Modest contribution of metabolomic data to genomic prediction of breeding values for feed conversion ratio in pigs

Feed efficiency is an economically important but costly trait to measure in pig breeding. Previous studies have shown that integrating metabolomic data, such as proton nuclear magnetic resonance (¹H NMR) - derived metabolomic profiles, into genomic prediction models can improve the accuracy of estimated breeding values (EBVs) - for example, using a univariate metabolomic-genomic best linear unbiased prediction (MGBLUP) model for malting quality traits in barley and for average daily gain (ADG) in pigs using NMR-based metabolomic features (MFs). In this study, we extend this approach to predict feed conversion ratio (FCR) in pigs. We tested two hypotheses: (1) incorporating NMR metabolomic data into a univariate MGBLUP model increases the accuracy of EBVs for FCR compared with a univariate genomic BLUP (GBLUP) model, and (2) a bivariate MGBLUP model that jointly analyses FCR and the correlated trait ADG further improves EBV accuracy compared with a univariate MGBLUP model. We tested these hypotheses using an offspring-validation design, allowing prediction of EBVs for animals lacking individual FCR records.

The experimental population comprised 8,174 Duroc pigs (4,027 males from a test station and 4,147 females from breeding herds). To evaluate the accuracy of EBVs for FCR, males with FCR records were used as the training population, and females without FCR records served as the validation population. These validation females had offspring with recorded FCR, and EBV accuracy was assessed by correlating their EBVs with the corrected phenotypes of their offspring.

Incorporating metabolomic data into the univariate MGBLUP model generated EBVs for FCR that were 2.3% more accurate than EBVs from the univariate GBLUP model (0.394 vs. 0.385). The bivariate MGBLUP model that jointly analysed FCR and ADG generated EBVs for FCR that were 1.2% more accurate than EBVs from the bivariate GBLUP model (0.432 vs. 0.427).

The increases in accuracy were modest and statistically insignificant, reflecting limitations in the current metabolomic data, such as single-time-point sampling. Even so, the observed improvements suggest that metabolomic information may provide complementary information beyond genomic data. With further refinements in sampling strategies and genetic models, metabolomics could contribute to improving prediction accuracy in animal breeding.