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/
• Monarch API v3: https://api-v3.monarchinitiative.org/v3/docs
• API docs: https://api-v3.monarchinitiative.org/v3/docs
• HPO browser: https://hpo.jax.org/

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

Base URL: https://api-v3.monarchinitiative.org/v3/docs

import requests

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

def monarch_get(endpoint, params=None):
    """Make a GET request to the Monarch API."""
    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)

def get_genes_for_phenotypes(hpo_ids, limit=50, offset=0):
    """
    Find genes associated with a list of HPO phenotype terms.
    Core use case: rare disease differential diagnosis.

    Args:
        hpo_ids: List of HPO term IDs (e.g., ["HP:0001250", "HP:0004322"])
        limit: Maximum number of results
    """
    params = {
        "terms": hpo_ids,
        "limit": limit,
        "offset": offset
    }
    return monarch_get("semsim/termset-pairwise-similarity/analyze", params)

def phenotype_to_gene(hpo_ids):
    """
    Return genes whose phenotypes match the given HPO terms.
    Uses semantic similarity scoring.
    """
    # Use the /association endpoint for direct phenotype-gene links
    all_genes = []
    for hpo_id in hpo_ids:
        data = monarch_get("association/all", {
            "subject": hpo_id,
            "predicate": "biolink:has_phenotype",
            "category": "biolink:GeneToPhenotypicFeatureAssociation",
            "limit": 50
        })
        for assoc in data.get("items", []):
            all_genes.append({
                "phenotype_id": hpo_id,
                "gene_id": assoc.get("object", {}).get("id"),
                "gene_name": assoc.get("object", {}).get("name"),
                "evidence": assoc.get("evidence_type")
            })
    return all_genes

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

3. Disease-to-Gene Associations

def get_genes_for_disease(disease_id, limit=100):
    """
    Get all genes associated with a disease.
    Disease IDs: OMIM:146300, MONDO:0007739, ORPHANET:558, etc.
    """
    params = {
        "object": disease_id,
        "category": "biolink:DiseaseToDiseaseAssociation",
        "limit": limit
    }
    # Use the gene-disease association endpoint
    gene_params = {
        "subject": disease_id,
        "category": "biolink:GeneToPhenotypicFeatureAssociation",
        "limit": limit
    }

    data = monarch_get("association/all", {
        "object": disease_id,
        "predicate": "biolink:has_phenotype",
        "limit": limit
    })
    return data

def get_disease_genes(disease_id, limit=100):
    """Get genes causally linked to a disease."""
    data = monarch_get("association/all", {
        "subject_category": "biolink:Gene",
        "object": disease_id,
        "predicate": "biolink:causes",
        "limit": limit
    })
    return data.get("items", [])

# 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/all", {
        "subject": gene_id,
        "predicate": "biolink:has_phenotype",
        "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/all", {
        "subject": gene_id,
        "object_category": "biolink:Disease",
        "limit": limit
    })
    return data.get("items", [])

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

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)

def compare_disease_phenotypes(disease_id_1, disease_id_2):
    """
    Compare two diseases by semantic similarity of their phenotype profiles.
    Returns similarity score using HPO hierarchy.
    """
    params = {
        "subjects": [disease_id_1],
        "objects": [disease_id_2],
        "metric": "ancestor_information_content"
    }
    return monarch_get("semsim/compare", params)

# Example: Compare Dravet syndrome with CDKL5-deficiency disorder
similarity = compare_disease_phenotypes("MONDO:0100135", "MONDO:0014917")

7. Cross-Species Orthologs

def get_orthologs(gene_id, species=None):
    """
    Get orthologs of a human gene in model organisms.
    Useful for finding animal models of human diseases.
    """
    params = {"limit": 50}
    if species:
        params["subject_taxon"] = species

    data = monarch_get("association/all", {
        "subject": gene_id,
        "predicate": "biolink:orthologous_to",
        "limit": 50
    })
    return data.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"

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

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

        for item in data.get("items", []):
            gene_id = item.get("subject", {}).get("id")
            gene_name = item.get("subject", {}).get("name")
            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 docs: 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/