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Genetic dissection of heat and drought stress QTLs in phenology-controlled synthetic-derived recombinant inbred lines in spring wheat

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Abstract

Abiotic stresses that affect wheat production—heat (H) and drought (D)—often occur concurrently. The genetic dissection of stress tolerance in a population with large range of phenology is difficult due to the confounding effects. We developed a recombinant inbred line (RIL) population of 276 entries with a narrow range of phenology, from a cross between a synthetic-derived parent (SYN-D: Croc 1/Aegilops squarrosa (224)//Opata) and an elite line (Weebill 1) to (a) understand the individual and combined effects of H and D stresses on yield and related traits, (b) identify the genetic basis of individual and combined stress tolerance, and (c) know the genetics of stress tolerance that can be explored from the line SYN-D. Phenotypic analysis indicated that the detrimental effect of combined stresses was greater than their individual effects. We constructed a genetic map—2771.5 cM—of the population with 569 SNPs (231 DArTseq and 338 Illumina bead chip 90 K array) and identified 71 QTLs, in which eight were common among stresses. We identified five QTL hotspots for yield and related traits under D, H, and H + D in chromosomes 2A (20.5 to 30.5 cM), 3D (92.5 to 108.5 cM), 6D (68.5 to 73.5 cM), 6D (125.5 to 135.5 cM), and 7B (40.5 to 61.5 cM). Among the 71 identified QTLs, SYN-D contributed 37 QTLs (52%) and Weebill 1 contributed 34 QTLs (48%). SYN-D also contributed the common thousand-grain weight QTL detected under H, D, and H + D, which can be used in molecular-assisted breeding.

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Abbreviations

QTL:

quantitative trait locus

RILs:

recombinant inbred lines

YLD:

grain yield m−2

TGW:

thousand-grain weight

GN:

grain number m−2

DTH:

days to heading

DTA:

days to anthesis

DTM:

days to maturity

PH:

plant height

CTvg, CTllg:

canopy temperature at vegetative and grain-filling stage, respectively

NDVIemg, NDVIvg, NDVIllg:

normalized difference vegetation index at emergence, vegetative, and grain-filling stage, respectively

SNP:

single-nucleotide polymorphism

BLUP:

best linear unbiased prediction

References

  • Acuña-Galindo MA, Mason RE, Subramanian NK, Hays DB (2015) Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Sci 55(2):477–492

    Article  Google Scholar 

  • Ahmad P, Wani MR, Azooz MM, Tran L-SP (2014) Improvement of crops in the era of climatic changes, 2014 July 2018. Springer, New York

  • Araus JL, Slafer GA, Royo C, Serret MD (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27(6):377–412

    Article  Google Scholar 

  • Babar M, Reynolds M, Van Ginkel M, Klatt A, Raun W, Stone M (2006) Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci 46(3):1046–1057

    Article  Google Scholar 

  • Bennett D, Izanloo A, Edwards J, Kuchel H, Chalmers K, Tester M, Reynolds M, Schnurbusch T, Langridge P (2012a) 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(4):697–711

    Article  PubMed  Google Scholar 

  • Bennett D, Reynolds M, Mullan D, Izanloo A, Kuchel H, Langridge P, Schnurbusch T (2012b) Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments. Theor Appl Genet 125(7):1473–1485

    Article  PubMed  Google Scholar 

  • Bhusal N, Sarial AK, Sharma P, Sareen S (2017) Mapping QTLs for grain yield components in wheat under heat stress. PLoS One 12(12):e0189594

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Challinor AJ, Watson J, Lobell D, Howden S, Smith D, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang 4(4):287–291

    Article  Google Scholar 

  • Christopher M, Chenu K, Jennings R, Fletcher S, Butler D, Borrell A, Christopher J (2018) QTL for stay-green traits in wheat in well-watered and water-limited environments. Field Crop Res 217:32–44

    Article  Google Scholar 

  • Collard B, Jahufer M, Brouwer J, Pang E (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142(1–2):169–196

    Article  CAS  Google Scholar 

  • Collins NC, Tardieu F, Tuberosa R (2008) Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiol 147(2):469–486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cooper M, Woodruff D, Eisemann R, Brennan P, DeLacy I (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(3–4):492–502

