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Marker-Assisted Breeding for Abiotic Stress Tolerance in Crop Plants

  • Shabir H. Wani
  • Mukesh Choudhary
  • Pardeep Kumar
  • Nudrat Aisha Akram
  • Challa Surekha
  • Parvaiz Ahmad
  • Satbir S. Gosal
Chapter

Abstract

The last few decades are evident of the economical uses of utilizing molecular markers of interested genes in plant breeding programs. The potential benefits of these traced markers of bona fide traits enhanced the feasibility of this marker-assisted selection (MAS). In traditional breeding programs, selection is carried out on morphological basis without knowledge of genetics. Regarding the recent yield issues, ecological problems, and enhancing populations, characters related to environmental stress tolerance, disease resistant, mineral and osmotic requirement, etc. should be the public attention. The molecular-assisted selection technology suggests a rapid progress in choosing stress-acclimated crop plants with expanding accuracy of selection. Molecular-aided selection is promising pyramid target traits in a single progeny more conveniently and precisely in few selected generations and little accidental harms. As it is a cost-effective and less time-consuming strategy, it can be suggested for long-term improvement in stress tolerance of economically important crops with some limitations.

References

  1. Abdel-Haleem H, Carter TE, Purcell LC et al (2012) Mapping of quantitative trait loci for canopy-wilting trait in soybean (Glycine max L. Merr). Theor Appl Genet 125:837.  https://doi.org/10.1007/s00122-012-1876-9 CrossRefPubMedGoogle Scholar
  2. Abdel-Haleem H, Carter TE, Rufty TW et al (2014) Quantitative trait loci controlling aluminum tolerance in soybean: candidate gene and single nucleotide polymorphism marker discovery. Mol Breed 33:851.  https://doi.org/10.1007/s11032-013-9999-5 CrossRefGoogle Scholar
  3. Allam M, Revilla P, Djemel A et al (2016) Identification of QTLs involved in cold tolerance in sweet x field corn. Euphytica 208:353.  https://doi.org/10.1007/s10681-015-1609-7 CrossRefGoogle Scholar
  4. Almeida GD, Makumbi D, Magorokosho C et al (2013) QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance. Theor Appl Genet 126:583.  https://doi.org/10.1007/s00122-012-2003-7 CrossRefPubMedGoogle Scholar
  5. Almeida GD, Nair S, Bore’m A, Cairns J, Trachsel S, Ribaut JM et al (2014) Molecular mapping across three populations reveals a QTL hotspot region on chromosome 3 for secondary traits associated with drought tolerance in tropical maize. Mol Breed 34:701–715 pmid:25076840CrossRefPubMedPubMedCentralGoogle Scholar
  6. Ashraf M, Afzal M, Ahmed R, Maqsood MA, Shahzad SM, Tahir MA, Akhtar N, Aziz A (2012) Growth response of salt-sensitive and salt-tolerant sugarcane genotypes to potassium nutrition under salt stress. Arch Agron Soil Sci 58:385–398CrossRefGoogle Scholar
  7. Azam F, Chang X, Jing R (2015) Mapping QTL for chlorophyll fluorescence kinetics parameters at seedling stage as indicators of heat tolerance in wheat. Euphytica 202:245.  https://doi.org/10.1007/s10681-014-1283-1 CrossRefGoogle Scholar
  8. Baltazar MD, Ignacio JCI, Thomson MJ, Ismail AM, Mendioro MS, Septiningsih EM (2014) QTL mapping for tolerance of anaerobic germination from IR64 and the aus landrace Nanhi using SNP genotyping. Euphytica 197:251–260CrossRefGoogle Scholar
