Physiology and Molecular Biology of Plants

, Volume 22, Issue 2, pp 179–192 | Cite as

Field level evaluation of rice introgression lines for heat tolerance and validation of markers linked to spikelet fertility

  • V. Vishnu Prasanth
  • Kumari Ramana Basava
  • M. Suchandranath Babu
  • Venkata Tripura V.G.N.
  • S. J. S. Rama Devi
  • S. K. Mangrauthia
  • S. R. Voleti
  • N. SarlaEmail author
Research Article


Rice lines derived from wild species and mutants can serve as a good resource for favorable alleles for heat tolerance. In all, 48 stable lines including 17 KMR3/O. rufipogon introgression lines (KMR3 ILs), 15 Swarna/O. nivara ILs (Swarna ILs) along with their parents, Nagina 22 (N22) and its 4 EMS induced mutants and 7 varieties were evaluated for heat tolerance under irrigated conditions under field in two seasons, wet season 2012 using poly cover house method and dry season 2013 using late sown method. Spikelet fertility (SF), yield per plant (YP) and heat susceptibility index (HSI) for these two traits were considered as criteria to assess heat tolerance compared to control. Four KMR3 ILs and eight Swarna ILs were identified as heat tolerant based on SF and YP and their HSIs in both wet and dry seasons. S-65 and S-70 showed low SF and high YP consistently in response to heat in both seasons. We provide evidence that SF alone may not be the best criterion to assess heat tolerance and including YP is important as lines with low SF but high YP and vice versa were identified under heat stress. Out of 49 SSR markers linked to spikelet fertility, 18 were validated for five traits. RM229 in wet season and RM430 and RM210 in dry season were significantly associated with both SF and its HSI under heat stress. RM430 was also significantly associated with both YP and its HSI in dry season. Thirty two candidate genes were identified close to nine markers associated with traits under heat stress.


Heat stress Wild rice Introgression lines Spikelet fertility Heat susceptibility index Association mapping 



Introgression line


Quantitative trait loci


Simple sequence repeat


Spikelet fertility


Yield per plant


Heat susceptibility index


Accumulated heat degree days


Linkage disequilibrium


General linear model


Mixed linear model



This work was supported by National Innovations in Climate Resilient Agriculture (NICRA), Indian Council of Agricultural Research (ICAR), Ministry of Agriculture, Govt. of India [F. No. Phy/NICRA/2011-2012].

Supplementary material

12298_2016_350_MOESM1_ESM.doc (70 kb)
Supplementary Table 1 (DOC 69 kb)
12298_2016_350_MOESM2_ESM.doc (60 kb)
Supplementary Table 2 (DOC 60 kb)
12298_2016_350_MOESM3_ESM.doc (125 kb)
Supplementary Table 3 (DOC 125 kb)
12298_2016_350_MOESM4_ESM.doc (84 kb)
Supplementary Table 4 (DOC 84 kb)
12298_2016_350_MOESM5_ESM.doc (72 kb)
Supplementary Table 5 (DOC 72 kb)
12298_2016_350_MOESM6_ESM.doc (1.2 mb)
Supplementary Figure 1 (DOC 1243 kb)
12298_2016_350_MOESM7_ESM.doc (197 kb)
Supplementary Figure 2 (DOC 197 kb)


