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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 and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-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 Epigenomics Mapping Consortium cell types, which we used to interpret regulatory SNPs in the NHGRI-EBI 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. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages 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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