A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays
Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogatin...
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| Published in | Bioinformatics Vol. 23; no. 12; pp. 1459 - 1467 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
15.06.2007
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1367-4811 1460-2059 |
| DOI | 10.1093/bioinformatics/btm131 |
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| Abstract | Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. Availability: R functions are available upon request from the authors. Contact: yxiao@itsa.ucsf.edu and rufang@biostat.ucsf.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
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| AbstractList | Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. Availability: R functions are available upon request from the authors. Contact: yxiao@itsa.ucsf.edu and rufang@biostat.ucsf.eduSupplementary information: Supplementary data are available at Bioinformatics online. Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. Availability: R functions are available upon request from the authors. Contact: yxiao@itsa.ucsf.edu and rufang@biostat.ucsf.edu Supplementary information: Supplementary data are available at Bioinformatics online. Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls.MOTIVATIONModern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls.We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods.RESULTSWe developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods.R functions are available upon request from the authors.AVAILABILITYR functions are available upon request from the authors. Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. Availability: R functions are available upon request from the authors. Contact: yxiao@itsa.ucsf.edu and rufang@biostat.ucsf.edu Supplementary information: Supplementary data are available at Bioinformatics online. MOTIVATION: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. RESULTS: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. AVAILABILITY: R functions are available upon request from the authors. CONTACT: yxiaotsa.ucsf.edu and rufangiostat.ucsf.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. Availability: R functions are available upon request from the authors. Contact: yxiao@itsa.ucsf.edu and rufang@biostat.ucsf.edu Supplementary information: Supplementary data are available at Bioinformatics online. Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. R functions are available upon request from the authors. |
| Author | Yang, Y.H. Yeh, Ru-Fang Xiao, Yuanyuan Segal, Mark R. |
| Author_xml | – sequence: 1 givenname: Yuanyuan surname: Xiao fullname: Xiao, Yuanyuan organization: Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA and School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia – sequence: 2 givenname: Mark R. surname: Segal fullname: Segal, Mark R. organization: Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA and School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia – sequence: 3 givenname: Y.H. surname: Yang fullname: Yang, Y.H. organization: Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA and School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia – sequence: 4 givenname: Ru-Fang surname: Yeh fullname: Yeh, Ru-Fang organization: Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA and School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia |
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| Keywords | Measurement Allelic imbalance DNA chip Genotype Gene expression Microarray Algorithm Clusterin Original document Computer program Single nucleotide polymorphism Bioinformatics Comparative study |
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| References_xml | – volume: 1 start-page: 1 year: 2002 ident: 2023041105083042600_ article-title: Variance stabilization applied to microarray data calibration and to the quantification of differetial expression publication-title: Bioinformatics – volume-title: Technical report. year: 2006 ident: 2023041105083042600_ article-title: BRLMM: an improved genotype calling method for the genechip human mapping 500 k array set – volume: 49 start-page: 803 year: 1993 ident: 2023041105083042600_ article-title: Model-based gaussian and non-gaussian clustering publication-title: Biometrics doi: 10.2307/2532201 – volume: 4 start-page: 249 year: 2003 ident: 2023041105083042600_ article-title: Exploration, normalization, and summaries of high density oligonucleotide array probe level data publication-title: Biostatistics doi: 10.1093/biostatistics/4.2.249 – volume: 22 start-page: 7 year: 2006 ident: 2023041105083042600_ article-title: A genotype calling algorithm for affymetrix SNP arrays publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti741 – volume: 21 start-page: 1958 year: 2005 ident: 2023041105083042600_ article-title: Dynamic model based algorithms for screening and genotyping over 100 k SNPs on oligonucleotide microarrays publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti275 – volume: 39 start-page: 1 year: 1977 ident: 2023041105083042600_ article-title: Maximum likelihood from incomplete data via EM algorithm (with discussion) publication-title: J. R. Stat. Soc. B doi: 10.1111/j.2517-6161.1977.tb01600.x – volume: 20 start-page: 53 year: 1987 ident: 2023041105083042600_ article-title: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis publication-title: J. Comput. Appl. Math doi: 10.1016/0377-0427(87)90125-7 – volume-title: Technical report. year: 2006 ident: 2023041105083042600_ article-title: Exploration, normalization, and genotype calls of high density oligonucleotide SNP array data – volume: 1 start-page: e65 year: 2005 ident: 2023041105083042600_ article-title: Allele-specific amplification in cancer revealed by SNP array analysis publication-title: PLoS Comput. Biol doi: 10.1371/journal.pcbi.0010065 – volume: 34 start-page: e28 year: 2006 ident: 2023041105083042600_ article-title: Genotyping pooled dna using 100 k SNP microarrays: A step towards genomewide association scans publication-title: Nucleic Acids Res doi: 10.1093/nar/gnj027 – volume: 65 start-page: 6071 year: 2005 ident: 2023041105083042600_ article-title: A robust algorithm for copy number detection for high-density oligonucleotide single nucleotide polymorphism genotyping arrays publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-05-0465 – volume: 22 start-page: 1942 year: 2006 ident: 2023041105083042600_ article-title: GEL: a novel genotype calling algorithm using empirical likelihood publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl341 – volume: 23 start-page: 57 year: 2006 ident: 2023041105083042600_ article-title: SNiPer- HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl536 – volume: 39 start-page: 949 year: 2004 ident: 2023041105083042600_ article-title: Detection of large-scale variation in the human genome publication-title: Nat. Genet doi: 10.1038/ng1416 – volume: 19 start-page: 2397 year: 2003 ident: 2023041105083042600_ article-title: Algorithms for large-scale genotyping microarrays publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg332 – volume: 97 start-page: 611 year: 2002 ident: 2023041105083042600_ article-title: Model-based clustering, discriminant analysis, and density estimation publication-title: JASA doi: 10.1198/016214502760047131 – volume: 426 start-page: 789 year: 2003 ident: 2023041105083042600_ article-title: The international hapmap project publication-title: Nature doi: 10.1038/nature02168 |
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| Snippet | Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive... Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and... MOTIVATION: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive... |
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| SubjectTerms | Algorithms Alleles Bioinformatics Biological and medical sciences Cluster Analysis Computational Biology - methods Fundamental and applied biological sciences. Psychology General aspects Genotype Haplotypes Humans Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Polymorphism, Single Nucleotide Statistical methods |
| Title | A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays |
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