type stringclasses 2 values | id stringlengths 2 11 |
|---|---|
Gene | 10 |
Gene | 1111 |
Gene | 13740 |
Gene | CECAD |
Gene | 2024 |
Gene | 295 |
Gene | 416 |
Gene | 35 |
Gene | DNA |
Gene | 11 |
Gene | 18 |
Gene | 20 |
Gene | 30 |
Gene | 21 |
Gene | 12 |
Gene | 13 |
Gene | 22 |
Gene | 23 |
Gene | 14 |
Gene | 15 |
Gene | 16 |
Gene | 17 |
Gene | 417 |
Gene | 435 |
Gene | 06 |
Gene | 2025 |
Gene | OA |
Gene | 29 |
Gene | 100 |
Gene | 31 |
Gene | 32 |
Gene | 33 |
Gene | 34 |
Gene | 36 |
Gene | 37 |
Gene | 38 |
Gene | 39 |
Gene | 40 |
Gene | 41 |
Gene | 42 |
Gene | 43 |
Gene | 44 |
Gene | 24 |
Gene | 28 |
Gene | 25 |
Gene | 27 |
Gene | 418 |
SNP | rs7412 |
SNP | rs429358 |
Gene | 53 |
Gene | RAD54L |
Gene | BLMWRN |
Gene | IGF |
Gene | 51 |
Gene | YWHAG |
Gene | 52 |
Gene | ULK1 |
Gene | POT1 |
Gene | IIS |
Gene | APOE |
Gene | 1021 |
Gene | 68 |
Gene | 88 |
Gene | 45 |
Gene | 47 |
Gene | AKT1 |
Gene | AKT3 |
Gene | FOXO4 |
Gene | IGF2 |
Gene | INS |
Gene | PIK3CA |
Gene | SGK |
Gene | SGK2 |
Gene | FOXO3 |
Gene | 48 |
Gene | 50 |
Gene | 419 |
Gene | 56 |
Gene | MTP |
Gene | 57 |
Gene | 58 |
Gene | SNP |
Gene | 54 |
Gene | GWAS |
Gene | TP53 |
Gene | GHSR |
Gene | 59 |
Gene | 60 |
Gene | 71 |
Gene | 72 |
Gene | 73 |
Gene | 74 |
Gene | 55 |
Gene | 75 |
Gene | 77 |
Gene | 420 |
SNP | rs2440012 |
SNP | rs2075650 |
SNP | rs4420638 |
SNP | rs6857 |
End of preview. Expand
in Data Studio
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)
🧬 Smulders Longevity Extracted Dataset
This dataset was extracted from the publication:
Genetics of human longevity: From variants to genes to pathways
Journal of Internal Medicine, 2023 — Smulders et al.
DOI: 10.1111/joim.13690
It contains structured gene names and SNP identifiers mentioned throughout the paper.
📄 Dataset Description
| Column | Description |
|---|---|
| type | Entry type: Gene or SNP |
| id | The gene name or SNP ID |
The gene names are uppercase identifiers, and SNPs follow the common rs format (e.g., rs429358).
🔧 Usage Instructions
Load in Python
import pandas as pd
df = pd.read_parquet("smulders_longevity_extracted.parquet")
print(df.head())
🚀 Use Cases
- Gene prioritization for longevity research
- Mapping SNPs from literature to existing aging gene databases
- Input for polygenic risk score (PRS) modeling
- Enhancing datasets like LongevityMap with literature-derived signals
📚 Citation
If you use this dataset, please cite the original paper:
Smulders, Y. M., et al. (2023). Genetics of human longevity: From variants to genes to pathways. Journal of Internal Medicine.
https://doi.org/10.1111/joim.13690
🙏 Acknowledgments
Extracted and compiled by Iris Lee for longevity research and hackathon use.
- Downloads last month
- 11