DNA methylation signatures associated with COVID severity

Zhiyu Kang

PhD, Biostatistics

Weihua Guan, Sandra Safo, Christopher Tignanelli, Elizabeth Lusczek, Kaifeng Yang

Weihua Guan

COVID severity, DNA methylation


Goals: We aim to characterize DNA methylation signatures that predict the development of severe COVID-19 using data from two multi-center, placebo-controlled randomized clinical trials. We also test for association between epigenetic aging and COVID-19 severity.

Methods: Blood DNA methylation were measured from 110 COVID patients across 246,604 CpG sites. Patients were dichotomized as having mild or moderate/severe COVID-19 disease. After screening CpG sites based on their association with COVID-19 severity, elastic net model was applied to identify the importance of CpG sites. Differentially methylated region (DMR) analysis was performed to identify genes regions that are associated with the outcome. Epigenetic aging (PhenoAge) was also constructed for association with COVID-19 severity.

Results: After adjusting for estimated cell types and age, we identified 6 CpG sites consistently selected by elastic nets based on cross validation. They are annotated to genes: CMPK2, PARP9, DTX3L, C3orf21, IMPDH1, CALHM1. The elastic net model yields an average AUC of 0.982. We also identified 76 DMRs ranked by Fisher’s multiple comparison statistics, with the top DMRs annotated to PARP9, WRAP73, CPNE2 and LPCAT1. Epigenetic age acceleration was found to be associated with covid severity (odds ratio: 1.29, 95% CI: (1.13, 1.48), p = 1.4E-4).

Conclusion: We identified DNA methylation signatures that are likely to predict COVID-19 severity. Our results also suggested an important role of biological aging in COVID-19 development. Our findings will provide potential tools for COVID-19 treatment and control.

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