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

Area of Science:

• Animal Genetics
• Genomics
• Metabolomics

Background:

• Accurate genetic merit prediction is crucial for accelerating genetic improvement in pigs.
• Traits like feed intake, feed conversion, backfat, daily gain, and loin depth are economically important but challenging to measure directly.
• This study explored integrating blood serum metabolite profiles from two developmental stages into genomic prediction models.

Purpose of the Study:

• To assess the impact of integrating metabolite profiles on genomic prediction accuracy for five key economic traits in pigs.
• To compare different integration strategies, including single and multiple timepoint metabolite data combined with genomic data.
• To determine the value of metabolite profiles as an intermediate omics layer for enhancing breeding programs.

Main Methods:

• Utilized a BayesC modeling framework on 1,637 pigs with complete phenotype, genotype, and metabolite data.
• Evaluated seven models: genotype-only, metabolite-only (10-week, 20-week, or both), and combined genotype-metabolite models.
• Analyzed average daily feed intake (DFI), feed conversion ratio (FCR), backfat thickness (BF), test daily gain (TDG), and loin depth (LD).

Main Results:

• Genomic prediction accuracy consistently improved with the integration of metabolite profiles.
• Models combining genotype and metabolite data (G+M2, G+M1+M2) showed notable increases in accuracy for DFI (0.31 to 0.41) and FCR (0.27 to 0.33).
• The greatest gains for BF and TDG were observed with G+M2 and G+M1+M2 models, while LD benefited most from combining both timepoints (G+M1+M2).

Conclusions:

• Integrating metabolite profiles, particularly from the 20-week stage or combined timepoints, enhances genomic prediction accuracy across various traits.
• Metabolite profiles serve as a valuable intermediate omics layer, providing complementary biological insights.
• Tailoring integration strategies to specific traits and sampling times maximizes the potential of metabolite data for pig genetic improvement.