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geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation
MOTIVATION: We have developed geneAttribution, an R package that assigns candidate causal gene(s) to a risk variant identified by a genetic association study such as a GWAS. The method combines user-supplied functional annotation such as expression quantitative trait loci (eQTL) or Hi-C genome confo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408921/ https://www.ncbi.nlm.nih.gov/pubmed/28035029 http://dx.doi.org/10.1093/bioinformatics/btw698 |
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author | Wuster, Arthur Chang, Diana Behrens, Timothy W Bhangale, Tushar R |
author_facet | Wuster, Arthur Chang, Diana Behrens, Timothy W Bhangale, Tushar R |
author_sort | Wuster, Arthur |
collection | PubMed |
description | MOTIVATION: We have developed geneAttribution, an R package that assigns candidate causal gene(s) to a risk variant identified by a genetic association study such as a GWAS. The method combines user-supplied functional annotation such as expression quantitative trait loci (eQTL) or Hi-C genome conformation data and reports the most likely candidate genes. In the absence of annotation data, geneAttribution relies on the distances between the genes and the input variant. AVAILABILITY AND IMPLEMENTATION: The package is freely available from http://www.bioconductor.org/. A quick-start vignette is included with the package. |
format | Online Article Text |
id | pubmed-5408921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-54089212017-05-03 geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation Wuster, Arthur Chang, Diana Behrens, Timothy W Bhangale, Tushar R Bioinformatics Applications Notes MOTIVATION: We have developed geneAttribution, an R package that assigns candidate causal gene(s) to a risk variant identified by a genetic association study such as a GWAS. The method combines user-supplied functional annotation such as expression quantitative trait loci (eQTL) or Hi-C genome conformation data and reports the most likely candidate genes. In the absence of annotation data, geneAttribution relies on the distances between the genes and the input variant. AVAILABILITY AND IMPLEMENTATION: The package is freely available from http://www.bioconductor.org/. A quick-start vignette is included with the package. Oxford University Press 2017-02-15 2016-11-29 /pmc/articles/PMC5408921/ /pubmed/28035029 http://dx.doi.org/10.1093/bioinformatics/btw698 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Wuster, Arthur Chang, Diana Behrens, Timothy W Bhangale, Tushar R geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation |
title | geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation |
title_full | geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation |
title_fullStr | geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation |
title_full_unstemmed | geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation |
title_short | geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation |
title_sort | geneattribution: trait agnostic identification of candidate genes associated with noncoding variation |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408921/ https://www.ncbi.nlm.nih.gov/pubmed/28035029 http://dx.doi.org/10.1093/bioinformatics/btw698 |
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