README.md

---
license: mit
language:
  - en
pretty_name: Clinvar Annotations
---

part of 

# 🧬 Genomic Reasoning Agent
### LLM-driven agentic system for personal genomic variant interpretation

[![HuggingFace](https://img.shields.io/badge/🤗_HuggingFace-Space-yellow)](https://huggingface.co/spaces/mioulin/genomic-reasoning-agent)
[![Dataset](https://img.shields.io/badge/🤗_Dataset-genomic--qa-blue)](https://huggingface.co/datasets/mioulin/genomic-reasoning-qa)
[![Model](https://img.shields.io/badge/🤗_Model-genomic--reasoning--llm-green)](https://huggingface.co/mioulin/genomic-reasoning-grpo)
[![License: MIT](https://img.shields.io/badge/License-MIT-purple)](LICENSE)

---

## Overview

This project builds a **multi-step reasoning agent** that interprets personal genomic data from 23andMe against biomedical knowledge databases (ClinVar, GWAS Catalog, gnomAD). The agent is trained with **GRPO** (Group Relative Policy Optimization) using fully verifiable reward signals — no human labelers needed.

The core insight mirrors DeepSeek-R1's approach to mathematics: **genomic variant interpretation has verifiable ground truth** (ClinVar classifications, GWAS p-values, population frequencies), making it ideal for RL-based reasoning training.

```
23andMe SNPs (631K)
        ↓
  ClinVar Annotation          ← rs1801133 → MTHFR → Pathogenic
        ↓
 Tool-Using LLM Agent         ← 5 tools: lookup / scan / haplotype / stats / reward
        ↓
   GRPO Training Loop         ← verifiable reward from ClinVar ground truth
        ↓
  Reasoning Model             ← factual · calibrated · evidence-grounded
        ↓
   HF Spaces Demo             ← upload 23andMe → ask questions → reasoning trace
```

---

## Motivation

Standard LLMs hallucinate on genomic questions. This project trains a model that:

- **Cites sources** (ClinVar review status, GWAS p-values, PubMed IDs)
- **Shows reasoning chains** (mechanism → evidence → conclusion)
- **Calibrates uncertainty** (risk factor ≠ diagnosis)
- **Uses tools** to look up live databases rather than relying on memorised weights

The training signal is 100% verifiable — reward is computed by checking responses against ClinVar annotations, not scored by humans.

---

## Repository Structure

```
genomic-reasoning-agent/
├── genomic_pipeline.py              # Step 1: Parse 23andMe .txt → DataFrame
├── clinvar_pipeline_full.py         # Step 2: Annotate variants via ClinVar API
├── genomic_agent_huggingface.py     # Step 3: smolagents tool-using agent (5 tools)
├── train_grpo.py                    # Step 4: GRPO training with TRL
├── app.py                           # HF Spaces Gradio UI
├── data/
│   ├── clinvar_annotations.json     # 12 variants with full ClinVar metadata
│   └── genomic_qa_dataset.json      # 36 Q&A pairs (3 task types × 12 variants)
└── README.md
```

---

## Pipeline: Step by Step

### Step 1 — Parse 23andMe

Reads the raw `.txt` export (Build GRCh37) into a DataFrame of 631,455 SNPs.

```python
from genomic_pipeline import parse_23andme
df = parse_23andme("genome_23andme.txt")
# → 631,455 SNPs across chromosomes 1–22, X, Y, MT
# → 104,617 heterozygous (16.6%) | 522,909 homozygous (82.8%)
```

### Step 2 — ClinVar Annotation

Queries NCBI E-utilities and GWAS Catalog for each rsID. Builds a Q&A dataset with verifiable answers.

```python
from clinvar_pipeline_full import query_clinvar_batch, build_qa_dataset
annotations = query_clinvar_batch(rsids, email="your@email.com")
qa_dataset  = build_qa_dataset(annotations, genome_df)
# → 12 variants annotated
# → 36 Q&A pairs: variant_interpretation / genotype_interpretation / pathway_reasoning
```

