name: scientific-variant-interpretation description: | 遺伝子バリアント臨床解釈スキル。ClinVar / gnomAD / COSMIC / ACMG ガイドラインに 基づく病原性評価、薬理ゲノミクス(PharmGKB/ClinPGx)、バリアント-表現型相関の エビデンスグレーディング。ToolUniverse の Variant Interpretation パラダイムを統合。 「バリアントの病原性を評価して」「pharmacogenomics 解析して」で発火。 tu_tools:
- key: clinvar name: ClinVar description: 臨床的バリアント解釈データベース
Scientific Variant Interpretation
遺伝子バリアントの臨床的解釈スキル。ACMG/AMP ガイドラインに準拠した 病原性分類、薬理ゲノミクスによる薬物応答予測、体細胞変異の ドライバー/パッセンジャー判定を統合的に実行する。
When to Use
- SNV/Indel の病原性を ACMG 基準で分類するとき
- 薬理ゲノミクス (PGx) による薬物応答予測を行うとき
- がん体細胞変異のドライバー判定を行うとき
- 希少疾患の原因バリアント同定を支援するとき
- バリアント-表現型相関のエビデンスを評価するとき
Quick Start
バリアント解釈パイプライン
Input: Variant (HGVS notation / rsID / genomic coordinates)
↓
Step 1: Annotation
- ClinVar 臨床意義
- gnomAD 集団頻度
- InterVar ACMG 自動分類
↓
Step 2: Population Frequency
- gnomAD allele frequency
- 民族・集団別頻度
- 稀少性判定 (AF < 0.01 or 0.001)
↓
Step 3: Functional Impact
- CADD / REVEL / AlphaMissense スコア
- 保存度 (PhyloP, GERP++)
- スプライス予測 (SpliceAI)
↓
Step 4: Clinical Evidence
- ClinVar submissions
- COSMIC (体細胞変異)
- Literature evidence (PubMed)
↓
Step 5: ACMG Classification
- 28 基準の系統的適用
- 5 段階分類 (P, LP, VUS, LB, B)
↓
Output: Variant Interpretation Report
Phase 1: ACMG/AMP 分類
28 基準の系統的評価
ACMG_CRITERIA = {
# Pathogenic - Very Strong
"PVS1": "Null variant (nonsense, frameshift, canonical splice) in LOF-intolerant gene",
# Pathogenic - Strong
"PS1": "Same amino acid change as established pathogenic variant",
"PS2": "De novo (confirmed parentage) in patient with disease",
"PS3": "Functional study shows damaging effect",
"PS4": "Prevalence in affected >> controls (OR > 5)",
# Pathogenic - Moderate
"PM1": "In mutational hot spot or critical functional domain",
"PM2": "Absent from controls (or extremely rare in gnomAD)",
"PM3": "Detected in trans with pathogenic variant (recessive)",
"PM4": "Protein length change (in-frame del/ins in non-repeat region)",
"PM5": "Novel missense at same position as established pathogenic",
"PM6": "Assumed de novo (parentage not confirmed)",
# Pathogenic - Supporting
"PP1": "Co-segregation with disease in multiple family members",
"PP2": "Missense in gene with low rate of benign missense",
"PP3": "Computational evidence supports deleterious effect",
"PP4": "Patient phenotype highly specific for gene",
"PP5": "Reputable source reports as pathogenic",
# Benign - Stand-alone
"BA1": "Allele frequency > 5% in any population",
# Benign - Strong
"BS1": "Allele frequency greater than expected for disorder",
"BS2": "Observed in healthy adult (for early-onset/penetrant disorder)",
"BS3": "Functional study shows no damaging effect",
"BS4": "Lack of segregation in affected family members",
# Benign - Supporting
"BP1": "Missense in gene where only truncating cause disease",
"BP2": "Observed in trans with pathogenic variant (dominant)",
"BP3": "In-frame del/ins in repetitive region",
