SNPPicker is a post-processor to optimize the selection of tag SNPs from common bin-tagging programs. SNPPicker uses a multi-step search strategy in combination with a statistical model to produce optimal genotyping panels. SNPPicker’s algorithm is also designed to optimize tag SNP selection for multi-population panels. It accounts for several assay-specific constraints such as predicted assay failure of SNPs and avoidance of SNPs that are too close. The latter issue affects one third of all SNPs in the 1000 genomes project pilot 1 data.SNPPicker automates the design of tag SNP genotyping panels by maximizing the likelihood of successfully genotyping the selected SNPs while minimizing the number of tag SNPs to assay. Geno-typing success is a function of two properties: the genotyping probability of a bin (or cluster of bins) statistically derived from the individual genotyping probability of each SNP; and (for some platforms) the proximity distance between SNPs. The genotyping probabilities currently used by SNPPicker are derived a from pro-spective analysis of the performance of genotyping assay and the probability model can be updated or changed for other platforms. SNP proximity is a strictly enforced constraint
Bioinformatics Program, Division of Biomedical Statistics and Informatics, Mayo Clinic Research
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BMC Bioinformatics. 2011 May 2;12:129.
SNPPicker: high quality tag SNP selection across multiple populations.
Sicotte H, Rider DN, Poland GA, Dhiman N, Kocher JP.