, 215:103 | Cite as

Genetic dissection of epistatic and QTL by environment interaction effects in three bread wheat genetic backgrounds for yield-related traits under saline conditions

  • Mojtaba Jahani
  • Ghasem Mohammadi-NejadEmail author
  • Babak Nakhoda
  • Loren H. Rieseberg


Salt stress represents a major impediment to global wheat production. Development of wheat varieties that offer tolerance to salt stress would increase productivity. Here we report on the results of a genetic study of salt tolerance in bread wheat across multiple genetic backgrounds and environments, with the goal of identifying quantitative trait loci (QTLs) for 9 yield-related traits that are both genetic background independent and environmentally stable. Three RIL populations derived from crosses between a super salt tolerant landrace (Roshan) and 3 bread-wheat cultivars (Falat, Sabalan, Superhead#2) that vary in salt tolerance were phenotyped in three environments. Genetic maps were constructed for each RIL population and independent analyses of each population/environment combination revealed significant associations of 92 genomic regions with the traits evaluated. Joint analyses of yield-related traits across all populations revealed a strong genetic background effect, with no QTLs shared across all genetic backgrounds. Fifty-seven QTLs identified in the independent analysis co-localized with those in the joint analysis. Overall, only 3 QTLs displayed significant epistatic interactions. Additionally, a total of 67 QTLs were identified in QTL analysis across environments, two of these (QSPL.3A, QBYI.7B-1) were both stable and not reported previously. Such novel and stable QTLs may accelerate marker-assisted breeding of new highly productive and salt tolerant bread-wheat varieties.


Bread wheat Epistatic effect Genetic background QTL by environment effect QTL mapping Salt stress 


Supplementary material

10681_2019_2426_MOESM1_ESM.xlsx (5.1 mb)
Supplementary material 1 (XLSX 5224 kb)
10681_2019_2426_MOESM2_ESM.pdf (90 kb)
Supplementary Figure 1 Schematic diagram of recombinant inbred line population development. Each individual is shown as a pair of homologous chromosomes (color coded by parent genome) in order to illustrate the genome of each RIL as a combination of different segments of its parental genomes (PDF 89 kb)
10681_2019_2426_MOESM3_ESM.pdf (105 kb)
Supplementary Figure 2 Temperature and precipitation information of environments (Location-Year) for experiments (PDF 105 kb)
10681_2019_2426_MOESM4_ESM.pdf (427 kb)
Supplementary Figure 3 Distribution of traits in 3 RIL populations across different environments. Plant height (PHT), spike length (SPL), spike weight (SPW), weight of kernels in plant (WKP), thousand kernel weight (TKW), spike per plant (SPP), grain yield per m2 (GYLD), biological yield per m2 (BYI), harvest index (HAI). (PDF 426 kb)
10681_2019_2426_MOESM5_ESM.pdf (359 kb)
Supplementary Figure 4 Independent analysis, LOD profile of plant height (PHT), spike length (SPL), spike weight (SPW), weight of kernels in plant (WKP), thousand kernel weight (TKW), spikes per plant (SPP), grain yield per m2 (GYLD), biological yield per m2 (BYI), harvest index (HAI) in Roshan*Falat population at Kerman-2013, Kerman-2012, Yazd-2011 environments (PDF 359 kb)
10681_2019_2426_MOESM6_ESM.pdf (328 kb)
