PAG-VI: APPLYING THE BINS CONCEPT TO COMPARATIVE MAP DATA IN RICE AND MAIZE

PAG-VI  Plant & Animal Genome VI Conference

Town & Country Hotel, San Diego, CA, January 18-22, 1998.


W93

APPLYING THE BINS CONCEPT TO COMPARATIVE MAP DATA IN RICE AND MAIZE

GEORGIA L. DAVIS1, Edward H. Coe2, Masahiro Yano3, Michael D. McMullen2, Takuji Sasaki4, Mary Polacco2

  1. USDA,ARS,MSA, Corn Host Plant Resistance Research Unit, PO Box 5367, Mississippi State, MS 39762
  2. USDA,ARS,MWA, Plant Genetics Research Unit, 210 Curtis Hall, Columbia, MO 65211
  3. Rice Genome Research Program, National Institute of Agrobiological Resources 2-1-2, Kannondai, Tsukuba, Ibaraki 305, JAPAN
  4. Rice Genome Research Program, STAFF Institute, 446-1, Ippaizuka, Kamiyokoba, Tsukuba, Ibaraki 305, JAPAN

Rice and maize represent two of the most economically important crops in the world marketplace. Both species are diploids, have well-defined molecular maps, and have a large number of ESTs available. In recent years numerous studies have provided evidence to support the existence of synteny among the genes in different grass species including rice and maize. The efficiency of molecular mapping and gene discovery research in a single species can be significantly increased by integrating the information from two species. The concept of bins as a division of the chromosome into smaller units for gene discovery has been utilized in humans and maize. Bin size in maize is approximately 20cM. Maize genetic data including ESTs, SSRs, phenotypic mutants, and QTLs can be examined by individual map or across maps in 20 cM bins defined by a common set of core markers. In order to enhance the gene discovery potential of an integrated rice-maize map, we used cDNAs distributed approximately every 20 cM in maize as probes on the Kasalath x Nipponbare rice mapping population. In addition to providing links between the maize and rice maps, these common cDNAs allow us to link the information from other maize maps containing ESTs, phenotypic mutants, and QTLs together. Using the composite bins we can quickly identify potentially interesting associations between molecular markers and phenotypes to enhance our understanding of plant development and quantitative trait expression.


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