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

Dandan Huang, Xianfu Yi, Shijie Zhang, Zhanye Zheng, Panwen Wang, Chenghao Xuan, Pak Chung Sham, Junwen Wang, Mulin Jun Li

Research output: Contribution to journalArticle

9 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)W114-W120
JournalNucleic acids research
Volume46
Issue numberW1
DOIs
StatePublished - Jul 2 2018

ASJC Scopus subject areas

  • Genetics

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