihood estimation of some spatial models.","volume":"22","author":"Martin","year":"1992","journal-title":"Commun. Stat. Theory Methods"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"221","DOI":"10.2307\/2532615","article-title":"REML estimation for survival models with frailty.","volume":"49","author":"McGilchrist","year":"1993","journal-title":"Biometrics"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"461","DOI":"10.2307\/2532138","article-title":"Regression with frailty in survival analysis.","volume":"47","author":"McGilchrist","year":"1991","journal-title":"Biometrics"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1001\/jamaneurol.2014.1491","article-title":"Effects of multiple genetic loci on age at onset in late-onset Alzheimer disease: a genome-wide association study.","volume":"71","author":"Naj","year":"2014","journal-title":"JAMA Neurol."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","article-title":"A simplex method for function minimization.","volume":"7","author":"Nelder","year":"1965","journal-title":"Comput. J."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/S0167-9473(02)00321-3","article-title":"Chebyshev approximation of log-determinants of spatial weight matrices.","volume":"45","author":"Pace","year":"2004","journal-title":"Comput. Stat. Data Anal."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1002\/gepi.20043","article-title":"Random-effects Cox proportional hazards model: general variance components methods for time-to-event data.","volume":"28","author":"Pankratz","year":"2005","journal-title":"Genet. Epidemiol."},{"key":"2023031009165112200_"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1111\/j.0006-341X.2000.01016.x","article-title":"Estimation of multivariate frailty models using penalized partial likelihood.","volume":"56","author":"Ripatti","year":"2000","journal-title":"Biometrics"},{"key":"2023031009165112200_","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1023\/A:1020566821163","article-title":"Maximum likelihood inference for multivariate frailty models using an automated Monte Carlo EM algorithm.","volume":"8","author":"Ripatti","year":"2002","journal-title":"Lifetime Data Anal."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1093\/biomet\/ass025","article-title":"Positive definite estimators of large covariance matrices.","volume":"99","author":"Rothman","year":"2012","journal-title":"Biometrika"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"2253","DOI":"10.3168\/jds.S0022-0302(05)72901-5","article-title":"Survival analysis applied to genetic evaluation for female fertility in dairy cattle.","volume":"88","author":"Schneider","year":"2005","journal-title":"J. Dairy Sci."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1159\/000363105","article-title":"The kinship2 R package for pedigree data.","volume":"78","author":"Sinnwell","year":"2014","journal-title":"Hum. Hered."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1182\/blood-2012-03-413591","article-title":"Common variation at 6p21.31 (BAK1) influences the risk of chronic lymphocytic leukemia.","volume":"120","author":"Slager","year":"2012","journal-title":"Blood"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/ng.2843","article-title":"A genome-wide association study identifies multiple susceptibility loci for chronic lymphocytic leukemia.","volume":"46","author":"Speedy","year":"2014","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1038\/ejhg.2017.78","article-title":"A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.","volume":"25","author":"Staley","year":"2017","journal-title":"Eur. J. Hum. Genet. EJHG"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1038\/ng.2410","article-title":"Rapid variance components-based method for whole-genome association analysis.","volume":"44","author":"Svishcheva","year":"2012","journal-title":"Nat. Genet."},{"key":"2023031009165112200_","unstructured":"Therneau, T , 2003\u2003On mixed-effect Cox models, sparse matrices, and modeling data from large pedigrees. Available at: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.9.6767"},{"key":"2023031009165112200_","unstructured":"Therneau, T , 2015\u2003Mixed effects Cox models. CRAN repository. https:\/\/repo.bppt.go.id\/cran\/web\/packages\/coxme\/vignettes\/coxme.pdf"},{"key":"2023031009165112200_","unstructured":"Therneau, T M, and Thomas \u00a0Lumley, 2015\u2003\u201cPackage \u2018survival\u2019.\u201d R Top Doc. 128: 112. https:\/\/cran.r-project.org\/web\/packages\/survival\/survival.pdf"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.2527\/2001.7951166x","article-title":"Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications.","volume":"79","author":"Tsuruta","year":"2001","journal-title":"J. Anim. Sci."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1137\/16M1104974","article-title":"Fast estimation of tr(f(A)) via stochastic Lanczos quadrature.","volume":"38","author":"Ubaru","year":"2017","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"439","DOI":"10.2307\/2061224","article-title":"The impact of heterogeneity in individual frailty on the dynamics of mortality.","volume":"16","author":"Vaupel","year":"1979","journal-title":"Demography"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1080\/01621459.1989.10478873","article-title":"Regression analysis of multivariate incomplete failure time data by modeling marginal distributions.","volume":"84","author":"Wei","year":"1989","journal-title":"J. Am. Stat. Assoc."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1111\/j.0006-341X.2003.00135.x","article-title":"A bivariate frailty model with a cure fraction for modeling familial correlations in diseases.","volume":"59","author":"Wienke","year":"2003","journal-title":"Biometrics"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ajhg.2010.11.011","article-title":"GCTA: a Tool for genome-wide complex trait analysis.","volume":"88","author":"Yang","year":"2011","journal-title":"Am. J. Hum. Genet."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1080\/08898489509525394","article-title":"Correlated individual frailty: an advantageous approach to survival analysis of bivariate data.","volume":"5","author":"Yashin","year":"1995","journal-title":"Math. Popul. Stud."},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.3168\/jds.S0022-0302(02)74226-4","article-title":"Heritability, reliability of genetic evaluations and response to selection in proportional hazard models.","volume":"85","author":"Yazdi","year":"2002","journal-title":"J. Dairy Sci."},{"key":"2023031009165112200_","unstructured":"Ypma, J , 2014\u2003Introduction to nloptr: an R interface to NLopt. Tech. rep. http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.488.6344&rep=rep1&type=pdf"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neuron.2015.11.013","article-title":"Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse.","volume":"89","author":"Zhang","year":"2016","journal-title":"Neuron"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"3326","DOI":"10.1093\/bioinformatics\/bts606","article-title":"A high-performance computing toolset for relatedness and principal component analysis of SNP data.","volume":"28","author":"Zheng","year":"2012","journal-title":"Bioinformatics"},{"key":"2023031009165112200_","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1038\/ng.2310","article-title":"Genome-wide efficient mixed model analysis for association studies.","volume":"44","author":"Zhou","year":"2012","journal-title":"Nat. Genet."}],"container-title":["Genetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/genetics\/article-pdf\/215\/1\/41\/49498492\/genetics0041.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/genetics\/article-pdf\/215\/1\/41\/49498492\/genetics0041.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T12:27:19Z","timestamp":1722515239000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/genetics\/article\/215\/1\/41\/5930490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,1]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,5,1]]},"published-print":{"date-parts":[[2020,5,1]]}},"URL":"https:\/\/doi.org\/10.1534\/genetics.119.302940","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/729285","asserted-by":"object"}]},"ISSN":["1943-2631"],"issn-type":[{"value":"1943-2631","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,5,1]]},"published":{"date-parts":[[2020,5,1]]}}}