# Scientific Gwas Catalog > GWAS カタログスキル。NHGRI-EBI GWAS Catalog REST API によるゲノム ワイド関連研究メタデータ・関連シグナル・形質・遺伝子座検索。 ToolUniverse 連携: gwas。 - Skill: `nahisaho/scientific-gwas-catalog-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add nahisaho/scientific-gwas-catalog-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-gwas-catalog-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: nahisaho (https://skillmd.com/u/nahisaho) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/nahisaho/scientific-gwas-catalog-2 --- # Scientific GWAS Catalog NHGRI-EBI GWAS Catalog REST API を活用した GWAS メタデータ 解析・遺伝子座レベル解釈パイプラインを提供する。 ## When to Use - GWAS Catalog から疾患/形質の関連バリアントを検索するとき - 遺伝的関連シグナルのエフェクトサイズ・P値を取得するとき - 特定遺伝子座の LD ブロック情報を解析するとき - 多形質 PheWAS-like 解析を実施するとき - GWAS サマリ統計量を下流解析に準備するとき - 公開 GWAS データから PRS ウェイトを抽出するとき --- ## Quick Start ## 1. GWAS 関連シグナル検索 ```python import requests import pandas as pd import numpy as np GWAS_BASE = "https://www.ebi.ac.uk/gwas/rest/api" def gwas_search_associations(trait=None, gene=None, variant=None, p_upper=5e-8, limit=100): """ GWAS Catalog — 関連シグナル検索。 Parameters: trait: str — 形質/疾患 EFO ID or 名前 (例: "EFO_0001645") gene: str — 遺伝子名 (例: "BRCA1") variant: str — rsID (例: "rs1234567") p_upper: float — P値上限 limit: int — 最大結果数 """ if trait: url = f"{GWAS_BASE}/efoTraits/{trait}/associations" elif gene: url = f"{GWAS_BASE}/associations/search/findByGene" elif variant: url = f"{GWAS_BASE}/singleNucleotidePolymorphisms/{variant}/associations" else: url = f"{GWAS_BASE}/associations" params = {"size": limit} if gene: params["geneName"] = gene resp = requests.get(url, params=params, timeout=30) resp.raise_for_status() data = resp.json() associations = data.get("_embedded", {}).get("associations", []) results = [] for assoc in associations: p_value = assoc.get("pvalue", 1.0) if p_value and float(p_value) > p_upper: continue loci = assoc.get("loci", [{}]) genes = [] for locus in loci: for gene_info in locus.get("authorReportedGenes", []): genes.append(gene_info.get("geneName", "")) snps = [] for snp_info in assoc.get("snps", []): snps.append(snp_info.get("rsId", "")) results.append({ "association_id": assoc.get("associationId", ""), "p_value": float(p_value) if p_value else None, "p_value_mlog": assoc.get("pvalueMantissa", 0), "or_beta": assoc.get("orPerCopyNum", None), "beta_num": assoc.get("betaNum", None), "beta_direction": assoc.get("betaDirection", ""), "ci": assoc.get("range", ""), "risk_allele_freq": assoc.get("riskFrequency", ""), "snps": "; ".join(snps), "genes": "; ".join(genes), "trait": assoc.get("efoTraits", [{}])[0].get("trait", "") if assoc.get("efoTraits") else "", "study_accession": assoc.get("study", {}).get( "accessionId", ""), }) df = pd.DataFrame(results) print(f"GWAS associations: {len(df)} results " f"(trait={trait}, gene={gene}, p<{p_upper})") return df.sort_values("p_value") if not df.empty else df ``` ## 2. GWAS 研究メタデータ検索 ```python def gwas_search_studies(query=None, efo_trait=None, limit=50): """ GWAS Catalog — 研究メタデータ検索。 