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.
Similar content being viewed by others
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
References
Ain Q, Rasheed A, Anwar A, Mahmood T, Imtiaz M, Xia X, He Z, Quraishi UM (2015) Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Front Plant Sci 6:743. https://doi.org/10.3389/fpls.2015.00743
Appels R, Eversole K, Feuillet C, Keller B, Rogers J, Stein N, The International Wheat Genome Sequencing Consortium (IWGSC) (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science. https://doi.org/10.1126/science.aar7191
Bennett D, Izanloo A, Edwards J, Kuchel H, Chalmers K, Tester M, Reynolds M, Schnurbusch T, Langridge P (2012) Identification of novel quantitative trait loci for days to ear emergence and flag leaf glaucousness in a bread wheat (Triticum aestivum L.) population adapted to southern Australian conditions. Theor Appl Genet 124:697–711. https://doi.org/10.1007/s00122-011-1740-3
Bentley AR, Horsnell R, Werner CP, Turner AS, Rose GA, Bedard C, Howell P, Wilhelm EP, Mackay IJ, Howells RM, Greenland A, Laurie DA, Gosman N (2013) Short, natural, and extended photoperiod response in BC2F4 lines of bread wheat with different Photoperiod-1 (Ppd-1) alleles. J Exp Bot 64:1783–1793. https://doi.org/10.1093/jxb/ert038
Boer MP, Wright D, Feng L, Podlich DW, Luo L, Cooper M, van Eeuwijk FA (2007) 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. Genetics 177:1801–1813. https://doi.org/10.1534/genetics.107.071068
Bonneau J, Taylor J, Parent B, Bennett D, Reynolds M, Feuillet C, Langridge P, Mather D (2013) Multi-environment analysis and improved mapping of a yield-related QTL on chromosome 3B of wheat. Theor Appl Genet 126:747–761. https://doi.org/10.1007/s00122-012-2015-3
Bouffier B (2014) Genetic and ecophysiological dissection of tolerance to drought and heat stress in bread wheat: from environmental characterization to QTL detection. Ph.D. Thesis. Agricultural sciences. Université Blaise Pascal-Clermont-Ferrand II
Brancourt-Hulmel M (1999) Crop diagnosis and probe genotypes for interpreting genotype environment interaction in winter wheat trials. Theor Appl Genet 99:1018–1030
Brancourt-Hulmel M, Doussinault G, Lecomte C, Bérard P, Le Buanec B, Trottet M (2003) Genetic improvement of agronomic traits of winter wheat cultivars released in France from 1946 to 1992. Crop Sci 43:37–45
Braun H-J, Rajaram S (1996) CIMMYT’s approach to breeding for wide adaptation. Euphytica 92:175–183
Breseghello F (2005) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177. https://doi.org/10.1534/genetics.105.044586
Brisson N, Gate P, Gouache D, Charmet G, Oury F-X, Huard F (2010) Why are wheat yields stagnating in Europe? a comprehensive data analysis for France. Field Crops Res 119:201–212. https://doi.org/10.1016/j.fcr.2010.07.012
Browning BL, Browning SR (2016) Genotype imputation with millions of reference samples. Am J Hum Genet 98:116–126. https://doi.org/10.1016/j.ajhg.2015.11.020
Budak H, Hussain B, Khan Z, Ozturk NZ, Ullah N (2015) From genetics to functional genomics: improvement in drought signaling and tolerance in wheat. Front Plant Sci 6:1012. https://doi.org/10.3389/fpls.2015.01012
Butler JD, Byrne PF, Mohammadi V, Chapman PL, Haley SD (2005) Agronomic performance of Rht alleles in a spring wheat population across a range of moisture levels. Crop Sci 45:939–947. https://doi.org/10.2135/cropsci2004.0323
Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual. State Qld Dep Prim Ind Fish Brisb
Calderini DF, Slafer GA (1998) Changes in yield and yield stability in heat during the 20th century. Field Crops Res 57:335–347. https://doi.org/10.1016/S0378-4290(98)00080-X
Cassman KG (1999) Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. Proc Natl Acad Sci USA 96:5952–5959
Chapman SC (2008) Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. Euphytica 161:195–208. https://doi.org/10.1007/s10681-007-9623-z
Chatelin MH, Aubry C, Poussin JC, Meynard JM, Massé J, Verjux N, Gate P, Le Bris X (2005) DéciBlé, a software package for wheat crop management simulation. Agric Syst 83:77–99. https://doi.org/10.1016/j.agsy.2004.03.003