  9. Barakat MN, Saleh MS, Al-Doss AA et al (2015) Mapping of QTLs associated with abscisic acid and water stress in wheat. Biol Plant 59:291.  https://doi.org/10.1007/s10535-015-0499-9 CrossRefGoogle Scholar
  10. Chankaew S, Isemura T, Naito K et al (2014) QTL mapping for salt tolerance and domestication related traits in Vigna marina subsp. oblonga, a halophytic species. Theor Appl Genet 127:691.  https://doi.org/10.1007/s00122-013-2251-1 CrossRefPubMedGoogle Scholar
  11. Christopher J, Christopher M, Jennings R et al (2013) QTL for root angle and number in a population developed from bread wheats (Triticum aestivum) with contrasting adaptation to water-limited environments. Theor Appl Genet. 126:1563.  https://doi.org/10.1007/s00122-013-2074-0 CrossRefPubMedGoogle Scholar
  12. Collard, B. C. Y., Jahufer, M. Z. Z., Brouwer, J. B., & Pang, E. C. K. (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.Google Scholar
  13. Dai J, Bai G, Zhang D, Hong D (2013) Validation of quantitative trait loci for aluminum tolerance in Chinese wheat landrace FSW. Euphytica 192:171–179.  https://doi.org/10.1007/s10681-012-0807-9 CrossRefGoogle Scholar
  14. Devi EL, Devi CP, Kumar S, Sharma S, Beemrote A, Chongtham SK, Akoijam R (2017) Marker assisted selection (MAS) towards generating stress tolerant crop plants. Plant Gene 11:205–218Google Scholar
  15. Dufey I, Draye X, Lutts S, Lorieux M, Martinez C, Bertin P (2015) Novel QTLs in an interspecific backcross Oryza sativa x Oryza glaberrima for resistance to iron toxicity in rice. Euphytica 204:609.  https://doi.org/10.1007/s10681-014-1342-7 CrossRefGoogle Scholar
  16. Esten Mason R, Mondal S, Beecher FW et al (2011) Genetic loci linking improved heat tolerance in wheat (Triticumaestivum L.) to lower leaf and spike temperatures under controlled conditions. Euphytica 180:181.  https://doi.org/10.1007/s10681-011-0349-6 CrossRefGoogle Scholar
  17. Forster BP, Ellis RP, Thomas WTB et al (2000) The development and application of molecular markers for abiotic stress tolerance in barley. J Exp Bot 51:19–27CrossRefPubMedGoogle Scholar
  18. Fukao T, Xiong L (2013) Genetic mechanisms conferring adaptation to submergence and drought in rice: simple or complex? Curr Opin Plant Biol 16:196–204CrossRefPubMedGoogle Scholar
  19. Fukuda A, Shiratsuchi H, Fukushima A, Yamaguchi H, Mochida H, Terao T, Ogiwara H (2012) Detection of chromosomal regions affecting iron concentration in rice shoots subjected to excess ferrous iron using chromosomal segment substitution lines between Japonica and Indica. Plant Prod Sci 15:183–191CrossRefGoogle Scholar
  20. George MT, Luseko AC, Deogracious P et al (2013) Marker assisted selection for common bean diseases improvement in Tanzania: prospects and future needs. InTech, U.K, pp 121–147  https://doi.org/10.5772/52823
  21. Golabadi M, Arzani A, MirmohammadiMaibody SAM et al (2011) Identification of microsatellite markers linked with yield components under drought stress at terminal growth stages in durum wheat. Euphytica 177:207.  https://doi.org/10.1007/s10681-010-0242-8 CrossRefGoogle Scholar
  22. Gonzaga ZJC, Carandang J, Sanchez DL et al (2016) Mapping additional QTLs from FR13A to increase submergence tolerance in rice beyond SUB1. Euphytica 209:627.  https://doi.org/10.1007/s10681-016-1636-z CrossRefGoogle Scholar
