PART OF A SPECIAL ISSUE ON GROWTH AND ARCHITECTURAL MODELLING A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes

BACKGROUND AND AIMS Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. METHODS The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. KEY RESULTS Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. CONCLUSIONS We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.

[1]  J. Zhu,et al.  Quantitative trait loci analysis for the developmental behavior of tiller number in rice (Oryza sativa L.) , 1998, Theoretical and Applied Genetics.

[2]  Takeshi Horie,et al.  A process model for explaining genotypic and environmental variation in growth and yield of rice based on measured plant N accumulation. , 2009 .

[3]  Graeme L. Hammer,et al.  Assessing climatic risk to sorghum production in water-limited subtropical environments. I.Development and testing of a simulation model , 1994 .

[4]  Mark E. Cooper,et al.  Modelling Crop Improvement in a G×E×M Framework via Gene–Trait–Phenotype Relationships , 2009 .

[5]  A. Paterson,et al.  Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches , 1999, Theoretical and Applied Genetics.

[6]  Guifu Liu,et al.  Genetic analysis on tiller number and plant height per plant in rice (Oryza sativa L.) , 2006 .

[7]  Jun Zhu,et al.  QTL analysis of genetic main effects and genotype × environment interaction effects for yield components in rice (Oryza saliva L.) , 2005 .

[8]  Xinyou Yin,et al.  Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics. , 2008, The New phytologist.

[9]  Przemyslaw Prusinkiewicz,et al.  The Algorithmic Beauty of Plants , 1990, The Virtual Laboratory.

[10]  Paul-Henry Cournède,et al.  Quantitative genetics and functional-structural plant growth models: simulation of quantitative trait loci detection for model parameters and application to potential yield optimization. , 2007, Annals of botany.

[11]  Xinyou Yin,et al.  Extension of a biochemical model for the generalized stoichiometry of electron transport limited C3 photosynthesis , 2004 .

[12]  Yang Xc,et al.  Genetic modification of plant architecture and variety improvement in rice , 2008 .

[13]  B. Andrieu,et al.  A process-based model to simulate nitrogen distribution in wheat (Triticum aestivum) during grain-filling. , 2008, Functional plant biology : FPB.

[14]  X. Yang,et al.  Genetic modification of plant architecture and variety improvement in rice , 2008, Heredity.

[15]  G. Khush.  Green revolution: the way forward , 2001, Nature Reviews Genetics.

[16]  Graeme L. Hammer,et al.  Future contributions of crop modelling—from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement , 2002 .

[17]  Martin J. Kropff,et al.  Use of component analysis in QTL mapping of complex crop traits : A case study on yield in barley , 2002 .

[18]  B. Andrieu,et al.  A 3D Architectural and Process-based Model of Maize Development , 1998 .

[19]  Xiaopeng Zhang,et al.  Plant growth modelling and applications: the increasing importance of plant architecture in growth models. , 2007, Annals of botany.

[20]  F. Tardieu.  Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. , 2003, Trends in plant science.

[21]  Jun Zhu,et al.  Molecular dissection of developmental behavior of plant height in rice (Oryza sativa L.). , 1998, Genetics.

[22]  Cao Gang.  Study on Epistatic Effects and QTL × Environment Interaction Effects of QTLs for Panicle Length in Rice ( Oryza sativa L ) , 2001 .

[23]  B. Walsh,et al.  Models for navigating biological complexity in breeding improved crop plants. , 2006, Trends in plant science.

[24]  Michael Henke,et al.  A Rule-Based Functional-Structural Model of Rice Considering Source and Sink Functions , 2009, 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications.

[25]  Leonidas J. Guibas,et al.  Robust Monte Carlo methods for light transport simulation , 1997 .

[26]  B. Andrieu,et al.  Towards a generic architectural model of tillering in Gramineae, as exemplified by spring wheat (Triticum aestivum). , 2005, The New phytologist.

[27]  Paul C. Struik,et al.  Functional-Structural Plant Modelling in Crop Production , 2007 .

[28]  Winfried Kurth,et al.  MORPHOLOGICAL MODELS OF PLANT GROWTH : POSSIBILITIES AND ECOLOGICAL RELEVANCE , 1994 .

[29]  J. Zhu,et al.  [QTL analysis for epistatic effects and QTL x environment interaction effects on final height of rice (Oryza sativa L.)]. , 2001, Yi chuan xue bao = Acta genetica Sinica.

[30]  Gerhard Buck-Sorlin,et al.  The rule-based language XL and the modelling environment GroIMP illustrated with simulated tree competition. , 2008, Functional plant biology : FPB.

[31]  Winfried Kurth,et al.  Barley morphology, genetics and hormonal regulation of internode elongation modelled by a relational growth grammar. , 2005, The New phytologist.

[32]  Z. Jun.  QTL Analysis of Genetic Main Effects and Genotype (Environment Interaction Effects for Yield Components in Rice Oryza sativa L.) , 2002 .

