Journal of Plant Biology

, Volume 54, Issue 4, pp 237–250 | Cite as

Identification of QTLs for Drought-Related Traits in Alien Introgression Lines Derived from Crosses of Rice (Oryza sativa cv. IR64) × O. glaberrima under Lowland Moisture Stress

  • Isaac Kofi Bimpong
  • Rachid Serraj
  • Joong Hyoun Chin
  • Joie Ramos
  • Evelyn M. T. Mendoza
  • Jose E. Hernandez
  • Merlyn S. Mendioro
  • Darshan S. Brar


Drought is a major abiotic stress that limits rice productivity in rain-fed and upland ecosystems. African rice, Oryza glaberrima, has low yields but is tolerant to drought and other stresses. We evaluated 513 BC2F3 progenies from alien introgression lines (AILs) that were derived from crosses of Oryza sativa (IR64) × O. glaberrima. They were assessed for yield and other traits when grown under drought at two locations. Such conditions reduced grain production by 59% compared with the recurrent parent (IR64). However, 33 AILs had higher yields, thus demonstrating their potential as genetic material for transferring drought-related traits from O. glaberrima to O. sativa. A set of 200 AILs was selectively genotyped with 173 simple sequence repeat and sequenced tagged site markers. Molecular analysis showed that a mean of 4.5% of the O. glaberrima genome was introgressed in BC2F3 AILs. Our analysis revealed 33 quantitative trait loci (QTLs; including 10 novel) for different traits. O. glaberrima contributed 50% of the alleles to those newly identified QTLs, with one for grain yield per plant (ypp9.1) being new. A QTL at RM208 on chromosome 2 positively affected yield under stress, accounting for 22% of the genetic variation. Our identification of drought-related QTLs for yield and yield components will be useful to future research efforts in marker-assisted selection.


AIL Alien introgression line Drought Oryza glaberrima O. sativa QTL SSR STS 



Alien introgression line




International Rice Research Institute




Likelihood ratio statistics


Quantitative trait locus


Simple sequence repeat


Sequenced tagged site



This research, part of a Ph.D. study by I.K. Bimpong, was made possible thanks to funding from the Rockefeller Foundation (IRRI Ref. No.: DPPC2004-76) through the International Rice Research Institute. The authors are also grateful to Ms. Socorro Carrandang, Ms. Eloisa Suiton, Mr. Rolly Torres, and the Wide Hybridization group at IRRI for their technical assistance with both field and laboratory aspects.

Supplementary material

12374_2011_9161_MOESM1_ESM.doc (94 kb)
Supplementary Table 1 Chi-square (χ 2) values for markers showing segregation distortion in the test cross progeny (DOC 94 kb)
12374_2011_9161_MOESM2_ESM.ppt (102 kb)
Supplementary Figure 1 Segregation distortion detected on 12 rice chromosomes; χ 2 values for each marker are shown on y-axis; χ2 >12.5 corresponds to significance threshold at P = 0.00. (PPT 102 kb)