  23. Ha BK, Vuong TD, Velusamy V et al (2013) Genetic mapping of quantitative trait loci conditioning salt tolerance in wild soybean (Glycine soja) PI 483463. Euphytica 193:79.  https://doi.org/10.1007/s10681-013-0944-9 CrossRefGoogle Scholar
  24. Hamwieh A, Imtiaz M, Malhotra RS (2013) Multi-environment QTL analyses for drought-related traits in a recombinant inbred population of chickpea (Cicer arientinum L.). Theor Appl Genet 126:1025–1038.  https://doi.org/10.1007/s00122-012-2034-0 CrossRefPubMedGoogle Scholar
  25. Hoque MMI, Jun Z, Guoying W (2015) Mapping QTLs associated with salinity tolerance in maize at seedling stage. Int J 3(10):1–23Google Scholar
  26. Hossain H, Rahman MA, Alam MS, Singh RK (2015) Mapping of quantitative trait loci associated with reproductive-stage salt tolerance in rice. J Agron Crop Sci 201(1):17–31CrossRefGoogle Scholar
  27. Iglesias-García R, Prats E, Fondevilla S, Satovic Z, Rubiales D (2015) Quantitative trait loci associated to drought adaptation in pea (Pisum sativum L.). Plant Mol Biol Rep 33:1768.  https://doi.org/10.1007/s11105-015-0872-z CrossRefGoogle Scholar
  28. Jena K, Mackill D (2008) Molecular markers and their use in marker assisted selection in rice. Crop Sci 48:1266–1276CrossRefGoogle Scholar
  29. Jiang GL (2013) Molecular markers and marker-assisted breeding in plants. In: Plant breeding from laboratories to fields. Intech, Croatia, pp 45–83Google Scholar
  30. Jongdee B, Fukai S, Cooper M (2002) Leaf water potential and osmotic adjustment as physiological traits to improve drought tolerance in rice. Field Crops Res 76:153–163CrossRefGoogle Scholar
  31. Kage U, Kumar A, Dhokane D, Karre S, Kushalappa AC (2016) Functional molecular markers for crop improvement. Crit Rev Biotechnol 36(5):917–930CrossRefPubMedGoogle Scholar
  32. Kiriga WJ, Yu Q, Bill R (2016) Breeding and genetic engineering of drought-resistant crops. Int J Agric Crop 9(1):7–12Google Scholar
  33. Klein A, Houtin H, Rond C, Marget P, Jacquin F, Boucherot K, Huart M, Rivière N, Boutet G, Lejeune-Hénaut I, Burstin J (2014) QTL analysis of frost damage in pea suggests different mechanisms involved in frost tolerance. Theor Appl Genet 127:1319–1330CrossRefPubMedGoogle Scholar
  34. Kretzschmar T, Pelayo MA, Trijatmiko KR, Gabunada LF, Alam R, Jimenez R et al (2015) A trehalose-6-phosphate phosphatase enhances anaerobic germination tolerance in rice. Nat Plants 1:15124.  https://doi.org/10.1038/nplants.2015.124 CrossRefPubMedGoogle Scholar
  35. Ku LX, Sun ZH, Wang CL, Zhang J, Zhao RF, Liu HY, Chen YH (2012) QTL mapping and epistasis analysis of brace root traits in maize. Mol Breed 30(2):697–708CrossRefGoogle Scholar
  36. Lang NT, Nha CT, HA PTT, Buu BC (2013) Quantitative trait loci (QTLs) associated with drought tolerance in rice (Oryza sativa L.). SABRAO J Breed Genet 45(3):409–421Google Scholar
  37. Lateef DD (2015) DNA marker technologies in plants and applications for crop improvements. J Biosci Med 3:7–18Google Scholar
  38. Leon TBD, Linscombe S, Subudhi PK (2016) Molecular dissection of seedling salinity tolerance in rice (Oryza sativa L.) using a high-density GBS-based SNP linkage map. Rice 9:52.  https://doi.org/10.1186/s12284-016-0125-2 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Li C, Sun B, Li Y, Liu C, Wu X, Zhang D, Wang T (2016) Numerous genetic loci identified for drought tolerance in the maize nested association mapping populations. BMC Genomics 17(1):894CrossRefPubMedPubMedCentralGoogle Scholar
