Development of genome-wide SNP assays for rice

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  • McCouch Susan R.
    Department of Plant Breeding and Genetics, Cornell University
  • Zhao Keyan
    Department of Biological Statistics and Computational Biology, Cornell University Department of Genetics, Stanford University
  • Wright Mark
    Department of Plant Breeding and Genetics, Cornell University Department of Biological Statistics and Computational Biology, Cornell University
  • Tung Chih-Wei
    Department of Plant Breeding and Genetics, Cornell University
  • Ebana Kaworu
    National Institute of Agrobiological Sciences
  • Thomson Michael
    International Rice Research Institute
  • Reynolds Andy
    Department of Biological Statistics and Computational Biology, Cornell University
  • Wang Diane
    Department of Plant Breeding and Genetics, Cornell University
  • DeClerck Genevieve
    Department of Plant Breeding and Genetics, Cornell University
  • Ali Md. Liakat
    Rice Research and Extension Center, University of Arkansas
  • McClung Anna
    USDA ARS, Dale Bumpers National Rice Research Center
  • Eizenga Georgia
    USDA ARS, Dale Bumpers National Rice Research Center
  • Bustamante Carlos
    Department of Biological Statistics and Computational Biology, Cornell University Department of Genetics, Stanford University

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Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation in eukaryotic genomes. SNPs may be functionally responsible for specific traits or phenotypes, or they may be informative for tracing the evolutionary history of a species or the pedigree of a variety. As genetic markers, SNPs are rapidly replacing simple sequence repeats (SSRs) because they are more abundant, stable, amenable to automation, efficient, and increasingly cost-effective. The integration of high throughput SNP genotyping capability promises to accelerate genetic gain in a breeding program, but also imposes a series of economic, organizational and technical hurdles. To begin to address these challenges, SNP-based resources are being developed and made publicly available for broad application in rice research. These resources include large SNP datasets, tools for identifying informative SNPs for targeted applications, and a suite of custom-designed SNP assays for use in marker-assisted and genomic selection, association and QTL mapping, positional cloning, pedigree analysis, variety identification and seed purity testing. SNP resources also make it possible for breeders to more efficiently evaluate and utilize the wealth of natural variation that exists in both wild and cultivated germplasm with the aim of improving the productivity and sustainability of agriculture.<br>

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