TABLE 1.
Epigenetic clocks based on Illumina human DNA methylation arrays
| Clock | No. CpGs | Error (Years) | Generation of error estimate (type of validation data set used) | No. of samples in training | Method used to find age‐associated CpGs | Age range of training | Cell types/Tissue used for training | Additional functional tissues/Cells | Reference |
|---|---|---|---|---|---|---|---|---|---|
| Bocklandt | 88 | 5.2 | Leave‐one‐out | 68 (34 twin pairs) | CpGs with q < 0.05 & absolute corr >0.57 with age | 21–55 | Saliva | ‐ | Bocklandt et al. (2011) |
| Koch & Wagner | 5 | 11 | Independent validation data set | 150 | Pavlidis Template Matching | 16–72 | Fibroblasts, keratinocytes, epithelial, peripheral blood | Saliva, breast organoid | Koch and Wagner (2011) |
| Passage Number | 6 | ‐ | ‐ | ‐ | Pavlidis Template Matching | ‐ | Fibroblasts, mesenchymal stem cells | ‐ | Koch et al. (2012) |
| Horvath (Pan‐Tissue) | 353 | Median Absolute Deviance 3.6 | Independent validation data set | 3931 | Elastic net regression | 0–100 | 51 different tissues/cell types including blood, brain, muscle | ‐ | Horvath (2013) |
| Skin & Blood (S&B) | 391 | No overall MAD for all tissues /cell types | Independent validation data set | 896 | Elastic net regression | 0–94 | Fibroblasts, keratinocytes, buccal cells, endothelial cells, lymphoblastoid, skin, blood, saliva | Brain, neurons, glia, liver, bone | Horvath et al. (2018) |
| Zhang (Elastic Net) | 514 | RMSE 2.04 | Independent validation data set | 13,661 | Elastic net regression | 2–104 | Whole blood, saliva | Breast, liver, adipose, muscle, endometrium | Zhang, Vallerga, et al. (2019) |
| Zhang (BLUP) | 319,607 | RMSE ~2.04 | Independent validation data set | 13,661 | Best linear unbiased prediction | 2–104 | Whole blood, saliva | ‐ | Zhang, Vallerga, et al. (2019) |
| Hannum | 71 | RMSE 4.9 | Independent validation data set | 482 | FDR to filter significant CpGs then elastic net | 19–101 | Whole blood | ‐ | Hannum et al. (2013) |
| Weidner (102 CpG) | 102 | 3.3 | Independent validation data set | 575 | CpGs selected by pearson corr (r > 0.85 or r < −0.85) | 0–78 | Whole blood | ‐ | Weidner et al. (2014) |
| Weidner (99 CpG) | 99 | 4.1 | Independent validation data set | 656 | CpGs derived from 102 previous CpGs in Weidner et al. (2014) | 19–101 | Whole blood | ‐ | Weidner et al. (2014) |
| Weidner/Lin (3 CpG) | 3 | 7.6 | Independent validation data set | 656 | Three CpGs selected from 102 previous CpGs, recursive feature elimination | 19–101 | Whole blood | ‐ | Weidner et al. (2014), Lin et al. (2016) |
| Boroni Skin | 2,266 | RMSE 4.98 | Random segregation of validation data set from training | 249 | Elastic net regression | 18–95 | Dermis, epidermis, whole skin | ‐ | Boroni et al. (2020) |
| Pediatric‐Buccal‐ Epigenetic (PedBE) | 94 | 0.35 | Independent validation data set | 1,032 | Elastic net regression | 0–19.5 | Buccal epithelial cells | ‐ | McEwen et al. (2019) |
Age‐associated CpGs are selected and weighted in a linear model, resulting in epigenetic age predictors (epigenetic clocks). Error (years) is based on mean absolute deviation (MAD) unless otherwise stated.