Literature DB >> 18653518

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

Eleni Giannoulatou1, Christopher Yau, Stefano Colella, Jiannis Ragoussis, Christopher C Holmes.   

Abstract

UNLABELLED: 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/.

Mesh:

Year:  2008        PMID: 18653518     DOI: 10.1093/bioinformatics/btn386

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  38 in total

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10.  Integrated study of copy number states and genotype calls using high-density SNP arrays.

Authors:  Wei Sun; Fred A Wright; Zhengzheng Tang; Silje H Nordgard; Peter Van Loo; Tianwei Yu; Vessela N Kristensen; Charles M Perou
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