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Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs.

Miriam Piles1, Carlos Fernandez-Lozano2, María Velasco-Galilea3

  • 1Animal Breeding and Genetics Program, Institute of Agriculture and Food Research and Technology (IRTA), Torre Marimon s/n, 08140, Caldes de Montbui, Barcelona, Spain. miriam.piles@irta.es.

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Summary

Machine learning and RNA-Seq successfully identified genes linked to feed efficiency in pigs. Liver tissue provided better classification of residual feed intake than duodenum, revealing key biomarkers for this trait.

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Area of Science:

  • Animal Genetics
  • Genomics
  • Bioinformatics

Background:

  • Molecular mechanisms of residual feed intake (RFI) in pigs remain largely unknown.
  • Previous genome-wide association studies and gene expression analyses have yielded inconsistent results.
  • This study leverages machine learning (ML) and transcriptomic data to identify genes associated with feed efficiency (FE).

Purpose of the Study:

  • To identify genes associated with feed efficiency (FE) in pigs using transcriptomic (RNA-Seq) data.
  • To apply machine learning algorithms to classify pigs based on residual feed intake (RFI).
  • To discover potential predictive biomarkers for FE in pigs.

Main Methods:

  • Computed RFI based on metabolic body weight, average daily gain, and backfat gain.
  • Performed RNA-Seq on liver and duodenum tissues from high and low RFI pigs.
  • Utilized machine learning algorithms (Support Vector Machine, Random Forest, Elastic Net) to predict RFI class from gene expression data.

Main Results:

  • Elastic Net (ENET) achieved the best classification accuracy using 200 liver genes (AUROC: 0.85) and 100 duodenum genes (AUROC: 0.76).
  • Identified canonical pathways and candidate genes previously linked to FE, including NRF2-mediated oxidative stress response and PPARα/RXRα activation.
  • Discovered specific genes (e.g., DNAJC6, MAPK8 in duodenum; SMOX, CLOCK in liver) associated with FE.

Conclusions:

  • ML algorithms and RNA-Seq data effectively classify pigs into high or low RFI groups.
  • Gene expression data from liver tissue yielded superior classification performance compared to duodenum.
  • Identified novel genes in liver and duodenum that serve as predictive biomarkers for feed efficiency in pigs.