Bioinformatic analysis for age prediction using epigenetic clocks: Application to fisheries management and conservation biology - Anastasiadi, Dafni; Piferrer, Francesc (Frontiers in Marine Science, 2023)
Epigenetic clocks are accurate tools for age prediction and are of great interest for fisheries management and conservation biology. Here, we review the necessary computational steps and tools in order to build an epigenetic clock in any species focusing on fish. Currently, a bisulfite conversion method which allows the distinction of methylated and unmethylated cytosines is the recommended method to be performed at single nucleotide resolution. Typically, reduced representation bisulfite sequencing methods provide enough coverage of CpGs to select from for age prediction while the exact implemented method depends on the specific objectives and cost of the study. Sequenced reads are controlled for their quality, aligned to either a reference or a deduced genome and methylation levels of CpGs are extracted. Methylation values are obtained in biological samples of fish that cover the widest age range possible. Using these datasets, machine learning statistical procedures and, in particular, penalized regressions, are applied in order to identify a set of CpGs the methylation of which in combination is enough to accurately predict age. Training and test datasets are used to build the optimal model or “epigenetic clock”, which can then be used to predict age in independent samples. Once a set of CpGs is robustly identified to predict age in a given species, DNA methylation in only a small number of CpGs is necessary, thus, sequencing efforts including data and money resources can be adjusted to interrogate a small number of CpGs in a high number of samples. Implementation of this molecular resource in routine evaluations of fish population structure is expected to increase in the years to come due to high accuracy, robustness and decreasing costs of sequencing. In the context of overexploited fish stocks, as well as endangered fish species, accurate age prediction with easy-to-use tools is much needed for improved fish populations management and conservation.