tooluniverse-pathway-disease-genetics

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Original

English

🇨🇳

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Chinese

COMPUTE, DON'T DESCRIBE

计算，而非描述

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

当分析需要计算（统计、数据处理、评分、富集分析）时，通过Bash编写并运行Python代码。不要描述你会做什么——直接执行并报告实际结果。使用ToolUniverse工具检索数据，然后用Python（pandas、scipy、statsmodels、matplotlib）进行分析。

Pathway-Disease Genetics: GWAS to Drug Targets via Pathways

通路-疾病遗传学：通过通路实现从GWAS到药物靶点的转化

Connect genome-wide association study (GWAS) variants to biological pathways for mechanistic understanding and drug target discovery.

将全基因组关联研究（GWAS）变异关联至生物通路，以实现机制解析和药物靶点发现。

When to Use

适用场景

"What pathways are disrupted in [disease] based on GWAS?"
"Which GWAS genes for [trait] are in druggable pathways?"
"Map [SNP/variant] to its causal gene and pathway"
"Find drug targets from GWAS data for [disease]"
"What is the eQTL evidence for [gene] in [tissue]?"

“基于GWAS数据，[疾病]中哪些通路被破坏？”
“[性状]的GWAS基因中哪些位于可成药通路？”
“将[SNP/变异]映射到其致病基因和通路”
“从[疾病]的GWAS数据中寻找药物靶点”
“[组织]中[基因]的eQTL证据是什么？”

Core Reasoning Principles

核心推理原则

Gene-to-Pathway Reasoning

基因到通路的推理

A gene found in GWAS doesn't tell you which pathway is dysregulated. To connect gene -> pathway -> disease mechanism, ask: what biological process does this gene participate in? Use Reactome/KEGG to find pathways, then ask: which of these pathways is relevant to the disease phenotype?

For example, TCF7L2 is the strongest T2D GWAS gene. It participates in the Wnt signaling pathway. The question is then: how does disrupted Wnt signaling impair beta-cell function or insulin secretion? That reasoning step — from pathway membership to disease mechanism — requires combining pathway data with tissue expression (GTEx) and disease biology.

Non-coding GWAS variants (the majority) rarely affect the nearest gene. They act through regulatory elements that alter expression of genes sometimes hundreds of kilobases away. Always check eQTL evidence before assuming the nearest gene is causal.

GWAS中发现的基因无法直接告知你哪些通路失调。要建立基因→通路→疾病机制的关联，需思考：该基因参与哪些生物学过程？使用Reactome/KEGG寻找通路，然后思考：这些通路中哪一个与疾病表型相关？

例如，TCF7L2是2型糖尿病（T2D）中最显著的GWAS基因，它参与Wnt信号通路。接下来的问题是：Wnt信号通路失调如何损害β细胞功能或胰岛素分泌？这一推理步骤——从通路成员身份到疾病机制——需要结合通路数据、组织表达数据（GTEx）和疾病生物学知识。

非编码GWAS变异（占大多数）很少影响最近的基因。它们通过调控元件发挥作用，改变有时相距数十万个碱基对的基因表达。在假设最近的基因为致病基因之前，务必检查eQTL证据。

Pathway Convergence

通路收敛性

Multiple disease genes mapping to the same pathway is stronger evidence than a single gene. If 5 GWAS hits for a disease all map to the NF-kB pathway, that is strong mechanistic evidence — the pathway is likely causal, not just coincidentally hit. If GWAS genes scatter across unrelated pathways, the mechanism is unclear, and you may need to look at upstream regulators or gene network hubs that connect the scattered genes.

When running enrichment (Reactome, KEGG, STRING), prioritize pathways that appear across multiple databases. A pathway enriched in all three is more reliable than one that appears in only one analysis.

