Observing correlations is tedious because so many gene combinations are involved
“Comparing different types of epigenomic data is difficult because it involves a variety of different data subsets that cannot normally be analyzed together, including various methods in which DNA gets modified,” said Dr. Wei Wang, a professor of cellular and molecular medicine and professor of chemistry and biochemistry at the UC San Diego School of Medicine.
Of these clusters, 13 were particularly enriched in these cells. Although some of them were expected due to their association with immune responses, others were completely unexpected, such as Huntington’s Disease Signaling. “When we found those, we looked at the genes that are expressed differently in RA versus non-RA. We are able to then go back and look at what genes are involved with either susceptibility or the severity of RA. When we did that, we got lists of hundreds of genes. We then can look into whether there are patterns to those genes that fall into pathway,” said Dr. Firestein.