SNiPA

SNiPA annotates and analyzes bi-allelic genomic variants (SNPs and SNVs) by integrating linkage disequilibrium and multiple annotation datasets to facilitate functional interpretation and linking variants to genes, phenotypic trait associations, and expression and metabolic QTLs.

Key Features:

• Bi-allelic variant annotation: Provides functional annotation for single nucleotide polymorphisms (SNPs) and single nucleotide variations (SNVs).
• Linkage disequilibrium data: Utilizes LD information derived from the 1000 Genomes Project for analysis of non-random allele associations.
• Proxy variant identification: Identifies LD-based proxy variants to link associated variants to nearby genetic signals.
• Integrated annotation layers: Combines multiple annotation datasets into a unified dataset for each variant.
• Gene annotations: Associates variants with gene models and gene-level information.
• Phenotypic trait associations: Links variants to reported trait and disease associations.
• Quantitative trait loci (QTLs): Includes expression QTLs (eQTLs) and metabolic QTLs for molecular phenotype associations.
• Heterogeneous data integration: Harmonizes diverse genomic data formats into a consolidated annotation framework.

Scientific Applications:

• GWAS interpretation: Facilitates interpretation of genome-wide association study signals by providing LD context and functional annotations.
• Variant-to-gene mapping: Aids in linking associated variants to candidate genes using LD and gene annotations.
• QTL and molecular mechanism exploration: Supports mapping of variants to expression and metabolic QTLs to explore molecular consequences.
• Candidate gene/pathway prioritization: Assists in prioritizing genes and pathways underlying disease or trait associations.

Methodology:

Performs LD-based proxy analysis using linkage disequilibrium derived from the 1000 Genomes Project and overlays these results with integrated annotation layers including gene annotations, phenotype associations, expression QTLs, and metabolic QTLs.