Chenu K (2015) Characterizing the crop environment—nature, significance and applications. In: Crop Physiology. Elsevier, pp 321–348
Chenu K, Cooper M, Hammer GL, Mathews KL, Dreccer MF, Chapman SC (2011) Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia. J Exp Bot 62:1743–1755. https://doi.org/10.1093/jxb/erq459
Christopher JT, Christopher MJ, Borrell AK, Fletcher S, Chenu K (2016) Stay-green traits to improve wheat adaptation in well-watered and water-limited environments. J Exp Bot 67:5159–5172. https://doi.org/10.1093/jxb/erw276
Cooper M, Fox PN (1996) Environmental characterization based on probe and reference genotypes. In: Cooper, Hammer GL (eds) Plant adaption and crop improvement. CAB International, Wallingford, pp 529–547
Cooper M, Woodruff DR, Eisemann RL, Brennan PS, DeLacy IH (1995) A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theor Appl Genet 90:492–502. https://doi.org/10.1007/BF00221995
Cormier F, Faure S, Dubreuil P, Heumez E, Beauchêne K, Lafarge S, Praud S, Le Gouis J (2013) A multi-environmental study of recent breeding progress on nitrogen use efficiency in wheat (Triticum aestivum L.). Theor Appl Genet 126:3035–3048. https://doi.org/10.1007/s00122-013-2191-9
Cormier F, Le Gouis J, Dubreuil P, Lafarge S, Praud S (2014) A genome-wide identification of chromosomal regions determining nitrogen use efficiency components in wheat (Triticum aestivum L.). Theor Appl Genet 127:2679–2693. https://doi.org/10.1007/s00122-014-2407-7
Cox DR, Snell EJ (1989) Analysis of Binary Data, Second Edition. Chapman and Hall/CRC
Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393. https://doi.org/10.1198/108571106X154443
Denis JB (1988) Two way analysis using covariates. Statistics 19:123–132. https://doi.org/10.1080/02331888808802080
Desclaux D (1996) De l’intérêt de génotypes révélateurs de facteurs limitants dans l’analyse des interactions génotype x milieu chez le soja (Glycine max. L. Merill). Institut national polytechnique de Toulouse. Spécialité: Biologie et Technologie végétales
Edae EA, Byrne PF, Haley SD, Lopes MS, Reynolds MP (2014) Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 127:791–807. https://doi.org/10.1007/s00122-013-2257-8
FAO (2018). FAOSTAT. http://www.fao.org/faostat/
Farooq M, Hussain M, Siddique KHM (2014) Drought stress in wheat during flowering and grain-filling periods. Crit Rev Plant Sci 33:331–349. https://doi.org/10.1080/07352689.2014.875291
Finger R (2010) Evidence of slowing yield growth—the example of Swiss cereal yields. Food Policy 35:175–182. https://doi.org/10.1016/j.foodpol.2009.11.004
Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Crop Pasture Sci 14:742–754
Fischer R, Maurer R (1978) Drought resistance in spring wheat cultivars. I. Grain yield responses. Aust J Agric Res 29:897–912. https://doi.org/10.1071/AR9780897
FranceAgriMer (2017) Les variétés de céréales à paille préférées des agriculteurs. In: Terre-Net Média. https://www.terre-net.fr/observatoire-technique-culturale/appros-phytosanitaire/article/quelles-varietes-des-cereales-a-paille-avez-vous-semees-et-recoltees-216-129258.html
Frensham A, Cullis B, Verbyla A (1997) Genotype by environment variance heterogeneity in a two-stage analysis. Biometrics 53:1373–1383. https://doi.org/10.2307/2533504
Gahlaut (2012) Genetic dissection of water stress tolerance in bread wheat. Ph.D. Thesis, Chaudhary Charan Singh University, Meerut, India, 2016
Gao X, Becker LC, Becker DM, Starmer JD, Province MA (2009) Avoiding the high Bonferroni penalty in genome-wide association studies. Genet Epidemiol 34:100–105. https://doi.org/10.1002/gepi.20430
Gate P (1995) Ecophysiologie du blé: de la plante à la culture. Lavoisier, France
Gelman A (2005) Analysis of variance? why it is more important than ever. Ann Stat 33:1–53. https://doi.org/10.1214/009053604000001048
Gervois S, Ciais P, de Noblet-Ducoudré N, Brisson N, Vuichard N, Viovy N (2008) Carbon and water balance of European croplands throughout the 20th century. Glob Biogeochemical Cycles 22, GB2022. https://doi.org/10.1029/2007gb003018
