Disease-Ancestry Network - DANCE

GWAS-hits, human complex phenotypes and population genetic variability in a Network-based approach.

SNP-Disease Network

The SNP-Disease network is a bipartite network where nodes are either phenotypes or SNPs and each phenotype is connected by an edge to a SNP if there is an association - a GWAS-hit. Thus, different phenotypes interconnect if they share risk SNPs. Ancestry information is represented as a property of the SNP node and consists of risk-allele frequencies in different populations and Fst values between pairs of populations.

Disease-Disease Network

The Disease-Disease Network is a projection of the SNP-Disease Network and captures the relationships between diseases by linking them if they share SNPs. It is implemented as an edge-weighted network with weights representing the Jaccard’s Coefficient for each pair of phenotypes. Also, phenotype nodes are annotated with its medical class for visual investigation.

Query sessions.

     DANCE is database and web tool to investigate associations between traits and common SNPs. Our data is a subset of both 1000 Genomes Project data and NHGRI GWAS Catalog. DANCE integrates data from the 1000 Genomes Project (Phase 3) and the NHGRI GWAS Catalog (v. March 2020) databases, comprising data from 3885 public GWAS extracted from GWAS Catalog. DANCE database stores 149.262 associations between 4208 phenotypes and 120.748 risk-alleles. DANCE was developed in the Laboratory of Human Genetic Diversity (LDGH). LDGH's research integrates concepts and tools of population genetics, genetic epidemiology, bioinformatics and functional genetics. The overarching objective is to enhance our understanding of evolution and the genetic bases of complex traits and diseases, with an emphasis on immune-related genes and their influence on infection and cancer. We have a particular interest in native and admixed populations of Latin America in the context of global human genomic diversity. More about LDGH in : http://www.ldgh.com.br/

Funding agencies for research:


LDGH 2020