# Gene Disease Association

> Gene-Disease Association Analysis - Analyze gene-disease associations: NCBI gene metadata, OpenTargets disease associations, TCGA expression, and Monarch phenotypes. Use this skill for medical genetics tasks involving get gene metadata by gene name get associated targets by disease efoId get gene expression across cancers get joint associated diseases by HPO ID list. Combines 4 tools from 4 SCP server(s).

- Skill: `gabrielmoreira/gene-disease-association` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/gene-disease-association`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/gene-disease-association/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/gene-disease-association

---


# Gene-Disease Association Analysis

**Discipline**: Medical Genetics | **Tools Used**: 4 | **Servers**: 4

## Description

Analyze gene-disease associations: NCBI gene metadata, OpenTargets disease associations, TCGA expression, and Monarch phenotypes.

## Tools Used

- **`get_gene_metadata_by_gene_name`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI`
- **`get_associated_targets_by_disease_efoId`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets`
- **`get_gene_expression_across_cancers`** from `tcga-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA`
- **`get_joint_associated_diseases_by_HPO_ID_list`** from `monarch-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch`

## Workflow

1. Get gene metadata from NCBI
2. Get disease-target associations from OpenTargets
3. Analyze TCGA cancer expression
4. Check Monarch disease associations

## Test Case

### Input
```json
{
    "gene_name": "TP53",
    "disease_efo": "EFO_0000311"
}
```

### Expected Steps
1. Get gene metadata from NCBI
2. Get disease-target associations from OpenTargets
3. Analyze TCGA cancer expression
4. Check Monarch disease associations

## Usage Example

> **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn).

```python
import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client

SERVERS = {
    "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI",
    "opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets",
    "tcga-server": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA",
    "monarch-server": "https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch"
}

async def connect(url, stack):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
    read, write, _ = await stack.enter_async_context(transport)
    ctx = ClientSession(read, write)
    session = await stack.enter_async_context(ctx)
    await session.initialize()
    return session

def parse(result):
    try:
        if hasattr(result, 'content') and result.content:
            c = result.content[0]
            if hasattr(c, 'text'):
                try: return json.loads(c.text)
                except: return c.text
        return str(result)
    except: return str(result)

async def main():
    async with AsyncExitStack() as stack:
        # Connect to required servers
        sessions = {}
        sessions["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack)
        sessions["opentargets-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", stack)
        sessions["tcga-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", stack)
        sessions["monarch-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch", stack)

        # Execute workflow steps
        # Step 1: Get gene metadata from NCBI
        result_1 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_name", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get disease-target associations from OpenTargets
        result_2 = await sessions["opentargets-server"].call_tool("get_associated_targets_by_disease_efoId", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Analyze TCGA cancer expression
        result_3 = await sessions["tcga-server"].call_tool("get_gene_expression_across_cancers", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Check Monarch disease associations
        result_4 = await sessions["monarch-server"].call_tool("get_joint_associated_diseases_by_HPO_ID_list", arguments={})
        data_4 = parse(result_4)
        print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

        # Cleanup
        print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())
```