Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.

key messageTrait Genome-to-Phenome (G2P) dimensionality and "breeding context" combine to influence the realised prediction skill of different whole genome prediction (WGP) methods. Theory and empirical evidence both suggest there is likely to be "No Free Lunch" for prediction-based breeding. Ensembles of diverse sets of G2P models provide a framework to expose and investigate the high G2P dimensionality of trait genetic architecture for WGP applications. Artificial Intelligence and Machine Learning (AI-ML) prediction algorithms contribute novel trait G2P model diversity to ensemble-based WGP. Prediction-based breeding leveraging ensembles of G2P models creates new opportunities to identify and design novel paths for genetic gain. Improving our understanding of trait genetic architecture is motivated by creating new opportunities to enhance breeding methodology, create new selection trajectories for crop improvement, and accelerate rates of genetic gain. With access to high-throughput sequencing, phenotyping and envirotyping technologies we can model the complex multidimensional relationships between sequence variation and trait phenotypic variation that are under the influences of selection. Using the framework of the diversity prediction theorem, we consider applications of ensembles of diverse trait genome-to-phenome (G2P) models. Crop growth models (CGM) are an example of a hierarchical framework for studying the influences of quantitative trait loci (QTL) within trait networks and their interactions with different environments to determine yield. Hybrid CGM-G2P models combine elements of CGMs, to understand how trait networks influence crop yield performance, with trait G2P models, to understand influences of trait genetic architecture on selection trajectories. We discuss hybrid CGM-G2P models and their potential applications to enhance ensemble-based prediction. Multi-environment trials conducted across breeding cycles can be designed to include contrasting environments to expose the different CGM-G2P dimensions of the trait by environment interactions that are influential on selection trajectories. Artificial intelligence and machine learning (AI-ML) algorithms can be applied as components of ensembles to improve gene discovery and quantification of allele effects for traits to enhance G2P prediction applications. We use the trait flowering time in the maize TeoNAM experiment to illustrate and motivate further investigations of how to leverage ensembles of G2P models for prediction-based breeding.