Research disease mechanisms with genomics
disease-researchskillsetup L2★35
ammawla/encode-toolkit ↗What it does
Connect GWAS variants to ENCODE regulatory elements for disease mechanism discovery
Best for
Disease mechanism research where non-coding variants need interpretation, or identifying therapeutic targets from epigenomic evidence rather than coding-only databases.
Inputs
- · GWAS variant list (SNPs, effect sizes)
- · disease/trait phenotype
- · disease-relevant tissue(s)
Outputs
- · variant-to-cCRE mapping
- · enrichment statistics (S-LDSC heritability partition)
- · drug target candidates (Open Targets integration)
Requires
- · ENCODE (926,535 cCREs)
- · GWAS Catalog
- · Open Targets Platform
- · SCREEN portal
- · ClinicalTrials.gov
- · PubMed
Preconditions
90% of disease variants are non-coding; correct tissue selection critical (disease-tissue-mapping table required)
Failure modes
- · wrong tissue chosen → regulatory elements miss disease mechanism
- · heritability enrichment inverted if control regions misspecified
- · drug target not druggable (no chemical matter) → false positive
Trust signals
- · Maurano et al. 2012 cited (76.6% GWAS SNPs in DNase hotspots)
- · S-LDSC partitioning (Finucane 2015)
- · ABC model for enhancer-gene links (Nasser 2021)