Theoretical and Applied Genetics

, Volume 125, Issue 4, pp 807–815 | Cite as

Identification of QTLs with main, epistatic and QTL × environment interaction effects for salt tolerance in rice seedlings under different salinity conditions

  • Zhoufei Wang
  • Jinping Cheng
  • Zhiwei Chen
  • Ji Huang
  • Yongmei Bao
  • Jianfei WangEmail author
  • Hongsheng ZhangEmail author
Original Paper


Salt tolerance of rice (Oryza sativa L.) at the seedling stage is one of the major determinants of its stable establishment in saline soil. One population of recombinant inbred lines (RILs, F 2:9) derived from a cross between the salt-tolerant variety Jiucaiqing and the salt-sensitive variety IR26 was used to determine the genetic mechanism of four salt tolerance indices, seedling height (SH), dry shoot weight (DSW), dry root weight (DRW) and Na/K ratios (Na/K) in roots after 10 days in three salt concentrations (0.0, 0.5 and 0.7 % NaCl). The main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) were detected by QTL IciMapping program using single environment phenotypic values. Eleven M-QTLs and 11 E-QTLs were identified for the salt tolerance indices. There were six M-QTLs and two E-QTLs identified for SH, three M-QTLs and five E-QTLs identified for DSW, two M-QTLs and one E-QTL identified for DRW, and three E-QTLs identified for Na/K. The phenotypic variation explained by each M-QTL and E-QTL ranged from 7.8 to 23.9 % and 13.3 to 73.7 %, respectively. The QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values. Six M-QTLs and five E-QTLs were identified. The phenotypic variation explained by each QTL and QTL × environment interaction ranged from 0.95 to 6.90 % and 0.02 to 0.50 %, respectively. By comparing chromosomal positions of these M-QTLs with those previously identified, five M-QTLs qSH1.3, qSH12.1, qSH12.2, qDSW12.1 and qDRW11 might represent novel salt tolerance genes. Five selected RILs with high salt tolerance had six to eight positive alleles of the M-QTLs, indicating that pyramiding by marker-assisted selection (MAS) of M-QTLs can be applied in rice salt tolerance breeding programs.


Salt Tolerance Salinity Stress Total Phenotypic Variance Seedling Height Positive Allele 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the National Natural Science Foundation of China (Grant No. 31000748), the Natural Science Foundation of Jiangsu Province (Grant No. BK2010452). We thank reviewers for the careful reading of the manuscript and constructive comments.


