REALGAR

REALGAR: Integration of GWAS and Multi-Omics Data for Gene-Disease Analysis

REALGAR integrates disease-specific and tissue-specific omics datasets with genome-wide association study (GWAS) findings to support gene-centric analysis of gene-disease associations and functional validation study design.

Key Features:

• Multi-Omics Integration: Combines GWAS results with transcriptomic, epigenomic, and expression quantitative trait loci (eQTL) data across cell and tissue types.
• Large-Scale Dataset Aggregation: Incorporates over 170,000 transcriptomic and epigenomic datasets from the Gene Expression Omnibus under diverse disease, treatment, and exposure conditions.
• Gene-Centric Analysis: Enables integrated evaluation of gene transcription, transcription factor binding sites, and regulatory variation associated with disease.

Scientific Applications:

• Airway Disease Research: Supports hypothesis generation and functional validation of genes associated with asthma and related airway diseases.
• Post-GWAS Interpretation: Facilitates interpretation of GWAS signals using cell type- and tissue-specific regulatory context.

Methodology:

REALGAR aggregates GWAS-identified variants and integrates them with transcriptomic, epigenomic, transcription factor binding site, and eQTL datasets from the Gene Expression Omnibus to enable cross-layer analysis of regulatory and transcriptional mechanisms underlying complex diseases.