Genomic predictions for crossbred dairy cattle

Detalhes bibliográficos
Autor(a) principal: VanRaden, P. M.
Data de Publicação: 2020
Outros Autores: Tooker, M. E., Chud, T. C.S. [UNESP], Norman, H. D., Megonigal, J. H., Haagen, I. W., Wiggans, G. R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3168/jds.2019-16634
http://hdl.handle.net/11449/199814
Resumo: Genomic evaluations are useful for crossbred as well as purebred populations when selection is applied to commercial herds. Dairy farmers had already spent more than $1 million to genotype over 32,000 crossbred animals before US genomic evaluations became available for those animals. Thus, new tools were needed to provide accurate genomic predictions for crossbreds. Genotypes for crossbreds are imputed more accurately when the imputation reference population includes purebreds. Therefore, genotypes of 6,296 crossbred animals were imputed from lower-density chips by including either 3,119 ancestors or 834,367 genotyped animals in the reference population. Crossbreds in the imputation study included 733 Jersey × Holstein F1 animals, 55 Brown Swiss × Holstein F1 animals, 2,300 Holstein backcrosses, 2,026 Jersey backcrosses, 27 Brown Swiss backcrosses, and 502 other crossbreds of various breed combinations. Another 653 animals appeared to be purebreds that owners had miscoded as a different breed. Genomic breed composition was estimated from 60,671 markers using the known breed identities for purebred, progeny-tested Holstein, Jersey, Brown Swiss, Ayrshire, and Guernsey bulls as the 5 traits (breed fractions) to be predicted. Estimates of breed composition were adjusted so that no percentages were negative or exceeded 100%, and breed percentages summed to 100%. Another adjustment set percentages above 93.5% equal to 100%, and the resulting value was termed breed base representation (BBR). Larger percentages of missing alleles were imputed by using a crossbred reference population rather than only the closest purebred reference population. Crossbred predictions were averages of genomic predictions computed using marker effects for each pure breed, which were weighted by the animal's BBR. Marker and polygenic effects were estimated separately for each breed on the all-breed scale instead of within-breed scales. For crossbreds, genomic predictions weighted by BBR were more accurate than the average of parents' breeding values and slightly more accurate than predictions using only the predominant breed. For purebreds, single-trait predictions using only within-breed data were as accurate as multi-trait predictions with allele effects in different breeds treated as correlated effects. Crossbred genomic predicted transmitting abilities were implemented by the Council on Dairy Cattle Breeding in April 2019 and will aid producers in managing their breeding programs and selecting replacement heifers.
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spelling Genomic predictions for crossbred dairy cattlecrossbreedinggenomic evaluationimputationmultiple breedGenomic evaluations are useful for crossbred as well as purebred populations when selection is applied to commercial herds. Dairy farmers had already spent more than $1 million to genotype over 32,000 crossbred animals before US genomic evaluations became available for those animals. Thus, new tools were needed to provide accurate genomic predictions for crossbreds. Genotypes for crossbreds are imputed more accurately when the imputation reference population includes purebreds. Therefore, genotypes of 6,296 crossbred animals were imputed from lower-density chips by including either 3,119 ancestors or 834,367 genotyped animals in the reference population. Crossbreds in the imputation study included 733 Jersey × Holstein F1 animals, 55 Brown Swiss × Holstein F1 animals, 2,300 Holstein backcrosses, 2,026 Jersey backcrosses, 27 Brown Swiss backcrosses, and 502 other crossbreds of various breed combinations. Another 653 animals appeared to be purebreds that owners had miscoded as a different breed. Genomic breed composition was estimated from 60,671 markers using the known breed identities for purebred, progeny-tested Holstein, Jersey, Brown Swiss, Ayrshire, and Guernsey bulls as the 5 traits (breed fractions) to be predicted. Estimates of breed composition were adjusted so that no percentages were negative or exceeded 100%, and breed percentages summed to 100%. Another adjustment set percentages above 93.5% equal to 100%, and the resulting value was termed breed base representation (BBR). Larger percentages of missing alleles were imputed by using a crossbred reference population rather than only the closest purebred reference population. Crossbred predictions were averages of genomic predictions computed using marker effects for each pure breed, which were weighted by the animal's BBR. Marker and polygenic effects were estimated separately for each breed on the all-breed scale instead of within-breed scales. For crossbreds, genomic predictions weighted by BBR were more accurate than the average of parents' breeding values and slightly more accurate than predictions using only the predominant breed. For purebreds, single-trait predictions using only within-breed data were as accurate as multi-trait predictions with allele effects in different breeds treated as correlated effects. Crossbred genomic predicted transmitting abilities were implemented by the Council on Dairy Cattle Breeding in April 2019 and will aid producers in managing their breeding programs and selecting replacement heifers.USDA Agricultural Research Service Animal Genomics and Improvement LaboratoryDepartamento de Ciências Exatas Universidade Estadual Paulista (Unesp) Faculdade de Ciências Agrárias e Veterinárias, JaboticabalCouncil on Dairy Cattle BreedingDepartamento de Ciências Exatas Universidade Estadual Paulista (Unesp) Faculdade de Ciências Agrárias e Veterinárias, JaboticabalAnimal Genomics and Improvement LaboratoryUniversidade Estadual Paulista (Unesp)Council on Dairy Cattle BreedingVanRaden, P. M.Tooker, M. E.Chud, T. C.S. [UNESP]Norman, H. D.Megonigal, J. H.Haagen, I. W.Wiggans, G. R.2020-12-12T01:50:02Z2020-12-12T01:50:02Z2020-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1620-1631http://dx.doi.org/10.3168/jds.2019-16634Journal of Dairy Science, v. 103, n. 2, p. 1620-1631, 2020.1525-31980022-0302http://hdl.handle.net/11449/19981410.3168/jds.2019-166342-s2.0-85076547176Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Dairy Scienceinfo:eu-repo/semantics/openAccess2024-06-06T13:42:22Zoai:repositorio.unesp.br:11449/199814Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:15:18.250948Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Genomic predictions for crossbred dairy cattle
title Genomic predictions for crossbred dairy cattle
spellingShingle Genomic predictions for crossbred dairy cattle
VanRaden, P. M.
