ArticleGenetics, selection, evolution : GSE2023
Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley.
Article in Genetics, selection, evolution : GSE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Modest contribution of metabolomic data to genomic prediction of breeding values for feed conversion ratio in pigs.Genetics, selection, evolution : GSE · 2026Article
- Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions.Environmental microbiome · 2026Article
- FSBLUP: a novel strategy of fusion similarity matrix construction via optimally integrating intermediate omics data to enhance genomic prediction.Genome biology · 2026Article
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction.Plants (Basel, Switzerland) · 2025Article
- Integrating Longitudinal Metabolite Profiles Improves Trait Prediction in Pigs in a Trait- and Timepoint-Dependent Manner.bioRxiv : the preprint server for biology · 2025Article
- Semi-parametric validation of genomic predictions and polygenic risk scores with the Blupf90 software suite.G3 (Bethesda, Md.) · 2025Article
- Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-GBLUP).Genetics, selection, evolution : GSE · 2025Article
- Metabolomic-genomic prediction realizes small increases in accuracy of estimated breeding values for daily gain in pigs.Genetics, selection, evolution : GSE · 2025Article
- Article
- Genomic prediction for yield and malting traits in barley using metabolomic and near-infrared spectra.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Efficient large-scale genomic prediction in approximate genome-based kernel model.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
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5 authors.
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Abstract
backgroundMetabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method.
resultsFor all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known.
conclusionsMGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species.
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