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A unified mixed model method for association mapping that accounts for multiple levels of relatedness

dc.contributor.authorYu, J.
dc.contributor.authorPressoir, G.
dc.contributor.authorBriggs, W.H.
dc.contributor.authorVroh Bi, Irie
dc.contributor.authorYamasaki, M.
dc.contributor.authorDoebley, J.F.
dc.contributor.authorMcmullen, M.D.
dc.contributor.authorGaut, B.S.
dc.contributor.authorNielsen, D.M.
dc.contributor.authorHolland, J.B.
dc.contributor.authorKresovich, S.
dc.date.accessioned2019-12-04T11:31:04Z
dc.date.available2019-12-04T11:31:04Z
dc.date.issued2006
dc.identifier.citationYu, J., Pressoir, G., Briggs, W.H., Vroh Bi, I., Yamasaki, M., Doebley, J.F., … & Kresovich, S. (2006). A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics, 38, 203-208.
dc.identifier.issn1061-4036
dc.identifier.urihttps://hdl.handle.net/20.500.12478/5440
dc.description.abstractAs population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure1,2. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.
dc.description.sponsorshipNational Science Foundation
dc.description.sponsorshipUnited States Department of Agriculture
dc.language.isoen
dc.subjectPopulation Structure
dc.subjectPlant Genetics
dc.subjectPhenotypes
dc.subjectGene Expression
dc.titleA unified mixed model method for association mapping that accounts for multiple levels of relatedness
dc.typeJournal Article
dc.description.versionPeer Review
cg.contributor.affiliationCornell University
cg.contributor.affiliationUniversity of Wisconsin
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationUniversity of Missouri
cg.contributor.affiliationUnited States Department of Agriculture
cg.contributor.affiliationUniversity of California
cg.contributor.affiliationNorth Carolina State University
cg.coverage.regionAcp
cg.coverage.regionAfrica
cg.coverage.regionNorth America
cg.coverage.regionWest Africa
cg.coverage.regionSouth America
cg.coverage.countryUnited States
cg.coverage.countryNigeria
cg.coverage.countryColombia
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectLivelihoods
cg.iitasubjectPlant Genetic Resources
cg.iitasubjectBioscience
cg.iitasubjectGenetic Improvement
cg.accessibilitystatusLimited Access
local.dspaceid103797
cg.identifier.doihttps://doi.org/10.1038/ng1702


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