See more from this Session: Symposium--Incorporating Genomic Knowledge Into Crop Simulation Models
Developments in molecular technologies have allowed the focus of practical crop improvement to shift from the level of the individual (genotype) to the level of genomic region (quantitative trait locus - QTL). The ability to inexpensively and densely map genomes has facilitated development of molecular breeding strategies. However, their applicability to complex traits is limited by context-dependent gene effects attributed to gene-gene and gene-environment interactions, which restrict predictive power of associations of genes/genomic regions with phenotypic responses. Despite this limitation, it is possible to design molecular breeding strategies for complex traits that on average will outperform phenotypic selection. But this requires gene-to-phenotype (G-to-P) models of the traits that are able to account for the context-dependent effects.
There is a range of approaches for G-to-P prediction for complex traits operating at broad levels of biological organisation. Gene network models have potential to account for gene context dependencies but require advanced knowledge of network structure and dynamics. Model species (e.g. Arabidopsis) provide opportunities to capture such knowledge. However, the issue of scaling from network to whole plant phenotypic response remains, unless direct associations exist, as for example with transition to flowering. Functional whole-plant models (crop models) have potential to account for environment context dependencies as they attempt to encapsulate the dynamic plant-environment interactions. It is plausible to link the vector of coefficients defining the plant characteristics in a whole-plant model to genomic regions, but the issue of scaling from these coefficients to gene level can be problematic, e.g. as some of these coefficients are not related to an easily seperable eco-physiological process.
In this paper we discuss our approaches to this challenge in dealing with key traits of leaf growth, nitrogen dynamics and phenological development, and measuring the value of genomic knowledge (about gene action and /or QTL effects associated with key controls). The examples encompass applications in understanding the genetic basis of traits, as well as providing direct input to breeding system design through use of environment characterisation and trait and/or QTL-based selection during the process of selection. Our current projects are extending this work to study how genetic diversity in trait response and changes in breeding systems might best allow adaptation to climate change environments in Australian cereal cropping systems.