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| Main Authors: | , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Conservation physiology
2026
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| Online Access: | https://pubmed.ncbi.nlm.nih.gov/41756354/ |
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| _version_ | 1868266079684395010 |
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| author | Photopoulou, Theoni Durbach, Ian Pirotta, Enrico Barratclough, Ashley Schwacke, Lori H Takeshita, Ryan Himes Boor, Gina K Harris, Catriona M Tyack, Peter L Thomas, Len |
| author_facet | Photopoulou, Theoni Durbach, Ian Pirotta, Enrico Barratclough, Ashley Schwacke, Lori H Takeshita, Ryan Himes Boor, Gina K Harris, Catriona M Tyack, Peter L Thomas, Len Photopoulou, Theoni Durbach, Ian Pirotta, Enrico Barratclough, Ashley Schwacke, Lori H Takeshita, Ryan Himes Boor, Gina K Harris, Catriona M Tyack, Peter L Thomas, Len |
| collection | PubMed - marine biology |
| contents | Methods for analysing wildlife DNA methylation data. Photopoulou, Theoni Durbach, Ian Pirotta, Enrico Barratclough, Ashley Schwacke, Lori H Takeshita, Ryan Himes Boor, Gina K Harris, Catriona M Tyack, Peter L Thomas, Len The analysis of DNA methylation data for wildlife conservation is gaining momentum as the technology for quantifying the methylome becomes mainstream. The use of epigenetic information extracted from tissue samples can be used for estimating chronological age, individual traits and phenotypic variation. Methylation data present an exciting opportunity to study wildlife populations, with the potential to provide insights into age structure, vital rates and health. However, the statistical methodology for answering the emerging research questions has been developed and mostly applied in the human biomedical setting. We review the key methodologies commonly used in wildlife settings, and methods that have been used only in human studies so far that could improve our understanding of wildlife epigenomic changes. We show how the different methods relate to each other and how they link to research questions, illustrating each approach with data from a case study, a large dataset from wild bottlenose dolphins ( spp.) from the US southeast and Gulf coast. Estimating chronological age from models called epigenetic clocks and understanding the relationship between epigenetic indicators of health and exposure to stressors are both key goals in wildlife settings; however, we show that a single model cannot do both accurately. This is a fundamental limitation of clock-type models and might explain why some age-related health conditions have been found to be related to epigenetic age and others not. Decoupling the analysis of age and health is challenging because the two are confounded but is especially important in wildlife settings where age prediction is often the main analytical objective. |
| format | Artículo científico |
| id | pubmed_41756354 |
| institution | PubMed |
| language | en |
| publishDate | 2026 |
| publisher | Conservation physiology |
| record_format | pubmed |
| spellingShingle | Methods for analysing wildlife DNA methylation data. Photopoulou, Theoni Durbach, Ian Pirotta, Enrico Barratclough, Ashley Schwacke, Lori H Takeshita, Ryan Himes Boor, Gina K Harris, Catriona M Tyack, Peter L Thomas, Len Methods for analysing wildlife DNA methylation data. Photopoulou, Theoni Durbach, Ian Pirotta, Enrico Barratclough, Ashley Schwacke, Lori H Takeshita, Ryan Himes Boor, Gina K Harris, Catriona M Tyack, Peter L Thomas, Len The analysis of DNA methylation data for wildlife conservation is gaining momentum as the technology for quantifying the methylome becomes mainstream. The use of epigenetic information extracted from tissue samples can be used for estimating chronological age, individual traits and phenotypic variation. Methylation data present an exciting opportunity to study wildlife populations, with the potential to provide insights into age structure, vital rates and health. However, the statistical methodology for answering the emerging research questions has been developed and mostly applied in the human biomedical setting. We review the key methodologies commonly used in wildlife settings, and methods that have been used only in human studies so far that could improve our understanding of wildlife epigenomic changes. We show how the different methods relate to each other and how they link to research questions, illustrating each approach with data from a case study, a large dataset from wild bottlenose dolphins ( spp.) from the US southeast and Gulf coast. Estimating chronological age from models called epigenetic clocks and understanding the relationship between epigenetic indicators of health and exposure to stressors are both key goals in wildlife settings; however, we show that a single model cannot do both accurately. This is a fundamental limitation of clock-type models and might explain why some age-related health conditions have been found to be related to epigenetic age and others not. Decoupling the analysis of age and health is challenging because the two are confounded but is especially important in wildlife settings where age prediction is often the main analytical objective. |
| title | Methods for analysing wildlife DNA methylation data. |
| url | https://pubmed.ncbi.nlm.nih.gov/41756354/ |