  1. Ahmadi N, Negrão S, Katsantonis D, Frouin J, Ploux J, Letourmy P, Droc G, Babo P, Trindade H, Bruschi G, Greco R, Oliveira MM, Piffanelli P, Courtois B (2011) Targeted association analysis identified japonica rice varieties achieving Na+/K+ homeostasis without the allelic make-up of the salt tolerant indica variety Nona Bokra. Theor Appl Genet 123:881–895PubMedCrossRefGoogle Scholar
  2. Carlborg Ö, Jacobsson L, Ahgren P, Siegel P, Andersson L (2006) Epistasis and the release of genetic variation during long-term selection. Nat Genet 38:418–420PubMedCrossRefGoogle Scholar
  3. Chen X, Temnykh S, Xu Y, Cho YG, McCouch SR (1997) Development of a microsatellite framework map providing genome-wide coverage in rice (Oryza sativa L.). Theor Appl Genet 95:553–567CrossRefGoogle Scholar
  4. Dellaporta SL, Wood T, Hicks TB (1983) A plant DNA mini preparation: version II. Plant Mol Bio Rep 1:19–21CrossRefGoogle Scholar
  5. Flowers TJ, Koyama ML, Flowers SA, Sudhakar C, Singh KP, Yeo AR (2000) QTL: their place in engineering tolerance of rice to salinity. J Exp Bot 51:99–106PubMedCrossRefGoogle Scholar
  6. Golldack D, Quigley F, Michalowski CB, Kamasani UR, Bohnert HJ (2003) Salinity stress-tolerant and sensitive rice (Oryza sativa L.) regulate AKT1-type potassium channel transcripts differently. Plant Mol Biol 51:71–81PubMedCrossRefGoogle Scholar
  7. Gulati A, Jaiwal PK (1993) Comparative salt responses of callus cultures of Vigna radiata to various osmotic and ionic stresses. J Plant Physiol 141:120–124CrossRefGoogle Scholar
  8. Horie T, Costa A, Kim TH, Han MJ, Horie R, Leung HY, Miyao A, Hirochika H, An G, Schroeder JI (2007) Rice OsHKT2;1 transporter mediates large Na+ influx component into K+-starved roots for growth. EMBO J 26:3003–3014PubMedCrossRefGoogle Scholar
  9. Koyama ML, Levesley A, Koebner RMD, Flowers TJ, Yeo AR (2001) Quantitative trait loci for component physiological traits determining salt tolerance in rice. Plant Physiol 125:406–422PubMedCrossRefGoogle Scholar
  10. Kubo T, Yoshimura A (2005) Epistasis underlying female sterility detected in hybrid breakdown in a Japonica-Indica cross of rice (Oryza sativa L.). Theor Appl Genet 110:346–355PubMedCrossRefGoogle Scholar
  11. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181PubMedCrossRefGoogle Scholar
  12. Lang TN, Yanagihara S, Buu BC (2001) A microsatellite marker for a gene conferring salt on rice at the vegetative and reproductive stages. Sabrao J Breed Genet 33:1–10Google Scholar
  13. Lee KS, Choi WY, Ko JC, Kim TS, Gregorio GB (2003) Salinity tolerance of japonica and indica rice (Oryza sativa L.) at the seedling stage. Planta 216:1043–1046PubMedGoogle Scholar
  14. Lee SY, Ahn JH, Cha YS, Yun DW, Lee MC, Ko JC, Lee KS, Eun MY (2006) Mapping of quantitative trait loci for salt tolerance at the seedling stage in rice. Mol Cells 21:192–196PubMedGoogle Scholar
  15. Li ZK, Pinson SRM, Paterson AH, Park WD, Stansel JW (1997) Epistasis for three grain yield components in rice (Oryza sativa L.). Genetics 145:453–465PubMedGoogle Scholar
  16. Li H, Ye G, Wang J (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175:361–374PubMedCrossRefGoogle Scholar
  17. Li H, Ribaut JM, Li Z, Wang J (2008) Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor Appl Genet 116:243–260PubMedCrossRefGoogle Scholar
  18. Lin HX, Zhu MZ, Yano MJ, Gao P, Liang ZW, Su WA, Hu XH, Ren ZH, Chao DY (2004) QTLs for Na+ and K+ uptake of the shoots and roots controlling rice salt tolerance. Theor Appl Genet 108:253–260PubMedCrossRefGoogle Scholar
  19. Malmberg RL, Held S, Waits A, Mauricio R (2005) Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse. Genetics 171:2013–2027PubMedCrossRefGoogle Scholar
  20. McCouch SR, CGSNL (Committee on Gene Symbolization, Nomenclature, Linkage, Rice Genetics Cooperative) (2008) Gene nomenclature system for rice. Rice 1:72–84CrossRefGoogle Scholar
  21. Mohan A, Kulwal P, Singh R, Kumar V, Mir RR, Kumar J, Prasad M, Balyan HS, Gupta PK (2009) Genome-wide QTL analysis for pre-harvest sprouting tolerance in bread wheat. Euphytica 168:319–328CrossRefGoogle Scholar
  22. Pandit A, Rai V, Bal S, Sinha S, Kumar V, Chauhan M, Gautam RK, Singh R, Sharma PC, Singh AK, Gaikwad K, Sharma TR, Mohapatra T, Singh NK (2010) Combining QTL mapping and transcriptome profiling of bulked RILs for identification of functional polymorphism for salt tolerance genes in rice (Oryza sativa L.). Mol Genet Genomics 284:121–136PubMedCrossRefGoogle Scholar
  23. Prasad SR, Bagali PG, Hittalmani S, Shashidhar HE (2000) Molecular mapping of quantitative trait loci associated with seedling tolerance to salt stress in rice (Oryza sativa L.). Curr Sci 78:162–164Google Scholar
  24. Ravi K, Vadez V, Isobe S, Mir RR, Guo Y, Nigam SN, Gowda MV, Radhakrishnan T, Bertioli DJ, Knapp SJ, Varshney RK (2011) Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.). Theor Appl Genet 122:1119–1132PubMedCrossRefGoogle Scholar
