Scientific Rare Disease Genetics

Name: scientific-rare-disease-genetics
Rating: 76
Author: nahisaho

OMIM / Orphanet / DisGeNET / IMPC を統合した希少疾患遺伝学パイプラインを提供する。

When to Use

•希少疾患の原因遺伝子を同定するとき
•OMIM で遺伝子-疾患の Mendelian 関連を調べるとき
•Orphanet で希少疾患分類や有病率を検索するとき
•DisGeNET で疾患-遺伝子関連スコア (GDA) を取得するとき
•IMPC マウスノックアウト表現型と比較するとき

Quick Start

1. OMIM 遺伝子-疾患マッピング

python

import requests
import pandas as pd

OMIM_API = "https://api.omim.org/api"


def search_omim(query, api_key, include="geneMap"):
    """
    OMIM データベース検索。

    Parameters:
        query: str — 検索語 (遺伝子名、疾患名)
        api_key: str — OMIM API キー
        include: str — "geneMap", "clinicalSynopsis", "all"

    ToolUniverse:
        OMIM_search(query=query)
        OMIM_get_entry(mim_number=mim_number)
        OMIM_get_gene_map(gene_symbol=gene_symbol)
        OMIM_get_clinical_synopsis(mim_number=mim_number)
    """
    params = {
        "search": query,
        "include": include,
        "format": "json",
        "apiKey": api_key,
    }
    resp = requests.get(f"{OMIM_API}/entry/search", params=params)
    resp.raise_for_status()
    data = resp.json()

    entries = data.get("omim", {}).get("searchResponse", {}).get("entryList", [])
    results = []
    for entry in entries:
        e = entry.get("entry", {})
        gene_map = e.get("geneMap", {})
        results.append({
            "mim_number": e.get("mimNumber"),
            "title": e.get("titles", {}).get("preferredTitle", ""),
            "gene_symbols": gene_map.get("geneSymbols", ""),
            "chromosome": gene_map.get("computedCytoLocation", ""),
            "phenotypes": [
                p.get("phenotype", "")
                for p in gene_map.get("phenotypeMapList", [])
            ],
            "inheritance": [
                p.get("phenotypeMappingKey", "")
                for p in gene_map.get("phenotypeMapList", [])
            ],
        })

    df = pd.DataFrame(results)
    print(f"OMIM search '{query}': {len(df)} entries")
    return df

2. Orphanet 希少疾患分類

python

ORPHANET_API = "https://api.orphadata.com"


def search_orphanet_diseases(query):
    """
    Orphanet 希少疾患検索。

    ToolUniverse:
        Orphanet_search_diseases(query=query)
        Orphanet_search_by_name(name=query)
        Orphanet_get_disease(orpha_code=code)
        Orphanet_get_genes(orpha_code=code)
        Orphanet_get_classification(orpha_code=code)
    """
    resp = requests.get(
        f"{ORPHANET_API}/rd-cross-referencing",
        params={"query": query}
    )
    resp.raise_for_status()
    data = resp.json()

    results = []
    for item in data if isinstance(data, list) else [data]:
        results.append({
            "orpha_code": item.get("ORPHAcode", ""),
            "name": item.get("Preferred term", ""),
            "prevalence_class": item.get("Prevalence", {}).get("PrevalenceClass", ""),
            "inheritance": item.get("TypeOfInheritance", []),
            "age_of_onset": item.get("AgeOfOnset", []),
            "genes": item.get("DisorderGeneAssociationList", []),
        })

    df = pd.DataFrame(results)
    print(f"Orphanet search '{query}': {len(df)} diseases")
    return df

3. DisGeNET 疾患-遺伝子関連スコア

python

DISGENET_API = "https://www.disgenet.org/api"


def get_disease_gene_associations(disease_id, api_key):
    """
    DisGeNET GDA スコアによる疾患-遺伝子関連取得。

    Parameters:
        disease_id: str — UMLS CUI (e.g., "C0023264") or disease name
        api_key: str — DisGeNET API key

    ToolUniverse:
        DisGeNET_search_disease(query=disease_id)
        DisGeNET_get_disease_genes(disease_id=disease_id)
        DisGeNET_search_gene(query=gene)
        DisGeNET_get_gene_diseases(gene_symbol=gene)
        DisGeNET_get_variant_diseases(variant_id=variant)
    """
    headers = {"Authorization": f"Bearer {api_key}"}
    resp = requests.get(
        f"{DISGENET_API}/gda/disease/{disease_id}",
        headers=headers
    )
    resp.raise_for_status()
    data = resp.json()

    results = []
    for gda in data:
        results.append({
            "gene_symbol": gda.get("gene_symbol", ""),
            "gene_id": gda.get("geneid", ""),
            "gda_score": gda.get("score", 0),
            "ei": gda.get("ei", 0),  # Evidence Index
            "el": gda.get("el", ""),  # Evidence Level
            "n_pmids": gda.get("pmid_count", 0),
            "source": gda.get("source", ""),
        })

    df = pd.DataFrame(results)
    if not df.empty:
        df = df.sort_values("gda_score", ascending=False)

    print(f"DisGeNET '{disease_id}': {len(df)} gene associations, "
          f"top GDA score={df['gda_score'].max():.3f}" if len(df) > 0 else "")
    return df

