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Updated: Jan 15, 2026

Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood

Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood

Published on: November 28, 2018

Using transcriptomic data to improve the prediction of immunity traits in pigs.

T Jové-Juncà¹, V P Haas², M P L Calus³

¹IRTA, Animal Breeding and Genetics, Torre Marimon, 08140 Caldes de Montbui, Spain.

Animal : an International Journal of Animal Bioscience

|January 13, 2026

Summary

Whole blood RNA sequencing data can predict pig immunity traits and stress indicators, improving breeding selection for robustness. Integrating genomic and transcriptomic data, especially with the GTCi model, enhances prediction accuracy for health-related traits.

Keywords:

Best Linear Unbiased prediction Genomic prediction RNA sequencing Robustness Swine

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Last Updated: Jan 15, 2026

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Published on: November 28, 2018

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Published on: March 1, 2024

Area of Science:

Animal Genetics and Genomics
Immunology
Bioinformatics

Background:

Improving pig robustness through breeding selection is crucial for animal health.
Health-related traits are increasingly considered in breeding programs.
Whole blood RNA sequencing offers potential for predicting complex traits.

Purpose of the Study:

To evaluate the predictive performance of transcriptomic data for immunity-related traits, stress indicators, and carcass weight in pigs.
To compare genomic (G), transcriptomic (T), and combined (GT) models for trait prediction.
To assess advanced multiomic models accounting for G-T interactions.

Main Methods:

Utilized whole blood RNA sequencing data from 255 commercial Duroc pigs.
Employed mixed models including genomic, transcriptomic, and combined effects (G, T, GT).
Evaluated models like GTC, GTCi, and a multiomics relationship matrix model to handle G-T redundancy and interactions.

Main Results:

Models incorporating gene expression data explained more variance than genomic models for immunity and stress traits, excluding carcass weight.
Transcriptomic effects significantly improved model fit and prediction accuracy for immunity traits, especially T helper cell abundance, γδ T cell abundance, haptoglobin, and leukocyte counts.
Models accounting for genomic-transcriptomic interactions, particularly the GTCi model, achieved the highest prediction accuracies.

Conclusions:

Whole blood gene expression data is valuable for predicting pig immunity traits.
Accurate modeling of interactions between genomic and transcriptomic effects is essential for maximizing prediction accuracy in multiomic studies.
This approach enhances the potential for improving pig robustness and health through advanced breeding strategies.