Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/7255
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dc.contributor.authorLee, Sang Hongen
dc.contributor.authorGoddard, M Een
dc.contributor.authorVisscher, P Men
dc.contributor.authorVan Der Werf, Julius Hen
dc.date.accessioned2011-04-29T16:45:00Z
dc.date.issued2010en
dc.identifier.citationGenetics Selection Evolution, 42(22), p. 1-14en
dc.identifier.issn0999-193Xen
dc.identifier.issn1297-9686en
dc.identifier.urihttps://hdl.handle.net/1959.11/7255en
dc.description.abstractBackground: In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased. Methods: In this study, we constructed genetic covariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate variance components in a heterogenous population of ~ 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the variances due to family, cage and genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects. Results and conclusions: We show that the use of genome-wide SNP information can disentangle confounding factors to estimate genetic variances by separating genetic and non-genetic effects. The estimated variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance variance.en
dc.languageenen
dc.publisherBioMed Central Ltden
dc.relation.ispartofGenetics Selection Evolutionen
dc.titleUsing the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traitsen
dc.typeJournal Articleen
dc.identifier.doi10.1186/1297-9686-42-22en
dc.subject.keywordsAnimal Breedingen
local.contributor.firstnameSang Hongen
local.contributor.firstnameM Een
local.contributor.firstnameP Men
local.contributor.firstnameJulius Hen
local.subject.for2008070201 Animal Breedingen
local.subject.seo2008830399 Livestock Raising not elsewhere classifieden
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailslee38@une.edu.auen
local.profile.emailpeter.visscher@qimr.edu.auen
local.profile.emailjvanderw@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20110326-141738en
local.publisher.placeLondon, United Kingdomen
local.format.startpage1en
local.format.endpage14en
local.peerreviewedYesen
local.identifier.volume42en
local.identifier.issue22en
local.contributor.lastnameLeeen
local.contributor.lastnameGoddarden
local.contributor.lastnameVisscheren
local.contributor.lastnameVan Der Werfen
dc.identifier.staffune-id:slee38en
dc.identifier.staffune-id:mgoddar5en
dc.identifier.staffune-id:jvanderwen
local.profile.orcid0000-0003-2512-1696en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:7423en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleUsing the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traitsen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.description.statisticsepubsVisitors: 306<br />Views: 306<br />Downloads: 0en
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