Genetic relationships between spring emergence, canopy phenology and biomass yield increase the accuracy of genomic prediction in Miscanthus

Christopher Davey, Paul Robson, Sarah Hawkins, Kerrie Farrar, John Clifton-Brown, Iain Donnison, Gancho Slavov

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
140 Downloads (Pure)

Abstract

Miscanthus has potential as a bioenergy crop but the rapid development of high-yielding varieties is challenging. Previous studies have suggested that phenology and canopy height are important determinants of biomass yield. Furthermore, while genome-wide prediction was effective for a broad range of traits, the predictive ability for yield was very low. We therefore developed models clarifying the genetic associations between spring emergence, consequent canopy phenology and dry biomass yield. The timing of emergence was a moderately strong predictor of early-season elongation growth (genetic correlation >0.5), but less so for growth later in the season and for the final yield (genetic correlation <0.1). In contrast, early-season canopy height was consistently more informative than emergence for predicting biomass yield across datasets for two species in Miscanthus and two growing seasons. We used the associations uncovered through these models to develop selection indices that are expected to increase the response to selection for yield by as much as 21% and improve the performance of genome-wide prediction by an order of magnitude. This multivariate approach could have an immediate impact in operational breeding programmes, as well as enable the integration of crop growth models and genome-wide prediction
Original languageEnglish
Pages (from-to)5093-5102
Number of pages9
JournalJournal of Experimental Botany
Volume68
Issue number18
DOIs
Publication statusPublished - 12 Oct 2017

Keywords

  • breeding
  • canopy phenology
  • emergence
  • genomic selection
  • genomic prediction
  • Miscanthus
  • biomass yield
  • quantitative genetics
  • selection indices
  • bioenergy crops
  • genomics

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