Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/58426
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dc.contributor.authorLi, Lien
dc.contributor.authorGurman, P Men
dc.contributor.authorSwan, A Aen
dc.contributor.authorTier, Ben
dc.date.accessioned2024-04-18T00:48:18Z-
dc.date.available2024-04-18T00:48:18Z-
dc.date.issued2023-
dc.identifier.citationAnimal Production Science, 63(11), p. 1086-1094en
dc.identifier.issn1836-5787en
dc.identifier.issn1836-0939en
dc.identifier.urihttps://hdl.handle.net/1959.11/58426-
dc.description.abstract<p><b>Context:</b> The accuracy of estimated breeding values (EBVs) is an important metric in genetic evaluation systems in Australia. With reduced costs for DNA genotyping due to advances in molecular technology, more and more animals have been genotyped for EBVs. The rapid increase in genotyped animals has grown beyond the capacity of the current genomic best linear unbiased prediction (GBLUP) method. <b>Aims:</b> This study aimed to implement and evaluate a new single-nucleotide polymorphism (SNP)–BLUP model for the computation of prediction error variances (PEVs) to accommodate the increasing number of genotyped animals in beef and sheep single-step genetic evaluations in Australia. <b>Methods:</b> First, the equivalence of PEV estimates obtained from both GBLUP and SNP-BLUP models was demonstrated. Second, the computing resources required by each model were compared. Third, within the SNP-BLUP model, the PEVs obtained from subsets of SNP were evaluated against those from the complete dataset. Fourth, the new model was tested in the Australian Merino sheep and Angus beef cattle datasets. <b>Key results:</b> The PEVs of genotyped animals calculated from the SNP–BLUP model were equivalent to the PEVs derived from the GBLUP model. The SNP–BLUP model used much less time than did the GBLUP model when the number of genotyped animals was larger than the number of SNPs. Within the SNP–BLUP model, the running time could be further reduced using a subset of SNPs makers, with high correlations (>0.97) observed between the PEVs obtained from the complete dataset and subsets. However, it is important to exercise caution when selecting the size of the subsets in the SNP–BLUP model, as reducing the subset size may result in an increase in the bias of the PEVs. <b>Conclusions:</b> The new SNP-BLUP model for PEV calculation for genotyped animals outperforms the current GBLUP model. A new accuracy program has been developed for the Australian genetic evaluation system which uses much less memory and time to compute accuracies. <b>Implications:</b> The new model has been implemented in routine sheep and beef genetic evaluation systems in Australia. This development ensures that the calculation of accuracies is sustainable, with increasing numbers of animals with genotypes.</p>en
dc.languageenen
dc.publisherCSIRO Publishingen
dc.relation.ispartofAnimal Production Scienceen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleApproximating prediction error variances and accuracies of estimated breeding values from a SNP–BLUP model for genotyped individualsen
dc.typeJournal Articleen
dc.identifier.doi10.1071/AN23027en
local.contributor.firstnameLien
local.contributor.firstnameP Men
local.contributor.firstnameA Aen
local.contributor.firstnameBen
local.profile.schoolAnimal Genetics and Breeding Uniten
local.profile.schoolAnimal Genetics and Breeding Uniten
local.profile.schoolAnimal Genetics and Breeding Uniten
local.profile.schoolAnimal Genetics and Breeding Uniten
local.profile.emaillli4@une.edu.auen
local.profile.emailpgurman@une.edu.auen
local.profile.emailaswan@une.edu.auen
local.profile.emailbtier@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeAustraliaen
local.format.startpage1086en
local.format.endpage1094en
local.peerreviewedYesen
local.identifier.volume63en
local.identifier.issue11en
local.access.fulltextYesen
local.contributor.lastnameLien
local.contributor.lastnameGurmanen
local.contributor.lastnameSwanen
local.contributor.lastnameTieren
dc.identifier.staffune-id:lli4en
dc.identifier.staffune-id:pgurmanen
dc.identifier.staffune-id:aswanen
dc.identifier.staffune-id:btieren
local.profile.orcid0000-0002-3601-9729en
local.profile.orcid0000-0002-4375-115Xen
local.profile.orcid0000-0001-8048-3169en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/58426en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleApproximating prediction error variances and accuracies of estimated breeding values from a SNP–BLUP model for genotyped individualsen
local.relation.fundingsourcenoteThis research was funded by Meat and Livestock Australia (MLA) project L.GEN.2024, the University of New England and NSW Department of Primary Industries.en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorLi, Lien
local.search.authorGurman, P Men
local.search.authorSwan, A Aen
local.search.authorTier, Ben
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/d629236a-eafe-4036-bb44-9e5c017d68c8en
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2023en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/d629236a-eafe-4036-bb44-9e5c017d68c8en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/d629236a-eafe-4036-bb44-9e5c017d68c8en
local.subject.for2020300305 Animal reproduction and breedingen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.date.moved2024-04-18en
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
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