多个疾病基因映射到同一通路的证据比单个基因更强。如果某疾病的5个GWAS位点都映射到NF-kB通路，这就是强有力的机制证据——该通路很可能是致病的，而非偶然命中。如果GWAS基因分散在不相关的通路中，则机制尚不明确，你可能需要研究连接这些分散基因的上游调控因子或基因网络枢纽。

进行富集分析（Reactome、KEGG、STRING）时，优先考虑在多个数据库中出现的通路。在三个数据库中均富集的通路比仅在一个分析中出现的通路更可靠。

Druggability Reasoning

可成药性推理

A pathway with existing drugs targeting its components is more actionable than a novel pathway. Before proposing a target as novel, check: are any pathway members already drug targets? Use DGIdb and OpenTargets to survey approved and clinical-stage drugs in the pathway.

Priorities: (1) approved drug for a different indication hitting a GWAS-supported target = strong repurposing opportunity; (2) drug in clinical trials hitting a GWAS-supported target = accelerated validation path; (3) druggable gene with no existing drugs + strong GWAS evidence = novel target opportunity.

"Undruggable" by current modalities does not mean permanently undruggable. Flag such genes but do not dismiss them — they may be actionable via gene therapy, RNA therapeutics, or downstream pathway intervention.

已有药物靶向其组分的通路比新通路更具可操作性。在提出新靶点之前，需检查：该通路中是否有成员已成为药物靶点？使用DGIdb和OpenTargets调查通路中已获批和临床阶段的药物。

优先级：(1) 针对其他适应症的获批药物作用于GWAS支持的靶点 = 强有力的药物重定位机会；(2) 临床试验中的药物作用于GWAS支持的靶点 = 加速验证路径；(3) 无现有药物的可成药基因 + 强有力的GWAS证据 = 新靶点机会。

当前技术手段下的“不可成药”并不意味着永久不可成药。标记此类基因但不要轻易排除——它们可能通过基因治疗、RNA疗法或下游通路干预实现可成药。

Tool Selection Guide

工具选择指南

Phase 1: Disease Resolution and GWAS Collection

阶段1：疾病解析与GWAS数据收集

Resolve disease name to ontology ID first:

OpenTargets_multi_entity_search_by_query_string(queryString=<disease>)

— returns EFO/MONDO IDs

Collect GWAS signals:

gwas_search_associations(query=<disease>)

— broad search

gwas_get_variants_for_trait(trait=<trait>, p_value_threshold=5e-8)

— genome-wide significant hits

gwas_get_snps_for_gene(gene_symbol=<gene>)

— gene-centric search

Gotcha:

gwas_get_associations_for_trait

is broken — use

gwas_search_associations

instead.

gwas_get_snps_for_gene

uses

gene_symbol

, not

mapped_gene

首先将疾病名称解析为本体ID：

OpenTargets_multi_entity_search_by_query_string(queryString=<disease>)

— 返回EFO/MONDO ID

收集GWAS信号：

gwas_search_associations(query=<disease>)

— 广泛搜索

gwas_get_variants_for_trait(trait=<trait>, p_value_threshold=5e-8)

— 全基因组显著位点

gwas_get_snps_for_gene(gene_symbol=<gene>)

— 以基因为中心的搜索

注意事项：

gwas_get_associations_for_trait

已失效——请改用

gwas_search_associations

。

gwas_get_snps_for_gene

使用

gene_symbol

，而非

mapped_gene

。

Phase 2: Variant Annotation and eQTL Evidence

阶段2：变异注释与eQTL证据

Annotate variants:

EnsemblVEP_annotate_rsid(variant_id=<rsid>)

— functional consequence, nearest gene

Response format is variable: list,
```
{data, metadata}
```
, or
```
{error}
```
— handle all three

Query eQTL evidence in tissue relevant to the disease (e.g., pancreas for T2D, brain for neurological):

GTEx_query_eqtl(gene_input=<gene>, tissue=<tissue>)

— never pass empty

gene_input

GTEx_get_expression_summary(gene_input=<gene>)

— expression across all tissues

GTEx_get_median_gene_expression(gencode_id=[<versioned_id>], tissue_site_detail_id=[<tissue>])

— use versioned Ensembl IDs (e.g.,

ENSG00000148737.11

) and

gtex_v8

注释变异：

EnsemblVEP_annotate_rsid(variant_id=<rsid>)

