Accelerated Domestication of Miscanthus: Towards Integration of Growth Modelling and Genomic Selection

We present ongoing activities aimed at developing an integrated approach combining growth modelling with linkage/association mapping and genomic selection in the bioenergy grass Miscanthus. As a first step, we developed multiple regression models relating dry biomass yield to a large set of phenological and morphological traits. 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 dry biomass yield (genetic correlation < 0.1). In contrast, early-season growth rate (i.e., canopy height by June) was consistently more informative than emergence for predicting biomass yield across data sets for two species of Miscanthus and two growing seasons. We then used the associations detected through these models to build selection indices that can lead to moderate increases (i.e., 1-3%) in the response to selection for biomass yield. More importantly, combining these indices with genomic selection can lead to substantial improvements in predictive ability (i.e., from three- to six-fold), making this approach directly applicable in operational breeding programmes. Finally, we outline ongoing efforts to scale up these studies using large field trials (>1000 genotypes) at the Institute of Biological, Environmental and Rural Sciences in Aberystwyth (UK).