{"ID": "PMID:29771388", "lab": {"title": "External Lab", "display_title": "External Lab", "uuid": "d3412190-317a-4837-823f-3892b9f641a4", "@type": ["Lab", "Item"], "@id": "/labs/external-lab/", "status": "current", "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin", "role.lab_submitter", "submits_for.d3412190-317a-4837-823f-3892b9f641a4"]}}, "url": "https://www.ncbi.nlm.nih.gov/pubmed/29771388", "tags": ["4dn-external-reuse"], "award": {"name": "external-award", "uuid": "12a92962-8265-4fc0-b2f8-cf14f05db58b", "center_title": "External", "project": "External", "description": "Funding source is from outside 4DN.", "status": "current", "center": "External", "display_title": "EXTERNAL AWARD", "@id": "/awards/external-award/", "@type": ["Award", "Item"], "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin"]}}, "title": "GWAS4D: multidimensional analysis of context-specific regulatory variant for  human complex diseases and traits.", "status": "current", "aliases": ["4dn-dcic-lab:PMID:29771388"], "authors": ["Huang D", "Yi X", "Zhang S", "Zheng Z", "Wang P", "Xuan C", "Sham PC", "Wang J", "Li MJ"], "journal": "Nucleic acids research", "abstract": "Genome-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.", "date_created": "2024-02-14T19:51:07.271401+00:00", "published_by": "External", "submitted_by": {"error": "no view permissions"}, "last_modified": {"modified_by": {"error": "no view permissions"}, "date_modified": "2024-03-15T16:03:35.079435+00:00"}, "date_published": "2018-07-02", "public_release": "2024-03-15", "schema_version": "2", "project_release": "2024-03-15", "@id": "/publications/fac5325e-bcd9-4e6a-b0e9-237607f09ea5/", "@type": ["Publication", "Item"], "uuid": "fac5325e-bcd9-4e6a-b0e9-237607f09ea5", "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin"]}, "display_title": "Huang D et al. (2018) PMID:29771388", "external_references": [], "short_attribution": "Huang D et al. (2018)", "@context": "/terms/", "aggregated-items": {}, "validation-errors": []}