Skip to main content
Log in

Identification of Markers for Root Traits Related to Drought Tolerance Using Traditional Rice Germplasm

  • Original Paper
  • Published:
Molecular Biotechnology Aims and scope Submit manuscript

Abstract

Drought is one of the important constraints affecting rice productivity worldwide. The vigorous shoot and deep root system help to improve drought resistance. In present era, genome-wide association study (GWAS) is the preferred method for mapping of QTLs for complex traits such as root and drought tolerance traits. In the present study, 114 rice genotypes were evaluated for various root and shoot traits under water stress conditions. All genotypes showed a significant amount of variation for various root and shoot traits. Correlation analysis revealed that high dry shoot weight and fresh shoot weight is associated with root length, root volume, fresh root weight and dry root weight. A total of 11 significant marker-trait associations were detected for various root, shoot and drought tolerance traits with the coefficient of determination (R2) ranging from 18.99 to 53.41%. Marker RM252 and RM212 showed association with three root traits which suggests their scope for improvement of root system. In the present study, a novel QTL was detected for root length associated with RM127, explaining 19.30% of variation. The marker alleles with increasing phenotypic effects for root and drought-tolerant traits can be exploited for improvement of root and drought tolerance traits using marker-assisted selection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Kumar, S., Dwivedi, S. K., Basu, S., Kumar, G., Mishra, J. S., Koley, T. K., Rao, K. K., Choudhary, A. K., Mondal, S., Kumar, S., Bhakta, N., Bhatt, B. P., Paul, R. K., & Kumar, A. (2020). Anatomical, agromorphological and physiological changes in rice under cumulative and stage specific drought conditions prevailed in eastern region of India. Field Crops Research, 245, 107658.

    Article  Google Scholar 

  2. Yang, X., Wang, B., & Chen, L. (2019). The different influences of drought stress at the flowering stage on rice physiological traits, grain yield, and quality. Science and Reports, 9, 3742.

    Article  CAS  Google Scholar 

  3. Intergovernmental Panel Climate Change (IPCC). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 2014; Core Writing Team. In R. K. Pachauri, L. A. Meyer (Eds.) IPCC: Geneva, Switzerland, p 151.

  4. Trenberth, K. E., Dai, A., Schrier, G. V. D., Jones, P. D., Barichivich, J., Briffa, K. R., & Sheffield, J. (2014). Global warming and changes in drought. Nature Climate Change, 4, 17–22.

    Article  Google Scholar 

  5. Zhang, J., Zhang, S., Cheng, M., Jiang, H., Zhang, X., Peng, C., Lu, X., Zhang, M., & Jin, J. (2018). Effect of drought on agronomic traits of rice and wheat: A meta-analysis. International Journal of Environmental Research and Public Health, 15, 839.

    Article  PubMed Central  Google Scholar 

  6. Daryanto, S., Wang, L., & Jacinthe, P. A. (2017). Global synthesis of drought effects on cereal, legume, tuber and root crops production: A review. Agricultural Water Management, 179, 18–33.

    Article  Google Scholar 

  7. Kim, W., Iizumi, T., Nishimori, M. (2019). Global patterns of crop production losses associated with droughts from 1983 to 2009. J Appl Meteorol Climatol, 1233–1244.

  8. Kim, Y., Chung, Y. S., Lee, E., Tripathi, P., Heo, S., & Kim, K. H. (2020). Root response to drought stress in rice (Oryza sativa L.). Int J Mol Sci, 21(4), 1513.

    Article  CAS  PubMed Central  Google Scholar 

  9. Ahmadi, N., Audebert, A., Bennett, M. J., Bishopp, A., de Oliveira, A. C., Courtois, B., Diedhiou, A., Diévart, A., Gantet, P., & Ghesquière, A. (2014). The roots of future rice harvests. Rice, 7, 29.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Dixit, S., Singh, A., & Kumar, A. (2014). Rice breeding for high grain yield under drought: A strategic solution to a complex problem. Int J Agron, 2014, 15.

    Article  Google Scholar 

  11. Palta, J. A., & Yang, J. (2014). Crop root system behaviour and yield. Field Crops Res, 165, 1–4.

    Article  Google Scholar 

  12. Canales, F. J., Nagel, K. A., Müller, C., Rispail, N., & Prats, E. (2019). Deciphering root architectural traits involved to cope with water deficit in Oat. Frontiers in Plant Science, 10, 1558.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wasaya, A., Zhang, X., Fang, Q., & Yan, Z. (2018). Root phenotyping for drought tolerance: A review. Agronomy, 8(11), 241.

