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- Title
Array-based genotyping in S.cerevisiae using semi-supervised clustering.
- Authors
Richard Bourgon; Eugenio Mancera; Alessandro Brozzi; Lars M. Steinmetz; Wolfgang Huber
- Abstract
Motivation: Microarrays provide an accurate and cost-effective method for genotyping large numbers of individuals at high resolution. The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals. Several issues inherent to short oligonucleotide arraysâcross-hybridization, or variability in probe response to targetâhave the potential to produce genotyping errors. There is a need for improved statistical methods for array-based genotyping. Results: We developed ssGenotyping (ssG), a multivariate, semi-supervised approach for using microarrays to genotype haploid individuals at thousands of polymorphic sites. Using a meiotic recombination dataset, we show that ssG is more accurate than existing supervised classification methods, and that it produces denser marker coverage. The ssG algorithm is able to fit probe-specific affinity differences and to detect and filter spurious signal, permitting high-confidence genotyping at nucleotide resolution. We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probes specific target sequence is unchanged. As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssGs semi-supervised approach, on the other hand, adapts automatically. Availability: The ssGenotyping software is implemented in R. It is currently available for download (www.ebi.ac.uk/â¼bourgon/yeast_genotyping/ssG) and is being submitted to Bioconductor. Contact: bourgon@ebi.ac.uk Supplementary information: Supplementary data and a version including color figures are available at Bioinformatics online.
- Publication
Bioinformatics, 2009, Vol 25, Issue 8, p1056
- ISSN
1367-4803
- Publication type
Academic Journal
- DOI
10.1093/bioinformatics/btp104