A genotype calling algorithm for the Illumina BeadArray platform
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 geno...
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| Published in | Bioinformatics Vol. 23; no. 20; pp. 2741 - 2746 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
15.10.2007
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI | 10.1093/bioinformatics/btm443 |
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| Abstract | 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 |
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| AbstractList | 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 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. 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 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 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.MOTIVATIONLarge-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.RESULTSWe 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.AVAILABILITYThe C++ executable for the algorithm described here is available by request from the authors. 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. |
| Author | Small, Kerrin S. Kwiatkowski, Dominic P. Inouye, Michael Clark, Taane G. Gwilliam, Rhian Teo, Yik Y. Deloukas, Panagiotis |
| AuthorAffiliation | 1 Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK 2 Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK |
| AuthorAffiliation_xml | – name: 2 Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK – name: 1 Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK |
| Author_xml | – sequence: 1 givenname: Yik Y. surname: Teo fullname: Teo, Yik Y. organization: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN and Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK – sequence: 2 givenname: Michael surname: Inouye fullname: Inouye, Michael organization: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN and Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK – sequence: 3 givenname: Kerrin S. surname: Small fullname: Small, Kerrin S. organization: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN and Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK – sequence: 4 givenname: Rhian surname: Gwilliam fullname: Gwilliam, Rhian organization: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN and Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK – sequence: 5 givenname: Panagiotis surname: Deloukas fullname: Deloukas, Panagiotis organization: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN and Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK – sequence: 6 givenname: Dominic P. surname: Kwiatkowski fullname: Kwiatkowski, Dominic P. organization: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN and Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK – sequence: 7 givenname: Taane G. surname: Clark fullname: Clark, Taane G. organization: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN and Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19883526$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/17846035$$D View this record in MEDLINE/PubMed |
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| Keywords | Genotype Algorithm Bioinformatics |
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| Notes | The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. istex:19C7D12E9B8EC95D74F294F1C29A734F406375BA To whom correspondence should be addressed. ark:/67375/HXZ-2R19P6XD-J ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| References | Bolstad (2023041105585924000_) 2003; 19 Affymetrix Inc (2023041105585924000_) 2006 Gunderson (2023041105585924000_) 2006; 7 Di (2023041105585924000_) 2005; 21 Rabbee (2023041105585924000_) 2006; 1 Rioux (2023041105585924000_) 2007; 39 Kermani (2023041105585924000_) 2005 Saxena (2023041105585924000_) 2007; 316 Scott (2023041105585924000_) 2007; 316 Gudmundsson (2023041105585924000_) 2007; 39 Plagnol (2023041105585924000_) 2007; 3 Steemers (2023041105585924000_) 2007; 2 Xiao (2023041105585924000_) 2007; 27 Moorhead (2023041105585924000_) 2006; 14 Carvalho (2023041105585924000_) 2007; 8 The Wellcome Trust Case Control Consortium (2023041105585924000_) 2007; 447 Yeager (2023041105585924000_) 2007; 39 |
| References_xml | – volume: 447 start-page: 661 year: 2007 ident: 2023041105585924000_ article-title: Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls publication-title: Nature doi: 10.1038/nature05911 – volume: 27 start-page: 1459 year: 2007 ident: 2023041105585924000_ article-title: A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm131 – volume: 21 start-page: 1958 year: 2005 ident: 2023041105585924000_ article-title: Dynamic model based algorithms for screening and genotyping over 100K SNPs on oligonucleotide microarrays publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti275 – year: 2006 ident: 2023041105585924000_ article-title: BRLMM: an improved genotype calling method for the GenChip Human Mapping 500K Array Set – volume: 19 start-page: 185 year: 2003 ident: 2023041105585924000_ article-title: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias publication-title: Bioinformatics doi: 10.1093/bioinformatics/19.2.185 – volume: 39 start-page: 631 year: 2007 ident: 2023041105585924000_ article-title: Genome-wide association study identifies a second prostate cancer susceptibility variantat 8q24 publication-title: Nat. Genet doi: 10.1038/ng1999 – volume: 7 start-page: 641 year: 2006 ident: 2023041105585924000_ article-title: Whole-genome genotyping of haplotype tag single nucleotide polymorphisms publication-title: Pharmacogenomics doi: 10.2217/14622416.7.4.641 – volume: 14 start-page: 207 year: 2006 ident: 2023041105585924000_ article-title: Optimal genotype determination in highly multiplexed SNP data publication-title: Eur. J. Hum. Genet doi: 10.1038/sj.ejhg.5201528 – volume: 39 start-page: 645 year: 2007 ident: 2023041105585924000_ article-title: Genome-wide association study of prostate cancer identifies a second risk locus at 8q24 publication-title: Nat. Genet doi: 10.1038/ng2022 – volume: 316 start-page: 1341 year: 2007 ident: 2023041105585924000_ article-title: A genome-wide association study of Type 2 diabetes in Finns detects multiple susceptibility variants publication-title: Science doi: 10.1126/science.1142382 – volume: 8 start-page: 485 year: 2007 ident: 2023041105585924000_ article-title: Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data publication-title: Biostatistics doi: 10.1093/biostatistics/kxl042 – volume: 3 start-page: e74 year: 2007 ident: 2023041105585924000_ article-title: A method to address differential bias in genotyping in large-scale association studies publication-title: PLoS Genet doi: 10.1371/journal.pgen.0030074 – volume: 316 start-page: 1331 year: 2007 ident: 2023041105585924000_ article-title: Genome-wide association analysis identifies loci for Type 2 diabetes and triglyceride levels publication-title: Science doi: 10.1126/science.1142358 – volume: 39 start-page: 596 year: 2007 ident: 2023041105585924000_ article-title: Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy n disease pathogenesis publication-title: Nat. Genet doi: 10.1038/ng2032 – volume: 1 start-page: 7 year: 2006 ident: 2023041105585924000_ article-title: A genotype calling algorithm for Affymetrix SNP arrays publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti741 – year: 2005 ident: 2023041105585924000_ article-title: Artificial intelligence and global normalization methods for genotyping – volume: 2 start-page: 41 year: 2007 ident: 2023041105585924000_ article-title: Whole genome genotyping technologies on the BeadArray platform publication-title: Biotechnol. J doi: 10.1002/biot.200600213 |
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| Snippet | Motivation: Large-scale genotyping relies on the use of unsupervised automated calling algorithms to assign genotypes to hybridization data. A number of such... Large-scale genotyping relies on the use of unsupervised automated calling algorithms to assign genotypes to hybridization data. A number of such calling... MOTIVATION: Large-scale genotyping relies on the use of unsupervised automated calling algorithms to assign genotypes to hybridization data. A number of such... |
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| SubjectTerms | 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 |
| Title | A genotype calling algorithm for the Illumina BeadArray platform |
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