From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time - Bustos-Korts, Daniela; Malosetti, Marcos; Chenu, Karine; Chapman, Scott; Boer, Martin P.; Zheng, Bangyou; van Eeuwijk, Fred A. (Frontiers in Plant Science, 2019)
Genotype by environment interaction (G×E) for the target trait (i.e. yield) is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have a different level of G×E. Here, we provide a genotype to phenotype (G2P) modelling approach to characterize the adaptation landscape, considering different levels in trait hierarchy and their interplay over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear responses over time and complex trait dependencies and apply it here to wheat crops in Australia. APSIM parameters had a genetic basis of 300 QTLs sampled from a Gamma distribution following the same shape and rate as observed for real data. In the simulations, G×E arises over the growing season, starting from basic traits without G×E. Insight in how G×E arises helps to improve on the phenotype prediction across environments and optimize the network of testing environments. As a result of the organisation of traits in a hierarchy and the interactions between the traits, G×E for the target trait can occur even when underlying traits do not show G×E. Our approach creates a tangible adaptation landscape that can be useful to i) study the relationships between traits over time and across environments, ii) evaluate genotype-to-phenotype models for multiple traits and environments, iii) compare models with an increased integration of statistical and biological aspects, iv) develop network models that allow visualizing how causality is propagated in biological systems and v) design efficient high throughput phenotyping schedules and methods.