Metabolomic-Genomic prediction drastically improves prediction accuracy of breeding values in crop breeding

Abstract

AbstractMetabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. Objective was to investigate heritabilities and accuracies of genetic evaluation of malting quality (MQ) traits by integrating both genomic and metabolomic information. In total, 2,430 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). Plot heritabilities ranged from 0.060 to 0.279 for GBLUP, and increased to range from 0.123 to 0.283 for MGBLUP. LOLO scheme yielded higher accuracies than LOYO scheme, and MGBLUP yielded higher accuracies than GBLUP regardless of cross-validation scheme.Author SummaryMetabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. We carried out a study on 2,430 plots of 562 malting spring barley lines and 24,018 Nuclear Magnetic Resonance intensities measured on each wort sample, to investigate the prediction of breeding values of five malting quality traits by integrating genomic and metabolomic information using a metabolomics-genomic model. Results showed an increase in prediction accuracy for this model compared to a genomic model.