A genotype calling algorithm for the Illumina BeadArray platform

• Published in: Bioinformatics Vol. 23; no. 20; pp. 2741 - 2746
• Main Authors: Teo, Yik Y., Inouye, Michael, Small, Kerrin S., Gwilliam, Rhian, Deloukas, Panagiotis, Kwiatkowski, Dominic P., Clark, Taane G.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.10.2007; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological and medical sciences; Chromosome Mapping - instrumentation; Chromosome Mapping - methods; Cluster Analysis; Fundamental and applied biological sciences. Psychology; General aspects; Genotype; Genotypes; Hybridization; In Situ Hybridization, Fluorescence - instrumentation; In Situ Hybridization, Fluorescence - methods; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Oligonucleotide Array Sequence Analysis - instrumentation; Oligonucleotide Array Sequence Analysis - methods; Polymorphism, Single Nucleotide - genetics; Sequence Analysis, DNA - instrumentation; Sequence Analysis, DNA - methods; Genotype; Algorithm; Bioinformatics
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btm443

Motivation: Large-scale genotyping relies on the use of unsupervised automated calling algorithms to assign genotypes to hybridization data. A number of such calling algorithms have been recently established for the Affymetrix GeneChip genotyping technology. Here, we present a fast and accurate genotype calling algorithm for the Illumina BeadArray genotyping platforms. As the technology moves towards assaying millions of genetic polymorphisms simultaneously, there is a need for an integrated and easy-to-use software for calling genotypes. Results: We have introduced a model-based genotype calling algorithm which does not rely on having prior training data or require computationally intensive procedures. The algorithm can assign genotypes to hybridization data from thousands of individuals simultaneously and pools information across multiple individuals to improve the calling. The method can accommodate variations in hybridization intensities which result in dramatic shifts of the position of the genotype clouds by identifying the optimal coordinates to initialize the algorithm. By incorporating the process of perturbation analysis, we can obtain a quality metric measuring the stability of the assigned genotype calls. We show that this quality metric can be used to identify SNPs with low call rates and accuracy. Availability: The C++ executable for the algorithm described here is available by request from the authors. Contact: teo@well.ox.ac.uk or tgc@well.ox.ac.uk