**Sample annotation:**

| rsID | Gene | Genotype | Significance | Condition |
|------|------|----------|-------------|-----------|
| rs1801133 | MTHFR | GG | Pathogenic/Likely pathogenic | Homocystinuria |
| rs429358 + rs7412 | APOE | TT / CC | risk factor | Alzheimer disease |
| rs9939609 | FTO | AT | risk factor | Obesity |
| rs762551 | CYP1A2 | AC | drug response | Caffeine metabolism |

### Step 3 — Tool-Using Agent (smolagents)

A `ToolCallingAgent` with 5 tools that plans multi-step queries across databases.

```python
from smolagents import ToolCallingAgent, InferenceClientModel
from genomic_agent_huggingface import (
    VariantLookupTool,    # rsID → ClinVar + genotype
    GeneScannerTool,      # gene/trait → all patient variants
    HaplotypeCallerTool,  # APOE ε2/ε3/ε4 from two SNPs
    GenomeStatsTool,      # 631K SNPs overview
    RewardEvaluatorTool,  # GRPO reward score (used during training)
)
model = InferenceClientModel("meta-llama/Llama-3.1-8B-Instruct")
agent = ToolCallingAgent(tools=[...], model=model, max_steps=10)
answer = agent.run("What is my APOE haplotype and what does it mean?")
```

**6-step reasoning trace for "Give me a genomic health summary":**

```
Step 1 [genome_stats]       → 631,455 SNPs | 16.6% heterozygous
Step 2 [variant_lookup]     → rs1801133 | MTHFR | GG | Pathogenic
Step 3 [call_haplotype]     → APOE ε3/ε3 | Neutral Alzheimer's risk
Step 4 [scan_gene_variants] → dopamine: ANKK1 GG (risk), COMT GG (Val/Val)
Step 5 [scan_gene_variants] → caffeine: CYP1A2 AC (intermediate), ADORA2A CT
Step 6 [evaluate_reasoning] → reward: 0.93 / 1.00 (excellent)
```

### Step 4 — GRPO Training

Trains the base LLM to reason better about genomic questions using reinforcement learning with verifiable rewards — no human annotation required.

```python
from train_grpo import train, TrainingConfig
config = TrainingConfig(
    model_name="meta-llama/Llama-3.1-8B-Instruct",
    num_epochs=3,
    num_generations=4,        # G: completions per question
    beta=0.04,                # KL penalty
    use_lora=True,
)
trainer = train(config)
```

**Reward function — 6 verifiable components:**

| Component | Weight | Verifiable against |
|-----------|--------|--------------------|
| Factual accuracy | 0.35 | ClinVar clinical significance |
| Condition coverage | 0.25 | ClinVar associated conditions |
| Gene mention | 0.15 | ClinVar gene annotation |
| Reasoning chain | 0.15 | Presence of causal language |
| Uncertainty calibration | 0.05 | Hedging language |
| Response completeness | 0.05 | Word count |

**Training progression (simulated):**

```
untrained  reward=0.06  |█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░|
epoch_1    reward=0.56  |████████████████░░░░░░░░░░░░░░|
epoch_3    reward=0.71  |█████████████████████░░░░░░░░░|
epoch_5    reward=0.93  |███████████████████████████░░░|
epoch_10   reward=1.00  |██████████████████████████████|
```

**GRPO advantage formula:**

```
advantage_i = (reward_i − mean(rewards)) / std(rewards)
No critic network. No value function. No human labeler.
Just relative comparison within each group of G=4 completions.
```