Parameters: query: str — フリーテキスト検索 efo_trait: str — EFO 形質 ID limit: int — 最大結果数 """ if efo_trait: url = f"{GWAS_BASE}/efoTraits/{efo_trait}/studies" else: url = f"{GWAS_BASE}/studies/search/findByDiseaseTrait" params = {"size": limit} if query: params["diseaseTrait"] = query resp = requests.get(url, params=params, timeout=30) resp.raise_for_status() data = resp.json() studies = data.get("_embedded", {}).get("studies", []) results = [] for s in studies: results.append({ "accession": s.get("accessionId", ""), "title": s.get("title", ""), "pubmed_id": s.get("publicationInfo", {}).get( "pubmedId", ""), "author": s.get("publicationInfo", {}).get( "author", {}).get("fullname", ""), "journal": s.get("publicationInfo", {}).get( "publication", ""), "date": s.get("publicationInfo", {}).get( "publicationDate", ""), "initial_sample_size": s.get("initialSampleSize", ""), "replication_sample_size": s.get( "replicationSampleSize", ""), "ancestry": s.get("ancestries", []), }) df = pd.DataFrame(results) print(f"GWAS studies: {len(df)} results") return df ``` ## 3. GWAS 形質検索・PheWAS ```python def gwas_phewas(variant_rsid, p_threshold=5e-8): """ GWAS Catalog — バリアント PheWAS (形質横断検索)。 Parameters: variant_rsid: str — rsID (例: "rs7903146") p_threshold: float — P値閾値 """ url = (f"{GWAS_BASE}/singleNucleotidePolymorphisms/" f"{variant_rsid}/associations") resp = requests.get(url, params={"size": 500}, timeout=30) resp.raise_for_status() data = resp.json() associations = data.get("_embedded", {}).get("associations", []) results = [] for assoc in associations: p_val = assoc.get("pvalue", 1.0) if p_val and float(p_val) > p_threshold: continue for trait in assoc.get("efoTraits", []): results.append({ "variant": variant_rsid, "trait": trait.get("trait", ""), "efo_uri": trait.get("shortForm", ""), "p_value": float(p_val) if p_val else None, "or_beta": assoc.get("orPerCopyNum", None), "study": assoc.get("study", {}).get( "accessionId", ""), }) df = pd.DataFrame(results) if not df.empty: df = df.sort_values("p_value") print(f"PheWAS {variant_rsid}: {len(df)} trait associations") return df ``` ## 4. GWAS 統合パイプライン ```python def gwas_catalog_pipeline(trait_query, output_dir="results"): """ GWAS Catalog 統合パイプライン。 Parameters: trait_query: str — 形質/疾患名 output_dir: str — 出力ディレクトリ """ from pathlib import Path output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) # 1) 研究検索 studies = gwas_search_studies(query=trait_query) studies.to_csv(output_dir / "gwas_studies.csv", index=False) # 2) 関連シグナル assocs = gwas_search_associations(gene=None, trait=None) assocs.to_csv(output_dir / "gwas_associations.csv", index=False) # 3) トップバリアントの PheWAS if not assocs.empty: top_snps = assocs["snps"].str.split("; ").explode().unique()[:5] phewas_all = [] for rsid in top_snps: if rsid.startswith("rs"): phewas = gwas_phewas(rsid) phewas_all.append(phewas) if phewas_all: phewas_df = pd.concat(phewas_all, ignore_index=True) phewas_df.to_csv(output_dir / "phewas.csv", index=False) print(f"GWAS pipeline: {output_dir}") return {"studies": studies, "associations": assocs} ``` --- ## ToolUniverse 連携 | TU Key | ツール名 | 連携内容 | |--------|---------|---------| | `gwas` | GWAS Catalog | 関連シグナル・形質・研究メタデータ検索 | ## パイプライン統合 ``` disease-research → gwas-catalog → variant-interpretation (DisGeNET/OMIM) (GWAS Catalog) (ACMG/AMP) │ │ ↓ population-genetics ──┘ variant-effect-prediction (Fst/PCA) │ (CADD/SpliceAI) ↓ precision-oncology (臨床的意義判定) ``` ## パイプライン出力 | ファイル | 説明 | 次スキル | |---------|------|---------| | `results/gwas_studies.csv` | GWAS 研究メタデータ | → literature-search | | `results/gwas_associations.csv` | 関連シグナル | → variant-interpretation | | `results/phewas.csv` | PheWAS 結果 | → disease-research |