AsianScientist (Dec. 12, 2018) – In a study published in Nature Methods, researchers in China have developed a statistical algorithm for identifying sites of DNA methylation (DNAm) in genomes.
DNAm is a covalent modification of DNA which can regulate gene activity, with implications on health and disease. Identifying changes in DNAm that correlate with disease risk is the main aim of epigenome-wide association studies (EWAS) in large numbers of individuals. However, tissues profiled in EWAS are complex mixtures of many different cell types, each with its own characteristic DNAm profile, which can confound analyses.
In the present study, researchers led by Professor Andrew Teschendorff at the Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, developed a statistical algorithm called ‘CellDMC,’ which allows researchers to pinpoint not only the specific genomic sites that have undergone DNAm, but also the cell types in which these DNAm alterations occur. They demonstrated that their algorithm can identify DNAm changes with over 90 percent sensitivity.
Using CellDMC, the researchers were able to show that many DNAm changes associated with rheumatoid arthritis occur in one particular subtype of immune cells, the B-cells. Applied to the analysis of samples from patients who smoked tobacco, their algorithm could identify epigenetic pathways linking smoking to lung cancer.
The researchers propose that CellDMC will allow researchers in the EWAS community to identify the relevant cell types altered in disease, without the need for laborious cell sorting or generating single-cell methylomes, both of which are expensive and time-consuming procedures. Their technology could facilitate the identification and development of epigenetic disease risk biomarker assays, paving the way for personalized medicine.
The article can be found at: Zheng et al. (2018) Identification of Differentially Methylated Cell Types in Epigenome-wide Association Studies.
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Source: Chinese Academy of Sciences; Photo: Shutterstock.
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