1. The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
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Joel Rozowsky, Jorg Drenkow, Yucheng T Yang, Gamze Gursoy, Timur Galeev, Beatrice Borsari, Charles B Epstein, Kun Xiong, Jinrui Xu, Jiahao Gao, Keyang Yu, Ana Berthel, Zhanlin Chen, Fabio Navarro, Jason Liu, Maxwell S Sun, James Wright, Justin Chang, Christopher JF Cameron, Noam Shoresh, Elizabeth Gaskell, Jessika Adrian, Sergey Aganezov, François Aguet, Gabriela Balderrama-Gutierrez, Samridhi Banskota, Guillermo Barreto Corona, Sora Chee, Surya B Chhetri, Gabriel Conte Cortez Martins, Cassidy Danyko, Carrie A Davis, Daniel Farid, Nina P Farrell, Idan Gabdank, Yoel Gofin, David U Gorkin, Mengting Gu, Vivian Hecht, Benjamin C Hitz, Robbyn Issner, Melanie Kirsche, Xiangmeng Kong, Bonita R Lam, Shantao Li, Bian Li, Tianxiao Li, Xiqi Li, Khine Zin Lin, Ruibang Luo, Mark Mackiewicz, Jill E Moore, Jonathan Mudge, Nicholas Nelson, Chad Nusbaum, Ioann Popov, Henry E Pratt, Yunjiang Qiu, Srividya Ramakrishnan, Joe Raymond, Leonidas Salichos, Alexandra Scavelli, Jacob M Schreiber, Fritz J Sedlazeck, Lei Hoon See, Rachel M Sherman, Xu Shi, Minyi Shi, Cricket Alicia Sloan, J Seth Strattan, Zhen Tan, Forrest Y Tanaka, Anna Vlasova, Jun Wang, Jonathan Werner, Brian Williams, Min Xu, Chengfei Yan, Lu Yu, Christopher Zaleski, Jing Zhang, Kristin Ardlie, J Michael Cherry, Eric M Mendenhall, William S Noble, Zhiping Weng, Morgan E Levine, Alexander Dobin, Barbara Wold, Ali Mortazavi, Bing Ren, Jesse Gillis, Richard M Myers, Michael P Snyder, Jyoti Choudhary, Aleksandar Milosavljevic, Michael C Schatz, Roderic Guigó, Bradley E Bernstein, Thomas R Gingeras, and Mark Gerstein
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Genetic variants ,Genomics ,Preprint ,Computational biology ,Biology ,Personal genomics - Abstract
Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for ∼25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
- Published
- 2021
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