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

Phenotypes in livestock are shaped by genetic variation as well as downstream regulatory mechanisms, making the prediction of complex traits a key challenge for animal breeding. Transcriptomic data represent an intermediate biological layer between genotypes and phenotypes and may capture regulatory signals not fully explained by genomic information alone.

Objective: This study evaluated the contribution of blood transcriptomic data, alone or combined with genomic information, to predict six immune, stress, and production traits in 255 Duroc pigs.

Methods: Four traits were closely related to the sampled tissue and timepoint, whereas two were less biologically relevant. Bayesian regression methods (BayesC and RKHS) and a neural network linear mixed model were compared using either all transcripts or subsets selected by Partial Least Squares (PLS).

Results: High prediction accuracy was obtained for immunity-related traits, such as gamma delta T cells and leukocyte counts, with correlations of 0.74 and 0.67, respectively, when transcriptomic data were used. Moderate improvements were observed for cortisol prediction, whereas single−nucleotide polymorphisms (SNP)-based models performed best for carcass weight. PLS-based feature selection showed that a small subset of features can perform equally well or better than the whole transcriptomic dataset and identified biologically relevant candidate genes, including MAF, SOX13, DDIT4, and FOS.

Conclusion: Blood transcriptomic data substantially improved prediction performance for traits biologically related to the sampled tissue, whereas SNP-based models performed better for less relevant traits, and combining omics provided only modest and non-significant gains; feature selection was essential to enhance prediction performance, computational efficiency, and to facilitate the identification of immune-related candidate genes.