Genomic prediction of bull fertility in US Jersey dairy cattle

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.

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Bibliographic Details
Main Authors: Rezende, Fernanda M., Nani, Juan Pablo, Peñagaricano, Francisco
Format: info:ar-repo/semantics/artículo biblioteca
Language:eng
Published: Elsevier 2019-04
Subjects:Ganado de Leche, Razas (animales), Genética, Genómica, Fertilidad, Toro, Dairy Cattle, Breeds (animals), Genetics, Genomics, Fertility, Bulls, Raza Jersey, Estados Unidos,
Online Access:https://www.sciencedirect.com/science/article/pii/S0022030219301079
http://hdl.handle.net/20.500.12123/5151
https://doi.org/10.3168/jds.2018-15810
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spelling oai:localhost:20.500.12123-51512019-05-20T12:02:16Z Genomic prediction of bull fertility in US Jersey dairy cattle Rezende, Fernanda M. Nani, Juan Pablo Peñagaricano, Francisco Ganado de Leche Razas (animales) Genética Genómica Fertilidad Toro Dairy Cattle Breeds (animals) Genetics Genomics Fertility Bulls Raza Jersey Estados Unidos 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 2019-05-20T12:00:50Z 2019-05-20T12:00:50Z 2019-04 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng info:eu-repo/semantics/openAccess application/pdf Elsevier Journal of Dairy Science 102 (4) : 3230-3240 (April 2019)
institution INTA AR
collection DSpace
country Argentina
countrycode AR
component Bibliográfico
access En linea
databasecode dig-inta-ar
tag biblioteca
region America del Sur
libraryname Biblioteca Central del INTA Argentina
language eng
topic Ganado de Leche
Razas (animales)
Genética
Genómica
Fertilidad
Toro
Dairy Cattle
Breeds (animals)
Genetics
Genomics
Fertility
Bulls
Raza Jersey
Estados Unidos
Ganado de Leche
Razas (animales)
Genética
Genómica
Fertilidad
Toro
Dairy Cattle
Breeds (animals)
Genetics
Genomics
Fertility
Bulls
Raza Jersey
Estados Unidos
spellingShingle Ganado de Leche
Razas (animales)
Genética
Genómica
Fertilidad
Toro
Dairy Cattle
Breeds (animals)
Genetics
Genomics
Fertility
Bulls
Raza Jersey
Estados Unidos
Ganado de Leche
Razas (animales)
Genética
Genómica
Fertilidad
Toro
Dairy Cattle
Breeds (animals)
Genetics
Genomics
Fertility
Bulls
Raza Jersey
Estados Unidos
Rezende, Fernanda M.
Nani, Juan Pablo
Peñagaricano, Francisco
Genomic prediction of bull fertility in US Jersey dairy cattle
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.
format info:ar-repo/semantics/artículo
topic_facet Ganado de Leche
Razas (animales)
Genética
Genómica
Fertilidad
Toro
Dairy Cattle
Breeds (animals)
Genetics
Genomics
Fertility
Bulls
Raza Jersey
Estados Unidos
author Rezende, Fernanda M.
Nani, Juan Pablo
Peñagaricano, Francisco
author_facet Rezende, Fernanda M.
Nani, Juan Pablo
Peñagaricano, Francisco
author_sort Rezende, Fernanda M.
title Genomic prediction of bull fertility in US Jersey dairy cattle
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
publisher Elsevier
publishDate 2019-04
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
work_keys_str_mv AT rezendefernandam genomicpredictionofbullfertilityinusjerseydairycattle
AT nanijuanpablo genomicpredictionofbullfertilityinusjerseydairycattle
AT penagaricanofrancisco genomicpredictionofbullfertilityinusjerseydairycattle
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