"BP4": "Computational evidence suggests no impact",
"BP5": "Variant found in case with alternate molecular basis",
"BP6": "Reputable source reports as benign",
"BP7": "Synonymous with no splicing impact predicted",
}
def classify_acmg(criteria_met):
"""
適用された ACMG 基準から最終分類を導出。
Richards et al., Genetics in Medicine 2015
"""
pathogenic_criteria = [c for c in criteria_met if c.startswith(("PVS", "PS", "PM", "PP"))]
benign_criteria = [c for c in criteria_met if c.startswith(("BA", "BS", "BP"))]
pvs = [c for c in pathogenic_criteria if c.startswith("PVS")]
ps = [c for c in pathogenic_criteria if c.startswith("PS")]
pm = [c for c in pathogenic_criteria if c.startswith("PM")]
pp = [c for c in pathogenic_criteria if c.startswith("PP")]
ba = [c for c in benign_criteria if c.startswith("BA")]
bs = [c for c in benign_criteria if c.startswith("BS")]
bp = [c for c in benign_criteria if c.startswith("BP")]
# Pathogenic Rules
if (len(pvs) >= 1 and (len(ps) >= 1 or len(pm) >= 2 or
(len(pm) == 1 and len(pp) == 1) or len(pp) >= 2)):
return "Pathogenic"
if len(ps) >= 2:
return "Pathogenic"
# Likely Pathogenic
if len(pvs) >= 1 and len(pm) == 1:
return "Likely Pathogenic"
if len(ps) >= 1 and (len(pm) >= 1 or len(pm) >= 2):
return "Likely Pathogenic"
# Benign
if len(ba) >= 1:
return "Benign"
if len(bs) >= 2:
return "Benign"
# Likely Benign
if len(bs) >= 1 and len(bp) >= 1:
return "Likely Benign"
if len(bp) >= 2:
return "Likely Benign"
return "Variant of Uncertain Significance (VUS)"
Phase 2: 薬理ゲノミクス (PGx)
PharmGKB / CPIC ガイドライン
PGX_GENES = {
"CYP2D6": {
"drugs": ["codeine", "tramadol", "tamoxifen", "atomoxetine"],
"phenotypes": ["Poor Metabolizer", "Intermediate", "Normal", "Ultrarapid"],
},
"CYP2C19": {
"drugs": ["clopidogrel", "voriconazole", "escitalopram", "omeprazole"],
"phenotypes": ["Poor Metabolizer", "Intermediate", "Normal", "Rapid", "Ultrarapid"],
},
"CYP2C9": {
"drugs": ["warfarin", "phenytoin", "celecoxib"],
"phenotypes": ["Poor Metabolizer", "Intermediate", "Normal"],
},
"DPYD": {
"drugs": ["fluorouracil", "capecitabine"],
"phenotypes": ["Poor Metabolizer", "Intermediate", "Normal"],
},
"TPMT": {
"drugs": ["azathioprine", "mercaptopurine", "thioguanine"],
"phenotypes": ["Poor Metabolizer", "Intermediate", "Normal"],
},
"HLA-B": {
"variants": {"*57:01": "abacavir hypersensitivity", "*58:01": "allopurinol SJS/TEN"},
},
}
def pgx_recommendation(gene, phenotype, drug):
"""
CPIC ガイドラインに基づく薬物用量推奨。
"""
recommendations = {
"avoid": "代替薬の使用を推奨",
"reduce_dose": "標準用量の 25-50% に減量",
"standard_dose": "標準用量で開始",
"increase_dose": "標準用量で効果不十分の可能性、増量を検討",
}
# 実際の推奨は CPIC テーブルから取得
return recommendations
Phase 3: 体細胞変異解釈
OncoKB / COSMIC エビデンスレベル
## Somatic Variant Interpretation
### Oncogenicity Classification
| Level | Description |
|-------|-------------|
| Oncogenic | Functionally validated driver |
