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\begin{document}

\title{Blood DNA methylation-based age estimation in domestic cats, Tsushima
leopard cats (\emph{Prionailurus bengalensis euptilurus}), and
\emph{Panthera} species using machine learning models}



\author[1]{Huiyuan Qi}%
\author[1]{Qi Luan Lim}%
\author[1]{Kodzue Kinoshita}%
\author[2]{Nobuyoshi Nakajima}%
\author[1]{Miho Inoue-Murayama}%
\affil[1]{Kyoto University Wildlife Research Center}%
\affil[2]{National Institute for Environmental Studies}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
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 (\emph{Felis catus}, 139
samples), Tsushima leopard cats (\emph{Prionailurus bengalensis
euptilurus}, 84 samples), and five \emph{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 \emph{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 \emph{Panthera} spp. using two of the five genetic
regions, suggesting an even lower cost to use our models for future
applications.%
\end{abstract}%



\sloppy


\textbf{Blood DNA methylation-based age estimation in domestic cats,
Tsushima leopard cats (\emph{Prionailurus bengalensis euptilurus}),
and\emph{Panthera} species using machine learning models}

\textbf{Running title: Epigenetic clocks in seven Felidae species}

Huiyuan Qi\textsuperscript{1}, Qi Luan Lim\textsuperscript{1+}, Kodzue
Kinoshita\textsuperscript{1++}, Nobuyoshi Nakajima\textsuperscript{2},
Miho Inoue-Murayama\textsuperscript{1*}

\textsuperscript{1} Wildlife Research Center, Kyoto University, Kyoto
606-8203, Japan

\textsuperscript{2} Biodiversity Division, National Institute for
Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan

\textsuperscript{+}Current address: Department of Biology, Faculty of
Science, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia

\textsuperscript{++}Current address: Graduate School of Asian and African
Area Studies, Kyoto University, Kyoto 606-8501, Japan

\textsuperscript{*}Corresponding author:

Phone: +81-75-771-4375;

Email: murayama.miho.5n@kyoto-u.ac.jp

\textbf{Abstract}

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 (\emph{Felis~catus} , 139
samples), Tsushima leopard cats
(\emph{Prionailurus~bengalensis~euptilurus} , 84 samples), and
five~\emph{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~\emph{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~\emph{Panthera}
~spp. using two of the five genetic regions, suggesting an even lower
cost to use our models for future applications.

\textbf{Keywords}

DNA methylation, age estimation, domestic cat, Tsushima leopard
cat,\emph{Panthera} , endangered Felidae species

\section*{Introduction}

{\label{introduction}}

Age information is important for both ex-situ and in-situ wildlife
conservation, as it is relevant to individual behavior, health,
fertility, and mortality (Blomqvist \& Sten, 1982; Kirkwood \& Austad,
2000; Nussey et al., 2013; Youn et al., 2022; Zhao et al., 2019). Age
estimation can help determine the demographic characteristics and
predict the current and future extinction risks of wildlife populations
(Lacy, 2019; Oli \& Dobson, 2003). Through age estimation in injured or
dead wild individuals, it is also possible to examine the relationships
between age and the causes of injury or mortality (Conroy et al., 2019;
Thorel et al., 2020). If certain age groups are found to have
particularly high mortality rates, the search for the causes of
mortality and preventive measures can then be undertaken. For rescued
wild individuals, information on age would help inform appropriate
health care and enrichment activities to maximize positive welfare
experienced by individuals at different age classes, which would also be
beneficial for more efficient breeding programs (Caselli et al., 2022;
Eskelinen et al., 2015; Hecht, 2021).

Age estimation methods based on morphology observations/measurements and
mark-recapture had been conducted, such as longtime tracking and direct
observation in primates (Mori, 1979), mark-recapture in bats (Wilkinson
\& Brunet-Rossinni, 2009), and scar/speckle-counting in cetaceans
(Hartman et al., 2016; Yagi et al., 2023). However, these age
measurement methods are difficult to implement for species that are hard
to be observed or recaptured or do not show significant age-related
changes in appearance. In many mammals, age estimation through measuring
the development and eruption of teeth and bones is also widely applied
(Chevallier et al., 2017; White et al., 2016), but this requires either
dead bodies or capturing and long-term restraint of live animals for the
measurements.

Molecular aging markers as new less-invasive age estimation
methods---able to determine individuals' age by sampling and analyzing
only a small amount of biological material---have been put in the
spotlight (Bocklandt et al., 2011; Gruber et al., 2021; Petkovich et
al., 2017; Xia et al., 2017). DNA methylation is one of the
representatives which has been established by previous studies to be one
of the most accurate age markers (Horvath, 2013; Li et al., 2018;
Paoli-Iseppi et al., 2017; Stubbs et al., 2017). DNA methylation is an
epigenetic process by which a methyl group is transferred onto the C5
position of cytosine to form 5-methylcytosine, which in mammals occurs
predominantly on cytosines in cytosine--guanine dinucleotide (CpG) sites
(Bogdanovi\selectlanguage{polish}ć \selectlanguage{english}\& Veenstra, 2009). The age-associated changes in DNA
methylation levels in CpG sites in gene promoters affect gene
transcription levels and result in loss of body functions (Field et al.,
2018). DNA methylation-based epigenetic clock has been developed in many
species, such as humans (\emph{Homo sapiens} ), house mice (\emph{Mus
musculus} ), domestic dogs (\emph{Canis familiaris} ), gray wolves
(\emph{Canis lupus} ), domestic cats (\emph{Felis catus} ), chimpanzees
(\emph{Pan troglodytes} ), humpback whales (\emph{Megaptera
novaeangliae} ), Bechstein's bats (\emph{Myotis bechsteinii} ), and
plains zebras (\emph{Equus quagga} ) (Han et al., 2018; Horvath, 2013;
Ito et al., 2018; Larison et al., 2021; Maegawa et al., 2010; Polanowski
et al., 2014; Qi et al., 2021; Raj et al., 2021; Thompson et al., 2017;
Wright et al., 2018; Zbie\selectlanguage{polish}ć-\selectlanguage{english}Piekarska et al., 2015), etc.

