Dataset Information

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

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.

SUBMITTER: Li MJ

PROVIDER: S-EPMC5356314 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.

Li Mulin Jun MJ Li Miaoxin M Liu Zipeng Z Yan Bin B Pan Zhicheng Z Huang Dandan D Liang Qian Q Ying Dingge D Xu Feng F Yao Hongcheng H Wang Panwen P Kocher Jean-Pierre A JA Xia Zhengyuan Z Sham Pak Chung PC Liu Jun S JS Wang Junwen J

Genome biology 20170316 1

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 ex ...[more]

PMID: 28302177

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Project description:With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings.We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence.Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

Project description:BackgroundExpression of the androgen receptor (AR) is associated with androgen-dependent proliferation arrest and terminal differentiation of normal prostate epithelial cells. Additionally, activation of the AR is required for survival of benign luminal epithelial cells and primary cancer cells, thus androgen deprivation therapy (ADT) leads to apoptosis in both benign and cancerous tissue. Escape from ADT is known as castration-resistant prostate cancer (CRPC). In the course of CRPC development the AR typically switches from being a cell-intrinsic inhibitor of normal prostate epithelial cell proliferation to becoming an oncogene that is critical for prostate cancer cell proliferation. A clearer understanding of the context dependent activation of the AR and its target genes is therefore desirable.MethodsImmortalized human prostate basal epithelial EP156T cells and progeny cells that underwent epithelial to mesenchymal transition (EMT), primary prostate epithelial cells (PrECs) and prostate cancer cell lines LNCaP, VCaP and 22Rv1 were used to examine context dependent restriction and activation of the AR and classical target genes, such as KLK3. Genome-wide gene expression analyses and single cell protein analyses were applied to study the effect of different contexts.ResultsA variety of growth conditions were tested and found unable to activate AR expression and transcription of classical androgen-dependent AR target genes, such as KLK3, in prostate epithelial cells with basal cell features or in mesenchymal type prostate cells. The restriction of androgen- and AR-dependent transcription of classical target genes in prostate basal epithelial cells was at the level of AR expression. Exogenous AR expression was sufficient for androgen-dependent transcription of AR target genes in prostate basal epithelial cells, but did not exert a positive feedback on endogenous AR expression. Treatment of basal prostate epithelial cells with inhibitors of epigenetic gene silencing was not efficient in inducing androgen-dependent transcription of AR target genes, suggesting the importance of missing cofactor(s).ConclusionsRegulatory mechanisms of AR and androgen-dependent AR target gene transcription are insufficiently understood and may be critical for prostate cancer initiation, progression and escape from standard therapy. The present model is useful for the study of context dependent activation of the AR and its transcriptome.

Dataset Information

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

Publications

cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.

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