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Genome-Wide Analysis Of Yield In Europe: Allelic Effects Vary With Drought And Heat Scenarios1[OPEN]

E. Millet, Chris Welcker, W. Kruijer, S. Negro, Aude Coupel-Ledru, S. Nicolas, J. Laborde, C. Bauland, Sébastien Praud, Nicolas Ranc, T. Presterl, R. Tuberosa, Z. Bedő, X. Draye, B. Usadel, A. Charcosset, F. V. van Eeuwijk, F. Tardieu
Published 2016 · Biology

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A genome-wide analysis of maize yield in identify genomic regions associated with adaptation to scenarios with drought or heat stresses. Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years × 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.
This paper references
The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis
M. Malosetti (2013)
Clustering of Environmental Parameters Discriminates Drought and Heat Stress Bread Wheat Trials
Bruno Bouffier (2015)
DUF581 Is Plant Specific FCS-Like Zinc Finger Involved in Protein-Protein Interaction
Muhammed Jamsheer K (2014)
Recovering Power in Association Mapping Panels with Variable Levels of Linkage Disequilibrium
R. Rincent (2014)
Two-mode clustering of genotype by trait and genotype by environment data
Jos A. Hageman (2010)
Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize
S. Salvi (2007)
A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array
S. Unterseer (2014)
FaST linear mixed models for genome-wide association studies
C. Lippert (2011)
Analysis of variance: Why it is more important than ever?
A. Gelman (2005)
Breeding Technologies to Increase Crop Production in a Changing World
M. Tester (2010)
Marker-Based Estimation of Heritability in Immortal Populations
W. Kruijer (2014)
Mapping QTLs regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize.
R. Tuberosa (2002)
Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes.
B. Parent (2010)
Are source and sink strengths genetically linked in maize plants subjected to water deficit? A QTL study of the responses of leaf growth and of Anthesis-Silking Interval to water deficit.
C. Welcker (2007)
Root-ABA1, a major constitutive QTL, affects maize root architecture and leaf ABA concentration at different water regimes.
S. Giuliani (2005)
Genetic Architecture of Flowering Time in Maize As Inferred From Quantitative Trait Loci Meta-analysis and Synteny Conservation With the Rice Genome
F. Chardon (2004)
Is Change in Ovary Carbon Status a Cause or a Consequence of Maize Ovary Abortion in Water Deficit during Flowering?1[OPEN]
V. Oury (2016)
Quantitative Trait Loci for Grain Yield and Adaptation of Durum Wheat (Triticum durum Desf.) Across a Wide Range of Water Availability
M. Maccaferri (2008)
Control of leaf growth by abscisic acid: hydraulic or non-hydraulic processes?
F. Tardieu (2010)
Use of trial clustering to study QTL × environment effects for grain yield and related traits in maize
L. Moreau (2004)
Crop evapotranspiration. Guidelines for computing crop water requirements
R. Allen (1998)
RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics
Marc Lohse (2012)
R: A language and environment for statistical computing.
R. Team (2014)
Any trait or trait-related allele can confer drought tolerance: just design the right drought scenario.
F. Tardieu (2012)
Genome-wide association study of leaf architecture in the maize nested association mapping population
Feng Tian (2011)
Genetic dissection of maize phenology using an intraspecific introgression library
Silvio Salvi (2010)
Dissection and modelling of abiotic stress tolerance in plants.
F. Tardieu (2010)
Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields.
S. Chapman (2000)
Expression analysis of a gene family in loblolly pine (Pinus taeda L.) induced by water deficit stress
V. Padmanabhan (2004)
Glycosylation of a Fasciclin-Like Arabinogalactan-Protein (SOS5) Mediates Root Growth and Seed Mucilage Adherence via a Cell Wall Receptor-Like Kinase (FEI1/FEI2) Pathway in Arabidopsis
Debarati Basu (2016)
Climate Trends and Global Crop Production Since 1980
D. Lobell (2011)
Towards parsimonious ecophysiological models that bridge ecology and agronomy.
Boris Parent (2016)
A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)
M. Malosetti (2007)
Detection and use of QTL for complex traits in multiple environments.
F. V. van Eeuwijk (2010)
Combining Quantitative Trait Loci Analysis and an Ecophysiological Model to Analyze the Genetic Variability of the Responses of Maize Leaf Growth to Temperature and Water Deficit1
M. Reymond (2003)
Aquaporins: Highly Regulated Channels Controlling Plant Water Relations1
F. Chaumont (2014)
Mapping QTLs and QTL × environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods
M. Vargas (2005)
Climate Impacts on Agriculture: Implications for Crop Production
J. L. Hatfield (2011)
Is there potential to adapt soybean (Glycine max Merr.) to future [CO₂]? An analysis of the yield response of 18 genotypes in free-air CO₂ enrichment.
Kristen A Bishop (2015)
Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures
Jarrod R. Welch (2010)
A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome
M. Ganal (2011)
Characterizing drought stress and trait influence on maize yield under current and future conditions.
M. Harrison (2014)
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
M. Boer (2007)
Conceptual framework for drought phenotyping during molecular breeding.
G. H. Salekdeh (2009)
RNA-Seq Analysis Reveals MAPKKK Family Members Related to Drought Tolerance in Maize
Ya Jun Liu (2015)
Efficient multivariate linear mixed model algorithms for genome-wide association studies.
Xiaoping Zhou (2014)
Genome-wide transcriptome analysis of two maize inbred lines under drought stress
J. Zheng (2009)
The B73 Maize Genome: Complexity, Diversity, and Dynamics
P. Schnable (2009)
A closed-form equation for predicting the hydraulic conductivity of unsaturated soils
M. T. Genuchten (1980)
Status and Prospects of Association Mapping in Plants
C. Zhu (2008)
Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia.
K. Chenu (2011)
Multi-environment analysis and improved mapping of a yield-related QTL on chromosome 3B of wheat
J. Bonneau (2012)
Future scenarios for plant phenotyping.
F. Fiorani (2013)
CYSTM, a novel cysteine-rich transmembrane module with a role in stress tolerance across eukaryotes
Thiago Motta Venancio (2010)
Plant factors controlling seed set in maize : the influence of silk, pollen, and ear-leaf water status and tassel heat treatment at pollination.
J. B. Schoper (1987)
Arabidopsis and primary photosynthetic metabolism - more than the icing on the cake.
M. Stitt (2010)
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
Diego Jarquín (2013)
The Genetic Architecture of Maize Flowering Time
E. Buckler (2009)
Quantitative Trait Loci and Crop Performance under Abiotic Stress: Where Do We Stand?
N. Collins (2008)
Making the most of 'omics' for crop breeding.
P. Langridge (2011)
Ovary Apical Abortion under Water Deficit Is Caused by Changes in Sequential Development of Ovaries and in Silk Growth Rate in Maize1[OPEN]
V. Oury (2015)
A model to estimate the temperature of a maize apex from meteorological data
Lydie Guilioni (2000)
Using multi-environment sugar beet variety trials to screen for drought tolerance
J. Pidgeon (2006)
Pummelo Fruit Transcript Homologous to Ripening-Induced Genes
C. Canel (1995)
Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize
Héloïse Giraud (2014)
Use of multi-model ensembles from global climate models for assessment of climate change impacts.
M. Semenov (2010)
Prioritizing quantitative trait loci for root system architecture in tetraploid wheat
M. Maccaferri (2016)

