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Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs
BACKGROUND: There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental v...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629156/ https://www.ncbi.nlm.nih.gov/pubmed/37936078 http://dx.doi.org/10.1186/s12711-023-00851-w |
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author | Lenoir, Guillaume Flatres-Grall, Loïc Muñoz-Tamayo, Rafael David, Ingrid Friggens, Nicolas C. |
author_facet | Lenoir, Guillaume Flatres-Grall, Loïc Muñoz-Tamayo, Rafael David, Ingrid Friggens, Nicolas C. |
author_sort | Lenoir, Guillaume |
collection | PubMed |
description | BACKGROUND: There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR). RESULTS: The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (− 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (− 0.76 ± 0.06) and residual feed intake (− 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm. CONCLUSIONS: Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs. |
format | Online Article Text |
id | pubmed-10629156 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106291562023-11-08 Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs Lenoir, Guillaume Flatres-Grall, Loïc Muñoz-Tamayo, Rafael David, Ingrid Friggens, Nicolas C. Genet Sel Evol Research Article BACKGROUND: There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR). RESULTS: The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (− 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (− 0.76 ± 0.06) and residual feed intake (− 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm. CONCLUSIONS: Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs. BioMed Central 2023-11-07 /pmc/articles/PMC10629156/ /pubmed/37936078 http://dx.doi.org/10.1186/s12711-023-00851-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Article Lenoir, Guillaume Flatres-Grall, Loïc Muñoz-Tamayo, Rafael David, Ingrid Friggens, Nicolas C. Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs |
title | Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs |
title_full | Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs |
title_fullStr | Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs |
title_full_unstemmed | Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs |
title_short | Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs |
title_sort | disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629156/ https://www.ncbi.nlm.nih.gov/pubmed/37936078 http://dx.doi.org/10.1186/s12711-023-00851-w |
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