    Article  Google Scholar 

  14. Comas, L. H., Becker, S. R., Cruz, V. M. V., Byrne, P. F., & Dierig, D. A. (2013). Root traits contributing to plant productivity under drought. Frontiers in Plant Science, 4, 442.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Uga, Y., Sugimoto, K., Ogawa, S., Rane, J., Ishitani, M., Hara, N., Kitomi, Y., Inukai, Y., Ono, K., & Kanno, N. (2013). Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nature Genetics, 45, 1097–1102.

    Article  CAS  PubMed  Google Scholar 

  16. Verma, H., Borah, J. L., & Sarma, R. N. (2019). Variability assessment for root and drought tolerance traits and genetic diversity analysis of rice germplasm using SSR markers. Science and Reports, 9, 16513.

    Article  CAS  Google Scholar 

  17. Uga, Y., Kitomi, Y., Ishikawa, S., & Yano, M. (2015). Genetic improvement for root growth angle to enhance crop production. Breeding Science, 65(2), 111–119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Vikram, P., Swamy, B., Dixit, S., Ahmed, H. U., Cruz, M. T. S., Singh, A. K., & 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, 89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Uga, Y., Okuno, K., & Yano, M. (2011). Dro1, a major QTL involved in deep rooting of rice under upland field conditions. J Expt Bot, 62(8), 2485–2494.

    Article  CAS  Google Scholar 

  20. Muthu, V., Abbai, R., Nallathambi, J., Rahman, H., Ramasamy, S., & Kambale, R. (2020). Pyramiding QTLs controlling tolerance against drought, salinity, and submergence in rice through marker assisted breeding. PLoS One, 15(1), e0227421.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Anyaoha, C. O., Fofana, M., Gracen, V., Tongoona, P., & Mande, S. (2019). Introgression of two drought QTLs into FUNAABOR-2 early generation backcross progenies under drought stress at reproductive stage. Rice Science, 26(1), 32141.

    Article  Google Scholar 

  22. Dharmappa, P. M., Doddaraju, P., Malagondanahalli, M. V., Rangappa, R. B., Mallikarjuna, N. M., Rajendrareddy, S. H., Ramanjinappa, R., Mavinahalli, R. P., Prasad, T. G., & Udayakumar, M. (2019). Introgression of root and water use efficiency traits enhances water productivity: An evidence for physiological breeding in rice (Oryza sativa L.). Rice, 12, 14.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Dixit, S., Singh, A., Sandhu, N., Bhandari, A., Vikram, P., & Kumar, A. (2017). Combining drought and submergence tolerance in rice: Marker assisted breeding and QTL combination effects. Molecular Breeding, 37, 143.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wijerathna, Y. M. A. M. (2015). Marker Assisted Selection: Biotechnology Tool for Rice Molecular Breeding. Adv Crop Sci Tech, 3, 4.

    Google Scholar 

  25. Bernardo, R. (2008). Molecular markers and selection for complex traits in plants: Learning from the last 20 years. Crop Science, 48, 1649–1664.

    Article  Google Scholar 

  26. Bernier, J., Kumar, A., Venuprasad, R., Spaner, D., Verulkar, S., Mandal, N. P., Sinha, P. K., Peeraju, P., Dongre, P. R., Mahto, R. N., & Atlin, G. (2009). Characterization of the effect of a QTL for drought resistance in rice, qtl12.1, over a range of environments in the Philippines and eastern India. Euphytica, 166, 207–217.

    Article  Google Scholar 

  27. Hoang, G. T., Van Dinh, L., & Nguyen, T. T. (2019). Genome-wide association study of a panel of vietnamese rice landraces reveals new QTLs for tolerance to water deficit during the vegetative phase. Rice, 12, 4.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Korte, A., & Farlow, A. (2013). The advantages and limitations of trait analysis with GWAS: A review. Plant Methods, 9, 29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Swamy, B. P. M., Shamsudin, N. A. A., Rahman, S. N. A., Mauleon, R., Ratnam, W., Sta Cruz, M. T., & Kumar, A. (2017). Association mapping of yield and yield-related traits under reproductive stage drought stress in rice (Oryza sativa L.). Rice, 10, 21.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Hirschhorn, J., & Daly, M. (2005). Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics, 6, 95–108.

