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ACMG/AMP Variant Classification

ACMG/AMP变异分类

ACMG Reasoning

ACMG推理逻辑

Each criterion (PS, PM, PP for pathogenic; BS, BP for benign) contributes a weighted piece of evidence for or against pathogenicity. The classification is the COMBINATION of all activated criteria, not any single criterion. Do not overweight a single finding.
The hierarchy is: PVS1 (very strong) > PS (strong) > PM (moderate) > PP (supporting). On the benign side: BA1 (stand-alone) > BS (strong) > BP (supporting). A frameshift in a LOF-intolerant gene (PVS1) plus a ClinVar expert-panel pathogenic entry (PS1) is pathogenic. A single PP criterion alone is not. The combination rule is what matters.
Two common errors to avoid: (1) seeing a "Pathogenic" ClinVar entry and stopping — that is PP5 (supporting) unless it has expert-panel review, not automatic confirmation; (2) dismissing a variant because one predictor says "tolerated" — discordant predictors mean neither PP3 nor BP4 applies, which is neutral evidence, not benign evidence.
Always apply criteria conservatively. When evidence is ambiguous, leave the criterion unmet. Cite the source for every criterion you activate so clinicians can audit the reasoning.
KEY PRINCIPLES:
  1. Criteria-driven — cite which criteria were activated and why
  2. Conservative — do not upgrade a criterion when evidence is ambiguous
  3. Gene-aware — adjust thresholds based on gene mechanism (LOF vs. gain-of-function)
  4. Population-calibrated — use ancestry-specific gnomAD frequencies, not just global AF
  5. Transparent — show evidence for each criterion
  6. Source-referenced — every criterion activation must cite the database/tool source
  7. English-first queries — always use English terms in tool calls; respond in user's language
每项标准（致病性相关：PS、PM、PP；良性相关：BS、BP）都会为致病性评估提供带有权重的支持或反对证据。最终分类是所有触发标准的组合结果，而非单一标准的结论。切勿过度重视单一发现。
权重层级为：PVS1（极强）> PS（强）> PM（中等）> PP（支持性）。良性相关的层级为：BA1（独立判定）> BS（强）> BP（支持性）。LOF不耐受基因中的移码突变（PVS1）加上ClinVar专家判定的致病性条目（PS1），即可判定为致病性。仅单一PP标准不足以判定致病性，组合规则才是关键。
需避免两种常见错误：(1) 看到ClinVar中的“致病性”条目就停止分析——除非该条目经过专家评审，否则仅为PP5（支持性）证据，而非自动确认；(2) 因某一预测工具显示“耐受”就排除变异——预测结果不一致意味着PP3和BP4均不适用，属于中性证据，而非良性证据。
应用标准时务必保守。当证据模糊时，应判定该标准未满足。为每个触发的标准注明来源，以便临床医生审核推理过程。
核心原则:
  1. 标准驱动——明确标注触发的标准及原因
  2. 保守严谨——证据模糊时不得升级标准权重
  3. 基因机制适配——根据基因机制（LOF vs. 功能获得）调整阈值
  4. 人群校准——使用特定祖先群体的gnomAD频率，而非仅全球频率
  5. 透明化——展示每项标准的证据
  6. 来源可追溯——每个触发的标准必须标注数据库/工具来源
  7. 查询优先用英文——工具调用时始终使用英文术语；以用户使用的语言回复

LOOK UP, DON'T GUESS

查资料，勿猜测

When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
对任何科学事实存疑时，先搜索数据库（PubMed、UniProt、ChEMBL、ClinVar等），而非凭记忆推理。经数据库验证的答案永远比猜测更可靠。

When to Use

使用场景

  • "Classify BRCA2 c.5946delT using ACMG criteria"
  • "Is this VUS pathogenic? NM_000059.4:c.7397T>C"
  • "Apply ACMG guidelines to rs28897743"
  • "What is the pathogenicity of CFTR p.Arg117His?"
  • "ACMG classification for TP53 R248W"
  • "用ACMG标准分类BRCA2 c.5946delT"
  • "这个VUS是否具有致病性？NM_000059.4:c.7397T>C"
  • "将ACMG指南应用于rs28897743"
  • "CFTR p.Arg117His的致病性如何？"
  • "TP53 R248W的ACMG分类"

