1. Overview
1.1. Introduction
Aging is a multifaceted, time-dependent process influenced by various factors, including genetics, lifestyle, nutrition, mental well-being, as well as social and environmental conditions. Consequently, the aging speed can significantly differ among individuals, rendering chronological age (i.e., the number of years a person has been alive) an inadequate indicator of a person’s overall health status and predictive value for disease onset and treatment responses. In contrast, biological (or physiological) age employs bio-physiological measurements to more accurately gauge an individual’s life clock [1] [2].
DNA methylation-based biological age estimation has been widely used; however, a universally applicable bioinformatics tool is currently lacking. The Epical package provides a number of commands to calcuate epigenetic ages from DNA methylation data generated from Illumina HumanMethylation450 BeadChip (450K), MethylationEPIC v1.0 (850K) or MethylationEPIC v2.0 array.
1.2. Available clocks for Human
Clock_name |
Predictor CpGs |
Unit |
Tissue |
Method |
|
1 |
353 |
Year |
Pan-tissue |
Elastic Net |
|
2 |
110 |
Year |
Pan-tissue |
Elastic Net |
|
3 |
391 |
Year |
Skin & blood |
Elastic Net |
|
4 |
513 |
Year |
Blood |
Elastic Net |
|
5 |
71 |
Year |
Blood |
Elastic Net |
|
6 |
514 |
Year |
Blood, Saliva |
Elastic Net |
|
7 |
319607 |
Year |
Blood, Saliva |
BLUP |
|
8 |
20318 |
Year |
Pan-tissue |
DNN |
|
9 |
347 |
Year |
Brain cortex |
Elastic Net |
|
10 |
200 |
Year |
Skeletal muscle |
Elastic Net |
|
11 |
n/a |
n/a |
n/a |
EM |
|
12 |
140 |
Kb |
Blood |
Elastic Net |
|
13 |
111 |
Year |
Blood |
Elastic Net |
|
14 |
94 |
Year |
Buccal epithelial |
Elastic Net |
|
15 |
96 |
Day |
Cord blood |
LASSO |
|
16 |
176 |
Day |
Cord blood |
LASSO |
|
17 |
148 |
Week |
Cord blood |
Elastic Net |
|
18 |
62 |
Week |
Placental tissues |
Elastic Net |
|
19 |
546 |
Week |
Placental tissues |
Elastic Net |
|
20 |
558 |
Week |
Placental tissues |
Elastic Net |
|
21 |
395 |
Week |
Placental tissues |
Elastic Net |
Note
Input data for these clocks are generated from Illumina ‘BeadChip’ methylation array.
The “EPM” algorithem needs user provide training data.
All the commands (i.e. Clock_name) are case-sensitive.
1.3. Available clocks for Mouse (Mus Musculus)
Clock_name |
Predictor CpGs |
Unit |
Tissue |
Method |
|
1 |
435 |
Day |
Pan-tissue |
Elastic Net |
|
2 |
329 |
Day |
Pan-tissue |
Elastic Net |
|
3 |
148 |
Day |
Liver |
Elastic Net |
|
4 |
90 |
Day |
Blood |
Elastic Net |
Note
Input data for these clocks are generated from RRBS (Reduced-Representation Bisulfite Sequencing) or WGBS (Whole Genome Bisulfite Sequencing).
WLMT = Whole Life Multiple Tissue. This clock was trained from mice aged 6 to 30 months.
YOMT = YOung Multiple Tissue. This clock was trained from mice aged 0 to 10 months.