cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.
cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.
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DOI：
10.1186/s13059-017-1177-3
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发表时间：
2017-03-16
期刊：
影响因子：
12.3
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Li MJ;Li M;Liu Z;Yan B;Pan Z;Huang D;Liang Q;Ying D;Xu F;Yao H;Wang P;Kocher JA;Xia Z;Sham PC;Liu JS;Wang J
由于高度上下文特定的基因调控，预测特定组织或细胞类型的调控变异仍然具有挑战性。通过将大规模的表观基因组图谱与广泛的人类组织/细胞类型中的表达数量性状基因座(EQTL)联系起来，我们识别了预测变异调控潜力的关键染色质特征。我们提出了CEIP，一个联合可能性框架，用于以依赖于上下文的方式估计变体的调控概率。我们的方法表现出显著的Gwas信号丰富，并优于现有的特定细胞类型的方法。此外，使用表型相关的表观基因组对GWAS单核苷酸多态进行加权，提高了基于基因的关联检验的统计能力。本文的在线版本(doi：10.1186/s13059-017-1177-3)包含补充材料，授权用户可以使用。
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. The online version of this article (doi:10.1186/s13059-017-1177-3) contains supplementary material, which is available to authorized users.