    Article  CAS  PubMed  Google Scholar 

  • Cossani CM, Reynolds MP (2015) Heat stress adaptation in elite lines derived from synthetic hexaploid wheat. Crop Sci 55(6):2719–2735

    Article  CAS  Google Scholar 

  • Crespo-Herrera LA, Crossa J, Huerta-Espino J, Autrique E, Mondal S, Velu G, Vargas M, Braun HJ, Singh RP (2017) Genetic yield gains in CIMMYT’s international elite spring wheat yield trials by modeling the genotype × environment interaction. Crop Sci 57(2):789–801

    Article  Google Scholar 

  • Del Blanco I, Rajaram S, Kronstad W (2001) Agronomic potential of synthetic hexaploid wheat-derived populations. Crop Sci 41(3):670–676

    Article  Google Scholar 

  • Dreisigacker S, Tiwari R, Sheoran S (2013) Laboratory manual: ICAR-CIMMYT molecular breeding course in wheat. ICAR. BMZ. CIMMYT, Haryana

  • Fleury D, Jefferies S, Kuchel H, Langridge P (2010) Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot 61(12):3211–3222

    Article  CAS  PubMed  Google Scholar 

  • Food and Agriculture Organization (FAO) 2017. FAOSTAT database. http://faostat.fao.org/beta/en/ (Accessed 14 Jan 2017)

  • Gao F, Wen W, Liu J, Rasheed A, Yin G, Xia X, Wu X, He Z (2015) Genome-wide linkage mapping of QTL for yield components, plant height and yield-related physiological traits in the Chinese wheat cross Zhou 8425B/Chinese spring. Front Plant Sci 6:1099

    PubMed  PubMed Central  Google Scholar 

  • Golabadi M, Arzani A, Maibody SM, Tabatabaei BS, Mohammadi S (2011) Identification of microsatellite markers linked with yield components under drought stress at terminal growth stages in durum wheat. Euphytica 177(2):207–221

    Article  Google Scholar 

  • Golan G, Oksenberg A, Peleg Z (2015) Genetic evidence for differential selection of grain and embryo weight during wheat evolution under domestication. J Exp Bot 66(19):5703–5711

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Groos C, Robert N, Bervas E, Charmet G (2003) Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. Theor Appl Genet 106(6):1032–1040

    Article  CAS  PubMed  Google Scholar 

  • Gupta PK, Balyan HS, Gahlaut V (2017) QTL analysis for drought tolerance in wheat: present status and future possibilities. Agronomy 7(1):5

    Article  CAS  Google Scholar 

  • Hazratkulova S, Sharma RC, Alikulov S, Islomov S, Yuldashev T, Ziyaev Z, Khalikulov Z, Ziyadullaev Z, Turok J (2012) Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress. Plant Breed 131(6):716–721

    Article  Google Scholar 

  • Huang X, Kempf H, Ganal M, Röder M (2004) Advanced backcross QTL analysis in progenies derived from a cross between a German elite winter wheat variety and a synthetic wheat (Triticum aestivum L.). Theor Appl Genet 109(5):933–943

    Article  CAS  PubMed  Google Scholar 

  • Kato K, Miura H, Sawada S (2000) Mapping QTLs controlling grain yield and its components on chromosome 5A of wheat. Theor Appl Genet 101(7):1114–1121

    Article  CAS  Google Scholar 

  • King R, von Wettstein-Knowles P (2000) Epicuticular waxes and regulation of ear wetting and pre-harvest sprouting in barley and wheat. Euphytica 112(2):157–166

    Article  Google Scholar 

  • Kirigwi F, Van Ginkel M, Brown-Guedira G, Gill B, Paulsen GM, Fritz A (2007) Markers associated with a QTL for grain yield in wheat under drought. Mol Breed 20(4):401–413

    Article  CAS  Google Scholar 

  • Kosambi DD (2016) The estimation of map distances from recombination values. In: DD Kosambi. Springer, pp 125–130

