Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array
Accurate assignment of copy number at known copy number variant (CNV) loci is important for both increasing understanding of the structural evolution of genomes as well as for carrying out association studies of copy number with disease. As with calling SNP genotypes, the task can be framed as a clu...
| Những tác giả chính: | , , , |
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| Định dạng: | Journal article |
| Ngôn ngữ: | English |
| Được phát hành: |
2011
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| _version_ | 1826266106716749824 |
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| author | Cardin, N Holmes, C Donnelly, P Marchini, J |
| author_facet | Cardin, N Holmes, C Donnelly, P Marchini, J |
| author_sort | Cardin, N |
| collection | OXFORD |
| description | Accurate assignment of copy number at known copy number variant (CNV) loci is important for both increasing understanding of the structural evolution of genomes as well as for carrying out association studies of copy number with disease. As with calling SNP genotypes, the task can be framed as a clustering problem but for a number of reasons assigning copy number is much more challenging. CNV assays have lower signal-to-noise ratios than SNP assays, often display heavy tailed and asymmetric intensity distributions, contain outlying observations and may exhibit systematic technical differences among different cohorts. In addition, the number of copy-number classes at a CNV in the population may be unknown a priori. Due to these complications, automatic and robust assignment of copy number from array data remains a challenging problem. We have developed a copy number assignment algorithm, CNVCALL, for a targeted CNV array, such as that used by the Wellcome Trust Case Control Consortium's recent CNV association study. We use a Bayesian hierarchical mixture model that robustly identifies both the number of different copy number classes at a specific locus as well as relative copy number for each individual in the sample. This approach is fully automated which is a critical requirement when analyzing large numbers of CNVs. We illustrate the methods performance using real data from the Wellcome Trust Case Control Consortium's CNV association study and using simulated data. © 2011 Wiley-Liss, Inc. |
| first_indexed | 2024-03-06T20:33:54Z |
| format | Journal article |
| id | oxford-uuid:31f8c8f6-a578-493d-bb5c-78cba9acb5f7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:33:54Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:31f8c8f6-a578-493d-bb5c-78cba9acb5f72022-03-26T13:11:12ZBayesian hierarchical mixture modeling to assign copy number from a targeted CNV arrayJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:31f8c8f6-a578-493d-bb5c-78cba9acb5f7EnglishSymplectic Elements at Oxford2011Cardin, NHolmes, CDonnelly, PMarchini, JAccurate assignment of copy number at known copy number variant (CNV) loci is important for both increasing understanding of the structural evolution of genomes as well as for carrying out association studies of copy number with disease. As with calling SNP genotypes, the task can be framed as a clustering problem but for a number of reasons assigning copy number is much more challenging. CNV assays have lower signal-to-noise ratios than SNP assays, often display heavy tailed and asymmetric intensity distributions, contain outlying observations and may exhibit systematic technical differences among different cohorts. In addition, the number of copy-number classes at a CNV in the population may be unknown a priori. Due to these complications, automatic and robust assignment of copy number from array data remains a challenging problem. We have developed a copy number assignment algorithm, CNVCALL, for a targeted CNV array, such as that used by the Wellcome Trust Case Control Consortium's recent CNV association study. We use a Bayesian hierarchical mixture model that robustly identifies both the number of different copy number classes at a specific locus as well as relative copy number for each individual in the sample. This approach is fully automated which is a critical requirement when analyzing large numbers of CNVs. We illustrate the methods performance using real data from the Wellcome Trust Case Control Consortium's CNV association study and using simulated data. © 2011 Wiley-Liss, Inc. |
| spellingShingle | Cardin, N Holmes, C Donnelly, P Marchini, J Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array |
| title | Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array |
| title_full | Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array |
| title_fullStr | Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array |
| title_full_unstemmed | Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array |
| title_short | Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array |
| title_sort | bayesian hierarchical mixture modeling to assign copy number from a targeted cnv array |
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