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Incorporating genomic and transcriptomic effects in joint linear and non-linear structural models for predicting

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Blood transcriptomic data significantly improve prediction of immune traits in pigs. Feature selection enhances accuracy and identifies key genes, outperforming genomic data for relevant traits.

Keywords:
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Area of Science:

  • Animal Genomics and Breeding
  • Transcriptomics and Gene Expression Analysis
  • Quantitative Genetics and Trait Prediction

Background:

  • Livestock phenotypes result from complex interactions between genetic variation and regulatory mechanisms.
  • Predicting complex traits is crucial for efficient animal breeding but remains challenging.
  • Transcriptomic data offer insights into regulatory signals bridging genotypes and phenotypes.

Purpose of the Study:

  • To assess the predictive value of blood transcriptomic data for six immune, stress, and production traits in Duroc pigs.
  • To compare the performance of transcriptomic data alone versus combined with genomic information.
  • To evaluate different statistical models and feature selection methods for trait prediction.

Main Methods:

  • Utilized blood transcriptomic and genomic (SNP) data from 255 Duroc pigs.
  • Employed Bayesian regression (BayesC, RKHS) and neural network linear mixed models.
  • Applied Partial Least Squares (PLS) for transcriptomic feature selection.

Main Results:

  • High prediction accuracies achieved for immunity-related traits (e.g., gamma delta T cells, leukocyte counts) using transcriptomic data (r=0.74, r=0.67).
  • Moderate improvement for cortisol prediction (r=0.39); SNP-based models excelled for carcass weight (r=0.45).
  • PLS feature selection identified key genes (MAF, SOX13, DDIT4, FOS) and improved prediction efficiency.

Conclusions:

  • Blood transcriptomics substantially enhance prediction for traits relevant to the sampled tissue.
  • SNP-based models are superior for traits less biologically related to blood.
  • Feature selection is critical for optimizing prediction performance, computational efficiency, and gene discovery.