Nevertheless, these resources have only collected limited or specific GWAS summary statistics to date and were not particularly designed to prioritize causal variants. However, statistical fine-mapping technologies were developed to identify underlying causality from GWAS summary information (17). Although some recent GWASs reported their associated signals along with the fine-mapping results, majority of existing GWASs did not point out potential causal variants in each significant locus. At present, there is no resource that follows a consistent procedure to systematically fine-map potential trait/disease causal variants at the genome-wide level. Moreover, online manipulation of genome-wide summary statistics involves intensive network data transmission load, and it is laborious for researchers to deploy fine-mapping pipeline on their own. Despite several web-based visualization tools, including LocusExplorer (18) and LocusZoom.js (19), that attempt to provide researchers with options for displaying potential causal variants, an online resource that can efficiently help visualize and operate genome-wide summary statistics and elucidate underlying causal signatures is still lacking. Finally, statistical fine-mapping usually fails to distinguish a true causal variant from extremely high LD (20); therefore, the integration of fine-scale functional annotation information is required to further prioritize fine-mapped variants.
