Alterlab Monarch

Monarch Initiative Database

Overview

The Monarch Initiative (https://monarchinitiative.org/) is a multi-species integrated knowledgebase that links genes, diseases, and phenotypes across humans and model organisms. It integrates data from over 40 sources including OMIM, ORPHANET, HPO (Human Phenotype Ontology), ClinVar, MGI (Mouse Genome Informatics), ZFIN (Zebrafish), RGD (Rat), FlyBase, and WormBase.

Monarch enables:

• Mapping phenotypes across species to identify candidate disease genes
• Finding all genes associated with a disease or phenotype
• Discovering model organisms for human diseases
• Navigating the HPO hierarchy for phenotype ontology queries

Key resources:

• Monarch portal: https://monarchinitiative.org/
• API v3 base URL: https://api-v3.monarchinitiative.org/v3/api (all endpoints live under /v3/api/...)
• Interactive API docs (Swagger UI): https://api-v3.monarchinitiative.org/v3/docs
• HPO browser: https://hpo.jax.org/

Scripts

scripts/query_monarch.py — query the Monarch Initiative API v3 (stdlib only, JSON to stdout):

python scripts/query_monarch.py entity HP:0001250        # look up an entity (gene/disease/HPO)
python scripts/query_monarch.py search epilepsy          # text search the knowledge graph
python scripts/query_monarch.py associations HGNC:1100   # associations for an entity

When to Use This Skill

Use Monarch when:

• Rare disease gene discovery: What genes are associated with my patient's phenotypes (HPO terms)?
• Phenotype similarity: Are two diseases similar based on their phenotypic profiles?
• Cross-species modeling: Are there mouse/zebrafish models for my disease of interest?
• HPO term lookup: Retrieve HPO term names, definitions, and ontology hierarchy
• Disease-phenotype mapping: List all HPO terms associated with a specific disease
• Gene-phenotype associations: What phenotypes are caused by variants in a gene?
• Ortholog-phenotype mapping: Use animal model phenotypes to infer human gene function

Core Capabilities

1. Monarch API v3

The real endpoints live under /v3/api (the /v3/docs URL is only the Swagger UI, not a request base). Association items come back flat — read item["subject"], item["subject_label"], item["predicate"], item["object"], item["object_label"], item["object_category"], etc. There is no nested item["object"]["id"].

import requests

BASE_URL = "https://api-v3.monarchinitiative.org/v3/api"

def monarch_get(endpoint, params=None):
    """GET a Monarch API v3 endpoint and return parsed JSON."""
    url = f"{BASE_URL}/{endpoint}"
    response = requests.get(url, params=params, headers={"Accept": "application/json"})
    response.raise_for_status()
    return response.json()

2. Phenotype-to-Gene Association (Pheno2Gene)

For a GeneToPhenotypicFeatureAssociation, the gene is the subject and the phenotype is the object. To go from a phenotype to its genes, filter on object=<HPO> and subject_category=biolink:Gene.

def phenotype_to_gene(hpo_ids, limit=100):
    """
    Return genes whose phenotypes match the given HPO terms (flat per-term links).
    Core use case: rare disease differential diagnosis.

    Args:
        hpo_ids: List of HPO term IDs (e.g., ["HP:0001250", "HP:0004322"])
    """
    all_genes = []
    for hpo_id in hpo_ids:
        data = monarch_get("association", {
            "object": hpo_id,
            "subject_category": "biolink:Gene",
            "category": "biolink:GeneToPhenotypicFeatureAssociation",
            "limit": limit,
        })
        for assoc in data.get("items", []):
            all_genes.append({
                "phenotype_id": hpo_id,
                "gene_id": assoc.get("subject"),
                "gene_name": assoc.get("subject_label"),
                "predicate": assoc.get("predicate"),
            })
    return all_genes

# Example: Find genes associated with seizures and short stature
hpo_terms = ["HP:0001250", "HP:0004322"]  # Seizure, Short stature
genes = phenotype_to_gene(hpo_terms)

3. Disease-to-Gene Associations

A causal gene-disease link is a CausalGeneToDiseaseAssociation (gene = subject, disease = object, predicate biolink:causes). To list genes for a disease, filter on object=<disease> and that category.

def get_disease_genes(disease_id, limit=100):
    """
    Get genes causally linked to a disease.
    Disease IDs: MONDO:0007739, OMIM:146300, ORPHANET:558, etc.
    """
    data = monarch_get("association", {
        "object": disease_id,
        "category": "biolink:CausalGeneToDiseaseAssociation",
        "limit": limit,
    })
    return data.get("items", [])

# Example: genes causally linked to Huntington disease
for assoc in get_disease_genes("MONDO:0007739"):
    print(f"  {assoc.get('subject_label')} ({assoc.get('subject')})")

# MONDO disease IDs (preferred over OMIM for cross-ontology queries)
# MONDO:0007739 - Huntington disease
# MONDO:0009061 - Cystic fibrosis
# OMIM:104300 - Alzheimer disease, susceptibility to, type 1

4. Gene-to-Phenotype and Disease

def get_phenotypes_for_gene(gene_id, limit=100):
    """
    Get all phenotypes associated with a gene.
    Gene IDs: HGNC:7884, NCBIGene:4137, etc.
    """
    data = monarch_get("association", {
        "subject": gene_id,
        "category": "biolink:GeneToPhenotypicFeatureAssociation",
        "limit": limit,
    })
    return data.get("items", [])

def get_diseases_for_gene(gene_id, limit=100):
    """Get diseases caused by variants in a gene."""
    data = monarch_get("association", {
        "subject": gene_id,
        "category": "biolink:CausalGeneToDiseaseAssociation",
        "limit": limit,
    })
    return data.get("items", [])

# Example: What diseases does BRCA1 cause? (flat fields: object / object_label)
brca1_diseases = get_diseases_for_gene("HGNC:1100")
for assoc in brca1_diseases:
    print(f"  {assoc.get('object_label')} ({assoc.get('object')})")

5. HPO Term Lookup

def get_hpo_term(hpo_id):
    """Fetch information about an HPO term."""
    return monarch_get(f"entity/{hpo_id}")

def search_hpo_terms(query, limit=20):
    """Search for HPO terms by name."""
    params = {
        "q": query,
        "category": "biolink:PhenotypicFeature",
        "limit": limit
    }
    return monarch_get("search", params)

# Example: look up the HPO term for seizures
seizure_term = get_hpo_term("HP:0001250")
print(f"Name: {seizure_term.get('name')}")
print(f"Definition: {seizure_term.get('description')}")

# Search for related terms
epilepsy_terms = search_hpo_terms("epilepsy")
for term in epilepsy_terms.get("items", [])[:5]:
    print(f"  {term['id']}: {term['name']}")

6. Semantic Similarity (Disease Comparison)

semsim/compare is a POST endpoint taking a JSON body of two HPO term sets (subjects, objects) and an optional metric. Valid metrics: ancestor_information_content (default), jaccard_similarity, phenodigm_score. The response includes average_score and best_score.

def compare_phenotype_sets(subject_hpo_ids, object_hpo_ids,
                           metric="ancestor_information_content"):
    """
    Compare two sets of HPO terms by semantic similarity over the HPO hierarchy.
    Pass each disease's HPO profile as a term set (not the disease CURIE itself).
    """
    body = {
        "subjects": subject_hpo_ids,
        "objects": object_hpo_ids,
        "metric": metric,
    }
    resp = requests.post(f"{BASE_URL}/semsim/compare", json=body)
    resp.raise_for_status()
    return resp.json()

# Example: compare two phenotype profiles
similarity = compare_phenotype_sets(
    ["HP:0001250", "HP:0001263"],  # Seizure, Global developmental delay
    ["HP:0001250", "HP:0004322"],  # Seizure, Short stature
)
print(similarity["average_score"], similarity["best_score"])

7. Cross-Species Orthologs

def get_orthologs(gene_id, taxon=None, limit=50):
    """
    Get orthologs of a human gene in model organisms.
    Useful for finding animal models of human diseases.
    Each item exposes object / object_label / object_taxon_label (e.g. Mus musculus).
    """
    params = {
        "subject": gene_id,
        "predicate": "biolink:orthologous_to",
        "limit": limit,
    }
    if taxon:
        params["object_taxon"] = taxon  # e.g. "NCBITaxon:10090" for mouse
    return monarch_get("association", params).get("items", [])

# NCBI Taxonomy IDs for common model organisms:
# Mouse: 10090 (Mus musculus)
# Zebrafish: 7955 (Danio rerio)
# Fruit fly: 7227 (Drosophila melanogaster)
# C. elegans: 6239
# Rat: 10116 (Rattus norvegicus)

8. Full Workflow: Rare Disease Gene Prioritization

import requests
import pandas as pd

def rare_disease_gene_finder(patient_hpo_terms, candidate_gene_ids=None, top_n=20):
    """
    Find genes that match a patient's HPO phenotype profile.

