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Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population

SIMPLE SUMMARY: The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to support the early selection of sires. Our results show that we can select sires accor...

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Autores principales: Salimiyekta, Yasamin, Vaez-Torshizi, Rasoul, Abbasi, Mokhtar Ali, Emmamjome-Kashan, Nasser, Amin-Afshar, Mehdi, Guo, Xiangyu, Jensen, Just
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8697866/
https://www.ncbi.nlm.nih.gov/pubmed/34944268
http://dx.doi.org/10.3390/ani11123492
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author Salimiyekta, Yasamin
Vaez-Torshizi, Rasoul
Abbasi, Mokhtar Ali
Emmamjome-Kashan, Nasser
Amin-Afshar, Mehdi
Guo, Xiangyu
Jensen, Just
author_facet Salimiyekta, Yasamin
Vaez-Torshizi, Rasoul
Abbasi, Mokhtar Ali
Emmamjome-Kashan, Nasser
Amin-Afshar, Mehdi
Guo, Xiangyu
Jensen, Just
author_sort Salimiyekta, Yasamin
collection PubMed
description SIMPLE SUMMARY: The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to support the early selection of sires. Our results show that we can select sires according to their daughters’ early lactation performance before they finish first lactation. Cross-validation results show that early selection accuracy can be high, and such an early selection can decrease the generation interval and lead to an increased genetic gain in the Iranian Holstein population. ABSTRACT: The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.
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spelling pubmed-86978662021-12-24 Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population Salimiyekta, Yasamin Vaez-Torshizi, Rasoul Abbasi, Mokhtar Ali Emmamjome-Kashan, Nasser Amin-Afshar, Mehdi Guo, Xiangyu Jensen, Just Animals (Basel) Article SIMPLE SUMMARY: The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to support the early selection of sires. Our results show that we can select sires according to their daughters’ early lactation performance before they finish first lactation. Cross-validation results show that early selection accuracy can be high, and such an early selection can decrease the generation interval and lead to an increased genetic gain in the Iranian Holstein population. ABSTRACT: The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population. MDPI 2021-12-07 /pmc/articles/PMC8697866/ /pubmed/34944268 http://dx.doi.org/10.3390/ani11123492 Text en © 2021 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
Salimiyekta, Yasamin
Vaez-Torshizi, Rasoul
Abbasi, Mokhtar Ali
Emmamjome-Kashan, Nasser
Amin-Afshar, Mehdi
Guo, Xiangyu
Jensen, Just
Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population
title Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population
title_full Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population
title_fullStr Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population
title_full_unstemmed Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population
title_short Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population
title_sort random regression model for genetic evaluation and early selection in the iranian holstein population
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8697866/
https://www.ncbi.nlm.nih.gov/pubmed/34944268
http://dx.doi.org/10.3390/ani11123492
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