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Article Dans Une Revue BMC Bioinformatics Année : 2008

GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population

Résumé

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.
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hal-02666065 , version 1 (31-05-2020)

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Eleni Giannoulatou, Christopher Yau, Stefano Colella, Jiannis Ragoussis, Chrisopher C. Holmes. GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. BMC Bioinformatics, 2008, 24 (19), pp.2209-2214. ⟨10.1093/bioinformatics/btn386⟩. ⟨hal-02666065⟩
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