Supplementary Figure 5 Independent analysis, LOD profile of plant height (PHT), spike length (SPL), spike weight (SPW), thousand kernel weight (TKW), grain yield per m2 (GYLD), biological yield per m2 (BYI), harvest index (HAI) in Roshan*Sabalan population at Kerman-2013, Kerman-2012, Yazd-2011 environments (PDF 327 kb)
10681_2019_2426_MOESM7_ESM.pdf (209 kb)
Supplementary Figure 6 Independent analysis, LOD profile of plant height (PHT), spike length (SPL), thousand kernel weight (TKW), grain yield per m2 (GYLD), in Roshan*Superhead#2 population at Kerman-2013, Kerman-2012, Yazd-2011 environments (PDF 208 kb)


  1. Akbari M, Wenzl P, Caig V, Carling J, Xia L, Yang S, Uszynski G, Mohler V, Lehmensiek A, Kuchel H (2006) Diversity arrays technology (DArT) for high-throughput profiling of the hexaploid wheat genome. Theor Appl Genet 113(8):1409–1420CrossRefGoogle Scholar
  2. Azadi A, Mardi M, Hervan EM, Mohammadi SA, Moradi F, Tabatabaee MT, Pirseyedi SM, Ebrahimi M, Fayaz F, Kazemi M (2015) QTL mapping of yield and yield components under normal and salt-stress conditions in bread wheat (Triticum aestivum L.). Plant Mol Biol Report 33(1):102–120CrossRefGoogle Scholar
  3. Bernier J, Atlin GN, Serraj R, Kumar A, Spaner D (2008) Breeding upland rice for drought resistance. J Sci Food Agric 88(6):927–939CrossRefGoogle Scholar
  4. Budak H, Shearman R, Parmaksiz I, Gaussoin R, Riordan T, Dweikat I (2004) Molecular characterization of buffalograss germplasm using sequence-related amplified polymorphism markers. Theor Appl Genet 108(2):328–334CrossRefGoogle Scholar
  5. Castillo A, Budak H, Varshney RK, Dorado G, Graner A, Hernandez P (2008) Transferability and polymorphism of barley EST-SSR markers used for phylogenetic analysis in Hordeum chilense. BMC Plant Biol 8(1):97CrossRefGoogle Scholar
  6. Courtois B, Shen L, Petalcorin W, Carandang S, Mauleon R, Li Z (2003) Locating QTLs controlling constitutive root traits in the rice population IAC 165 × Co39. Euphytica 134(3):335–345CrossRefGoogle Scholar
  7. Cui F, Zhao C, Ding A, Li J, Wang L, Li X, Bao Y, Li J, Wang H (2014) Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations. Theor Appl Genet 127(3):659–675CrossRefGoogle Scholar
  8. Cui T, He K, Chang L, Zhang X, Xue J, Liu J (2017) QTL mapping for leaf area in maize (Zea mays L.) under multi-environments. J Integr Agric 16(4):800–808CrossRefGoogle Scholar
  9. Dehdari A, Rezai A, Maibody SAM (2005) Salt tolerance of seedling and adult bread wheat plants based on ion contents and agronomic traits. Commun Soil Sci Plant Anal 36(15–16):2239–2253CrossRefGoogle Scholar
  10. FAO (2018) Online statistical database: food balance. Food and Agricultural Organization of the United Nations. Available online at
  11. Flowers T, Flowers S (2005) Why does salinity pose such a difficult problem for plant breeders? Agric Water Manag 78(1–2):15–24CrossRefGoogle Scholar
  12. Flowers T, Yeo A (1995) Breeding for salinity resistance in crop plants: where next? Funct Plant Biol 22(6):875–884CrossRefGoogle Scholar
  13. 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
  14. 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):1032–1040CrossRefGoogle Scholar
  15. Guan P, Lu L, Jia L, Kabir MR, Zhang J, Zhao Y, Xin M, Hu Z, Yao Y, Ni Z (2018) Global QTL analysis identifies genomic regions on chromosomes 4A and 4B harboring stable loci for yield-related traits across different environments in wheat (Triticum aestivum L.). Front Plant Sci 9:529CrossRefGoogle Scholar