  1. Aluko G, Martinez C, Tohme J, Castano C, Bergman C, Oard JH (2004) QTL mapping of grain quality traits from the interspecific cross Oryza sativa × O. glaberrima. Theor Appl Genet 109:630–639PubMedCrossRefGoogle Scholar
  2. Bernier J, Kumar A, Venuprasad R, Spaner D, Atlin G (2007) A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Sci 47:507–518CrossRefGoogle Scholar
  3. Bimpong IK, Carpena AL, Borromeo TH, Mendioro MS, Brar DS (2004) Nematode resistance of backcross derivatives of Oryza sativa L crosses with Oryza glaberrima Steud. and molecular characterization of introgression. From a thesis (IK Bimpong) submitted to the University of The Philippines, Los BañosGoogle Scholar
  4. Brar DS, Khush GS (2006) Cytogenetic manipulation and germplasm enhancement of rice (Oryza sativa L.), In: RJ Singh, PP Jauhar (eds) Genetic Resources, Chromosome Engineering and Crop Improvement, Vol II. CRC Press, Boca Raton, FL, USA, pp 115–158Google Scholar
  5. Bremner JM (1996) Nitrogen—total. In: DL Sparks et al. (eds) Methods of Soil Analysis, Part 3. Chemical Methods. SSSA Book Set 5. Soil Science Society of America/American Society of Agronomy, Madison, WI, USA. 1085–1121Google Scholar
  6. Brondani C, Rangel PHN, Brondani RPV, Ferreira ME (2002) QTL mapping and introgression of yield-related traits from Oryza glumaepatula to cultivated rice (Oryza sativa) using microsatellite markers. Theor Appl Genet 104:1192–1203PubMedCrossRefGoogle Scholar
  7. Causse MA, Fulto TM, Cho YG, Ahn SN, Chuncongs EJ (1994) Saturated molecular map of the rice genome based on an interspecific backcross population. Genetics 138:1251–1274PubMedGoogle Scholar
  8. Chen J, Ding J, Ouyang Y, Du H, Yang J, Cheng K, Zhao J, Qiu S, Zhang X, Yao J, Liu K, Wang L, Xu C, Li X, Xue Y, Xia M, Ji Q, Lu J, Xu M, Zhang Q (2008) A triallelic system of S5 is a major regulator of the reproductive barrier and compatibility of indicajaponica hybrids in rice. PNAS 105:11436–11441PubMedCrossRefGoogle Scholar
  9. Chin JH, Kim JH, Kwon SW, Cho YI, Piao ZZ, Han LZ, Koh HJ (2007) Identification of subspecies-specific RAPD markers in rice. Korean J Breed 35:102–108Google Scholar
  10. Cho C, Suh JP, Choi IS, Hong HC, Baek MK, Kang KH, Kim YG, Ahn SN, Choi HC, Hwang HG, Moon HP (2003) QTLs analysis of yield and its related traits in wild rice relative Oryza rufipogon. Treat Crop Res 4:19–29Google Scholar
  11. Dellaporta SC, Wood J, Hicks TB (1983) A plant DNA mini preparation: version II. Plant Mol Biol Rep 1:19–21CrossRefGoogle Scholar
  12. Dingkuhn M, Audebert A, Jones MP, Etienne K, Sow A (1999) Control of stomatal conductance and leaf rolling in O. sativa and O. glaberrima upland rice. Field Crops Res 61:223–236CrossRefGoogle Scholar
  13. Doi K, Taguchi K, Yoshomura A (1999) RFLP mapping of S20 and S21 for F1 semi-sterility found in backcross progeny of Oryza sativa and O. glaberrima. Rice Genet Newslett 16:65–68Google Scholar
  14. FAO (2008) FAO-STAT Data Base. Food and Agriculture Organization of the UN, Rome, ItalyGoogle Scholar
  15. Grandillo S, Tanksley SD (2005) Advanced backcross QTL analysis: results and perspectives. In: Tuberosa R, Phillips RL, Gale M (eds) Proceedings of the International Congress, “In the Wake of the Double Helix: From the Green Revolution to the Gene Revolution”, 27–31 May 2003. Bologna, Italy, pp 115–132Google Scholar
