Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins
Data files
Nov 08, 2023 version files 6.64 KB
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 = 0.34) 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.
README: 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
->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
->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
->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
-> This is the R scripts for the statistical analysis examined on the Yagi et al. (2023).