Literature DB >> 31324929

Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.).

Gaëtan Touzy1,2, Renaud Rincent3, Matthieu Bogard4, Stephane Lafarge2, Pierre Dubreuil2, Agathe Mini3, Jean-Charles Deswarte5, Katia Beauchêne6, Jacques Le Gouis3, Sébastien Praud7.   

Abstract

KEY MESSAGE: Environmental clustering helps to identify QTLs associated with grain yield in different water stress scenarios. These QTLs could be useful for breeders to improve grain yields and increase genetic resilience in marginal environments. Drought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world. The acquisition of new high-yielding and stress-tolerant varieties is therefore necessary and requires improved understanding of the physiological and genetic bases of drought resistance. A panel of 210 elite European varieties was evaluated in 35 field trials. Grain yield and its components were scored in each trial. A crop model was then run with detailed climatic data and soil water status to assess the dynamics of water stress in each environment. Varieties were registered from 1992 to 2011, allowing us to test timewise genetic progress. Finally, a genome-wide association study (GWAS) was carried out using genotyping data from a 280 K SNP chip. The crop model simulation allowed us to group the environments into four water stress scenarios: an optimal condition with no water stress, a post-anthesis water stress, a moderate-anthesis water stress and a high pre-anthesis water stress. Compared to the optimal water condition, grain yield losses in the stressed conditions were 3.3%, 12.4% and 31.2%, respectively. This environmental clustering improved understanding of the effect of drought on grain yields and explained 20% of the G × E interaction. The greatest genetic progress was obtained in the optimal condition, mostly represented in France. The GWAS identified several QTLs, some of which were specific of the different water stress patterns. Our results make breeding for improved drought resistance to specific environmental scenarios easier and will facilitate genetic progress in future environments, i.e., water stress environments.

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Year:  2019        PMID: 31324929     DOI: 10.1007/s00122-019-03393-2

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  44 in total

1.  Environment characterisation for the interpretation of environmental effect and genotype x environment interaction.

Authors:  Xavier Lacaze; Pierre Roumet
Journal:  Theor Appl Genet       Date:  2004-09-15       Impact factor: 5.699

2.  A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.

Authors:  Jianming Yu; Gael Pressoir; William H Briggs; Irie Vroh Bi; Masanori Yamasaki; John F Doebley; Michael D McMullen; Brandon S Gaut; Dahlia M Nielsen; James B Holland; Stephen Kresovich; Edward S Buckler
Journal:  Nat Genet       Date:  2005-12-25       Impact factor: 38.330

3.  Pvclust: an R package for assessing the uncertainty in hierarchical clustering.

Authors:  Ryota Suzuki; Hidetoshi Shimodaira
Journal:  Bioinformatics       Date:  2006-04-04       Impact factor: 6.937

4.  Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture.

Authors:  K G Cassman
Journal:  Proc Natl Acad Sci U S A       Date:  1999-05-25       Impact factor: 11.205

5.  Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars.

Authors:  Flavio Breseghello; Mark E Sorrells
Journal:  Genetics       Date:  2005-08-03       Impact factor: 4.562

6.  Positional cloning of the wheat vernalization gene VRN1.

Authors:  L Yan; A Loukoianov; G Tranquilli; M Helguera; T Fahima; J Dubcovsky
Journal:  Proc Natl Acad Sci U S A       Date:  2003-05-01       Impact factor: 11.205

Review 7.  Mapping QTLs regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize.

Authors:  Roberto Tuberosa; Silvio Salvi; Maria Corinna Sanguineti; Pierangelo Landi; Marco Maccaferri; Sergio Conti
Journal:  Ann Bot       Date:  2002-06       Impact factor: 4.357

8.  A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize.

Authors:  Martin P Boer; Deanne Wright; Lizhi Feng; Dean W Podlich; Lang Luo; Mark Cooper; Fred A van Eeuwijk
Journal:  Genetics       Date:  2007-10-18       Impact factor: 4.562

9.  Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare.

Authors:  X Lacaze; P M Hayes; A Korol
Journal:  Heredity (Edinb)       Date:  2008-10-22       Impact factor: 3.821

10.  'Green revolution' genes encode mutant gibberellin response modulators.

Authors:  J Peng; D E Richards; N M Hartley; G P Murphy; K M Devos; J E Flintham; J Beales; L J Fish; A J Worland; F Pelica; D Sudhakar; P Christou; J W Snape; M D Gale; N P Harberd
Journal:  Nature       Date:  1999-07-15       Impact factor: 49.962

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  8 in total

1.  Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions.

Authors:  R Rincent; M Malosetti; B Ababaei; G Touzy; A Mini; M Bogard; P Martre; J Le Gouis; F van Eeuwijk
Journal:  Theor Appl Genet       Date:  2019-09-27       Impact factor: 5.699

2.  Genomic analysis for heat and combined heat-drought resilience in bread wheat under field conditions.

Authors:  Michael O Itam; Ryosuke Mega; Yasir S A Gorafi; Yuji Yamasaki; Izzat S A Tahir; Kinya Akashi; Hisashi Tsujimoto
Journal:  Theor Appl Genet       Date:  2021-10-16       Impact factor: 5.699

3.  Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits.

Authors:  Michel Colombo; Pierre Roumet; Christophe Salon; Christian Jeudy; Mickael Lamboeuf; Stéphane Lafarge; Anne-Valérie Dumas; Pierre Dubreuil; Wa Ngo; Brice Derepas; Katia Beauchêne; Vincent Allard; Jacques Le Gouis; Renaud Rincent
Journal:  Front Plant Sci       Date:  2022-03-25       Impact factor: 5.753

4.  Identification of QTLs affecting post-anthesis heat stress responses in European bread wheat.

Authors:  Gaëtan Touzy; Stéphane Lafarge; Elise Redondo; Vincent Lievin; Xavier Decoopman; Jacques Le Gouis; Sébastien Praud
Journal:  Theor Appl Genet       Date:  2022-01-05       Impact factor: 5.574

5.  Grain carbon isotope composition is a marker for allocation and harvest index in wheat.

Authors:  Jean-Baptiste Domergue; Cyril Abadie; Julie Lalande; Jean-Charles Deswarte; Eric Ober; Valérie Laurent; Céline Zimmerli; Philippe Lerebour; Laure Duchalais; Camille Bédard; Jérémy Derory; Thierry Moittie; Marlène Lamothe-Sibold; Katia Beauchêne; Anis M Limami; Guillaume Tcherkez
Journal:  Plant Cell Environ       Date:  2022-05-09       Impact factor: 7.947

6.  Wheat individual grain-size variance originates from crop development and from specific genetic determinism.

Authors:  Aurore Beral; Renaud Rincent; Jacques Le Gouis; Christine Girousse; Vincent Allard
Journal:  PLoS One       Date:  2020-03-26       Impact factor: 3.240

7.  Cytosolic TaGAPC2 Enhances Tolerance to Drought Stress in Transgenic Arabidopsis Plants.

Authors:  Lin Zhang; Hanwen Zhang; Shushen Yang
Journal:  Int J Mol Sci       Date:  2020-10-12       Impact factor: 5.923

Review 8.  Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection.

Authors:  Etienne Paux; Stéphane Lafarge; François Balfourier; Jérémy Derory; Gilles Charmet; Michael Alaux; Geoffrey Perchet; Marion Bondoux; Frédéric Baret; Romain Barillot; Catherine Ravel; Pierre Sourdille; Jacques Le Gouis
Journal:  Biology (Basel)       Date:  2022-01-17
  8 in total

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