Felidae species vary in the breadth of their geographic occurrence as
apex predators of the ecosystems (Li et al., 2016). Except for the
domestic cat (\emph{Felis catus} ), most species are at threat
(\emph{Felidae, The IUCN Red List of Threatened Species} , 2023). While
feline conservation has received much attention, few studies have
focused on improving age assessment for conservation
implications---recent publications for tigers and lions were based on
teeth measurement (Sharma et al., 2022; White et al., 2016). There has
been a lack of studies on DNA methylation-based age estimation that can
be conveniently implemented for live animals, except for domestic cats
(Qi et al., 2021; Raj et al., 2021). It would be interesting, other than
building age estimation models for each species, to study whether the
same age estimation models could be shared among the Felidae species.
Although the body size and living environments of felids show large
variations, the genetic distance among the cat families is relatively
close---there is strong collinearity among felid genomes coupled with
recent genetic divergence (Cho et al., 2013; Davis et al., 2009;
Wurster-Hill \& Centerwall, 1982). The occurrences of interspecific
hybrids in big as well as small cats in captivity (Gray, 1972) also
supported this genome collinearity. These include many
\emph{Panthera}hybrids: ligers (between male lions and female tigers),
tigons (between male tigers and female lions), jaglions (between male
jaguars and female lions), tipards (between male tigers and female
leopards), and leopon (between male leopard and female lion)---as well
as domestic cat breeds that are inter-specific hybrids, such as the
Bengal and Savannah.

In our study, we aimed to construct age estimation models covering both
small cat species and \emph{Panthera} species. Another goal was to
compare the difference in DNA methylation profiles of different cat
species and discuss the possibility of constructing common age
estimation models applicable to multiple cat species. The domestic cat
(\emph{Felis catus} ) and Tsushima leopard cat (\emph{Prionailurus
bengalensis euptilurus} ) are chosen as our target small cat species.
The domestic cat is a beloved companion animal and model animal for
other small felines. The age estimation model developed for the domestic
cat is a great reference for other endangered small felines for which
adequate sample sizes are difficult to obtain. The Tsushima leopard cat,
on the other hand, is a critically endangered species whose age
estimation is needed to improve its conversation strategies. The
Tsushima leopard cat is an isolated population of Amur leopard cat
(\emph{Prionailurus bengalensis euptilurus} ) that only lives on
Tsushima Island in Japan, with only about 100 wild individuals and 30
captive individuals still surviving (Inoue, 2021). Fortunately, rescue
and breeding have begun since 1997, and over time many samples have been
collected. Besides the two small cat species, we also included
five\emph{Panthera} spp. that required urgent conservation---jaguars
(\emph{Panthera onca} ), Amur leopards (\emph{Panthera pardus
orientalis} ), African lions (\emph{Panthera leo leo} ), snow leopards
(\emph{Panthera uncia} ), and Amur tigers (\emph{Panthera tigris
altaica} ).

Sex and health conditions may be factors contributing to epigenetic
aging (Bors et al., 2021; Qi et al., 2021). Therefore, we also assessed
whether these influence our models. In other words, we assessed whether
our models are universal to individuals of different sexes and health
conditions. If true, this would imply that our models have high
applicability in the conservation context, in which rescued individuals
often have varied conditions.

\section*{Materials and Methods}

{\label{materials-and-methods}}

\textbf{Ethics statement}

All methods were carried out in accordance with relevant guidelines and
regulations. The study was carried out in compliance with the ARRIVE
guidelines. All experimental protocols were approved by the ethical
committee of Wildlife Research Center of Kyoto University, and all
sample collection and experiments were conducted with permission from
the ethical committee (approval numbers: WRC-2019\&2020-012A,
WRC-2021\&2022-013A, and WRC-2022-010A). All domestic cat samples were
obtained with the consent of the cat owners. Other Felidae species
samples were collected with the approval of each zoo/conservation
center. Approval from the Ministry of the Environment Japan was also
obtained for the Tsushima leopard cat samples.

\textbf{Sample collection}

\emph{Domestic cat samples}

A total of 139 residual blood samples from clinical health check-ups of
105 domestic cats during the months of July to September 2020 were
obtained from the Kyoto Medical Center, Daktari Animal Hospital, and
Anicom Specialty Medical Institute Inc. The information recorded by the
veterinarians on age, breed, neuter status, sex, and health condition
was provided by the institutions. All domestic cat samples were stored
at -80 \selectlanguage{ngerman}°C for less than one month before DNA extraction. Age ranged from
0.41 to 21.04 years, and the female-to-male sex ratio (F:M) was 50:55 =
10:11. Most domestic cat samples were obtained from mixed-breed
individuals (n = 65). The remaining individuals (n = 40) were purebred
cats. As there were only a few samples for each cat breed (n \selectlanguage{english}[?] 8), the
influence of breed was not estimated. Most, except six, of the 112 adult
individuals over 3.5 years old were neutered, while only one out of 15
individuals under 2 years old were neutered. Because of the extreme
disparity in the age distributions between neutered and unneutered
individuals, it was implausible to find similar-age individuals from the
two groups. Therefore, we did not investigate the effect of neutering on
the accuracy of age estimation. Health conditions and other detailed
information can be found in Table 1 and Appendix S1.

\emph{Tsushima leopard cat samples}

Tsushima leopard cat blood samples were collected and banked during
health checks in Tsushima Wildlife Conservation Center and zoos from
2006 to 2021. These samples were stored under \selectlanguage{english}-20 degC for 0--1 year or
\selectlanguage{english}-80 degC for 0--15 years. We included in the study 84 samples of known age
from 19 captive-born individuals (age ranged 0.50--15.32 yr; F:M = 9:10;
Table 1) and 15 samples from four rescued wild-born individuals of
unknown age (F:M = 3:1; Table 2). Health conditions and other detailed
information can be found in Appendix S2.

\emph{Panthera spp. samples}

A total of 96 blood samples from 35 individuals of \emph{Panthera} spp.
including jaguars, Amur leopards, African lions, snow leopards, and Amur
tigers were collected from routine health checks in Kobe Oji Zoo, Japan
from 2001 to 2022 and stored at \selectlanguage{english}-80 degC for 0--21 years. The sample size
for each species is summarized in Table 1. Health conditions and other
detailed information can be found in Appendix S3. Considering the small
sample sizes of each species, small genetic distances, and similar
lifespans (Li et al., 2016; Tacutu et al., 2018), we merged
all\emph{Panthera} spp. samples into one dataset for all subsequent
analyses.

