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tooluniverse-gwas-snp-interpretation

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GWAS SNP Interpretation Skill

GWAS SNP 解读技能

Overview

概述

Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context.
Use Cases:
  • "Interpret rs7903146" (TCF7L2 diabetes variant)
  • "What diseases is rs429358 associated with?" (APOE Alzheimer's variant)
  • "Clinical significance of rs1801133" (MTHFR variant)
  • "Is rs12913832 in any fine-mapped loci?" (Eye color variant)
通过整合多来源证据，为GWAS研究中的遗传变异（SNP）提供全面的临床和生物学背景解读。
适用场景：
  • "解读rs7903146"（TCF7L2糖尿病相关变异）
  • "rs429358与哪些疾病相关？"（APOE阿尔茨海默病相关变异）
  • "rs1801133的临床意义"（MTHFR基因变异）
  • "rs12913832是否属于精细定位位点？"（眼睛颜色相关变异）

What It Does

功能说明

The skill provides a comprehensive interpretation of SNPs by:
  1. SNP Annotation: Retrieves basic variant information including genomic coordinates, alleles, functional consequence, and mapped genes
  2. Association Discovery: Finds all GWAS trait/disease associations with statistical significance
  3. Fine-Mapping Evidence: Identifies credible sets the variant belongs to (fine-mapped causal loci)
  4. Gene Mapping: Uses Locus-to-Gene (L2G) predictions to identify likely causal genes
  5. Clinical Summary: Aggregates evidence into actionable clinical significance
该技能通过以下方式提供SNP的全面解读：
  1. SNP注释：获取基本变异信息，包括基因组坐标、等位基因、功能影响及映射基因
  2. 关联发现：查找所有具有统计学显著性的GWAS性状/疾病关联
  3. 精细定位证据：识别变异所属的可信集合（精细定位的因果位点）
  4. 基因映射：利用位点-基因（L2G）预测识别潜在因果基因
  5. 临床总结：整合证据形成可落地的临床意义结论

Workflow

工作流程

User Input: rs7903146
    ↓
[1] SNP Lookup
    → Get location, consequence, MAF
    → gwas_get_snp_by_id
    ↓
[2] Association Search
    → Find all trait/disease associations
    → gwas_get_associations_for_snp
    ↓
[3] Fine-Mapping (Optional)
    → Get credible set membership
    → OpenTargets_get_variant_credible_sets
    ↓
[4] Gene Predictions
    → Extract L2G scores for causal genes
    → (embedded in credible sets)
    ↓
[5] Clinical Summary
    → Aggregate evidence
    → Identify key traits and genes
    ↓
Output: Comprehensive Interpretation Report
用户输入: rs7903146
    ↓
[1] SNP 查找
    → 获取位置、功能影响、次要等位基因频率（MAF）
    → gwas_get_snp_by_id
    ↓
[2] 关联搜索
    → 查找所有性状/疾病关联
    → gwas_get_associations_for_snp
    ↓
[3] 精细定位（可选）
    → 获取可信集合成员信息
    → OpenTargets_get_variant_credible_sets
    ↓
[4] 基因预测
    → 提取因果基因的L2G评分
    → （嵌入可信集合数据中）
    ↓
[5] 临床总结
    → 整合所有证据
    → 识别关键性状与基因
    ↓
输出: 全面解读报告

Data Sources

数据来源

GWAS Catalog (EMBL-EBI)

GWAS Catalog（EMBL-EBI）

  • SNP annotations: Functional consequences, mapped genes, population frequencies
  • Associations: P-values, effect sizes, study metadata
  • Coverage: 350,000+ publications, 670,000+ associations
  • SNP注释：功能影响、映射基因、人群频率
  • 关联信息：P值、效应量、研究元数据
  • 覆盖范围：35万+出版物，67万+关联数据

Open Targets Genetics

Open Targets Genetics

  • Fine-mapping: Statistical credible sets from SuSiE, FINEMAP methods
  • L2G predictions: Machine learning-based gene prioritization
  • Colocalization: QTL evidence for causal genes
  • Coverage: UK Biobank, FinnGen, and other large cohorts
  • 精细定位：基于SuSiE、FINEMAP方法的统计可信集合
  • L2G预测：基于机器学习的基因优先级排序
  • 共定位：因果基因的QTL证据
  • 覆盖范围：英国生物样本库、FinnGen及其他大型队列

Input Parameters

输入参数

Required

必填参数

  • rs_id
    (str): dbSNP rs identifier
    • Format: "rs" + number (e.g., "rs7903146")
    • Must be valid rsID in GWAS Catalog
  • rs_id
    （字符串）：dbSNP的rs编号
    • 格式："rs" + 数字（例如："rs7903146"）
    • 必须是GWAS Catalog中存在的有效rsID