  16. Harushima Y, Yano M, Shomura A, Sato M, Shimano T, Kuboki Y, Yamamoto T, Lin SY, Antonio BA, Parco A, Kajiya H, Huang N, Yamamoto K, Nagamura Y, Kurata N, Khush GS, Sasaki T (1998) A high-density rice genetic linkage map with 2275 markers using a single F2 population. Genetics 148:479–494PubMedGoogle Scholar
  17. Helmke PA, Sparks DL (1996) Lithium, sodium, potassium, rubidium, and cesium. In: Bigham JM (ed) Methods of Soil Analysis, Part 3, Chemical Methods. SSSA Book Set 5. Soil Science Society of America/American Society of Agronomy, Madison, pp 551–574Google Scholar
  18. Heuer S, Lu X, Chin JH, Tanaka JP, Kanamori H, Matsumoto T, De Leon T, Ulat VJ, Ismail AM, Yano M, Wissuwa M (2009) Comparative sequence analyses of the major quantitative trait locus Phosphorus uptake 1 (Pup1) reveal a complex genetic structure. Plant Biotech J 7:456–471CrossRefGoogle Scholar
  19. Hu FY, Xu P, Deng XN, Zhou JW, Li J, Tao DY (2006) Molecular mapping of a pollen killer gene S29(t) in Oryza glaberrima and co-linear analysis with S22 in O. glumaepatula. Euphytica 151:273–278CrossRefGoogle Scholar
  20. IRRI (1996) Standard Evaluation System for Rice. International Rice Research Institute, Los Baños, Laguna, The PhilippinesGoogle Scholar
  21. Ji XM, Raveendran M, Oane R, Ismail A, Lafitte R, Bruskiewich R, Cheng SH, Bennett J (2005) Tissue-specific expression and drought responsiveness of cell-wall invertase genes of rice at flowering. Plant Mol Biol 59:945–964PubMedCrossRefGoogle Scholar
  22. Jones MP, Dingkuhn M, Aluko GK, Monde S (1997) Interspecific O. sativa L. O. glaberrima Steud.: progenies in upland rice improvement. Euphytica 92:237–246CrossRefGoogle Scholar
  23. Kinoshita T (1995) Report of committee on gene symbolization, nomenclature and linkage groups. Rice Genet Newslett 12:9–93Google Scholar
  24. Koehn M (1928) Pflanzenernaehr., Bodenk., AII:50Google Scholar
  25. Koide Y, Ikenaga M, Sawamura N, Nishimoto D, Matsubara K, Onishi K, Kanazawa K, Sano Y (2008a) The evolution of sex-independent transmission ratio distortion involving multiple allelic interactions at a single locus in rice. Genetics 180:409–420PubMedCrossRefGoogle Scholar
  26. Koide Y, Onishi K, Nishimoto D, Baruah AR, Kanazawa A, Sano Y (2008b) Sex-independent transmission ratio distortion system responsible for reproductive barriers between Asian and African rice species. New Phytol 179:888–900PubMedCrossRefGoogle Scholar
  27. Kumar A, Bernier J, Verlukar S, Lafitte HR, Atlin G (2008) Breeding for drought tolerance: direct selection for yield, response to selection and use of drought tolerant donors. Field Crops Res 107:221–231CrossRefGoogle Scholar
  28. Lafitte HR, Li ZK, Vijayakumar CHM, Gao YM, Shi Y, Xu JL, Fu BY, Yu SB, Ali AJ, Domingo J, Maghirang R, Torres R, Mackill DJ (2006) Improvements of rice drought tolerance through backcross breeding: evaluation of donors and selection in drought nurseries. Field Crops Res 97:77–86CrossRefGoogle Scholar
  29. Li Z, Zhu Y (1988) Rice male sterile cytoplasm and fertility restoration. In: Hybrid rice. International Rice Research Institute, Manila, the Philippines. pp 85–102Google Scholar
  30. Li J, Xiao J, Grandillo S, Jiang L, Wan Y, Deng Q, Yuan L, McCouch SR (2004) QTL detection for rice grain quality traits using an interspecific backcross population derived from cultivated Asian (O. sativa L.) and African (O. glaberrima S.) rice. Genome 47:697–704PubMedCrossRefGoogle Scholar
  31. Li J, Xu P, Deng X, Zhou J, Hu F, Wan J, Tao D (2008) Identification of four genes for stable hybrid sterility and an epistatic QTL from a cross between Oryza sativa and Oryza glaberrima. Euphytica 164:699–708CrossRefGoogle Scholar
