GWAS3D

GWAS3D prioritizes and annotates noncoding genetic variants by integrating chromatin state, functional genomics, sequence motifs, conservation, and chromosome interaction data to assess regulatory impact and link GWAS signals to regulatory elements.

Key Features:

• Integration of Diverse Annotations: GWAS3D analyzes variants using cell type-specific chromatin state, epigenetic modifications, sequence motifs, and cross-species conservation annotations.
• Inference of Regulatory Elements: The tool infers regulatory elements from genome-wide chromosome interaction data, chromatin marks in 16 different cell types, and 73 regulatory factor motifs from the Encyclopedia of DNA Elements project.
• Reprioritization of Genetic Variants: Variants are reprioritized based on association with functional elements, risk haplotypes, binding affinities, conservation levels, and P-values from original GWAS studies.
• Cell Type-Specific Analysis: Analyses incorporate cell type-specific chromatin marks and interactions to enable context-dependent interpretation, as demonstrated for low-density lipoprotein cholesterol studies.
• Comprehensive Annotations and Visualizations: GWAS3D generates extensive annotations and visualizations to aid interpretation of regulatory annotations and variant prioritization.

Scientific Applications:

• Prioritization of regulatory variants: Prioritize and annotate variants in enhancers and promoters to identify candidates underlying GWAS loci.
• Linking GWAS loci to target genes: Integrate chromosome interaction and functional annotations to suggest regulatory connections between variants and target genes.
• Dissecting molecular mechanisms of complex traits: Combine multi-omic annotations to investigate genotype-phenotype relationships and regulatory mechanisms in diseases and traits.

Methodology:

Collecting and integrating annotations from genomic datasets; inferring regulatory elements using genome-wide chromosome interaction data and chromatin modification marks across cell types; reprioritizing genetic variants by combining functional relevance, conservation, binding affinities, risk haplotypes, and GWAS P-values.