Epigraphdb Tools

Configuration File: epigraphdb_tools.json Tool Type: Local Tools Count: 8

This page contains all tools defined in the epigraphdb_tools.json configuration file.

Available Tools

EpiGraphDB_get_drugs_for_trait (Type: EpiGraphDBTool)

Find drugs that target genes associated with a GWAS risk factor trait, ranked by supporting evide…

EpiGraphDB_get_drugs_for_trait tool specification

Tool Information:

• Name: EpiGraphDB_get_drugs_for_trait

• Type: EpiGraphDBTool

• Description: Find drugs that target genes associated with a GWAS risk factor trait, ranked by supporting evidence and exposed through recoverable offset pagination. unique_drug_count is the full total; continue with next_offset while has_more is true. Example: ‘LDL cholesterol’ returns hundreds of genetically supported drug candidates.

Parameters:

• trait (string) (required) GWAS risk factor trait name (e.g., ‘LDL cholesterol’, ‘Body mass index’, ‘C-reactive protein’, ‘Systolic blood pressure’).

• pval_threshold (number) (optional) GWAS p-value threshold for gene-trait associations (default 1e-4).

• limit (integer) (optional) Number of unique drugs per page (1-500; default 30).

• offset (integer) (optional) Zero-based offset into evidence-ranked unique drugs.

Example Usage:

query = {
    "name": "EpiGraphDB_get_drugs_for_trait",
    "arguments": {
        "trait": "example_value"
    }
}
result = tu.run(query)

EpiGraphDB_get_gene_drug_associations (Type: EpiGraphDBTool)

Get pharmacogenomics drug-gene associations from CPIC and PharmGKB databases via EpiGraphDB. Retu…

EpiGraphDB_get_gene_drug_associations tool specification

Tool Information:

• Name: EpiGraphDB_get_gene_drug_associations

• Type: EpiGraphDBTool

• Description: Get pharmacogenomics drug-gene associations from CPIC and PharmGKB databases via EpiGraphDB. Returns drugs associated with a gene with clinical evidence levels. Example: ‘DPYD’ returns fluorouracil with CPIC Level A evidence.

Parameters:

• gene_name (string) (optional) Gene symbol to find drug associations for (e.g., ‘TP53’, ‘EGFR’, ‘DPYD’, ‘CYP2D6’).

• gene_symbol (string) (optional) Alias for gene_name. Gene symbol (e.g., ‘CYP2D6’, ‘BRCA1’).

• pval_threshold (number) (optional) P-value threshold (default 0.05).

Example Usage:

query = {
    "name": "EpiGraphDB_get_gene_drug_associations",
    "arguments": {
    }
}
result = tu.run(query)

EpiGraphDB_get_genetic_correlations (Type: EpiGraphDBTool)

Get strong genetic correlations (|rg| > 0.8) between a GWAS trait and other traits with recoverab…

EpiGraphDB_get_genetic_correlations tool specification

Tool Information:

• Name: EpiGraphDB_get_genetic_correlations

• Type: EpiGraphDBTool

• Description: Get strong genetic correlations (|rg| > 0.8) between a GWAS trait and other traits with recoverable offset pagination. total_count, has_more, and next_offset expose the complete result set. NOTE: the graph is sparse and exact-label sensitive; an empty result means no strong-rg edge in this graph, not no shared genetics. Genetic correlation is NOT causal evidence. Example traits with data: ‘Waist circumference’, ‘Hip circumference’, ‘Body fat percentage’.

Parameters:

• trait (string) (required) Exact, case-sensitive GWAS trait label (e.g., ‘Waist circumference’, ‘Hip circumference’). Many labels have no node in the sparse genetic-correlation graph and return empty.

• pval_threshold (number) (optional) Accepted for compatibility but NOT applied — the endpoint always returns the fixed |rg| > 0.8 set.

• limit (integer) (optional) Number of correlations per page (1-500; default 50).

• offset (integer) (optional) Zero-based correlation offset. Continue with next_offset while has_more is true.

Example Usage:

query = {
    "name": "EpiGraphDB_get_genetic_correlations",
    "arguments": {
        "trait": "example_value"
    }
}
result = tu.run(query)

EpiGraphDB_get_literature_evidence (Type: EpiGraphDBTool)

Get SemMedDB literature triple evidence supporting a GWAS trait via EpiGraphDB, with recoverable …

EpiGraphDB_get_literature_evidence tool specification

Tool Information:

• Name: EpiGraphDB_get_literature_evidence

• Type: EpiGraphDBTool

• Description: Get SemMedDB literature triple evidence supporting a GWAS trait via EpiGraphDB, with recoverable offset pagination. Returns subject-predicate-object triples mined from PubMed and linked to GWAS hits, each citing a supporting article (pubmed_id). Continue with next_offset while has_more is true. Because the upstream endpoint does not return an exact match count, total_count is null until the final page is reached. Example: trait=’body mass index’ returns more than 1,000 supporting rows at pval_threshold=1e-4.

Parameters:

• trait (string) (required) GWAS trait name (e.g., ‘body mass index’, ‘LDL cholesterol’, ‘coronary heart disease’). Matched case-insensitively as a substring of GWAS labels.

• pval_threshold (number) (optional) GWAS p-value threshold for the trait’s GWAS hits (default 1e-8).

