MSK-KP

MSK-KP integrates large-scale omics and genomic datasets to aggregate and analyze human, cellular, and model organism genetic data for discovery and prioritization of causal variants, candidate genes, biological pathways, and drug targets in musculoskeletal diseases.

Key Features:

• Integration of Diverse Data Sources: Aggregates omics and genomic datasets from human studies, cellular experiments, model organisms, high-throughput genotyping technologies, and biobank collections.
• Hypothesis-free GWAS: Supports genome-wide association studies that identify genetic loci linked to musculoskeletal conditions.
• Fine-mapping of Genetic Loci: Applies statistical genetics approaches to fine-map loci and prioritize plausibly causal variants and candidate genes.
• Cross-Species Data Integration: Incorporates complementary evidence from model organisms to inform human translational interpretation.
• Biological Pathway and Drug Target Prioritization: Identifies and prioritizes biological pathways and evaluates candidate genes and pathways as potential therapeutic targets, including assessment of side-effect profiles and new indications.

Scientific Applications:

• Mechanistic discovery: Elucidates biological mechanisms underlying musculoskeletal diseases by combining genetic loci, candidate genes, and pathway analyses.
• Cross-species translation: Bridges findings from model organisms to human disease to support translational hypotheses.
• Genetic locus discovery: Identifies and validates novel genetic loci associated with musculoskeletal disorders using GWAS and fine-mapping.
• Therapeutic target identification: Prioritizes candidate genes and pathways for therapeutic development based on integrated omics and genetic evidence.
• Drug prioritization and repurposing: Evaluates potential drug targets, side-effect profiles, and new indications informed by integrated genomic and pathway data.

Methodology:

Integration of large-scale genomic and omics datasets using advanced statistical genetics techniques, hypothesis-free GWAS, fine-mapping of genetic loci to identify causal variants, and incorporation of complementary evidence from model organisms to support translational interpretation.