Advertisement

Molecular Breeding

, 39:34 | Cite as

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

  • Caiyun Liu
  • Sivakumar SukumaranEmail author
  • Etienne Claverie
  • Carolina Sansaloni
  • Susanne Dreisigacker
  • Matthew Reynolds
Article
  • 184 Downloads

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.

Keywords

Synthetics QTL Heat and drought stress Spring wheat 

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

Notes

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/).

Author contribution

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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11032_2019_938_Fig7_ESM.png (188 kb)
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)

11032_2019_938_MOESM1_ESM.tif (688 kb)
High-resolution image (TIF 688 kb)
11032_2019_938_Fig8_ESM.png (1.9 mb)
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)

11032_2019_938_MOESM2_ESM.tif (5.9 mb)
High-resolution image (TIF 6083 kb)
11032_2019_938_Fig9_ESM.png (1.3 mb)
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)

11032_2019_938_MOESM3_ESM.tif (2.4 mb)
High-resolution image (TIF 2459 kb)
11032_2019_938_Fig10_ESM.png (35 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)

11032_2019_938_MOESM4_ESM.tif (203 kb)
High-resolution image (TIF 202 kb)
11032_2019_938_MOESM5_ESM.xlsx (21 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)
11032_2019_938_MOESM6_ESM.xlsx (9 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)
11032_2019_938_MOESM7_ESM.xlsx (555 kb)
Supplementary Table 3 Genetic data and linkage map of the SYN-D/Weebill 1 RIL population. (XLSX 554 kb)
11032_2019_938_MOESM8_ESM.xlsx (12 kb)
Supplementary Table 4 QTL detected for traits of SYN-D/Weebill 1 RIL population under drought stress in 2010 season. (XLSX 11 kb)
11032_2019_938_MOESM9_ESM.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)
11032_2019_938_MOESM10_ESM.xlsx (15 kb)
Supplementary Table 6 QTL detected for traits of SYN-D/Weebill 1 RIL population under two H + D environments. (XLSX 15 kb)
11032_2019_938_MOESM11_ESM.xlsx (12 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)