• limit (integer) (optional) Number of literature evidence rows per page (1-1000; default 100).

• offset (integer) (optional) Zero-based row offset. Continue with next_offset while has_more is true.

Example Usage:

query = {
    "name": "EpiGraphDB_get_literature_evidence",
    "arguments": {
        "trait": "example_value"
    }
}
result = tu.run(query)

EpiGraphDB_get_mendelian_randomization (Type: EpiGraphDBTool)

Get Mendelian Randomization (MR) evidence using IEU OpenGWAS data with recoverable offset paginat…

EpiGraphDB_get_mendelian_randomization tool specification

Tool Information:

• Name: EpiGraphDB_get_mendelian_randomization

• Type: EpiGraphDBTool

• Description: Get Mendelian Randomization (MR) evidence using IEU OpenGWAS data with recoverable offset pagination. Provide exposure_trait, outcome_trait, or both: a single trait retrieves all MR edges in that direction, while both constrain the relationship. Returns MR estimates (beta, SE, p-value) from multiple instruments/methods. Continue with next_offset while has_more is true.

Parameters:

• exposure_trait (string) (optional) GWAS trait name for the exposure (e.g., ‘Body mass index’, ‘LDL cholesterol’, ‘Type 2 diabetes’). Use EpiGraphDB_search_opengwas to find exact trait names.

• outcome_trait (string) (optional) GWAS trait name for the outcome (e.g., ‘Coronary heart disease’, ‘Stroke’, ‘Alzheimer disease’). Use EpiGraphDB_search_opengwas to find exact trait names.

• pval_threshold (number) (optional) P-value threshold for instrument selection (default 1e-5).

• limit (integer) (optional) Number of MR results per page (1-1000; default 50).

• offset (integer) (optional) Zero-based MR result offset. Continue with next_offset while has_more is true.

Example Usage:

query = {
    "name": "EpiGraphDB_get_mendelian_randomization",
    "arguments": {
    }
}
result = tu.run(query)

EpiGraphDB_map_gwas_to_efo (Type: EpiGraphDBTool)

Map GWAS trait names to EFO (Experimental Factor Ontology) terms for standardization. Returns EFO…

EpiGraphDB_map_gwas_to_efo tool specification

Tool Information:

• Name: EpiGraphDB_map_gwas_to_efo

• Type: EpiGraphDBTool

• Description: Map GWAS trait names to EFO (Experimental Factor Ontology) terms for standardization. Returns EFO IDs and labels corresponding to a GWAS trait. Useful for cross-database trait harmonization. Example: ‘Body mass index’ maps to EFO:0004340.

Parameters:

• trait (string) (required) GWAS trait name to map to EFO (e.g., ‘Body mass index’, ‘coronary artery disease’, ‘type 2 diabetes’).

Example Usage:

query = {
    "name": "EpiGraphDB_map_gwas_to_efo",
    "arguments": {
        "trait": "example_value"
    }
}
result = tu.run(query)

EpiGraphDB_search_gene (Type: EpiGraphDBTool)

Search for gene information in EpiGraphDB by gene symbol or Ensembl ID. Returns gene metadata inc…

EpiGraphDB_search_gene tool specification

Tool Information:

• Name: EpiGraphDB_search_gene

• Type: EpiGraphDBTool

• Description: Search for gene information in EpiGraphDB by gene symbol or Ensembl ID. Returns gene metadata including druggability classification, chromosomal location, and gene type. Fuzzy name search supports up to the upstream maximum of 200 rows but the source exposes no offset; when a page is full, truncated=true and total_found=null warn that more matches may exist. Use an exact Ensembl gene_id or narrow the name in that case.

Parameters:

• gene_name ([‘string’, ‘null’]) (optional) Gene symbol to search for (e.g., ‘BRCA1’, ‘TP53’, ‘EGFR’). Either gene_name or gene_id required.

• gene_id ([‘string’, ‘null’]) (optional) Ensembl gene ID (e.g., ‘ENSG00000012048’). Either gene_name or gene_id required.

• limit (integer) (optional) Maximum results to return (default 10, upstream max 200). A full fuzzy-name page is marked truncated because the upstream endpoint has no offset.

Example Usage:

query = {
    "name": "EpiGraphDB_search_gene",
    "arguments": {
    }
}
result = tu.run(query)

EpiGraphDB_search_opengwas (Type: EpiGraphDBTool)

Search the IEU OpenGWAS database for GWAS studies by trait name. Returns matching GWAS study IDs …

EpiGraphDB_search_opengwas tool specification

Tool Information:

• Name: EpiGraphDB_search_opengwas

• Type: EpiGraphDBTool

• Description: Search the IEU OpenGWAS database for GWAS studies by trait name. Returns matching GWAS study IDs and trait names with similarity scores. OpenGWAS IDs (e.g., ‘ieu-a-2’) are needed as inputs for Mendelian Randomization analyses. Example: ‘body mass index’ returns ‘ieu-a-2’, ‘ieu-b-40’, etc.

Parameters:

• query (string) (required) Trait or phenotype name to search for in OpenGWAS (e.g., ‘body mass index’, ‘coronary heart disease’, ‘type 2 diabetes’, ‘blood pressure’).

Example Usage:

query = {
    "name": "EpiGraphDB_search_opengwas",
    "arguments": {
        "query": "example_value"
    }
}
result = tu.run(query)

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