tooluniverse-gwas-trait-to-gene

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GWAS Trait-to-Gene Discovery

基于GWAS的性状关联基因发现

Discover genes associated with diseases and traits using genome-wide association studies (GWAS)

利用全基因组关联研究（GWAS）发现与疾病和性状相关的基因

Overview

概述

This skill enables systematic discovery of genes linked to diseases/traits by analyzing GWAS data from two major resources:

GWAS Catalog (EBI/NHGRI): Curated catalog of published GWAS with >500,000 associations
Open Targets Genetics: Fine-mapped GWAS signals with locus-to-gene (L2G) predictions

本技能通过分析两大核心资源的GWAS数据，可系统性发现与疾病/性状相关的基因：

GWAS Catalog（EBI/NHGRI）：精心整理的已发表GWAS目录，包含超过50万条关联数据
Open Targets Genetics：经过精细定位的GWAS信号，提供基因座-基因（L2G）预测

Use Cases

适用场景

Clinical Research

"What genes are associated with type 2 diabetes?"
"Find genetic risk factors for coronary artery disease"
"Which genes contribute to Alzheimer's disease susceptibility?"

Drug Target Discovery

Identify genes with strong genetic evidence for disease causation
Prioritize targets based on L2G scores and replication across studies
Find genes with genome-wide significant associations (p < 5e-8)

Functional Genomics

Map disease-associated variants to candidate genes
Analyze genetic architecture of complex traits
Understand polygenic disease mechanisms

临床研究

"哪些基因与2型糖尿病相关？"
"寻找冠心病的遗传风险因素"
"哪些基因会增加阿尔茨海默病的易感性？"

药物靶点发现

识别具有强遗传致病证据的基因
根据L2G评分和跨研究重复验证结果优先选择靶点
寻找达到全基因组显著性关联的基因（p < 5e-8）

功能基因组学

将疾病相关变异映射到候选基因
分析复杂性状的遗传结构
理解多基因疾病的发病机制

Workflow

工作流程

1. Trait Search → Search GWAS Catalog by disease/trait name
       ↓
2. SNP Aggregation → Collect genome-wide significant SNPs (p < 5e-8)
       ↓
3. Gene Mapping → Extract mapped genes from associations
       ↓
4. Evidence Ranking → Score by p-value, replication, fine-mapping
       ↓
5. Annotation (Optional) → Add L2G predictions from Open Targets

1. 性状搜索 → 根据疾病/性状名称搜索GWAS Catalog
       ↓
2. SNP聚合 → 收集达到全基因组显著性的SNP（p < 5e-8）
       ↓
3. 基因映射 → 从关联数据中提取映射的基因
       ↓
4. 证据排序 → 按p值、重复验证结果、精细定位结果评分
       ↓
5. 注释（可选） → 加入来自Open Targets的L2G预测

Key Concepts

核心概念

Genome-wide Significance

Standard threshold: p < 5×10⁻⁸
Accounts for multiple testing burden across ~1M common variants
Higher confidence: p < 5×10⁻¹⁰ or replicated across studies

Gene Mapping Methods

Positional: Nearest gene to lead SNP
Fine-mapping: Statistical refinement to credible variants
Locus-to-Gene (L2G): Integrative score combining multiple evidence types

Evidence Confidence Levels

High: L2G score > 0.5 OR multiple studies with p < 5e-10
Medium: 2+ studies with p < 5e-8
Low: Single study or marginal significance

全基因组显著性

标准阈值：p < 5×10⁻⁸
用于校正约100万个常见变异的多重检验偏差
更高置信度：p < 5×10⁻¹⁰ 或在多项研究中重复验证

基因映射方法

位置映射：将SNP分配给距离最近的基因
精细定位：通过统计方法缩小到可信变异位点
基因座-基因（L2G）：整合多种证据类型的综合评分

证据置信水平

高：L2G评分>0.5 或多项研究中p < 5e-10
中：2项及以上研究中p < 5e-8
低：仅单一研究或显著性边缘

Required ToolUniverse Tools

所需ToolUniverse工具

GWAS Catalog (11 tools)

