GWAS药物靶点发现工具：从遗传学数据到药物研发与再利用

tooluniverse-gwas-drug-discovery by mims-harvard/tooluniverse

120 周安装量

1,200 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-gwas-drug-discovery

科研工具生物信息学数据处理

🇨🇳中文介绍

GWAS 到药物靶点发现

将全基因组关联研究（GWAS）转化为可操作的药物靶点和再利用机会。

重要提示：在工具调用中始终使用英文术语。以用户的语言进行回复。

概述

此技能通过以下方式将 GWAS 的遗传学发现与药物开发联系起来：

识别遗传风险因素 - 寻找与疾病相关的基因
评估成药性 - 评估哪些基因可以被药物靶向
靶点优先级排序 - 根据遗传证据强度对候选靶点进行排序
寻找现有药物 - 发现已批准/研究中的化合物
识别再利用机会 - 将药物匹配到新的适应症

关键见解：有遗传学支持的靶点获得临床批准的概率高出 2 倍（Nelson 等人，Nature Genetics 2015）。

工作流程步骤

步骤 1：GWAS 基因发现

输入：疾病/性状名称（例如，"type 2 diabetes", "Alzheimer disease"）

过程：查询 GWAS Catalog 获取关联性，按显著性（p < 5x10^-8）过滤，将变异映射到基因，汇总证据。

工具：

gwas_get_associations_for_trait - 按疾病获取关联性
gwas_search_associations - 灵活搜索
- SNP 特异性关联

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步骤 2：成药性评估

输入：来自步骤 1 的基因列表

过程：检查靶点类别，评估可操作性，评估安全性，检查是否存在工具化合物或结构。

OpenTargets_get_target_tractability_by_ensemblID - 成药性评估
OpenTargets_get_target_classes_by_ensemblID - 靶点分类
OpenTargets_get_target_safety_profile_by_ensemblID - 安全性数据
OpenTargets_get_target_genomic_location_by_ensemblID - 基因组背景

步骤 3：靶点优先级排序

Target Score = (GWAS Score x 0.4) + (Druggability x 0.3) + (Clinical Evidence x 0.2) + (Novelty x 0.1)

按综合评分对靶点进行排序。生成靶点档案。

步骤 4：现有药物搜索

过程：搜索药物-靶点关联，查找已批准药物和临床候选药物，获取 MOA 和适应症数据。

OpenTargets_get_associated_drugs_by_disease_efoId - 针对疾病的已知药物
OpenTargets_get_drug_mechanisms_of_action_by_chemblId - 药物 MOA
ChEMBL_get_target_activities - 生物活性数据
ChEMBL_get_drug_mechanisms / ChEMBL_search_drugs - 药物数据

步骤 5：临床证据与安全性

FDA_get_adverse_reactions_by_drug_name - 安全性数据
FDA_get_active_ingredient_info_by_drug_name - 药物成分
OpenTargets_get_drug_warnings_by_chemblId - 药物警告

步骤 6：再利用机会

将药物靶点匹配到新的疾病基因，评估机制匹配度，检查禁忌症，估计再利用概率。

from tooluniverse import ToolUniverse
tu = ToolUniverse(use_cache=True)
tu.load_tools()

# Step 1: Get GWAS associations
associations = tu.tools.gwas_get_associations_for_trait(trait="type 2 diabetes")

# Step 2: Assess druggability
tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(ensemblID="ENSG00000148737")

# Step 3: Find existing drugs
drugs = tu.tools.OpenTargets_get_associated_drugs_by_disease_efoId(efoId="EFO_0001360")

按类别划分的所有工具

GWAS 与遗传学：

gwas_get_associations_for_trait / gwas_search_associations / gwas_get_associations_for_snp
OpenTargets_search_gwas_studies_by_disease / OpenTargets_get_variant_credible_sets

OpenTargets_get_target_tractability_by_ensemblID / OpenTargets_get_target_classes_by_ensemblID
OpenTargets_get_target_safety_profile_by_ensemblID / OpenTargets_get_target_genomic_location_by_ensemblID

OpenTargets_get_associated_drugs_by_disease_efoId / OpenTargets_get_drug_mechanisms_of_action_by_chemblId
ChEMBL_get_target_activities / ChEMBL_get_drug_mechanisms / ChEMBL_search_drugs