Optional

可选参数

  • include_credible_sets
    (bool, default=True): Query fine-mapping data
    • True: Complete interpretation (slower, ~10-30s)
    • False: Fast associations only (~2-5s)
  • p_threshold
    (float, default=5e-8): Genome-wide significance threshold
  • max_associations
    (int, default=100): Maximum associations to retrieve
  • include_credible_sets
    （布尔值，默认值：True）：查询精细定位数据
    • True：完整解读（速度较慢，约10-30秒）
    • False：仅快速获取关联数据（约2-5秒）
  • p_threshold
    （浮点数，默认值：5e-8）：全基因组显著性阈值
  • max_associations
    （整数，默认值：100）：最大获取关联数据数量

Output Format

输出格式

Returns 
SNPInterpretationReport
containing:
返回包含以下内容的
SNPInterpretationReport
：

1. SNP Basic Info

1. SNP基本信息

python
{
    'rs_id': 'rs7903146',
    'chromosome': '10',
    'position': 112998590,
    'ref_allele': 'C',
    'alt_allele': 'T',
    'consequence': 'intron_variant',
    'mapped_genes': ['TCF7L2'],
    'maf': 0.293
}
python
{
    'rs_id': 'rs7903146',
    'chromosome': '10',
    'position': 112998590,
    'ref_allele': 'C',
    'alt_allele': 'T',
    'consequence': 'intron_variant',
    'mapped_genes': ['TCF7L2'],
    'maf': 0.293
}

2. Trait Associations

2. 性状关联数据

python
[
    {
        'trait': 'Type 2 diabetes',
        'p_value': 1.2e-128,
        'beta': '0.28 unit increase',
        'study_id': 'GCST010555',
        'pubmed_id': '33536258',
        'effect_allele': 'T'
    },
    ...
]
python
[
    {
        'trait': 'Type 2 diabetes',
        'p_value': 1.2e-128,
        'beta': '0.28 unit increase',
        'study_id': 'GCST010555',
        'pubmed_id': '33536258',
        'effect_allele': 'T'
    },
    ...
]

3. Credible Sets (Fine-Mapping)

3. 可信集合（精细定位）

python
[
    {
        'study_id': 'GCST90476118',
        'trait': 'Renal failure',
        'finemapping_method': 'SuSiE-inf',
        'p_value': 3.5e-42,
        'predicted_genes': [
            {'gene': 'TCF7L2', 'score': 0.863}
        ],
        'region': '10:112950000-113050000'
    },
    ...
]
python
[
    {
        'study_id': 'GCST90476118',
        'trait': 'Renal failure',
        'finemapping_method': 'SuSiE-inf',
        'p_value': 3.5e-42,
        'predicted_genes': [
            {'gene': 'TCF7L2', 'score': 0.863}
        ],
        'region': '10:112950000-113050000'
    },
    ...
]

4. Clinical Significance

4. 临床意义总结

Genome-wide significant associations with 100 traits/diseases:
  - Type 2 diabetes
  - Diabetic retinopathy
  - HbA1c levels
  ...

Identified in 20 fine-mapped loci.
Predicted causal genes: TCF7L2
与100种性状/疾病存在全基因组显著性关联：
  - 2型糖尿病
  - 糖尿病视网膜病变
  - 糖化血红蛋白（HbA1c）水平
  ...

该变异存在于20个精细定位位点中。
预测因果基因：TCF7L2

Example Usage

示例用法

See 
QUICK_START.md
for platform-specific examples.
请查看
QUICK_START.md
获取平台特定示例。

Tools Used

使用工具

GWAS Catalog Tools

GWAS Catalog工具

  1. gwas_get_snp_by_id
    : Get SNP annotation
  2. gwas_get_associations_for_snp
    : Get all trait associations
  1. gwas_get_snp_by_id
    ：获取SNP注释信息
  2. gwas_get_associations_for_snp
    ：获取所有性状关联数据

Open Targets Tools

Open Targets工具

  1. OpenTargets_get_variant_info
    : Get variant details with population frequencies
  2. OpenTargets_get_variant_credible_sets
    : Get fine-mapping credible sets with L2G
  1. OpenTargets_get_variant_info
    ：获取带有人群频率的变异详情
  2. OpenTargets_get_variant_credible_sets
    ：获取包含L2G评分的精细定位可信集合

Interpretation Guide

解读指南

P-value Significance Levels

P值显著性水平

  • p < 5e-8: Genome-wide significant (strong evidence)
  • p < 5e-6: Suggestive (moderate evidence)
  • p < 0.05: Nominal (weak evidence)
  • p < 5e-8：全基因组显著性（强证据）
  • p < 5e-6：提示性关联（中等证据）
  • p < 0.05：名义关联（弱证据）

