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Published on: June 23, 2012
SNPPicker: high quality tag SNP selection across multiple populations.
Hugues Sicotte1, David N Rider, Gregory A Poland
1Department of Biomedical Statistics and Informatics, Mayo Clinic, 200 1st street SW, Rochester, MN 55905, USA.
BMC Bioinformatics
|May 4, 2011
Summary
SNPPicker optimizes tag SNP selection for custom genotyping panels, improving success rates by considering platform factors and user preferences. This tool enhances the design of multi-population panels for genetic variation studies.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Linkage Disequilibrium (LD) bin-tagging algorithms reduce the number of single nucleotide polymorphisms (SNPs) genotyped while capturing population genetic variation.
- Existing algorithms for selecting tag SNPs for custom genotyping panels often neglect platform-specific factors influencing genotyping success and user-defined constraints.
Purpose of the Study:
- To develop an application, SNPPicker, that optimizes tag SNP selection for designing custom genotyping panels.
- To maximize the genotyping success rate of selected tag SNPs by incorporating platform-specific factors and user preferences.
Main Methods:
- SNPPicker employs a multi-step search strategy and a statistical model to select optimal tag SNPs.
- The application considers user preferences for functional SNPs as a secondary selection criterion.
- It optimizes tag SNP selection for panels designed to tag multiple populations and incorporates assay-specific constraints (e.g., Illumina's GoldenGate and Infinium assays).
Main Results:
- SNPPicker successfully optimizes tag SNP selection for custom genotyping panels.
- The application enhances the design of multi-population panels, maximizing genotyping success.
- It accommodates user constraints, including runtime limitations and assay-specific requirements.
Conclusions:
- A novel application, SNPPicker, has been developed to maximize the success of custom multi-population genotyping panels.
- SNPPicker integrates user constraints and platform-specific factors for improved SNP selection.
- The software is available as open-source, with Perl scripts, Java source code, and executables provided for download.
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