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

• Published in: Bioinformatics Vol. 32; no. 8; pp. 1247 - 1249
• Main Authors: Araújo, Gilderlanio S, Lima, Lucas Henrique C, Schneider, Silvana, Leal, Thiago P, da Silva, Ana Paula C, Vaz de Melo, Pedro O S, Tarazona-Santos, Eduardo, Scliar, Marília O, Rodrigues, Maíra R
• Format: Journal Article
• Language: English
• Published: England 15.04.2016
• Subjects: Bioinformatics; Browsers (computer); Dance; Diseases; Genetic Variation; Genetics; Genome; Genome-Wide Association Study; Human; Humans; Internet; Networks; Phenotype; Populations
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btv708

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. 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. 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). gilderlanio@gmail.com or maira.r.rodrigues@gmail.com Supplementary data are available at Bioinformatics online.