Iranian Journal of Field Crop Science

Iranian Journal of Field Crop Science

Detection of informative markers associated with rice root traits under drought stress in the growth early stages using association analysis

Document Type : Research Paper

Authors
Hossein Sabouri 1
sharife mohamadalegh 2
Ahmad Reza Dadras 3
1 Associate Professor, University of Gonbad-e-Kavoos, Iran
2 M.Sc. Student, University of Gonbad-e-Kavoos, Iran
3 Former Ph.D. Student of Plant Breeding, Department of Agronomy and Plant Breeding, University of Guilan, Iran
10.22059/ijfcs.2017.137585.653991
Abstract
Developing a deep root system is an important strategy for avoiding of drought stress in rice. In order to improve rice root system, at the first stage, must be detected genomic regions controlling important root traits. In order to achieve this aim, a study was conducted using 192 rice genotypes under drought stress and hydroponic culture. Manitol with -5 bar concertration was used to apply osmotic stress in seedling stage. In addition of shoot mass, root mass, plant mass and root thickness, length of shoot and root were recorded on 7th, 14th, 21th, 28th and 35th days after transferring to hydroponic culture. Genotyping of population was performed using primer combinations of restriction enzymes of EcoRI and MseI. To identify genomic regions associated with controlling loci of the traits, were used five statistical models with two GLM and MLM procedures in TASSEL software. The markers of E100-M140-3, E100-M160-7, E110-M140-9, E100-M140-3, E100-M150-19, E100-M160-7, E100-M160-11, E110-M140-1 and E110-M140-9 were detected as the most important markers. Since these markers explained significant percentage of the phenotype variations, can be used as candidate markers in further studies such as conversion to SCAR markers marker assisted selection for drought stress tolerance after validation.
Keywords
association analysis
drought tolerance
rice (Oryza sativa L.)
Root trait
Subjects
  1. Ataee, R., Mohammadi, V. A., Taleii, A. & Naghavi, M. R. (2013). Association mapping in barley roots. Iranian Journal of Field Crop, 44(2), 347-357. (In Farsi)
  2. Bassam, B. J., Caetano-Anolles, G. & Gresshoff, P. M. (1991). Fast and sensitive silver staining of DNA in polyacrylamide gels. Analytical Biochemistry, 196: 80-83.
  3. Courtois, B., Shen, L., Petalcorin, W., Carandang, S., Mauleon, R. & Li1, Z. (2003). Locating QTLs controlling constitutive root traits in the rice population IAC 165 × Co39. Euphytica, 134: 335–345.
  4. Dadras, A. R., Sabouri, H., Mohammadinejad, Gh., Sabouri A. & Shoai-Deylami, M. (2014). Association analysis, genetic diversity and structure analysis of tobacco based on AFLP markers. Molecular Biology Reports, 41(5), 3317-3329.
  5. Donnelly, P. (2008). Progress and challenges in genome-wide association studies in humans. Nature, 456, 728-731.
  6. Evanno, G., Reganut, E. & Goudet, J. (2005). Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology, 14, 2611-2620.
  7. 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 Researche, 122(1), 1-13
  8. Hardy, O. J., & Vekemans, X. (2002). SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Resources, 2(4), 618-620.‏
  9. Hwang, E. Y. (2008). Association analysis in soybean. Ph.D. Thesis. University of Maryland, College Park.
  10. Jun, T. H., Van, K., Kim, M. Y., Lee, S. H. & Walker, D. R. (2008). Association analysis using SSR markers to find QTL for seed protein content in soybean. Euphytica, 62, 179-191.
  11. Kafi, M., Borzuee, A., Salehi, M., Kamandi, A., Masumi, A. & Nabati, J. (2009). Physiology and environmental stress in plants. (Pp 500). Jihad-e-Daneshgahi Mashhad Publications, Mashhad, Iran.
  12. Kang, S. Y., Lee, K. J., Lee, G. J., Kim, J. B., Chung, S. J., Song, J. Y., ... & Kim, D. S. (2010). Development of AFLP and STS markers linked to a waterlogging tolerance in Korean soybean landraces. Biologia Plantarum, 54(1), 61-68.‏
  13. Lafitte, H. R., Yongsheng, G., Yan, S. & Li, Z. K. (2007). Whole plant responses, key processes, and adaptation to drought stress: the case of rice. Journal of ExperimentalBotany, 58, 169-175.
  14. Álvarez, M. F., Mosquera, T. & Blair, M. W. (2014). The use of association genetics approaches in plant breeding. Plant Breeding Reviews, 38, 17-68.‏