    Article  CAS  PubMed  Google Scholar 

  31. Myers, N. (1988). Threatened biotas: “hots spots” in tropical forests. The Environmentalist, 8(3), 187–208.

    Article  CAS  PubMed  Google Scholar 

  32. Travis, A. J., Norton, G. J., Datta, S., Sarma, R., Dasgupta, T., Savio, F. L., Macaulay, M., Hedley, P. E., McNally, K. L., Sumon, M. H., Islam, M. R., & Price, A. H. (2015). Assessing the genetic diversity of rice originating from Bangladesh, Assam and West Bengal. Rice, 8(1), 1–9.

    Article  Google Scholar 

  33. Civán, P., Craig, H., Cox, C. J., & Brown, T. A. (2015). Three geographically separate domestications of Asian rice. Nat Plants, 1, 15164.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kim, H. J., Jung, J., Singh, N., Greenberg, A., Doyle, J. J., Tyagi, W., Chung, J. W., Kimball, J., Hamilton, R. S., & McCouch, S. R. (2016). Population dynamics among six major groups of the Oryzarufipogon species complex, wild relative of cultivated Asian rice. Rice, 9, 56.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Norton, G. J., Travis, A. J., Douglas, A., Fairley, S., De PaivaAlves, E., Ruang-areerate, P., Naredo, M. E. B., McNally, K. L., Hossain, M., Islam, M. R., & Price, A. H. (2018). Genome wide association mapping of grain and straw biomass traits in the rice bengal and assam aus panel (BAAP) grown under alternate wetting and drying and permanently flooded irrigation. Frontiers in Plant Science, 9, 1223.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Verma, H., Devi, K., Baruah, A. R., & Sarma, R. N. (2020). Relationship of root aquaporin genes, OsPIP1;3, OsPIP2;4, OsPIP2;5, OsTIP2;1 and OsNIP2;1 expression with drought tolerance in rice. Indian J Genet, 80(1), 50–57.

    Article  CAS  Google Scholar 

  37. Umakanth, B., Vishalakshi, B., Kumar, P. S., Devi, S. J. S. R., Bhadana, V. P., Senguttuvel, P., Kumar, S., Sharma, S. K., Sharma, P. K., Prasad, M. S., & Madhav, M. S. (2017). Diverse rice landraces of North-East India enables the identification of novel genetic resources for Magnaporthe resistance. Frontiers in Plant Science, 8, 1500.

    Article  PubMed  PubMed Central  Google Scholar 

  38. McNally, K., Childs, K. L., Bohnert, R., Davidson, R. M., & Zhao, K. (2009). Genome wide SNP variation reveals relationships among landraces and modern varieties of rice. Proceedings of the National Academy of Sciences, 106, 12273–12278.

    Article  CAS  Google Scholar 

  39. Yadav, S., Sandhu, N., Singh, V. K., Catolos, M., & Kumar, A. (2019). Genotyping-by-sequencing based QTL mapping for rice grain yield under reproductive stage drought stress tolerance. Scientific reports, 9, 14326.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. IRRI. (2002). Standard evaluation system for rice. International Rice Research Institute.

    Google Scholar 

  41. Reynolds, S. G. (1970). The gravimetric method of soil moisture determination, Part 1 A study of equipments and methodological problems. J Hydrology, 11, 258–273.

    Article  Google Scholar 

  42. Plaschke, J., Ganal, M. W., & Röder, M. S. (1995). Detection of genetic diversity in closely related bread wheat using microsatellite markers. TAG. Theoretical and Applied Genetics. , 91, 1001–1007.

    Article  CAS  PubMed  Google Scholar 

  43. Panaud, O., Chen, X., McCouch, S. R. (1996). Frequency of microsatellite sequences in rice (Oryza sativa L). Genome, 38(l), 1170–1176

  44. Sangeetha, A., Malhotra, P. K., Bhatia, V. K., & Rajendra, P. (2008). Statistical package for agricultural research (SPAR 2.0). J Indian Soc Agric Stat, 62, 65–74.

    Google Scholar 

  45. Wei, T., & Simko, V. (2017). R package “corrplot”: visualization of a Correlation Matrix (Version 0.84). https://cran.r-project.org/web/packages/corrplot/corrplot.pdf.

