Here, we develop a web server GWAS3D (http://jjwanglab.org/gwas3d) to systematically analyze the probability of genetic variants affecting regulatory pathways and underlying disease/trait associations by integrating chromatin state, functional genomics, sequence motifs and cross-species conservation for a set of GWAS data or variant list. We first collected and curated genome-wide chromosome interaction (5C, Hi-C, ChIA-PET) data, enhancer/insulator/promoter marks [H3K4me1, H3K27ac, p300, CCCTC-binding factor (CTCF), DHS] and ChromHMM predicted functional elements in 16 different cell types. Using those regulatory regions, we mapped genetic variants to the reference genome and evaluated the binding affinity changes of regulatory factors by scanning 73 ENCODE motifs. Finally, we combined original GWAS signal, risk haplotype, binding affinity significance and conservation information to prioritize the genetic variants. In addition, the system provides comprehensive annotation and visualization to help users to interpret the results. Comparing with existing software and databases, GWAS3D uses the latest information to build a one-stop web-based tool for clinicians and biologists to evaluate the deleteriousness of disease/trait-associated variants that affect transcription regulation on a broader spectrum, especially on non-coding genetic variation.
