Modelling gene-trait-crop relationships: past experiences and future prospects

Classical crop models have long been established to understand crop responses to environmental factors, by integrating quantitative functional relationships for various physiological processes. In view of the potential added value of robust crop modelling to classical quantitative genetics, model-input parameters or traits are increasingly considered to represent ‘genetic coefficients’. A number of case studies, in which the effects of quantitative trait loci or genes have been incorporated into existing ecophysiological models to replace model-input traits, have shown promise of using models in analyzing genotype-phenotype relationships of more complex crop traits. Studies of functional genomics will increasingly enable the elucidation of the molecular genetic basis of these modelinput traits. To fulfil the great expectations from this integrated modelling, crop models should be upgraded based on understandings at lower organizational levels. The recently proposed ‘crop systems biology’, which combines modern genomics, traditional physiology and biochemistry, and advanced modelling, is believed ultimately to realize the expected roles of in silico modelling in narrowing genotypephenotype gaps. We will summarise recent research activities and express our opinions on perspectives for modelling genotype-by-environment interactions at crop level. INTRODUCTION A major challenge in fieldand greenhousecrop production today is breeding for genotypes and realizing their potential in given environments to produce sufficient quality products while maintaining the sustainability of production systems and resource use. This goal can be achieved via creating phenotypes of complex traits at the level of the crop – the community of mutually interacting plants, usually of the same species. A thorough insight into gene-trait-crop relationships is therefore crucial. Currently, there is an increasing recognition amongst geneticists and breeders (e.g. Tuberosa and Salvi, 2006; Dwivedi et al., 2007; Langridge and Fleury, 2011; Messina et al., 2011) and physiologists (e.g. Chenu et al., 2009; Zhu et al., 2011) of immediate need for computational tools to assist breeders more effectively in translating and integrating the outputs from high-throughput genomics research, and to help resolving genotype-byenvironment interactions (G×E) efficiently and selecting the best technology interventions and associated breeding systems for their target traits and target environments. Here, we present our views on elucidating the gene-trait-crop relationships by integrating modern plant biology, traditional crop science and advanced systems modelling. CROP MODELLING TO ASSIST BREEDING Process-based physiological models of crop growth quantify causality between relevant physiological processes and responses of these processes to environmental variables, and, therefore, allow predictions of crop yields not restricted to the environments in which the model parameters have been derived. Crop models require Proc. IV IS on HortiModel 2012 Eds.: Weihong Luo et al. Acta Hort. 957, ISHS 2012 182 environmental inputs (i.e. weather variables and management options) and physiological inputs. The latter inputs are used as model parameters for characterizing genotypic differences. These parameters are also referred to as ‘genetic coefficients’ (White and Hoogenboom, 1996; Mavromatis et al., 2001) or ‘model-input traits’ (Yin et al., 2000a), implying that model-input parameters might be (at least partly) under genetic control. As model parameters can reflect certain genetic characteristics, crop modelling has long been considered a useful computational tool to assist breeding (Loomis et al., 1979; Boote et al., 2001). Shorter et al. (1991) have long proposed collaborative efforts between breeders, physiologists and modellers, using models as a framework to integrate physiology with breeding. Given the common experience that crop models based on physiologically sound mechanisms can quantify and integrate responses of crop yield to both genetic and environmental factors, crop physiologists, breeders and modellers have explored the potential of using crop models in various aspects of breeding. These activities include: (1) identifying main yield-determining traits, both under poor and conducive environments for crop growth (Yin et al., 2000b; Heuvelink et al., 2007; Semenov and Halford, 2009), (2) defining optimum selection environments in order to maximize selection progress (Aggarwal et al., 1997), (3) optimizing single trait values (Boote and Tollenaar, 1994; Setter et al., 1995; Yin et al., 1997), (4) designing ideotypes in which trade-offs between conflicting crop traits are properly evaluated (Penning de Vries, 1991; Dingkuhn et al., 1993; Kropff et al., 1995; Haverkort and Kooman, 1997), and (5) assisting multi-location testing (Dua et al., 1990) and explaining G×E (Mavromatis et al., 2001; Van Eeuwijk et al., 2005). All these studies, based on model simulations, are to give suggestions that breeders may use. Stam (1998) and Koornneef and Stam (2001), from a geneticist’s and breeder’s point of view, expressed their concerns about this model-based approach that ignores the inheritance of the model-input traits. For example, for designing ideotypes by modelling, it is assumed, either tacitly or explicitly, that these traits can be combined at will in a single genotype. Such an assumption ignores the possible existence of constraints, feedback mechanisms and correlations among the traits. Constraints might be imposed simply by the fact that little genetic variation exists in the genetic material available for selection. Thus, models may not identify those traits for which gain via breeding may be easiest (Jackson et al., 1996). Correlations between the traits, due either to a tight linkage between genes or to a single gene that affects multiple traits (pleiotropy), may seriously hamper the realization of an ideotype (for example, an earlymaturing potato cultivar with high resistance against late blight). Knowledge of the genetic basis of phenotypic variation, even described in terms of model-input traits, is crucial for a successful breeding programme (Stam, 1998). Therefore, understanding the inheritance of the model parameters within the framework is required (Stam, 1998). To assist the development of efficient breeding strategies, crop modelling requires quantitative understanding of the inheritance of the model-input parameters. INTEGRATION OF CROP MODELLING WITH GENETICS In genetics, complex crop traits can be unravelled into the effects of individual QTL – quantitative trait loci (Paterson et al., 1988), commonly using the materials of a segregating population derived from a bi-parental cross. This QTL approach can also be applied to model-input parameters to elucidate their inheritance (Yin et al., 1999a, b). A common result of QTL analysis of complex crop traits is that QTL expression is usually conditional on the environment and this greatly impedes the application of QTLmapping information for manipulating complex traits (Stratton, 1998). Crop models can potentially be of help in this respect to better address genotype-phenotype relationships, provided that model-input parameters can be easily measured (Yin et al., 2004) and vary little with environmental conditions (Reymond et al., 2003; Tardieu, 2003). Model-input parameters (or ‘genetic coefficients’), reflect effects of genetic origin in the way that one set of parameters represents one genotype (Tardieu, 2003). Hence, the models manifest