安全性与临床：

FDA_get_adverse_reactions_by_drug_name / FDA_get_active_ingredient_info_by_drug_name
OpenTargets_get_drug_warnings_by_chemblId

PubMed_search_articles / EuropePMC_search_articles / ClinicalTrials_search

多人群 GWAS：包含跨种族荟萃分析以获得稳健信号
功能验证：通过 eQTL、pQTL、共定位分析确认
网络分析：按通路（KEGG、Reactome）对 GWAS 命中结果进行分组
安全性评估：检查 gnomAD pLI、GTEx 表达、PharmaGKB
批量操作：使用 tu.run_batch() 对多个靶点进行并行查询

问题	解决方案
疾病无 GWAS 命中结果	尝试更广泛的性状名称，检查同义词，使用 OpenTargets
基因不属于可成药类别	考虑抗体/反义寡核苷酸等模式，检查通路邻近基因
靶点无现有药物	靶点可能为新靶点 - 在 ChEMBL 中检查工具化合物
L2G 评分低	变异可能具有调控作用 - 检查 eQTL/pQTL 证据

REFERENCE.md - 详细概念、成药性层级、临床转化、局限性、伦理
EXAMPLES.md - 用例（亨廷顿病、阿尔茨海默病、糖尿病）及成功案例
REPORT_TEMPLATE.md - 输出报告模板及评分标准
PROCEDURES.md - 分步实施流程
QUICK_START.md - 快速入门指南
相关技能：tooluniverse-drug-repurposing, disease-intelligence-gatherer, tooluniverse-sdk

🇺🇸English

GWAS-to-Drug Target Discovery

Transform genome-wide association studies (GWAS) into actionable drug targets and repurposing opportunities.

IMPORTANT : Always use English terms in tool calls. Respond in the user's language.

Overview

This skill bridges genetic discoveries from GWAS with drug development by:

Identifying genetic risk factors - Finding genes associated with diseases
Assessing druggability - Evaluating which genes can be targeted by drugs
Prioritizing targets - Ranking candidates by genetic evidence strength
Finding existing drugs - Discovering approved/investigational compounds
Identifying repurposing opportunities - Matching drugs to new indications

Key insight : Targets with genetic support have 2x higher probability of clinical approval (Nelson et al., Nature Genetics 2015).

Workflow Steps

Step 1: GWAS Gene Discovery

Input : Disease/trait name (e.g., "type 2 diabetes", "Alzheimer disease")

Process : Query GWAS Catalog for associations, filter by significance (p < 5x10^-8), map variants to genes, aggregate evidence.

Tools :

gwas_get_associations_for_trait - Get associations by disease
gwas_search_associations - Flexible search
gwas_get_associations_for_snp - SNP-specific associations
OpenTargets_search_gwas_studies_by_disease - Curated GWAS data
OpenTargets_get_variant_credible_sets - Fine-mapped loci with L2G predictions

Step 2: Druggability Assessment

Input : Gene list from Step 1

Process : Check target class, assess tractability, evaluate safety, check for tool compounds or structures.

Tools :

OpenTargets_get_target_tractability_by_ensemblID - Druggability assessment
OpenTargets_get_target_classes_by_ensemblID - Target classification
OpenTargets_get_target_safety_profile_by_ensemblID - Safety data
OpenTargets_get_target_genomic_location_by_ensemblID - Genomic context

Step 3: Target Prioritization

Scoring Formula :

Target Score = (GWAS Score x 0.4) + (Druggability x 0.3) + (Clinical Evidence x 0.2) + (Novelty x 0.1)

Rank targets by composite score. Generate target dossiers.

Step 4: Existing Drug Search

Process : Search drug-target associations, find approved drugs and clinical candidates, get MOA and indication data.