Tool Parameter Reference

工具参数参考

ToolKey ParametersNotes
VariantValidator_validate_variant
variant_description
, 
genome_build
, 
select_transcripts
genome_build="GRCh38"
VariantValidator_gene2transcripts
gene_symbol
Returns MANE Select transcript
MyVariant_query_variants
query
HGVS or rsID. Returns ClinVar, gnomAD, CADD, REVEL, SIFT, PolyPhen
EnsemblVEP_annotate_hgvs
hgvs_notation
Consequence, colocated variants, ancestry gnomAD
gnomad_search_variants
query
rsID to gnomAD variant ID
gnomad_get_variant
variant_id
Per-ancestry population frequencies
gnomad_get_gene_constraints
gene_symbol
pLI, LOEUF, mis_z
ClinVar_search_variants
query
Variable response format: list OR 
{status, data}
ClinVar_get_variant_details
variant_id
ClinVar numeric ID
civic_get_variants_by_gene
gene_id
CIViC numeric gene ID (NOT symbol). Known: BRAF=5, BRCA2=19
UniProt_get_function_by_accession
accession
Returns list of strings
InterPro_get_entries_for_protein
accession
Domain architecture by UniProt accession
alphafold_get_prediction
qualifier
UniProt accession; pLDDT confidence
PubMed_search_articles
query
, 
limit
Returns list of dicts
MyGene_query_genes
query
Filter by 
symbol
match (first hit may not match)
工具关键参数说明
VariantValidator_validate_variant
variant_description
, 
genome_build
, 
select_transcripts
genome_build="GRCh38"
VariantValidator_gene2transcripts
gene_symbol
返回MANE Select转录本
MyVariant_query_variants
query
HGVS或rsID。返回ClinVar、gnomAD、CADD、REVEL、SIFT、PolyPhen数据
EnsemblVEP_annotate_hgvs
hgvs_notation
变异后果、共定位变异、特定祖先群体gnomAD数据
gnomad_search_variants
query
将rsID转换为gnomAD变异ID
gnomad_get_variant
variant_id
各祖先群体的频率数据
gnomad_get_gene_constraints
gene_symbol
pLI、LOEUF、mis_z值
ClinVar_search_variants
query
响应格式可变：列表或
{status, data}
ClinVar_get_variant_details
variant_id
ClinVar数字ID
civic_get_variants_by_gene
gene_id
CIViC数字基因ID（非基因符号）。已知：BRAF=5，BRCA2=19
UniProt_get_function_by_accession
accession
返回字符串列表
InterPro_get_entries_for_protein
accession
通过UniProt accession获取结构域架构
alphafold_get_prediction
qualifier
UniProt accession；pLDDT置信度
PubMed_search_articles
query
, 
limit
返回字典列表
MyGene_query_genes
query
按
symbol
匹配过滤（首个结果可能不匹配）

