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Tong et al. construct simulations using DNA methylation data to quantify what proportion of the predictive accuracy of epigenetic clocks could be explained by stochastic methylation changes, suggesting that stochasticity contributes more toward the accuracy of chronological rather than biological age predictions.
In a longitudinal population-based cohort, Liu et al. demonstrate that integrating polygenic risk scores and the gut microbiome improved prediction, over traditional risk factors, for heart disease, diabetes, Alzheimer disease and prostate cancer.
Identifying individuals at risk of developing Alzheimer’s disease is an important task. Here Tang et al. leverage electronic health records to predict Alzheimer’s disease onset, and utilize knowledge networks to prioritize shared genes behind the clinical data as well as facilitate contextualization based on sex.