  40. Lou Q, Chen L, Mei H, Wei H, Feng F, Wang P, Xia H, Li T, Luo L (2015) Quantitative trait locus mapping of deep rooting by linkage and association analysis in rice. J Exp Bot 66(15):4749–4757CrossRefPubMedPubMedCentralGoogle Scholar
  41. Lubberstedt T, Melchinger AE, Fahr S et al (1998) QTL mapping in testcrosses of flint lines of maize: III. Comparison across populations for forage traits. Crop Sci 38:1278–1289CrossRefGoogle Scholar
  42. Lubberstedt T, Zein I, Andersen JR et al (2005) Development and application of functional markers in maize. Euphytica 146:101–108CrossRefGoogle Scholar
  43. Malik S, Rahman M, Malik TA (2015) Genetic mapping of potential QTLs associated with drought tolerance in wheat. J Anim Plant Sci 25(4):1032–1040Google Scholar
  44. Manangkil OE, Vu HTT, Mori N, Yoshida S, Nakamura C (2013) Mapping of quantitative trait loci controlling seedling vigor in rice (Oryza sativa L.) under submergence. Euphytica 192:63–75.  https://doi.org/10.1007/s10681-012-0857-z CrossRefGoogle Scholar
  45. Mano Y, Omori F, Takeda K (2012) Construction of intraspecific linkage maps, detection of a chromosome inversion, and mapping of QTL for constitutive root aerenchyma formation in the teosinte Zeanica raguensis. Mol Breed 29(1):137–146CrossRefGoogle Scholar
  46. Mathews KL, Malosetti M, Chapman S et al (2008) Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117:1077–1091CrossRefPubMedGoogle Scholar
  47. McCartney C, Somers D, Fedak G et al (2007) The evaluation of FHB resistance QTLs introgressed into elite Canadian spring wheat germplasm. Mol Breed 20:209–221CrossRefGoogle Scholar
  48. Merchuk-Ovnat L, Barak V, Fahima T, Ordon F, Lidzbarsky GA, Krugman T, Saranga Y (2016) Ancestral QTL alleles from wild emmer wheat improve drought resistance and productivity in modern wheat cultivars. Front Plant Sci 7:452.  https://doi.org/10.3389/fpls.2016.00452 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Messmer R, Fracheboud Y, Bänziger M, Stamp P, Ribaut JM (2011) Drought stress and tropical maize: QTLs for leaf greenness, plant senescence, and root capacitance. Field Crop Res 124(1):93–103CrossRefGoogle Scholar
  50. Miedaner T, Korzun V (2012) Marker-assisted selection for disease resistance in wheat and barley breeding. Phytopathology 102:560–566CrossRefPubMedGoogle Scholar
  51. Mohamed A, Ali R, Elhassan O et al (2014) First products of DNA marker-assisted selection in sorghum released for cultivation by farmers in sub-saharan. Africa J Plant Sci Mol Breed 3:1–10CrossRefGoogle Scholar
  52. Mukeshimana G, Butare L, Cregan PB, Blair MW, Kelly JD (2014) Quantitative trait loci associated with drought tolerance in common bean. Crop Sci 54:923–938.  https://doi.org/10.2135/cropsci2013.06.0427 CrossRefGoogle Scholar
  53. Nakashima K, Ito Y, Yamaguchi-Shinozaki K (2009) Transcriptional regulatory networks in response to abiotic stresses in Arabidopsis and grasses. Plant Physiol 149:88–95CrossRefPubMedPubMedCentralGoogle Scholar
  54. Nikolić A, Anđelković V, Dodig D, Ignjatović-Micić D (2011) Quantitative trait loci for yield and morphological traits in maize under drought stress. Genetika 43(2):263–276CrossRefGoogle Scholar
