Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs.

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Genetics Selection Evolution Pub Date : 2023-11-07 DOI:10.1186/s12711-023-00851-w
Guillaume Lenoir, Loïc Flatres-Grall, Rafael Muñoz-Tamayo, Ingrid David, Nicolas C Friggens
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Abstract

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.

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解开能量分配的动态,开发肥猪健壮性的指标。
背景:人们越来越需要提高育肥猪的健壮性,但这种特性很难表现出来。我们的第一个目标是通过对生长的纵向能量分配系数建模,开发肥猪健壮性的替代品,并将该分配系数的环境方差视为健壮性的代表。第二个目标是估计其遗传参数以及与所选性状和常规收集的表型的相关性。2015年至2022年,共有5848头来自PietrainNN父系的猪在AXIOM公猪检测站(法国因德雷河畔阿扎伊)接受了检测。这个农场配备了一个自动喂养系统,记录每次访问时的个体体重和饲料摄入量。我们使用动态线性回归模型来表征育肥期可用累积净能量(根据采食量估计)和累积增重之间分配系数的演变。纵向能量分配系数采用两步法进行分析,以估计系数的遗传方差和残差方差中的遗传方差,称为对数变换平方残差(LSR),表现出较低的遗传力(0.05 ± 0.01),与平均日生长具有高度有利的遗传相关性(- 0.71 ± 0.06),以及与饲料转化率的不利遗传相关性(- 0.76 ± 0.06)和残余进料(- 0.83 ± 0.06)。使用LSR的估计繁殖值将种群分为四类,结果表明,具有最低估计繁殖值的动物是具有最差稳健性表型指标值的动物,这些表型指标是使用农场常规收集的记录进行评估的。结论:本研究的结果表明,在育肥猪的育种计划中,可以考虑根据生长分配系数的环境方差的估计育种值来选择健壮性。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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