Knowledge of individual age can help both in-situ and ex-situ
conservation programs to design more efficient and suitable management
plans for targeted wildlife species. DNA methylation is one of the
epigenetic aging markers that has emerged as a promising tool that can
estimate age with high accuracy using only a tiny amount of biological
material, which can be collected in a minimally invasive way. Although
the conservation of Felidae species has received great attention,
studies rarely focus on the development of age estimation models. Here,
we sequenced five genetic regions and used 4–25 selected CpG sites to
build age estimation models with several machine learning methods, using
blood samples of seven Felidae species—ranging from small to big, and
domestic to endangered species: domestic cats (Felis catus, 139
samples), Tsushima leopard cats (Prionailurus bengalensis
euptilurus, 84 samples), and five Panthera species (96 samples).
The models built achieved satisfactory accuracy—the mean absolute
deviation of the best models was 1.80, 1.30, and 1.55 years in domestic
cats, Tsushima leopard cats, and Panthera spp., respectively. Our
models in domestic cats and Tsushima leopard cats were applicable to all
individuals regardless of health conditions and sex, indicating high
applicability of our models to samples collected from diverse
situations, e.g., rescued individuals in the context of conservation. We
also showed the possibility of developing universal age estimation
models for the five Panthera spp. using two of the five genetic
regions, suggesting an even lower cost to use our models for future
applications.