New residual feed intake criterion for longitudinal data

David, Ingrid; Tran, Van-Hung Huynh; Gilbert, Hélène

doi:10.1186/s12711-021-00641-2

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…RFI in the RRM was higher both at the start and end of the test period, which demonstrated the different genetic backgrounds in pig growth. The same tendency of the heritability estimates during the whole process were also reported (Shirali et al, 2017b;Coyne et al, 2017;David et al, 2021). A permanent environmental effect was also included in our RRM models, and the estimates of permanent environmental variance changed gently, which indicated that RFI was less influenced by permanent environmental effects in our study.…”

Section: Discussionsupporting

confidence: 77%

A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs

et al. 2022

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Residual feed intake (RFI) is considered as a measurement of feed efficiency, which is greatly related to the growth performance in pigs. Daily feeding records can be obtained from automatic feeders. In general, RFI is usually calculated from the total measurement records during the whole test period. This measurement cannot reflect genetic changes in different growth periods during the test. A random regression model (RRM) provides a method to model such type of longitudinal data. To improve the accuracy of genetic prediction for RFI, the RRM and regular animal models were applied in this study, and their prediction performances were compared. Both traditional pedigree-based relationship matrix (A matrix) and pedigree and genomic information-based relationship matrix (H matrix) were applied for these two models. The results showed that, the prediction accuracy of the RRM was higher than that of the animal model, increasing 24.2% with A matrix and 40.9% with H matrix. Furthermore, genomic information constantly improved the accuracy of evaluation under each evaluation model. In conclusion, longitudinal traits such as RFI can describe feed efficiency better, and the RRM with both pedigree and genetic information was superior to the animal model. These results provide a feasible method of genomic prediction using longitudinal data in animal breeding.

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Section: Discussionsupporting

confidence: 77%

A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs

et al. 2022

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“…These animals were from seven generations of a divergent selection experiment for RFI applied at the end of each test period (110 kg) [1]. Animal management and phenotype measurements are described in David et al [32] and Huynh-Tran et al [19]. The numbers of animals and records per low (LRFI), high RFI (HRFI) lines and generation are in Additional file 1: Table S2.…”

Section: Methodsmentioning

confidence: 99%

“…The correlations between SBV l and u ySBV l were slightly lower but still large for SBV 1 (0.92 and 0.95 for LRFI and HRFI, respectively) and SBV 2 (0.94 for both lines), but lower for SBV 3 (0.78 for both lines). Since the first two SBV have been identified as sufficient to select for a desired trajectory pattern of RFI over time in pigs [32], using the model on summarized phenotypes seems a good alternative (similar accuracy with less computing time) to select for RFI trajectories in routine evaluation.…”

Section: Pedigreementioning

confidence: 99%

Quality of breeding value predictions from longitudinal analyses, with application to residual feed intake in pigs

et al. 2022

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Background An important goal in animal breeding is to improve longitudinal traits. The objective of this study was to explore for longitudinal residual feed intake (RFI) data, which estimated breeding value (EBV), or combination of EBV, to use in a breeding program. Linear combinations of EBV (summarized breeding values, SBV) or phenotypes (summarized phenotypes) derived from the eigenvectors of the genetic covariance matrix over time were considered, and the linear regression method (LR method) was used to facilitate the evaluation of their prediction accuracy. Results Weekly feed intake, average daily gain, metabolic body weight, and backfat thickness measured on 2435 growing French Large White pigs over a 10-week period were analysed using a random regression model. In this population, the 544 dams of the phenotyped animals were genotyped. These dams did not have own phenotypes. The quality of the predictions of SBV and breeding values from summarized phenotypes of these females was evaluated. On average, predictions of SBV at the time of selection were unbiased, slightly over-dispersed and less accurate than those obtained with additional phenotypic information. The use of genomic information did not improve the quality of predictions. The use of summarized instead of longitudinal phenotypes resulted in predictions of breeding values of similar quality. Conclusions For practical selection on longitudinal data, the results obtained with this specific design suggest that the use of summarized phenotypes could facilitate routine genetic evaluation of longitudinal traits.

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“…In a previous study [48], we considered the average estimate of α t during the evaluated period and not the daily estimates, and obtained a lower heritability (0.16 ± 0.05) but this was based on a different dataset. For RFI, David et al [49] reported heritabilities ranging from 0.19 ± 0.06 to 0.28 ± 0.06, using an RR model for weekly estimates of RFI over 10 weeks in pigs.…”

Section: Estimation Of the Genetic Variance Of The Allocation Coeffic...mentioning

confidence: 99%

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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New residual feed intake criterion for longitudinal data

Cited by 6 publications

References 49 publications

A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs

A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs

Quality of breeding value predictions from longitudinal analyses, with application to residual feed intake in pigs

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

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