Graybosch RA, Peterson CJ (2010) Genetic improvement in winter wheat yields in the great plains of North America, 1959–2008. Crop Sci 50:1882–1890. https://doi.org/10.2135/cropsci2009.11.0685
Gupta P, Balyan H, Gahlaut V (2017) QTL analysis for drought tolerance in wheat: present status and future possibilities. Agronomy 7:5. https://doi.org/10.3390/agronomy7010005
Hammer G, Jordan D (2007) An integrated systems approach to crop improvement. Scale and complexity in plant systems research: Gene-Plant-Crop Relations, 45–61. Wagening UR–Frontis Ser No 21 Springer Dordecht Neth, pp 45–61
Heslot N, Akdemir D, Sorrells ME, Jannink J-L (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127:463–480. https://doi.org/10.1007/s00122-013-2231-5
Holzworth DP, Huth NI, Devoil PG, Zurcher EJ, Herrmann NI, McLean G, Chenu K, van Oosterom EJ, Snow V, Murphy C, Moore AD, Brown H, Whish JPM, Verrall S, Fainges J, Bell LW, Peake AS, Poulton PL, Hochman Z, Thorburn PJ, Gaydon DS, Dalgliesh NP, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li FY, Wang E, Hammer GL, Robertson MJ, Dimes JP, Whitbread AM, Hunt J, van Rees H, McClelland T, Carberry PS, Hargreaves JNG, MacLeod N, McDonald C, Harsdorf J, Wedgwood S, Keating BA (2014) APSIM – Evolution towards a new generation of agricultural systems simulation. Environ Model Softw 62:327–350. https://doi.org/10.1016/j.envsoft.2014.07.009
Jones RM, Mather K (1958) Interaction of genotype and environment in continuous variation: II. Analysis. Biometrics 14:489–498. https://doi.org/10.2307/2527515
Kendall MG, Stuart A (1979) The advanced theory of statistics, 4th edn. Oxford University Press, Griffin
Lacaze X, Roumet P (2004) Environment characterisation for the interpretation of environmental effect and genotype x environment interaction. Theor Appl Genet 109:1632–1640. https://doi.org/10.1007/s00122-004-1786-6
Lacaze X, Hayes PM, Korol A (2009) Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare. Heredity 102:163–173. https://doi.org/10.1038/hdy.2008.76
Langer SM, Longin CFH, Würschum T (2014) Flowering time control in European winter wheat. Front Plant Sci 5:537. https://doi.org/10.3389/fpls.2014.00537
Lantican MA, Pingali PL, Rajaram S (2001) Growth in wheat yield potential in marginal environments. Sponsored by the CIMMYT Wheat Program, Oregon State University, USAID, and AgriPro: 73–79
Le Bris X, Soenen B, Laberdesque M, Maunas M, Gouache D, Lorgeou J, Cohan J, Laurent F, Bouthier A, Garcia C (2016) “CHN”, a crop model to add value to phenotyping and approach genetic variation for RUE and WUE. Conference: Recent progress in drought tolerance: from genetics to modelling. At: Montpellier, France
Le Couviour F, Faure S, Poupard B, Flodrops Y, Dubreuil P, Praud S (2011) Analysis of genetic structure in a panel of elite wheat varieties and relevance for association mapping. Theor Appl Genet 123:715–727. https://doi.org/10.1007/s00122-011-1621-9
Lecomte C (2005) L’évaluation expérimentale des innovations variétales. Proposition d’outils d’analyse de l’interaction génotype-milieu adaptés à la diversité des besoins et des contraintes des acteurs de la filière semences. Ph.D. Thesis. Agronomie. INAPG (AgroParisTech)
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833–835. https://doi.org/10.1038/nmeth.1681
Löffler CM, Wei J, Fast T, Gogerty J, Langton S, Bergman M, Merrill B, Cooper M (2005) Classification of maize environments using crop simulation and geographic information systems. Crop Sci 45:1708–1716. https://doi.org/10.2135/cropsci2004.0370
Ly D, Huet S, Gauffreteau A, Rincent R, Touzy G, Mini A, Jannink J-L, Cormier F, Paux E, Lafarge S, Le Gouis J, Charmet G (2018) Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression. Field Crops Res 216:32–41. https://doi.org/10.1016/j.fcr.2017.08.020
Malosetti M, Ribaut JM, Vargas M, Crossa J, van Eeuwijk FA (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica 161:241–257. https://doi.org/10.1007/s10681-007-9594-0
