DOI: 10.5061/dryad.p8cz8w9z3
title: A superLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits

description: Background: DNA methylation (DNAm) provides a window to characterize the
 impacts of environmental exposures and the biological aging process.
 Epigenetic clocks are often trained on DNAm using penalized regression of
 CpG sites, but recent evidence suggests potential benefits of training
 predictors on principal components. Results: We developed a pipeline to
 simultaneously train three epigenetic predictors; a traditional CpG Clock,
 a PCA Clock, and a SuperLearner PCA Clock (SL PCA). We gathered publicly
 available DNAm datasets to generate i) a novel childhood epigenetic clock,
 ii) a reconstructed Hannum adult blood clock, and iii) as a proof of
 concept, a predictor of polybrominated biphenyl exposure using the three
 developmental methodologies. We used correlation coefficients and median
 absolute error to assess fit between predicted and observed measures, as
 well as agreement between duplicates. The SL PCA clocks improved fit with
 observed phenotypes relative to the PCA clocks or CpG clocks across
 several datasets. We found evidence for higher agreement between duplicate
 samples run on alternate DNAm arrays when using SL PCA clocks relative to
 traditional methods. Analyses examining associations between relevant
 exposures and epigenetic age acceleration (EAA) produced more precise
 effect estimates when using predictions derived from SL PCA clocks.
 Conclusions: We introduce a novel method for the development of DNAm-based
 predictors that combines the improved reliability conferred by training on
 principal components with advanced ensemble-based machine learning.
 Coupling SuperLearner with PCA in the predictor development process may be
 especially relevant for studies with longitudinal designs utilizing
 multiple array types, as well as for the development of predictors of more
 complex phenotypic traits.
description: Three epigenetic predictors generated using public DNA
 methylation datasets, including a novel childhood epigenetic clock, a
 revisiting of the Hannum adult epigenetic clock, and a novel predictor of
 polybrominated biphenyl exposure. 
description: # A SuperLearner-based Pipeline for the Development of DNA
 Methylation-derived Predictors of Phenotypic Traits
 [https://doi.org/10.5061/dryad.p8cz8w9z3](https://doi.org/10.5061/dryad.p8cz8w9z3) ## Description of the data and file structure DNA methylation-based predictors have emerged as key biomarkers for environmental health research, and it is vital that these predictors maximize the signal-to-noise ratio and are capable of modeling complex exposure-response relationships. Leveraging these methodological advances, we developed a pipeline capable of simultaneously training DNA methylation-based predictors using 3 methods; 1) the traditional approach based on elastic net regression of the CpG matrix, 2) elastic net regression of the principal component matrix, and 3) the ensemble prediction derived from running a SuperLearner model on the principal component matrix. To compare these 3 clock development methodologies, we trained 3 epigenetic predictors using each of the three training methodologies; 1) a novel childhood clock, 2) a revisiting of the traditional Hannum clock, and 3) a novel predictor of polybrominated biphenyl exposure.  The training model data for each of the predictors is stored in the corresponding RData objects: * "Childhood_Clock_Inputs.RData" * "Hannum_Clock_Inputs.RData" * "PBB_Inputs.RData"   The code necessary for generating predictions from these predictors as well as code for generating new predictors using the methodology developed in this analysis are stored "`SL_PCA_Clock_Functions.R`", and a detailed user guide is provided in "00_SLPCA_Predictor_Guide.HTML" ## Sharing/Access information * DNA methylation and phenotypic data used to construct all predictors, as well as most testing datasets, is publicly available on the Gene Expression Omnibus, with the GEO accession numbers specified in the main manuscript.