A genomic predictor for age at sexual maturity for mammalian species

Abstract

Abstract Age at sexual maturity is a key life history trait that can be used to predict population growth rates and develop life history models. In many wild animal species, the age at sexual maturity is not accurately quantified. This results in a reduced ability to accurately model demography of wild populations. Recent studies have indicated the potential for CpG density within gene promoters to be predictive of other life history traits, specifically maximum lifespan. Here, we have developed a machine learning model using gene promoter CpG density to predict the mean age at sexual maturity in mammalian species. In total, 91 genomes were used to identify 101 unique gene promoters predictive of age at sexual maturity across males and females. We found these gene promoters to be most predictive of age at sexual maturity in females (R2 = 0.881) compared to males (R2 = 0.758). The median absolute error rate was also found to be lower in females (0.427 years) compared to males (0.785 years). This model provides a novel method for species‐level age at sexual maturity prediction without the need for long‐term monitoring. This study also highlights a potential epigenetic mechanism for the onset of sexual maturity, indicating the possibility of using epigenetic biomarkers for this important life history trait.

Keywords

• CpG density
• elastic net regression
• epigenetics
• machine learning
• mammals
• sexual maturity