Cepip: Context-Dependent Epigenomic Weighting for Prioritization of Regulatory Variants and Disease-Associated Genes
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.
- Author (aut): Li, Mulin Jun
- Author (aut): Li, Miaoxin
- Author (aut): Liu, Zipeng
- Author (aut): Yan, Bin
- Author (aut): Pan, Zhicheng
- Author (aut): Huang, Dandan
- Author (aut): Liang, Qian
- Author (aut): Ying, Dingge
- Author (aut): Xu, Feng
- Author (aut): Yao, Hongcheng
- Author (aut): Wang, Panwen
- Author (aut): Kocher, Jean-Pierre A.
- Author (aut): Xia, Zhengyuan
- Author (aut): Sham, Pak Chung
- Author (aut): Liu, Jun S.
- Author (aut): Wang, Junwen
- Contributor (ctb): College of Health Solutions