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The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet
- Source :
- Genetics Selection Evolution, Vol 56, Iss 1, Pp 1-12 (2024)
- Publication Year :
- 2024
- Publisher :
- BMC, 2024.
-
Abstract
- Abstract Background Improving pigs’ ability to digest diets with an increased dietary fiber content is a lever to improve feed efficiency and limit feed costs in pig production. The aim of this study was to determine whether information on the gut microbiota and host genetics can contribute to predict digestive efficiency (DE, i.e. digestibility coefficients of energy, organic matter, and nitrogen), feed efficiency (FE, i.e. feed conversion ratio and residual feed intake), average daily gain, and daily feed intake phenotypes. Data were available for 1082 pigs fed a conventional or high-fiber diet. Fecal samples were collected at 16 weeks, and DE was estimated using near‑infrared spectrometry. A cross-validation approach was used to predict traits within the same diet, for the opposite diet, and for a combination of both diets, by implementing three models, i.e. with only genomic (Gen), only microbiota (Micro), and both genomic and microbiota information (Micro+Gen). The predictive ability with and without sharing common sires and breeding environment was also evaluated. Prediction accuracy of the phenotypes was calculated as the correlation between model prediction and phenotype adjusted for fixed effects. Results Prediction accuracies of the three models were low to moderate ( 0.52). Prediction accuracies were not affected by the stratification of diets in the reference and validation sets and were in the same order of magnitude within the same diet, for the opposite diet, and for the combination of both diets. Prediction accuracies of the three models were significantly higher when pigs in the reference and validation populations shared common sires and breeding environment than when they did not (P
- Subjects :
- Animal culture
SF1-1100
Genetics
QH426-470
Subjects
Details
- Language :
- German, English, French
- ISSN :
- 12979686
- Volume :
- 56
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Genetics Selection Evolution
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.22b47a61afef499cb4a87b8a6c5b789b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12711-023-00865-4