  25. Ren ZH, Gao JP, Li LG, Cai XL, Huang W, Chao DY, Zhu MZ, Wang ZY, Luan S, Lin HX (2005) A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nat Genet 37:1141–1146PubMedCrossRefGoogle Scholar
  26. Ruan SL, Ma HS, Wang SH, Fu YP, Xin Y, Liu WZ, Wang F, Tong JX, Wang SZ, Chen HZ (2011) Proteomic identification of OsCYP2, a rice cyclophilin that confers salt tolerance in rice (Oryza sativa L.) seedlings when overexpressed. BMC Plant Biol 11:34PubMedCrossRefGoogle Scholar
  27. Sabouri H, Rezai AM, Moumeni A, Kavousi A, Katouzi M, Sabouri A (2009) QTLs mapping of physiological traits related to salt tolerance in young rice seedlings. Biol Plant 53:657–662CrossRefGoogle Scholar
  28. Sanguinetti CJ, Neto ED, Simpson AJ (1994) Rapid silver staining and recovery of PCR products separated on polyacrylamide gels. Biotechniques 17:914–921PubMedGoogle Scholar
  29. Shen X, Zhang T, Guo W, Zhu X, Zhang X (2006) Mapping fiber and yield QTLs with main, epistatic and QTL × environment interaction effects in recombinant inbred lines of upland cotton. Crop Sci 46:61–66CrossRefGoogle Scholar
  30. Takehisa H, Shimodate T, Fukuta Y, Ueda T, Yano M, Yamaya T, Kameya T, Sato T (2004) Identification of quantitative trait loci for plant growth of rice in paddy field flooded with salt water. Field Crops Res 89:85–95CrossRefGoogle Scholar
  31. Tuyen DD, Lal SK, Xu DH (2010) Identification of a major QTL allele from wild soybean (Glycine soja Sieb. and Zucc.) for increasing alkaline salt tolerance in soybean. Theor Appl Genet 121:229–236PubMedCrossRefGoogle Scholar
  32. Villalta I, Bernet GP, Carbonell EA, Asins MJ (2007) Comparative QTL analysis of salinity tolerance in terms of fruit yield using two Solanum populations of F7 lines. Theor Appl Genet 114:1001–1017PubMedCrossRefGoogle Scholar
  33. Villalta I, Reina-Sánchez A, Bolarín MC, Cuartero J, Belver A, Venema K, Carbonell EA, Asins MJ (2008) Genetic analysis of Na+ and K+ concentrations in leaf and stem as physiological components of salt tolerance in Tomato. Theor Appl Genet 116:869–880PubMedCrossRefGoogle Scholar
  34. Wang J, Drayton MC, George J, Cogan NO, Baillie RC, Hand ML, Kearney GA, Erb S, Wilkinson T, Bannan NR, Forster JW, Smith KF (2010a) Identification of genetic factors influencing salt stress tolerance in white clover (Trifolium repens L.) by QTL analysis. Theor Appl Genet 120:607–619PubMedCrossRefGoogle Scholar
  35. Wang ZF, Wang JF, Wang FH, Bao YM, Wu YY, Zhang HS (2010b) Segregation analysis of rice seed germination under cold stress using major gene plus polygene mixed inheritance model. Seed Sci Technol 38:104–113Google Scholar
  36. Wang Y, Li H, Zhang L, Lu W, Wang J (2011a) On the use of mathematically-derived traits in QTL mapping. Mol Breed. doi: 10.1007/s11032-011-9580-z PubMedGoogle Scholar
  37. Wang ZF, Wang FH, Zhou R, Wang J, Zhang HS (2011b) Identification of quantitative trait loci for cold tolerance during the germination and seedling stages in rice (Oryza sativa L.). Euphytica 181:405–413CrossRefGoogle Scholar
  38. Wang ZF, Wang JF, Bao YM, Wu YY, Zhang HS (2011c) Quantitative trait loci controlling rice seed germination under salt stress. Euphytica 178:297–307CrossRefGoogle Scholar
  39. Würschum T, Maurer HP, Schulz B, Möhring J, Reif JC (2011) Genome-wide association mapping reveals epistasis and genetic interaction networks in sugar beet. Theor Appl Genet 123:109–118PubMedCrossRefGoogle Scholar
  40. Xing YZ, Tan YF, Hua JP, Sun XL, Xu CG, Zhang QF (2002) Characterization of the main effects, epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice. Theor Appl Genet 105:248–257PubMedCrossRefGoogle Scholar
  41. Yan S, Tang Z, Su W, Sun W (2005) Proteomic analysis of salt stress-responsive proteins in rice root. Proteomics 5:235–244PubMedCrossRefGoogle Scholar
  42. Yang J, Hu CC, Ye XZ, Zhu J (2005) QTLNetwork 2.0. Available at Institute of Bioinformatics, Zhejiang University, Hangzhou, China
  43. Yang X, Guo Y, Yan J, Zhang J, Song T, Rocheford T, Li JS (2009) Major and minor QTL and epistasis contribute to fatty acid compositions and oil concentration in high-oil maize. Theor Appl Genet 120:665–678PubMedCrossRefGoogle Scholar
  44. Yoshida S, Forno DA, Cock JH, Gomez KA (1976) Laboratory manual for physiological studies of rice. IRRI, Los BañosGoogle Scholar
  45. Zeng ZB (2005) Modeling quantitative trait loci and interpretation of models. Genetics 169:1711–1725PubMedCrossRefGoogle Scholar
  46. Zhu JK (2001) Plant salt tolerance. Trends Plant Sci 6:66–71PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Zhoufei Wang
    • 1
  • Jinping Cheng
    • 1
  • Zhiwei Chen
    • 1
  • Ji Huang
    • 1
  • Yongmei Bao
    • 1
  • Jianfei Wang
    • 1
    Email author
  • Hongsheng Zhang
    • 1
    Email author
  1. 1.The Laboratory of Seed Science and Technology, State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingPeople’s Republic of China

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