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Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging

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Abstract

Stochastic epigenetic mutations (SEMs) have been proposed as novel aging biomarkers to capture heterogeneity in age-related DNA methylation changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. Because SEMs are defined by their outlier status, it is critical to differentiate extreme values due to technical noise or data artifacts from those due to real biology. Using technical replicate data, we found SEM detection is not reliable: across 3 datasets, 24 to 39% of hypoSEM and 46 to 67% of hyperSEM are not shared between replicates. We identified factors influencing SEM reliability—including blood cell type composition, probe beta-value statistics, genomic location, and presence of SNPs. We used these factors in a training dataset to build a machine learning-based filter that removes unreliable SEMs, and found this filter enhances reliability in two independent validation datasets. We assessed associations between SEM loads and aging phenotypes in the Framingham Heart Study and discovered that associations with aging outcomes were in large part driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations were preserved after filtering out unreliable SEMs and were enhanced after adjusting for blood cell composition. Finally, we utilized these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which uses parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.

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Data availability

The datasets comprising technical replicates utilized in this research are publicly accessible on the NCBI Gene Expression Omnibus (GEO) under accession numbers GSE55763 and GSE174422. However, due to the sensitive nature of the health data contained within the Framingham Heart Study (FHS) dataset, researchers interested in accessing this data will need to submit an application through the database of Genotypes and Phenotypes (dbGaP) at https://dbgap.ncbi.nlm.nih.gov/aa/ (dbGaP accession number: phs000724.v7.p11). The SEMdetectR software package developed in this study is available on GitHub at https://github.com/HigginsChenLab/SEMdetectR. The repository includes the source code and documentation to facilitate utilization by other researchers in the community.

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Funding

This project was principally supported by the funding awarded to YM from the Biomarker Network (R24 AG037898), and to AHC and ML by the National Institute on Aging (R01AG057912 and R01AG065403).

Author information

Authors and Affiliations

  1. Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

    Yaroslav Markov

  2. Altos Labs, San Diego Institute of Sciences, San Diego, CA, USA

    Morgan Levine

  3. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

    Albert T. Higgins-Chen

  4. Department of Pathology, Yale University School of Medicine, New Haven, CT, USA

    Albert T. Higgins-Chen

Authors
  1. Yaroslav Markov
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  2. Morgan Levine
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  3. Albert T. Higgins-Chen
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Contributions

YM, ML, and AHC conceived the project and study design and obtained and cleaned the data. YM performed all analyses, trained the machine learning models, generated the figures, and developed the SEMdetectR software package. ML and AHC supervised, provided feedback, and provided code for analysis of mortality and reliability. YM and AHC authored the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Albert T. Higgins-Chen.

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Competing interests

AHC has received consulting fees from FOXO Technologies, Inc., and TruDiagnostic for work unrelated to the present manuscript. ML is a founding PI of Altos Labs. ML and AHC hold patents for epigenetic clocks they developed, unrelated to the present manuscript. All other authors declare no competing interests.

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Markov, Y., Levine, M. & Higgins-Chen, A.T. Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging. GeroScience (2024). https://doi.org/10.1007/s11357-024-01191-3

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