Genomic prediction and genotype-by-environment interaction analysis of crown and stem rust in ryegrasses in european multi-site trials

Abstract

Climate change calls for novel approaches to include environmental effects in future breeding programs for forage crops. A set of ryegrasses (Lolium) varieties was evaluated in multiple European environments for crown rust (Puccinia coronata f. sp. lolii) and stem rust (P. graminis f. sp. graminicola) resistance. Additive Main Effect and Multiplicative Interaction (AMMI) analysis revealed significant genotype (G) and environment (E) effects as well as the interaction of both factors (G × E). Genotypes plus Genotype-by-Environment interaction (GGE) analysis grouped the tested environments in multiple mega-environments for both traits suggesting the presence of an environmental effect on the ryegrasses performances. The best performing varieties in the given mega-environments showed high resistance to crown as well as stem rust, and overall, tetraploid varieties performed better than diploid. Furthermore, we modeled G × E using a marker x environment interaction (M × E) model to predict the performance of varieties tested in some years but not in others. Our results showed that despite the limited number of varieties, the high number of observations allowed us to predict both traits’ performances with high accuracy. The results showed that genomic prediction using multi environmental trials could enhance breeding programs for the crown and stem rust in ryegrasses.