A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking.

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components from CpG-level data as input for biological age prediction. Our retrained principal-component versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of principal component-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies, and clinical trials of aging interventions.

Personalized reproductive medicine on the brink: progress, opportunities and challenges ahead.

Significant progress has been made in several fields of medicine towards personalizing treatment recommendations based on individual patient genotype. As the number of clinical and genetic biomarkers available to physicians has increased, predictive models able to integrate the contributions of multiple variables simultaneously have become valuable tools for medical decision making. Leveraging genotype information and multivariate predictive models holds the promise of bringing greater efficiency to, and reducing the costs of, fertility treatments. This work reviews the advances that have been made in genetic biomarker discovery and predictive modelling for fertility treatment outcomes. We also discuss some of the limitations of these studies for translation to clinical diagnostics and the challenges that remain.Personalized medicine holds the promise of allowing doctors to create 'bespoke' treatment recommendations for each patient based on multiple clinical variables such as age and hormone concentrations combined with the patient's genetic sequence information. A number of challenges remain for the field of reproductive medicine to make the research discoveries necessary to usher in this new era of personalized fertility care. Here, we discuss some of these challenges and make recommendations for overcoming them.