Robustness scores in fattening pigs based on routinely collected phenotypes: determination and genetic parameters

Lenoir, Guillaume; Flatres-Grall, Loïc; Friggens, Nicolas; David, Ingrid

doi:10.1093/jas/skac157

Cited by 4 publications

(11 citation statements)

References 25 publications

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“…A pre-treatment process was performed on BW and FI, recorded each time the animal went into the AFS, to validate them, identify quality issues and convert them on a daily scale. This process followed the procedure proposed by Revilla et al (2022) and modified by Lenoir et al (2022a). In summary, on the scale of the visit, a quadratic regression of BW on age + age 2 for each animal was applied to eliminate aberrant BW.…”

Section: Methodsmentioning

confidence: 99%

“…The provided diets were non-limiting in amino acids. The boar testing station environmental and technical conditions are described in detail in Lenoir et al (2022a). The pedigrees contained 27276 animals across 20 generations.…”

Section: Study Populationmentioning

confidence: 99%

“…There were strong and unfavorable relationship between LSR and feed efficiency traits, FCR and RFI. This could be related to the positive correlation between ADG and FCR, which was affected by the way these two traits were estimated (Lenoir et al, 2022a). The traits ADG and FCR used in selection were measured over an identical period for all pigs but were not standardized between starting and finishing weights.…”

Section: Genetic Correlations Between Robustness and Production Traitsmentioning

confidence: 99%

“…Accordingly, some of the animals tested reached their mature weight before testing, which led to a drop in feed conversion or residual feed intake even if they had previously a strong growth. Thus, there were two different types of finisher pigs with low FCR or RFI: those which had a strong growth but did not approach their mature weight during the testing period, and those with a low daily feed intake associated with a low, near maturity, growth (Lenoir et al, 2022a). We performed an additional analysis where we standardized the trait FCR between 40 and 100kg, the genetic correlation with LSR remained unfavorable but less strong, -0.34±0.14.…”

Section: Genetic Correlations Between Robustness and Production Traitsmentioning

confidence: 99%

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Disentangling the dynamics of energy allocation to provide a proxy of robustness in fattening pigs

Lenoir

Flatres-Grall²,

Muñoz-Tamayo

et al. 2022

Preprint

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There is a growing need to improve robustness characteristics in fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a robustness proxy on the basis of modelling of longitudinal energetic allocation coefficient to growth for fattening pigs. Consequently, the environmental variance of this allocation coefficient was considered as a proxy of robustness. The second objective was to estimate its genetic parameters and correlation with traits under selection as well with phenotypes routinely collected on farms. A total of 5848 pigs, from Pietrain NN paternal line, were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm was equipped with automatic feeding system, recording individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between cumulative net energy available, estimated from feed intake, and cumulative weight gain during fattening period. Longitudinal energetic allocation coefficients were analysed using a two-step approach, to estimate both its genetic variance and the genetic variance in the residual variance, trait LSR. The LSR trait, that could be interpreted as an indicator of the response of the animal to perturbations/stress, showed low heritability (0.05 +/- 0.01). The trait LSR had high favourable genetic correlations with average daily growth (-0.71+/-0.06) and unfavourable with feed conversion ratio (-0.76+/-0.06) and residual feed intake (-0.83+/-0.06). The analysis of the relationship between estimated breeding values (EBV) LSR quartiles and phenotypes routinely collected on farms shows the most favourable situation for animals from quartile with the weakest EBV LSR, i.e., the most robust. These results show that selection for robustness based on deviation from energetic allocation coefficient to growth can be considered in breeding programs for fattening pigs.

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Section: Methodsmentioning

confidence: 99%

Section: Study Populationmentioning

confidence: 99%

Section: Genetic Correlations Between Robustness and Production Traitsmentioning

confidence: 99%

Section: Genetic Correlations Between Robustness and Production Traitsmentioning

confidence: 99%

See 2 more Smart Citations

Disentangling the dynamics of energy allocation to provide a proxy of robustness in fattening pigs

Lenoir

Flatres-Grall²,

Muñoz-Tamayo

et al. 2022

Preprint

Self Cite

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show abstract

“…However, robustness is a complex trait that is difficult to phenotype ( Friggens et al., 2017 ). Today, traits included in breeding goals that may be associated with robustness are mainly related to the health status of animals during a specific period or to the longevity of reproductive animals ( Knap, 2005 ; Berghof et al., 2019 ; Knap and Doeschl-Wilson, 2020 ; Lenoir et al., 2022 ). The second option consists of estimating the environmental sensitivity of animals by modeling and integrating the corresponding genetic value into the selection objective.…”

Section: Introductionmentioning

confidence: 99%

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs

Le,

Rohmer,

David

2024

Journal of Animal Science

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Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and to quantify the robustness of animals in a genetic framework using different sets of simulation, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake and feeding rate). Based on the simulations, the specificity of the UpDown method was high (> 0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than three days. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78 and 0.83 for individual, pen and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

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

Lenoir,

Flatres-Grall,

Muñoz-Tamayo

et al. 2023

Genet Sel Evol

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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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Robustness scores in fattening pigs based on routinely collected phenotypes: determination and genetic parameters

Cited by 4 publications

References 25 publications

Disentangling the dynamics of energy allocation to provide a proxy of robustness in fattening pigs

Disentangling the dynamics of energy allocation to provide a proxy of robustness in fattening pigs

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs

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

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