    Args:
        patient_hpo_terms: List of HPO IDs from clinical assessment
        candidate_gene_ids: Optional list to restrict search
        top_n: Number of top candidates to return
    """
    BASE_URL = "https://api-v3.monarchinitiative.org/v3/api"

    # 1. Find genes associated with each phenotype
    gene_phenotype_counts = {}

    for hpo_id in patient_hpo_terms:
        data = requests.get(
            f"{BASE_URL}/association",
            params={
                "object": hpo_id,
                "subject_category": "biolink:Gene",
                "category": "biolink:GeneToPhenotypicFeatureAssociation",
                "limit": 100,
            }
        ).json()

        for item in data.get("items", []):
            gene_id = item.get("subject")        # flat field, not item["subject"]["id"]
            gene_name = item.get("subject_label")
            if gene_id:
                if gene_id not in gene_phenotype_counts:
                    gene_phenotype_counts[gene_id] = {"name": gene_name, "count": 0, "phenotypes": []}
                gene_phenotype_counts[gene_id]["count"] += 1
                gene_phenotype_counts[gene_id]["phenotypes"].append(hpo_id)

    # 2. Rank by number of matching phenotypes
    ranked = sorted(gene_phenotype_counts.items(),
                    key=lambda x: -x[1]["count"])[:top_n]

    results = []
    for gene_id, info in ranked:
        results.append({
            "gene_id": gene_id,
            "gene_name": info["name"],
            "matching_phenotypes": info["count"],
            "total_patient_phenotypes": len(patient_hpo_terms),
            "phenotype_overlap": info["count"] / len(patient_hpo_terms),
            "matching_hpo_terms": info["phenotypes"]
        })

    return pd.DataFrame(results)

# Example usage
patient_phenotypes = [
    "HP:0001250",  # Seizures
    "HP:0004322",  # Short stature
    "HP:0001252",  # Hypotonia
    "HP:0000252",  # Microcephaly
    "HP:0001263",  # Global developmental delay
]
candidates = rare_disease_gene_finder(patient_phenotypes)
print(candidates[["gene_name", "matching_phenotypes", "phenotype_overlap"]].to_string())

Query Workflows

Workflow 1: HPO-Based Differential Diagnosis

1. Extract HPO terms from clinical notes or genetics consultation
2. Run phenotype-to-gene query against Monarch
3. Rank candidate genes by number of matching phenotypes
4. Cross-reference with gnomAD (constraint scores) and ClinVar (variant evidence)
5. Prioritize genes with high pLI and known pathogenic variants

Workflow 2: Disease Model Discovery

1. Identify gene or disease of interest
2. Query Monarch for cross-species orthologs
3. Find phenotype associations in model organism databases
4. Identify experimental models that recapitulate human disease features

Workflow 3: Phenotype Annotation of Novel Genes

1. For a gene with unknown function, query all known phenotype associations
2. Map to HPO hierarchy to understand affected body systems
3. Cross-reference with OMIM and ORPHANET for disease links

Common Identifier Prefixes

Best Practices

• Use MONDO IDs for diseases — they unify OMIM/ORPHANET/MESH identifiers
• Use HPO IDs for phenotypes — the standard for clinical phenotype description
• Handle pagination: Large queries may require iterating with offset parameter
• Semantic similarity is better than exact match: Ancestor HPO terms catch related phenotypes
• Cross-validate with ClinVar and OMIM: Monarch aggregates many sources; quality varies
• Use HGNC IDs for genes: More stable than gene symbols across database versions

Additional Resources

• Monarch portal: https://monarchinitiative.org/
• API v3 base: https://api-v3.monarchinitiative.org/v3/api — Swagger UI: https://api-v3.monarchinitiative.org/v3/docs
• HPO browser: https://hpo.jax.org/
• MONDO ontology: https://mondo.monarchinitiative.org/
• Citation: Shefchek KA et al. (2020) Nucleic Acids Research. PMID: 31701156
• Phenomizer (HPO-based diagnosis): https://hpo.jax.org/