Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated With Drought Response in Maize: A “Gene-to-Phenotype” Modeling Approach (original) (raw)

Journal Article

Karine Chenu ,

Queensland Primary Industries and Fisheries

, Agricultural Production Systems Research Unit (APSRU), Department of Employment, Economic Development and Innovation, Toowoomba, Queensland 4350, Australia

Institut National de la Recherche Agronomique

, Unité Mixte de Recherche 759, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France

Corresponding author: Queensland Primary Industries and Fisheries, 203 Tor St., Toowoomba, QLD 4350, Australia. E-mail: [email protected]

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Scott C Chapman ,

CSIRO Plant Industry, Queensland Bioscience Precinct

, St. Lucia, Queensland 4067, Australia and

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François Tardieu ,

Institut National de la Recherche Agronomique

, Unité Mixte de Recherche 759, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France

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Greg McLean ,

Queensland Primary Industries and Fisheries

, Agricultural Production Systems Research Unit (APSRU), Department of Employment, Economic Development and Innovation, Toowoomba, Queensland 4350, Australia

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Claude Welcker ,

Institut National de la Recherche Agronomique

, Unité Mixte de Recherche 759, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France

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Graeme L Hammer

University of Queensland, School of Land

, Crop and Food Sciences, APSRU, Brisbane, Queensland 4072, Australia

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Accepted:

09 September 2009

Published:

01 December 2009

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Karine Chenu, Scott C Chapman, François Tardieu, Greg McLean, Claude Welcker, Graeme L Hammer, Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated With Drought Response in Maize: A “Gene-to-Phenotype” Modeling Approach, Genetics, Volume 183, Issue 4, 1 December 2009, Pages 1507–1523, https://doi.org/10.1534/genetics.109.105429
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Abstract

Under drought, substantial genotype–environment (G × E) interactions impede breeding progress for yield. Identifying genetic controls associated with yield response is confounded by poor genetic correlations across testing environments. Part of this problem is related to our inability to account for the interplay of genetic controls, physiological traits, and environmental conditions throughout the crop cycle. We propose a modeling approach to bridge this “gene-to-phenotype” gap. For maize under drought, we simulated the impact of quantitative trait loci (QTL) controlling two key processes (leaf and silk elongation) that influence crop growth, water use, and grain yield. Substantial G × E interaction for yield was simulated for hypothetical recombinant inbred lines (RILs) across different seasonal patterns of drought. QTL that accelerated leaf elongation caused an increase in crop leaf area and yield in well-watered or preflowering water deficit conditions, but a reduction in yield under terminal stresses (as such “leafy” genotypes prematurely exhausted the water supply). The QTL impact on yield was substantially enhanced by including pleiotropic effects of these QTL on silk elongation and on consequent grain set. The simulations obtained illustrated the difficulty of interpreting the genetic control of yield for genotypes influenced only by the additive effects of QTL associated with leaf and silk growth. The results highlight the potential of integrative simulation modeling for gene-to-phenotype prediction and for exploiting G × E interactions for complex traits such as drought tolerance.

© Genetics 2009

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