TABLE 4.
Mouse epigenetic clocks
| Clock | Number of CpGs | Correlation (R 2) | Generation of error estimate (Type of validation data set) | Number of samples in training data | Method used to find age‐associated CpGs | Age range of training samples (Months) | Cell types/Tissue used for training | Reference |
|---|---|---|---|---|---|---|---|---|
| Wang | 107 | 0.91 | Independent validation data set | 148 | Elastic net | 0.2–26 | Liver | Wang et al. (2017) |
| Petkovich | 90 | >0.90 | Independent validation data set | 141 | Elastic net | 3–35 | Partial blood | Petkovich et al. (2017) |
| Stubbs Multi‐Tissue | 329 | 0.7 | Training data sets partitioned and mixed with two external data sets to make up validation data set | 129 | Elastic net | 0.2–9.5 | Liver, lung, heart, muscle, spleen, cerebellum, cortex | Stubbs et al. (2017) |
| Meer | 435 | 0.89 | Random segregation of validation data set from training | ~333 | Elastic net | 0.2–35 | Blood, heart, cortex, liver, lung, muscle, spleen, cerebellum, pro B cells, follicular B cells | Meer et al. (2018) |
| Thompson All CpGs (Ridge) | 582 | 0.79 | Leave‐one‐batch‐out | 893 | Ridge Regression | 0–30 | Various tissues including adipose, blood, kidney, liver, lung, muscle, spleen | Thompson et al. (2018) |
| Thompson All CpGs (Elastic Net) | 582 | 0.82 | Leave‐one‐batch‐out | 893 | Elastic net | 0–30 | "" | Thompson et al. (2018) |
| Thompson Conserved CpGs (Ridge) | 273 | 0.64 | Leave‐one‐batch‐out | 893 | Ridge Regression | 0–30 | "" | Thompson et al. (2018) |
| Thompson Conserved CpGs (Elastic Net) | 273 | 0.68 | Leave‐one‐batch‐out | 893 | Elastic net | 0–30 | "" | Thompson et al. (2018) |
| Wood Mouse Clock | 9 | 0.88 | Same data set used for training | 48 | LASSO | 3–16 | Ear punch samples | Little et al. (2020) |
All clocks were trained on mouse RRBS data (with the exception of Wang et al., which used both RRBS and WGBS, and the Wood Mouse Clock, which used a targeted PCR approach combined with Oxford Nanopore).