  55. Nikolić A, Ignjatović-Micić D, Dodig D, Anđelković V, andLazić-Jančić V (2012) Identification of QTLs for yield and drought-related traits in maize: assessment of their causal relationships. Biotechnol Biotechnol Equip 26(3):2952–2960CrossRefGoogle Scholar
  56. Nikolić A, Anđelković V, Dodig D, Mladenović-Drinić S, Kravić N, andIgnjatović-Micić D (2013) Identification of QTL-s for drought tolerance in maize, II: yield and yield components. Genetika 45(2):341–350CrossRefGoogle Scholar
  57. O’Boyle PD, James D, Kelly JD, Kirk WW (2007) Use of marker-assisted selection to breed for resistance to common bacterial blight in common bean. J Am Soc Hortic Sci 132(3):381–386Google Scholar
  58. Oliveira LK, Melo LC, Brondani C, Peloso MJD, Brondani RPV (2008) Backcross assisted by microsatellite markers in common bean. Genet Mol Res 7(4):1000–1010CrossRefPubMedGoogle Scholar
  59. Osman KA, Tang B, Wang Y, Chen J, Yu F, Li L et al (2013) Dynamic QTL analysis and candidate gene mapping for waterlogging tolerance at maize seedling stage. PLoS One 8(11):e79305CrossRefPubMedPubMedCentralGoogle Scholar
  60. Paliwal R, Roder MS, Kumar U, Srivastava JP, Joshi AK (2012) QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.). Theor Appl Genet. 125:561–575.  https://doi.org/10.1007/s00122-012-1853-3 CrossRefPubMedGoogle Scholar
  61. Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M (2017) Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front Plant Sci 8:1–15PubMedPubMedCentralGoogle Scholar
  62. Prasanna B, Pixley K, Warburton ML, Xie C-X (2010) Molecular marker-assisted breeding options for maize improvement in Asia. Mol Breed 26:339–356CrossRefGoogle Scholar
  63. Prince SJ, Beena R, Michael GS, Senthivel S, Chandra BR (2015) Mapping consistent Rice (Oryza sativa L.) yield QTLs under drought stress in target rainfed environments. Rice 8:25.  https://doi.org/10.1186/s12284-015-0053-6 CrossRefPubMedCentralGoogle Scholar
  64. Pushpavalli R, Krishnamurthy L, Thudi M, Gaur PM, Rao MV, Siddique KHM, Colmer TD, Turner NC, Varshney RK, Vadez V (2015) Two key genomic regions harbour QTLs for salinity tolerance in ICCV 2 × JG 11 derived chickpea (Cicer arietinum L.) recombinant inbred lines. BMC Plant Biol 15:124.  https://doi.org/10.1186/s12870-015-0491-8 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Rahman H, Pekic S, Lazic-Jancic V, Quarrie SA, Shah SM, Pervez A, Shah MM (2011) Molecular mapping of quantitative trait loci for drought tolerance in maize plants. Genet Mol Res 10(2):889–901CrossRefPubMedGoogle Scholar
  66. Reddy VRP (2017) New concepts in plant breeding and genetics. Adv Plants Agric Res 7(1):00245.  https://doi.org/10.15406/apar.2017.07.00245 CrossRefGoogle Scholar
  67. Revilla P, Rodríguez VM, Ordás A, Rincent R, Charcosset A, Giauffret C, Melchinger AE, Schön C-C, Bauer E, Altmann T et al (2016) Association mapping for cold tolerance in two large maize inbred panels. BMC Plant Biol 16(1):1–10CrossRefGoogle Scholar
  68. Rozema J, Flowers T (2008) Crops for a salinized world. Science 322:1478–1480CrossRefGoogle Scholar
  69. Ruane J, Sonnino A (2007) Marker-assisted selection as a tool for genetic improvement of crops, livestock, forestry and fish in developing countries: an overview of the issues. FAO, Rome, pp 3–13Google Scholar
  70. Rumanti IA, Nugraha Y, Wening RH, Gonzaga ZJC, Nasution A, Kusdiaman D, Septiningsih EM (2016) Development of high-yielding rice varieties suitable for swampy lands in Indonesia. Plant Breed Biotechnol 4(4):413–425CrossRefGoogle Scholar