\textbf{DNA extraction and bisulfite conversion}

Genomic DNA of all samples was extracted using the DNeasy Blood \&
Tissue Kit (QIAGEN GmbH, Hilden, Germany), followed by bisulfite
conversion with the EZ DNA Methylation-Gold Kit (Zymo Research, Irvine,
CA, USA) according to the manufacturer's protocol.

\textbf{Gene regions, primer design and PCR conditions}

We targeted five DNA regions adjacent to five genes, namely \emph{TCF21,
PRMT8, DLX5, RALYL,} and \emph{ELOVL2} . These genes are found to have
age-related methylation rate changes in dogs (Lowe et al., 2018) and
humans (Bekaert et al., 2015). The genes are involved in many
housekeeping/essential pathways, and their abnormal gene expression
levels are detected in several types of cancer tissues (Safran et al.,
2021; Stelzer et al., 2016). Homogeneous gene regions were searched
against the reference genomes of each target species using BLAST+ 2.11.0
(Altschul et al., 1990; Camacho et al., 2009) provided by the National
Centre for Biotechnology Information (NCBI). The reference genomes that
were used are as follows: F.catus\_Fca126\_mat1.0 (GCF\_018350175.1) for
the domestic cat, Fcat\_Pben\_1.1\_paternal\_pri (GCF\_016509475.1) for
the Tsushima leopard cat, PanOnc\_v1\_BIUU (GCA\_004023805.1) for the
jaguar, Panpar1.0 (GCF\_001857705.1) for the leopard,
P.leo\_Ple1\_pat1.1 (GCF\_018350215.1) for the lion, Puncia\_PCG\_1.0
(GCF\_023721935.1) for the snow leopard, and P.tigris\_Pti1\_mat1.1
(GCF\_018350195.1) for the tiger. Bisulfited DNA was subjected to PCR
amplification with TaKaRa EpiTaq\selectlanguage{english} HS (Takara Bio, Shiga, Japan), using
the primers and PCR conditions in Table 3 (the sequence information on
the National Center for Biotechnology Information {[}NCBI{]} for each
target region and each species was listed in Supplementary Table 1). The
singleplex PCR reactions were prepared separately for each primer pair
at a final volume of 20 \selectlanguage{greek}μ\selectlanguage{english}L, containing 2 \selectlanguage{greek}μ\selectlanguage{english}L of bisulfited DNA (5--10
ng), 0.5 U TaKara EpiTaq HS, 1X EpiTaq PCR Buffer
(Mg\textsuperscript{2+} free), 2.5 mM MgCl\textsubscript{2}, 0.3 mM dNTP
mixture, and 500 nM each of forward and reverse primers.

\textbf{Next-generation sequencing}

NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1 and Set
2) and NEBNext Ultra II DNA Library Prep Kit for Illumina (New England
Biolabs, Ipswich, MA) were used to generate MiSeq compatible barcoded
DNA sequencing libraries for each gene amplicon and sample, following
the manufacturer's protocol. Barcoded libraries were pooled together in
equimolar amounts to make a single pooled library after concentration
measurement with Tapestation D1000 (Agilent, Santa Clara, USA).
Next-generation sequencing was performed on Illumina MiSeq with the
MiSeq Reagent Kit v2 (500 cycles) (Illumina, San Diego, CA, USA).

\textbf{Methylation data organizing}

Quality check and trimming of sequence data were performed using fastp
v0.20.1 with base quality threshold -q set to 20. The quality-filtered
sequences were aligned to the bisulfite-converted reference genome of
respective species using these parameters: --score\_min L, 0, -0.4,
--non\_directional in Bismark v0.22.3. The bisulfite-converted reference
genomes were generated with bismark\_genome\_preparation. The count of
methylated CpG sites and total coverage was output in cov.gz files with
bismark\_methylation\_extractor (--comprehensive) and processed using
bsseq 1.26.0. package (Hansen et al., 2012) in R 4.0.5 (R Core Team,
2021). CpG sites with coverage lower than 1000 were removed from further
analysis. Samples---0 to 2 samples for each species---with extremely
lower coverage than others were not counted in the \emph{Sample
collection} part in Materials and Methods and were also excluded from
the analysis.

\textbf{Age estimation model}

Age was estimated by the methylation rates of selected CpG sites. We
obtained 105--108 CpG sites in the five target regions for each species
(Appendix S4), however, narrowing down candidate features (i.e., CpG
sites) was important to reduce model complexity and reduce overfitting.
For this, we applied two methods---elastic net regression and
XGBoost-RFECV (Recursive Feature Elimination Cross Validation on eXtreme
Gradient Boosting) combined with leave-one-individual-out
cross-validation (LOIOCV) to achieve less biased feature selection. In
each method, the feature selection procedure was repeated a number of
times equal to the number of individuals---each time excluding samples
from one individual and keeping the samples of remaining individuals for
feature selection, then iterating over all individuals.

The first method, elastic net regression, is a mix of ridge and lasso
regression which perform automatic feature shrinkage together with
regression. We performed elastic net regression and its hyperparameter
tuning with cv.glmnet using the default 10-fold cross-validation in
glmnet 4.1-3 package (Friedman et al., 2010) in R 4.0.5 (R Core Team,
2021). Optimized alpha (0 to 1 with a 0.01 interval) and lambda were
determined when the mean absolute difference between predicted age and
chronological age (i.e., mean absolute error or deviation, MAE or MAD)
of the training sets was the smallest. The second method, XGBoost-RFECV,
fitted an XGBoost regression model and removed the weakest features in
each cross-validation until a specified number of features was reached.
XGBoost-RFECV was processed using xgboost 1.7.3 (Chen \& Guestrin, 2016)
and scikit-learn 1.2.0 packages (Pedregosa et al., 2011) in Python 3.8.8
(Van Rossum \& Drake Jr, 2009), using these settings: default settings
for XGBregressor; step = 1, min feature to select = 5, cross validation
= 5, scoring = 'neg\_mean\_absolute\_error' for rfecv.

