DOI: 10.5061/dryad.9zw3r22kr
title: Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins

description: Age is necessary information for the study of life history of wild
 animals. A general method to estimate the age of odontocetes is counting
 dental growth layer groups (GLGs). However, this method is highly invasive
 as it requires the capture and handling of individuals to collect their
 teeth. Recently, the development of DNA-based age estimation methods has
 been actively studied as an alternative to such invasive methods, of which
 many have used biopsy samples. However, if DNA-based age estimation can be
 developed from fecal samples, age estimation can be performed without
 touching or disrupting individuals, thus establishing an entirely
 non-invasive method. We developed an age estimation model using
 the methylation rate of two gene regions, GRIA2 and CDKN2A, measured
 through methylation-sensitive high-resolution melting (MS-HRM) from fecal
 samples of wild Indo-Pacific bottlenose dolphins (Tursiops aduncus). The
 age of individuals was known through conducting longitudinal individual
 identification surveys underwater. Methylation rates were quantified from
 36 samples. Both gene regions showed a significant correlation between age
 and methylation rate. The age estimation model was constructed based on
 the methylation rates of both genes which achieved sufficient accuracy
 (after LOOCV: MAE = 5.08, R2 = 0.34) for the ecological studies of the
 Indo-Pacific bottlenose dolphins, with a lifespan of 40-50 years. This is
 the first study to report the use of non-invasive fecal samples to
 estimate the age of marine mammals.
description: # Non-invasive age estimation based on fecal DNA using
 methylation-sensitive high-resolution melting for Indo-Pacific bottlenose
 dolphins. Authors: Genfu Yagi, Huiyuan Qi, Kana Arai, Yuki F. Kita,
 Kazunobu Kogi, Tadamichi Morisaka*, Motoi Yoshioka, Miho Inoue-Murayama*
 Corresponding authors: Tadamichi Morisaka (Graduate School of
 Bioresources, Mie University, Tsu, Mie 514-8507, Japan;
 e-mail:chaka@bio.mie-u.ac.jp), Miho Inoue-Murayama (Wildlife Research
 Center, Kyoto University, Kyoto, Kyoto 606-8203, Japan; e-mail:
 mailto:murayama.miho.5n@kyoto-u.ac.jp) File creation: This file was
 generated in 2023-07-09 by Genfu Yagi ABSTRACT Age is necessary
 information for the study of life history of wild animals. A general
 method to estimate the age of odontocetes is counting dental growth layer
 groups (GLGs). However, this method is highly invasive as it requires the
 capture and handling of individuals to collect their teeth. Recently, the
 development of DNA-based age estimation methods has been actively studied
 as an alternative to such invasive methods, of which many have used biopsy
 samples. However, if DNA-based age estimation can be developed from fecal
 samples, age estimation can be performed without touching or disrupting
 individuals, thus establishing an entirely non-invasive method. We
 developed an age estimation model using the methylation rate of two gene
 regions, GRIA2 and CDKN2A, measured through methylation-sensitive
 high-resolution melting (MS-HRM) from fecal samples of wild Indo-Pacific
 bottlenose dolphins (Tursiops aduncus). The age of individuals was known
 through conducting longitudinal individual identification surveys
 underwater. Methylation rates were quantified from 36 samples. Both gene
 regions showed a significant correlation between age and methylation rate.
 The age estimation model was constructed based on the methylation rates of
 both genes which achieved sufficient accuracy (after LOOCV: MAE = 5.08, R2
 = 0.33) for the ecological studies of the Indo-Pacific bottlenose
 dolphins, with a lifespan of 40-50 years. This is the first study to
 report the use of non-invasive fecal samples to estimate the age of marine
 mammals. DATA & FILE OVERVIEW 1: age_estimation_dataset.csv
 -&gt;This is the dataset that we used to develop the age estimation
 model in Yagi et al. (2023) . 'sample' column showed the sample
 collection years. 'id' column showed individual identification
 numbers. Column 'sex' indicates the sex of the individual that
 defecated (0: female, 1: male). Column 'age' indicates the age
 of the defecated individual in the year of sample collection.
 'GRIA2_ave_df' and 'CDKN2A_ave_df' indicates the
 average df value quantified by the HRM analysis for GRIA2 and CDKN2A.
 'GRIA2_ave_methyl_%' and 'CDKN2A_ave_methyl_%' column
 indicates the average of methylation rates calculated by the
 'GRIA2_ave_df' and 'CDKN2A_ave_df' columns.
 'baby' column indicates the female nursing state (0: nursing, 1:
 not nursing). 2: cdkn2a_dataset_for_standard_curve.csv -&gt;This is
 the dataset that we used to develop the standard curve for cdkn2a.
 'Methyl_rate' column indicates the conroled methyl rate (e.g.
 0.1 means 10% of methylation rate). 'Df' column indicates the
 quantified Df value by HRM. 3: gria2_dataset_for_standard_curve.csv
 -&gt;This is the dataset that we used to develop the standard curve
 for gria2.'Methyl_rate' column indicates the conroled methyl
 rate (e.g. 0.1 means 10% of methylation rate). 'Df' column
 indicates the quantified Df value by HRM. 4: Rscript-SVR_model.R -&gt;
 This is the R scripts for the statistical analysis examined on the Yagi et
 al. (2023).