Academic journal article Genetics

Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated with Drought Response in Maize: A "Gene-to-Phenotype" Modeling Approach

Academic journal article Genetics

Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated with Drought Response in Maize: A "Gene-to-Phenotype" Modeling Approach

Article excerpt

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.

CROP yield varies greatly among genotypes, but this genetic variation is not consistent among environments. This presents a major challenge to plant breeding. Environmentally stable quantitative trait loci (QTL) were found for drought adaptation traits at the organ level. But what impacts do such QTL have on crop yield? We developed a "gene-to-phenotype" modeling approach that integrates physiological processes and incorporates their genetic controls. We estimated in silico the genotype-environment interactions that organ-level QTL might generate on yield in different drought conditions. Such a modeling approach opens new avenues for crop improvement.

Genotype-environment interactions-statistical vs. predictive modeling approaches: Genotype-environment (G × E) interactions impede plant breeding progress for complex traits such as yield and confound the interpretation of genetic controls of adaptive traits. In drought-prone regions, the size of the yield variance component for G × E interactions is frequently greater than the variance associated with genotype main effects (see COOPER and HAMMER 1996 for examples in wheat, sorghum, maize, and rice). Over the last century, numerous statistical methodologies have been developed to analyze these effects and to predict the expected yield of genotypes across and/or within subsets of environments (i.e., to measure "stable/ broad" and/or "specific" adaptation). In recent years, mixed models have increasingly dealt with heterogeneity effects across and within trials to explain genetic correlations among environments (e.g., SMITH et al. 2001). The use of these models has been extended to identify quantitative trait loci (QTL) associated with yield variation and to estimate how these are influenced by various environment covariables (e.g., BOER et al. 2007). Recent developments of this method enable one to account for genetic correlations among both traits and environments (MALOSETTI et al. 2006, 2008) and to detect QTL for nonlinear functions (MA et al. 2002; MALOSETTI et al. 2006), thus providing a powerful method for eco-physiologically inspired genetic models. New methods to characterize local environments as experienced by the plants (i.e., as influenced by G × E interactions) have also been proposed (e.g., MUCHOW et al. 1996; CHELLE 2005; SADOK et al. 2007; CHENU et al. …

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