There are challenges when annotating the molecular basis of human disease, particularly because 93% of disease loci (the specific, fixed physical location on a chromosome where a particular gene or other DNA sequence is located) are non-coding.
Read the original publication of this study here: Regulatory genomic circuitry of human disease loci by integrative epigenomics
The study aimed to further note and explain the molecular basis of human disease
Regulatory Genomic Circuitry of Human Disease Loci by Integrative Epigenomics
Researchers at the Massachusetts Institute of Technology and the Broad Institute have come up with a new epigenomics map, known as “epigenome integration across multiple annotation projects” or EpiMap, which is a compendium comprising 10,000 epigenomic maps across 833 samples.
The goal was to help untangle functional features of the genome on traits, tissues, or disease states.
A wide range of regulatory marks was used to define high-resolution enhancers, chromatin states, enhancer modules, downstream targets, upstream regulators, the disease variants, and the interpretation of these disease variants.
The information was used to interpret 30,000 genetic loci associated with 540 traits, putative causal nucleotide variants in enriched tissue enhancers, predicting trait-relevant tissues, and candidate tissue-specific target genes for each.
The investigations also allowed researchers to start determining genetic loci with monotropic effects and those marked by pleiotropy or many phenotypic expressions.
- The results show the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.
- The authors hope that their predictions, made publicly available, are usable broadly in industry and academia to help explain genetic variants and their mechanisms of action, help target therapies to the most promising targets, and help speed up drug development for many disorders.
- The study allows us to see further into how the genome actually functions.
- The 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.
You can read the original publication of this study here: Regulatory genomic circuitry of human disease loci by integrative epigenomics