  46. Neuwirth, E. (2014). Package ‘RColorBrewer’. https://cran.rproject.org/web/packages/RColorBrewer/RColorBrewer.pdf.

  47. Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., & Buckler, E. S. (2007). TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics, 23, 2633–2635.

    Article  CAS  PubMed  Google Scholar 

  48. Zhang, Z., Ersoz, E., Lai, C. Q., Todhunter, R. J., Tiwari, H. K., Gore, M. A., Bradbury, J., Yu, J., Arnett, D. K., Ordovas, J. M., & Buckler, E. (2010). Mixed linear model approach adapted for genome-wide association studies. Nature Genetics, 42, 355–360.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rincent, R., Moreau, L., Monod, H., Kuhn, E., Melchinger, A., Malvar, R. A., MorenoGonzalez, J., Nicolas, S., Madur, D., Combes, V., Dumas, F., Altmann, T., Brunel, D., Ouzunova, M., Flament, P., Dubreuil, P., Charcosset, A., & MaryHuard, T. (2014). Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics, 197, 375–387.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Das, G., Patra, J. K., & Baek, K. H. (2017). Insight into MAS: A molecular tool for development of stress resistant and quality of rice through gene stacking. Frontiers in Plant Science, 8, 985.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Balasubramanian, S., Schwartz, C., Singh, A., Warthmann, N., Kim, M. C., Maloof, J. N., Loudet, O., Trainer, G. T., Dabi, T., & Borevitz, J. O. (2009). QTL mapping in new Arabidopsis thaliana advanced intercross-recombinant inbred lines. PLoS One, 4(2), e4318.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Ibrahim, A. K., Zhang, L., Niyitanga, S., Afzal, M. Z., Zhang, L., Zhang, L., & Qi, J. (2020). Principles and approaches of association mapping in plant breeding. Tropical Plant Biol, 13, 212–224.

    Article  Google Scholar 

  53. Zhu, C., Gore, M., Buckler, E. S., & Yu, J. (2008). Status and prospects of association mapping in plants. The plant genome, 1, 5–20.

    Article  CAS  Google Scholar 

  54. Pritchard, J., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155, 945–959.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Yu, Y., Lee, H. O., Chin, J. H., Park, H. Y., & Yoo, S. C. (2017). The complete chloroplast genome sequence of Oryza sativa aus-type variety Nagina-22 (Poaceae). Mitochondrial DNA Part B: Resources, 2(2), 819–820.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Agrama, H. A., Eizenga, G. C., & Yan, W. (2007). Association mapping of yield and its components in rice cultivars. Molecular Breeding, 19, 341–356.

    Article  Google Scholar 

  57. Dixit, S., Swamy, B. P. M., Vikram, P., Ahmed, H. U., Cruz, M. T. S., Amante, M., Atri, D., Leung, H., & Kumar, A. (2012). Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities. TAG. Theoretical and Applied Genetics. , 125, 155–169.

    Article  PubMed  Google Scholar 

  58. Qu, Y., Ping, M., Hongliang, Z., Chen, Y., Gao, Y., Tian, Y., Wen, F., & Li, Z. (2008). Mapping QTLs of root morphological traits at different growth stages in rice. Genetica, 133, 187–200.

    Article  PubMed  Google Scholar 

  59. Xing, Z., Tan, F., Hua, P., Sun, L., Xu, G., & Zhang, Q. (2002). Characterization of the main effects, epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice. TAG. Theoretical and Applied Genetics. , 105, 248–257.

    Article  CAS  PubMed  Google Scholar 

  60. Kamoshita, A., Wade, L. J., Ali, M. L., Pathan, M. S., Zjang, J., & Sarkarung, S. (2002). Mapping QTL for root morphology of a rice population adapted to rainfed lowland conditions. TAG. Theoretical and Applied Genetics. , 104, 880–893.

    Article  CAS  PubMed  Google Scholar 

  61. Prince, S. J., Beena, R., Gomez, S. M., Sentivel, S., & Babu, R. C. (2015). Mapping consistent rice (Oryza sativa L.) yield QTLs under drought stress in target rainfed environments. Rice, 8, 25.

