| Autor: |
Boix CA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA., James BT; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Park YP; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada., Meuleman W; Altius Institute for Biomedical Sciences, Seattle, WA, USA., Kellis M; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. manoli@mit.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. manoli@mit.edu. |
| Abstrakt: |
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 incomplete 1-3 . 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 traits 4 , 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. |
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