Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system

Abstract Background Genomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our o...

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Main Authors: Siyi Liu, Tianxiong Yao, Dong Chen, Shijun Xiao, Liqing Chen, Zhiyan Zhang
Format: Article
Language:deu
Published: BMC 2023-03-01
Series:Genetics Selection Evolution
Online Access:https://doi.org/10.1186/s12711-023-00794-2
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author Siyi Liu
Tianxiong Yao
Dong Chen
Shijun Xiao
Liqing Chen
Zhiyan Zhang
author_facet Siyi Liu
Tianxiong Yao
Dong Chen
Shijun Xiao
Liqing Chen
Zhiyan Zhang
author_sort Siyi Liu
collection DOAJ
description Abstract Background Genomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our objective was to explore the potential application of genomic selection of PB animals using genotypes of CB animals with extreme phenotypes in a three-way crossbreeding system as the reference population. Using real genotyped PB as ancestors, we simulated the production of 100,000 pigs for a Duroc x (Landrace x Yorkshire) DLY crossbreeding system. The predictive performance of breeding values of PB animals for CB performance using genotypes and phenotypes of (1) PB animals, (2) DLY animals with extreme phenotypes, and (3) random DLY animals for traits of different heritabilities ( $${h}^{2}$$ h 2 = 0.1, 0.3, and 0.5) was compared across different reference population sizes (500 to 6500) and prediction models (genomic best linear unbiased prediction (GBLUP) and Bayesian sparse linear mixed model (BSLMM)). Results Using a reference population consisting of CB animals with extreme phenotypes showed a definite predictive advantage for medium- and low-heritability traits and, in combination with the BSLMM model, significantly improved selection response for CB performance. For high-heritability traits, the predictive performance of a reference population of extreme CB phenotypes was comparable to that of PB phenotypes when the effect of the genetic correlation between PB and CB performance ( $${r}_{pc}$$ r pc ) on the accuracy obtained with a PB reference population was considered, and the former could exceed the latter if the reference size was large enough. For the selection of the first and terminal sires in a three-way crossbreeding system, prediction using extreme CB phenotypes outperformed the use of PB phenotypes, while the optimal design of the reference group for the first dam depended on the percentage of individuals from the corresponding breed that the PB reference data comprised and on the heritability of the target trait. Conclusions A commercial crossbred population is promising for the design of the reference population for genomic prediction, and selective genotyping of CB animals with extreme phenotypes has the potential for maximizing genetic improvement for CB performance in the pig industry.
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spelling doaj.art-f8a5842e82784ae9aa129868778277a62023-04-03T05:13:40ZdeuBMCGenetics Selection Evolution1297-96862023-03-0155112010.1186/s12711-023-00794-2Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding systemSiyi Liu0Tianxiong Yao1Dong Chen2Shijun Xiao3Liqing Chen4Zhiyan Zhang5National Key Laboratory for Swine Genetics, Breeding and Production Technology, Jiangxi Agricultural UniversityNational Key Laboratory for Swine Genetics, Breeding and Production Technology, Jiangxi Agricultural UniversityNational Key Laboratory for Swine Genetics, Breeding and Production Technology, Jiangxi Agricultural UniversityNational Key Laboratory for Swine Genetics, Breeding and Production Technology, Jiangxi Agricultural UniversityNational Key Laboratory for Swine Genetics, Breeding and Production Technology, Jiangxi Agricultural UniversityNational Key Laboratory for Swine Genetics, Breeding and Production Technology, Jiangxi Agricultural UniversityAbstract Background Genomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our objective was to explore the potential application of genomic selection of PB animals using genotypes of CB animals with extreme phenotypes in a three-way crossbreeding system as the reference population. Using real genotyped PB as ancestors, we simulated the production of 100,000 pigs for a Duroc x (Landrace x Yorkshire) DLY crossbreeding system. The predictive performance of breeding values of PB animals for CB performance using genotypes and phenotypes of (1) PB animals, (2) DLY animals with extreme phenotypes, and (3) random DLY animals for traits of different heritabilities ( $${h}^{2}$$ h 2 = 0.1, 0.3, and 0.5) was compared across different reference population sizes (500 to 6500) and prediction models (genomic best linear unbiased prediction (GBLUP) and Bayesian sparse linear mixed model (BSLMM)). Results Using a reference population consisting of CB animals with extreme phenotypes showed a definite predictive advantage for medium- and low-heritability traits and, in combination with the BSLMM model, significantly improved selection response for CB performance. For high-heritability traits, the predictive performance of a reference population of extreme CB phenotypes was comparable to that of PB phenotypes when the effect of the genetic correlation between PB and CB performance ( $${r}_{pc}$$ r pc ) on the accuracy obtained with a PB reference population was considered, and the former could exceed the latter if the reference size was large enough. For the selection of the first and terminal sires in a three-way crossbreeding system, prediction using extreme CB phenotypes outperformed the use of PB phenotypes, while the optimal design of the reference group for the first dam depended on the percentage of individuals from the corresponding breed that the PB reference data comprised and on the heritability of the target trait. Conclusions A commercial crossbred population is promising for the design of the reference population for genomic prediction, and selective genotyping of CB animals with extreme phenotypes has the potential for maximizing genetic improvement for CB performance in the pig industry.https://doi.org/10.1186/s12711-023-00794-2
spellingShingle Siyi Liu
Tianxiong Yao
Dong Chen
Shijun Xiao
Liqing Chen
Zhiyan Zhang
Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system
Genetics Selection Evolution
title Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system
title_full Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system
title_fullStr Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system
title_full_unstemmed Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system
title_short Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system
title_sort genomic prediction in pigs using data from a commercial crossbred population insights from the duroc x landrace x yorkshire three way crossbreeding system
url https://doi.org/10.1186/s12711-023-00794-2
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