    Article  PubMed Central  Google Scholar 

  62. Swamy, M. B. P., Vikram, P., Dixit, S., Ahmed, H. U., & Kumar, A. (2011). Meta-analysis of grain yield QTL identified during agricultural drought in grasses showed consensus. BMC Genomics, 12, 319.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Kanagaraj, P., Prince, K. S. J., Annie Sheeba, J., Biji, K. R., Paul, S. B., Senthil, A., & Chandra Babu, R. (2010). Microsatellite markers linked to drought resistance in rice (Oryza sativa L.). Cursos e Congresos da Universidade de Santiago de Compostela, 98, 836–839.

    CAS  Google Scholar 

  64. Gomez, S. M., Boopathi, N. M., Kumar, S. S., Ramasubramanian, T., Chengsong, Z., Jeyaprakash, P., Senthil, A., & Babu, R. C. (2010). Molecular mapping and location of QTLs for drought-resistance traits in indica rice (Oryza sativa L.) lines adapted to target environments. Acta Physiologiae Plantarum, 32, 355–364.

    Article  Google Scholar 

  65. Zhao, X. Q., Xu, J. L., Zhao, M., Lafitte, R., Zhu, L. H., Fu, B. Y., Gao, Y. M., & Li, Z. K. (2008). QTLs affecting morpho-physiological traits related to drought tolerance detected in overlapping introgression lines of rice (Oryza sativa L.). Plant Science, 174(6), 618–625.

    Article  CAS  Google Scholar 

  66. Sabar, M., Shabir, G., Shah, S. M., Aslam, K., Naveed, S. A., & Arif, M. (2019). Identification and mapping of QTLs associated with drought tolerance traits in rice by a cross between Super Basmati and IR55419-04. Breeding Science, 69(1), 169–178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Babu, R. C., Nguyen, B. D., Chamarerk, V., Shanmugasundaram, P., Chezhian, P., Jeyaprakash, P., Ganesh, S. K., Palchamy, A., Sadasivam, S., Sarkarung, S., Wade, L. J., & Nguyen, H. T. (2003). Genetic analysis of drought resistance in rice by molecular markers: Association between secondary traits and field performance. Crop Science, 43, 1457–1469.

    Article  CAS  Google Scholar 

  68. Salunkhe, A. S., Poornima, R., Prince, K. S., Kanagaraj, P., Sheeba, J. A., Amudha, K., Suji, K. K., Senthil, A., & Babu, R. C. (2011). Fine mapping QTL for drought resistance traits in rice (Oryza sativa L.) using bulk segregant analysis. MolBiotechnol, 49(1), 90–95.

    CAS  Google Scholar 

  69. Sandhu, N., & Kumar, A. (2017). Bridging the rice yield gaps under drought: QTLs genes and their use in breeding programs. Agronomy, 7(2), 27.

    Article  CAS  Google Scholar 

  70. Zheng, H. G., Babu, R. C., Pathan, M. S., Ali, L., Huang, N., Courtois, B., & Nguyen, H. T. (2000). Quantitative trait loci for root-penetration ability and root thickness in rice: Comparison of genetic backgrounds. Genome, 43(1), 53–61.

    Article  CAS  PubMed  Google Scholar 

  71. Singh, S., Pradhan, S. K., Singh, A. K., & Singh, O. N. (2012). Marker validation in recombinant inbred lines and random varieties of rice for drought tolerance. AJCS, 6(4), 606–612.

    Google Scholar 

  72. Zhao, Y., Jiang, C. H., Rehman, R. M. A., Zhang, H. L., Li, J., & Li, Z. C. (2019). Genetic analysis of roots and shoots in rice seedling by association mapping. Genes Genomics, 41, 95–105.

    Article  CAS  PubMed  Google Scholar 

  73. Lanceras, J. C., Pantuwan, G., Jongdee, B., & Toojinda, T. (2004). Quantitative trait loci associated with drought tolerance at reproductive stage in rice. Plant Physiology, 135(1), 384–399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. An, H., Liu, K., Wang, B., Tian, Y., Ge, Y., & Zhang, Y. (2019). Genome-wide association study identifies QTLs conferring salt tolerance in rice. Plant Breeding, 139, 73–82.

    Article  CAS  Google Scholar 

  75. Zheng, H., Wang, J., Zhao, H., Liu, H., Sun, J., Guo, J., & Zou, D. (2015). Genetic structure, linkage disequilibrium and association mapping of salt tolerance in japonica rice germplasm at the seedling stage. Molecular Breeding, 35, 152.

