NEW YORK – A team led by researchers at the Massachusetts Institute of Technology and the Broad Institute has annotated a wide range of regulatory marks across dozens of cell or tissue types, coming up with a new epigenomics map that can help untangle functional features of the genome in relation to tissues, traits, or disease states.
"Epigenomics allows us to peek at what each cell marked as important in every cell type, and thus understand how the genome actually functions," senior and corresponding author Manolis Kellis, a computer scientist and artificial intelligence researcher affiliated with MIT and the Broad Institute, said in a statement, calling the map "the circuitry of the human genome."
For a paper appearing in Nature on Wednesday, Kellis and his colleagues pulled together available data across 18 directly assessed or imputed regulatory marks — including chromatin, enhancer, and other regulatory profiles — in 833 reference epigenomes from dozens of tissue types. These samples, originally characterized for the ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation, and other prior efforts, allowed the researchers to gather a resource known as the "epigenome integration across multiple annotation projects," or EpiMap.
The publicly available EpiMap resource contains data spanning some 10,000 epigenetic marks in samples from 33 tissue or cell types, the team reported, making it possible to annotate millions of enhancer sites falling into modules with related biological activity.
"[W]e not only have the genes, we not only have the noncoding annotations, but we have the modules, the upstream regulators, the downstream targets, the disease variants, and the interpretation of these disease variants," Kellis explained, adding that the investigators behind the study "hope that our predictions will be used broadly in industry and in academia to help elucidate genetic variants and their mechanisms of action, help target therapies to the most promising targets, and help accelerate drug development for many disorders."
With such data in hand, the researchers began predicting additional regulatory features in the genome. They also got a closer look at noncoding genetic loci and tissue types implicated in hundreds of traits and diseases through genome-wide association and other studies, including clues to the causal variants and tissue types that may be most relevant to traits or conditions such as coronary artery disease or breast cancer.
The resource also made it possible to begin distinguishing between genetic loci with monotropic effects and those marked by pleiotropy, in which distinct genetic variants and regulatory features converged on shared gene targets, the team explained, noting that EpiMap is expected to contribute to a range of future research efforts.
"Our work enables many future studies: hierarchical and multi-resolution tree-based analyses of gene regulation and GWAS; machine learning-based gene circuitry and combinatorial regulatory motif analyses; more sophisticated network analyses of our tissue-trait, trait-trait, and tissue-tissue relationships; and guiding the experimental prioritization, methodological development, and validation experiments, which can continue to further our understanding of gene regulation and human disease circuitry," the authors concluded.