  • Kosina P, Reynolds M, Dixon J, Joshi A (2007) Stakeholder perception of wheat production constraints, capacity building needs, and research partnerships in developing countries. Euphytica 157(3):475–483

    Article  Google Scholar 

  • Langridge P, Reynolds MP (2015) Genomic tools to assist breeding for drought tolerance. Curr Opin Biotechnol 32:130–135

    Article  CAS  PubMed  Google Scholar 

  • Lopes MS, Reynolds MP (2010) Partitioning of assimilates to deeper roots is associated with cooler canopies and increased yield under drought in wheat. Funct Plant Biol 37(2):147–156

    Article  Google Scholar 

  • Lopes MS, Reynolds MP (2011) Drought adaptive traits and wide adaptation in elite lines derived from resynthesized hexaploid wheat. Crop Sci 51(4):1617–1626

    Article  Google Scholar 

  • Maccaferri M, Sanguineti MC, Corneti S, Ortega JLA, Salem MB, Bort J, DeAmbrogio E, del Moral LFG, Demontis A, El-Ahmed A (2008) Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability. Genetics 178(1):489–511

    Article  PubMed  PubMed Central  Google Scholar 

  • Marti J, Bort J, Slafer G, Araus J (2007) Can wheat yield be assessed by early measurements of normalized difference vegetation index? Ann Appl Biol 150(2):253–257

    Article  Google Scholar 

  • Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3(3):269–283

    Article  Google Scholar 

  • Merah O, Deléens E, Souyris I, Monneveux P (2000) Effect of glaucousness on carbon isotope discrimination and grain yield in durum wheat. J Agron Crop Sci 185(4):259–265

    Article  Google Scholar 

  • Millet EJ, Welcker C, Kruijer W, Negro S, Coupel-Ledru A, Nicolas SD, Laborde J, Bauland C, Praud S, Ranc N (2016) Genome-wide analysis of yield in Europe: allelic effects vary with drought and heat scenarios. Plant Physiol 172(2):749–764

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mittler R (2006) Abiotic stress, the field environment and stress combination. Trends Plant Sci 11(1):15–19

    Article  CAS  PubMed  Google Scholar 

  • Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12(10):433–436

    Article  CAS  PubMed  Google Scholar 

  • Mujeeb-Kazi A, Rosas V, Roldan S (1996) Conservation of the genetic variation of Triticum tauschii (Coss.) Schmalh. (Aegilops squarrosa auct. non L.) in synthetic hexaploid wheats (T. turgidum L. s. lat. × T. tauschii; 2n = 6x = 42, AABBDD) and its potential utilization for wheat improvement. Genet Resour Crop Evol 43(2):129–134

    Article  Google Scholar 

  • Ogbonnaya FC, Rasheed A, Okechukwu EC, Jighly A, Makdis F, Wuletaw T, Hagras A, Uguru MI, Agbo CU (2017) Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments. Theor Appl Genet 130(9):1819–1835

    Article  PubMed  Google Scholar 

  • Olivares-Villegas JJ, Reynolds MP, McDonald GK (2007) Drought-adaptive attributes in the Seri/Babax hexaploid wheat population. Funct Plant Biol 34(3):189–203

    Article  Google Scholar 

  • Parent B, Tardieu F (2012) Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytol 194(3):760–774

    Article  PubMed  Google Scholar 

  • Pask A, Pietragalla J, Mullan D, Reynolds M (2012) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico

  • Peleg Z, Fahima T, Korol AB, Abbo S, Saranga Y (2011) Genetic analysis of wheat domestication and evolution under domestication. J Exp Bot 62(14):5051–5061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peng J, Ronin Y, Fahima T, Röder MS, Li Y, Nevo E, Korol A (2003) Domestication quantitative trait loci in Triticum dicoccoides, the progenitor of wheat. Proc Natl Acad Sci 100(5):2489–2494

    Article  CAS  PubMed  Google Scholar 

  • 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(6):1001–1021

    Article  PubMed  PubMed Central  Google Scholar 

  • Pradhan GP, Prasad PV, Fritz AK, Kirkham MB, Gill BS (2012) Effects of drought and high temperature stress on synthetic hexaploid wheat. Funct Plant Biol 39(3):190–198

