# Co-analysis of methylation platforms for signatures of biological aging in the domestic dog reveals previously unexplored confounding factors

## TL;DR

This study explores how methylation data from dogs can help understand biological aging and identifies challenges in creating accurate biological age predictors.

## Contribution

The study introduces a method to build biological age methylation clocks in dogs while accounting for phylogenetic relationships.

## Key findings

- Biological age methylation clocks in dogs are affected by population stratification.
- Methylation markers for biological age are rare and do not overlap between datasets.
- Effective biological age predictions require heavy parameterization.

## Abstract

Chronological age reveals the number of years an individual has lived since birth. By contrast, biological age varies between individuals of the same chronological age at a rate reflective of physiological decline. Differing rates of physiological decline are related to longevity and result from genetics, environment, behavior, and disease. The creation of methylation biological age predictors is a long-standing challenge in aging research due to the lack of individual pre-mortem longevity data. The consistent differences in longevity between domestic dog breeds enable the construction of biological age estimators which can, in turn, be contrasted with methylation measurements to elucidate mechanisms of biological aging. We draw on three flagship methylation studies using distinct measurement platforms and tissues to assess the feasibility of creating biological age methylation clocks in the dog. We expand epigenetic clock building strategies to accommodate phylogenetic relationships between individuals, thus controlling for the use of breed standard metrics. We observe that biological age methylation clocks are affected by population stratification and require heavy parameterization to achieve effective predictions. Finally, we observe that methylation-related markers reflecting biological age signals are rare and do not colocalize between datasets.