Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/63682
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dc.contributor.authorHerrera, Jesus Rommel Ven
dc.contributor.authorFlores, Ester Ben
dc.contributor.authorDuijvesteijn, Naomien
dc.contributor.authorMoghaddar, Nasiren
dc.contributor.authorVan Der Werf, Juliusen
dc.date.accessioned2024-10-25T04:09:02Z-
dc.date.available2024-10-25T04:09:02Z-
dc.date.issued2021-
dc.identifier.citationFrontiers in Genetics, v.12, p. 1-7en
dc.identifier.issn1664-8021en
dc.identifier.urihttps://hdl.handle.net/1959.11/63682-
dc.description.abstract<p>The objective of this study was to compare the accuracies of genomic prediction for milk yield, fat yield, and protein yield from Philippine dairy buffaloes using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with the accuracies based on pedigree BLUP (pBLUP). To also assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted breeding values (BVs) was also calculated. Two data sets were analyzed. The GENO data consisting of all female buffaloes that have both phenotypes and genotypes (n = 904 with 1,773,305-days lactation records) were analyzed using pBLUP and GBLUP. The ALL data, consisting of the GENO data plus females with phenotypes but not genotyped (n = 1,975 with 3,821,305-days lactation records), were analyzed using pBLUP and ssGBLUP. Animals were genotyped with the Affymetrix 90k buffalo genotyping array. After quality control, 60,827 single-nucleotide polymorphisms were used for downward analysis. A pedigree file containing 2,642 animals was used for pBLUP and ssGBLUP. Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set. To assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated. Results showed that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP. Average GBLUP and ssGBLUP accuracies were 0.24 and 0.29, respectively, whereas average pBLUP accuracies (for GENO and ALL data) were 0.21 and 0.22, respectively. Slopes of the two genomic methods were also closer to one, indicating lesser bias, compared to pBLUP. Average GBLUP and ssGBLUP slopes were 0.89 and 0.84, respectively, whereas the average pBLUP (for GENO and ALL data) slopes were 0.80 and 0.54, respectively.</p>en
dc.languageenen
dc.publisherFrontiers Research Foundationen
dc.relation.ispartofFrontiers in Geneticsen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAccuracy of Genomic Prediction for Milk Production Traits in Philippine Dairy Buffaloesen
dc.typeJournal Articleen
dc.identifier.doi10.3389/fgene.2021.682576en
dcterms.accessRightsUNE Greenen
dc.subject.keywordsGBLUPen
dc.subject.keywordsbiasen
dc.subject.keywordsaccuracy of genomic predictionen
dc.subject.keywordspBLUPen
dc.subject.keywordsGenetics & Heredityen
dc.subject.keywordsdairy buffaloen
dc.subject.keywordsssGBLUPen
local.contributor.firstnameJesus Rommel Ven
local.contributor.firstnameEster Ben
local.contributor.firstnameNaomien
local.contributor.firstnameNasiren
local.contributor.firstnameJuliusen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailnduijves@une.edu.auen
local.profile.emailnmoghad4@une.edu.auen
local.profile.emailjvanderw@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeSwitzerlanden
local.identifier.runningnumber682576en
local.format.startpage1en
local.format.endpage7en
local.peerreviewedYesen
local.identifier.volume12en
local.access.fulltextYesen
local.contributor.lastnameHerreraen
local.contributor.lastnameFloresen
local.contributor.lastnameDuijvesteijnen
local.contributor.lastnameMoghaddaren
local.contributor.lastnameVan Der Werfen
dc.identifier.staffune-id:nduijvesen
dc.identifier.staffune-id:nmoghad4en
dc.identifier.staffune-id:jvanderwen
local.profile.orcid0000-0002-3600-7752en
local.profile.orcid0000-0003-2512-1696en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/63682en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleAccuracy of Genomic Prediction for Milk Production Traits in Philippine Dairy Buffaloesen
local.relation.fundingsourcenoteResearch was supported by the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Developmentā€”Department of Science and Technology (PCAARRD-DOST) and PCC. Scholarship of JRVH was provided by the Philippine Carabao Center- Southeast Asian Regional Center for Graduate Study and Research in Agriculture (PCC-SEARCA).en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorHerrera, Jesus Rommel Ven
local.search.authorFlores, Ester Ben
local.search.authorDuijvesteijn, Naomien
local.search.authorMoghaddar, Nasiren
local.search.authorVan Der Werf, Juliusen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/b0f9a511-b21c-4309-99c7-101ff5985ac6en
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2021en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/b0f9a511-b21c-4309-99c7-101ff5985ac6en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/b0f9a511-b21c-4309-99c7-101ff5985ac6en
local.subject.for20203003 Animal productionen
local.subject.seo2020tbden
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
Appears in Collections:Journal Article
School of Environmental and Rural Science
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