After the feature selection (i.e., the selection of CpG sites), to build
age estimation regression models, we applied two methods---elastic net
regression and Support Vector Machine radial (SVMr) on new,
feature-extracted datasets. Both regression models are widely applied in
many DNA methylation-based age estimation studies with high estimation
accuracy (Bors et al., 2021; Krivonosov et al., 2022; Qi et al., 2021;
Raj et al., 2021; Thompson et al., 2017; Vidaki et al., 2021; Xu et al.,
2015). The setting of elastic net regression is the same as what was
described in the above feature selection. For SVMr, we used the Python
package scikit-learn 1.2.0 (Pedregosa et al., 2011) to tune the
hyperparameters, i.e., gamma and cost, by GridsearchCV and SVR
functions: gamma in the range of 2\textsuperscript{-15} to
2\textsuperscript{6}, cost in the range of 2\textsuperscript{-5} to
2\textsuperscript{16}, cv = 5, and scoring =
'neg\_mean\_absolute\_error'. The model construction was also
accompanied by LOIOCV---this time excluding samples from one individual,
building models on remaining samples, predicting the ages of the
excluded samples and repeating the process on all individuals---to
achieve less biased age estimation models. For \emph{Panthera} spp.
samples, in addition to LOIOCV, leave-one-species-out cross-validation
(LOSpeciesOCV), in which samples of a species instead of an individual
were left out, was also conducted.

To investigate what variables influenced the difference between
predicted age and chronological age---\selectlanguage{greek}Δ\selectlanguage{english}Age (predicted age -
chronological age) in each group (i.e. domestic cats, Tsushima leopard
cats, and \emph{Panthera} spp.), we applied linear mixed models with
individual ID as a random effect using the R package lmerTest 3.1.3
(Kuznetsova et al., 2017). Age, sex, and health condition were used as
the explanatory variables. Regarding the variable `health condition',
since domestic cats had comparably large samples, healthy (n = 34) and
several disease categories could be assigned: cancer (n = 13), chronic
kidney disease (CKD, n = 48), diabetes (n = 23), digestive disease (n =
9), other diseases (n = 33). Note that several individuals had multiple
diseases, therefore, the sum of the health categories was larger than
the total sample size. The small sample sizes for each disease category
in Tsushima leopard cats and \emph{Panthera} spp. made separate analyses
of disease groups statistically less convincing, therefore, we merged
samples of different diseases into a single `diseased' group: Tsushima
leopard cats comprised 47 healthy and 37 diseased individuals,
and\emph{Panthera} spp. had 83 healthy and 13 diseased individuals.

\section*{Results}

{\label{results}}

\subsection*{Correlation between methylation rates and chronological
age}

{\label{correlation-between-methylation-rates-and-chronological-age}}

About one hundred CpGs were detected and used in the analysis for each
group---domestic cats (n = 106), Tsushima leopard cats (n = 108),
and\emph{Panthera} spp. (n = 105). CpGs in at least one group were
listed in Appendix S4, and 80.8\% (97 out of 120) were overlapping in
all groups. The correlation coefficients between methylation rate of
CpGs and chronological age ranged -0.12-0.77 (median = 0.26) for
domestic cats, -0.18-0.77 (median = 0.21) for Tsushima leopard cats, and
-0.15-0.92 (median = 0.44) for \emph{Panthera} spp. (Appendix S4).

\subsection*{Age estimation model}

{\label{age-estimation-model}}

\emph{Domestic cat}

Features, or CpG sites, which were selected more than 89 times (or
84.8\%) in 105 cross-validations based either on elastic net regression
or XGBoost-RFECV methods (Supplementary Figure 1a, 1b), were put to
trial as the explanatory variables in subsequent regression models for
age estimation. This threshold (the grey dashed line in Supplementary
Figure 1a, 1b) was the point below which the frequency (i.e., the number
of times that CpGs were selected in cross-validations) fell off sharply.
Seventeen CpGs (frequency = 102, 97.1\% of 105 cross-validations, the
mean absolute difference between predicted age and chronological age
{[}MAD{]} = 1.92 yr), fourteen CpGs (frequency = 105, 100\%, MAD = 1.80
yr) from all five target regions led to the best model in elastic net
feature selection-elastic net regression and elastic net feature
selection-SVMr (i.e., elastic net-based feature selection, followed by
elastic net regression or SVMr) (Figure 1, Supplementary Figure 1a). In
contrast, XGBoost-RFECV chose much fewer CpGs compared to the elastic
net regression method. Seven CpGs from three regions (\emph{TCF21,
PRMT8,} and \emph{ELOVL2} ; Figure 1) which were selected [?] 94 times
(89.5\%, Supplementary Figure 1b) led to the best regression models in
XGBoost-RFECV-elastic net regression (MAD = 2.20 yr) and
XGBoost-RFECV-SVMr (MAD = 1.89 yr) (Figure 1, Supplementary Figure 1b).
Among these models, the best estimation accuracy (smallest MAD = 1.80
yr) was obtained by elastic net feature selection-SVMr (Figure 2a).
Within-individual changes in the predicted age of those sampled multiple
times were plotted in Supplementary Figure 2. The correlation
coefficients between chronological age and methylation rate of the 14
CpGs used in the best model were not limited to only high values; e.g.,
two selected CpGs from \emph{TCF21} had a correlation coefficient under
0.2 (Figure 1).

\emph{Tsushima leopard cat}

CpG sites that were selected more than 11 times (57.9\%) in a total of
19 cross-validations (Supplementary Figure 1c, 1d) were included as
explanatory variables in the age prediction models. In the case of
elastic net feature selection, 25 CpGs from all five target regions that
were selected [?] 14 times (73.7\% of 19 cross-validations) led to the
best models built by elastic net feature selection-elastic net
regression (MAD = 1.30 yr) and elastic net feature selection-SVMr (MAD =
1.46 yr) (Figure 1, Supplementary Figure 1c). Eleven CpGs (selection
frequency [?] 11, 57.9\% of 19 cross-validations, MAD = 2.54 yr) from four
gene regions (\emph{TCF21, PRMT8, DLX5,} and \emph{ELOVL2} ) and five
CpGs (selection frequency [?] 16, 84.2\%, MAD = 1.84 yr) from three gene
regions (\emph{PRMT8, DLX5,} and \emph{ELOVL2} ) contributed to the best
age prediction models built by XGBoost-RFECV-elastic net regression and
XGBoost-RFECV-SVMr (Figure 1, Supplementary Figure 1d). Among these
models, elastic net feature selection-elastic net regression showed the
best accuracy (MAD = 1.30 yr) (Figure 2b). Within-individual changes in
predicted age were plotted in Supplementary Figure 3. Similar to the
domestic cats, selected CpGs were not limited to those with a high
correlation with chronological age, e.g., CpGs in \emph{TCF21} (Figure
1).

