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Description

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant’s regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.

ContributorsLi, Mulin Jun (Author) / Li, Miaoxin (Author) / Liu, Zipeng (Author) / Yan, Bin (Author) / Pan, Zhicheng (Author) / Huang, Dandan (Author) / Liang, Qian (Author) / Ying, Dingge (Author) / Xu, Feng (Author) / Yao, Hongcheng (Author) / Wang, Panwen (Author) / Kocher, Jean-Pierre A. (Author) / Xia, Zhengyuan (Author) / Sham, Pak Chung (Author) / Liu, Jun S. (Author) / Wang, Junwen (Author) / College of Health Solutions (Contributor)
Created2017-03-16