  1. Bai XF, Luo LJ, Yan WH, Kovi MR, Xing YZ (2011) Quantitative trait loci for rice yield-related traits using recombinant inbred lines derived from two diverse cultivars. J Genet 90:209–215CrossRefPubMedGoogle Scholar
  2. Borba CD, Oliveria RPV, Brondani F, Breseghello ASG, Coelho JA, Mendonca PHN, Rangel BC (2010) Association mapping for yield and grain quality traits in rice (Oryza sativa L.). Genet Mol Biol 33:515–524CrossRefGoogle Scholar
  3. Bradbury PJ, Zhang Z, Kroon DE, Casstevens RM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635CrossRefPubMedGoogle Scholar
  4. Bui CB, Pham TTH, Bui PT, et al. (2014) Quantitative trait loci associated with heat tolerance in rice (Oryza sativa L.). Plant Breed Biotech 2(1):14–24CrossRefGoogle Scholar
  5. Cao L, Zhao J, Zhan X, Li D, He L, Cheng S (2003) Mapping QTLs for heat tolerance and correlation between heat tolerance and photosynthetic rate in rice. Chin J Rice Sci 17:223–227Google Scholar
  6. Casa A, Pressoir G, Brown P, Mitchell S, Rooney W, et al. (2008) Community resources and strategies for association mapping in sorghum. Crop Sci 48:30–40CrossRefGoogle Scholar
  7. Chen Q, Yu S, Li C, Mou T (2008) Identification of QTLs for heat tolerance at flowering stage in rice. Sci Agric Sin 41:315–321Google Scholar
  8. Cheng L, Wang J, Veronica U, Meng L, Wang Y, Sun Y, Zhu L, Xu J, Li Z (2012) Genetic analysis of Cold tolerance at seedling stage and heat tolerance at anthesis in Rice (Oryza sativa L.). J Integr Agr 11(3):359–367CrossRefGoogle Scholar
  9. Courtois B, Audebert A, Dardou A, Roques S, Ghneim-Herrera T, et al. (2013) Genome-wide association mapping of root traits in a japonica rice panel. PLoS One 8(11):e78037CrossRefPubMedPubMedCentralGoogle Scholar
  10. d’Alpoim Guedes J, Jin G, Bocinsky RK (2015) The impact of climate on the spread of rice to North-Eastern China: a new look at the data from Shandong Province. PLoS One 10(6):e0130430. doi: 10.1371/journal.pone.0130430 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620CrossRefPubMedGoogle Scholar
  12. Fischer RA, Maurer R (1978) Drought resistance in spring wheat cultivars. I. Grain yield responses in spring wheat. Aust J Agric Sci 29:892–912Google Scholar
  13. IPCC: Intergovernmental panel on climate change (2007) Climate change and its impacts in the near and long term under different scenarios. In Climate Change Impact of high-temperature stress on rice 9 2007: Synthesis Report (Eds The Core Writing Team, R. K. Pachauri & A. Reisinger), pp. 43–54. Geneva, SwitzerlandGoogle Scholar
  14. IPCC:Intergovernmental panel on climate change (2013) Summary for policymakers. in ‘climate change 2013: the physical science basis. Contribution of Working group I to the Fifth Assessment Report of the Intergovernmental panel on climate change’. (eds TF Stocker, D Qin, GK Plattner, M Tignor, SK Allen, J Boschung, A Nauels, Y Xia, V Bex, PM Midgley) pp. 1–28. (Cambridge University Press: Cambridge)Google Scholar
  15. Jagadish S, Muthurajan R, Oane R, Wheeler T, Heuer S, Bennett J, Craufurd Q (2010b) Physiological and proteomic approaches to address heat tolerance during anthesis in rice. J Exp Bot 61:143–156CrossRefPubMedGoogle Scholar
  16. Jagadish SVK, Cairns J, Lafitte R, Wheeler T, Price A, Craufurd P (2010a) Genetic analysis of heat tolerance at anthesis in rice. Crop Sci 50:1633–1641CrossRefGoogle Scholar
  17. Jagadish SVK, Craufurd PQ, Wheeler TR (2007) High temperature stress and spikelet fertility in rice (Oryza sativa L.). J Exp Bot 58:1627–1635CrossRefPubMedGoogle Scholar
  18. Jena KK, Mackill DJ (2008) Molecular markers and their use in marker-assisted selection in rice. Crop Sci 48(4):1266–1276CrossRefGoogle Scholar
  19. Jiang GH, Xu CG, Li XH, He YQ (2004) Characterization of the genetic basis for yield and its component traits of rice revealed by doubled haploid population. Acta Genet Sin 31:63–72PubMedGoogle Scholar
  20. Liao JL, Zhang HY, Liu JB, Zhong PA, Huang YJ (2012) Identification of candidate genes related to rice grain weight under high-temperature stress. Plant Sci 196:32–43CrossRefPubMedGoogle Scholar
  21. Liao JL, Zhang HY, Shao XL, Zhong PA, Huang YJ (2011) Identification on heat tolerance of backcross recombinant rice lines and screening of backcross introgression lines with heat tolerance at milky stage. Rice Sci 18:279–286CrossRefGoogle Scholar