    Article  Google Scholar 

  • Prasad P, Pisipati S, Momčilović I, Ristic Z (2011) Independent and combined effects of high temperature and drought stress during grain filling on plant yield and chloroplast EF-Tu expression in spring wheat. J Agron Crop Sci 197(6):430–441

    Article  CAS  Google Scholar 

  • Quarrie S, Pekic Quarrie S, Radosevic R, Rancic D, Kaminska A, Barnes J, Leverington M, Ceoloni C, Dodig D (2006) Dissecting a wheat QTL for yield present in a range of environments: from the QTL to candidate genes. J Exp Bot 57(11):2627–2637

    Article  CAS  PubMed  Google Scholar 

  • Quraishi UM, Abrouk M, Murat F, Pont C, Foucrier S, Desmaizieres G, Confolent C, Riviere N, Charmet G, Paux E (2011) Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution. Plant J 65(5):745–756

    Article  CAS  PubMed  Google Scholar 

  • Reynolds M, Langridge P (2016) Physiological breeding. Curr Opin Plant Biol 31:162–171

    Article  PubMed  Google Scholar 

  • Reynolds M, Dreccer F, Trethowan R (2006) Drought-adaptive traits derived from wheat wild relatives and landraces. J Exp Bot 58(2):177–186

    Article  PubMed  CAS  Google Scholar 

  • Reynolds M, Manes Y, Izanloo A, Langridge P (2009) Phenotyping approaches for physiological breeding and gene discovery in wheat. Ann Appl Biol 155(3):309–320

    Article  Google Scholar 

  • Richards R, Rawson H, Johnson D (1986) Glaucousness in wheat: its development and effect on water-use efficiency, gas exchange and photosynthetic tissue temperatures. Funct Plant Biol 13(4):465–473

    Article  Google Scholar 

  • Rizhsky L, Liang H, Shuman J, Shulaev V, Davletova S, Mittler R (2004) When defense pathways collide. The response of Arabidopsis to a combination of drought and heat stress. Plant Physiol 134(4):1683–1696

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Saghai-Maroof MA, Soliman KM, Jorgensen RA, Allard R (1984) Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location, and population dynamics. Proc Natl Acad Sci 81(24):8014–8018

    Article  CAS  PubMed  Google Scholar 

  • Sansaloni C, Petroli C, Jaccoud D, Carling J, Detering F, Grattapaglia D, Kilian A (2011) Diversity arrays technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. In: BMC proceedings. vol 7. BioMed Central, p P54

  • Shi S, Azam FI, Li H, Chang X, Li B, Jing R (2017a) Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes. Euphytica 213(11):246

    Article  Google Scholar 

  • Shi W, Hao C, Zhang Y, Cheng J, Zhang Z, Liu J, Yi X, Cheng X, Sun D, Xu Y (2017b) A combined association mapping and linkage analysis of kernel number per spike in common wheat (Triticum aestivum L.). Front Plant Sci 8:1412

    Article  PubMed  PubMed Central  Google Scholar 

  • Simmonds J, Fish L, Leverington-Waite M, Wang Y, Howell P, Snape J (2008) Mapping of a gene (Vir) for a non-glaucous, viridescent phenotype in bread wheat derived from Triticum dicoccoides, and its association with yield variation. Euphytica 159(3):333–341

    Article  CAS  Google Scholar 

  • Singh RP, Huerta-Espino J, Sharma R, Joshi AK, Trethowan R (2007) High yielding spring bread wheat germplasm for global irrigated and rainfed production systems. Euphytica 157(3):351–363

    Article  Google Scholar 

  • Singh S, Vikram P, Sehgal D, Burgueño J, Sharma A, Singh SK, Sansaloni CP, Joynson R, Brabbs T, Ortiz C (2018) Harnessing genetic potential of wheat germplasm banks through impact-oriented-prebreeding for future food and nutritional security. Sci Rep 8(1):12527

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sourdille P, Charmet G, Trottet M, Tixier M, Boeuf C, Negre S, Barloy D, Bernard M (1998) Linkage between RFLP molecular markers and the dwarfing genes Rht‐B1 and Rht‐D1 in wheat. Hereditas 128(1):41–46

