Although some catalogs of GWAS, such as the NHGRI-EBI GWAS Catalog (8), GWASdb (9) and GRASP (10), have been curating statistically significant variants for years, most of these resources only focus on the reported GWAS signals in each publication, particularly those with the most significant P-value at the associated locus. However, given the complexity of the linkage disequilibrium (LD) structure in the investigated GWAS cohort and statistical fluctuations of association analysis, several GWAS leading variants may not necessarily be trait/disease causal variants (11). Therefore, previous curation projects inevitably missed many causal signals and only provided marker information on genetic associations. Fortunately, recent GWAS communities advocate the public release of GWAS summary statistics, and some pertinent data have been archived in existing databases including LD Hub (12), GWAS Catalog (8), PhenoScanner (13), MR-Base (14), Gene ATLAS (15) and GWAS ATLAS (16). 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
