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- Title
GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population.
- Authors
Eleni Giannoulatou; Christopher Yau; Stefano Colella; Jiannis Ragoussis; Christopher C. Holmes
- Abstract
Summary: Current genotyping algorithms typically call genotypes by clustering allele-specific intensity data on a single nucleotide polymorphism (SNP) by SNP basis. This approach assumes the availability of a large number of control samples that have been sampled on the same array and platform. We have developed a SNP genotyping algorithm for the Illumina Infinium SNP genotyping assay that is entirely within-sample and does not require the need for a population of control samples nor parameters derived from such a population. Our algorithm exhibits high concordance with current methods and >99% call accuracy on HapMap samples. The ability to call genotypes using only within-sample information makes the method computationally light and practical for studies involving small sample sizes and provides a valuable independent quality control metric for other population-based approaches. Availability: http://www.stats.ox.ac.uk/~giannoul/GenoSNP/ Contact: cholmes@stats.ox.ac.uk
- Publication
Bioinformatics, 2008, Vol 24, Issue 19, p2209
- ISSN
1367-4803
- Publication type
Academic Journal
- DOI
10.1093/bioinformatics/btn386