\emph{Panthera spp.}

CpG sites with a frequency of selection \textgreater{} 20 (57.1\% of 35
cross-validations) were trialed to build age prediction models
(Supplementary Figure 1e, 1f). Selected CpGs of \emph{Panthera} spp.
were vastly different from the domestic cats and Tsushima leopard cats.
Those selected were mainly from \emph{DLX5} and \emph{ELOVL2} , and
their correlations with chronological age were also at a higher range
(cor \textgreater{} 0.5 for most CpGs) (Figure 1). In particular, six
CpGs (frequency = 35, 100\% of 35 cross-validations, MAD = 1.60 yr),
nine CpGs (frequency [?] 34, 97.1\%, MAD = 1.59 yr), eight CpGs (frequency
[?] 23, 65.7\%, MAD = 1.66 yr), ten CpGs (frequency [?] 20, 57.1\%, MAD =
1.55 yr) found in \emph{DLX5} and \emph{ELOVL2} led to the best model in
elastic net feature selection-elastic net regression, elastic net
feature selection-SVMr, XGBoost-RFECV-elastic net regression, and
XGBoost-RFECV-SVMr, respectively (Figure 1, Supplementary Figure 1e,1f).
XGBoost-RFECV-SVMr model gave the best accuracy (MAD = 1.55 yr, Figure
2c). Within-individual changes in the predicted age of the best model
were plotted in Supplementary Figure 4.

Estimation accuracy was only slightly lower in LOSpeciesOCV. We tried
CpG sites with a frequency of selection \textgreater{} 3 (60\% of five
cross-validations) to build the age prediction models (Supplementary
Figure 1g,1h). Nine CpGs of \emph{DLX5,} \emph{RALYL} , and
\emph{ELOVL2}(frequency [?] 4, 80\% of five cross-validations, MAD = 1.72
yr), four CpGs of \emph{DLX5} and \emph{ELOVL2} (frequency [?] 5, 100\%,
MAD = 1.76 yr), ten CpGs of \emph{DLX5,} \emph{RALYL} , and
\emph{ELOVL2} (frequency [?] 3, 60\%, MAD = 2.00 yr), and ten CpGs of
\emph{DLX5,} \emph{RALYL,}and \emph{ELOVL2} (frequency [?] 3, 60\%, MAD =
1.73 yr) led to the best models in elastic net feature selection-elastic
net regression, elastic net feature selection-SVMr,
XGBoost-RFECV-elastic net regression, and XGBoost-RFECV-SVMr,
respectively (Figure 1, Supplementary Figure 1g,1h). Elastic net feature
selection-elastic net regression led to the slightest difference between
predicted age and chronological age (MAD = 1.72 yr, Figure 2d).

\subsection*{Factors affecting the deviation of predicted age from
chronological
age}

{\label{factors-affecting-the-deviation-of-predicted-age-from-chronological-age}}

The correlations between \selectlanguage{greek}Δ\selectlanguage{english}Age and chronological age are visualized in
Figure 3. The results of the mixed linear regression for investigating
the factors influencing \selectlanguage{greek}Δ\selectlanguage{english}Age were summarized in Table 4. Chronological
age had a significant influence on \selectlanguage{greek}Δ\selectlanguage{english}Age in all three groups: domestic
cats (estimate = -1.11, \emph{p} \textless{} 0.001), Tsushima leopard
cats (estimate = -0.73, \emph{p} \textless{} 0.001), and
\emph{Panthera}spp. (estimate = -0.57, \emph{p} \textless{} 0.05). Sex
did not contribute to the differences in any group. In the case of
health conditions, no significant contribution to \selectlanguage{greek}Δ\selectlanguage{english}Age was found for
domestic cats and Tsushima leopard cats, on the other hand,
surprisingly, the diseased had smaller \selectlanguage{greek}Δ\selectlanguage{english}Age than the healthy (estimate =
-1.68, \emph{p}\textless{} 0.001, Figure 4) in \emph{Panthera} spp. The
R-squared results were as follows: domestic cats (marginal\emph{R}
\textsuperscript{2} = 0.21, conditional\emph{R} \textsuperscript{2} =
0.52), Tsushima leopard cats (marginal\emph{R} \textsuperscript{2} =
0.21, conditional\emph{R} \textsuperscript{2} = 0.21), and
\emph{Panthera} spp. (marginal\emph{R} \textsuperscript{2} = 0.19,
conditional\emph{R} \textsuperscript{2} = 0.67).

\subsection*{Age prediction on wild samples of Tsushima leopard
cats}

{\label{age-prediction-on-wild-samples-of-tsushima-leopard-cats}}

The predicted ages of wild-born Tsushima leopard cat individuals of
unknown age were summarized in Table 2. The estimated ages of samples
taken at short intervals, such as within one year, also showed variation
to some extent (e.g., Leocat\_w1, mean = 14.25 yr, SD = 3.91 yr;
Leocat\_w4, mean = 10.21 yr, SD = 1.44 yr).

