Article

A genotype calling algorithm for the Illumina BeadArray platform

Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.
Bioinformatics (Impact Factor: 4.98). 11/2007; 23(20):2741-6. DOI: 10.1093/bioinformatics/btm443
Source: PubMed

ABSTRACT

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.
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.
The C++ executable for the algorithm described here is available by request from the authors.

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Available from: Taane G Clark, Jan 10, 2014
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    • "Genotyping, data calling and automated QC Samples were assayed on the Illumina Human610-Quad BeadChip using the Infinium HD Super Assay (Illumina, San Diego, CA, USA); beadchips were scanned with an iScan. Intensity data, normalised according to the standard Illumina algorithm, was extracted and genotypes called using Illuminus[16]. Sample call rate was calculated and Illuminus re-run using only the samples with a call rate of at least 90 % (to improve cluster definition).Samples having a call rate of less than 95 % or having autosomal heterozygosity values in the tail of the distribution were excluded. "
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    Full-text · Article · Dec 2016 · Genome Medicine
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    • "A number of algorithms are available for processing the raw signal of paired allele intensities into discrete genotype calls (AA, AB, BB) for each SNP in each sample. Current methods include: GenCall [11], Illumina’s proprietary method implemented in the GenomeStudio software; GenoSNP [12]; Illuminus [13]; CRLMM [14-16]; Birdseed [17] and BeagleCall [18]. Three new methods have been proposed recently to meet the challenge of calling low frequency/rare variants on the Illumina platform (M 3[7], zCall [19] and OptiCall [8]). "
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    ABSTRACT: Background SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering. Results Here we develop KRLMM, a new method for converting raw intensities into genotype calls that aims to overcome this issue. Our method is unique in that it applies careful between sample normalization and allows a variable number of clusters k (1, 2 or 3) for each SNP, where k is predicted using the available data. We compare our method to four genotyping algorithms (GenCall, GenoSNP, Illuminus and OptiCall) on several Illumina data sets that include samples from the HapMap project where the true genotypes are known in advance. All methods were found to have high overall accuracy (> 98%), with KRLMM consistently amongst the best. At low minor allele frequency, the KRLMM, OptiCall and GenoSNP algorithms were observed to be consistently more accurate than GenCall and Illuminus on our test data. Conclusions Methods that tailor their approach to calling low frequency variants by either varying the number of clusters (KRLMM) or using information from other SNPs (OptiCall and GenoSNP) offer improved accuracy over methods that do not (GenCall and Illuminus). The KRLMM algorithm is implemented in the open-source crlmm package distributed via the Bioconductor project (http://www.bioconductor.org).
    Full-text · Article · May 2014 · BMC Bioinformatics
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    • "The samples were genotyped using the Illumina HumanHap610Q array. The normalized intensity data was used by the Illluminus calling algorithm [31] to assign genotypes. No calls were assigned if an individual's most likely genotype was called with a posterior probability threshold of less than 0.95. "
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    Full-text · Article · May 2014 · PLoS ONE
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