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README.md

@@ -1,3 +1,50 @@
----
-license: mit
----
+---
+# 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/alzheimer's-variant-tutorial-data", split="test")
+df = dataset.to_pandas()
+print(df.head())
+```