Simulating Genotype-Phenotype Interaction Using Extended Functional-Structural Plant Models: Approaches, Applications and Potential Pitfalls

Xu, Linhao; Buck-Sorlin, Gerhard

doi:10.1007/978-3-319-20562-5_2

Cited by 7 publications

(10 citation statements)

References 35 publications

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“…A methodology involving hierarchical mixed effects models and testing of variance components is proposed in Baey et al, (in preparation) and should be used in order to clearly identify which parameters can be considered constant or varying among the population of genotypes. Finally, being able to identify plant model parameters varying among genotypes from high-throughput phenotyping data is the first step toward the integration of genetic control into plant models (Baldazzi et al, 2016; Xu and Buck-Sorlin, 2016) via QTL or association mapping (Myles et al, 2009). …”

Section: Discussionmentioning

confidence: 99%

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

Viaud¹,

Cournède²

2017

Front. Plant Sci.

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A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of Arabidopsis thaliana over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for A. thaliana and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes.

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Section: Discussionmentioning

confidence: 99%

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

Viaud¹,

Cournède²

2017

Front. Plant Sci.

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“…Finally, the extension of such models coupling FSPM and genetic information to larger germplasms and to the generation of a new simulated mapping population, as recently proposed in rice (Xu and Buck-Sorlin, 2016), will likely be a new research avenue in interface with eco-physiology, process-based, and gene-based modeling (Technow et al, 2015). For perennials, the extension to multi-years development of trees will represent another complexity that could be tackled based on knowledge on genetic determinisms over years (Segura et al, 2008, 2009).…”

Section: Discussionmentioning

confidence: 99%

Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture

Migault¹,

Pallas²,

Costes³

2017

Front. Plant Sci.

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In crops, optimizing target traits in breeding programs can be fostered by selecting appropriate combinations of architectural traits which determine light interception and carbon acquisition. In apple tree, architectural traits were observed to be under genetic control. However, architectural traits also result from many organogenetic and morphological processes interacting with the environment. The present study aimed at combining a FSPM built for apple tree, MAppleT, with genetic determinisms of architectural traits, previously described in a bi-parental population. We focused on parameters related to organogenesis (phyllochron and immediate branching) and morphogenesis processes (internode length and leaf area) during the first year of tree growth. Two independent datasets collected in 2004 and 2007 on 116 genotypes, issued from a ‘Starkrimson’ × ‘Granny Smith’ cross, were used. The phyllochron was estimated as a function of thermal time and sylleptic branching was modeled subsequently depending on phyllochron. From a genetic map built with SNPs, marker effects were estimated on four MAppleT parameters with rrBLUP, using 2007 data. These effects were then considered in MAppleT to simulate tree development in the two climatic conditions. The genome wide prediction model gave consistent estimations of parameter values with correlation coefficients between observed values and estimated values from SNP markers ranging from 0.79 to 0.96. However, the accuracy of the prediction model following cross validation schemas was lower. Three integrative traits (the number of leaves, trunk length, and number of sylleptic laterals) were considered for validating MAppleT simulations. In 2007 climatic conditions, simulated values were close to observations, highlighting the correct simulation of genetic variability. However, in 2004 conditions which were not used for model calibration, the simulations differed from observations. This study demonstrates the possibility of integrating genome-based information in a FSPM for a perennial fruit tree. It also showed that further improvements are required for improving the prediction ability. Especially temperature effect should be extended and other factors taken into account for modeling GxE interactions. Improvements could also be expected by considering larger populations and by testing other genome wide prediction models. Despite these limitations, this study opens new possibilities for supporting plant breeding by in silico evaluations of the impact of genotypic polymorphisms on plant integrative phenotypes.

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“…weather variables) and physiological inputs. The latter model-input traits can be used as genotype-specific model parameters to characterize genotypic differences; in fact, it can be surmised that they are, at least partly, under genetic control ( Yin et al , 2016 ).…”

Section: Genotype-specific Modelling To Support Breedingmentioning

confidence: 99%

“…Breeders can use models to: (i) identify the main yield-determining traits and their most significant markers, (ii) define the best selective environments for maximizing the progress of selection, (iii) optimize single-trait values, (iv) design ideotypes, and (v) assist multi-location testing ( Gu et al , 2014 ; Yin et al , 2016 ).…”

Section: Genotype-specific Modelling To Support Breedingmentioning

confidence: 99%

Bridging the genotype–phenotype gap in 3D

Struik

2016

EXBOTJ

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Simulating Genotype-Phenotype Interaction Using Extended Functional-Structural Plant Models: Approaches, Applications and Potential Pitfalls

Cited by 7 publications

References 35 publications

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture

Bridging the genotype–phenotype gap in 3D

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