We developed a novel method for identifying SNPs widely distributed throughout the coding and non-coding regions of a genome. The method uses large-scale parallel pyrosequencing technology in combination with bioinformatics tools. We used this method to generate approximately 23,000 candidate SNPs throughout the Macaca mulattagenome. We estimate that over 60% of the SNPs will be of high frequency and useful for mapping QTLs, genetic management, and studies of individual\ relatedness, whereas other less frequent SNPs may be useful as population specific markers for ancestry identification. We have created a web resource called MamuSNP to view the SNPs and associated information online. This resource will also be useful for researchers using a wide variety of Macaca species in their research.
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