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Area of Science:

  • Animal Science
  • Microbiome Research
  • Genomics and Bioinformatics

Background:

  • The gut microbiome plays a crucial role in host physiology, influencing growth and metabolism.
  • Understanding the predictive power of the microbiome for animal production traits is essential for optimizing livestock management.
  • Previous studies have explored microbiome-host interactions, but predictive modeling for production traits requires further investigation.

Purpose of the Study:

  • To evaluate the predictive capacity of microbiome composition at different growth stages for growth and carcass traits in crossbred pigs.
  • To compare the effectiveness of various statistical and machine learning models in utilizing microbiome data for prediction.
  • To determine the optimal timing for microbiome data collection to maximize predictive accuracy.

Main Methods:

  • Microbiome composition was analyzed from 1039 pigs at three time points: weaning, 15 weeks, and 22 weeks.
  • Prediction accuracy was assessed using correlation between actual and predicted phenotypes via five-fold cross-validation.
  • Models included Bayesian Lasso, Random Forest, Gradient Boosting, and Reproducing Kernel Hilbert Space, compared against a null model.

Main Results:

  • Inclusion of microbiome data significantly increased prediction accuracy for growth and carcass traits compared to the null model.
  • Microbiome data from later time points (15 and 22 weeks) yielded higher prediction accuracies (0.30-0.50+) than data from weaning.
  • Model choice had marginal effects on predictions; averaging predictions across models was suggested as a robust strategy.

Conclusions:

  • Microbiome composition is a valuable predictor of pig growth and carcass traits, particularly fatness.
  • Later-stage microbiome data are more effective for prediction, suggesting optimal sampling times.
  • Future research should integrate microbiome and host genome data for enhanced predictive capabilities and explore impacts on feed efficiency and meat quality.