Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency
Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.
KeywordsGenotype-to-phenotype Phenotypic prediction Trait physiology QTL Functional genomics Crop improvement
This paper summarises the research of a team of people, which would not have been possible without their enthusiasm and dedication nor without the financial support of a number of funders including Australian Research Council, Grains Research and Development Corporation, DuPont Pioneer, European Union FP7, and Australian Centre for International Agricultural Research. The authors acknowledge Crop and Pasture Science (CSIRO Publishing) and Journal of Experimental Botany (Oxford Journals) for permissions to re-use figures.
- Cooper M, Hammer GL (1996) Synthesis of strategies for crop improvement. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, ICRISAT & IRRI, Wallingford, pp 591–623Google Scholar
- Cooper M, Chapman SC, Podlich DW, Hammer GL (2002) The GP problem: quantifying gene-to-phenotype relationships. In Silico Biol 2:151–164. http://www.bioinfo.de/journals.html
- Gholipoor M, Prasad PVV, Mutava RN, Sinclair TR (2010) Genetic variability of transpiration response to vapor pressure deficit among sorghum genotypes. Field Crops Res 119:85–90Google Scholar
- Hammer G (2006) Pathways to prosperity: breaking the yield barrier in sorghum. Agric Sci 19:16–22Google Scholar
- Hammer GL, Jordan DR (2007) An integrated systems approach to crop improvement. In: Spiertz JHJ, Struik PC, van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations, vol 21, Wageningen UR – Frontis series. Springer, The Netherlands, pp 45–61Google Scholar
- Hammer GL, McLean G, Chapman S, Zheng B, Doherty A, Harrison MT, van Oosterom E, Jordan D (2014) Crop design for specific adaptation in variable dryland production environments. Crop Pasture Sci 65:614–626. http://www.publish.csiro.au/nid/40/paper/CP14088.htm
- Messina C, Hammer G, Dong Z, Podlich D, Cooper M (2009) Modelling crop improvement in a G*E*M framework via gene-trait-phenotype relationships. In: Sadras VO, Calderini D (eds) Crop physiology: applications for genetic improvement and agronomy. Academic/Elsevier, The Netherlands, pp 235–265CrossRefGoogle Scholar
- Morrell PL, Buckler ES, Ross-Ibarra J (2012) Crop genomics: advances and applications. Nat Rev Genet 13:85–96Google Scholar
- Ray JD, Samson BK, Sinclair TR (1997) Vegetative growth and soil water extraction of two maize hybrids during water deficits. Field Crops Res 52:135–142Google Scholar
- Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol 131:664–675PubMedPubMedCentralCrossRefGoogle Scholar
- Sadok W, Sinclair TR (2009a) Genetic variability of transpiration response to vapor pressure deficit among soybean (Glycine max [L.] Merr.) genotypes selected from a recombinant inbred line population. Field Crops Res 113:156–160Google Scholar
- van Oosterom EJ, Hammer GL, Chapman SC, Doherty A, Mace E, Jordan DR (2006) Predicting flowering time in sorghum using a simple gene network: functional physiology or fictional functionality? In: Borrell AK et al. (eds) Proceedings of the 5th Australian sorghum conference, Gold Coast, 30 Jan–2 Feb 2006Google Scholar