Phase 0: Variant Validation and Normalization

阶段0：变异验证与标准化

Wrong HGVS or wrong transcript cascades errors through every downstream criterion. Validate first.
  1. Get MANE Select transcript: 
    VariantValidator_gene2transcripts(gene_symbol="BRCA2")
  2. Validate variant: 
    VariantValidator_validate_variant(variant_description="NM_000059.4:c.5946delT", genome_build="GRCh38", select_transcripts="mane_select")
  3. Resolve gene IDs: 
    MyGene_query_genes(query="BRCA2")
    — extract Ensembl ID and UniProt accession. Filter results by 
    symbol == 'BRCA2'
    (first hit may not match).
  4. Record: HGVS coding, HGVS protein, genomic coordinates, variant type (frameshift/missense/nonsense/splice/synonymous/in-frame indel).
Accepted inputs: HGVS coding (NM_000059.4:c.5946delT), HGVS protein (BRCA2 p.Val600Glu), rsID (rs28897743), gene+change (BRCA1 c.68_69del), genomic coordinates.
错误的HGVS或转录本会导致后续所有标准分析出错。务必先验证。
  1. 获取MANE Select转录本：
    VariantValidator_gene2transcripts(gene_symbol="BRCA2")
  2. 验证变异：
    VariantValidator_validate_variant(variant_description="NM_000059.4:c.5946delT", genome_build="GRCh38", select_transcripts="mane_select")
  3. 解析基因ID：
    MyGene_query_genes(query="BRCA2")
    ——提取Ensembl ID和UniProt accession。按
    symbol == 'BRCA2'
    过滤结果（首个结果可能不匹配）。
  4. 记录信息：HGVS编码变异、HGVS蛋白变异、基因组坐标、变异类型（移码/错义/无义/剪接/同义/框内插入缺失）。
接受的输入格式：HGVS编码（NM_000059.4:c.5946delT）、HGVS蛋白（BRCA2 p.Val600Glu）、rsID（rs28897743）、基因+变异信息（BRCA1 c.68_69del）、基因组坐标。

Phase 1: Population Frequency (BA1, BS1, BS2, PM2)

阶段1：人群频率（BA1、BS1、BS2、PM2）

Population AF is among the strongest evidence in either direction. A variant at >5% in any population is almost certainly benign (BA1 — stand-alone, no further analysis needed). Absent from gnomAD supports pathogenicity (PM2, now usually applied as PM2_Supporting per ClinGen guidance).
Use ancestry-specific AF, not just global. A variant at 8% in East Asian populations but rare globally is benign in that ancestry context. For BS1, the threshold depends on disease prevalence and inheritance — the default is 1% for common diseases, 0.1% for rare.
python
gnomad_search_variants(query="rs28897743")          # get gnomAD variant ID
gnomad_get_variant(variant_id="...")                 # per-ancestry frequencies
gnomad_get_gene_constraints(gene_symbol="BRCA2")     # pLI, LOEUF, mis_z
MyVariant_query_variants(query="rs28897743")          # fallback: gnomad_genome.af
If gnomAD data is unavailable, note the gap and continue — absence of data is not the same as evidence of absence.
人群等位基因频率（AF）是致病性或良性方向最强的证据之一。在任何人群中频率>5%的变异几乎可以确定为良性（BA1——独立判定，无需进一步分析）。在gnomAD中未检出的变异支持致病性（PM2，根据ClinGen指南通常作为PM2_Supporting应用）。
使用特定祖先群体的AF，而非仅全球频率。某变异在东亚人群中频率为8%但全球罕见，在该祖先群体背景下属于良性。BS1的阈值取决于疾病患病率和遗传模式——默认常见疾病为1%，罕见疾病为0.1%。
python
gnomad_search_variants(query="rs28897743")          # 获取gnomAD变异ID
gnomad_get_variant(variant_id="...")                 # 获取各祖先群体频率
gnomad_get_gene_constraints(gene_symbol="BRCA2")     # 获取pLI、LOEUF、mis_z值
MyVariant_query_variants(query="rs28897743")          # 备选方案：获取gnomad_genome.af
若gnomAD数据不可用，需注明数据缺口并继续分析——数据缺失不等于无证据。

Phase 2: Computational Predictions (PP3, BP4)

