Multi-omics analysis

Metabolomics adds only marginal increases to prediction accuracy when genomic and pedigree information are available

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Abstract

We tested the hypothesis that including metabolomic information in prediction models increases the accuracy of estimated breeding values (EBV) when pedigree and genomic markers are already available. Using stochastic simulation, we compared MGBLUP - a single-trait BLUP model using phenotypes, pedigree, genomic markers, and metabolomics - with GBLUP, which used phenotypes, pedigree, and genomic markers only. We simulated a breeding scheme with four generations of random selection that was designed to maximise the potential benefits of metabolomics. Accuracy for the trait was estimated for animals born in generation 4, which were genotyped for 54218 genetic markers, measured for 60 metabolites, but were not phenotyped for the trait. Animals in generations 1-3 were phenotyped for the trait, genotyped, and measured for 60 metabolites. The trait and 60 metabolites were controlled by 7702 QTL that were in linkage disequilibrium with the genetic markers, they were moderately heritable (h2 = 0.2), there were genetic correlations between the trait and each of the metabolites (ra = 0.08-0.30), and all residuals among the trait and metabolites were uncorrelated. We found that MGBLUP generated EBVs that were 7.3% more accurate than those generated by GBLUP (0.649 vs 0.604). While encouraging, we consider this increase in accuracy to be modest because it was generated in a simulation deliberately designed to maximise the potential benefits of MGBLUP. Our findings indicate that when pedigree and genomic markers are available, metabolomics offers only marginal increases in predictive accuracy - increases that are unlikely to justify the cost of collecting metabolomic information in routine breeding-value prediction.

Keywords: 2026

How to Cite:

Henryon, M., Jensen, J., Sarup, P., Chu, T., Ostersen, T., Sørensen, A., Guo, X. & Christensen, O., (2026) “Metabolomics adds only marginal increases to prediction accuracy when genomic and pedigree information are available”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286128. doi: https://doi.org/10.31274/wcgalp.23857

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