Genomic prediction of bull fertility in US Jersey dairy cattle
- Autores
- Rezende, Fernanda M.; Nani, Juan Pablo; Peñagaricano, Francisco
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.
EEA Rafaela
Fil: Rezende, Fernanda M. University of Florida. Department of Animal Sciences; Estados Unidos. Universidade Federal de Uberlândia. Faculdade de Medicina Veterinária; Brasil
Fil: Nani, Juan Pablo. University of Florida. Department of Animal Sciences; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina
Fil: Peñagaricano, Francisco. University of Florida. Department of Animal Sciences; Estados Unidos. University of Florida. University of Florida Genetics Institute; Estados Unidos - Fuente
- Journal of Dairy Science 102 (4) : 3230-3240 (April 2019)
- Materia
-
Ganado de Leche
Razas (animales)
Genética
Genómica
Fertilidad
Toro
Dairy Cattle
Breeds (animals)
Genetics
Genomics
Fertility
Bulls
Raza Jersey
Estados Unidos - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/5151
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Genomic prediction of bull fertility in US Jersey dairy cattleRezende, Fernanda M.Nani, Juan PabloPeñagaricano, FranciscoGanado de LecheRazas (animales)GenéticaGenómicaFertilidadToroDairy CattleBreeds (animals)GeneticsGenomicsFertilityBullsRaza JerseyEstados UnidosService sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.EEA RafaelaFil: Rezende, Fernanda M. University of Florida. Department of Animal Sciences; Estados Unidos. Universidade Federal de Uberlândia. Faculdade de Medicina Veterinária; BrasilFil: Nani, Juan Pablo. University of Florida. Department of Animal Sciences; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Peñagaricano, Francisco. University of Florida. Department of Animal Sciences; Estados Unidos. University of Florida. University of Florida Genetics Institute; Estados UnidosElsevier2019-05-20T12:00:50Z2019-05-20T12:00:50Z2019-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S0022030219301079http://hdl.handle.net/20.500.12123/51510022-0302https://doi.org/10.3168/jds.2018-15810Journal of Dairy Science 102 (4) : 3230-3240 (April 2019)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-04T09:47:58Zoai:localhost:20.500.12123/5151instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-04 09:47:59.064INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
dc.title.none.fl_str_mv |
Genomic prediction of bull fertility in US Jersey dairy cattle |
title |
Genomic prediction of bull fertility in US Jersey dairy cattle |
spellingShingle |
Genomic prediction of bull fertility in US Jersey dairy cattle Rezende, Fernanda M. Ganado de Leche Razas (animales) Genética Genómica Fertilidad Toro Dairy Cattle Breeds (animals) Genetics Genomics Fertility Bulls Raza Jersey Estados Unidos |
title_short |
Genomic prediction of bull fertility in US Jersey dairy cattle |
title_full |
Genomic prediction of bull fertility in US Jersey dairy cattle |
title_fullStr |
Genomic prediction of bull fertility in US Jersey dairy cattle |
title_full_unstemmed |
Genomic prediction of bull fertility in US Jersey dairy cattle |
title_sort |
Genomic prediction of bull fertility in US Jersey dairy cattle |
dc.creator.none.fl_str_mv |
Rezende, Fernanda M. Nani, Juan Pablo Peñagaricano, Francisco |
author |
Rezende, Fernanda M. |
author_facet |
Rezende, Fernanda M. Nani, Juan Pablo Peñagaricano, Francisco |
author_role |
author |
author2 |
Nani, Juan Pablo Peñagaricano, Francisco |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ganado de Leche Razas (animales) Genética Genómica Fertilidad Toro Dairy Cattle Breeds (animals) Genetics Genomics Fertility Bulls Raza Jersey Estados Unidos |
topic |
Ganado de Leche Razas (animales) Genética Genómica Fertilidad Toro Dairy Cattle Breeds (animals) Genetics Genomics Fertility Bulls Raza Jersey Estados Unidos |
dc.description.none.fl_txt_mv |
Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions. EEA Rafaela Fil: Rezende, Fernanda M. University of Florida. Department of Animal Sciences; Estados Unidos. Universidade Federal de Uberlândia. Faculdade de Medicina Veterinária; Brasil Fil: Nani, Juan Pablo. University of Florida. Department of Animal Sciences; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina Fil: Peñagaricano, Francisco. University of Florida. Department of Animal Sciences; Estados Unidos. University of Florida. University of Florida Genetics Institute; Estados Unidos |
description |
Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-05-20T12:00:50Z 2019-05-20T12:00:50Z 2019-04 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://www.sciencedirect.com/science/article/pii/S0022030219301079 http://hdl.handle.net/20.500.12123/5151 0022-0302 https://doi.org/10.3168/jds.2018-15810 |
url |
https://www.sciencedirect.com/science/article/pii/S0022030219301079 http://hdl.handle.net/20.500.12123/5151 https://doi.org/10.3168/jds.2018-15810 |
identifier_str_mv |
0022-0302 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
Journal of Dairy Science 102 (4) : 3230-3240 (April 2019) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
reponame_str |
INTA Digital (INTA) |
collection |
INTA Digital (INTA) |
instname_str |
Instituto Nacional de Tecnología Agropecuaria |
repository.name.fl_str_mv |
INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
repository.mail.fl_str_mv |
tripaldi.nicolas@inta.gob.ar |
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