阶段2：计算预测（PP3、BP4）

No single predictor is definitive. The reasoning is: concordance across multiple independent predictors provides supporting evidence. Discordance means neither PP3 nor BP4 applies — it is neutral, not benign.
PP3 (supporting pathogenic) applies when the majority of predictors agree damaging, or when REVEL >= 0.7 alone (sufficient per ClinGen guidance). BP4 (supporting benign) requires ALL predictors to agree benign, or REVEL < 0.15 or CADD < 15. These criteria apply only to missense variants.
python
MyVariant_query_variants(query="...")        # REVEL, CADD PHRED, AlphaMissense, SIFT, PolyPhen
EnsemblVEP_annotate_hgvs(hgvs_notation="...") # consequence, SpliceAI deltas
For non-missense variants, skip PP3/BP4 and focus on SpliceAI scores in Phase 5.
单一预测工具的结果不具有决定性。推理逻辑为：多个独立预测工具结果一致时提供支持性证据。结果不一致则PP3和BP4均不适用——属于中性证据，而非良性证据。
当多数预测工具判定为有害，或仅REVEL≥0.7时（符合ClinGen指南要求），适用PP3（支持致病性）。BP4（支持良性）要求所有预测工具均判定为良性，或REVEL<0.15、CADD<15。这些标准仅适用于错义变异。
python
MyVariant_query_variants(query="...")        # 获取REVEL、CADD PHRED、AlphaMissense、SIFT、PolyPhen数据
EnsemblVEP_annotate_hgvs(hgvs_notation="...") # 获取变异后果、SpliceAI delta值
对于非错义变异，跳过PP3/BP4，重点关注阶段5的SpliceAI评分。

Phase 3: Clinical Database Evidence (PS1, PM5, PP5, BP6)

阶段3：临床数据库证据（PS1、PM5、PP5、BP6）

ClinVar aggregates clinical lab classifications. The reasoning: if the same amino acid change (different nucleotide) is established pathogenic, that is strong evidence (PS1) because the mechanism is the amino acid change. If a different pathogenic missense occurs at the same residue, that is moderate evidence (PM5) — the residue is functionally important.
PP5 applies when ClinVar shows Pathogenic with >= 2-star review (criteria provided, multiple submitters). Weight by the number of concordant submitters. Conflicting ClinVar interpretations mean neither PP5 nor BP6 should be applied. ClinGen has proposed downweighting PP5/BP6 — treat them as supporting, not strong.
python
ClinVar_search_variants(query="BRCA2 c.5946delT")
ClinVar_get_variant_details(variant_id="...")
civic_get_variants_by_gene(gene_id=19)   # BRCA2 CIViC ID is 19
ClinVar汇总了临床实验室的分类结果。推理逻辑：若相同氨基酸改变（不同核苷酸）已被确定为致病性，则属于强证据（PS1），因为致病机制源于氨基酸改变。若同一残基存在其他致病性错义变异，则属于中等证据（PM5）——该残基具有重要功能。
当ClinVar显示致病性且评审星级≥2（提供标准依据、多提交者）时，适用PP5。根据一致提交者的数量调整权重。ClinVar解读存在冲突时，PP5和BP6均不适用。ClinGen建议降低PP5/BP6的权重——视为支持性证据，而非强证据。
python
ClinVar_search_variants(query="BRCA2 c.5946delT")
ClinVar_get_variant_details(variant_id="...")
civic_get_variants_by_gene(gene_id=19)   # BRCA2的CIViC ID为19

Phase 4: Functional Domain and Protein Analysis (PM1, PP2, BP1)

阶段4：功能结构域与蛋白分析（PM1、PP2、BP1）

Variants in well-established functional domains with known pathogenic variant enrichment are more likely pathogenic. PM1 (moderate pathogenic) requires the variant to be in a hotspot domain with low benign variation — use InterPro domain architecture and UniProt active/binding sites to assess.
PP2 and BP1 are mutually exclusive. PP2 (supporting pathogenic) applies to missense in genes where missense is the known mechanism and benign missense rate is low (mis_z > 3.09). BP1 (supporting benign) applies to missense in genes where only truncating variants cause disease (LOF-only mechanism) — a missense in such a gene is unlikely to be pathogenic.
python
UniProt_get_function_by_accession(accession="P51587")        # active sites, binding sites
InterPro_get_entries_for_protein(accession="P51587")          # domain architecture
alphafold_get_prediction(qualifier="P51587")                   # pLDDT > 90 = structured region
gnomad_get_gene_constraints(gene_symbol="BRCA2")              # mis_z for PP2/BP1
位于已知致病性变异富集的成熟功能结构域内的变异更可能具有致病性。PM1（中等致病性）要求变异位于热点结构域且良性变异率低——使用InterPro结构域架构和UniProt活性/结合位点进行评估。
PP2和BP1互斥。PP2（支持致病性）适用于错义变异，且该基因的致病机制为错义变异、良性错义变异率低（mis_z>3.09）。BP1（支持良性）适用于仅截短变异致病的基因（LOF唯一机制）——此类基因中的错义变异不太可能具有致病性。
python
UniProt_get_function_by_accession(accession="P51587")        # 获取活性位点、结合位点
InterPro_get_entries_for_protein(accession="P51587")          # 获取结构域架构
alphafold_get_prediction(qualifier="P51587")                   # pLDDT>90=结构化区域
gnomad_get_gene_constraints(gene_symbol="BRCA2")              # 获取mis_z值以评估PP2/BP1

