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Population Structure and Breeding Patterns of 145 U.S. Rice Cultivars Based on SSR Marker Analysis


This article in CS

  1. Vol. 45 No. 1, p. 66-76
    Received: Dec 18, 2003

    * Corresponding author(s): thtai@ucdavis.edu
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  1. Hong Lua,
  2. Marc A. Redusc,
  3. Jason R. Coburnb,
  4. J. Neil Rutgerc,
  5. Susan R. McCouchb and
  6. Thomas H. Tai *d
  1. a Pioneer Hi-Bred International, A DuPont Company, 7300 NW 62nd Ave., Johnston, IA 50131-1004, USA
    c USDA-ARS Dale Bumpers National Rice Research Center, Stuttgart, AR 72160, USA
    b Dep. of Plant Breeding, Cornell Univ., Ithaca, NY 14853, USA
    d USDA-ARS Crops Pathology and Genetics Research Unit, Dep. of Agronomy and Range Science, Univ. of California, Davis, CA 95616, USA


This study was undertaken to investigate the population structure of U.S. rice (Oryza sativa L.). A total of 115 U.S. rice cultivars and 30 ancestral accessions introduced from Asia were genotyped by means of 169 simple sequence repeat (SSR) markers that are well distributed throughout the rice genome. SSR-based clustering analysis identified three groups of U.S. rice cultivars that were recognizable as temperate japonica with short to medium grains, tropical japonica with medium grains, and tropical japonica with long grains. Indica cultivars were represented among ancestral accessions, but always clustered independently. Indica germplasm has been used for cultivar improvement, but never directly in U.S. rice production. Cluster analysis of cultivars based on four time periods representing their first release date or introduction (1900–1929, 1930–1959, 1960–1979, and 1980–2000) resulted in the identification of the same three groups. This suggests that the population structure in U.S. rice was established before 1930 and remains essentially intact today, despite a large amount of controlled crossing and artificial selection as a part of the breeding process. Fifty-seven percent of U.S. rice cultivars were developed from intragroup crosses, indicating the availability of substantial genetic variability within each group. Similar results were obtained using genetic distance-based and model-based clustering methods. Information about population structure and associated phenotypic characteristics recognized by geneticists and breeders paves the way for coordinated association mapping studies in the future.

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