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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/18977
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dc.contributor.authorLee, Sang Hongen
dc.contributor.authorVan Der Werf, Julius Hen
dc.date.accessioned2016-05-09T15:00:00Z-
dc.date.issued2016-
dc.identifier.citationBioinformatics, 32(9), p. 1420-1422en
dc.identifier.issn1367-4811en
dc.identifier.issn1367-4803en
dc.identifier.urihttps://hdl.handle.net/1959.11/18977-
dc.description.abstractWe have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations.en
dc.languageenen
dc.publisherOxford University Pressen
dc.relation.ispartofBioinformaticsen
dc.titleMTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic informationen
dc.typeJournal Articleen
dc.identifier.doi10.1093/bioinformatics/btw012en
dcterms.accessRightsGolden
dc.subject.keywordsGenomicsen
dc.subject.keywordsQuantitative Genetics (incl. Disease and Trait Mapping Genetics)en
dc.subject.keywordsAnimal Breedingen
local.contributor.firstnameSang Hongen
local.contributor.firstnameJulius Hen
local.subject.for2008070201 Animal Breedingen
local.subject.for2008060408 Genomicsen
local.subject.for2008060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)en
local.subject.seo2008970108 Expanding Knowledge in the Information and Computing Sciencesen
local.subject.seo2008970107 Expanding Knowledge in the Agricultural and Veterinary Sciencesen
local.subject.seo2008970106 Expanding Knowledge in the Biological Sciencesen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailslee38@une.edu.auen
local.profile.emailjvanderw@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20160502-123725en
local.publisher.placeUnited Kingdomen
local.format.startpage1420en
local.format.endpage1422en
local.identifier.scopusid84966377318en
local.peerreviewedYesen
local.identifier.volume32en
local.identifier.issue9en
local.title.subtitlean efficient algorithm for multivariate linear mixed model analysis based on genomic informationen
local.access.fulltextYesen
local.contributor.lastnameLeeen
local.contributor.lastnameVan Der Werfen
dc.identifier.staffune-id:slee38en
dc.identifier.staffune-id:jvanderwen
local.profile.orcid0000-0003-2512-1696en
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:19178en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleMTG2en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.relation.grantdescriptionNHMRC/APP1080157en
local.relation.grantdescriptionARC/DP160102126en
local.relation.grantdescriptionARC/DE130100614en
local.search.authorLee, Sang Hongen
local.search.authorVan Der Werf, Julius Hen
local.uneassociationUnknownen
local.identifier.wosid000376106100023en
local.year.published2016en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/fb14b615-e39a-4b99-97ac-82036e729ac9en
local.subject.for2020300305 Animal reproduction and breedingen
local.subject.for2020310509 Genomicsen
local.subject.for2020310506 Gene mappingen
local.subject.seo2020280115 Expanding knowledge in the information and computing sciencesen
local.subject.seo2020280101 Expanding knowledge in the agricultural, food and veterinary sciencesen
local.subject.seo2020280102 Expanding knowledge in the biological sciencesen
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