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GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis
Motivation: Genome-wide association studies are beginning to elucidate how our genetic differences contribute to susceptibility and severity of disease. While computational tools have previously been developed to support various aspects of genome-wide association studies, there is currently a need f...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2820681/ https://www.ncbi.nlm.nih.gov/pubmed/20053839 http://dx.doi.org/10.1093/bioinformatics/btp714 |
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author | Fong, Christine Ko, Dennis C. Wasnick, Michael Radey, Matthew Miller, Samuel I. Brittnacher, Mitchell |
author_facet | Fong, Christine Ko, Dennis C. Wasnick, Michael Radey, Matthew Miller, Samuel I. Brittnacher, Mitchell |
author_sort | Fong, Christine |
collection | PubMed |
description | Motivation: Genome-wide association studies are beginning to elucidate how our genetic differences contribute to susceptibility and severity of disease. While computational tools have previously been developed to support various aspects of genome-wide association studies, there is currently a need for informatics solutions that facilitate the integration of data from multiple sources. Results: Here we present GWAS Analyzer, a database driven web-based tool that integrates genotype and phenotype data, association analysis results and genomic annotations from multiple public resources. GWAS Analyzer contains features for browsing these interrelated data, exploring phenotypic values by family or genotype, and filtering association results based on multiple criteria. The utility of the tool has been demonstrated by a genome-wide association study of human in vitro susceptibility to bacterial infection. GWAS Analyzer facilitated management of large sets of phenotype and genotype data, analysis of phenotypic variation and heritability, and most importantly, generation of a refined set of candidate single nucleotide polymorphisms (SNPs). The tool revealed a SNP that was experimentally validated to be associated with increased cell death among Salmonella infected HapMap cell lines. Availability: http://www.nwrce.org/gwas-analyzer Contact: mbrittna@u.washington.edu Supplementary Information: Supplementary data are available at Bioinformatics online. |
format | Text |
id | pubmed-2820681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-28206812010-02-12 GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis Fong, Christine Ko, Dennis C. Wasnick, Michael Radey, Matthew Miller, Samuel I. Brittnacher, Mitchell Bioinformatics Original Papers Motivation: Genome-wide association studies are beginning to elucidate how our genetic differences contribute to susceptibility and severity of disease. While computational tools have previously been developed to support various aspects of genome-wide association studies, there is currently a need for informatics solutions that facilitate the integration of data from multiple sources. Results: Here we present GWAS Analyzer, a database driven web-based tool that integrates genotype and phenotype data, association analysis results and genomic annotations from multiple public resources. GWAS Analyzer contains features for browsing these interrelated data, exploring phenotypic values by family or genotype, and filtering association results based on multiple criteria. The utility of the tool has been demonstrated by a genome-wide association study of human in vitro susceptibility to bacterial infection. GWAS Analyzer facilitated management of large sets of phenotype and genotype data, analysis of phenotypic variation and heritability, and most importantly, generation of a refined set of candidate single nucleotide polymorphisms (SNPs). The tool revealed a SNP that was experimentally validated to be associated with increased cell death among Salmonella infected HapMap cell lines. Availability: http://www.nwrce.org/gwas-analyzer Contact: mbrittna@u.washington.edu Supplementary Information: Supplementary data are available at Bioinformatics online. Oxford University Press 2010-02-15 2010-01-06 /pmc/articles/PMC2820681/ /pubmed/20053839 http://dx.doi.org/10.1093/bioinformatics/btp714 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Fong, Christine Ko, Dennis C. Wasnick, Michael Radey, Matthew Miller, Samuel I. Brittnacher, Mitchell GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis |
title | GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis |
title_full | GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis |
title_fullStr | GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis |
title_full_unstemmed | GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis |
title_short | GWAS Analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis |
title_sort | gwas analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2820681/ https://www.ncbi.nlm.nih.gov/pubmed/20053839 http://dx.doi.org/10.1093/bioinformatics/btp714 |
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