  71. Sangodele EA, Hanchinal RR, Hanamaratti NG, Shenoy V, Kumar MV (2014) Analysis of drought tolerant QTL linked to physiological and productivity component traits under water-stress and non-stress in rice (Oryza sativa L.). Int J Curr Res Acd Rev 2(5):108–113Google Scholar
  72. Septiningsih EM, Sanchez DL, Singh N et al (2012) Identifying novel QTLs for submergence tolerance in rice cultivars IR72 and Madabaru. Theor Appl Genet 124:867.  https://doi.org/10.1007/s00122-011-1751-0 CrossRefPubMedGoogle Scholar
  73. Sharma AD, Sharma H, Lightfoot DA (2011) The genetic control of tolerance to aluminum toxicity in the ‘Essex’ by ‘Forrest’ recombinant inbred line population. Theor Appl Genet 122:687–694.  https://doi.org/10.1007/s00122-010-1478-3 CrossRefPubMedGoogle Scholar
  74. Simova-Stoilova L, Vassileva V, Feller U (2016) Selection and breeding of suitable crop genotypes for drought and heat periods in a changing climate: which morphological and physiological properties should be considered. Agriculture 6(2):1–19CrossRefGoogle Scholar
  75. Singh BD, Singh AK (2015) Linkage mapping of molecular markers and oligogenes. In: Marker-assisted plant breeding: principles and practices. SpringerNature, pp 151–183Google Scholar
  76. Slafer GA, Araus JL, Royo C, Del Moral LFG (2005) Promising eco-physiological traits for genetic improvement of cereal yields in Mediterranean environments. Ann Appl Biol 146:61–70CrossRefGoogle Scholar
  77. Talukder SK, Babar MA, Vijayalakshmi K, Poland J, Prasad PV, Bowden R, Fritz A (2014) Mapping QTL for the traits associated with heat tolerance in wheat (Triticum aestivum L.). BMC Genet 15:97CrossRefPubMedPubMedCentralGoogle Scholar
  78. Tiwari C, Wallwork H, Kumar U, Dhari R, Arun B, Mishra VK, Reynolds MP, Joshi AK (2013) Molecular mapping of high temperature tolerance in bread wheat adapted to the Eastern Gangetic Plain region of India. Field Crop Res 154:201–210CrossRefGoogle Scholar
  79. Tollefson J (2011) Drought-tolerant maize gets US debut. Nature 469:144CrossRefPubMedGoogle Scholar
  80. Toojinda T, Baird E, Booth A et al (1998) Introgression of quantitative trait loci (QTLs) determining stripe rust resistance in barley: an example of marker-assisted line development. Theor Appl Genet 96:123–131CrossRefGoogle Scholar
  81. Turki N, Shehzad T, Harrabi M et al (2015) Detection of QTLs associated with salinity tolerance in durum wheat based on association analysis. Euphytica 201:29.  https://doi.org/10.1007/s10681-014-1164-7 CrossRefGoogle Scholar
  82. Vadez V, Krishnamurthy L, Thudi M, Anuradha C, Colmer TD, Turner NC, Siddique KHM, Gaur PM, Varshney RK (2012) Assessment of ICCV 2 3 JG 62 chickpea progenies shows sensitivity of reproduction to salt stress and reveals QTL for seed yield and yield components. Mol Breed 30(1):9–21CrossRefGoogle Scholar
  83. Venuprasad R, Bool M, Quiatchon L et al (2012) A large-effect QTL for rice grain yield under upland drought stress on chromosome 1. Mol Breed 30:535–547CrossRefGoogle Scholar
  84. Witcombe JR, Virk DS (2001) Number of crosses and population size for participatory and classical plant breeding. Euphytica 122:451–462CrossRefGoogle Scholar
  85. Wu LB, Shhadi MY, Gregorio G, Matthus E, Becker M, Frei M (2014) Genetic and physiological analysis of tolerance to acute iron toxicity in rice. Rice 7:8CrossRefPubMedPubMedCentralGoogle Scholar
