dnamphenoagenonprc
Original Levine-lab non-polycomb PhenoAge contribution using 458 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 | non-polycomb PhenoAge contribution |
| Species | Homo sapiens |
| Tissue | whole blood |
| Data type | DNA methylation |
| Model type | weighted linear score |
| Year | 2022 |
Use with pyaging
import pyaging as pya
pya.pred.predict_age(adata, ["dnamphenoagenonprc"])
Browse every clock in the pyaging Clock Catalogue.
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.']
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