Skip to Main content Skip to Navigation
Journal articles

Model-based QTL detection is sensitive to slight modifications in model formulation

Abstract : Classical crop models have been developed to predict crop yield and quality, and they are based on physiological and environmental inputs. After molecular discoveries, models should integrate genetic variation to allow predictions that are more genotype-dependent. An interesting approach, Quantitative Trait Locus (QTL)-based ecophysiological modeling, has shown promising results for the design of ideotypes that are adapted to biotic and abiotic stresses, but there are still limitations to attaining a fully integrated model. The aim of this case study is to clarify the impact of choosing different model equations (closely related and with different numbers of parameters) and optimization methods on the detection of QTLs controlling the parameters of crop growth. Different growth equations were parameterized based on a genetic population by following different approaches. The correlations between parameters were analyzed, and two different strategies were adopted to address the correlation issue. QTL analysis was performed on the optimized values of the parameters of the growth equations and on the observed dry mass (DM) data to validate the QTLs detected. Overall, models and strategies resulted in different QTLs being detected. Similar LOD profiles but with peaks of different heights were observed, some of which were significant, resulting in different numbers of QTLs. In some cases, peaks had slightly different positions or were absent. Even closely related growth models led to the detection of different QTLs. The goodness of fit and complexity of the growth models were found to be insufficient to select the best model. Calculating parameters independently of observed data may not be a good strategy, whereas setting parameters independent of the genotype is recommended. Given the large-scale global optimization problem and the strong correlations between parameters, the two algorithms tested showed poor performance. Currently, the lack of effective algorithms is the main obstacle to answering the question posed. The authors therefore suggest testing different model formulations and comparing the QTLs detected before choosing the best formulation to use in an ecophysiological modeling approach based on QTLs.
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download

https://hal.inrae.fr/hal-02619010
Contributor : Migration Prodinra Connect in order to contact the contributor
Submitted on : Monday, May 25, 2020 - 4:00:41 PM
Last modification on : Tuesday, February 9, 2021 - 11:48:03 AM

File

2019_Barrasso_Plos One_1.pdf
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Collections

Citation

Caterina Barrasso, Mohamed-Mahmoud Memah, Michel Génard, Bénédicte Quilot-Turion. Model-based QTL detection is sensitive to slight modifications in model formulation. PLoS ONE, Public Library of Science, 2019, 14 (10), pp.1-24. ⟨10.1371/journal.pone.0222764⟩. ⟨hal-02619010⟩

Share

Metrics

Record views

4332

Files downloads

85