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Integrating, summarizing and visualizing GWAS-hits and human diversity with DANCE (Disease-ANCEstry networks).

Authors :
Araújo GS
Lima LH
Schneider S
Leal TP
da Silva AP
Vaz de Melo PO
Tarazona-Santos E
Scliar MO
Rodrigues MR
Source :
Bioinformatics (Oxford, England) [Bioinformatics] 2016 Apr 15; Vol. 32 (8), pp. 1247-9. Date of Electronic Publication: 2015 Dec 15.
Publication Year :
2016

Abstract

Motivation: The 1000 Genomes Project (1KGP) and thousands of Genome-Wide Association Studies (GWAS) performed during the last years have generated an enormous amount of information that needs to be integrated to better understand the genetic architecture of complex diseases in different populations. This integration is important in areas such as genetics, epidemiology, anthropology, as well as admixture mapping design and GWAS-replications. Network-based approaches that explore the genetic bases of human diseases and traits have not yet incorporated information on genetic diversity among human populations.<br />Results: We propose Disease-ANCEstry networks (DANCE), a graph-based web tool that allows to integrate and visualize information on human complex phenotypes and their GWAS-hits, as well as their risk allele frequencies in different populations. DANCE provides an interactive way to explore the human SNP-Disease Network and its projection, a Disease-Disease Network. With these functionalities, DANCE fills a gap in our ability to handle and understand the knowledge generated by GWAS and 1KGP. We provide a number of case studies that show how DANCE can be used to explore the relationships between human complex diseases, their genetic bases and variability in different human populations.<br />Availability and Implementation: DANCE is freely available at http://ldgh.com.br/dance/ We recommend using DANCE with Mozilla Firefox, Safari, Chrome or Internet Explorer (v9 or v10).<br />Contact: gilderlanio@gmail.com or maira.r.rodrigues@gmail.com<br />Supplementary Information: Supplementary data are available at Bioinformatics online.<br /> (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Details

Language :
English
ISSN :
1367-4811
Volume :
32
Issue :
8
Database :
MEDLINE
Journal :
Bioinformatics (Oxford, England)
Publication Type :
Academic Journal
Accession number :
26673785
Full Text :
https://doi.org/10.1093/bioinformatics/btv708