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

• Published in: Nucleic acids research Vol. 46; no. W1; pp. W114 - W120
• Main Authors: Huang, Dandan, Yi, Xianfu, Zhang, Shijie, Zheng, Zhanye, Wang, Panwen, Xuan, Chenghao, Sham, Pak Chung, Wang, Junwen, Li, Mulin Jun
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 02.07.2018
• Subjects: Computational Biology - trends; Genetic Diseases, Inborn; Genome-Wide Association Study; Genomics - methods; Humans; Polymorphism, Single Nucleotide - genetics; Quantitative Trait Loci - genetics; Software; Web Server Issue
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gky407

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.