Mykkänen, J. | bioRxiv Science

bioRxiv2026

Traditional disease risk factors outperform epigenetic clocks as predictors of non-communicable disease morbidity in a middle-aged cohort

DNA methylation-based epigenetic clocks have been highlighted as promising biomarkers of ageing, and they have been shown to robustly predict morbidity and mortality. However, current literature is lacking a formal analysis of the increased prediction accuracy, or the added value, of the epigenetic clocks over traditional risk factors of common chronic diseases. Here, we have compared the most commonly used epigenetic clocks and traditional risk factors as predictors of incidence of ageing-associated non-communicable chronic disease in a 7-to-9-year follow-up in a middle-aged population cohort (n=1108, aged 34 to 49 years at baseline). In our cohort, a statistical model consisting of a combination of traditional risk factors outperforms any model including an epigenetic clock. The added value of epigenetic clock measurements over simple and affordable traditional risk factors should be clearly established, if epigenetic clocks are to be used in clinical settings or as tools of personal health monitoring.

molecular biology↗

bioRxiv2021

Permutation-based significance analysis reduces the type 1 error rate in bisulfite sequencing data analysis of human umbilical cord blood samples

BackgroundDNA methylation patterns are largely established in-utero and might mediate the impacts of in-utero conditions on later health outcomes. Associations between perinatal DNA methylation marks and pregnancy-related variables, such as maternal age and gestational weight gain, have been earlier studied with methylation microarrays, which typically cover less than 2 % of human CpG sites. To detect such associations outside these regions, we chose the bisulfite sequencing approach. MethodsWe collected and curated all available clinical data on 200 newborn infants; whose umbilical cord blood samples were analyzed with the reduced representation bisulfite sequencing (RRBS) method. A generalized linear mixed effects model was fit for each high coverage CpG site, followed by spatial and multiple testing adjustment of P values to identify differentially methylated cytosines (DMCs) and regions (DMRs) associated with clinical variables such as maternal age, mode of delivery, and birth weight. Type 1 error rate was then evaluated with a permutation analysis. ResultsWe discovered a strong inflation of spatially adjusted P values through the permutation analysis, which we then applied for empirical type 1 error control. Based on empirically estimated significance thresholds, very little differential methylation was associated with any of the studied clinical variables, other than sex. With this analysis workflow, the sex-associated differentially methylated regions were highly reproducible across studies, technologies, and statistical models. ConclusionsThe inflation of P values was caused by a common method for spatial adjustment and DMR detection, implemented in tools comb-p and RADMeth. With standard significance thresholds, type 1 error rates were high with both these implementations, across alternative parameter settings and analysis strategies. We conclude that comb-p and RADMeth are convenient methods for the detection of differentially methylated regions, but the statistical significance should either be determined empirically or before the spatial adjustment. Our RRBS data analysis workflow is available in https://github.com/EssiLaajala/RRBS_workflow.

bioinformatics↗