| Likely Oncogenic | Strong computational/indirect evidence |
| VUS | Insufficient evidence |
| Likely Benign | Evidence against oncogenicity |
| Benign | Confirmed passenger |
### Therapeutic Actionability (OncoKB Levels)
| Level | Description |
|-------|-------------|
| 1 | FDA-approved, same tumor type |
| 2 | Standard care, different tumor type |
| 3A | Clinical evidence in same tumor type |
| 3B | Clinical evidence in different tumor type |
| 4 | Preclinical evidence |
| R1 | Resistance to approved therapy |
| R2 | Resistance to investigational therapy |
Report Template
# Variant Interpretation Report
**Variant**: [HGVS notation]
**Gene**: [gene symbol]
**Date**: [date]
## 1. Variant Summary
| Feature | Value |
|---------|-------|
| Genomic location | |
| Transcript | |
| Protein change | |
| Variant type | |
## 2. Population Frequency
| Database | Frequency | Population |
|----------|-----------|------------|
## 3. In Silico Predictions
| Tool | Score | Prediction |
|------|-------|------------|
| CADD | | |
| REVEL | | |
| AlphaMissense | | |
| SpliceAI | | |
## 4. Clinical Evidence
### 4.1 ClinVar
### 4.2 Literature
### 4.3 Functional Studies
## 5. ACMG Classification
| Criterion | Applied | Evidence |
|-----------|---------|----------|
**Final Classification**: [P/LP/VUS/LB/B]
## 6. Pharmacogenomic Implications
(該当する場合)
## 7. Treatment Implications
(がん体細胞変異の場合)
## 8. Recommendations
Completeness Checklist
- バリアント注釈: HGVS、rsID、ゲノム座標の正規化
- 集団頻度: gnomAD 全集団 + 民族別
- In silico 予測: CADD + REVEL + 少なくとも 1 追加ツール
- ClinVar: 全サブミッションの確認
- ACMG 分類: 28 基準の系統的評価
- PGx: 該当遺伝子の場合 CPIC ガイドライン参照
Best Practices
- ACMG は系統的に: 全 28 基準を 1 つずつ評価し、根拠を明記
- 集団頻度はサブ集団で確認: 特定民族でのみ高頻度な場合がある
- 機能研究を重視: 計算予測よりも実験的エビデンスが優先
- ClinVar は星評価を確認: 3-4 星のエントリが最も信頼性が高い
- PGx は CPIC レベルを確認: Level A/B のみ臨床実装
References
Output Files
| ファイル | 形式 | 生成タイミング |
|---|---|---|
results/variant_report.md |
バリアント解釈レポート(Markdown) | 全解析完了時 |
results/variant_classification.json |
ACMG/AMP 分類データ(JSON) | 分類完了時 |
results/pgx_report.json |
薬理ゲノミクスレポート(JSON) | PGx 評価完了時 |
利用可能ツール
ToolUniverse SMCP 経由で利用可能な外部ツール。
| カテゴリ | 主要ツール | 用途 |
|---|---|---|
| ClinVar | clinvar_search_variants |
バリアントの病原性分類検索 |
| gnomAD | gnomad_get_gene_constraints |
遺伝子制約メトリクス(pLI / LOEUF) |
| ClinGen | ClinGen_get_gene_validity |
遺伝子-疾患の妥当性評価 |
| AlphaMissense | AlphaMissense_get_variant_score |
ミスセンス病原性予測スコア |
| PharmGKB | PharmGKB_search_variants |
薬理ゲノミクスバリアント検索 |
| CADD | CADD_get_variant_score |
バリアント有害性スコア |
| MyVariant | MyVariant_get_variant_annotation |
統合バリアントアノテーション |
参照スキル
| スキル | 連携 |
|---|---|
scientific-bioinformatics |
← ゲノムデータ・バリアントコール |
scientific-sequence-analysis |
← 配列コンテキスト・保存度情報 |
scientific-data-preprocessing |
← バリアントデータの前処理・正規化 |
scientific-clinical-decision-support |
→ バリアント解釈結果の臨床意思決定 |
scientific-academic-writing |
→ 研究成果の論文化 |
scientific-pharmacogenomics |
← Star アレル・代謝型・薬理ゲノミクス |