Integrating Longitudinal Metabolite Profiles Improves Trait Prediction in Pigs in a Trait- and Timepoint-Dependent Manner

BackgroundAccurate prediction of genetic merit is essential for accelerating genetic improvement in pigs, particularly for traits that are costly or difficult to measure directly. This study investigated the potential of integrating individual-level blood serum metabolite profiles sampled at two developmental stages (10-week and 20-week) into genomic prediction models for five economically important traits: average daily feed intake (DFI), feed conversion ratio (FCR), backfat thickness (BF), test daily gain (TDG), and loin depth (LD). Using a BayesC modeling framework, we analyzed 1,637 pigs from a single purebred population with complete phenotype, genotype, and metabolite-profile data. We evaluated seven models, including a genotype-only model (G), metabolite-only models using metabolite-profile data from 10-week (M1), 20-week (M2), or both timepoints (M1+M2), and combined models integrating genotypes with metabolite-profile data (G+M1, G+M2, G+M1+M2). ResultsHeritability estimates were generally low to moderate, ranging from 0.07 to 0.30 for the 10-week metabolite profile and from 0.04 to 0.28 for the 20-week metabolite profile. Prediction accuracy of phenotype consistently improved when metabolite-profile data were integrated into genotype-based models, although the magnitude of improvement varied depending on the trait and the timepoint of metabolite sampling. Prediction accuracy increased from 0.31 (G) to 0.41 (G+M2; G+M1+M2) for DFI, and from 0.27 (G) to 0.33 (G+M2; G+M1+M2) for FCR. The latter two models also delivered the largest gains over G for BF (from 0.41 to 0.45) and TDG (from 0.28 to 0.32). However, LD benefited the most when both 10-week and 20-week metabolite profiles were combined (G+M1+M2: 0.45 compared to G: 0.42). ConclusionsAcross all traits, models combining genotype data with metabolite profiles from one or multiple timepoints achieved the highest or equally high prediction accuracies compared to the genotype-only model, reflecting complementary biological insights captured by metabolite profiles. These findings highlight the potential value of metabolite-profile data as an intermediate omics layer to enhance genomic prediction, particularly when integration strategies are tailored to trait-specific biology and sampling timepoints.