Phase 5: Splice Impact Assessment (PVS1)

阶段5：剪接影响评估（PVS1）

PVS1 is the strongest single pathogenic criterion. A null variant (nonsense/frameshift/canonical splice/initiation codon) in a gene where LOF is the established mechanism can activate PVS1, but the full strength depends on context.
Apply PVS1 at full strength when: null variant + LOF is known mechanism + variant is not in the last exon or last 50bp of the penultimate exon + no rescue transcript exists. Downgrade to PVS1_Moderate if the variant is in the last exon or NMD escape is likely. Downgrade to PVS1_Supporting if a rescue transcript is possible or SpliceAI >= 0.5 but not a canonical splice site. Do NOT apply PVS1 if LOF mechanism is uncertain.
python
EnsemblVEP_annotate_hgvs(hgvs_notation="...")   # splice_donor_variant, splice_acceptor_variant
MyVariant_query_variants(query="...")             # SpliceAI deltas
gnomad_get_gene_constraints(gene_symbol="...")    # pLI >= 0.9 or LOEUF < 0.35 = LOF intolerant
PVS1是最强的单一致病性标准。当无义/移码/经典剪接/起始密码子变异发生在LOF为已知致病机制的基因中时，可触发PVS1，但权重需根据具体情况调整。
满足以下条件时，PVS1按全强度应用：无义变异+LOF为已知致病机制+变异不在最后一个外显子或倒数第二个外显子的最后50bp内+无拯救转录本存在。若变异位于最后一个外显子或可能发生无义介导的mRNA降解（NMD）逃逸，则降级为PVS1_Moderate。若存在潜在拯救转录本或SpliceAI≥0.5但非经典剪接位点，则降级为PVS1_Supporting。若LOF机制不确定，则不得应用PVS1。
python
EnsemblVEP_annotate_hgvs(hgvs_notation="...")   # 获取剪接供体变异、剪接受体变异
MyVariant_query_variants(query="...")             # 获取SpliceAI delta值
gnomad_get_gene_constraints(gene_symbol="...")    # pLI≥0.9或LOEUF<0.35=LOF不耐受

Phase 6: Literature and Functional Evidence (PS3, BS3, PP1, PP4)

阶段6：文献与功能证据（PS3、BS3、PP1、PP4）

Well-designed functional assays showing LOF (PS3) or normal function (BS3) can shift a classification decisively. PS3/BS3 can be downgraded (e.g., PS3_Supporting) for less rigorous assays. Not all functional assays qualify — ClinGen gene-specific guidance defines valid assays.
PP1 (co-segregation) upgrades to PP1_Strong at >= 7 informative meioses. PP4 applies when the patient's phenotype is highly specific for the gene's disease.
python
PubMed_search_articles(query="BRCA2 c.5946delT functional assay", limit=10)
PubMed_search_articles(query="BRCA2 c.5946delT segregation family", limit=5)
Criteria requiring clinical data (PS2, PS4, PM3, PM6, BS4, BP2, BP5) cannot be assessed automatically. Document as "Not Assessed" unless the user provides clinical context.
PM4 (protein length change in non-repeat region) and BP3 (in-frame indel in repeat) can be partially assessed from variant type. BP7 (synonymous, no splice impact) is assessable via SpliceAI < 0.1.
设计严谨的功能实验显示LOF（PS3）或功能正常（BS3）可决定性改变分类结果。实验严谨性不足时，PS3/BS3可降级（如PS3_Supporting）。并非所有功能实验都符合要求——ClinGen基因特异性指南定义了有效实验类型。
PP1（共分离）在≥7个信息性减数分裂时升级为PP1_Strong。当患者表型与该基因相关疾病高度特异性匹配时，适用PP4。
python
PubMed_search_articles(query="BRCA2 c.5946delT functional assay", limit=10)
PubMed_search_articles(query="BRCA2 c.5946delT segregation family", limit=5)
需要临床数据的标准（PS2、PS4、PM3、PM6、BS4、BP2、BP5）无法自动评估。除非用户提供临床背景，否则标注为“未评估”。
PM4（非重复区域蛋白长度改变）和BP3（重复区域框内插入缺失）可通过变异类型部分评估。BP7（同义变异、无剪接影响）可通过SpliceAI<0.1评估。

