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Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation |
| Brittany Baur1, Junha Shin1, Jacob Schreiber2, Shilu Zhang1, Yi Zhang3, Mohith Manjunath4, Jun Song4,5,6, William Stafford Noble2,7, Sushmita Roy1,8 |
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| Abstract |
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| Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants are typically unknown. Furthermore, a regulatory variant might influence a particular gene’s expression in a cell type or tissue-specific manner. Cell-type specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offers a powerful framework for understanding the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of model cell lines. To address this challenge, we have developed L-HiC-Reg, a Random Forests based regression method to predict high-resolution contact counts in new cell lines, and a network-based framework to identify candidate cell line-specific gene networks targeted by a set of variants from a Genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenome Consortium cell lines, which we used to interpret regulatory SNPs in the NHGRI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including Schizophrenia, Coronary Artery Disease (CAD) and Crohn’s disease. In CAD, we found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and associated network-based analysis pipeline offers a powerful resource to leverage long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes. |
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| 1. Wisconsin Institute for Discovery, University of Wisconsin-Madison |
| 2. Paul G. Allen School of Computer Science and Engineering, University of Washington |
| 3. Department of Bioengineering, University of Illinois at Urbana-Champaign |
| 4. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign |
| 5. Department of Physics, University of Illinois at Urbana-Champaign |
| 6. Cancer Center at Illinois, University of Illinois at Urbana-Champaign |
| 7. Department of Genome Sciences, University of Washington |
| 8. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison |
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