---
library_name: pyaging
tags:
- pyaging
- aging-clock
- biology
- dna-methylation
---

# dnamphenoageprc

Original Levine-lab polycomb-related PhenoAge contribution using 55 CpGs. Coefficients retain their original values; the 60.664 PhenoAge intercept is excluded from both partitions, so these scores are not standalone calibrated ages. The two contributions plus the original intercept reconstruct DNAmPhenoAge. Follows author imputation=FALSE: absent CpGs and supplied NA values contribute zero. The parent DNAmPhenoAge coefficients were fitted on InCHIANTI 450K whole-blood data; the selected sites also occur on 27K and EPIC arrays.

Model weights retain the original authors' terms; the pyaging software license does not relicense them.

| | |
|---|---|
| **Predicts** | polycomb-related PhenoAge contribution |
| **Species** | Homo sapiens |
| **Tissue** | whole blood |
| **Data type** | DNA methylation |
| **Model type** | weighted linear score |
| **Year** | 2022 |

## Use with pyaging

```python
import pyaging as pya

pya.pred.predict_age(adata, ["dnamphenoageprc"])
```

Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io).

## Citation

['Rozenblit, M., et al. Evidence of accelerated epigenetic aging of breast tissues in patients with breast cancer is driven by CpGs associated with polycomb-related genes. Clinical Epigenetics 14, 30 (2022).', 'Levine, M. E., et al. "An epigenetic biomarker of aging for lifespan and healthspan." Aging 10.4 (2018): 573-591.']

https://doi.org/10.1186/s13148-022-01249-z