References

  1. 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):477–492Google Scholar
  2. Ahmad P, Wani MR, Azooz MM, Tran L-SP (2014) Improvement of crops in the era of climatic changes, 2014 July 2018. Springer, New YorkGoogle Scholar
  3. Araus JL, Slafer GA, Royo C, Serret MD (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27(6):377–412Google Scholar
  4. 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):1046–1057Google Scholar
  5. 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):697–711PubMedGoogle Scholar
  6. 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):1473–1485PubMedGoogle Scholar
  7. Bhusal N, Sarial AK, Sharma P, Sareen S (2017) Mapping QTLs for grain yield components in wheat under heat stress. PLoS One 12(12):e0189594PubMedPubMedCentralGoogle Scholar
  8. 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):287–291Google Scholar
  9. 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 217:32–44Google Scholar
  10. 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):169–196Google Scholar
  11. Collins NC, Tardieu F, Tuberosa R (2008) Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiol 147(2):469–486PubMedPubMedCentralGoogle Scholar
  12. 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):492–502PubMedGoogle Scholar
  13. Cossani CM, Reynolds MP (2015) Heat stress adaptation in elite lines derived from synthetic hexaploid wheat. Crop Sci 55(6):2719–2735Google Scholar
  14. 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):789–801Google Scholar
  15. Del Blanco I, Rajaram S, Kronstad W (2001) Agronomic potential of synthetic hexaploid wheat-derived populations. Crop Sci 41(3):670–676Google Scholar
  16. Dreisigacker S, Tiwari R, Sheoran S (2013) Laboratory manual: ICAR-CIMMYT molecular breeding course in wheat. ICAR. BMZ. CIMMYT, HaryanaGoogle Scholar
  17. Fleury D, Jefferies S, Kuchel H, Langridge P (2010) Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot 61(12):3211–3222PubMedGoogle Scholar
  18. Food and Agriculture Organization (FAO) 2017. FAOSTAT database. http://faostat.fao.org/beta/en/ (Accessed 14 Jan 2017)
  19. 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 6:1099PubMedPubMedCentralGoogle Scholar
  20. 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):207–221Google Scholar
  21. 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):5703–5711PubMedPubMedCentralGoogle Scholar
  22. 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):1032–1040PubMedGoogle Scholar
  23. Gupta PK, Balyan HS, Gahlaut V (2017) QTL analysis for drought tolerance in wheat: present status and future possibilities. Agronomy 7(1):5Google Scholar
  24. 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):716–721Google Scholar
  25. 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):933–943PubMedGoogle Scholar
  26. 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):1114–1121Google Scholar
  27. 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):157–166Google Scholar
  28. 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):401–413Google Scholar
  29. Kosambi DD (2016) The estimation of map distances from recombination values. In: DD Kosambi. Springer, pp 125–130Google Scholar
  30. 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):475–483Google Scholar
  31. Langridge P, Reynolds MP (2015) Genomic tools to assist breeding for drought tolerance. Curr Opin Biotechnol 32:130–135PubMedGoogle Scholar
  32. 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):147–156Google Scholar
  33. Lopes MS, Reynolds MP (2011) Drought adaptive traits and wide adaptation in elite lines derived from resynthesized hexaploid wheat. Crop Sci 51(4):1617–1626Google Scholar
  34. 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):489–511PubMedPubMedCentralGoogle Scholar
  35. 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):253–257Google Scholar
  36. 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):269–283Google Scholar
  37. 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):259–265Google Scholar
  38. 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):749–764PubMedPubMedCentralGoogle Scholar
  39. Mittler R (2006) Abiotic stress, the field environment and stress combination. Trends Plant Sci 11(1):15–19PubMedGoogle Scholar
  40. Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12(10):433–436PubMedGoogle Scholar
  41. 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):129–134Google Scholar
  42. 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):1819–1835PubMedGoogle Scholar
  43. Olivares-Villegas JJ, Reynolds MP, McDonald GK (2007) Drought-adaptive attributes in the Seri/Babax hexaploid wheat population. Funct Plant Biol 34(3):189–203Google Scholar
  44. 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):760–774PubMedGoogle Scholar
  45. Pask A, Pietragalla J, Mullan D, Reynolds M (2012) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, MexicoGoogle Scholar
  46. 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):5051–5061PubMedPubMedCentralGoogle Scholar
  47. 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):2489–2494PubMedGoogle Scholar
  48. 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):1001–1021PubMedPubMedCentralGoogle Scholar
  49. 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):190–198Google Scholar
  50. 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):430–441Google Scholar
  51. 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):2627–2637PubMedGoogle Scholar
  52. 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):745–756PubMedGoogle Scholar
  53. Reynolds M, Langridge P (2016) Physiological breeding. Curr Opin Plant Biol 31:162–171PubMedGoogle Scholar
  54. Reynolds M, Dreccer F, Trethowan R (2006) Drought-adaptive traits derived from wheat wild relatives and landraces. J Exp Bot 58(2):177–186PubMedGoogle Scholar
  55. Reynolds M, Manes Y, Izanloo A, Langridge P (2009) Phenotyping approaches for physiological breeding and gene discovery in wheat. Ann Appl Biol 155(3):309–320Google Scholar
  56. 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):465–473Google Scholar
  57. 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):1683–1696PubMedPubMedCentralGoogle Scholar
  58. 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):8014–8018PubMedGoogle Scholar
  59. 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 P54Google Scholar
  60. 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):246Google Scholar
  61. 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 8:1412PubMedPubMedCentralGoogle Scholar
  62. 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):333–341Google Scholar
  63. 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):351–363Google Scholar
  64. 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):12527PubMedPubMedCentralGoogle Scholar
  65. 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):41–46Google Scholar
  66. 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:1–14Google Scholar
  67. 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 9:81PubMedPubMedCentralGoogle Scholar
  68. 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):26–45PubMedGoogle Scholar
  69. 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 315–318Google Scholar
  70. Tattaris M, Reynolds MP, Chapman SC (2016) A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci 7:1131PubMedPubMedCentralGoogle Scholar
  71. Trethowan R, Mujeeb-Kazi A (2008) Novel germplasm resources for improving environmental stress tolerance of hexaploid wheat. Crop Sci 48(4):1255–1265Google Scholar
  72. 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):3195–3210PubMedGoogle Scholar
  73. 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):33–41Google Scholar
  74. 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):1613–1628Google Scholar
  75. 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):11–19Google Scholar
  76. Voorrips R (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93(1):77–78PubMedGoogle Scholar
  77. 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):787–796PubMedPubMedCentralGoogle Scholar
  78. 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):119–123Google Scholar
  79. Wei T, Simko V, Levy M, Xie Y, Jin Y, Zemla J (2017) Package ‘corrplot’. Statistician 56:316–324Google Scholar
  80. Xu S (2008) Quantitative trait locus mapping can benefit from segregation distortion. Genetics 180(4):2201–2208PubMedPubMedCentralGoogle Scholar
  81. 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):34Google Scholar
  82. 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):45–52Google Scholar
  83. 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):543–552PubMedGoogle Scholar
  84. 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 Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Global Wheat ProgramInternational Maize and Wheat Improvement Center (CIMMYT)Mexico CityMexico
  2. 2.Dezhou Academy of Agricultural SciencesDezhouChina
  3. 3.Syngenta Crop Protection Münchwillen AGMünchwillenSwitzerland
  4. 4.Genetic Resources ProgramInternational Maize and Wheat Improvement Center (CIMMYT)Mexico CityMexico

Personalised recommendations