Li, Mulin JunLi, MiaoxinLiu, ZipengYan, BinPan, ZhichengHuang, DandanLiang, QianYing, DinggeXu, FengYao, HongchengWang, PanwenKocher, Jean-Pierre A.Xia, ZhengyuanSham, Pak ChungLiu, JunWang, Junwen2017-04-062017Li, M. J., M. Li, Z. Liu, B. Yan, Z. Pan, D. Huang, Q. Liang, et al. 2017. “cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.” Genome Biology 18 (1): 52. doi:10.1186/s13059-017-1177-3. http://dx.doi.org/10.1186/s13059-017-1177-3.http://nrs.harvard.edu/urn-3:HUL.InstRepos:32072194It 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. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1177-3) contains supplementary material, which is available to authorized users.en-USRegulatory variantVariant prioritizationDisease-susceptible geneCell type-specificEpigenomeJournal Article2017-04-0610.1186/s13059-017-1177-3