Amplification of the epigenetic (gestational) age acceleration signal

Abstract

Background Epigenetic (gestational) age acceleration (E(G)AA) is associated with environmental exposures and health outcomes in humans. However, E(G)AA is the residual term from a regression of epigenetic age (outcome) on chronological (gestational) age (predictor) and therefore strongly obscured by ‘noise’ from multiple sources. Here, we propose a simple procedure, based on regression, principal component analysis (PCA), and the Lasso, that amplifies E(G)AA signals. More specifically, we first regress given (gestational) age against each CpG used for epigenetic (gestational) age prediction. The CpGs are typically taken from one of several epigenetic clocks available. PCA is subsequently performed on the resulting matrix of residual vectors for each CpG as it projects the E(G)AA signal onto perpendicular principal components (PCs), thereby separating ‘signal’ from noise. Finally, we use the Lasso to select PCs associated with an outcome of interest. We apply our method to previous studies: EAA in patients with Down’s syndrome and Werner’s syndrome and EGAA of newborns exposed to prenatal smoking as well as associations with maternal BMI. Results The extracted EAA components computed using our proposed procedure revealed a significant association with Down’s syndrome (PB<0.05, Bonferroni adjusted for multiple testing) as well as for Werner’s Syndrome (PB<0.05). For EGAA we find a significant association with maternal prenatal smoking (PB<0.05, also Bonferroni adjusted) and maternal BMI (PB<0.05). Additionally, by examining the loadings of the PCs of interest, and contrary to residual EGAA, our method can identify implicated CpGs. Conclusions Our findings suggest that our proposed procedure leads to a remarkable amplification of the E(G)AA signal. Furthermore, our method reveals that E(G)AA is a composite signal that can be driven by multiple independent factors.