---

## Key Results from Real Genome Data

Running the full pipeline on a real 23andMe export (Zalina Dezhina, v5 chip):

| Variant | Gene | Genotype | Clinical Note |
|---------|------|----------|---------------|
| 🔴 rs1801133 | MTHFR | GG | Pathogenic — folate metabolism (p.Ala222Val) |
| 🟡 rs9939609 | FTO | **AT** | Risk factor — obesity, 1 risk allele (40.4% population) |
| 🧠 APOE | — | **ε3/ε3** | Neutral — most common haplotype, no elevated AD risk |
| 💊 rs762551 | CYP1A2 | **AC** | Drug response — intermediate caffeine metabolizer |
| 🟡 rs1800497 | ANKK1 | GG | Risk factor — reward pathway / DRD2 association |
| 🟢 rs6265 | BDNF | CC | Benign (Val/Val) — better episodic memory |

> ⚠️ This is a research and portfolio project, not medical advice.
> All interpretations are for educational purposes only.

---

## Tech Stack

| Layer | Technology |
|-------|-----------|
| Genome parsing | pandas, Python |
| Variant annotation | NCBI E-utilities (ClinVar), EBI GWAS Catalog |
| Agentic framework | [smolagents](https://github.com/huggingface/smolagents) (HuggingFace) |
| RL training | [TRL](https://github.com/huggingface/trl) GRPOTrainer |
| Fine-tuning | LoRA (PEFT) + 4-bit quantization (bitsandbytes) |
| Base model | meta-llama/Llama-3.1-8B-Instruct |
| Demo UI | Gradio (HF Spaces) |
| Evaluation | Per-task reward breakdown, 3 task types |

---

## HuggingFace Deployment

**Spaces (interactive demo):**
```
1. Create new Space → Gradio SDK
2. Upload: genomic_agent_huggingface.py, app.py, data/
3. Add secret: HF_TOKEN
4. Upload your 23andMe .txt → ask questions in chat
```

**Model Hub (trained weights):**
```python
trainer.push_to_hub("mioulin/genomic-reasoning-llm")
```

**Dataset Hub:**
```python
from datasets import Dataset
Dataset.from_list(qa_dataset).push_to_hub("mioulin/genomic-reasoning-qa")
```

---

## Connection to ML Scientist Role

This project was built to demonstrate the exact skills required for ML Scientist roles in AI×biology:

- **Agentic systems** — 5-tool ToolCallingAgent with multi-step planning
- **RL/RLHF training** — GRPO with verifiable reward, no human labelers
- **Biomedical data integration** — ClinVar, GWAS Catalog, gnomAD, PubMed
- **Evaluation framework** — 6-component reward breakdown across 3 task types
- **Real scientific domain** — 631,455 SNPs from real 23andMe genome
- **Reasoning over evidence** — multi-hop: SNP → gene → pathway → phenotype

---

## Running Locally

```bash
git clone https://huggingface.co/spaces/mioulin/genomic-reasoning-agent
cd genomic-reasoning-agent
pip install smolagents trl transformers accelerate peft datasets gradio
# Annotate your genome
python clinvar_pipeline_full.py \
  --genome your_23andme.txt \
  --output data/clinvar_annotations.json \
  --email your@email.com
# Run agent (requires HF token for model inference)
export HF_TOKEN=hf_...
python genomic_agent_huggingface.py
# Train with GRPO (requires GPU)
python train_grpo.py \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --epochs 3 --lora --G 4
```

---

## Author

**Zalina Dezhina, PhD**  
[![HuggingFace](https://img.shields.io/badge/🤗-mioulin-yellow)](https://huggingface.co/mioulin)

## Citation

```bibtex
@misc{dezhina2026genomic,
  title  = {Genomic Reasoning Agent: GRPO Training on Personal SNP Data},
  author = {Dezhina, Zalina},
  year   = {2026},
  url    = {https://huggingface.co/spaces/mioulin/genomic-reasoning-agent}
}
```

---

## License

MIT — research and educational use only.
Not intended for clinical or medical decision-making.

---

*Built with 🧬 smolagents · TRL · HuggingFace · ClinVar · 23andMe*