Gene-disease association analysis across DisGeNET, OpenTargets, Monarch, OMIM, GenCC, Orphanet. Cross-references multiple sources for evidence-graded association reports with concordance scoring (5/5 sources agree → strong, 1/5 → weak). Use for 'which diseases is gene X associated with' or 'which genes cause disease Y' queries with quantitative confidence.
Gene-disease association analysis across DisGeNET, OpenTargets, Monarch, OMIM, GenCC, Orphanet. Cross-references multiple sources for evidence-graded association reports with concordance scoring (5/5 sources agree → strong, 1/5 → weak). Use for 'which diseases is gene X associated with' or 'which genes cause disease Y' queries with quantitative confidence.
Gene-Disease Association Analysis
Systematically query and compare gene-disease associations across 6+ databases to produce a unified, evidence-graded report. Cross-references DisGeNET scores, OpenTargets evidence, Monarch Initiative cross-species data, OMIM Mendelian mappings, GenCC curated validity, and Orphanet rare disease links.
IMPORTANT: Always use English gene names and disease terms in tool calls. Respond in the user's language.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
Core Principles
Report-first approach - Create report file FIRST, then populate progressively
API KEY REQUIRED: DisGeNET tools require DISGENET_API_KEY environment variable. Without it, all DisGeNET calls will fail. Register at https://www.disgenet.org/api/#/Authorization for a free academic key.
Fallback if no key: Skip this phase and rely on OpenTargets (Phase 3) + Monarch (Phase 4) which are free and cover much of the same data.
# Gene -> diseases
disgenet_diseases = tu.tools.DisGeNET_search_gene(gene=gene_symbol, limit=20)
disgenet_gda = tu.tools.DisGeNET_get_gda(gene=gene_symbol, source="CURATED", min_score=0.3, limit=25)
# Disease -> genes (accepts name or UMLS CUI like "C0006142")
disgenet_genes = tu.tools.DisGeNET_search_disease(disease=disease_name, limit=20)
disgenet_ranked = tu.tools.DisGeNET_get_disease_genes(disease=disease_name, min_score=0.3, limit=50)
Interpreting DisGeNET scores: Higher scores reflect more evidence sources and stronger curation. Rather than memorizing cutoffs, ask: is this score driven by curated sources or text-mining? Use source="CURATED" to distinguish.
Phase 3: OpenTargets Associations
ot_diseases = tu.tools.OpenTargets_get_diseases_phenotypes_by_target_ensembl(ensemblId=ensembl_id)
ot_evidence = tu.tools.OpenTargets_target_disease_evidence(ensemblId=ensembl_id, efoId=efo_id)
# Both require pre-resolved Ensembl/EFO IDs. Use OpenTargets_multi_entity_search_by_query_string to discover IDs.
API KEY REQUIRED: OMIM tools require OMIM_API_KEY. Register at https://omim.org/api for academic access.
Fallback if no key: Use Monarch Initiative (biolink:CausalGeneToDiseaseAssociation from Phase 4) which includes OMIM data without requiring a key. Also use GenCC (below) which is fully open.
Evidence strength reasoning: A gene-disease association supported by multiple independent lines of evidence (genetic, functional, model organism) is stronger than one supported by a single study. Ask: how many independent sources support this link? Do they converge on the same mechanism?
Genetic evidence hierarchy: Mendelian segregation (gene mutation causes disease in family) > GWAS (statistical association in population) > candidate gene study (hypothesis-driven). The first proves causation. The second shows correlation. The third is hypothesis. OMIM/GenCC "Definitive" entries represent the top of this hierarchy; DisGeNET text-mining hits represent the bottom.
Cross-database concordance: If DisGeNET, OpenTargets, AND OMIM all link gene X to disease Y, that's strong concordance. If only one database shows the link, check why -- is it a single study indexed by that database? Concordance across databases does not equal independent evidence if they all cite the same primary study. Count the number of databases supporting each association, but reason about whether they represent truly independent evidence.
Mechanism reasoning: Knowing the gene's function helps evaluate the association. A gene encoding a liver enzyme being linked to liver disease is mechanistically plausible. The same gene being linked to a psychiatric disorder needs stronger evidence because the mechanism is less obvious. Use Harmonizome gene summaries and Monarch phenotype profiles to assess mechanistic plausibility.
Common Patterns
Gene-centric: MyGene ID resolution -> DisGeNET/OpenTargets/Monarch/OMIM/GenCC/Orphanet -> unified table ranked by concordance