Regulatory genomic circuitry of human disease loci by integrative epigenomics
Name
s41586-020-03145-z.pdf
Description
Published version
Size
25.41 MB
Format
Adobe PDF
Checksum (MD5)
35c8b3dcee80c84808038ffac4a5367d
Author(s)
Boix, Carles A
James, Benjamin T
Park, Yongjin P
Meuleman, Wouter
Kellis, Manolis
Date Issued
2021
Journal
Nature
Publisher
Springer Science and Business Media LLC
Citation
Boix, Carles A, James, Benjamin T, Park, Yongjin P, Meuleman, Wouter and Kellis, Manolis. 2021. "Regulatory genomic circuitry of human disease loci by integrative epigenomics." Nature, 590 (7845).
Version
Final published version
Abstract
© 2021, The Author(s). Annotating the molecular basis of human disease remains an unsolved challenge, as 93% of disease loci are non-coding and gene-regulatory annotations are highly incomplete1–3. Here we present EpiMap, a compendium comprising 10,000 epigenomic maps across 800 samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000 genetic loci that were associated with 540 traits4, predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Computational and Systems Biology Program
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41586-020-03145-Z
