XGR

XGR facilitates the biological interpretation of genomic summary data by integrating ontology, annotation, and network-driven methods to identify genes, pathways, and variant relationships from GWAS and eQTL datasets.

Key Features:

• Enhanced Interpretation: Leverages prior biological knowledge and relationships to improve the interpretability of genomic summary datasets.
• Ontology, Annotation, and Network-Driven Approaches: Incorporates ontology frameworks, detailed annotations, and systems biology network-driven methodologies to contextualize genetic data within biological networks and pathways.
• Application to GWAS and eQTL Data: Applies analyses to GWAS and eQTL summary datasets to investigate genomic landscapes linked to activated innate immune responses and immunological diseases.
• Disease Taxonomy Support: Provides genomic evidence supporting a taxonomy of diseases spanning autoimmune to autoinflammatory disorders.
• SNP-Modulated Gene Networks and Pathways: Defines single nucleotide polymorphism (SNP)-modulated gene networks and pathways and identifies those shared or unique across diseases.
• Functional, Phenotypic, and Epigenomic Annotations: Facilitates functional, phenotypic, and epigenomic annotation of genes and variants to enhance biological interpretation.
• Exploration of Annotation-Based Relationships: Enables exploration of relationships between genetic variants based on their annotations.

Scientific Applications:

• GWAS/eQTL interpretation: Identifies variant–gene–pathway associations from GWAS and eQTL summary statistics.
• Immune-response investigation: Characterizes genomic features associated with activated innate immune responses and common immunological diseases.
• Disease-spectrum characterization: Supports analysis of disease relationships across the autoimmune to autoinflammatory spectrum.
• Network and pathway comparison: Identifies shared and disease-specific SNP-modulated gene networks and pathways.
• Annotation-driven variant interpretation: Supports functional, phenotypic, and epigenomic interpretation of genes and variants.

Methodology:

Integrates ontology and annotation frameworks with systems biology network–driven approaches to contextualize genetic information, define SNP-modulated gene networks and pathways, and explore annotation-based relationships.