Traveling through large cities is complicated. Thankfully, our apps help us find a way through the maze and lead us to our destination. Now, a new method has come forward to do the same for the epigenomics of disease. A group led by Charles Breeze and Sonja Berndt (National Cancer Institute) have developed a program that guides us through the maze of epigenetic data to identify key disease-associated targets.
In the largest integrative analysis of GWAS to date (3,604 GWAS), they:
- Identified 2,057 novel associations, including key cell types for different types of cancer (B cells for CLL), autoimmune diseases (T cells), and BMI (mesenchymal stem cells)
- Focused on immune traits and diseases to show that their method finds targets not only for tissue-level data but also at the level of cell types, charting different cell type associations for each of the phenotypes analyzed and showing candidate causal cell types for each condition
Knowing the floor number of your destination can also be important and through this method you can also find different levels of epigenomic annotation associated for each cell type, including DNase I hotspots, Histone mark broad peaks, and Hidden Markov Model (HMM) chromatin states. Attesting to the utility of these annotations, they found known associations for DNase I hotspots can be found at the same tissue for enhancer-associated (H3K4me1) and promoter-associated (H3K4me3) marks, and in other cases for transcription-associated (H3K36me3) and repression associated (H3K27me3, H3K9me3) marks. Similar trends are found across chromatin states, which were analyzed in a 15-state model.
Knowledge of the most-visited destinations is also important and the same holds for epigenomic marks. In this study they find that H3K4me1 is the most informative mark overall for GWAS analysis, alongside DNase-seq data and H3K4me3 data.
As we know, all good apps must be on the web, and their method, FORGE2, is provided as an easy-to-use web tool for the community.
Go navigate all the epigenomics of disease in Genome Biology, January 2022.