---
license: mit
task_categories:
- tabular-regression
tags:
- biology
- genomics
pretty_name: "Alzheimer's GWAS variants (hg19)"
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: test
    path: "variants.csv"
---

# alzheimers-variant-tutorial-data

## Dataset Summary
This dataset contains summary statistics for 1,000 genomic variants associated with Alzheimer's disease. Each row represents a single-nucleotide polymorphism (SNP) mapped to the hg19 reference genome.

## Dataset Structure

- **Number of variants:** 1,000
- **Genome build:** hg19

### Data Fields
Based on the header of `variants.csv`:

| Column | Type | Description |
| :--- | :--- | :--- |
| `snpid` | string | Unique identifier in `chr:pos_ref_alt` format |
| `chrom` | string | Chromosome (e.g., `chr6`) |
| `pos` | int | Genomic position (hg19) |
| `alt` | string | Alternate allele (effect allele) |
| `ref` | string | Reference allele (non-effect allele) |
| `rsid` | string | Reference SNP cluster ID |
| `pval` | float | P-value of the association |
| `beta` | float | Regression coefficient (effect size) |
| `se` | float | Standard error of the beta |

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd

file_path = hf_hub_download(
    repo_id="Genentech/alzheimers-variant-tutorial-data",
    repo_type="dataset",
    filename="variants.csv"
)
df = pd.read_csv(file_path)
```