  22. Liao JL, Zhou HW, Zhang HY, Zhong PA, Huang YJ (2014) Comparative proteomic analysis of differentially expressed proteins in the early milky stage of rice grains during high temperature stress. J Exp Bot 65(2):655–671CrossRefPubMedGoogle Scholar
  23. Luo X, Wu S, Feng Tian F, Xin X, et al. (2011) Identification of heterotic loci associated with yield-related traits in Chinese common wild rice (Oryza rufipogon Griff.). Plant Sci 181:14–22CrossRefPubMedGoogle Scholar
  24. Marri PR, Sarla N, Reddy VLN, Siddiq EA (2005) Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet 6:33CrossRefPubMedPubMedCentralGoogle Scholar
  25. Matsui T, Omasa K (2002) Rice cultivars tolerant to high temperature at flowering: anther characteristics. Ann Bot 89:683–687CrossRefPubMedPubMedCentralGoogle Scholar
  26. Mei HW, Li ZK, Shu QY, Guo LB, Wang YP, Yu XQ, Ying CS, Luo LJ (2005) Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two backcross populations. Theor Appl Genet 110:649–659CrossRefPubMedGoogle Scholar
  27. 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 × Oryza rufipogon BC2F2 population evaluated in an upland environment. Theor Appl Genet 102(1):41–42CrossRefGoogle Scholar
  28. Morita S, Yonemaru JI, Takanashi JI (2005) Grain growth and endosperm cell size under high night temperatures in rice (Oryza sativa L.). Ann Bot 95:695–701CrossRefPubMedPubMedCentralGoogle Scholar
  29. Nakagawa H, Horie T, Matsui T (2002) In: Effects of climate change on rice production and adaptive technologies. Rice science: innovations and impact for livelihood. Mew TW, Brar DS, Peng S, Dawe D, Hardy B (eds) International Rice Research Institute, China, pp 635–657Google Scholar
  30. Nevo E, Chen G (2010) Drought and salt tolerances in wild relatives for wheat and barley improvement. Plant Cell Environ 33:670–685CrossRefPubMedGoogle Scholar
  31. Panigrahy M, Neelamraju S, Nageswarara Rao D, Ramanan R (2011) Heat tolerance in rice mutants is associated with reduced accumulation of reactive oxygen species. Biol Plant 55(4):721–724CrossRefGoogle Scholar
  32. Peng S, Huang J, Sheehy J, Laza R, Visperas R, Zhong X, Centeno G, GSK, Cassman K (2004) Rice yield decline with higher night temperature from global warming. Proc Natl Acad Sci U S A 101: 9971–9975Google Scholar
  33. Placido DF, Malachy T. Campbell, Jing J. Folsom, Xinping Cui, Greg R. Kruger, P. Stephen Baenziger, and Harkamal Walia (2013) Introgression of novel traits from a wild wheat relative improves drought adaptation in wheat. Plant Physiol 16: 1806–1819Google Scholar
  34. Poli Y, Ramana Kumari B, Panigrahy M, Vinukonda VP, Nageswara Rao D, Voleti SR, Subrahmanyam D, Sarla N (2013) Characterization of a Nagina22 rice mutant for heat tolerance and mapping of yield traits. Rice 6:36. doi: 10.1186/1939-8433-6-36 CrossRefPubMedPubMedCentralGoogle Scholar
  35. Prasad P, Boote K, Allen L, Sheehy J, Thomas J (2006) Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress. Field Crop Res 95:398–411CrossRefGoogle Scholar
  36. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  37. Sadras VO (2007) Evolutionary aspects of the trade-off between seed size and number in crops. Field Crop Res 100:125–138CrossRefGoogle Scholar
  38. Samuel AO Jr, Silva J, JH O (2010) Association mapping of grain quality and flowering time in elite japonica rice germplasm. J Cereal Sci 51(3):337–343Google Scholar
  39. Shi P, Tang L, Wang L, Sun T, Liu L, Cao W, et al. (2015a) Post-Heading heat stress in Rice of South China during 1981-2010. PLoS One 10(6):e0130642CrossRefPubMedPubMedCentralGoogle Scholar
  40. Shi W, Ishimaru T, Gannaban RB, Oane W, Jagadish SVK (2015b) Popular Rice (Oryza sativa L.) cultivars show contrasting responses to heat stress at gametogenesis and anthesis. Crop Sci 55:589–596CrossRefGoogle Scholar
  41. Singh RK, Bhat KV, Bhatia VS, Mohapatra T (2008) Singh NK (2008) association mapping for photoperiod insensitivity trait in soybean. Nat Aca Sci Lett 31:281–283Google Scholar
  42. Steele KA, Price AH, Shashidhar HE, Witcombe JR (2006) Marker-assisted selection to introgress rice QTLs controlling root traits into an Indian upland rice variety. Theor Appl Genet 112:208–221CrossRefPubMedGoogle Scholar
