Genome-wide screening identifies DNA methylation sites that regulate the blood proteome.

Background: Identifying DNA methylation sites that regulate the blood proteome is important for biomedical purposes. Materials & methods: Here the authors performed a genome-wide search to find DNA methylation sites that impact proteins. Results: The authors identified 165 methylation sites associated with 138 proteins. The authors noted hotspot genomic regions that control the levels of several proteins. For example, methylation of the ABO locus impacted 37 proteins and contributed to cardiometabolic comorbidities, including the severity of SARS-CoV-2 infection. The authors made these findings publicly available as a Unix software that identifies methylation sites that cause disease and reveals the underlying proteins. The authors underlined the software application by showing that components of innate immunity contribute to systolic blood pressure. Conclusion: This study provides a catalog of DNA methylation sites that regulate the proteome, and the results are available as freeware for biological insight.

Our lifestyle choices and interactions with the world around us are continuously printed in our DNA through a biochemical process known as epigenomic modification. Excessive epigenomic modification at a DNA site may cause disease. To prevent or treat disease, it is important to find such sites and remove the excessive epigenomic modification with medications or lifestyle changes. Here the authors searched for DNA sites that undergo epigenomic modification. The authors also investigated the mechanism whereby these sites cause disease. The authors found that there are DNA sites where reverting the epigenomic modification could have a big impact on the body. The authors have made these findings publicly available.

Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity.

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.