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

mouse.png

Clock_name

Predictor CpGs

Unit

Tissue

Method

1

Horvath13

353

Year

Pan-tissue

Elastic Net

2

Horvath13_shrunk

110

Year

Pan-tissue

Elastic Net

3

Horvath18

391

Year

Skin & blood

Elastic Net

4

Levine

513

Year

Blood

Elastic Net

5

Hannum

71

Year

Blood

Elastic Net

6

Zhang_EN

514

Year

Blood, Saliva

Elastic Net

7

Zhang_BLUP

319607

Year

Blood, Saliva

BLUP

8

AltumAge

20318

Year

Pan-tissue

DNN

9

Cortical

347

Year

Brain cortex

Elastic Net

10

MEAT

200

Year

Skeletal muscle

Elastic Net

11

EPM

n/a

n/a

n/a

EM

12

Lu_DNAmTL

140

Kb

Blood

Elastic Net

13

Ped_Wu

111

Year

Blood

Elastic Net

14

PedBE

94

Year

Buccal epithelial

Elastic Net

15

GA_Bohlin

96

Day

Cord blood

LASSO

16

GA_Haftorn

176

Day

Cord blood

LASSO

17

GA_Knight

148

Week

Cord blood

Elastic Net

18

GA_Mayne

62

Week

Placental tissues

Elastic Net

19

GA_Lee_CPC

546

Week

Placental tissues

Elastic Net

20

GA_Lee_RPC

558

Week

Placental tissues

Elastic Net

21

GA_Lee_rRPC

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)

mouse.png

Clock_name

Predictor CpGs

Unit

Tissue

Method

1

WLMT

435

Day

Pan-tissue

Elastic Net

2

YOMT

329

Day

Pan-tissue

Elastic Net

3

mmLiver

148

Day

Liver

Elastic Net

4

mmBlood

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.