Cepip: Context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes

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

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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 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.

Original languageEnglish (US)
Article number52
JournalGenome biology
Volume18
Issue number1
DOIs
StatePublished - Mar 16 2017

Keywords

  • Cell type-specific
  • Disease-susceptible gene
  • Epigenome
  • Regulatory variant
  • Variant prioritization

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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