  32. Lorieux M, Ndjionjo PN, Ghesquiere A (2000) A first interspecific Oryza sativa × Oryza glaberrima microsatellite-based genetic linkage map. Theor Appl Genet 100:593–601Google Scholar
  33. Manly KF, Cudmore RH, Meer JM (2002) Map manager QTX, cross-platform software for genetic mapping. Mamm Genom 12:930–932CrossRefGoogle Scholar
  34. Moncada P, Martinez CP, Borrero J, Chatel M, Gauch H, Guimaraes E, Tohme J, McCouch SR (2001) Quantitative trait loci for yield and yield components in an Oryza sativa x Oryza rufipogon BC2F2 population evaluated in an upland environment. Theor Appl Genet 102:41–52CrossRefGoogle Scholar
  35. Navabi A, Mather DE, Bernier J, Spaner DM, Atlin GN (2009) QTL detection with bidirectional and unidirectional selective genotyping: Marker-based and trait-based analyses. Theor Appl Genet 118:347–358PubMedCrossRefGoogle Scholar
  36. Nelson DW, Sommers LE (1996) Total carbon, organic carbon, and organic matter. In: AL Page et al. (eds) Methods of Soil Analysis, Part 2, Ed 2. Soil Science Society of America/American Society of Agronomy, Madison, WI, USA. pp 961–1010Google Scholar
  37. Olsen SR, Cole CV, Watanabe FS, Dean LA (1954) Estimation of available phosphorus in soils by extraction with sodium bicarbonate. US Department of Agriculture, Circular 939Google Scholar
  38. Plucknett DL, Smith NJH, Williams JT, Anishetty NM (1987) A case study in rice germplasm: IR36. In: Plucknett DL, Smith NJH, Williams JT, Anishetty NM (eds) Gene banks and the world's food. Princeton University Press, Princeton, pp 171–185Google Scholar
  39. Ponnamperuma FN, Cayton MT, Lantin RS (1981) Dilute hydrochloric acid as an extractant for available zinc, copper, and boron in rice soils. Plant Soil 61:297–310CrossRefGoogle Scholar
  40. Price AH, Steele KA, Moore BJ, Barraclough PB, Clark LJ (2000) A combined RFLP and AFLP linkage map of upland rice (Oryza sativa L.) used to identify QTLs for root-penetration ability. Theor Appl Genet 100:49–56CrossRefGoogle Scholar
  41. Rahman ML, Chu SH, Choi MS, Qiao YL, Jiang W, Piao R, Khanam S, Cho Y, Jeung J, Jena KK, Koh HJ (2007) Identification of QTLs for some agronomic traits in rice using an introgression line from Oryza minuta. Mol Cells 24:16–26PubMedGoogle Scholar
  42. Reddy MP, Sarla N, Laxminarayana SN, Reddy V, Siddiq EA (2005) Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet 6:33PubMedGoogle Scholar
  43. Saito K, Azoma K, Sie M (2010) Grain yield performance of selected lowland NERICA and modern Asian rice genotypes in West Africa. Crop Sci 50:281–291CrossRefGoogle Scholar
  44. Sano Y (1986) Sterility barriers between Oryza sativa and O. glaberrima. In: Rice genetics. International Rice Research Institute, Manila, the Philippines. pp 109–118Google Scholar
  45. SAS (2003) SAS/Stat User’s Guide, Version 9.1. SAS Institute, Inc., Cary, NC, USAGoogle Scholar
  46. Septiningsih EM, Trijatmiko KR, Moeljopawiro S, McCouch SR (2003) Identification of quantitative trait loci for grain quality in an advance backcross population derived from the Oryza sativa variety IR64 and the wild relative O. rufipogon. Theor Appl Genet 107:1411–1433Google Scholar
  47. Serraj R, Kumar A, McNally KL, Slamet-Loedin I, Bruskiewich R, Mauleon R, Cairns J, Hijmans RJ (2009) Improvement of drought resistance in rice. Adv Agron 103:41–98CrossRefGoogle Scholar