Classification Algorithm

分类算法

Combine criteria at their applied strength (after upgrades/downgrades):
Pathogenic: (1) PVS1 + ≥1 Strong; (2) PVS1 + ≥2 Moderate; (3) PVS1 + 1 Moderate + 1 Supporting; (4) PVS1 + ≥2 Supporting; (5) ≥2 Strong; (6) 1 Strong + ≥3 Moderate; (7) 1 Strong + 2 Moderate + ≥2 Supporting; (8) 1 Strong + 1 Moderate + ≥4 Supporting
Likely Pathogenic: (1) PVS1 + 1 Moderate; (2) 1 Strong + 1-2 Moderate; (3) 1 Strong + ≥2 Supporting; (4) ≥3 Moderate; (5) 2 Moderate + ≥2 Supporting; (6) 1 Moderate + ≥4 Supporting
Benign: (1) BA1 stand-alone; (2) ≥2 Strong benign
Likely Benign: (1) 1 Strong benign + 1 Supporting benign; (2) ≥2 Supporting benign
VUS: Criteria do not meet any threshold above, OR pathogenic and benign evidence conflict.
结合各标准的应用权重（升级/降级后）：
致病性：(1) PVS1 + ≥1项强证据；(2) PVS1 + ≥2项中等证据；(3) PVS1 + 1项中等证据 + 1项支持性证据；(4) PVS1 + ≥2项支持性证据；(5) ≥2项强证据；(6) 1项强证据 + ≥3项中等证据；(7) 1项强证据 + 2项中等证据 + ≥2项支持性证据；(8) 1项强证据 + 1项中等证据 + ≥4项支持性证据
可能致病性：(1) PVS1 + 1项中等证据；(2) 1项强证据 + 1-2项中等证据；(3) 1项强证据 + ≥2项支持性证据；(4) ≥3项中等证据；(5) 2项中等证据 + ≥2项支持性证据；(6) 1项中等证据 + ≥4项支持性证据
良性：(1) BA1独立判定；(2) ≥2项强良性证据
可能良性：(1) 1项强良性证据 + 1项支持性良性证据；(2) ≥2项支持性良性证据
意义未明变异（VUS）：标准未达到上述任何阈值，或致病性与良性证据冲突。

Output Format

输出格式

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ACMG Variant Classification Report

ACMG变异分类报告

Variant: [HGVS]

变异信息: [HGVS]

  • Gene: [symbol] | Transcript: [MANE Select] | Protein: [p.notation] | Type: [variant type]
  • 基因: [基因符号] | 转录本: [MANE Select] | 蛋白变异: [p.命名] | 变异类型: [变异类型]

Classification: [PATHOGENIC / LIKELY PATHOGENIC / VUS / LIKELY BENIGN / BENIGN]

分类结果: [致病性 / 可能致病性 / VUS / 可能良性 / 良性]

Evidence Summary

证据总结

Pathogenic Criteria Met

满足的致病性标准

| Criterion | Strength | Evidence | Source |
| 标准 | 权重 | 证据 | 来源 |

Benign Criteria Met

满足的良性标准

| Criterion | Strength | Evidence | Source |
| 标准 | 权重 | 证据 | 来源 |

Criteria Not Met (key ones with reasoning)

