CellDMC's new statistical algorithm: Helps identify epigenetic pathways associated with disease.

On November 30th, the international academic journal Nature Methods published a research paper by Andrew Teschendorff of the Institute of Computational Biology of the Shanghai Institute of Nutrition and Health of the Chinese Academy of Sciences, "Identify of the nutrition and health institute, "Identify of the methylated cell in epigenome-wide-wide sessions Association", which reported on a new "CellDMC" statistical algorithm that helps identify epigenetic pathways associated with disease, "cracking" the key difficulties in the analysis of epigenome associations.
DNA methylation (DNAm) is a covalent modification of DNA that regulates gene activity and is susceptible to disease risk factors. The main goal of
Epigenome-wide Association Analysis (EWAS) is to measure genome-wide DNA methylation in a large number of individuals and identify changes in DNA methylation associated with disease risk.
However, one of the main obstacles to this process is cell type heterogeneity: the tissues used by EWAS are complex mixtures of different cell types, each with its own dna methylation spectrum, which may produce erroneous analytical conclusions.
so far, there is no effective way to determine the cell type and epigenetic pathways that drive changes in DNA methylation.
Under the guidance of researcher Andrew Teschendorff, PhD student Zheng Shijie and others developed a new statistical algorithm called CellDMC that not only identifies changes in specific genomic sites, but also determines the type of cells that cause these CHANGEs in DNA methylation.
more than 90 percent sensitivity to DNA methylation changes compared to current common methods that do not recognize changes in DNA methylation.
then the researchers used several real EWAS data to perform algorithmic tests that showed that large amounts of DNA methylation associated with rheumatoid arthritis occurred in a specific blood cell subtype (B-cell), a change that is particularly important in the disease's mechanism.
in another test, the algorithm identified changes in DNA methylation in normal cells exposed to smoke carcinogens and lung cancer progenitor cells, helping to correlate epigenetic pathways associated with smoking with lung cancer.
note, there are some expensive and difficult-to-implement technologies in EWAS, such as cell sorting technology and single-cell methylation group sequencing.
CellDMC helps EWAS researchers identify changes in disease-related cell types without using these techniques.
the ability to detect changes in cell type related to disease-related and disease risk are important for identifying and developing epigenetic disease risk biomarkers and achieving P4 medical objectives.
the above-mentioned research was funded by the National Natural Science Foundation of China (31571359; 31771464; 31401120) and the Chinese Academy of Sciences.
Source: Shanghai Institute of Nutrition and Health.