  48. Suh JP, Ahn SN, Cho YC, Kang KH, Choi IS, Kim YG, Suh HS, Hong HC (2005) Mapping of QTLs for yield traits using an advanced backcross population from a cross between Oryza sativa and O. glaberrima. Korean J Breed 37:214–220Google Scholar
  49. Sumner ME, Miller WP (1996) Cation exchange capacity and exchange coefficients. In: DL Sparks (ed) Methods of Soil Analysis, Part 2, Chemical Properties, Ed 3.Soil Science Society of America/American Society of Agronomy, MadisonGoogle Scholar
  50. Tan XL, Vanavichit A, Amornsipal S, Trangoonrung S (1998) Genetic analysis of rice CMS-WA fertility restoration based on QTL mapping. Theor Appl Genet 97:994–999CrossRefGoogle Scholar
  51. Tanksley SD (1993) Mapping polygenes. Annu Rev Genet 27:205–233PubMedCrossRefGoogle Scholar
  52. Tanksley SD, Nelson JC (1996) Advanced backcross QTL analysis: a method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theor Appl Genet 92:191–203CrossRefGoogle Scholar
  53. van Berloo R (2008) Computer note: GGT 2.0: versatile software for visualization and analysis of genetic data. J Hered 99:232–236PubMedCrossRefGoogle Scholar
  54. Venuprasad R, Dalid CO, del Valle M, Zhao D, Espiritu M, Sta Cruz MT, Amante M, Kumar A, Atlin G (2009) Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theor Appl Genet 120:177–190PubMedCrossRefGoogle Scholar
  55. Wang J, Wan X, Crossa J, Crouch J, Weng J, Zhai H, Wan J (2006) QTL mapping of grain length in rice (Oryza sativa L.) using chromosome segment substitution lines. Genet Res 88:93–104PubMedCrossRefGoogle Scholar
  56. WARDA (2008) Africa rice trends 2007. Cotonou, Benin: Africa Rice Center (WARDA). 84 pGoogle Scholar
  57. Wu P, Zhang G, Huang N (1996) Identification of QTLs controlling quantitative characters in rice using RFLP markers. Euphytica 89:349–354Google Scholar
  58. Xiao J, Li J, Yuan L, Tanksley SD (1996) Identification of QTLs affecting traits of agronomic importance in recombinant inbred population derived from a subspecific cross. Theor Appl Genet 92:230–244CrossRefGoogle Scholar
  59. Xiao J, Li J, Grandillo S, Ahn SN, Yuan L (1998) Identification of trait improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 150:899–909PubMedGoogle Scholar
  60. Yao FY, Xu CG, Yu SB, Li JX, Gao YJ, Li X, Zhang Q (1997) Mapping and genetic analysis of two fertility restorer loci in the wild-abortive cytoplasmic male sterility system of rice (Oryza sativa L.). Euphytica 98:183–187CrossRefGoogle Scholar
  61. Zhang HD, Nettleton D, Soller M, Dekkers JCM (2005) Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL. Genet Res 86:77–87CrossRefGoogle Scholar
  62. Zhu QH, Ramm K, Shivakkumar R, Dennis ES, Upadhyaya NM (2004) The anther indehiscence1 gene encoding a single MYB domain protein is involved in anther development in rice. Plant Physiol 135:1514–1525PubMedCrossRefGoogle Scholar

Copyright information

© The Botanical Society of Korea 2011

Authors and Affiliations

  • Isaac Kofi Bimpong
    • 1
    • 4
  • Rachid Serraj
    • 1
    • 2
  • Joong Hyoun Chin
    • 1
  • Joie Ramos
    • 1
  • Evelyn M. T. Mendoza
    • 3
  • Jose E. Hernandez
    • 3
  • Merlyn S. Mendioro
    • 3
  • Darshan S. Brar
    • 1
  1. 1.Plant Breeding, Genetics and Biotechnology DivisionInternational Rice Research Institute (IRRI)Los BañosThe Philippines
  2. 2.International Centre for Agricultural Research in the Dry Areas (ICARDA)AleppoSyria
  3. 3.University of The Philippines Los Baños (UPLB)Los BañosThe Philippines
  4. 4.Africa Rice CentreSaint-LouisSenegal

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