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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.
- Li, 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)
Li, M. J., Li, M., Liu, Z., Yan, B., Pan, Z., Huang, D., . . . Wang, J. (2017). Cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes. Genome Biology, 18(1). doi:10.1186/s13059-017-1177-3
- 2017-05-18 02:47:26
- 2021-10-26 12:06:08
- 3 years ago