GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. 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/
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Stephen W. Erickson, Joshua C. Callaway: SNPMClust: Bivariate Gaussian Genotype Clustering and Calling for Illumina Microarrays (2016) not zbMATH
- Bengtsson, Henrik; Neuvial, Pierre; Speed, Terence P.: Tumorboost: normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays (2010) ioport
- Greenman, Chris D.; Bignell, Graham; Butler, Adam; Edkins, Sarah; Hinton, Jon: PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data (2010)
- Giannoulatou, Eleni; Yau, Christopher; Colella, Stefano; Ragoussis, Jiannis; Holmes, Christopher C.: Genosnp: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. (2008) ioport