Literature DB >> 29044609

Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand.

Santiago Alvarez Prado1, Llorenç Cabrera-Bosquet1, Antonin Grau1, Aude Coupel-Ledru1, Emilie J Millet1, Claude Welcker1, François Tardieu1.   

Abstract

Stomatal conductance is central for the trades-off between hydraulics and photosynthesis. We aimed at deciphering its genetic control and that of its responses to evaporative demand and water deficit, a nearly impossible task with gas exchanges measurements. Whole-plant stomatal conductance was estimated via inversion of the Penman-Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform. We have analysed jointly 4 experiments with contrasting environmental conditions imposed to a panel of 254 maize hybrids. Estimated whole-plant stomatal conductance closely correlated with gas-exchange measurements and biomass accumulation rate. Sixteen robust quantitative trait loci (QTLs) were identified by genome wide association studies and co-located with QTLs of transpiration and biomass. Light, vapour pressure deficit, or soil water potential largely accounted for the differences in allelic effects between experiments, thereby providing strong hypotheses for mechanisms of stomatal control and a way to select relevant candidate genes among the 1-19 genes harboured by QTLs. The combination of allelic effects, as affected by environmental conditions, accounted for the variability of stomatal conductance across a range of hybrids and environmental conditions. This approach may therefore contribute to genetic analysis and prediction of stomatal control in diverse environments.
© 2017 John Wiley & Sons Ltd.

Entities:  

Keywords:  GWAS; QTL by environment interaction; drought; evaporative demand; maize (Zea mays L.); phenomics; stomatal conductance; transpiration

Mesh:

Year:  2017        PMID: 29044609     DOI: 10.1111/pce.13083

Source DB:  PubMed          Journal:  Plant Cell Environ        ISSN: 0140-7791            Impact factor:   7.228


  16 in total

1.  Estimation of Plant and Canopy Architectural Traits Using the Digital Plant Phenotyping Platform.

Authors:  Shouyang Liu; Pierre Martre; Samuel Buis; Mariem Abichou; Bruno Andrieu; Frédéric Baret
Journal:  Plant Physiol       Date:  2019-08-16       Impact factor: 8.340

2.  Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria.

Authors:  Parthiban Thathapalli Prakash; Darshi Banan; Rachel E Paul; Maximilian J Feldman; Dan Xie; Luke Freyfogle; Ivan Baxter; Andrew D B Leakey
Journal:  J Exp Bot       Date:  2021-06-22       Impact factor: 6.992

3.  Uncovering hidden genetic variation in photosynthesis of field-grown maize under ozone pollution.

Authors:  Nicole E Choquette; Funda Ogut; Timothy M Wertin; Christopher M Montes; Crystal A Sorgini; Alison M Morse; Patrick J Brown; Andrew D B Leakey; Lauren M McIntyre; Elizabeth A Ainsworth
Journal:  Glob Chang Biol       Date:  2019-10-01       Impact factor: 13.211

4.  Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment.

Authors:  Silvere Vialet-Chabrand; Tracy Lawson
Journal:  J Exp Bot       Date:  2019-05-09       Impact factor: 6.992

5.  The foliar application of a mixture of semisynthetic chitosan derivatives induces tolerance to water deficit in maize, improving the antioxidant system and increasing photosynthesis and grain yield.

Authors:  Valquíria Mikaela Rabêlo; Paulo César Magalhães; Letícia Aparecida Bressanin; Diogo Teixeira Carvalho; Caroline Oliveira Dos Reis; Decio Karam; Antônio Carlos Doriguetto; Marcelo Henrique Dos Santos; Plínio Rodrigues Dos Santos Santos Filho; Thiago Corrêa de Souza
Journal:  Sci Rep       Date:  2019-06-03       Impact factor: 4.379

Review 6.  Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.

Authors:  Keiichi Mochida; Satoru Koda; Komaki Inoue; Takashi Hirayama; Shojiro Tanaka; Ryuei Nishii; Farid Melgani
Journal:  Gigascience       Date:  2019-01-01       Impact factor: 6.524

7.  Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies.

Authors:  Tsu-Wei Chen; Llorenç Cabrera-Bosquet; Santiago Alvarez Prado; Raphaël Perez; Simon Artzet; Christophe Pradal; Aude Coupel-Ledru; Christian Fournier; François Tardieu
Journal:  J Exp Bot       Date:  2019-04-29       Impact factor: 6.992

8.  Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System.

Authors:  Pascal Neveu; Anne Tireau; Nadine Hilgert; Vincent Nègre; Jonathan Mineau-Cesari; Nicolas Brichet; Romain Chapuis; Isabelle Sanchez; Cyril Pommier; Brigitte Charnomordic; François Tardieu; Llorenç Cabrera-Bosquet
Journal:  New Phytol       Date:  2018-08-28       Impact factor: 10.151

9.  To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Authors:  Santiago Alvarez Prado; Isabelle Sanchez; Llorenç Cabrera-Bosquet; Antonin Grau; Claude Welcker; François Tardieu; Nadine Hilgert
Journal:  J Exp Bot       Date:  2019-08-07       Impact factor: 6.992

10.  Carbon isotope composition, water use efficiency, and drought sensitivity are controlled by a common genomic segment in maize.

Authors:  Viktoriya Avramova; Adel Meziane; Eva Bauer; Sonja Blankenagel; Stella Eggels; Sebastian Gresset; Erwin Grill; Claudiu Niculaes; Milena Ouzunova; Brigitte Poppenberger; Thomas Presterl; Wilfried Rozhon; Claude Welcker; Zhenyu Yang; François Tardieu; Chris-Carolin Schön
Journal:  Theor Appl Genet       Date:  2018-09-22       Impact factor: 5.699

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