[33]  K. Hikosaka,et al.  Seasonal changes in temperature dependence of photosynthetic rate in rice under a free-air CO(2) enrichment. , 2006, Annals of botany.

[34]  Xinyou Yin,et al.  The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley , 1999, Heredity.

[35]  Cris Kuhlemeier,et al.  Plant architecture , 2002, EMBO reports.

[36]  M. Kropff,et al.  Possibility of Increasing Yield Potential of Rice by Reducing Panicle Height in the Canopy. I. Effects of Panicles on Light Interception and Canopy Photosynthesis. , 1995 .

[37]  Jun Zhu,et al.  QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations , 2008, Bioinform..

[38]  Yan Guo,et al.  New advances in virtual plant research , 2001 .

[39]  P. Wernecke,et al.  LEAFC3-N: a nitrogen-sensitive extension of the CO2 and H2O gas exchange model leafc3 parameterised and tested for winter wheat (Triticum aestivum L.) , 2005 .

[40]  G. Buck-Sorlin,et al.  A grammar-based model of barley including virtual breeding, genetic control and a hormonal metabolic network , 2007 .

[41]  J. Zhu,et al.  Impact of epistasis and QTL×environment interaction on the developmental behavior of plant height in rice (Oryza sativa L.) , 2001, Theoretical and Applied Genetics.

[42]  T. Hirano,et al.  The Effect of the Amount of Nitrogen Fertilizer on Starch Metabolism in Leaf Sheath of Japonica and Indica Rice Varieties during the Heading Period , 2005 .

[43]  Greg McLean,et al.  Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. , 2010, Journal of experimental botany.

[44]  Graeme L. Hammer,et al.  Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems , 2005 .

[45]  William J. Massman,et al.  Coupling biochemical and biophysical processes at the leaf level: an equilibrium photosynthesis model for leaves of C3 plants , 1995 .

[46]  Jan Vos,et al.  A flexible sigmoid function of determinate growth. , 2003, Annals of botany.

[47]  J. Goudriaan,et al.  Modelling Potential Crop Growth Processes , 1994, Current Issues in Production Ecology.

[48]  P. de Reffye,et al.  Parameter optimization and field validation of the functional-structural model GREENLAB for maize at different population densities. , 2007, Annals of botany.

[49]  Jun Zhu,et al.  Mapping the genetic architecture of complex traits in experimental populations , 2007, Bioinform..

[50]  G. Buck-Sorlin,et al.  A rule-based model of barley morphogenesis, with special respect to shading and gibberellic acid signal transduction. , 2007, Annals of botany.

[51]  Yan Guo,et al.  Comparison of architecture among different cultivars of hybrid rice using a spatial light model based on 3-D digitising. , 2008, Functional plant biology : FPB.

[52]  He Ci.  QTL Mapping for Developmental Behaviour of Leaf Sheath Height in Rice , 2000 .

[53]  J. Hanan,et al.  Rice morphogenesis and plant architecture: measurement, specification and the reconstruction of structural development by 3D architectural modelling. , 2005, Annals of botany.

[54]  D. Luquet,et al.  Orchestration of transpiration, growth and carbohydrate dynamics in rice during a dry-down cycle. , 2008, Functional plant biology : FPB.

[55]  B. Andrieu,et al.  Functional-structural plant modelling: a new versatile tool in crop science. , 2010, Journal of experimental botany.

[56]  Gerhard Buck-Sorlin,et al.  A dynamic 3D model of rape (Brassica napus L.) computing yield components under variable nitrogen fertilization regimes , 2007 .

[57]  Mark E. Cooper,et al.  Gene-to-phenotype models and complex trait genetics , 2005 .

[58]  Cao Gang.  QTL Analysis for Flag Leaf Length in a Rice DH Population under Multi Environments , 2007 .

[59]  Ole Kniemeyer.  Design and implementation of a graph grammar based language for functional-structural plant modelling , 2008 .

[60]  Xinyou Yin,et al.  Crop Systems Dynamics: An Ecophysiological Simulation Model for Genotype-By-Environment Interactions , 2005 .

[61]  Mikhail A. Semenov,et al.  Quantifying effects of simple wheat traits on yield in water-limited environments using a modelling approach , 2009 .

[62]  G. Hammer,et al.  Functional dynamics of the nitrogen balance of sorghum. II. Grain filling period. , 2010 .

[63]  K. Ishimaru,et al.  Identification of a new gene controlling plant height in rice using the candidate-gene strategy , 2004, Planta.

[64]  A dynamic 3 D model of rape ( Brassica napus L . ) computing yield components under variable nitrogen fertilisation regimes , 2007 .

[65]  G. Hammer,et al.  Functional dynamics of the nitrogen balance of sorghum: I. N demand of vegetative plant parts , 2010 .