Conformal Uncertainty Quantification for BayesAge Epigenetic Age Predictions

Epigenetic clocks predict chronological age from DNA methylation (DNAm) profiles, yet most provide point estimates without calibrated uncertainty. This limits their use when quantified error bounds are required. We apply conformal prediction to BayesAge, a maximum-likelihood clock that models nonlinear DNAm-age relationships using a small set of CpG loci and a count-based likelihood. Split conformal prediction yields distribution-free prediction intervals with finite-sample marginal coverage guarantees under exchangeability and requires only a single model fit per split. We also evaluate a locally scaled variant that produces age-dependent interval widths using a locally estimated error scale. In our targeted bisulfite sequencing cohort, split conformalized BayesAge attains near-nominal empirical coverage while preserving BayesAge point-prediction accuracy. The locally scaled variant yields wider intervals at older ages, but its coverage is less stable in small-calibration regimes, consistent with additional uncertainty from estimating the local scale. Relative to Monte Carlo intervals that propagate read-sampling variability and to higher-dimensional linear baselines (conformalized linear quantile regression and conformalized Lasso regression), conformalized BayesAge provides calibrated uncertainty using substantially fewer CpG sites and with weaker age-dependent structure in residuals. These results support conformal prediction as a practical approach for uncertainty quantification in DNAm-based age estimation.