  • Sukumaran S, Lopes M, Dreisigacker S, Reynolds M (2017) 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:1–14

  • Sukumaran S, Reynolds MP, Sansaloni C (2018) Genome-wide association analyses identify QTL hotspots for yield and component traits in durum wheat grown under yield potential, drought, and heat stress environments. Front Plant Sci 9:81

    Article  PubMed  PubMed Central  Google Scholar 

  • Tahmasebi S, Heidari B, Pakniyat H, McIntyre CL (2016) Mapping QTLs associated with agronomic and physiological traits under terminal drought and heat stress conditions in wheat (Triticum aestivum L.). Genome 60(1):26–45

    Article  PubMed  CAS  Google Scholar 

  • Talbot S, Ogbonnaya F, Chalmers K, Mather D, Appels R, Eastwood R, Lagudah E, Langridge P, Mackay M, McIntyre L (2008) Is synthetic hexaploid wheat a useful germplasm source for increasing grain size and yield in bread wheat breeding? In: Proceedings of the 11th International Wheat Genetics Symposium, 24–29 Aug. 2008. pp 315–318

  • Tattaris M, Reynolds MP, Chapman SC (2016) A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci 7:1131

    Article  PubMed  PubMed Central  Google Scholar 

  • Trethowan R, Mujeeb-Kazi A (2008) Novel germplasm resources for improving environmental stress tolerance of hexaploid wheat. Crop Sci 48(4):1255–1265

    Article  Google Scholar 

  • 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 69(13):3195–3210

    Article  CAS  PubMed  Google Scholar 

  • Tsunewaki K, Ebana K (1999) Production of near-isogenic lines of common wheat for glaucousness and genetic basis of this trait clarified by their use. Genes Genet Syst 74(2):33–41

    Google Scholar 

  • Tzarfati R, Barak V, Krugman T, Fahima T, Abbo S, Saranga Y, Korol A (2014) Novel quantitative trait loci underlying major domestication traits in tetraploid wheat. Mol Breed 34(4):1613–1628

    Article  Google Scholar 

  • Vargas M, Combs E, Alvarado G, Atlin G, Mathews K, Crossa J (2013) META: a suite of SAS programs to analyze multienvironment breeding trials. Agron J 105(1):11–19

    Article  Google Scholar 

  • Voorrips R (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93(1):77–78

    Article  CAS  PubMed  Google Scholar 

  • Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L (2014) Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol J 12(6):787–796

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Watanabe N, Takesada N, Shibata Y, Ban T (2005) Genetic mapping of the genes for glaucous leaf and tough rachis in Aegilops tauschii, the D-genome progenitor of wheat. Euphytica 144(1–2):119–123

    Article  CAS  Google Scholar 

  • Wei T, Simko V, Levy M, Xie Y, Jin Y, Zemla J (2017) Package ‘corrplot’. Statistician 56:316–324

    Google Scholar 

  • Xu S (2008) Quantitative trait locus mapping can benefit from segregation distortion. Genetics 180(4):2201–2208

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu Y-F, Li S-S, Li L-H, Ma F-F, Fu X-Y, Shi Z-L, Xu H-X, Ma P-T, An D-G (2017) QTL mapping for yield and photosynthetic related traits under different water regimes in wheat. Mol Breed 37(3):34

    Article  CAS  Google Scholar 

  • Yıldırım M, Eser V, Bedő Z, Bagci S, Molnár-Láng M, Láng L (2017) Synthetic wheat: an indispensable pre-breeding source for high yield and resistance to biotic and abiotic stresses in wheat improvement. J Crop Breeding Genet 3(2):45–52

    Google Scholar 

  • Yoshida T, Nishida H, Zhu J, Nitcher R, Distelfeld A, Akashi Y, Kato K, Dubcovsky J (2010) Vrn-D4 is a vernalization gene located on the centromeric region of chromosome 5D in hexaploid wheat. Theor Appl Genet 120(3):543–552

    Article  CAS  PubMed  Google Scholar 

  • Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x

    Article  Google Scholar 

Download references

Acknowledgments

This work was implemented by the CIMMYT as part of the projects ARCADIA, MasAgro Trigo, and MasAgro Biodiversidad in collaboration with the CIMMYT, made possible by the generous support of SAGARPA MasAgro Trigo, MasAgro Biodiversidad, IWYP, and ARCADIA. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of SAGARPA and ARCADIA. Dr. Caiyun Liu’s stay at the CIMMYT is sponsored by the China Scholarship Council (CSC)—CIMMYT scholarship (http://en.csc.edu.cn/).

