Literature DB >> 20038518

Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case-study of fruit and grain quality traits.

Nadia Bertin1, Pierre Martre, Michel Génard, Bénédicte Quilot, Christophe Salon.   

Abstract

Detailed information has arisen from research at gene and cell levels, but it is still incomplete in the context of a quantitative understanding of whole plant physiology. Because of their integrative nature, process-based simulation models can help to bridge the gap between genotype and phenotype and assist in deconvoluting genotype-by-environment (GxE) interactions for complex traits. Indeed, GxE interactions are emergent properties of simulation models, i.e. unexpected properties generated by complex interconnections between subsystem components and biological processes. They co-occur in the system with synergistic or antagonistic effects. In this work, different kinds of GxE interactions are illustrated. Approaches to link model parameters to genes or quantitative trait loci (QTL) are briefly reviewed. Then the analysis of GxE interactions through simulation models is illustrated with an integrated model simulation of peach (Prunus persica (L.) Batsch) fruit mass and sweetness, and with a model of wheat (Triticum aestivum L.) grain yield and protein concentration. This paper suggests that the management of complex traits such as fruit and grain quality may become possible, thanks to the increasing knowledge concerning the genetic and environmental regulation of organ size and composition and to the development of models simulating the complex aspects of metabolism and biophysical behaviours at the plant and organ levels.

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Year:  2009        PMID: 20038518     DOI: 10.1093/jxb/erp377

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  20 in total

1.  Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.

Authors:  Junfei Gu; Xinyou Yin; Chengwei Zhang; Huaqi Wang; Paul C Struik
Journal:  Ann Bot       Date:  2014-07-01       Impact factor: 4.357

2.  A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions.

Authors:  Amélie Mathieu; Tiphaine Vidal; Alexandra Jullien; QiongLi Wu; Camille Chambon; Benoit Bayol; Paul-Henry Cournède
Journal:  Ann Bot       Date:  2018-08-27       Impact factor: 4.357

3.  Rapid Chlorophyll a Fluorescence Light Response Curves Mechanistically Inform Photosynthesis Modeling.

Authors:  Jonathan R Pleban; Carmela R Guadagno; David S Mackay; Cynthia Weinig; Brent E Ewers
Journal:  Plant Physiol       Date:  2020-03-09       Impact factor: 8.340

4.  Using chromosome introgression lines to map quantitative trait loci for photosynthesis parameters in rice (Oryza sativa L.) leaves under drought and well-watered field conditions.

Authors:  Junfei Gu; Xinyou Yin; Paul C Struik; Tjeerd Jan Stomph; Huaqi Wang
Journal:  J Exp Bot       Date:  2011-10-06       Impact factor: 6.992

5.  Frost trends and their estimated impact on yield in the Australian wheatbelt.

Authors:  Bangyou Zheng; Scott C Chapman; Jack T Christopher; Troy M Frederiks; Karine Chenu
Journal:  J Exp Bot       Date:  2015-04-28       Impact factor: 6.992

6.  Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model.

Authors:  Matthieu Bogard; Catherine Ravel; Etienne Paux; Jacques Bordes; François Balfourier; Scott C Chapman; Jacques Le Gouis; Vincent Allard
Journal:  J Exp Bot       Date:  2014-08-22       Impact factor: 6.992

7.  In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management.

Authors:  Pierre Martre; Jianqiang He; Jacques Le Gouis; Mikhail A Semenov
Journal:  J Exp Bot       Date:  2015-03-24       Impact factor: 6.992

8.  Inter-Species Comparative Analysis of Components of Soluble Sugar Concentration in Fleshy Fruits.

Authors:  Zhanwu Dai; Huan Wu; Valentina Baldazzi; Cornelis van Leeuwen; Nadia Bertin; Hélène Gautier; Benhong Wu; Eric Duchêne; Eric Gomès; Serge Delrot; Françoise Lescourret; Michel Génard
Journal:  Front Plant Sci       Date:  2016-05-19       Impact factor: 5.753

9.  Developmental and growth controls of tillering and water-soluble carbohydrate accumulation in contrasting wheat (Triticum aestivum L.) genotypes: can we dissect them?

Authors:  M Fernanda Dreccer; Scott C Chapman; Allan R Rattey; Jodi Neal; Youhong Song; John Jack T Christopher; Matthew Reynolds
Journal:  J Exp Bot       Date:  2012-12-03       Impact factor: 6.992

10.  Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments.

Authors:  Bangyou Zheng; Ben Biddulph; Dora Li; Haydn Kuchel; Scott Chapman
Journal:  J Exp Bot       Date:  2013-07-19       Impact factor: 6.992

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