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Inferring the chronological and biological age of individuals is fundamental to population ecology and our understanding of ageing itself, its evolution, and the biological processes that affect or even cause ageing. Epigenetic clocks based on DNA methylation (DNAm) at specific CpG sites show a strong correlation with chronological age in humans, and discrepancies between inferred and actual chronological age predict morbidity and mortality. Recently, a growing number of epigenetic clocks have been developed in non‐model animals and we here review these studies. We also conduct a meta‐analysis to assess the effects of different aspects of experimental protocol on the performance of epigenetic clocks for non‐model animals. Two measures of performance are usually reported, the R2 of the association between the predicted and chronological age, and the mean/median absolute deviation (MAD) of estimated age from chronological age, and we argue that only the MAD reflects accuracy. R2 for epigenetic clocks based on the HorvathMammalMethylChip4 was higher and the MAD scaled to age range lower, compared with other DNAm quantification approaches. Scaled MAD tended to be lower among individuals in captive populations, and decreased with an increasing number of CpG sites. We conclude that epigenetic clocks can predict chronological age with relatively high accuracy, suggesting great potential in ecological epigenetics. We discuss general aspects of epigenetic clocks in the hope of stimulating further DNAm‐based research on ageing, and perhaps more importantly, other key traits. [ABSTRACT FROM AUTHOR]

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species. [ABSTRACT FROM AUTHOR]