Paternal germ line aging: DNA methylation age prediction from human sperm

Background The relationship between aging and epigenetic profiles has been highlighted in many recent studies. Models using somatic cell methylomes to predict age have been successfully constructed. However, gamete aging is quite distinct and as such age prediction using sperm methylomes is ineffective with current techniques. Results We have produced a model that utilizes human sperm DNA methylation signatures to predict chronological age by utilizing methylation array data from a total of 329 samples. The dataset used for model construction includes infertile patients, sperm donors, and individuals from the general population. Our model is capable of accurately predicting age with an R2 of 0.928 in our test data set. We additionally investigated the repeatability of prediction by processing the same sample on 6 different arrays and found very robust age prediction with an average standard deviation of only 0.877 years. Additionally, we found that smokers have approximately 5% increased age profiles compared to ‘never smokers.’ Conclusions The predictive model described herein was built to offer researchers the ability to assess “germ line age” by accessing sperm DNA methylation signatures at genomic regions affected by age. Our data suggest that this model can predict an individual’s chronological age with a high degree of accuracy regardless of fertility status and with a high degree of repeatability. Additionally, our data appear to show age acceleration patterns as a result of smoking suggesting that the aging process in sperm may be impacted by environmental factors, though this effect appears to be quite subtle.