\section*{Discussion}

{\label{discussion}}

Our study successfully estimated age in many Felidae species with
satisfactory accuracies: domestic cats (MAD = 1.80 yr, accuracy reaching
91.4\% of 21 yr---the oldest sample), Tsushima leopard cats (MAD = 1.30
yr, 91.5\% of 15.3 yr), and \emph{Panthera} spp. (MAD = 1.55 yr, 93.5\%
of 23.7 yr), which include jaguars, Amur leopards, African lions, snow
leopards, and Amur tigers. Although the accuracy of domestic cats was
lower than the previously reported MAD = 0.83 yr in Raj et al. (2021),
in which 34 CpGs were selected from 38k CpGs available from the
HorvathMammalMethylChip40, the accuracy obtained in this study is still
comparable to the accuracies from previous age estimation studies that
used less than 30 CpGs, e.g., 95\% in house mouses (\emph{Mus musculus}
; Han et al., 2018), 73\% in chimpanzees (\emph{Pan troglodytes} , Ito
et al., 2018), and 93\% in beluga whales (\emph{Delphinapterus leucas} ,
(Bors et al., 2021). Unlike Raj et al. (2021), we aimed to achieve an
acceptable age estimation accuracy using only a few gene regions,
allowing the design of a project with a scale and budget more suitable
for conservation applications. Next-generation sequencing is cost- and
time-effective when a large number of samples is analyzed simultaneously
(about \$7 per sample based on five markers in our study). To our
knowledge, our study is the first study analyzing several Felidae
species, from small cat species to big cat species
(\emph{Panthera}spp.), with appreciable sample sizes.

Selected CpGs of domestic cats and Tsushima leopard cats were found to
be more similar to each other (i.e., all five targeted genes contributed
to models) (Figure 1) than the \emph{Panthera} spp. (i.e.,
fundamentally, only \emph{DLX5} and \emph{ELOVL2} contributed to the
models). However, although most CpGs overlapped between the domestic
cats and Tsushima leopard cats, the correlations with chronological age
were different; applying the domestic cat model directly to the Tsushima
leopard cat dataset resulted in a sharp decline in estimation accuracy
(Supplementary Table 2). Consequently, at least for the five genomic
regions explored in this study, it seems not advisable to combine all
samples to construct a common age estimation model for felids. However,
a common age estimation model for \emph{Panthera} spp. seemed plausible,
which is supported by the high prediction accuracy (MAD = 1.72 yr)
maintained in the \emph{Panthera} LOSpeciesOCV model (Figure 2d). The
closer genetic distance among \emph{Panthera} spp. could be the reason
for the relatively high estimation accuracy (Li et al., 2016). Since
only two regions were used to construct the age estimation model
for\emph{Panthera} spp., the cost could be reduced to less than
half---only about \$3 per DNA sample. However, fewer samples in some
\emph{Panthera}spp.---especially jaguars, African lions, and Amur
tigers, cast difficulty in assuring that our model is well performed for
these species. In the future, this should be addressed with a larger
sample size ample for constructing a more reliable common model for
the\emph{Panthera} spp. as well as separate models for each species, to
furtherly investigate whether a common model or separate models is best
suited for the \emph{Panthera} genus.

In our study, we conducted stepwise feature selection and regression to
find the best age prediction model. In feature selection, we employed a
wrapper method (XGBoost-RFECV) and an embedded method (elastic net) to
select the optimal combination of CpGs. This is unlike filtering
methods, which measure the relevance of features (CpGs) by their
correlation with the dependent variable (chronological age) and select
the highly correlated features to construct the subsequent regression
model. Wrapper methods and embedded methods could consider the
correlation between features and in turn find an optimal subset of
features (Guyon \& Elisseeff, 2003). Wrapper methods measure the
usefulness of all the combinations of features by first training the
models on them before selecting the optimal combination with the best
estimation accuracy in the training models to build the final regression
model in the next step. Embedded methods are implemented by regression
models which have built-in regularization functions that give the
importance of features and, therefore, naturally conduct the feature
selection during model construction. During feature selection, we found
that some CpGs with a low correlation (e.g., [?] 0.02, green tiles in
Figure 1) between the methylation rate and chronological age were
selected, which when removed from the models resulted in lower accuracy
in the prediction models, e.g., MAD changed from 1.92 yr to 2.33 yr in
the elastic net feature selection-elastic net regression model of the
domestic cats. This suggests that it is important not to select CpGs
based solely on the correlation coefficients alone (i.e., filtering
method). When the number of genes used is small, the variation of
correlationships of CpGs with age is low, to begin with. Furthermore,
given that in some cases CpGs with high correlations with age are likely
to cluster at a limited number of gene locations, focusing only on the
magnitude of the correlation coefficients will bias toward the selection
of only CpGs with a similar correlation with age, therefore resulting in
a low diversity of explanatory variables in age estimation models and
consequently, low accuracy in the age estimates. In this study, we also
introduced LOIOCV in the feature selection step and only used CpGs that
were selected by most cross-validations, which made the selection
results less dependent on our dataset to ensure higher applicability to
future samples.

Unlike most previous studies, we included both healthy samples and
samples with many types of disease in our models. In the context of
wildlife conservation, rescued individuals of unknown age may be healthy
or diseased, therefore an age estimation model that could be applied at
a satisfactory accuracy on individuals with varied health conditions
would be required. Our models for domestic cats and Tsushima leopard
cats did not find significant estimation differences between the healthy
and unhealthy samples (Table 4), suggesting that our models for the two
species are robust to estimate ages in samples whose health conditions
are unknown. We noted a better estimation accuracy in the diseased
samples of \emph{Panthera} spp. than the healthy samples (Figure 4,
Table 4). Most of these diseased samples were from individuals between
the ages of 10 and 20 years, which may still be relatively healthy
considering their ages, and thus expressed a young epigenetic age.
Nevertheless, there were only a few diseased samples (n = 13) compared
to the healthy samples (n = 83) for \emph{Panthera} spp.; therefore, it
was still difficult to estimate the effect of disease on the age
estimation.

Our models, like many previous studies (El Khoury et al., 2019; Prado et
al., 2021; Raj et al., 2021), tended to underestimate the age of older
individuals. As suggested by El Khoury et al. (2019), one explanation
could be the saturation of methylation rates of targeted CpGs, i.e.,
CpGs already reach either full methylation or complete de-methylation
before the individuals age further. Another assumption is that
long-lived individuals are biologically younger and have a younger
predicted epigenetic age than their chronological age.