— 功能影响、最近基因

响应格式不固定：列表、
```
{data, metadata}
```
或
```
{error}
```
— 需处理所有三种格式

查询与疾病相关组织中的eQTL证据（例如，T2D对应胰腺，神经系统疾病对应大脑）：

GTEx_query_eqtl(gene_input=<gene>, tissue=<tissue>)

— 切勿传入空的

gene_input

GTEx_get_expression_summary(gene_input=<gene>)

— 所有组织中的表达情况

GTEx_get_median_gene_expression(gencode_id=[<versioned_id>], tissue_site_detail_id=[<tissue>])

— 使用带版本号的Ensembl ID（例如，

ENSG00000148737.11

）和

gtex_v8

Phase 3: Pathway Enrichment

阶段3：通路富集分析

Run enrichment across multiple databases and cross-validate results:

ReactomeAnalysis_pathway_enrichment(identifiers="P04637 P38398 ...")

— space-separated UniProt STRING, not an array

Reactome_map_uniprot_to_pathways(uniprot_id=<id>)

— per-gene pathway membership

Reactome_get_participants(pathway_id=<R-HSA-XXXXX>)

— all genes in a pathway

KEGG_get_gene_pathways(gene_id=<kegg_id>)

— KEGG pathways for one gene

kegg_search_pathway(query=<disease_or_process>)

— keyword search

STRING_functional_enrichment(protein_ids=[<genes>], species=9606)

— GO/KEGG/Reactome with FDR

PANTHER_enrichment(gene_list="GENE1,GENE2,...", organism=9606, annotation_dataset="GO:0008150")

— comma-separated STRING, not array

MetaCyc note: Currently unavailable (BioCyc requires authentication). Use KEGG or Reactome for metabolic pathways.

在多个数据库中进行富集分析并交叉验证结果：

ReactomeAnalysis_pathway_enrichment(identifiers="P04637 P38398 ...")

— 空格分隔的UniProt字符串，而非数组

Reactome_map_uniprot_to_pathways(uniprot_id=<id>)

— 单个基因的通路成员身份

Reactome_get_participants(pathway_id=<R-HSA-XXXXX>)

— 某通路中的所有基因

KEGG_get_gene_pathways(gene_id=<kegg_id>)

— 单个基因的KEGG通路

kegg_search_pathway(query=<disease_or_process>)

— 关键词搜索

STRING_functional_enrichment(protein_ids=[<genes>], species=9606)

— 带FDR校正的GO/KEGG/Reactome富集分析

PANTHER_enrichment(gene_list="GENE1,GENE2,...", organism=9606, annotation_dataset="GO:0008150")

— 逗号分隔的字符串，而非数组

MetaCyc说明： 当前不可用（BioCyc需要身份验证）。代谢通路分析请使用KEGG或Reactome。

Phase 4: Druggability and Drug Landscape

阶段4：可成药性与药物格局

```
DGIdb_get_gene_druggability(genes=[<gene_list>])
```
— categories: clinically actionable, druggable, etc.

DGIdb_get_drug_gene_interactions(genes=[<gene_list>])

— use

genes

param (array), not

gene_name

OpenTargets_get_associated_drugs_by_target_ensemblID(ensemblId=<id>)

— approved and clinical drugs

OpenTargets_target_disease_evidence(ensemblId=<id>, efoId=<disease_id>)

— genetic + other evidence score

DGIdb_get_gene_druggability(genes=[<gene_list>])

— 分类：临床可操作、可成药等

DGIdb_get_drug_gene_interactions(genes=[<gene_list>])

— 使用

genes

参数（数组），而非

gene_name

OpenTargets_get_associated_drugs_by_target_ensemblID(ensemblId=<id>)

— 已获批和临床阶段药物

OpenTargets_target_disease_evidence(ensemblId=<id>, efoId=<disease_id>)

— 遗传及其他证据评分

Three-Step Workflow

三步工作流

Step 1: GWAS to Causal Gene

步骤1：从GWAS到致病基因

Resolve disease ID via

OpenTargets_multi_entity_search_by_query_string

Pull GWAS hits with
```
gwas_get_variants_for_trait
```
(p < 5e-8)
Annotate each lead SNP with VEP — is it coding or non-coding?
For non-coding variants, check eQTL via GTEx in the disease-relevant tissue
Prioritize genes where: GWAS SNP is also a significant eQTL AND the tissue is biologically relevant to the disease
Cross-check with
```
OpenTargets_target_disease_evidence
```
for additional genetic evidence

Evidence tiers: High = GWAS p < 5e-8 + eQTL colocalization in relevant tissue + coding variant; Medium = GWAS p < 5e-8 + eQTL in any tissue; Low = GWAS p < 5e-8 + positional mapping only.

