title: Quantitative genetics and functional-structural plant growth models: simulation of quantitative trait loci detection for model parameters and application to potential yield optimization.
abstract: OBJECTIVE
  Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization.
  
  
  METHODS
  The GREENLAB model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination.
  
  
  CONCLUSIONS
  By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits--such as cob weight--and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GREENLAB maize model. The paper discusses the potentials of GREENLAB to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand.
authors.affiliation: Ecole Centrale of Paris, Laboratoire de Mathématiques Appliquées aux Systèmes, F-92295 Châtenay-Malabry cedex, France. veronique.letort@centraliens.net
authors.forename: Véronique
authors.initials: V
authors.lastname: Letort
authors.forename: Paul
authors.initials: P
authors.lastname: Mahe
authors.forename: Paul-Henry
authors.initials: PH
authors.lastname: Cournède
authors.forename: Philippe
authors.initials: P
authors.lastname: de Reffye
authors.forename: Brigitte
authors.initials: B
authors.lastname: Courtois
mesh_terms: D018533:Biomass; D003198:Computer Simulation; D008957:Models, Genetic; D008962:Models, Theoretical; D040641:Quantitative Trait Loci / Q000235:genetics*; D003313:Zea mays / Q000033:anatomy & histology / Q000235:genetics / Q000254:growth & development*