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Bioinformatics. 2016 Apr 15;32(8):1247-9. doi: 10.1093/bioinformatics/btv708. Epub 2015 Dec 15.

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

Author information

1
Department of General Biology.
2
Department of Computer Science and.
3
Department of Statistics, Federal University of Minas Gerais, Belo Horizonte, Brazil.

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.

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.

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).

CONTACT:

gilderlanio@gmail.com or maira.r.rodrigues@gmail.com

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26673785
DOI:
10.1093/bioinformatics/btv708
[Indexed for MEDLINE]

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