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M. Garcia (2019)
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F. V. van Eeuwijk (2019)
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Quantitative trait loci mapping for yield‐related traits under low and high planting densities in maize (Zea mays)
Qiang Yi (2020)
Barley varieties in semi‐controlled and natural conditions—Response to water shortage and changing environment
Maria Surma (2019)
Genetic Correlations Between Photosynthetic and Yield Performance in Maize Are Different Under Two Heat Scenarios During Flowering
Vlatko Galic (2019)
Seed Weight as a Covariate in Association and Prediction Studies for Biomass Traits in Maize Seedlings
V. Galic (2020)
Discovery of novel haplotypes for complex traits in landraces
M. Mayer (2020)
Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions
Renaud Rincent (2019)
Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel
Simon Rio (2018)
Genetic dissection of heat and drought stress QTLs in phenology-controlled synthetic-derived recombinant inbred lines in spring wheat
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Computational aspects underlying genome to phenome analysis in plants
A. Bolger (2019)
The Genetic Architecture for Phenotypic Plasticity of the Rice Grain Ionome
Yongjun Tan (2020)
Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies
S. Negro (2019)
From plant genomes to phenotypes.
Marie E. Bolger (2017)
Genome-wide signatures of flowering adaptation to climate temperature: Regional analyses in a highly diverse native range of Arabidopsis thaliana.
Daniel Tabas-Madrid (2018)
Plant Phenomics, From Sensors to Knowledge
F. Tardieu (2017)
Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat
M. Reynolds (2017)
Harnessing Phenotypic Plasticity to Improve Maize Yields
Aaron Kusmec (2018)
Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.)
Gaëtan Touzy (2019)
Genomic prediction of maize yield across European environmental conditions
E. Millet (2019)
Tracing the ancestry of modern bread wheats
C. Pont (2019)
A systems genetics approach reveals environment-dependent associations between SNPs, protein co-expression and drought-related traits in maize
Mélisande Blein-Nicolas (2019)
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E. Ainsworth (2016)
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