4. IMPC マウス表現型参照

python

IMPC_API = "https://www.ebi.ac.uk/mi/impc/solr"


def get_impc_mouse_phenotypes(gene_symbol):
    """
    IMPC マウスノックアウト表現型データ取得。

    ToolUniverse:
        IMPC_search_genes(query=gene_symbol)
        IMPC_get_gene_summary(gene_symbol=gene_symbol)
        IMPC_get_phenotypes_by_gene(gene_symbol=gene_symbol)
        IMPC_get_gene_phenotype_hits(gene_symbol=gene_symbol)
    """
    params = {
        "q": f"marker_symbol:{gene_symbol}",
        "rows": 100,
        "wt": "json",
    }
    resp = requests.get(f"{IMPC_API}/genotype-phenotype/select", params=params)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for doc in data.get("response", {}).get("docs", []):
        results.append({
            "gene_symbol": doc.get("marker_symbol", ""),
            "mp_term_id": doc.get("mp_term_id", ""),
            "mp_term_name": doc.get("mp_term_name", ""),
            "top_level_mp": doc.get("top_level_mp_term_name", []),
            "p_value": doc.get("p_value", None),
            "effect_size": doc.get("effect_size", None),
            "zygosity": doc.get("zygosity", ""),
            "procedure_name": doc.get("procedure_name", ""),
        })

    df = pd.DataFrame(results)
    if not df.empty and "p_value" in df.columns:
        df = df.sort_values("p_value")

    print(f"IMPC '{gene_symbol}': {len(df)} phenotype associations")
    return df

5. 遺伝子-表現型統合解析

python

def rare_disease_gene_analysis(gene_symbol, omim_api_key=None,
                                 disgenet_api_key=None):
    """
    全 DB 統合の希少疾患遺伝子プロファイリング。
    """
    profile = {"gene": gene_symbol, "sources": {}}

    # 1. OMIM
    if omim_api_key:
        try:
            omim_df = search_omim(gene_symbol, omim_api_key)
            profile["sources"]["omim"] = {
                "entries": len(omim_df),
                "phenotypes": omim_df["phenotypes"].explode().dropna().unique().tolist()
                    if not omim_df.empty else [],
            }
        except Exception as e:
            profile["sources"]["omim"] = {"error": str(e)}

    # 2. Orphanet
    try:
        orpha_df = search_orphanet_diseases(gene_symbol)
        profile["sources"]["orphanet"] = {
            "diseases": len(orpha_df),
            "names": orpha_df["name"].tolist() if not orpha_df.empty else [],
        }
    except Exception as e:
        profile["sources"]["orphanet"] = {"error": str(e)}

    # 3. DisGeNET
    if disgenet_api_key:
        try:
            dgn_df = get_disease_gene_associations(gene_symbol, disgenet_api_key)
            profile["sources"]["disgenet"] = {
                "associations": len(dgn_df),
                "max_gda_score": float(dgn_df["gda_score"].max())
                    if not dgn_df.empty else 0,
            }
        except Exception as e:
            profile["sources"]["disgenet"] = {"error": str(e)}

    # 4. IMPC
    try:
        impc_df = get_impc_mouse_phenotypes(gene_symbol)
        profile["sources"]["impc"] = {
            "phenotypes": len(impc_df),
            "top_phenotypes": impc_df["mp_term_name"].head(5).tolist()
                if not impc_df.empty else [],
        }
    except Exception as e:
        profile["sources"]["impc"] = {"error": str(e)}

    n_sources = sum(1 for v in profile["sources"].values() if "error" not in v)
    print(f"Rare disease profile '{gene_symbol}': {n_sources}/4 sources OK")
    return profile

References

Output Files

ファイル	形式
`results/omim_search.csv`	CSV
`results/orphanet_diseases.csv`	CSV
`results/disgenet_gda.csv`	CSV
`results/impc_phenotypes.csv`	CSV
`results/rare_disease_profile.json`	JSON

利用可能ツール

カテゴリ	主要ツール	用途
OMIM	`OMIM_search`	遺伝子/疾患検索
OMIM	`OMIM_get_entry`	MIM エントリ取得
OMIM	`OMIM_get_gene_map`	遺伝子マップ
OMIM	`OMIM_get_clinical_synopsis`	臨床概要
Orphanet	`Orphanet_search_diseases`	希少疾患検索
Orphanet	`Orphanet_get_disease`	疾患詳細
Orphanet	`Orphanet_get_genes`	関連遺伝子
Orphanet	`Orphanet_get_classification`	分類情報
Orphanet	`Orphanet_search_by_name`	名前検索
DisGeNET	`DisGeNET_search_disease`	疾患検索
DisGeNET	`DisGeNET_get_disease_genes`	疾患遺伝子
DisGeNET	`DisGeNET_get_gene_diseases`	遺伝子疾患
DisGeNET	`DisGeNET_get_variant_diseases`	バリアント疾患
IMPC	`IMPC_search_genes`	遺伝子検索
IMPC	`IMPC_get_gene_summary`	遺伝子サマリー
IMPC	`IMPC_get_phenotypes_by_gene`	表現型取得
IMPC	`IMPC_get_gene_phenotype_hits`	ヒット数

参照スキル

スキル	関連
`scientific-disease-research`	GWAS/Orphanet 疾患研究
`scientific-variant-interpretation`	ACMG バリアント解釈
`scientific-variant-effect-prediction`	病原性予測
`scientific-population-genetics`	集団遺伝学
`scientific-human-protein-atlas`	タンパク質発現

依存パッケージ

requests, pandas