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

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.

Abstract

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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Abbreviations

QTL:

Quantitative trait loci

GWAS:

Genome-wide association study

MET:

Multi-environment trials

G × E :

Genotype-by-environment

ETs:

Environmental types

OPT:

Optimal condition

LWD:

Late water deficit

MWD:

Medium water deficit

HWD:

High water deficit

PH:

Plant height

HD:

Heading date

SA:

Spikes per area

GPS:

Grains per spike

TKW:

Thousand kernel weight

GY:

Grain yield

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Acknowledgements

Part of the data were obtained thanks to the support of the PIA (Investment for the Future Program) Breedwheat (ANR-10-BTBR-03) and Phenome (ANR-11-INBS-0012) projects funded by the National Research Agency (ANR), FranceAgriMer, the French Plant Breeding Support Funds (FSOV-2012D), the European Regional Development Fund (FEDER), the Auvergne-Rhône-Alpes Region (CPER 2015-2020) and from INRA. The authors are also grateful to the ANRT (Association Nationale de la Recherche et de la Technologie) and ARVALIS Institut du végétal which supported the PhD thesis (2015/0686). We are especially grateful to Lauren Inchboard, José Osorio Y Fortea, Nadine Roquessalane and Accent Europe (http://www.accenteurope.fr) for editing the English of the manuscript.

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Correspondence to Sébastien Praud.

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Touzy, G., Rincent, R., Bogard, M. et al. Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.). Theor Appl Genet 132, 2859–2880 (2019). https://doi.org/10.1007/s00122-019-03393-2

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