Evaluating Plant Breeding Strategies by Simulating Gene Action and Dryland Environment Effects

In quantitative genetics, computer simulation is commonly used to evaluate alternative plant breeding strateFunctional genomics is the systematic study of genome-wide effects gies on the basis of stochastic descriptions of gene action of gene expression on organism growth and development with the ultimate aim of understanding how networks of genes influence traits. and interaction (e.g., Hospital et al., 1997; Podlich and Here, we use a dynamic biophysical cropping systems model (APSIMCooper, 1998). Our aim is to demonstrate how linkages Sorg) to generate a state space of genotype performance based on between gene action and crop performance in dryland 15 genes controlling four adaptive traits and then search this space environments can be investigated by combining the biousing a quantitative genetics model of a plant breeding program (QUphysical response simulation of the of crops to the moisGENE) to simulate recurrent selection. Complex epistatic and gene ture environment with the quantitative genetics simulaenvironment effects were generated for yield even though gene action tion of plant breeding programs. A review of present at the trait level had been defined as simple additive effects. Given research will reveal that substantial resources are being alternative breeding strategies that restricted either the cultivar matuinvested into the cellular and molecular basis for adaptarity type or the drought environment type, the positive ( ) alleles tion to dry environments while plant breeding compafor 15 genes associated with the four adaptive traits were accumulated at different rates over cycles of selection. While early maturing genonies and public programs continue to make advances in types were favored in the Severe-Terminal drought environment type, yield through conventional means. Exploitation of the late genotypes were favored in the Mild-Terminal and Midseason investment in the former requires the integration of drought environment types. In the Severe-Terminal environment, knowledge from agronomy and cellular, plant, and crop there was an interaction of the stay-green (SG) trait with other traits: physiology as well as plant breeding and quantitative Selection for alleles of the SG genes was delayed until alleles genetics. For those less familiar with the breeding and for genes associated with the transpiration efficiency and osmotic quantitative genetics, there are some useful background adjustment traits had been fixed. Given limitations in our current texts (e.g., Hallauer et al., 1988; Falconer and Mackay, understanding of trait interaction and genetic control, the results are 1996) in addition to Podlich and Cooper (1998) and not conclusive. However, they demonstrate how the per se complexity Chapman et al. (2002a). of gene gene environment interactions will challenge the application of genomics and marker-assisted selection in crop improvement In combining gene sequencing, gene cloning, and for dryland adaptation. plant transformation with biochemical and genome databases, scientists in private and public industry have identified and constructed genes that control relatively linear pathways like herbicide tolerance, disease resisW this paper focuses on the simulation of plant tance, and product quality (Somerville and Somerville, breeding programs from an understanding of gene 1999; Mazur et al., 1999). Molecular biology is beginning action, it is useful to begin with a description of how to investigate the role of the other genes that relate to connections between crop modeling, genomics (the unadaptation to the abiotic environment. For these genoderstanding of how multiple genes function together), type–environment systems, thousands of genes interact and plant breeding are developing. Crop simulation in complex ways to generate crop responses to the enmodels have been used to integrate physiological undervironment via mediation of responses over both short standing and evaluate alternative strategies of system time scales (e.g., cellular response to environment shocks management to account for the soil, climate, and agrolike frost) and long time scales (e.g., morphological nomic technologies available. The principal objective of growth responses of crop development and morphola plant-breeding program is the generation and selection ogy). Some pathways for abiotic adaptation are comparof new gene combinations to create genotypes with trait atively straightforward, e.g., direct cellular tolerance of performance that is superior to current genotypes, withsalt stresses (see review by Hasegawa et al., 2000). Howin the target population of environments (TPE) (Comever, it will be some time ( 20 yr?) before we understock, 1977). This objective applies equally to conventional, molecular, and combined approaches. stand how the interactions of developmental and signaling genes control yield of crops as a function of responses at the biochemical, cellular, plant, and canopy or crop S. Chapman, CSIRO Plant Industry, Long Pocket Lab., 120 Meiers Rd., Indooroopilly, 4068, QLD, Australia; M. Cooper and D. Podlich, levels of organization. Until then, we need to deal with School of Land and Food Sci., The University of Queensland, Brisadaptive traits at a more integrative level (i.e., traits bane, 4072, QLD, Australia; and G. Hammer, Agric. Prod. Syst. Res. observable at the plant or crop level) while beginning Unit, Queensland Dep. of Primary Industries, P.O. Box 102, Toowoomba, QLD, 4350, Australia. M. Cooper and D. Podlich, current address: Pioneer Hi-Bred Int., 7300 N.W. 62nd Ave., P.O. Box 1004, Abbreviations: G E, genotype environment; MET, multienvironJohnston, IA 50131. Received 1 May 2001. *Corresponding author ment trial; OA, osmotic adjustment; PH, flowering time; QTLs, quan([email protected]). titative trait loci; SG, stay-green; TE, transpiration efficiency; TPE, target population of environments. Published in Agron. J. 95:99–113 (2003).