  15. Ndjiondjop, M. N., Cisse, F., Futakuchi, K., Lorieux, M., Manneh, B., Bocco, R. & Fatondji, B. (2010). Effect of drought on rice (Oryza spp.) genotypes according to their drought tolerance level. Innovation andPartnerships to RealizeAfrica’s Rice Potential, SecondAfrica Rice Congress, Bamako, Mali, 22-26.
  16. Nordborg, M. & Weigel, D. (2008). Next-generation genetics in plants. Nature, 456, 720-723.
  17. Pocovi, M. I. & Mariotti, J. A. (2015). A bayesian approach to inferring the genetic population structure of sugarcane accessions from INTA (Argentina). Chilean Journal of Agricultural Research, 75(2), 152-159.
  18. Pritchard, J. K., Stephens, M. & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155, 945-959.
  19. Rohlf, F. (2000). NTSYS-PC, Numerical Taxonomy System for the PC ExeterSoftware, Version 2.1. Applied Biostatistics Inc Setauket, USA.
  20. Roy, J.K., Bandopadhyay, R., Rustgi, S., Balyan, H.S. & Gupta, P.K. (2006). Association analysis of agronomically important traits using SSR, SAMPL and AFLP markers in bread wheat. Current Science, 5, 683-689.
  21. Saghai-Maroof, M. A., Soliman, K. M., Jorgensen, R. A. & Allard, R. W. (1984). Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location, and population dynamics. In: Proceedings of the National Academy of Sciences , United States of America 81: 8014-8018
  22. Shaaf, S., Bihamta, M., Talee, A. & Naghavi, M. R. (2012).  Association analysis of single nucleotide variation inflowering time genes Ppd-H1, HvCO1 and HvG1 in the atmosphere (H.Vulgare). Journal of Modern Genetics, 7(2), 179-191.
  23. Spataro, G., Tiranti, B., Arcaleni, P., Bellucci, E., Attene, G., Papa, R., Spagnoletti Zeuli, P. & Negri, V. (2011). Genetic diversity and structure of a worldwide collection of Phaseolus coccineus L. Theoretical Applied Genetics, 122, 1281-1291.
  24. Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M. & Kumar, S. (2011). MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Molecular Evolutionary Genetics Analysis, 28(10), 2731-2739.
  25. Todaka, D., Nakashima, K., Shinozaki, K. & Yamaguchi-Shinozaki, K. (2012). Toward understanding transcriptional regulatory networks in abiotic stress responses and tolerance in rice. Rice, 5(1), 6.
  26. Uga, Y., Okuno, K. & Yano, M. (2011). Dro1, a major QTL involved in deep rooting of rice under upland field conditions. Journal of Experimental Botany, 62(8), 2485-2494.
  27. Van Berloo, R. (1999). GGT: Software for the display of graphical genotypes. Journal of Heredity, 90, 328-329.
  28. Vos, P., Hogers, R., Bleeker, M., Reijans, M., Lee, T. V. D., Hornes, M., Frijters, A., Pot, J., Peleman, J., Kuiper, M. & Zabeau, M. (1995). AFLP: a new technique for DNA fingerprinting. Nucleic Acids Research, 23, 4407-4414.
  29. Wang, H., Xu, X., Zhan, X., Zhai, R., Wu, W., Shen, X., Dai, G., Cao, L. & Cheng, S. (2013). Identification of qRL7, a major quantitative trait locus associated with rice root length in hydroponic conditions. Journal of Breeding Science, 63, 267-274.
  30. Wen, W., Mei, H., Feng, F., Yu, S., Huang, Z., Wu, J., Chen, L., Xu, X. & Luo, L. (2009). Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryza sativa L.). Theoretical Applied Genetics, 119, 459-470.
  31. Yoshida, S. & Hasegawa, S. (1982). The rice root system: its development and function. In: Proceedings of Drought resistance in crops withemphasis on rice. Los Banos, Philippines: International Rice Research Institute, 97-114.
  32. Yoshida, S., Forno, D. A., Cock, J. H. & Gomez, K. A. (1976). Laboratory manual for physiological studies of rice. IRRI, Los Banos, Philipines.
  33. Yu, J., Pressoir, G., Briggs, W. H., Vroh Bi, I., Yamasaki, M., Doebley, J. F., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., Kresovich, S. & Buckler, E. S. (2006). A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics, 38, 203-208.
  34. Zhou, J., You, A., Ma, Z., Zhu, L. & He, G. (2012). Association analysis of important agronomic traits in japonica rice germplasm. Journal of Biotechnology, 11(12), 2957-2970.
Iranian Journal of Field Crop Science
Volume 48, Issue 1 - Serial Number 1
Spring 2017
Pages 171-182

  • Receive Date 19 December 2015
  • Revise Date 10 April 2016
  • Accept Date 10 April 2016
  • Publish Date 22 May 2017