Optimal genotype determination in highly multiplexed SNP data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-02

AUTHORS

Martin Moorhead, Paul Hardenbol, Farooq Siddiqui, Matthew Falkowski, Carsten Bruckner, James Ireland, Hywel B Jones, Maneesh Jain, Thomas D Willis, Malek Faham

ABSTRACT

High-throughput genotyping technologies that enable large association studies are already available. Tools for genotype determination starting from raw signal intensities need to be automated, robust, and flexible to provide optimal genotype determination given the specific requirements of a study. The key metrics describing the performance of a custom genotyping study are assay conversion, call rate, and genotype accuracy. These three metrics can be traded off against each other. Using the highly multiplexed Molecular Inversion Probe technology as an example, we describe a methodology for identifying the optimal trade-off. The methodology comprises: a robust clustering algorithm and assessment of a large number of data filter sets. The clustering algorithm allows for automatic genotype determination. Many different sets of filters are then applied to the clustered data, and performance metrics resulting from each filter set are calculated. These performance metrics relate to the power of a study and provide a framework to choose the most suitable filter set to the particular study. More... »

PAGES

207

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/sj.ejhg.5201528

DOI

http://dx.doi.org/10.1038/sj.ejhg.5201528

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008259745

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16306880


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