For the age estimation models built on captive Tsushima leopard cats,
although we used LOIOCV to reduce the influence of different sample
sizes in different individuals, future sampling should be further
improved. We only had a small number of samples except for the age
classes 2--6 years old, and most samples over 6 years old came from only
two individuals (Leocat\_3 and Leocat\_5, Supplementary Figure 3). We
also predicted the ages of several wild-born Tsushima leopard cat
samples of unknown age. However, we could not ascertain the accuracy of
our estimates, because the ages of these individuals could not be
estimated by other methods (e.g., morphological methods) and were known
only as adult/old individuals. Referring to the rescue records and dates
of sampling and death in addition to the life span of Tsushima leopard
cats (about 15 yr at maximum, according to Inoue (2021)), we believe
that our estimates were appropriate to some extent. It should be noted
that the estimation variation existed even among samples that were
collected within one year from the same individual (SD ranged from 1 to
3 yr). Therefore, we recommend multiple sampling within a one-year
window or less to obtain an average predicted age as the final predicted
age---the same goes for other species (see within-individual age change
plot, Supplementary Figure 2--4). We were unsure whether wild and
captive individuals will have different estimations in our models.
Environmental effects are also associated with DNA methylation change
and age acceleration (Anderson et al., 2020; Oblak et al., 2021;
Petkovich et al., 2017). To investigate the accuracy of age estimation
on wild populations, a larger sample of wild-born individuals with known
age, especially subadult and young adult individuals, would be needed.

In conclusion, we successfully developed epigenetic clocks with
satisfactory accuracy for domestic cats (MAD = 1.80 yr), Tsushima
leopard cats (MAD = 1.30 yr), and \emph{Panthera} spp. (MAD = 1.55 yr)
using next-generation sequencing and machine learning algorithms. Our
models for domestic cats and Tsushima leopard cats are applicable to
individuals of varying conditions, seemingly not affected by health
conditions and sex. It was not recommended to build a common age
estimation model for all the target species using our markers. On the
other hand, we showed the possibility of developing a common model for
five \emph{Panthera} spp. Our within-individual changes in predicted age
implied that multiple sampling within a one-year window to obtain the
mean of predicted age as the final predicted age is advisable for future
applications.

\section*{Acknowledgments}

{\label{acknowledgments}}

We would like to express our sincere thanks to Kyoto Medical Center,
Daktari Animal Hospital and Dr. Minori Arahori of Anicom Specialty
Medical Institute Inc. for providing domestic cat samples and sample
information. We greatly appreciate Tsushima Wildlife Conservation
Center,\begin{CJK}{UTF8}{gbsn}　\end{CJK}\selectlanguage{english}Yokohama Zoological Gardens Zoorasia, Nagoya Higashiyama Zoo
and Botanical Gardens for providing Tsushima leopard cat samples and
sample information. We are grateful to Kobe Oji Zoo for providing
samples and sample information of \emph{Panthera} spp. This study was
financially supported by JSPS (KAKENHI Grant Number 20H00420 to MI-M,
20H03008 to K.K.) and the Environment Research and Technology
Development Fund (JPMEERF20214001) of the Environmental Restoration and
Conservation Agency of Japan (M.I.-M.).

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\section*{Data Accessibility
Statement}

{\label{data-accessibility-statement}}

The data and code of this study can be accessed
at\emph{https://doi.org/10.5061/dryad.3r2280gn4}

(It is still under review, and the doi will be activated after the
publishment. Our data and code are now also submitted
to\emph{Supplemental File for Review Only} ).

\section*{Author Contributions}

{\label{author-contributions}}

H.Q. designed and conducted the study and wrote the first draft of the
manuscript. Q.L.L. assisted in the laboratory work and the revision of
the manuscript. K.K. and M.I-M. designed the study, communicated with
the institutes for samples, and revised the manuscript. N.N. led the
Miseq-related work, including technical part and data analysis. All
authors approved the final manuscript for publication.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{Species} & \textbf{Scientific name} & \textbf{n} &
\textbf{Healthy} & \textbf{Individual} & \textbf{Female} & \textbf{Min.
age (yr)} & \textbf{Max. age (yr)} & \textbf{Ave. age
(yr)}\tabularnewline
\midrule
\endhead
Domestic cat & \emph{Felis catus} & 139 & 34 & 105 & 50 & 0.41 & 21.04 &
10.06\tabularnewline
Tsushima leopard cat & \emph{Prionailurus bengalensi euptilurus} & 84 &
47 & 19 & 9 & 0.50 & 15.32 & 6.53\tabularnewline
Jaguar & \emph{Panthera onca} & 4 & 4 & 3 & 1 & 10.79 & 19.73 &
16.33\tabularnewline
Amur leopard & \emph{Panthera pardus orientalis} & 34 & 34 & 8 & 3 &
0.92 & 17.94 & 13.30\tabularnewline
African Lion & \emph{Panthera leo leo} & 10 & 8 & 5 & 3 & 0.26 & 23.74 &
8.11\tabularnewline
Snow leopard & \emph{Panthera uncia} & 33 & 27 & 15 & 6 & 0.93 & 17.66 &
7.77\tabularnewline
Amur tiger & \emph{Panthera tigris altaica} & 15 & 10 & 4 & 3 & 3.58 &
18.13 & 13.41\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1} Description of samples of seven feline species used to
build age estimation models.

n: number of samples. Healthy: number of samples from healthy
individuals. Individual: number of individuals. Female: number of female
individuals.