  16. Han Y, Li D, Zhu D, Li H, Li X, Teng W, Li W (2012) QTL analysis of soybean seed weight across multi-genetic backgrounds and environments. Theor Appl Genet 125(4):671–683CrossRefGoogle Scholar
  17. Hedden P (2003) The genes of the green revolution. Trends Genet 19(1):5–9CrossRefGoogle Scholar
  18. Huang X, Cöster H, Ganal M, Röder M (2003) Advanced backcross QTL analysis for the identification of quantitative trait loci alleles from wild relatives of wheat (Triticum aestivum L.). Theor Appl Genet 106(8):1379–1389CrossRefGoogle Scholar
  19. Hussain B (2015) Modernization in plant breeding approaches for improving biotic stress resistance in crop plants. Turk J Agric For 39(4):515–530CrossRefGoogle Scholar
  20. Jahani M, Nematzadeh G, Dolatabadi B, Hashemi SH, Mohammadi-Nejad G (2014) Identification and validation of functional markers in a global rice collection by association mapping. Genome 57(6):355–362CrossRefGoogle Scholar
  21. Klahr A, Zimmermann G, Wenzel G, Mohler V (2007) Effects of environment, disease progress, plant height and heading date on the detection of QTLs for resistance to Fusarium head blight in an European winter wheat cross. Euphytica 154(1–2):17–28CrossRefGoogle Scholar
  22. Kosambi DD (1943) The estimation of map distances from recombination values. Ann Eugen 12(1):172–175CrossRefGoogle Scholar
  23. Kumar N, Kulwal P, Balyan H, Gupta P (2007) QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat. Mol Breed 19(2):163–177CrossRefGoogle Scholar
  24. Lafitte H, Ismail A, Bennett J (2004) Abiotic stress tolerance in rice for Asia: progress and the future. In: Fischer T, Turner N, Angus J, McIntyre L, Robertson M, Borrell A (eds) New directions for a diverse planet: proceedings of the 4th international crop science congress. Brisbane, AustraliaGoogle Scholar
  25. Le Rouzic A, Álvarez-Castro JM (2008) Estimation of genetic effects and genotype-phenotype maps. Evolut Bioinform 4:EBO-S756CrossRefGoogle Scholar
  26. Leamy L, Workman M, Routman E, Cheverud J (2005) An epistatic genetic basis for fluctuating asymmetry of tooth size and shape in mice. Heredity 94(3):316CrossRefGoogle Scholar
  27. Li ZK, Pinson S, Park W (1997) Epistasis for three grain yield components in rice (Oryza sativa L.). Genetics 145(2):453–465PubMedPubMedCentralGoogle Scholar
  28. Li H, Ribaut J-M, Li Z, Wang J (2008) Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor Appl Genet 116(2):243–260CrossRefGoogle Scholar
  29. Li S, Wang J, Zhang L (2015) Inclusive composite interval mapping of QTL by environment interactions in biparental populations. PLoS ONE 10(7):e0132414CrossRefGoogle Scholar
  30. Liu G, Jia L, Lu L, Qin D, Zhang J, Guan P, Ni Z, Yao Y, Sun Q, Peng H (2014) Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat. Theor Appl Genet 127(11):2415–2432CrossRefGoogle Scholar
  31. Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333(6042):616–620CrossRefGoogle Scholar
  32. Mathews KL, Malosetti M, Chapman S, McIntyre L, Reynolds M, Shorter R, van Eeuwijk F (2008) Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117(7):1077–1091CrossRefGoogle Scholar
  33. McFarland ML, Provin TL, Redmon LA, Boellstorff DE, McDonald AK, Stein LA, Wherley BG (2014) An index of salinity and boron tolerance of common native and introduced plant species in Texas. Texas A&M Agrilife Extension Service College Station, TexasGoogle Scholar
