A general framework for penalized mixed-effects multitask learning with applications on DNA methylation surrogate biomarkers creation

TL;DR

This paper introduces a flexible mixed-effects multitask learning framework for high-dimensional DNA methylation biomarker creation, improving prediction and interpretation over existing univariate methods.

Contribution

It develops a novel penalized estimation scheme with an EM algorithm for multivariate DNAm surrogate biomarkers in multi-center studies, handling structured dependence among outcomes.

Findings

Outperforms existing methods in predictive accuracy.

Provides better biological interpretation of biomarkers.

Successfully models multiple correlated risk factors.

Abstract

Recent evidence highlights the usefulness of DNA methylation (DNAm) biomarkers as surrogates for exposure to risk factors for non-communicable diseases in epidemiological studies and randomized trials. DNAm variability has been demonstrated to be tightly related to lifestyle behavior and exposure to environmental risk factors, ultimately providing an unbiased proxy of an individual state of health. At present, the creation of DNAm surrogates relies on univariate penalized regression models, with elastic-net regularizer being the gold standard when accomplishing the task. Nonetheless, more advanced modeling procedures are required in the presence of multivariate outcomes with a structured dependence pattern among the study samples. In this work we propose a general framework for mixed-effects multitask learning in presence of high-dimensional predictors to develop a multivariate DNAm biomarker from a multi-center study. A penalized estimation scheme based on an expectation-maximization algorithm is devised, in which any penalty criteria for fixed-effects models can be conveniently incorporated in the fitting process. We apply the proposed methodology to create novel DNAm surrogate biomarkers for multiple correlated risk factors for cardiovascular diseases and comorbidities. We show that the proposed approach, modeling multiple outcomes together, outperforms state-of-the-art alternatives, both in predictive power and bio-molecular interpretation of the results.