Transfer Elastic Net for Developing Epigenetic Clocks for the Japanese Population

Abstract

The 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 age as the response variable. Due to the large number of CpG sites and their correlation, Elastic Net is commonly used to train the models. However, existing standard epigenetic clocks, trained on multiracial data, may exhibit biases due to genetic and environmental differences among specific racial groups. Developing epigenetic clocks suitable for a specific single-race population requires collecting and analyzing hundreds or thousands of samples, which costs a lot of time and money. Therefore, an efficient method to construct accurate epigenetic clocks with smaller sample sizes is needed. We propose Transfer Elastic Net, a transfer learning approach that trains a model in the target population using the information of parameters estimated by the Elastic Net in a source population. Using this method, we constructed Horvath’s, Hannum’s, and Levine’s types of epigenetic clocks from blood samples of 143 Japanese subjects. The DNA methylation 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. Transfer Elastic Net can also be applied to develop epigenetic clocks for other specific populations, and is expected to be applied in various fields.

Keywords

• biological age
• DNA methylation
• epigenetics
• sparse regularization
• transfer learning