\textbf{Table 2} Predicted age and sample information of wild-born
individuals of Tsushima leopard cats.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Individual ID}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Sex}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Predicted age}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Sampling date}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Rescued date}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Estimated life stage} \textbf{at the time of rescue}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Death date}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
18.566109\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013/5/23\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2005/3/4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013/7/28\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
13.2239393\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013/6/21\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2005/3/4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013/7/28\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10.9485178\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013/7/28\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2005/3/4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013/7/28\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
M\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.57303445\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2015/3/12\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2010/8/6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Alive\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
M\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
16.7084207\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/1/11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2010/8/6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Alive\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12.2164717\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2018/2/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2015/12/26\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Alive\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7.51772785\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/6/9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2015/12/26\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Alive\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.59795102\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/6/28\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8.79926535\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/10/15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.59541378\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/11/19\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8.49976998\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/12/6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
11.1272976\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/12/16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.72330095\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/1/14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12.4008035\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/6/22\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leocat\_w4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
11.9512338\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/10/23\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2020/5/13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adult/Old\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2021/11/30\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} Primers and PCR conditions.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Primer} \textbf{name}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Proximal} \textbf{gene}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Primers}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Size (bp)}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{PCR conditions}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{NCBI sequence ID: position} \textbf{(Domestic cat)}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Reference}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Amp3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{TCF21}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
F: GTTTAGGAGAGGGGTTGTG R: TAATTAAATCTTACACTCCTTTTCCC\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
382\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
95\selectlanguage{ngerman}°C (3min) {[}95°C (30s), 59°C(30s), 72°C(30s){]} *40cycles 72°C
(5min)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NC\_058372.1: 120538405--120538786\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Lowe et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Amp4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{PRMT8}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
F: GTTTTTGGTTTTGAAGGTGGT R: ACTCAAATCAATCCCCCC\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
224\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
95\selectlanguage{ngerman}°C (3min) {[}95°C (30s), 59°C(30s), 72°C(30s){]} *40cycles 72°C
(5min)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NC\_058374.1: 38848878--38849102\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Lowe et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Amp8\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{DLX5}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
F: AATTAGATTTTTATTTGGGTGG R: CTCTAAAACCATTATTTTAAAAACC\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
384\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
95\selectlanguage{ngerman}°C (3min) {[}95°C (30s), 55°C(30s), 72°C(30s){]} *40cycles 72°C
(5min)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NC\_058369.1: 100169877--100170260\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Lowe et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Amp9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{RALYL}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
F: AGTTGGAGGAGTATGGTTTT R: ACAAAAATCTTATCAAAAAAAAAA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
261\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
95\selectlanguage{ngerman}°C (3min) {[}95°C (30s), 55°C(30s), 72°C(30s){]} *40cycles 72°C
(5min)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NC\_058385.1: 32317113--32317373\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Lowe et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Bs38\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{ELOVL2}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
F: TGTYGTYGYGGYGTTTTTTGT R: CCAAAAACRAACRACRAATCC\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
118\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
95\selectlanguage{ngerman}°C (3min) {[}95°C (30s), 57°C(30s), 72°C(30s){]} *45cycles\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NC\_058372.1: 17621162--17621279\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Bekaert et al., 2015\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Sequence information on the National Center for Biotechnology
Information (NCBI) for other species was summarized in Supplementary
Table 1.

\textbf{Table 4} Coefficients and \emph{p} -values for the mixed linear
regression of \selectlanguage{greek}Δ\selectlanguage{english}Age taken from the best age estimates of each species.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& & \textbf{Estimate} & \emph{p}\textbf{-value}\tabularnewline
\midrule
\endhead
\textbf{Domestic cat} & (Intercept) & -0.82423 & 0.228\tabularnewline
& \textbf{Chronological age (scaled)} & \textbf{-1.11480} &
\textbf{3.08e-06 ***}\tabularnewline
& Sex (Male) & 0.46577 & 0.252\tabularnewline
& Health condition (Healthy) & 0.27506 & 0.711\tabularnewline
& Health condition (Cancer) & 0.03697 & 0.964\tabularnewline
& Health condition (CKD) & 0.97602 & 0.135\tabularnewline
& Health condition (Diabetes) & 0.99081 & 0.149\tabularnewline
& Health condition (Digestive disease) & -0.32144 & 0.728\tabularnewline
& Health condition (Other diseases) & -0.60413 & 0.280\tabularnewline
\textbf{Tsushima leopard cat} & (Intercept) & -0.4318 &
0.316\tabularnewline
& \textbf{Chronological age (scaled)} & \textbf{-0.7295} &
\textbf{2.4e-05 ***}\tabularnewline
& Sex (Male) & 0.5932 & 0.164\tabularnewline
& Health condition (Diseased) & 0.2034 & 0.632\tabularnewline
Panthera & (Intercept) & 0.1551 & 0.711261\tabularnewline
& \textbf{Chronological age (scaled)} & \textbf{-0.5667} &
\textbf{0.010805 *}\tabularnewline
& Sex (Male) & 0.3346 & 0.541286\tabularnewline
& \textbf{Health condition (Diseased)} & \textbf{-1.6841} &
\textbf{0.000871 ***}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} CpG sites in proximity with five targeted gene regions
selected for different models and feline species, including the domestic
cat, Tsushima leopard cat, and the \emph{Panthera} group. The results of
the \emph{Panthera} group also included the case with
leave-one-species-out cross-validation, i.e.,
\emph{Panthera}(LOSpeciesOCV). Colored columns represent CpG sites that
were selected by age estimation models built by different methods---from
left to right for each species/species group were elastic net feature
selection-elastic net regression, elastic net feature selection-SVMr,
XGBoost-RFECV-elastic net regression, and XGBoost-RFECV-SVMr. 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
(i.e., domestic cat and Tsushima leopard cat) or did not exist/were not
shared in all \emph{Panthera} spp. Overall, all CpG sites had a positive
correlation or a negative but close to zero correlation with
chronological age.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/619852/articles/644152-blood-dna-methylation-based-age-estimation-in-domestic-cats-tsushima-leopard-cats-prionailurus-bengalensis-euptilurus-and-panthera-species-using-machine-learning-models}

\textbf{Figure 2} Correlations between predicted age and chronological
age in the best age estimation models of each species. Solid lines
represent the identity lines, in which predicted age = chronological
age. The distance between a dashed line and a solid line represents the
mean absolute difference (MAD) between predicted age and chronological
age. (a) MAD = 1.80 yr, (b) MAD = 1.30 yr, (c) MAD =1.55 yr, (d) MAD =
1.72 yr.

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/619852/articles/644152-blood-dna-methylation-based-age-estimation-in-domestic-cats-tsushima-leopard-cats-prionailurus-bengalensis-euptilurus-and-panthera-species-using-machine-learning-models}

\textbf{Figure 3} Correlation between \selectlanguage{greek}Δ\selectlanguage{english}Age and chronological age.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/619852/articles/644152-blood-dna-methylation-based-age-estimation-in-domestic-cats-tsushima-leopard-cats-prionailurus-bengalensis-euptilurus-and-panthera-species-using-machine-learning-models}

\textbf{Figure 4} Plots of correlation (a) between predicted age and
chronological age and (b) between \selectlanguage{greek}Δ\selectlanguage{english}Age and chronological age in the best
model of five \emph{Panthera} spp. Diseased samples are highlighted in
orange.

\selectlanguage{english}
\FloatBarrier
\end{document}

