cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes

  • Panwen Wang (Contributor)
  • Zhicheng Pan (Contributor)
  • Zhengyuan Xia (Contributor)
  • Mulin Jun Li (Contributor)
  • Dandan Huang (Contributor)
  • Zipeng Liu (Contributor)
  • Junwen Wang (Contributor)
  • Pak Chung Sham (Contributor)
  • Jun S. Liu (Contributor)
  • Dingge Ying (Contributor)
  • Qian Liang (Contributor)
  • Feng Xu (Contributor)
  • Hongcheng Yao (Contributor)
  • Bin Yan (Contributor)
  • Miaoxin Li (Contributor)
  • Jean Pierre A. Kocher (Contributor)



Abstract 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 variantâ 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.
Date made availableMar 16 2017
Publisherfigshare Academic Research System

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