Malosetti M, Ribaut JM, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front Physiol 4:1–17. https://doi.org/10.3389/fphys.2013.00044
Manès Y, Gomez HF, Puhl L, Reynolds M, Braun HJ, Trethowan R (2012) Genetic yield gains of the CIMMYT international semi-arid wheat yield trials from 1994 to 2010. Crop Sci 52:1543–1552. https://doi.org/10.2135/cropsci2011.10.0574
Mathews KL, Chapman SC, Trethowan R, Singh RP, Crossa J, Pfeiffer W, van Ginkel M, DeLacy I (2006) Global adaptation of spring bread and durum wheat lines near-isogenic for major reduced height genes. Crop Sci 46:603–613. https://doi.org/10.2135/cropsci2005.05-0056
Meynard J-M (1997) Which crop models for decision support in crop management? Example of the DECIBLE system. In: Proceeding of the INRA-KCW workshop on DSS, Laon
Meynard JM, Sebillotte M (1994) L’elaboration du rendement du ble, base pour l’etude des autres cereales a talles. In: L. Combe (Editeur), D. Picard (Editeur), L’elaboration du rendement des principales cultures annuelles, 31–53. Paris, FRA: INRA Editions. https://prodinra.inra.fr/record/119700
Millet E, Welcker C, Kruijer W, Negro S, Coupel-Ledru A, Nicolas S, Laborde J, Bauland C, Praud S, Ranc N, Presterl T, Tuberosa R, Bedo Z, Draye X, Usadel B, Charcosset A, van Eeuwijk F (2016) Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios. Plant Physiol 172:749–764. https://doi.org/10.1104/pp.16.00621
Möhring J, Piepho H-P (2009) Comparison of weighting in two-stage analysis of plant breeding trials. Crop Sci 49:1977–1988. https://doi.org/10.2135/cropsci2009.02.0083
Monteith JL (1977) Climate and the efficiency of crop production in Britain. Philos Trans R Soc Lond Biol Sci 281:277–294. https://doi.org/10.1098/rstb.1977.0140
Muchow RC, Cooper M, Hammer GL (1996) Characterizing environmental challenges using models. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 349–364
Murtagh F, Legendre P (2014) Ward’s hierarchical clustering method: clustering criterion and agglomerative algorithm. J Classif 31:274–295. https://doi.org/10.1007/s00357-014-9161-z
Mwadzingeni L, Shimelis H, Rees DJG, Tsilo TJ (2017) Genome-wide association analysis of agronomic traits in wheat under drought-stressed and non-stressed conditions. PLoS ONE 12:e0171692. https://doi.org/10.1371/journal.pone.0171692
Oury F-X, Godin C, Mailliard A, Chassin A, Gardet O, Giraud A, Heumez E, Morlais J-Y, Rolland B, Rousset M, Trottet M, Charmet G (2012) A study of genetic progress due to selection reveals a negative effect of climate change on bread wheat yield in France. Eur J Agron 40:28–38. https://doi.org/10.1016/j.eja.2012.02.007
Peltonen-Sainio P, Jauhiainen L, Laurila IP (2009) Cereal yield trends in northern European conditions: changes in yield potential and its realisation. Field Crops Res 110:85–90
Peng J, Richards DE, Hartley NM, Murphy GP, Devos KM, Flintham JE, Beales J, Fish LJ, Worland AJ, Pelica F, Sudhakar D, Christou P, Snape JW, Gale MD, Harberd NP (1999) ‘Green revolution’ genes encode mutant gibberellin response modulators. Nature 400:256–261. https://doi.org/10.1038/22307
Pinto RS, Reynolds MP (2015) Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution in bread wheat. Theor Appl Genet 128:575–585. https://doi.org/10.1007/s00122-015-2453-9
Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivares-Villegas J-J, Chapman SC (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021. https://doi.org/10.1007/s00122-010-1351-4
Pritchard JK, Wen X, Falush D (2010) Documentation for structure software: Version 2.3. 39
R Development Core Team (2011) R: A language and environment for statistical computing. Vienna, Austria : the R Foundation for Statistical Computing. ISBN: 3-900051-07-0. Available online at http://www.R-project.org/
Reynolds M, Manes Y, Izanloo A, Langridge P (2009) Phenotyping approaches for physiological breeding and gene discovery in wheat. Ann Appl Biol 155:309–320. https://doi.org/10.1111/j.1744-7348.2009.00351.x
