Investigating G2P relationships for multiple traits across selection cycles will allow us to investigate how well we can predict trait phenotypes across environments for individual plants and their progeny.
Thus, within quantitative genetics we can explore new modelling methods to predict an individual’s genotypic value and breeding value.
The selection treatments will increase the power to detect underlying quantitative trait loci and resolve functional genome variation determining trait phenotypic variation at different levels within the G2P trait hierarchy.
We will design new prediction methods for breeding by combining mathematical methods for describing and predicting trajectories on complex response surfaces with genetic simulations and experimental results; this will allow us to refine and improve our abilities to discover important trait G2P relationships, and predict trajectories in genetic and phenotypic space.
Emerging mathematical methods, such as Approximate Bayesian Computation, will help us discover new G2P modelling and prediction principles that can be applied broadly across species, and for diverse trait adaptation scenarios applicable to breeding and the evolution of natural systems.
The models and experimental resources we develop will provide gene-editing targets for further testing and improving the G2P model.
