Tomo, Y. | Search bioRxiv

bioRxiv2025

iComBat: An Incremental Framework for Batch Effect Correction in DNA Methylation Array Data

DNA methylation is associated with various diseases and aging; thus, longitudinal and repeated assessments of methylation patterns are crucial for revealing the mechanisms of disease onset and identifying factors associated with aging. The presence of batch effects influences the analysis of DNA methylation array data. Since existing methods for correcting batch effects are designed to correct all samples simultaneously, when data are incrementally measured and included, the correction of newly added data affects previous data. In this study, we propose an incremental framework for batch-effect correction based on ComBat, a location/scale adjustment approach using a Bayesian hierarchical model, and empirical Bayes estimation. Using numerical experiments and application to actual data, we demonstrate that the proposed method can correct newly included data without re-correcting the old data. The proposed method is expected to be useful for studies involving repeated measurements of DNA methylation, such as clinical trials of anti-aging interventions.

bioinformatics↗

bioRxiv2024

Transfer Elastic Net for Developing Epigenetic Clocks for the Japanese Population

MotivationThe epigenetic clock evaluates human biological age based on DNA methylation patterns. It takes the form of a regression model where the methylation ratio at CpG sites serves as the predictor, and chronological or adjusted age as the response variable. Due to the large number of CpG sites considered as candidate explanatory variables and their potential correlation, Elastic Net is commonly used to train the regression models. However, existing standard epigenetic clocks, trained on multiracial data, may exhibit biases due to genetic and environmental differences among specific racial groups. The development of epigenetic clocks suitable for a single-race population typically necessitates the collection of hundreds to thousands of samples to measure DNA methylation and other biomarkers, which costs a lot of time and money. Consequently, a method for developing accurate epigenetic clocks with relatively small sample sizes is needed. ResultsWe propose Transfer Elastic Net, a transfer learning approach that uses the parameter information from a linear regression model trained with the Elastic Net to estimate another model. Using this method, we constructed Horvaths, Hannums, and Levines types of epigenetic clocks using DNA methylation data from blood samples of 143 Japanese subjects. The data were transformed through principal component analysis to obtain more reliable clocks. The developed clocks demonstrated the smallest prediction errors compared to both the original clocks and those trained with the Elastic Net on the same Japanese data. Furthermore, the bias relative to the original clocks was reduced. Thus, we successfully developed epigenetic clocks that are well-suited for the Japanese population. Transfer Elastic Net can also be applied to develop epigenetic clocks for other specific populations, and is expected to be applied in various fields. Availabilityhttps://github.com/t-yui/TransferENet-EpigeneticClock

bioinformatics↗