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Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia

SIMPLE SUMMARY: This research aims to evaluate the possibility of introducing modern methods for the selection of dairy cattle in Serbia. A total of eight models were applied to estimate the genetic values and their accuracy based on the data of milk yield, milk fat yield, milk fat content, milk pro...

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Autores principales: Štrbac, Ljuba, Pracner, Doni, Šaran, Momčilo, Janković, Dobrila, Trivunović, Snežana, Ivković, Mirko, Tarjan, Laslo, Dedović, Nebojša
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951744/
https://www.ncbi.nlm.nih.gov/pubmed/36830383
http://dx.doi.org/10.3390/ani13040597
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author Štrbac, Ljuba
Pracner, Doni
Šaran, Momčilo
Janković, Dobrila
Trivunović, Snežana
Ivković, Mirko
Tarjan, Laslo
Dedović, Nebojša
author_facet Štrbac, Ljuba
Pracner, Doni
Šaran, Momčilo
Janković, Dobrila
Trivunović, Snežana
Ivković, Mirko
Tarjan, Laslo
Dedović, Nebojša
author_sort Štrbac, Ljuba
collection PubMed
description SIMPLE SUMMARY: This research aims to evaluate the possibility of introducing modern methods for the selection of dairy cattle in Serbia. A total of eight models were applied to estimate the genetic values and their accuracy based on the data of milk yield, milk fat yield, milk fat content, milk protein yield and milk protein content. A total of 6041 animals were included, and of them, 2565 had data for milk production traits. Two cases were studied, the first when only the first lactations were observed, and the second when all lactations were observed using a repeatability model. For the formation of a genomic relationship matrix, 58 K SNP data were used for a total of 1491 cows. Higher accuracy was obtained using models with repeated measurements. Multivariate analysis with repeated measurements showed the best results. An ssGBLUP methodology showed promising results. Expanding the genotyped population is necessary to achieve better accuracy. ABSTRACT: In this paper, six univariate and two multivariate best linear unbiased prediction (BLUP) models were tested for the estimation of breeding values (BV) in Holstein Friesian cattle in Serbia. Two univariate models were formed using the numerator relationship matrix (NRM), four using the genomic relationship matrix (GRM). Multivariate models contained only an NRM. Two cases were studied, the first when only first lactations were observed, and the second when all lactations were observed using a repeatability model. A total of 6041 animals were included, and of them, 2565 had data on milk yield (MY), milk fat yield (FY), milk fat content (FC), milk protein yield (PY) and milk protein content (PC). Finally, out of those 2565 cows, 1491 were genotyped. A higher accuracy of BV was obtained when using a combination of NRM and GRM compared to NRM alone in univariate analysis, while multivariate analysis with repeated measures gave the highest accuracy with all 6041 animals. When only genotyped animals were observed, the highest accuracy of the estimated BV was calculated by the ssGBLUPp model, and the lowest by the univariate BLUP model. In conclusion, the current breeding programs in Serbia should be changed to use multivariate analysis with repeated measurements until the optimal size of the reference population, which must include genotyping data on both bulls and cows, is reached.
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spelling pubmed-99517442023-02-25 Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia Štrbac, Ljuba Pracner, Doni Šaran, Momčilo Janković, Dobrila Trivunović, Snežana Ivković, Mirko Tarjan, Laslo Dedović, Nebojša Animals (Basel) Article SIMPLE SUMMARY: This research aims to evaluate the possibility of introducing modern methods for the selection of dairy cattle in Serbia. A total of eight models were applied to estimate the genetic values and their accuracy based on the data of milk yield, milk fat yield, milk fat content, milk protein yield and milk protein content. A total of 6041 animals were included, and of them, 2565 had data for milk production traits. Two cases were studied, the first when only the first lactations were observed, and the second when all lactations were observed using a repeatability model. For the formation of a genomic relationship matrix, 58 K SNP data were used for a total of 1491 cows. Higher accuracy was obtained using models with repeated measurements. Multivariate analysis with repeated measurements showed the best results. An ssGBLUP methodology showed promising results. Expanding the genotyped population is necessary to achieve better accuracy. ABSTRACT: In this paper, six univariate and two multivariate best linear unbiased prediction (BLUP) models were tested for the estimation of breeding values (BV) in Holstein Friesian cattle in Serbia. Two univariate models were formed using the numerator relationship matrix (NRM), four using the genomic relationship matrix (GRM). Multivariate models contained only an NRM. Two cases were studied, the first when only first lactations were observed, and the second when all lactations were observed using a repeatability model. A total of 6041 animals were included, and of them, 2565 had data on milk yield (MY), milk fat yield (FY), milk fat content (FC), milk protein yield (PY) and milk protein content (PC). Finally, out of those 2565 cows, 1491 were genotyped. A higher accuracy of BV was obtained when using a combination of NRM and GRM compared to NRM alone in univariate analysis, while multivariate analysis with repeated measures gave the highest accuracy with all 6041 animals. When only genotyped animals were observed, the highest accuracy of the estimated BV was calculated by the ssGBLUPp model, and the lowest by the univariate BLUP model. In conclusion, the current breeding programs in Serbia should be changed to use multivariate analysis with repeated measurements until the optimal size of the reference population, which must include genotyping data on both bulls and cows, is reached. MDPI 2023-02-08 /pmc/articles/PMC9951744/ /pubmed/36830383 http://dx.doi.org/10.3390/ani13040597 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Štrbac, Ljuba
Pracner, Doni
Šaran, Momčilo
Janković, Dobrila
Trivunović, Snežana
Ivković, Mirko
Tarjan, Laslo
Dedović, Nebojša
Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia
title Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia
title_full Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia
title_fullStr Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia
title_full_unstemmed Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia
title_short Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia
title_sort mathematical modeling and software tools for breeding value estimation based on phenotypic, pedigree and genomic information of holstein friesian cattle in serbia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951744/
https://www.ncbi.nlm.nih.gov/pubmed/36830383
http://dx.doi.org/10.3390/ani13040597
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