通过

OpenTargets_multi_entity_search_by_query_string

解析疾病ID

使用
```
gwas_get_variants_for_trait
```
获取GWAS位点（p < 5e-8）
用VEP注释每个先导SNP——它是编码区还是非编码区变异？
对于非编码变异，通过GTEx查询疾病相关组织中的eQTL证据
优先选择以下基因：GWAS SNP同时是显著eQTL，且相关组织与疾病生物学相关
使用
```
OpenTargets_target_disease_evidence
```
交叉验证以获取额外遗传证据

证据层级：高 = GWAS p < 5e-8 + 相关组织中的eQTL共定位 + 编码区变异；中 = GWAS p < 5e-8 + 任意组织中的eQTL；低 = GWAS p < 5e-8 + 仅位置映射。

Step 2: Gene Set to Pathway Enrichment

步骤2：从基因集到通路富集分析

Collect prioritized gene list from Step 1

Run

ReactomeAnalysis_pathway_enrichment

and

STRING_functional_enrichment

Map each gene to KEGG pathways via
```
KEGG_get_gene_pathways
```
Identify pathways appearing across multiple databases (convergence = stronger evidence)
For metabolic diseases, add tissue-specific network context via
```
humanbase_ppi_analysis
```
(all 5 params required:
```
gene_list
```
,
```
tissue
```
,
```
max_node
```
,
```
interaction
```
,
```
string_mode
```
)
Rank pathways by enrichment FDR x number of GWAS genes x biological plausibility

收集步骤1中筛选出的基因列表

运行

ReactomeAnalysis_pathway_enrichment

和

STRING_functional_enrichment

通过
```
KEGG_get_gene_pathways
```
将每个基因映射到KEGG通路
识别在多个数据库中出现的通路（收敛性 = 更强证据）
对于代谢疾病，通过
```
humanbase_ppi_analysis
```
添加组织特异性网络背景（需传入全部5个参数：
```
gene_list
```
,
```
tissue
```
,
```
max_node
```
,
```
interaction
```
,
```
string_mode
```
）
按富集FDR × GWAS基因数量 × 生物学合理性对通路排序

Step 3: Pathway to Drug Target

步骤3：从通路到药物靶点

From enriched pathways, extract all member genes via
```
Reactome_get_participants
```
and
```
KEGG_get_pathway_genes
```
Assess druggability via
```
DGIdb_get_gene_druggability
```

Look up existing drugs via

OpenTargets_get_associated_drugs_by_target_ensemblID

Cross-reference pathway genes with GWAS genes: overlap = top candidate
Classify each candidate: repurposing opportunity / novel target / undruggable (for now)

Final ranking: Genetic Evidence x Druggability x Pathway Centrality. Flag novel targets (strong genetic + no existing drugs) and repurposing opportunities (approved drug + genetic support in this disease).

通过
```
Reactome_get_participants
```
和
```
KEGG_get_pathway_genes
```
从富集通路中提取所有成员基因
通过
```
DGIdb_get_gene_druggability
```
评估可成药性

通过

OpenTargets_get_associated_drugs_by_target_ensemblID

查询现有药物

将通路基因与GWAS基因交叉对比：重叠部分为顶级候选靶点
对每个候选靶点分类：重定位机会 / 新靶点 / 当前不可成药

最终排序：遗传证据 × 可成药性 × 通路中心性。标记新靶点（强遗传证据 + 无现有药物）和重定位机会（已获批药物 + 该疾病中的遗传支持）。

Key Parameter Gotchas

关键参数注意事项

gwas_get_snps_for_gene

: use

gene_symbol

, not

mapped_gene

OpenTargets_multi_entity_search_by_query_string

: use

queryString

, not

query

```
GTEx_query_eqtl
```
:
```
gene_input
```
must never be empty
```
GTEx_get_median_gene_expression
```
: use versioned gencode IDs; use
```
gtex_v8
```