crossbreeding
genomic evaluation
imputation
multiple breed
title_short Genomic predictions for crossbred dairy cattle
title_full Genomic predictions for crossbred dairy cattle
title_fullStr Genomic predictions for crossbred dairy cattle
title_full_unstemmed Genomic predictions for crossbred dairy cattle
title_sort Genomic predictions for crossbred dairy cattle
author VanRaden, P. M.
author_facet VanRaden, P. M.
Tooker, M. E.
Chud, T. C.S. [UNESP]
Norman, H. D.
Megonigal, J. H.
Haagen, I. W.
Wiggans, G. R.
author_role author
author2 Tooker, M. E.
Chud, T. C.S. [UNESP]
Norman, H. D.
Megonigal, J. H.
Haagen, I. W.
Wiggans, G. R.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Animal Genomics and Improvement Laboratory
Universidade Estadual Paulista (Unesp)
Council on Dairy Cattle Breeding
dc.contributor.author.fl_str_mv VanRaden, P. M.
Tooker, M. E.
Chud, T. C.S. [UNESP]
Norman, H. D.
Megonigal, J. H.
Haagen, I. W.
Wiggans, G. R.
dc.subject.por.fl_str_mv crossbreeding
genomic evaluation
imputation
multiple breed
topic crossbreeding
genomic evaluation
imputation
multiple breed
description Genomic evaluations are useful for crossbred as well as purebred populations when selection is applied to commercial herds. Dairy farmers had already spent more than $1 million to genotype over 32,000 crossbred animals before US genomic evaluations became available for those animals. Thus, new tools were needed to provide accurate genomic predictions for crossbreds. Genotypes for crossbreds are imputed more accurately when the imputation reference population includes purebreds. Therefore, genotypes of 6,296 crossbred animals were imputed from lower-density chips by including either 3,119 ancestors or 834,367 genotyped animals in the reference population. Crossbreds in the imputation study included 733 Jersey × Holstein F1 animals, 55 Brown Swiss × Holstein F1 animals, 2,300 Holstein backcrosses, 2,026 Jersey backcrosses, 27 Brown Swiss backcrosses, and 502 other crossbreds of various breed combinations. Another 653 animals appeared to be purebreds that owners had miscoded as a different breed. Genomic breed composition was estimated from 60,671 markers using the known breed identities for purebred, progeny-tested Holstein, Jersey, Brown Swiss, Ayrshire, and Guernsey bulls as the 5 traits (breed fractions) to be predicted. Estimates of breed composition were adjusted so that no percentages were negative or exceeded 100%, and breed percentages summed to 100%. Another adjustment set percentages above 93.5% equal to 100%, and the resulting value was termed breed base representation (BBR). Larger percentages of missing alleles were imputed by using a crossbred reference population rather than only the closest purebred reference population. Crossbred predictions were averages of genomic predictions computed using marker effects for each pure breed, which were weighted by the animal's BBR. Marker and polygenic effects were estimated separately for each breed on the all-breed scale instead of within-breed scales. For crossbreds, genomic predictions weighted by BBR were more accurate than the average of parents' breeding values and slightly more accurate than predictions using only the predominant breed. For purebreds, single-trait predictions using only within-breed data were as accurate as multi-trait predictions with allele effects in different breeds treated as correlated effects. Crossbred genomic predicted transmitting abilities were implemented by the Council on Dairy Cattle Breeding in April 2019 and will aid producers in managing their breeding programs and selecting replacement heifers.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T01:50:02Z
2020-12-12T01:50:02Z
2020-02-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.3168/jds.2019-16634
Journal of Dairy Science, v. 103, n. 2, p. 1620-1631, 2020.
1525-3198
0022-0302
http://hdl.handle.net/11449/199814
10.3168/jds.2019-16634
2-s2.0-85076547176
url http://dx.doi.org/10.3168/jds.2019-16634
http://hdl.handle.net/11449/199814
identifier_str_mv Journal of Dairy Science, v. 103, n. 2, p. 1620-1631, 2020.
1525-3198
0022-0302
10.3168/jds.2019-16634
2-s2.0-85076547176
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Dairy Science
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1620-1631
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
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instname_str Universidade Estadual Paulista (UNESP)
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institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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