  34. Montooth KL, Marden JH, Clark AG (2003) Mapping determinants of variation in energy metabolism, respiration and flight in Drosophila. Genetics 165(2):623–635PubMedPubMedCentralGoogle Scholar
  35. Munns R, James RA, Läuchli A (2006) Approaches to increasing the salt tolerance of wheat and other cereals. J Exp Bot 57(5):1025–1043CrossRefGoogle Scholar
  36. Nadolska-Orczyk A, Rajchel IK, Orczyk W, Gasparis S (2017) Major genes determining yield-related traits in wheat and barley. Theor Appl Genet 130(6):1081–1098CrossRefGoogle Scholar
  37. Nyholt DR, LaForge KS, Kallela M, Alakurtti K, Anttila V, Färkkilä M, Hämaläinen E, Kaprio J, Kaunisto MA, Heath AC (2008) A high-density association screen of 155 ion transport genes for involvement with common migraine. Hum Mol Genet 17(21):3318–3331CrossRefGoogle Scholar
  38. Poustini K, Siosemardeh A (2004) Ion distribution in wheat cultivars in response to salinity stress. Field Crops Res 85(2–3):125–133CrossRefGoogle Scholar
  39. Prashant R, Kadoo N, Desale C, Kore P, Dhaliwal HS, Chhuneja P, Gupta V (2012) Kernel morphometric traits in hexaploid wheat (Triticum aestivum L.) are modulated by intricate QTL × QTL and genotype × environment interactions. J Cereal Sci 56(2):432–439CrossRefGoogle Scholar
  40. Price AH, Cairns JE, Horton P, Jones HG, Griffiths H (2002) Linking drought-resistance mechanisms to drought avoidance in upland rice using a QTL approach: progress and new opportunities to integrate stomatal and mesophyll responses. J Exp Bot 53(371):989–1004CrossRefGoogle Scholar
  41. 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):2627–2637CrossRefGoogle Scholar
  42. Ray DK, Ramankutty N, Mueller ND, West PC, Foley JA (2012) Recent patterns of crop yield growth and stagnation. Nat Commun 3:1293CrossRefGoogle Scholar
  43. Rebetzke GJ, Ellis MH, Bonnett DG, Richards RA (2007) Molecular mapping of genes for Coleoptile growth in bread wheat (Triticum aestivum L.). Theor Appl Genet 114(7):1173–1183. CrossRefPubMedGoogle Scholar
  44. Reif JC, Maurer HP, Korzun V, Ebmeyer E, Miedaner T, Würschum T (2011) Mapping QTLs with main and epistatic effects underlying grain yield and heading time in soft winter wheat. Theor Appl Genet 123(2):283CrossRefGoogle Scholar
  45. Saade S, Maurer A, Shahid M, Oakey H, Schmöckel SM, Negrão S, Pillen K, Tester M (2016) Yield-related salinity tolerance traits identified in a nested association mapping (NAM) population of wild barley. Sci Rep 6:32586CrossRefGoogle Scholar
  46. Sardouie-Nasab S, Mohammadi-Nejad G, Zebarjadi A (2013) Haplotype analysis of QTLs attributed to salinity tolerance in wheat (Triticum aestivum). Mol Biol Rep 40(7):4661–4671CrossRefGoogle Scholar
  47. Shi W, Hao C, Zhang Y, Cheng J, Zhang Z, Liu J, Yi X, Cheng X, Sun D, Xu Y (2017) A combined association mapping and linkage analysis of kernel number per spike in common wheat (Triticum aestivum L.). Front Plant Sci 8:1412CrossRefGoogle Scholar
  48. Venuprasad R, Bool M, Quiatchon L, Atlin G (2012) A QTL for rice grain yield in aerobic environments with large effects in three genetic backgrounds. Theor Appl Genet 124(2):323–332CrossRefGoogle Scholar
  49. Vikram P, Swamy BM, Dixit S, Ahmed HU, Cruz MTS, Singh AK, Kumar A (2011) qDTY 1.1, a major QTL for rice grain yield under reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet 12(1):89CrossRefGoogle Scholar
  50. Villalta I, Bernet G, Carbonell E, Asins M (2007) Comparative QTL analysis of salinity tolerance in terms of fruit yield using two solanum populations of F 7 lines. Theor Appl Genet 114(6):1001–1017CrossRefGoogle Scholar