GWAS Catalog（11款工具）

```
gwas_get_associations_for_trait
```
- Get all associations for a trait (sorted by p-value)
```
gwas_search_snps
```
- Search SNPs by gene mapping
```
gwas_get_snp_by_id
```
- Get SNP details (MAF, consequence, location)
```
gwas_get_study_by_id
```
- Get study metadata
```
gwas_search_associations
```
- Search associations with filters
```
gwas_search_studies
```
- Search studies by trait/cohort
```
gwas_get_associations_for_snp
```
- Get all associations for a SNP
```
gwas_get_variants_for_trait
```
- Get variants for a trait
```
gwas_get_studies_for_trait
```
- Get studies for a trait
```
gwas_get_snps_for_gene
```
- Get SNPs mapped to a gene
```
gwas_get_associations_for_study
```
- Get associations from a study

```
gwas_get_associations_for_trait
```
- 获取某一性状的所有关联数据（按p值排序）
```
gwas_search_snps
```
- 根据基因映射结果搜索SNP
```
gwas_get_snp_by_id
```
- 获取SNP详细信息（MAF、变异后果、位置）
```
gwas_get_study_by_id
```
- 获取研究元数据
```
gwas_search_associations
```
- 带筛选条件的关联数据搜索
```
gwas_search_studies
```
- 按性状/队列搜索研究
```
gwas_get_associations_for_snp
```
- 获取某一SNP的所有关联数据
```
gwas_get_variants_for_trait
```
- 获取某一性状的变异位点
```
gwas_get_studies_for_trait
```
- 获取某一性状的相关研究
```
gwas_get_snps_for_gene
```
- 获取与某一基因映射的SNP
```
gwas_get_associations_for_study
```
- 获取某一研究的关联数据

Open Targets Genetics (6 tools)

Open Targets Genetics（6款工具）

OpenTargets_search_gwas_studies_by_disease

- Search studies by disease ontology

```
OpenTargets_get_study_credible_sets
```
- Get fine-mapped loci for a study
```
OpenTargets_get_variant_credible_sets
```
- Get credible sets for a variant
```
OpenTargets_get_variant_info
```
- Get variant annotation (frequencies, consequences)
```
OpenTargets_get_gwas_study
```
- Get study metadata
```
OpenTargets_get_credible_set_detail
```
- Get detailed credible set information

OpenTargets_search_gwas_studies_by_disease

- 按疾病本体搜索研究

```
OpenTargets_get_study_credible_sets
```
- 获取某一研究的精细定位位点
```
OpenTargets_get_variant_credible_sets
```
- 获取某一变异的可信集合
```
OpenTargets_get_variant_info
```
- 获取变异注释信息（频率、后果）
```
OpenTargets_get_gwas_study
```
- 获取研究元数据
```
OpenTargets_get_credible_set_detail
```
- 获取可信集合的详细信息

Parameters

参数说明

Required

```
trait
```
- Disease/trait name (e.g., "type 2 diabetes", "coronary artery disease")

Optional

```
p_value_threshold
```
- Significance threshold (default: 5e-8)
```
min_evidence_count
```
- Minimum number of studies (default: 1)
```
max_results
```
- Maximum genes to return (default: 100)
```
use_fine_mapping
```
- Include L2G predictions (default: true)
```
disease_ontology_id
```
- Disease ontology ID for Open Targets (e.g., "MONDO_0005148")

必填参数

```
trait
```
- 疾病/性状名称（例如："2型糖尿病"、"冠心病"）

可选参数

```
p_value_threshold
```
- 显著性阈值（默认：5e-8）
```
min_evidence_count
```
- 最小研究数量（默认：1）
```
max_results
```
- 返回的最大基因数量（默认：100）
```
use_fine_mapping
```
- 是否包含L2G预测（默认：true）
```
disease_ontology_id
```
- Open Targets的疾病本体ID（例如："MONDO_0005148"）

Output Schema

输出格式

python

{
  "genes": [
    {
      "symbol": str,              # Gene symbol (e.g., "TCF7L2")
      "min_p_value": float,       # Most significant p-value
      "evidence_count": int,      # Number of independent studies
      "snps": [str],              # Associated SNP rs IDs
      "studies": [str],           # GWAS study accessions
      "l2g_score": float | null,  # Locus-to-gene score (0-1)
      "credible_sets": int,       # Number of credible sets
      "confidence_level": str     # "High", "Medium", or "Low"
    }
  ],
  "summary": {
    "trait": str,
    "total_associations": int,
    "significant_genes": int,
    "data_sources": ["GWAS Catalog", "Open Targets"]
  }
}

python

{
  "genes": [
    {
      "symbol": str,              # 基因符号（例如："TCF7L2"）
      "min_p_value": float,       # 最显著的p值
      "evidence_count": int,      # 独立研究数量
      "snps": [str],              # 关联的SNP rs编号
      "studies": [str],           # GWAS研究编号
      "l2g_score": float | null,  # 基因座-基因（L2G）评分（0-1）
      "credible_sets": int,       # 可信集合数量
      "confidence_level": str     # "High"、"Medium"或"Low"
    }
  ],
  "summary": {
    "trait": str,
    "total_associations": int,
    "significant_genes": int,
    "data_sources": ["GWAS Catalog", "Open Targets"]
  }
}

Example Results

结果示例

Type 2 Diabetes

TCF7L2:  p=1.2e-98, 15 studies, L2G=0.82 → High confidence
KCNJ11:  p=3.4e-67, 12 studies, L2G=0.76 → High confidence
PPARG:   p=2.1e-45, 8 studies,  L2G=0.71 → High confidence
FTO:     p=5.6e-42, 10 studies, L2G=0.68 → High confidence
IRS1:    p=8.9e-38, 6 studies,  L2G=0.54 → High confidence