ReactomeAnalysis_pathway_enrichment

identifiers

is space-separated STRING, not array

DGIdb_get_drug_gene_interactions

: use

genes

(array), not

gene_name

```
PANTHER_enrichment
```
:
```
gene_list
```
is comma-separated STRING, not array
```
humanbase_ppi_analysis
```
: all 5 params required
```
EnsemblVEP_annotate_rsid
```
: use
```
variant_id
```
, not
```
rsid
```
```
kegg_find_genes
```
: include
```
organism="hsa"
```
for human genes

gwas_get_snps_for_gene

: 使用

gene_symbol

，而非

mapped_gene

OpenTargets_multi_entity_search_by_query_string

: 使用

queryString

，而非

query

```
GTEx_query_eqtl
```
:
```
gene_input
```
绝不能为空
```
GTEx_get_median_gene_expression
```
: 使用带版本号的gencode ID；使用
```
gtex_v8
```

ReactomeAnalysis_pathway_enrichment

identifiers

是空格分隔的字符串，而非数组

DGIdb_get_drug_gene_interactions

: 使用

genes

（数组），而非

gene_name

```
PANTHER_enrichment
```
:
```
gene_list
```
是逗号分隔的字符串，而非数组
```
humanbase_ppi_analysis
```
: 必须传入全部5个参数
```
EnsemblVEP_annotate_rsid
```
: 使用
```
variant_id
```
，而非
```
rsid
```
```
kegg_find_genes
```
: 人类基因需添加
```
organism="hsa"
```

Limitations

局限性

GTEx eQTL lookup is not formal statistical colocalization (coloc/ENLOC) — treat as suggestive evidence
GWAS Catalog may not include recent publications; cross-check with Open Targets
Reactome and KEGG define pathways differently; some biology is in one but not the other
DGIdb druggability categories are heuristic — "undruggable" applies only to current modalities
eQTLs are tissue-specific; querying the wrong tissue may miss causal effects

GTEx eQTL查询并非正式的统计共定位（coloc/ENLOC）——视为提示性证据
GWAS Catalog可能未收录最新研究；需与Open Targets交叉验证
Reactome和KEGG对通路的定义不同；部分生物学内容仅存在于其中一个数据库
DGIdb的可成药性分类为启发式——“不可成药”仅适用于当前技术手段
eQTL具有组织特异性；查询错误组织可能会遗漏致病效应

tooluniverse-pathway-disease-genetics

Original

Translation

COMPUTE, DON'T DESCRIBE

计算，而非描述

Pathway-Disease Genetics: GWAS to Drug Targets via Pathways

通路-疾病遗传学：通过通路实现从GWAS到药物靶点的转化

When to Use

适用场景

Core Reasoning Principles

核心推理原则

Gene-to-Pathway Reasoning

基因到通路的推理

Pathway Convergence

通路收敛性

Druggability Reasoning

可成药性推理

Tool Selection Guide

工具选择指南

Phase 1: Disease Resolution and GWAS Collection

阶段1：疾病解析与GWAS数据收集

Phase 2: Variant Annotation and eQTL Evidence

阶段2：变异注释与eQTL证据

Phase 3: Pathway Enrichment

阶段3：通路富集分析

Phase 4: Druggability and Drug Landscape

阶段4：可成药性与药物格局

Three-Step Workflow

三步工作流

Step 1: GWAS to Causal Gene

步骤1：从GWAS到致病基因

Step 2: Gene Set to Pathway Enrichment

步骤2：从基因集到通路富集分析

Step 3: Pathway to Drug Target

步骤3：从通路到药物靶点

Key Parameter Gotchas

关键参数注意事项

Limitations

局限性

Related Skills

相关技能