  51. Wang X, Pang Y, Zhang J, Zhang Q, Tao Y, Feng B, Zheng T, Xu J, Li Z (2014) Genetic background effects on QTL and QTL × environment interaction for yield and its component traits as revealed by reciprocal introgression lines in rice. Crop J 2(6):345–357CrossRefGoogle Scholar
  52. Wei M, Fu J, Li X, Wang Y, Li Y (2009) Influence of dent corn genetic backgrounds on QTL detection for plant-height traits and their relationships in high-oil maize. J Appl Genet 50(3):225–234CrossRefGoogle Scholar
  53. Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, Kilian A (2004) Diversity Arrays Technology (DArT) for whole-genome profiling of barley. Proc Natl Acad Sci USA 101(26):9915–9920CrossRefGoogle Scholar
  54. Wu Q-H, Chen Y-X, Zhou S-H, Fu L, Chen J-J, Xiao Y, Zhang D, Ouyang S-H, Zhao X-J, Cui Y (2015) High-density genetic linkage map construction and QTL mapping of grain shape and size in the wheat population Yanda 1817 × Beinong6. PLoS ONE 10(2):e0118144CrossRefGoogle Scholar
  55. Würschum T, Langer SM, Longin CFH (2015) Genetic control of plant height in European winter wheat cultivars. Theor Appl Genet 128(5):865–874CrossRefGoogle Scholar
  56. Xu S, Jia Z (2007) Genomewide analysis of epistatic effects for quantitative traits in barley. Genetics 175(4):1955–1963CrossRefGoogle Scholar
  57. Xue D, Huang Y, Zhang X, Wei K, Westcott S, Li C, Chen M, Zhang G, Lance R (2009) Identification of QTLs associated with salinity tolerance at late growth stage in barley. Euphytica 169(2):187–196CrossRefGoogle Scholar
  58. Yao X, Wang J, Jin L, Wei W, Yang S, Zhang Y, Xu Z (2016) Comparison and analysis of QTLs for grain and hull thickness related traits in two recombinant inbred line (RIL) populations in rice (Oryza sativa L.). J Integr Agric 15(11):2437–2450CrossRefGoogle Scholar
  59. Zhang X, Yang S, Zhou Y, He Z, Xia X (2006) Distribution of the Rht-B1b, Rht-D1b and Rht8 reduced height genes in autumn-sown Chinese wheats detected by molecular markers. Euphytica 152(1):109–116CrossRefGoogle Scholar
  60. Zhang K, Tian J, Zhao L, Wang S (2008) Mapping QTLs with epistatic effects and QTL × environment interactions for plant height using a doubled haploid population in cultivated wheat. J Genet Genom 35(2):119–127CrossRefGoogle Scholar
  61. Zhang LY, Liu DC, Guo XL, Yang WL, Sun JZ, Wang DW, Zhang A (2010) Genomic distribution of quantitative trait loci for yield and yield-related traits in common wheat. J Integr Plant Biol 52(11):996–1007CrossRefGoogle Scholar
  62. Zheng BS, Le Gouis J, Leflon M, Rong WY, Laperche A, Brancourt-Hulmel M (2010) Using probe genotypes to dissect QTL × environment interactions for grain yield components in winter wheat. Theor Appl Genet 121(8):1501–1517CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Mojtaba Jahani
    • 1
  • Ghasem Mohammadi-Nejad
    • 1
    Email author
  • Babak Nakhoda
    • 2
  • Loren H. Rieseberg
    • 3
  1. 1.Department of Agronomy and Plant Breeding, College of AgricultureShahid-Bahonar University of KermanKermanIran
  2. 2.Department of Molecular Physiology, Agricultural Biotechnology Research Institute of IranResearch, Education and Extension Organization (AREEO)KarajIran
  3. 3.Department of Botany and Beaty Biodiversity CentreUniversity of British ColumbiaVancouverCanada

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