Datasets:
| # 1. Metadata Block | |
| license: mit | |
| task_categories: | |
| - tabular-regression | |
| tags: | |
| - biology | |
| - genomics | |
| pretty_name: "Alzheimer's GWAS variants (hg19)" | |
| size_categories: | |
| - 1K<n<10K | |
| # 2. Config & Split Management | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: test | |
| path: "variants.csv" | |
| # alzheimer's-variant-tutorial-data | |
| ## Dataset Summary | |
| This dataset contains summary statistics for 1,000 genomic variants. Each row represents a single-nucleotide polymorphism (SNP) mapped to the hg19 reference genome. | |
| ## Dataset Structure | |
| ### 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 datasets import load_dataset | |
| dataset = load_dataset("Genentech/alzheimers-variant-tutorial-data", split="test") | |
| df = dataset.to_pandas() | |
| print(df.head()) | |
| ``` |