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Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population

BACKGROUND: Carcass traits are crucial for broiler ducks, but carcass traits can only be measured postmortem. Genomic selection (GS) is an effective approach in animal breeding to improve selection and reduce costs. However, the performance of genomic prediction in duck carcass traits remains largel...

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Autores principales: Cai, Wentao, Hu, Jian, Fan, Wenlei, Xu, Yaxi, Tang, Jing, Xie, Ming, Zhang, Yunsheng, Guo, Zhanbao, Zhou, Zhengkui, Hou, Shuisheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163724/
https://www.ncbi.nlm.nih.gov/pubmed/37147656
http://dx.doi.org/10.1186/s40104-023-00875-8
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author Cai, Wentao
Hu, Jian
Fan, Wenlei
Xu, Yaxi
Tang, Jing
Xie, Ming
Zhang, Yunsheng
Guo, Zhanbao
Zhou, Zhengkui
Hou, Shuisheng
author_facet Cai, Wentao
Hu, Jian
Fan, Wenlei
Xu, Yaxi
Tang, Jing
Xie, Ming
Zhang, Yunsheng
Guo, Zhanbao
Zhou, Zhengkui
Hou, Shuisheng
author_sort Cai, Wentao
collection PubMed
description BACKGROUND: Carcass traits are crucial for broiler ducks, but carcass traits can only be measured postmortem. Genomic selection (GS) is an effective approach in animal breeding to improve selection and reduce costs. However, the performance of genomic prediction in duck carcass traits remains largely unknown. RESULTS: In this study, we estimated the genetic parameters, performed GS using different models and marker densities, and compared the estimation performance between GS and conventional BLUP on 35 carcass traits in an F(2) population of ducks. Most of the cut weight traits and intestine length traits were estimated to be high and moderate heritabilities, respectively, while the heritabilities of percentage slaughter traits were dynamic. The reliability of genome prediction using GBLUP increased by an average of 0.06 compared to the conventional BLUP method. The Permutation studies revealed that 50K markers had achieved ideal prediction reliability, while 3K markers still achieved 90.7% predictive capability would further reduce the cost for duck carcass traits. The genomic relationship matrix normalized by our true variance method instead of the widely used [Formula: see text] could achieve an increase in prediction reliability in most traits. We detected most of the bayesian models had a better performance, especially for BayesN. Compared to GBLUP, BayesN can further improve the predictive reliability with an average of 0.06 for duck carcass traits. CONCLUSION: This study demonstrates genomic selection for duck carcass traits is promising. The genomic prediction can be further improved by modifying the genomic relationship matrix using our proposed true variance method and several Bayesian models. Permutation study provides a theoretical basis for the fact that low-density arrays can be used to reduce genotype costs in duck genome selection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40104-023-00875-8.
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spelling pubmed-101637242023-05-07 Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population Cai, Wentao Hu, Jian Fan, Wenlei Xu, Yaxi Tang, Jing Xie, Ming Zhang, Yunsheng Guo, Zhanbao Zhou, Zhengkui Hou, Shuisheng J Anim Sci Biotechnol Research BACKGROUND: Carcass traits are crucial for broiler ducks, but carcass traits can only be measured postmortem. Genomic selection (GS) is an effective approach in animal breeding to improve selection and reduce costs. However, the performance of genomic prediction in duck carcass traits remains largely unknown. RESULTS: In this study, we estimated the genetic parameters, performed GS using different models and marker densities, and compared the estimation performance between GS and conventional BLUP on 35 carcass traits in an F(2) population of ducks. Most of the cut weight traits and intestine length traits were estimated to be high and moderate heritabilities, respectively, while the heritabilities of percentage slaughter traits were dynamic. The reliability of genome prediction using GBLUP increased by an average of 0.06 compared to the conventional BLUP method. The Permutation studies revealed that 50K markers had achieved ideal prediction reliability, while 3K markers still achieved 90.7% predictive capability would further reduce the cost for duck carcass traits. The genomic relationship matrix normalized by our true variance method instead of the widely used [Formula: see text] could achieve an increase in prediction reliability in most traits. We detected most of the bayesian models had a better performance, especially for BayesN. Compared to GBLUP, BayesN can further improve the predictive reliability with an average of 0.06 for duck carcass traits. CONCLUSION: This study demonstrates genomic selection for duck carcass traits is promising. The genomic prediction can be further improved by modifying the genomic relationship matrix using our proposed true variance method and several Bayesian models. Permutation study provides a theoretical basis for the fact that low-density arrays can be used to reduce genotype costs in duck genome selection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40104-023-00875-8. BioMed Central 2023-05-06 /pmc/articles/PMC10163724/ /pubmed/37147656 http://dx.doi.org/10.1186/s40104-023-00875-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Cai, Wentao
Hu, Jian
Fan, Wenlei
Xu, Yaxi
Tang, Jing
Xie, Ming
Zhang, Yunsheng
Guo, Zhanbao
Zhou, Zhengkui
Hou, Shuisheng
Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population
title Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population
title_full Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population
title_fullStr Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population
title_full_unstemmed Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population
title_short Strategies to improve genomic predictions for 35 duck carcass traits in an F(2) population
title_sort strategies to improve genomic predictions for 35 duck carcass traits in an f(2) population
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163724/
https://www.ncbi.nlm.nih.gov/pubmed/37147656
http://dx.doi.org/10.1186/s40104-023-00875-8
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