  86. Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48(2):391–407CrossRefGoogle Scholar
  87. Xu Y, Li S, Li L, Zhang X, Xu H, An D (2013) Mapping QTLs for salt tolerance with additive, epistatic and QTL 3 treatment interaction effects at seedling stage in wheat. Plant Breed 132:276–283CrossRefGoogle Scholar
  88. Xu, Y. (2010). Molecular plant breeding. Cabi. Wallingford , U.KGoogle Scholar
  89. Ye C, Tenorio FA, Argayoso MA, Laza MA, Koh H, Redoña ED, Jagadish KSV, Gregorio GB (2015) Identifying and confirming quantitative trait loci associated with heat tolerance at flowering stage in different rice populations. BMC Genet 16:41.  https://doi.org/10.1186/s12863-015-0199-7 CrossRefPubMedPubMedCentralGoogle Scholar
  90. Yu M, Chen G (2013) Conditional QTL mapping for waterlogging tolerance in two RILs populations of wheat. Springerplus 2:245CrossRefPubMedPubMedCentralGoogle Scholar
  91. Zaidi PH, Rashid Z, Vinayan MT, Almeida GD, Phagna RK, Babu R (2015) QTL mapping of agronomic waterlogging tolerance using recombinant inbred lines derived from tropical maize (Zea mays L.) germplasm. PLoS One 10(4):e0124350CrossRefPubMedPubMedCentralGoogle Scholar
  92. Zhang H, Cui F, Wang L, Li J, Ding A, Zhao C, Bao Y, Yang Q, Wang H (2013a) Conditional and unconditional QTL mapping of drought-tolerance-related traits of wheat seedling using two related RIL populations. J Genet 92:213–231CrossRefPubMedGoogle Scholar
  93. Zhang X, Tang B, Yu F, Li L, Wang M, Xue Y, andQiu F (2013b) Identification of major QTL for waterlogging tolerance using genome-wide association and linkage mapping of maize seedlings. Plant Mol Biol Report 31(3):594–606CrossRefGoogle Scholar
  94. Zhang X, Lu G, Long W, Zou X, Li F, Nishio T (2014) Recent progress in drought and salt tolerance studies in Brassica crops. Breed Sci 64(1):60–73CrossRefPubMedPubMedCentralGoogle Scholar
  95. Zhao L, Lei J, Huang Y, Zhu S, Chen H, Huang R, Peng Z, Tu Q, Shen X, Yan S (2016) Mapping quantitative trait loci for heat tolerance at anthesis in rice using chromosomal segment substitution lines. Breed Sci 66:358–366.  https://doi.org/10.1270/jsbbs.15084 CrossRefPubMedPubMedCentralGoogle Scholar
  96. Zhu JJ, Wang XP, Sun CX, Zhu XM, Meng LI, Zhang GD, Wang ZL (2011) Mapping of QTL associated with drought tolerance in a semi-automobile rain shelter in maize (Zea mays L.). Agric Sci China 10(7):987–996CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Shabir H. Wani
    • 1
    • 2
  • Mukesh Choudhary
    • 3
  • Pardeep Kumar
    • 3
  • Nudrat Aisha Akram
    • 4
  • Challa Surekha
    • 5
  • Parvaiz Ahmad
    • 6
  • Satbir S. Gosal
    • 7
  1. 1.Mountain Research Centre for Field Crops, Khudwani, Sher-e Kashmir University of Agricultural Sciences and Technology of KashmirSrinagarIndia
  2. 2.Department of Plant, Soil and Microbial SciencesMichigan State UniversityEast LansingUSA
  3. 3.ICAR-Indian Institute of Maize ResearchNew DelhiIndia
  4. 4.Department of BotanyGovernment College UniversityFaisalabadPakistan
  5. 5.Department of Biochemistry and BioinformaticsInstitute of Science, GITAM UniversityVisakhapatnamIndia
  6. 6.Botany and Microbiology DepartmentCollege of Science, King Saud UniversityRiyadhSaudi Arabia
  7. 7.Punjab Agricultural UniversityLudhianaIndia

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