  43. Swamy BPM, Kaladhar K, Ramesha MS, Viraktamath BC, Sarla N (2011) Molecular mapping of QTLs for yield and related traits in Oryza sativa cv Swarna x O. nivara (IRGC81848) backcross population. Rice Sci 18:178–186CrossRefGoogle Scholar
  44. Swamy BPM, Sarla N (2008) Yield enhancing QTLs from wild species. Biotechnol Adv 26:106–120CrossRefPubMedGoogle Scholar
  45. Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S. Computational and experimental analysis of microsatellites in rice (Oryza sativa L.) (2001) Frequency, length variation, transposon associations, and genetic marker potential. Genome Res 11(8):1441–1452Google Scholar
  46. Thalapati S, Madhusmita P, Lakshmanaik M, Prasad Babu A, Surendhar Reddy C, Anuradha K, Swamy BPM, Sarla N (2012) Variation and correlation of phenotypic traits contributing to high yield in KMR3-Oryza rufipogon introgression lines. Int J Plant Breed Genet 6:69–82CrossRefGoogle Scholar
  47. Tommasini L, Schnurbusch T, Fossati D, Mascher F, Keller B (2007) Association mapping of Stagonospora nodorum blotch resistance in modern European winter wheat varieties. Theor Appl Genet 115:697–708CrossRefPubMedGoogle Scholar
  48. Thomson MJ, Tai TH, McClung AM, Lai XH, Hinga ME, Lobos KB, Xu Y, Martinez CP, McCouch SR (2003) Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor Appl Genet 107:479–493CrossRefPubMedGoogle Scholar
  49. Vannirajan C, Vinod KK, Pereira A (2012) Molecular evaluation of genetic diversity and association studies in rice (Oryza sativa L.). J Genet 91(1)Google Scholar
  50. Wei H, Liu JP, Wang Y, Huang NR, Zhang XB, Wang LC, Zhang JW, Tu JM, Zhong XH (2013) A dominant major locus in chromosome 9 of rice (Oryza sativa L.) confers tolerance to 48 °C high temperature at seedling stage. J Hered 104:287–294CrossRefPubMedGoogle Scholar
  51. Welch J, Vincent J, Auffhammer M, Moya P, Dobermann A, Dawe D (2010) Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proc Natl Acad Sci U S A 107:14562–14567CrossRefPubMedPubMedCentralGoogle Scholar
  52. Xiao J, Li J, Grandillo S, Ahn SN, Yan L, Tanksley SD, McCouch SR (1998) Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 150:899–909PubMedPubMedCentralGoogle Scholar
  53. Xiao Y, Pan Y, Luo L, Zhang G, Deng H, Dai L, Liu X, Tang W, Chen L, Wang G (2011) Quantitative trait loci associated with seed set under high temperature stress at the flowering stage in rice. Euphytica 178:331–338CrossRefGoogle Scholar
  54. Xue DW, Jiang H, Hu J, Zhang XQ, Guo LB, Zeng DL, Dong GJ, Sun GC, Qian Q (2012) Characterization of physiological response and identification of associated genes under heat stress in rice seedlings. Plant Physiol Biochem 61:46–53CrossRefPubMedGoogle Scholar
  55. Ye C, May A, Argayoso A, Edilberto D, Redoñ A, Sierra SN, Laza MA, Dilla CJ, Youngjun MO, Thomson MJ, Chin J, Delaviñ CBA, Diaz GQ, Hernandez JE (2011) Mapping QTL for heat tolerance at flowering stage in rice using SNP markers. Plant Breed 131(1):33–41CrossRefGoogle Scholar
  56. Yue B, Xue W, Xiong L, et al. (2006) Genetic basis of drought resistance at reproductive stage in rice: separation of drought tolerance from drought avoidance. Genetics 172(2):1213–1228CrossRefPubMedPubMedCentralGoogle Scholar
  57. Zhang T, Yang L, Jiang K, Huang M, Sun Q, Chen W, J. Zheng J (2008) QTL mapping for heat tolerance of the tassel period of rice. Mol Plant Breed 6:867–873Google Scholar
  58. Zhao K, Aranzana MJ, Kim S, Lister C, Shindo C, et al. (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genet 19: 3(1):e4Google Scholar
  59. Zhao Z, Zhang L, Xiao Y, Zhang W, Zhai H, Wan J (2006) Identification of QTLs for heat tolerance at the booting stage in rice. Acta Agron Sin 32:640–644Google Scholar
  60. Zhou WH, Xue DW, Zhang G (2012) Identification and physiological characterization of thermo-tolerant rice genotypes. J Zhejiang University 38:1–9CrossRefGoogle Scholar

Copyright information

© Prof. H.S. Srivastava Foundation for Science and Society 2016

Authors and Affiliations

  • V. Vishnu Prasanth
    • 1
  • Kumari Ramana Basava
    • 1
  • M. Suchandranath Babu
    • 1
  • Venkata Tripura V.G.N.
    • 1
  • S. J. S. Rama Devi
    • 1
  • S. K. Mangrauthia
    • 1
  • S. R. Voleti
    • 1
  • N. Sarla
    • 1
    Email author
  1. 1.ICAR - Indian Institute of Rice Research (Directorate of Rice Research)HyderabadIndia

Personalised recommendations