未满足的关键标准（含推理）

Criteria Not Assessed (and why)

未评估的标准（及原因）

Detailed Evidence

详细证据

  • Population: gnomAD AF, ancestry max, homozygotes, gene constraints
  • Computational: predictor concordance
  • Clinical: ClinVar classification + review status, CIViC entries
  • Domain: InterPro domains, UniProt annotations
  • Splice: SpliceAI scores, canonical site status
  • Literature: key functional study findings
  • 人群数据：gnomAD频率、最高祖先群体频率、纯合子数量、基因约束值
  • 计算预测：预测工具一致性
  • 临床数据：ClinVar分类+评审状态、CIViC条目
  • 结构域分析：InterPro结构域、UniProt注释
  • 剪接分析：SpliceAI评分、经典剪接位点状态
  • 文献证据：关键功能研究结果

Classification Logic

分类逻辑

Applied rule: [e.g., "PVS1 + PM2_Supporting = Likely Pathogenic (LP rule 1)"]
应用规则: [例如："PVS1 + PM2_Supporting = 可能致病性（LP规则1）"]

Limitations

局限性

  • [Criteria not assessed and what data would be needed]

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  • [未评估的标准及所需补充数据]

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Common Patterns

常见模式

Pattern 1: Known pathogenic frameshift — "Classify BRCA2 c.5946delT" Phase 0 (validate) → Phase 1 (gnomAD absent, PM2_Supporting) → Phase 3 (ClinVar Pathogenic, PP5) → Phase 4 (DNA repair domain, PM1) → Phase 5 (frameshift + LOF gene, PVS1) → Phase 6 (literature PS3) Result: Pathogenic (PVS1 + PS3 + PM1 + PM2_Supporting + PP5)
Pattern 2: Missense VUS — "Is BRCA1 p.Arg1699Gln pathogenic?" Phase 0 → Phase 1 (rare, PM2_Supporting) → Phase 2 (REVEL 0.82, CADD 26, PP3) → Phase 3 (ClinVar VUS) → Phase 4 (BRCT domain, PM1) → Phase 6 (reduced activity, PS3_Moderate) Result: Likely Pathogenic (PS3_Moderate + PM1 + PM2_Supporting + PP3)
Pattern 3: Common benign variant — "ACMG for rs1800497" Phase 1 (gnomAD AF=0.21, BA1) → short-circuit. Result: Benign (BA1 stand-alone)
Pattern 4: Deep-intronic variant — "Classify NM_000059.4:c.7977+100A>G" Phase 1 (check AF) → Phase 5 (SpliceAI < 0.1) → Result: Likely Benign or VUS depending on frequency
模式1：已知致病性移码变异 — "分类BRCA2 c.5946delT" 阶段0（验证）→ 阶段1（gnomAD未检出，PM2_Supporting）→ 阶段3（ClinVar致病性，PP5）→ 阶段4（DNA修复结构域，PM1）→ 阶段5（移码变异+LOF基因，PVS1）→ 阶段6（文献支持PS3） 结果: 致病性（PVS1 + PS3 + PM1 + PM2_Supporting + PP5）
模式2：错义VUS — "BRCA1 p.Arg1699Gln是否具有致病性？" 阶段0 → 阶段1（罕见，PM2_Supporting）→ 阶段2（REVEL 0.82，CADD 26，PP3）→ 阶段3（ClinVar VUS）→ 阶段4（BRCT结构域，PM1）→ 阶段6（活性降低，PS3_Moderate） 结果: 可能致病性（PS3_Moderate + PM1 + PM2_Supporting + PP3）
模式3：常见良性变异 — "rs1800497的ACMG分类" 阶段1（gnomAD AF=0.21，BA1）→ 直接得出结论。结果: 良性（BA1独立判定）
模式4：深内含子变异 — "分类NM_000059.4:c.7977+100A>G" 阶段1（检查频率）→ 阶段5（SpliceAI < 0.1）→ 结果: 可能良性或VUS（取决于频率）