DOI: 10.5061/dryad.3r2280gn4
title: 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

description: 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.
description: # Datasets --- ### Appendix S1–S4 The files included the methylation data,
 sample information, and predicted age of each target species/species
 group. The data in the files are used to build age estimation models.
 'domestic cat' in the filename means the file is for the
 domestic cat; 'leopard cat' means for the Tsushima leopard cat;
 'panthera' means for the Panthera species (i.e., jaguar,
 leopard, lion, snow leopard, and tiger), and 'all' means for all
 the samples from all species. ### Appendix S5 The file contains the CpG
 selection results for the best age estimation model of each
 species/species group, the frequency of being selected in elastic net
 feature selection of each CpG site, correlation coefficients between the
 methylation rate and chronological age of each CpG site, and NCBI sequence
 ID with position. ### CpG No renamed fulllist\_all felidae.csv The file
 showed the list of CpGs, which were at least contained in one species. ###
 M%+sampleinfo\*.csv These files are the version of Appendix S1–S4 before
 adding the predicted age. ### indextable\_skf\_cor\*.csv Raw results of
 feature selection (correlation-based). ###
 indextable\_skf\_loio\_ela\*.csv Raw results of feature selection (elastic
 net-based, leave-one-individual-out cross-validation). ###
 indextable\_skf\_loso(\_raw)\_ela\*.csv Raw results of feature selection
 (elastic net-based, leave-one-species-out cross-validation). *P.S.
 Appendix S1-S5 are referred to in our paper. Other files were only used in
 the analysis.* # Description of the data sets and file structures ###
 Appendix S1–S4, M%+sampleinfo\*.csv * amp3_,amp4_, amp8_, amp9_, and
 bs38\_ in the head are the names of CpG sites. Columns with the heads
 showed the results of methylation rates. The proximal genes and positions
 in genomes could be referred to in Appendix S5 and CpG No renamed
 fulllist_all felidae.csv. * Health_condition_ed: health condition at the
 time of sampling (good, diseased). * Health_condition (Appendix S2–S4,
 species other than domestic cats): raw health condition data * Health
 condition information in Appendix S1 (domestic cats): *
 Health_condition_Healthy (column K): healthy sample Health_condition_CKD
 (column L): sample with chronic kidney disease Health_condition_Diabetes
 (column M): sample with diabetes Health_condition_Cancer (column N):
 sample with cancer Health_condition_DigestiveDisease (column O): sample
 with digestive diseases Health_condition_Others (column P): sample with
 other diseases * Fold: data was split into five folds (0–4) with similar
 age and species distribution using stratified k-fold. * Age_class: age
 class of each sample. * Predictedage_*: age predicted through the methods
 below. | Feature selection methods | Regression methods | Column name
 (after 'Predictedage\_') | | --------------------------- |
 ------------------------ | ------------------------------------ | |
 ---------elastic net------- | -------only once-------- | ela | | elastic
 net | elastic net | ela\_ela | | elastic net | SVMr | ela\_svmr | | cor ≥
 0.5 | elastic net | cor0\_5\_ela | | cor ≥ 0.7 | elastic net |
 cor0\_7\_ela | | cor ≥ 0.5 | SVMr | cor0\_5\_svmr | | cor ≥ 0.7 | SVMr |
 cos0\_7\_svmr | * For Appendix S2 and
 M%+sampleinfo_leopardcat_paper_final_fold+ageclass.csv *
 'Age_stage_at_time_of_protection' shows the age stages estimated
 when the individuals were protected from morphological methods. *
 'Death_date' shows the death date. No data here means the
 individuals are still alive in 2023. This data was not used in the
 analysis. * Empty cells mean no data. Captive-born individuals had no data
 in 'Age_stage_at_time_of_protection'. Wild-born individuals had
 no data in 'Age',
 'Health_condition_ed','Fold', 'Age_class',
 which were only available for captive-born individuals with age known. The
 predicted epigenetic age was only calculated using the best model and
 summarized in 'Predictedage_ela_svmr'. * For Appendix S3 and
 M%+sampleinfo_panthera_paper_final_fold+ageclass.csv, Appendix S4 and
 M%+sampleinfo_all_paper_final_fold+relative_ageclass.csv *
 'Predictedage_*_loso(_raw)' is age predicted under the model
 evaluation of leave-one-species-out-cross-validation. * For Appendix S4 *
 'Predictedage_* ' is the predicted relative age of each sample.
 'Predictedage_*_chronoloical age' is the predicted chronological
 age under the best models. * Empty cells mean no data. The summarizing
 standard for domestic cats and other species was different. Therefore,
 empty cells are in health condition-related columns. ### Appendix S5, CpG
 No renamed fulllist\_all felidae.csv * Columns E to M showed whether the
 CpG sites existed in each species group. 0 means the CpG does not exist in
 the species; 1 means the CpG exists in the species. Panthera_spp. (column
 L) included species in column G–K (i.e. jaguar, leopard, lion, snow
 leopard, and tiger). All_spp. (column M) included all species. ###
 Appendix S5 * Green, yellow, orange, and red columns represent different
 levels of correlation coefficients between methylation rates of selected
 CpG sites and chronological age. White columns are CpG sites that were not
 selected. Grey columns are CpG sites that did not exist in the species
 group. * Columns named "Features in the best model
 (correlation_coefficient)—Elastic net + SVMr (frequency ≥ 4 or 5)"
 showed the correlation coefficient between the chronological age and the
 methylation rates of features (i.e., CpGs) used in the best models.
 Elastic net-based feature selection followed by regression using SVMr
 (Elastic net + SVMr) produced the best models for all species groups. For
 some species groups, CpGs selected over four times in all five training
 data sets (frequency≥4) constructed the explanatory variables of the best
 models; for others, CpGs selected in all five training data sets
 (frequency ≥ 5) constructed the explanatory variables of the best models.
 # Code/Software 2023_Qi_etal_paper Rscript.R was run in R 4.3.1.
 2023_Qi_etal_Pythonscript.py was run in Python 3.8.8.