HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (27) Citing Articles via Google Scholar Google Scholar Articles by Chapman, S. Articles by Hammer, G. Search for Related Content PubMed Articles by Chapman, S. Articles by Hammer, G. Agricola Articles by Chapman, S. Articles by Hammer, G. Related Collections Water Stress Sorghum Crop Models Plant and Environment Interactions Crop Genetics

SYMPOSIUM PAPERS

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

^a CSIRO Plant Industry, Long Pocket Lab., 120 Meiers Rd., Indooroopilly, 4068, QLD, Australia
^b School of Land and Food Sci., The University of Queensland, Brisbane, 4072, QLD, Australia
^c Agric. Prod. Syst. Res. Unit, Queensland Dep. of Primary Industries, P.O. Box 102, Toowoomba, QLD, 4350, Australia

* Corresponding author (scott.chapman{at}csiro.au)

Received for publication May 1, 2001. Functional genomics is the systematic study of genome-wide effects of gene expression on organism growth and development with the ultimate aim of understanding how networks of genes influence traits. Here, we use a dynamic biophysical cropping systems model (APSIM-Sorg) to generate a state space of genotype performance based on 15 genes controlling four adaptive traits and then search this space using a quantitative genetics model of a plant breeding program (QU-GENE) to simulate recurrent selection. Complex epistatic and gene x environment effects were generated for yield even though gene action at the trait level had been defined as simple additive effects. Given alternative breeding strategies that restricted either the cultivar maturity type or the drought environment type, the positive (+) alleles for 15 genes associated with the four adaptive traits were accumulated at different rates over cycles of selection. While early maturing genotypes were favored in the Severe-Terminal drought environment type, late genotypes were favored in the Mild-Terminal and Midseason drought environment types. In the Severe-Terminal environment, there was an interaction of the stay-green (SG) trait with other traits: Selection for + alleles of the SG genes was delayed until + alleles for genes associated with the transpiration efficiency and osmotic adjustment traits had been fixed. Given limitations in our current understanding of trait interaction and genetic control, the results are not conclusive. However, they demonstrate how the per se complexity of gene x gene x environment interactions will challenge the application of genomics and marker-assisted selection in crop improvement for dryland adaptation.

Abbreviations: G x E, genotype x environment • MET, multienvironment trial • OA, osmotic adjustment • PH, flowering time • QTLs, quantitative trait loci • SG, stay-green • TE, transpiration efficiency • TPE, target population of environments

This article has been cited by other articles:

				N. C. Collins, F. Tardieu, and R. Tuberosa Quantitative Trait Loci and Crop Performance under Abiotic Stress: Where Do We Stand? Plant Physiology, June 1, 2008; 147(2): 469 - 486. [Full Text] [PDF]

				V. Letort, P. Mahe, P.-H. Cournede, P. de Reffye, and B. Courtois Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization Ann. Bot., May 1, 2008; 101(8): 1243 - 1254. [Abstract] [Full Text] [PDF]

				R. Tuberosa, S. Salvi, S. Giuliani, M. C. Sanguineti, M. Bellotti, S. Conti, and P. Landi Genome-wide Approaches to Investigate and Improve Maize Response to Drought Crop Sci., December 18, 2007; 47(Supplement_3): S-120 - S-141. [Abstract] [Full Text] [PDF]

				C. D. Messina, J. W. Jones, K. J. Boote, and C. E. Vallejos A Gene-Based Model to Simulate Soybean Development and Yield Responses to Environment Crop Sci., January 24, 2006; 46(1): 456 - 466. [Abstract] [Full Text] [PDF]

				G. L. Hammer, T. R. Sinclair, S. C. Chapman, and E. van Oosterom On Systems Thinking, Systems Biology, and the in Silico Plant Plant Physiology, March 1, 2004; 134(3): 909 - 911. [Abstract] [Full Text] [PDF]