No abstract

Men infected with SARS‐CoV‐2 are more likely to be admitted to the intensive care unit (ICU) compared to women. 1 Previously, we have reported that among hospitalized men with COVID‐19, 79% presented with androgenetic alopecia (AA) compared to 31‐53% that would be expected in a similar aged match population. 2 AA is known to be mediated by variations in the androgen receptor (AR) gene. 3 In addition, the only known promoter of the enzyme implicated in SARS‐CoV‐2 infectivity, TMPRSS2, is regulated by an androgen response element. 4 The polyglutamine repeat (CAG repeat) located in the AR gene is associated with androgen sensitivity and AA. 3 These observations led us to hypothesize that variations in the AR gene may predispose male COVID‐19 patients to increased disease severity.

Saliva and buccal samples are popular for epigenome wide association studies (EWAS) due to their ease of collection compared and their ability to sample a different cell lineage compared to blood. As these samples contain a mix of white blood cells and buccal epithelial cells that can vary within a population, this cellular heterogeneity may confound EWAS. This has been addressed by including cellular heterogeneity obtained through cytology at the time of collection or by using cellular deconvolution algorithms built on epigenetic data from specific cell types. However, to our knowledge, the two methods have not yet been compared. Here we show that the two methods are highly correlated in saliva and buccal samples (R = 0.84, P <0.0001) by comparing data generated from cytological staining and Infinium MethylationEPIC arrays and the EpiDISH deconvolution algorithm from buccal and saliva samples collected from twenty adults. In addition, by using an expanded dataset from both sample types, we confirmed our previous finding that age has a significant negative correlation with epithelial cell proportion in both sample types. However, children and adults showed a large within-population variation in cellular heterogeneity. Our results validate the use of the EpiDISH algorithm in estimating the effect of cellular heterogeneity in EWAS and showed DNA methylation generally underestimates the epithelial cell content obtained from cytology.