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Application of single-step genomic evaluation using social genetic effect model for growth in pig

OBJECTIVE: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in th...

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Autores principales: Hong, Joon Ki, Kim, Young Sin, Cho, Kyu Ho, Lee, Deuk Hwan, Min, Ye Jin, Cho, Eun Seok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Asian-Australasian Association of Animal Production Societies (AAAP) and Korean Society of Animal Science and Technology (KSAST) 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819686/
https://www.ncbi.nlm.nih.gov/pubmed/31480141
http://dx.doi.org/10.5713/ajas.19.0182
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author Hong, Joon Ki
Kim, Young Sin
Cho, Kyu Ho
Lee, Deuk Hwan
Min, Ye Jin
Cho, Eun Seok
author_facet Hong, Joon Ki
Kim, Young Sin
Cho, Kyu Ho
Lee, Deuk Hwan
Min, Ye Jin
Cho, Eun Seok
author_sort Hong, Joon Ki
collection PubMed
description OBJECTIVE: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ω constants for genomic relationships. METHODS: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (τ: 1), several weights (ω(xx), 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). RESULTS: The genetic variances and total heritability estimates (T(2)) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ω other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ω in both breeds, indicating the better accuracy of ω(0.1) models. Therefore, the optimal values of ω to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. CONCLUSION: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.
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spelling pubmed-68196862019-12-01 Application of single-step genomic evaluation using social genetic effect model for growth in pig Hong, Joon Ki Kim, Young Sin Cho, Kyu Ho Lee, Deuk Hwan Min, Ye Jin Cho, Eun Seok Asian-Australas J Anim Sci Article OBJECTIVE: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ω constants for genomic relationships. METHODS: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (τ: 1), several weights (ω(xx), 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). RESULTS: The genetic variances and total heritability estimates (T(2)) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ω other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ω in both breeds, indicating the better accuracy of ω(0.1) models. Therefore, the optimal values of ω to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. CONCLUSION: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices. Asian-Australasian Association of Animal Production Societies (AAAP) and Korean Society of Animal Science and Technology (KSAST) 2019-12 2019-08-26 /pmc/articles/PMC6819686/ /pubmed/31480141 http://dx.doi.org/10.5713/ajas.19.0182 Text en Copyright © 2019 by Asian-Australasian Journal of Animal Sciences This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Hong, Joon Ki
Kim, Young Sin
Cho, Kyu Ho
Lee, Deuk Hwan
Min, Ye Jin
Cho, Eun Seok
Application of single-step genomic evaluation using social genetic effect model for growth in pig
title Application of single-step genomic evaluation using social genetic effect model for growth in pig
title_full Application of single-step genomic evaluation using social genetic effect model for growth in pig
title_fullStr Application of single-step genomic evaluation using social genetic effect model for growth in pig
title_full_unstemmed Application of single-step genomic evaluation using social genetic effect model for growth in pig
title_short Application of single-step genomic evaluation using social genetic effect model for growth in pig
title_sort application of single-step genomic evaluation using social genetic effect model for growth in pig
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819686/
https://www.ncbi.nlm.nih.gov/pubmed/31480141
http://dx.doi.org/10.5713/ajas.19.0182
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