Korean Journal of Breeding Science (한국육종학회지)

The Korean Society of Breeding Science (한국육종학회)
권호 표지

SSR Analysis of Genetic Diversity and Nitrogen Use Efficiency Traits in Rice

  • Received : 2008.06.07
  • Published : 2008.06.10
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Abstract

A total of 41 microsatellite markers were used with 29 genotypes to examine the relationship between SSR polymorphisms and N-use efficiency related traits with a goal to identify the putative QTLs related to these traits. These primers yielded a total of 183 alleles (average 4.46 alleles per primer), and polymorphism information content (PIC) values of the SSRs ranged from 0.119 to 0.805 with mean value of 0.425. Correlation coefficients were obtained among the four N-use efficiency traits in the 34 accessions and significant positive correlations of relative ratios between grain yield and harvest index (r=0.3404) and total dry matter (r=0.7976), while N uptake showed a moderate level of correlation with the ratios of the grain yield and total dry matter, respectively. 36.5% (15/41) SSR markers were monomorphic among the 25 japonica accessions out of the 29 accessions. Association between SSR genotypes and phenotypic performances from the total (29) or japonica (25) accessions was tested based on a single point analysis. Three putative QTL regions were detected for the ratio of grain yield. These include the chromosomal region containing the RM283 locus on chromosome 1 and RM25 on chromosome 8 (all and japonica accessions) and the region with the SSR marker, RM206 on chromosome 11 (the japonica accessions). For the total dry matter ratio, two chromosomal regions were identified as the putative QTL region. One is the region with the SSR marker, RM162 on chromosome 6 (all and japonica accessions) and the other was the one with the SSR marker RM25 on chromosome 8 (the japonica accessions). Among these markers, RM25 showed associations with both traits.

Keywords

References

  1. Agrama H, Zakaria A, Said F, Tuinstra M. 1999. Identification of quantitative trait loci for nitrogen use efficiency in, aize. Mol Breed 5:187-195 https://doi.org/10.1023/A:1009669507144
  2. Anderson JA, Churchill GA, Autrique JE, Tanksley SD, and Sorrells ME. 1993. Optimizing parental selection for genetic linkage maps. Genome 36:181-186 https://doi.org/10.1139/g93-024
  3. Beavis WD 1994. The power and deceit of QTL experiments: lessons from comparative QTL studies. In; Annual corn sorghum research conference 49th. American Seed Trade Association, Washington, pp252-268
  4. Bernardo R 2004. What proportion of declared QTL in plants are false?, Theor Appl Genet 109: 419-424
  5. Bertin P, Gallais A. 2004. Physiological and genetic basis of nitrogen use efficiency. II. QTL detection and coincidences. Maydica 46:53-68
  6. Ender A, Schwenk K, Stdler T, Streit B and Schierwater B. 1996. RAPD identification of microsatellites in Daphnia. Molecular Ecology. 5:437-441 https://doi.org/10.1111/j.1365-294X.1996.tb00333.x
  7. Fang P, Wu P. 2001. QTL x N‐level interaction for plant height in rice (Oryza sativa L.). Plant and Soil 236: 237-242 https://doi.org/10.1023/A:1012787510201
  8. Gallais A, Hirel B. 2004. An approach to the genetics of nitrogen use efficiency in maize. J Exp Bot 55:295-306 https://doi.org/10.1093/jxb/erh006
  9. Hirel B, Bertin P, Quillere I, Bourdoncle W, Attagnant C, Dellay C, Gouy A, Cadiou S, Retailliau C, Falque M, Gallais A. 2001. Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize. Plant physiol. 125:1258-1270 https://doi.org/10.1104/pp.125.3.1258
  10. Ji HS, Koh HA, Park SO, McCouch SR. 1998. Varietal identification in Japonica rice using microsatellite DNA markers. Korean J. breed. 30(4):350-360
  11. Kwon SJ, Ahn SN, Hong HC, Hwang HG, Choi HC. 2002. SSR diversity in japonica rice cultivars and its association to several traits. Korean J. Breed. 34(1): 57-63
  12. Lian X, Xing Y, Yan H, Xu C, Li X, and Zhang Q. 2005. QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred population derived from an elite rice hybrid. Theor. Appl. Genet. 112: 85-96 https://doi.org/10.1007/s00122-005-0108-y
  13. Melchinger AE, Utz HF, Schon CC. 1998. Quantitative trait locus(QTL) mapping using different testers and indepenendent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149: 383-403
  14. Melchinger AE, Utz HF, Schon CC. 2004. QTL analysis of complex traits with cross validation, bootstrapping and other biometric methods. Euphytica 137:1-11 https://doi.org/10.1023/B:EUPH.0000040498.48379.68
  15. Rafalski JA, Vogel JM, Morgante M, Powell W, Andre C, Tingey SV. 1996. Generating and using DNA markers in plants. In: B. Birren, and E. Lai (Eds), Analysis of Non -mammalian Genomes ‐ A Practical Guide. Academic Press, New York. 75-134
  16. Saghai MA, Biyashev RM, Yang GP, Zhang Q, Allard RW. 1994. Extra ordinarily polymorphic microsatellite DNA in barley; species diversity, chromosomal locations and population dynamics. Proc. Natl. Acad. Sci. USA 91: 5466-5470 https://doi.org/10.1073/pnas.91.12.5466
  17. Welsh J, McClelland M. 1990. Fingerprinting genomes using PCR with arbitrary primers. Nucl. Acids Res. 18: 7213 https://doi.org/10.1093/nar/18.24.7213
  18. Williams JGK, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV. 1990. DNA polymorphisms amplified by arbitrary primers useful as genetic markers. Nucl. Acids Res. 18:6531-6535 https://doi.org/10.1093/nar/18.22.6531
  19. Wu KS, Tanksley SD. 1993. Abundance, polymorphism and genetic mapping of microsatellite in rice. Mol. Gen. Genet. 241:225-235 https://doi.org/10.1007/BF00280220