Can metabolic prediction be an alternative to genomic prediction in barley?

PLoS One. 2020 Jun 5;15(6):e0234052. doi: 10.1371/journal.pone.0234052. eCollection 2020.

Abstract

Like other crop species, barley, the fourth most important crop worldwide, suffers from the genetic bottleneck effect, where further improvements in performance through classical breeding methods become difficult. Therefore, indirect selection methods are of great interest. Here, genomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits in the nested association mapping (NAM) population HEB-25. We found prediction abilities of up to 0.93 for plant height with SNP markers and of up to 0.61 for flowering time with metabolites. Interestingly, prediction abilities in GP increased after reducing the number of incorporated SNP markers. The estimated effects of GP and MP were highly concordant, indicating MP as an interesting alternative to GP, being able to reflect a stable genotype-specific metabolite profile. In MP, sampling at an early developmental stage outperformed sampling at a later stage. The results confirm the value of GP for future breeding. With MP, an interesting alternative was also applied successfully. However, based on our results, usage of MP alone cannot be recommended in barley. Nevertheless, MP can assist in unravelling physiological pathways for the expression of agronomically important traits.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Chromosome Mapping
  • Genome, Plant*
  • Genotype
  • Hordeum / genetics*
  • Hordeum / metabolism
  • Metabolome*
  • Phenotype
  • Polymorphism, Single Nucleotide

Grants and funding

This work was funded by the European Fund for Regional Development (EFRE, https://ec.europa.eu/regional_policy/en/funding/erdf/) within the research focus “Molecular Biosciences as a Motor of Knowledge-Based Economics” (awarded to KP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.