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Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data
Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of ex...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4744743/ https://www.ncbi.nlm.nih.gov/pubmed/26037133 http://dx.doi.org/10.1002/humu.22818 |
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author | Watson, Christopher M. Crinnion, Laura A. Gurgel‐Gianetti, Juliana Harrison, Sally M. Daly, Catherine Antanavicuite, Agne Lascelles, Carolina Markham, Alexander F. Pena, Sergio D. J. Bonthron, David T. Carr, Ian M. |
author_facet | Watson, Christopher M. Crinnion, Laura A. Gurgel‐Gianetti, Juliana Harrison, Sally M. Daly, Catherine Antanavicuite, Agne Lascelles, Carolina Markham, Alexander F. Pena, Sergio D. J. Bonthron, David T. Carr, Ian M. |
author_sort | Watson, Christopher M. |
collection | PubMed |
description | Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution. |
format | Online Article Text |
id | pubmed-4744743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-47447432016-02-18 Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data Watson, Christopher M. Crinnion, Laura A. Gurgel‐Gianetti, Juliana Harrison, Sally M. Daly, Catherine Antanavicuite, Agne Lascelles, Carolina Markham, Alexander F. Pena, Sergio D. J. Bonthron, David T. Carr, Ian M. Hum Mutat Informatics Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution. John Wiley and Sons Inc. 2015-07-22 2015-09 /pmc/articles/PMC4744743/ /pubmed/26037133 http://dx.doi.org/10.1002/humu.22818 Text en © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Informatics Watson, Christopher M. Crinnion, Laura A. Gurgel‐Gianetti, Juliana Harrison, Sally M. Daly, Catherine Antanavicuite, Agne Lascelles, Carolina Markham, Alexander F. Pena, Sergio D. J. Bonthron, David T. Carr, Ian M. Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data |
title | Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data |
title_full | Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data |
title_fullStr | Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data |
title_full_unstemmed | Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data |
title_short | Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data |
title_sort | rapid detection of rare deleterious variants by next generation sequencing with optional microarray snp genotype data |
topic | Informatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4744743/ https://www.ncbi.nlm.nih.gov/pubmed/26037133 http://dx.doi.org/10.1002/humu.22818 |
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