L2G Score Interpretation

L2G评分解读

  • > 0.5: High confidence causal gene
  • 0.1-0.5: Moderate confidence
  • < 0.1: Low confidence
  • > 0.5：高置信度因果基因
  • 0.1-0.5：中等置信度
  • < 0.1：低置信度

Clinical Actionability

临床可操作性

  1. High: Multiple genome-wide significant associations + in credible sets + high L2G scores
  2. Moderate: Genome-wide significant associations but limited fine-mapping
  3. Low: Suggestive associations or limited replication
  1. 高：多个全基因组显著性关联 + 属于可信集合 + 高L2G评分
  2. 中：全基因组显著性关联但精细定位证据有限
  3. 低：仅提示性关联或重复验证证据不足

Limitations

局限性

  1. Variant ID Conversion: OpenTargets requires chr_pos_ref_alt format, which may need allele lookup
  2. Population Specificity: Associations may vary by ancestry
  3. Effect Sizes: Beta values are study-dependent (different phenotype scales)
  4. Causality: Associations don't prove causation; fine-mapping improves confidence
  5. Currency: Data reflects published GWAS; latest studies may not be included
  1. 变异ID转换：OpenTargets需要chr_pos_ref_alt格式，可能需要等位基因查询
  2. 人群特异性：关联结果可能因祖先族群不同而异
  3. 效应量：Beta值具有研究特异性（不同表型的度量标准不同）
  4. 因果关系：关联不等于因果；精细定位可提升置信度
  5. 数据时效性：数据基于已发表的GWAS研究，最新研究可能未纳入

Best Practices

最佳实践

  1. Use Full Interpretation: Enable 
    include_credible_sets=True
    for clinical decisions
  2. Check Multiple Variants: Look at other variants in the same locus
  3. Validate Populations: Consider ancestry-specific effect sizes
  4. Review Publications: Check original studies for context
  5. Integrate Evidence: Combine with functional data, eQTLs, pQTLs
  1. 使用完整解读模式：临床决策时启用
    include_credible_sets=True
  2. 检查同一位点的其他变异：查看同一基因座中的其他变异
  3. 验证人群特异性：考虑祖先族群特异性的效应量
  4. 查阅原始文献：查看原始研究获取更多背景信息
  5. 整合多维度证据：结合功能数据、eQTL、pQTL等信息

Technical Notes

技术说明

Performance

性能

  • Fast mode (no credible sets): 2-5 seconds
  • Full mode (with credible sets): 10-30 seconds
  • Bottleneck: OpenTargets GraphQL API rate limits
  • 快速模式（无可信集合）：2-5秒
  • 完整模式（含可信集合）：10-30秒
  • 性能瓶颈：OpenTargets GraphQL API的速率限制

Error Handling

错误处理

  • Invalid rs_id: Returns error message
  • No associations: Returns empty list with note
  • API failures: Graceful degradation (returns partial results)
  • 无效rsID：返回错误提示
  • 无关联数据：返回空列表并附带说明
  • API调用失败：优雅降级（返回部分结果）

Related Skills

相关技能

  • Gene Function Analysis: Interpret predicted causal genes
  • Disease Ontology Lookup: Understand trait classifications
  • PubMed Literature Search: Find original GWAS publications
  • Variant Effect Prediction: Functional consequence analysis
  • 基因功能分析：解读预测的因果基因
  • 疾病本体查询：理解性状分类
  • PubMed文献检索：查找原始GWAS研究文献
  • 变异效应预测：功能影响分析

References

参考文献

  1. GWAS Catalog: https://www.ebi.ac.uk/gwas/
  2. Open Targets Genetics: https://genetics.opentargets.org/
  3. GWAS Significance Thresholds: Fadista et al. 2016
  4. L2G Method: Mountjoy et al. 2021 (Nature Genetics)
  1. GWAS Catalog: https://www.ebi.ac.uk/gwas/
  2. Open Targets Genetics: https://genetics.opentargets.org/
  3. GWAS显著性阈值: Fadista et al. 2016
  4. L2G方法: Mountjoy et al. 2021（Nature Genetics）

Version

版本信息

  • Version: 1.0.0
  • Last Updated: 2026-02-13
  • ToolUniverse Version: >= 1.0.0
  • Tools Required: gwas_get_snp_by_id, gwas_get_associations_for_snp, OpenTargets_get_variant_credible_sets
  • 版本: 1.0.0
  • 最后更新: 2026-02-13
  • ToolUniverse版本: >= 1.0.0
  • 所需工具: gwas_get_snp_by_id, gwas_get_associations_for_snp, OpenTargets_get_variant_credible_sets