Tools :

OpenTargets_get_associated_drugs_by_disease_efoId - Known drugs for disease
OpenTargets_get_drug_mechanisms_of_action_by_chemblId - Drug MOA
ChEMBL_get_target_activities - Bioactivity data
ChEMBL_get_drug_mechanisms / ChEMBL_search_drugs - Drug data

Step 5: Clinical Evidence & Safety

Tools :

FDA_get_adverse_reactions_by_drug_name - Safety data
FDA_get_active_ingredient_info_by_drug_name - Drug composition
OpenTargets_get_drug_warnings_by_chemblId - Drug warnings

Step 6: Repurposing Opportunities

Match drug targets to new disease genes, assess mechanistic fit, check contraindications, estimate repurposing probability.

Quick Start

from tooluniverse import ToolUniverse
tu = ToolUniverse(use_cache=True)
tu.load_tools()

# Step 1: Get GWAS associations
associations = tu.tools.gwas_get_associations_for_trait(trait="type 2 diabetes")

# Step 2: Assess druggability
tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(ensemblID="ENSG00000148737")

# Step 3: Find existing drugs
drugs = tu.tools.OpenTargets_get_associated_drugs_by_disease_efoId(efoId="EFO_0001360")

All Tools by Category

GWAS & Genetics:

gwas_get_associations_for_trait / gwas_search_associations / gwas_get_associations_for_snp
OpenTargets_search_gwas_studies_by_disease / OpenTargets_get_variant_credible_sets

Target Assessment :

OpenTargets_get_target_tractability_by_ensemblID / OpenTargets_get_target_classes_by_ensemblID
OpenTargets_get_target_safety_profile_by_ensemblID / OpenTargets_get_target_genomic_location_by_ensemblID

Drug Discovery :

OpenTargets_get_associated_drugs_by_disease_efoId / OpenTargets_get_drug_mechanisms_of_action_by_chemblId
ChEMBL_get_target_activities / ChEMBL_get_drug_mechanisms / ChEMBL_search_drugs

Safety & Clinical:

FDA_get_adverse_reactions_by_drug_name / FDA_get_active_ingredient_info_by_drug_name
OpenTargets_get_drug_warnings_by_chemblId

Literature :

PubMed_search_articles / EuropePMC_search_articles / ClinicalTrials_search

Best Practices

Multi-ancestry GWAS : Include trans-ethnic meta-analyses for robust signals
Functional validation : Confirm with eQTL, pQTL, colocalization analysis
Network analysis : Group GWAS hits by pathway (KEGG, Reactome)
Safety assessment : Check gnomAD pLI, GTEx expression, PharmaGKB
Batch operations : Use tu.run_batch() for parallel queries across targets

Troubleshooting

Problem	Solution
No GWAS hits for disease	Try broader trait name, check synonyms, use OpenTargets
Gene not in druggable class	Consider antibody/antisense modalities, check pathway neighbors
No existing drugs for target	Target may be novel - check tool compounds in ChEMBL
Low L2G score	Variants may be regulatory - check eQTL/pQTL evidence

Reference Files

REFERENCE.md - Detailed concepts, druggability tiers, clinical translation, limitations, ethics
EXAMPLES.md - Use cases (Huntington's, Alzheimer's, diabetes) with success stories
REPORT_TEMPLATE.md - Output report template with scoring criteria
PROCEDURES.md - Step-by-step implementation procedures
QUICK_START.md - Quick start guide
Related skills: tooluniverse-drug-repurposing, disease-intelligence-gatherer, tooluniverse-sdk

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Repository

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GitHub Stars

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First Seen

Feb 19, 2026

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1,000 周安装

GWAS药物靶点发现工具：从遗传学数据到药物研发与再利用

🇨🇳中文介绍

GWAS 到药物靶点发现

概述

工作流程步骤

步骤 1：GWAS 基因发现

相关 Skills

步骤 2：成药性评估

步骤 3：靶点优先级排序

步骤 4：现有药物搜索

步骤 5：临床证据与安全性

步骤 6：再利用机会

快速开始

按类别划分的所有工具

最佳实践

故障排除

参考文件

🇺🇸English

GWAS-to-Drug Target Discovery

Overview

Workflow Steps

Step 1: GWAS Gene Discovery

Step 2: Druggability Assessment

Step 3: Target Prioritization

Step 4: Existing Drug Search

Step 5: Clinical Evidence & Safety

Step 6: Repurposing Opportunities

Quick Start

All Tools by Category

Best Practices

Troubleshooting

Reference Files

最新 Skills