Rimbert H, Darrier B, Navarro J, Kitt J, Choulet F, Leveugle M, Duarte J, Rivière N, Eversole K, Le Gouis J, on behalf The BreedWheat Consortium, on behalf The BreedWheat Consortium, Davassi A, Balfourier F, Le Paslier M-C, Berard A, Brunel D, Feuillet C, Poncet C, Sourdille P, Paux E (2018) High throughput SNP discovery and genotyping in hexaploid wheat. PLOS ONE 13: e0186329. https://doi.org/10.1371/journal.pone.0186329
Rincent R, Moreau L, Monod H, Kuhn E, Melchinger AE, Malvar RA, Moreno-Gonzalez J, Nicolas S, Madur D, Combes V, Dumas F, Altmann T, Brunel D, Ouzunova M, Flament P, Dubreuil P, Charcosset A, Mary-Huard T (2014) Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics 197:375–387. https://doi.org/10.1534/genetics.113.159731
Rodriguez-Alvarez MX, Boer MP, van Eeuwijk FA, Eilers PHC (2018) Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spat Stat 23:52–71
Rutkoski J, Poland J, Mondal S, Autrique E, Gonzalez Parez L, Crossa J, Reynolds M, Singh R (2016) Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat. G3 Genes Genom Genet 6:2799–2808. https://doi.org/10.1534/g3.116.032888
Sadras VO, Lake L, Chenu K, McMurray LS, Leonforte A (2012) Water and thermal regimes for field pea in Australia and their implications for breeding. Crop Past Sci 63:33–44. https://doi.org/10.1071/cp11321
Sheoran S, Malik R, Narwal S, Tyagi B, Mittal V, Kharub AS, Tiwari V, Sharma I (2015) Genetic and molecular dissection of drought tolerance in wheat and barley. J Wheat Res 7:1–13
Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP (2015) Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet 128:353–363. https://doi.org/10.1007/s00122-014-2435-3
Sukumaran S, Lopes M, Dreisigacker S, Reynolds M (2018) Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number. Theor Appl Genet 131:985–998. https://doi.org/10.1007/s00122-017-3037-7
Suzuki R, Shimodaira H (2006) Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22:1540–1542. https://doi.org/10.1093/bioinformatics/btl117
Tardieu F (2012) Any trait or trait-related allele can confer drought tolerance: just design the right drought scenario. J Exp Bot 63:25–31. https://doi.org/10.1093/jxb/err269
Trethowan RM, van Ginkel M, Rajaram S (2002) Progress in breeding wheat for yield and adaptation in global drought affected environments. Crop Sci 42:1441. https://doi.org/10.2135/cropsci2002.1441
Tricker PJ, ElHabti A, Schmidt J, Fleury D (2018) The physiological and genetic basis of combined drought and heat tolerance in wheat. J Exp Bot https://doi.org/10.1093/jxb/ery081
Tuberosa R (2002) Mapping QTLs regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize. Ann Bot 89:941–963. https://doi.org/10.1093/aob/mcf134
Van Eeuwijk FA, Kang MS, Denis JB (1996) Incorporating additional information on genotypes and environments in models for twoway genotype by environment tables. In: Gauch HG, Kang M (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 15–49
Van Eeuwijk FA, Bink MC, Chenu K, Chapman SC (2010) Detection and use of QTL for complex traits in multiple environments. Curr Opin Plant Biol 13:193–205. https://doi.org/10.1016/j.pbi.2010.01.001
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423. https://doi.org/10.3168/jds.2007-0980
Yan L, Loukoianov A, Tranquilli G, Helguera M, Fahima T, Dubcovsky J (2003) Positional cloning of the wheat vernalization gene VRN1. PNAS 100:6263–6268. https://doi.org/10.1073/pnas.0937399100
Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208. https://doi.org/10.1038/ng1702
Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421
Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Neumann F, Eichhorn A, Polley A, Jaenecke C, Ganal MW, Röder MS (2015) Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping. Front Plant Sci 6:644. https://doi.org/10.3389/fpls.2015.00644
Zhang J, Gizaw SA, Bossolini E, Hegarty J, Howell T, Carter AH, Akhunov E, Dubcovsky J (2018) Identification and validation of QTL for grain yield and plant water status under contrasting water treatments in fall-sown spring wheats. Theor Appl Genet. https://doi.org/10.1007/s00122-018-3111-9
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Albrecht E. Melchinger.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-019-03393-2