Alzheimer's Disease

APOE:    p=1.0e-450, 25 studies, L2G=0.95 → High confidence
BIN1:    p=2.3e-89,  18 studies, L2G=0.88 → High confidence
CLU:     p=4.5e-67,  16 studies, L2G=0.82 → High confidence
ABCA7:   p=6.7e-54,  14 studies, L2G=0.79 → High confidence
CR1:     p=8.9e-52,  13 studies, L2G=0.75 → High confidence

2型糖尿病

TCF7L2:  p=1.2e-98, 15项研究, L2G=0.82 → 高置信度
KCNJ11:  p=3.4e-67, 12项研究, L2G=0.76 → 高置信度
PPARG:   p=2.1e-45, 8项研究,  L2G=0.71 → 高置信度
FTO:     p=5.6e-42, 10项研究, L2G=0.68 → 高置信度
IRS1:    p=8.9e-38, 6项研究,  L2G=0.54 → 高置信度

阿尔茨海默病

APOE:    p=1.0e-450, 25项研究, L2G=0.95 → 高置信度
BIN1:    p=2.3e-89,  18项研究, L2G=0.88 → 高置信度
CLU:     p=4.5e-67,  16项研究, L2G=0.82 → 高置信度
ABCA7:   p=6.7e-54,  14项研究, L2G=0.79 → 高置信度
CR1:     p=8.9e-52,  13项研究, L2G=0.75 → 高置信度

Best Practices

最佳实践

1. Use Disease Ontology IDs for Precision

undefined

1. 使用疾病本体ID提高精准度

undefined

Instead of:

避免：

discover_gwas_genes("diabetes") # Ambiguous

discover_gwas_genes("diabetes") # 含义模糊

Use:

For drug targets, require strong evidence:

针对药物靶点，要求强证据：

discover_gwas_genes( "coronary artery disease", p_value_threshold=5e-10, # Stricter than GWAS threshold min_evidence_count=3, # Multiple independent studies use_fine_mapping=True # Include L2G predictions )


**3. Interpret Results Carefully**
- **Association ≠ Causation**: GWAS identifies correlated variants, not necessarily causal genes
- **Linkage Disequilibrium**: Lead SNP may tag the true causal variant in a nearby gene
- **Fine-mapping**: L2G scores provide better causal gene evidence than positional mapping
- **Functional Evidence**: Validate with orthogonal data (eQTLs, knockout models, etc.)

discover_gwas_genes( "coronary artery disease", p_value_threshold=5e-10, # 比GWAS标准阈值更严格 min_evidence_count=3, # 需多项独立研究验证 use_fine_mapping=True # 包含L2G预测 )


**3. 谨慎解读结果**
- **关联≠因果**：GWAS识别的是相关变异，不一定是致病基因
- **连锁不平衡**：主导SNP可能标记的是附近基因中的真正致病变异
- **精细定位**：L2G评分比位置映射能提供更可靠的致病基因证据
- **功能验证**：需结合正交数据（如eQTL、基因敲除模型等）进行验证

Limitations

局限性

Gene Mapping Uncertainty
- Positional mapping assigns SNPs to nearest gene (may be incorrect)
- Fine-mapping available for only a subset of studies
- Intergenic variants difficult to map
Population Bias
- Most GWAS in European populations
- Effect sizes may differ across ancestries
- Rare variants often under-represented
Sample Size Dependence
- Larger studies detect more associations
- Older small studies may have false negatives
- p-values alone don't indicate effect size
Validation Bug
- Some ToolUniverse tools have oneOf validation issues
- Use
```
validate=False
```
  parameter if needed
- This is automatically handled in the Python implementation

基因映射不确定性
- 位置映射将SNP分配给最近的基因（可能不准确）
- 仅部分研究支持精细定位
- 基因间变异难以映射
人群偏差
- 大多数GWAS研究基于欧洲人群
- 效应量可能因种族而异
- 罕见变异通常代表性不足
样本量依赖性
- 样本量更大的研究能检测到更多关联
- 早期小样本研究可能存在假阴性
- p值本身无法反映效应量大小
验证缺陷
- 部分ToolUniverse工具存在oneOf验证问题
- 必要时使用
```
validate=False
```
  参数
- Python实现中已自动处理此问题

Related Skills

Data Sources

数据来源

GWAS Catalog

Curator: EBI and NHGRI
URL: https://www.ebi.ac.uk/gwas/
Coverage: 100,000+ publications, 500,000+ associations
Update Frequency: Weekly

Open Targets Genetics

Curator: Open Targets consortium
URL: https://genetics.opentargets.org/
Coverage: Fine-mapped GWAS, L2G predictions, QTL colocalization
Update Frequency: Quarterly