geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation

Arthur Wuster; Diana Chang; Timothy W Behrens; Tushar R Bhangale

doi:10.1093/bioinformatics/btw698

geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation

Bioinformatics. 2017 Feb 15;33(4):599-600. doi: 10.1093/bioinformatics/btw698.

Authors

Arthur Wuster^{1

2}, Diana Chang¹, Timothy W Behrens¹, Tushar R Bhangale^{1

2}

Affiliations

¹ Department of Human Genetics.
² Department of Bioinformatics and Computational Biology, Genentech Inc, South San Francisco, CA 94080, USA.

Abstract

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.

Contact: wustera@gene.com.

MeSH terms

Genetic Association Studies / methods*
Genome, Human
Humans
Phenotype
Polymorphism, Genetic*
Promoter Regions, Genetic*
Quantitative Trait Loci
Software*