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

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.

Dissecting molecular regulatory mechanisms underlying noncoding susceptibility SNPs associated with 19 autoimmune diseases using multi-omics integrative analysis

“…In this study, we developed a new improved epigenetic functional scoring method to prioritize functional autoimmune SNPs through incorporating hundreds of immune cell-specific active epigenetic information. Some other comparable scoring methods are also developed, such as 3DSNP [13], FIRE [11], GWAS4D [14], IW-Scoring [15] or RegulomeDB [12]. Compared with these approaches, one distinct feature of our method was the integrating of immune cell-specific epigenetic information (Table S15), which might provide better evaluation for disease-specific functional autoimmune SNPs.…”

“…However, the prior labelled training data may be inaccurate and impractical due to the current knowledge limitation in functional roles underlying noncoding SNPs. Some other methods like RegulomeDB [12], 3DSNP [13], GWAS4D [14], IW-Scoring [15], Eigen [16], and FunSeq2 [17] either directly combined various epigenetic/regulatory features to rank SNP functionality or adopted a weighted scoring scheme by considering the relative importance of each feature to assign SNP functionality scores. However, these approaches principally incorporated epigenetic or transcriptional annotation across all cells or tissues, while omitting the cell-specific or context-specific regulation.…”

Whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants

“…in which highly linked SNPs achieve similar posterior probabilities of causality, requiring further investigation of the true causal variants by other computational strategies, such as functional annotation (56). By visualizing regional PHRED-scaled score spectrum of composite models across 5p15.33 TERT region, we found several PHRED score peaks of regBase_REG, regBase_REG_Common and regBase_CAN generally colocalize with significant disease-associated variants identified by existing GWASs, especially in the TERT promoter region ( Figure 5A and Supplementary Table S15).…”

RSAT Var-tools: an accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding

“…epigenomics and transcriptomics data. Still, these resources and tools have limitations hampering their usage in several organisms [6][7][8] , on new annotated variants [9][10][11] , and/or on analyses with personal collections of TF motifs [6,7,12] .…”

“…Major differences between these tools can be found in (i) their availability: web pages [9] , command line [14] or both [15] ); (ii) flexibility for the user to input their own data [16] ; (iii) usability (possibility to use several variant formats [7] ); (iv) results representation: figures and/or tables [10] ; (v) available organisms: only human [17] , or other organisms [16] ); and (vi) the possibility to calculate results on-the-fly [12] or access pre-calculated ones [9] . One of the major challenges, aside from the development itself, is the maintenance over time and tool updating.…”