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Article: GWAS4D: Multidimensional analysis of context-specific regulatory variant for human complex diseases and traits

TitleGWAS4D: Multidimensional analysis of context-specific regulatory variant for human complex diseases and traits
Authors
Issue Date2018
Citation
Nucleic Acids Research, 2018, v. 46, n. W1, p. W114-W120 How to Cite?
AbstractGenome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.
Persistent Identifierhttp://hdl.handle.net/10722/324501
ISSN
2023 Impact Factor: 16.6
2023 SCImago Journal Rankings: 7.048
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, Dandan-
dc.contributor.authorYi, Xianfu-
dc.contributor.authorZhang, Shijie-
dc.contributor.authorZheng, Zhanye-
dc.contributor.authorWang, Panwen-
dc.contributor.authorXuan, Chenghao-
dc.contributor.authorSham, Pak Chung-
dc.contributor.authorWang, Junwen-
dc.contributor.authorLi, Mulin Jun-
dc.date.accessioned2023-02-03T07:03:30Z-
dc.date.available2023-02-03T07:03:30Z-
dc.date.issued2018-
dc.identifier.citationNucleic Acids Research, 2018, v. 46, n. W1, p. W114-W120-
dc.identifier.issn0305-1048-
dc.identifier.urihttp://hdl.handle.net/10722/324501-
dc.description.abstractGenome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.-
dc.languageeng-
dc.relation.ispartofNucleic Acids Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleGWAS4D: Multidimensional analysis of context-specific regulatory variant for human complex diseases and traits-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1093/nar/gky407-
dc.identifier.pmid29771388-
dc.identifier.pmcidPMC6030885-
dc.identifier.scopuseid_2-s2.0-85050867122-
dc.identifier.volume46-
dc.identifier.issueW1-
dc.identifier.spageW114-
dc.identifier.epageW120-
dc.identifier.eissn1362-4962-
dc.identifier.isiWOS:000438374100020-

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