Variability of daily feed intake as an indicator of resilience in Pietrain pigs

There is a growing need to produce more resilient livestock that can cope with extreme environments and their associated impacts. Daily feed intake (DFI) is a promising metric for the development of resilience indicators (RIs), as reduced feed consumption is widely recognised as a clinical sign of disease. However, there is no consensus on which DFI-based RIs are the most informative. The objectives of this study were: (i) to evaluate the most common DFI-based RIs and their relationships with feed efficiency traits (FETs), (ii) to investigate the relationship between these indicators and progeny mortality rates on commercial farms, and (iii) to gain insight into their biological mechanisms. A total of 111 121 DFI records of 1 634 healthy purebred Pietrain boars were used, ranging from 47 to 90 days, with an average of 69 DFI records per boar. Three RIs were calculated: residual variance, RMSE and logarithm of the variance of the deviations from an expected pattern (LnVar). A classical animal model was used to estimate the variance components of each RI, and a bivariate model was implemented to estimate the genetic correlation between RIs and the FET. Furthermore, a preliminary mortality study was carried out using data from 6 889 progeny of 55 boars on farms catalogued as virulent for porcine reproductive and respiratory syndrome. Progeny mortality was calculated for each contemporary group (farm and batch) of the offspring, and sires were classified into three levels of resilience. A Bayesian linear model was used to determine whether the differences in progeny mortality rate between the levels of resilience were relevant. On the other hand, a weighted single-step genome-wide association study was performed for each RI and the FET, using a total of 1 216 genotypes. RIs showed moderate heritability (h<sup>2</sup> = 0.27-0.49) and moderate to strong genetic correlation with FET. Progeny of resilient sires had an 80% probability of at least a 2.5% higher survival rate. Overlapping genomic regions were found for RIs and feed conversion ratio, highlighting DOCK1, SYK and SPTLC1 genes for their potential roles in modulating immune responses and/or metabolism. The LnVar of deviations from the population mean was the most promising indicators (LnVar-Pop) for disease resilience, as it was the RI that better captured differences in progeny mortality. Furthermore, these results suggest a common biological basis for RIs and FET of interest to breeding programmes. Further studies are needed to validate them.

Authors

Institutions

Publication Details

Journal
animal
Published
2024-12-28
DOI
https://doi.org/10.1016/j.animal.2024.101415
Citations
6
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
Field-Weighted Citation Impact
3.30
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Variability of daily feed intake as an indicator of resilience in Pietrain pigs

Cristina Casto‐Rebollo, S. Gol, Pablo Núñez Demarco, Noelia Ibáñez‐Escriche et al.
6 citations
animal
Genetic and phenotypic traits in livestock
3.30
article

Variability of daily feed intake as an indicator of resilience in Pietrain pigs

Cristina Casto‐Rebollo, S. Gol, Pablo Núñez Demarco, Noelia Ibáñez‐Escriche, J. Reixach
article en
6 citations

Abstract

There is a growing need to produce more resilient livestock that can cope with extreme environments and their associated impacts. Daily feed intake (DFI) is a promising metric for the development of resilience indicators (RIs), as reduced feed consumption is widely recognised as a clinical sign of disease. However, there is no consensus on which DFI-based RIs are the most informative. The objectives of this study were: (i) to evaluate the most common DFI-based RIs and their relationships with feed efficiency traits (FETs), (ii) to investigate the relationship between these indicators and progeny mortality rates on commercial farms, and (iii) to gain insight into their biological mechanisms. A total of 111 121 DFI records of 1 634 healthy purebred Pietrain boars were used, ranging from 47 to 90 days, with an average of 69 DFI records per boar. Three RIs were calculated: residual variance, RMSE and logarithm of the variance of the deviations from an expected pattern (LnVar). A classical animal model was used to estimate the variance components of each RI, and a bivariate model was implemented to estimate the genetic correlation between RIs and the FET. Furthermore, a preliminary mortality study was carried out using data from 6 889 progeny of 55 boars on farms catalogued as virulent for porcine reproductive and respiratory syndrome. Progeny mortality was calculated for each contemporary group (farm and batch) of the offspring, and sires were classified into three levels of resilience. A Bayesian linear model was used to determine whether the differences in progeny mortality rate between the levels of resilience were relevant. On the other hand, a weighted single-step genome-wide association study was performed for each RI and the FET, using a total of 1 216 genotypes. RIs showed moderate heritability (h<sup>2</sup> = 0.27-0.49) and moderate to strong genetic correlation with FET. Progeny of resilient sires had an 80% probability of at least a 2.5% higher survival rate. Overlapping genomic regions were found for RIs and feed conversion ratio, highlighting DOCK1, SYK and SPTLC1 genes for their potential roles in modulating immune responses and/or metabolism. The LnVar of deviations from the population mean was the most promising indicators (LnVar-Pop) for disease resilience, as it was the RI that better captured differences in progeny mortality. Furthermore, these results suggest a common biological basis for RIs and FET of interest to breeding programmes. Further studies are needed to validate them.

animalVol. 19(2)
Universitat Politècnica de València (ES)
Climate action
Openalex Percentile: Top 7%
Genetic and phenotypic traits in livestock
3.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.