A robust computational framework for methylation age and disease-risk prediction based on pairwise learning.

Conventional epigenetic clocks encounter challenges in generalizability, especially when there are pronounced batch effects between the training and test datasets, restricting their clinical applicability for aging assessment. Here we present MAPLE, a robust computational framework for methylation age and disease-risk prediction through pairwise learning. MAPLE utilizes pairwise learning to discern the relative relationships between two DNA methylation profiles regarding age or disease risk. It effectively identifies aging- or disease-related biological signals while mitigating technical biases in the data. MAPLE outperforms five competing methods, achieving a median absolute error of 1.6 years across 31 benchmark tests from diverse studies, sequencing platforms, data preprocessing methods and tissue types. Furthermore, MAPLE performs well when assessing aging-related disease risk, with mean areas under the curve of 0.97 for disease identification and 0.85 for pre-disease status detection. Overall, we show that MAPLE has great potential for assessing epigenetic age and aging-related disease risk clinically.