    Article  CAS  Google Scholar 

  76. Xu, F., Bao, J., He, Q., & Park, Y. J. (2016). Genome-wide association study of eating and cooking qualities in different subpopulations of rice (Oryza sativa L.). BMC Genomics, 17, 633.

    Article  CAS  Google Scholar 

  77. Zhong, M., Wang, L., Yuan, J., Luo, L., Xu, C., & He, Y. Q. (2011). Identification of QTL affecting protein and amino acid contents in rice. Rice Science, 18(3), 187–195.

    Article  Google Scholar 

  78. Guo, L., Guo, W., Zhao, H., & Wang, J. (2015). Association mapping and resistant alleles’ analysis for japonica rice blast resistance. Plant Breeding, 134, 646–652.

    Article  CAS  Google Scholar 

  79. Eizenga, G. C., Jia, M. H., Jackson, A. K., Boykin, D. L., Ali, M. L., Shakiba, E., Tran, N. T., McCouch, S. R., & Edwards, J. D. (2019). Validation of yield component traits identified by genome-wide association mapping in a tropical japonica × tropical japonica rice biparental mapping population. Plant Genome, 12, 180021.

    Article  CAS  Google Scholar 

  80. Raju, B. R., Narayanaswamy, B. R., Mohankumar, M. V., Sumanth, K. K., Rajanna, M. P., Mohanraju, B., Udayakumar, M., & Sheshshayee, M. S. (2014). Root traits and cellular level tolerance hold the key in maintaining higher spikelet fertility of rice under water limited conditions. Functional Plant Biology, 41(9), 930–939.

    Article  PubMed  Google Scholar 

  81. Courtois, B., Audebert, A., & Dardou, A. (2013). Genome-wide association mapping of root traits in a japonica rice panel. PLoS One, 8, e78037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Henry, A., Gowda, V. R. P., Torres, R., Mcnally, K., & Serraj, R. (2011). Variation in root system architecture and drought response in rice (Oryza sativa): Phenotyping of the Oryza SNP panel in rainfed lowland fields. Field Crops Research, 120(2), 205–214.

    Article  Google Scholar 

  83. Gowda, V. R. P., Henry, A., Yamauchi, A., Shashidhar, H. E., & Serraj, R. (2011). Root biology and genetic improvement for drought avoidance in rice. Field Crops Research, 122(1), 1–13.

    Article  Google Scholar 

  84. Wasson, A. P., Richards, R. A., Chatrath, R., Misra, S. C., Prasad, S. V., & Rebetzke, G. J. (2012). Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. Journal of Experimental Botany, 63, 3485–3498.

    Article  CAS  PubMed  Google Scholar 

  85. Abd Allah, A., Badawy, S. A., & Zayed, & B., El-Gohary, A. . (2010). The role of root system traits in the drought tolerance of rice (Oryza sativa L.). World Acad Sci Eng Technol, 68, 1378–1382.

    Google Scholar 

  86. Kirkegaard, J. A., Lilley, J. M., Howe, G. N., & Graham, J. M. (2007). Impact of subsoil water use on wheat yield. Australian Journal of Agricultural Research, 58, 303–315.

    Article  Google Scholar 

  87. DeDorlodot, S., Forster, B., Pagès, L., Price, A., Tuberosa, R., & Draye, X. (2007). Root system architecture: Opportunities and constraints for genetic improvement of crops. Trends in Plant Science, 12, 474–481.

    Article  CAS  Google Scholar 

  88. Phung, N. T. P., Mai, C. D., & Hoang, G. T. (2016). Genome-wide association mapping for root traits in a panel of rice accessions from Vietnam. BMC Plant Biology, 16, 64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Kashiwagi, J., Krishnamurthy, L., Upadhyaya, H., Krishna, H., Chandra, S., & Vadez, V. (2005). Genetic variability of drought-avoidance root traits in the mini-core germplasm collection of chickpea (Cicer arietinum L.). Euphytica, 146, 213–222.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Harendra Verma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verma, H., Sarma, R.N. Identification of Markers for Root Traits Related to Drought Tolerance Using Traditional Rice Germplasm. Mol Biotechnol 63, 1280–1292 (2021). https://doi.org/10.1007/s12033-021-00380-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12033-021-00380-1

Keywords

Navigation