Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Background Epigenetic aging signatures can provide insights into the human aging process. Within the last decade many alternative epigenetic clocks have been described, which are typically based on linear regression analysis of DNA methylation at multiple CG dinucleotides (CpGs). However, this approach assumes that the epigenetic modifications follow either a continuous linear or logarithmic trajectory. In this study, we explored an alternative non-parametric approach using 2D-kernel density estimation (KDE) to determine epigenetic age. Results We used Illumina BeadChip profiles of blood samples of various studies, exemplarily selected the 27 CpGs with highest linear correlation with chronological age (R 2 > 0.7), and computed KDEs for each of them. The probability profiles for individual KDEs were further integrated by a genetic algorithm to assign an optimal weight to each CpG. Our weighted 2D-kernel density estimation model (WKDE) facilitated age-predictions with similar correlation and precision (R 2 = 0.81, median absolute error = 4 years) as other commonly used clocks. Furthermore, our approach provided a variation score, which reflects the inherent variation of age-related epigenetic changes at different CpG sites within a given sample. An increase of the variation score by one unit reduced the mortality risk by 9.2% (95% CI (0.8387, 0.9872), P <0.0160) in the Lothian Birth Cohort 1921 after adjusting for chronological age and sex. Conclusions We describe a new method using weighted 2D-kernel density estimation (WKDE) for accurate epigenetic age-predictions and to calculate variation scores, which provide an additional variable to estimate biological age.

Authors 7

1. Juan-Felipe Perez-Correa Aachen Institute for Stem Cell Biology

   RWTH Aachen University

   Affiliation as printed

   Helmholtz Institute for Biomedical Engineering, RWTH Aachen University Medical School, Aachen, Germany

   Institute for Stem Cell Biology, RWTH Aachen University Medical School, Aachen, Germany

2. Thomas Stiehl Aachen Institute for Computational Biomedicine-Disease Modeling

   RWTH Aachen University

   Affiliation as printed

   Institute for Computational Biomedicine - Disease Modeling, RWTH Aachen University, Aachen, Germany

3. Riccardo E. Marioni

   Institute of Genetics and Cancer · University of Edinburgh

   Affiliation as printed

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

4. Janie Corley

   University of Edinburgh

   Affiliation as printed

   Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK

5. Simon R. Cox

   University of Edinburgh

   Affiliation as printed

   Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK

6. Ivan G. Costa Aachen Institute for Computational Genomics

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute for Computational Genomics, RWTH Aachen University Hospital, Aachen, Germany

7. Wolfgang Wagner corresponding Aachen Institute for Stem Cell Biology

   RWTH Aachen University

   Affiliation as printed

   Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), Aachen, Germany

   Helmholtz Institute for Biomedical Engineering, RWTH Aachen University Medical School, Aachen, Germany

   Institute for Stem Cell Biology, RWTH Aachen University Medical School, Aachen, Germany