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
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🧬 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. ### 🧑💻 Team: MultiModalMillenials. Iris Lee (@iris8090)
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