Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models

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. Here,
we sequenced five targeted genetic regions and used 8–23 selected CpG
sites to build age estimation models with machine learning methods with
about only $3–7 per sample, 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 error of the best
models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima
leopard cats, and Panthera spp., respectively. Our
models in domestic cats and Tsushima leopard cats were applicable to
individuals regardless of health conditions, indicating the 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.