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Population Structure and Genetic Diversity in a Rice Core Collection (Oryza sativa L.) Investigated with SSR Markers

Author

Listed:
  • Peng Zhang
  • Jinquan Li
  • Xiaoling Li
  • Xiangdong Liu
  • Xingjuan Zhao
  • Yonggen Lu

Abstract

The assessment of genetic diversity and population structure of a core collection would benefit to make use of these germplasm as well as applying them in association mapping. The objective of this study were to (1) examine the population structure of a rice core collection; (2) investigate the genetic diversity within and among subgroups of the rice core collection; (3) identify the extent of linkage disequilibrium (LD) of the rice core collection. A rice core collection consisting of 150 varieties which was established from 2260 varieties of Ting's collection of rice germplasm were genotyped with 274 SSR markers and used in this study. Two distinct subgroups (i.e. SG 1 and SG 2) were detected within the entire population by different statistical methods, which is in accordance with the differentiation of indica and japonica rice. MCLUST analysis might be an alternative method to STRUCTURE for population structure analysis. A percentage of 26% of the total markers could detect the population structure as the whole SSR marker set did with similar precision. Gene diversity and MRD between the two subspecies varied considerably across the genome, which might be used to identify candidate genes for the traits under domestication and artificial selection of indica and japonica rice. The percentage of SSR loci pairs in significant (P

Suggested Citation

  • Peng Zhang & Jinquan Li & Xiaoling Li & Xiangdong Liu & Xingjuan Zhao & Yonggen Lu, 2011. "Population Structure and Genetic Diversity in a Rice Core Collection (Oryza sativa L.) Investigated with SSR Markers," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0027565
    DOI: 10.1371/journal.pone.0027565
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    References listed on IDEAS

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    1. Fraley, Chris & Raftery, Adrian, 2007. "Model-based Methods of Classification: Using the mclust Software in Chemometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i06).
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