File Download
  Links for fulltext
     (May Require Subscription)

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

Titlecepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes
Authors
Issue Date2017
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.genomebiology.com
Citation
Genome Biology, 2017, v. 18 n. 1, p. 52:1-15 How to Cite?
AbstractIt 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.
Persistent Identifierhttp://hdl.handle.net/10722/242937
ISSN
2012 Impact Factor: 10.288
2015 SCImago Journal Rankings: 9.860
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, J-
dc.contributor.authorLi, M-
dc.contributor.authorLiu, Z-
dc.contributor.authorYan, B-
dc.contributor.authorPan, Z-
dc.contributor.authorHuang, D-
dc.contributor.authorLiang, Q-
dc.contributor.authorYing, D-
dc.contributor.authorXu, F-
dc.contributor.authorYao, H-
dc.contributor.authorWang, P-
dc.contributor.authorKocher, JA-
dc.contributor.authorXia, Z-
dc.contributor.authorSham, PC-
dc.contributor.authorLiu, JS-
dc.contributor.authorWang, JJ-
dc.date.accessioned2017-08-25T02:47:34Z-
dc.date.available2017-08-25T02:47:34Z-
dc.date.issued2017-
dc.identifier.citationGenome Biology, 2017, v. 18 n. 1, p. 52:1-15-
dc.identifier.issn1474-7596-
dc.identifier.urihttp://hdl.handle.net/10722/242937-
dc.description.abstractIt 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.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.genomebiology.com-
dc.relation.ispartofGenome Biology-
dc.rightsGenome Biology. Copyright © BioMed Central Ltd.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titlecepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes-
dc.typeArticle-
dc.identifier.emailLi, J: mulin@hku.hk-
dc.identifier.emailYan, B: yanbin14@hku.hk-
dc.identifier.emailXia, Z: zyxia@hkucc.hku.hk-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.authorityYan, B=rp01940-
dc.identifier.authorityXia, Z=rp00532-
dc.identifier.authoritySham, PC=rp00459-
dc.identifier.authorityWang, JJ=rp00280-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s13059-017-1177-3-
dc.identifier.hkuros275265-
dc.identifier.volume18-
dc.identifier.issue1-
dc.identifier.spage52:1-
dc.identifier.epage15-
dc.identifier.isiWOS:000397114700001-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats