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Microbial and Genomic Information Synergistically Contribute to Predicting Swine Performance Across Production

Christian Maltecca1,2, Enrico Mancin3, Jicai Jiang1

  • 1Department of Animal Science, North Carolina State University, Raleigh, North Carolina, USA.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie
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PubMed
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Swine microbiota composition accurately predicts growth and carcass traits, outperforming genomic information for traits like back fat and daily gain. Combining microbiota and genomic data further enhances prediction accuracy for improved animal selection in precision farming.

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Area of Science:

  • Animal Science
  • Genomics
  • Microbiology

Background:

  • Microbiota composition is a key indicator of animal health and performance.
  • Precision farming requires accurate prediction tools for animal selection and management.
  • Genomic information has been used to predict animal performance, but its accuracy can be limited by environmental factors.

Purpose of the Study:

  • To compare the predictive ability of microbiota composition versus genomic information for swine performance traits.
  • To evaluate prediction accuracy across different production settings (nucleus and terminal populations) and time points (mid-test and off-test).
  • To assess the combined predictive power of microbiota and genomic data.

Main Methods:

  • Swine performance data and microbiota composition were collected from nucleus (NU) and terminal (TE) populations.
  • Machine learning models were used to predict traits like back fat, daily gain, and loin area using microbiota and/or genomic data.
  • Cross-validation was performed by training models on one population and testing on the other (NU-TE and TE-NU).

Main Results:

  • Microbiota composition consistently predicted most growth and carcass traits in both production settings and at both time points.
  • Microbiota achieved higher prediction accuracies than genomic information for back fat and daily gain at off-test.
  • Combining microbiota and genomic data yielded higher prediction accuracies than either data source alone for back fat and daily gain.

Conclusions:

  • Microbiota profiles are effective predictors of swine growth and carcass traits, especially fat deposition.
  • Microbiota composition holds significant potential as a tool for selecting animals across diverse production environments.
  • Integrating microbiota and genomic data offers a powerful approach for enhancing prediction accuracy in precision swine farming.