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Authors and Affiliations

  1. Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico

    Caiyun Liu, Sivakumar Sukumaran, Susanne Dreisigacker & Matthew Reynolds

  2. Dezhou Academy of Agricultural Sciences, Dezhou, 253011, Shandong, China

    Caiyun Liu

  3. Syngenta Crop Protection Münchwillen AG, Breitenloh 5, 4333, Münchwillen, Switzerland

    Etienne Claverie

  4. Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico City, Mexico

    Carolina Sansaloni

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  1. Caiyun Liu
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Contributions

C.L. and S.S. did the genetic and phenotypic analyses and drafted the manuscript; M.R. coordinated the phenotypic data collection; E.C. collected the phenotypic data; S.D. and C.S. did the genotyping; all authors reviewed and agreed on the manuscript.

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Correspondence to Sivakumar Sukumaran.

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Supplementary Figure 1

Pattern of variation in the traits (a) grain yield (YLD), (b) thousand-grain weight (TGW), (c) grain number (GN), (d) days to heading, (e) days to maturity (DTM), (f) plant height (PH), (g) canopy temperature at grain-filling stage (CTllg), and normalized difference vegetation index at vegetative stage (NDVIvg) of the SYN-D/Weebill 1 RILs population under drought stress in 2010 (D10), heat stress in 2010 (H10), head + drought in 2012 (HD12) and 2013 (HD13) environments. (PNG 187 kb)

High-resolution image (TIF 688 kb)

Supplementary Figure 2

Genetic linkage map of chromosomes 1A to 4D showing the location of QTL detected in the SYN-D/Weebill 1 RILs population grown under different environments. (PNG 1973 kb)

High-resolution image (TIF 6083 kb)

Supplementary Figure 3

Genetic linkage map of chromosomes 5A to 7D showing the location of QTL detected in the SYN-D/Weebill 1 RILs population grown under different environments. (PNG 1287 kb)

High-resolution image (TIF 2459 kb)

Supplementary Figure 4

Yield advantage of the SYN-D/Weebill 1 RILs based on the presence of parental yield favorable QTL allele contribution. (PNG 34 kb)

High-resolution image (TIF 202 kb)

Supplementary Table 1

Genotyping results of the major Rht, Ppd, Vrn, and Eps genes of the SYN-D/Weebill 1 RIL population (XLSX 21 kb)

Supplementary Table 2

Weather data of the experimental site during the crop growth season from 2010 to 2013 in Cd. Obregon, Mexico. (XLSX 8 kb)

Supplementary Table 3

Genetic data and linkage map of the SYN-D/Weebill 1 RIL population. (XLSX 554 kb)

Supplementary Table 4

QTL detected for traits of SYN-D/Weebill 1 RIL population under drought stress in 2010 season. (XLSX 11 kb)

Supplementary Table 5

QTL detected for traits of SYN-D/Weebill 1 RIL population under heat stress in 2010. (XLSX 11 kb)

Supplementary Table 6

QTL detected for traits of SYN-D/Weebill 1 RIL population under two H + D environments. (XLSX 15 kb)

Supplementary Table 7

QTL Located in the chromosome regions showing allele difference of high-yielding group and low-yielding group of SYN-D/Weebill 1 RIL population. (XLSX 11 kb)

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Liu, C., Sukumaran, S., Claverie, E. et al. Genetic dissection of heat and drought stress QTLs in phenology-controlled synthetic-derived recombinant inbred lines in spring wheat. Mol Breeding 39, 34 (2019). https://doi.org/10.1007/s11032-019-0938-y

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