We used several well-known GWAS loci as examples and found that CAUSALdb could identify potential causal variants that were verified or were about to be verified; moreover, it facilitated the identification of true causal variants in a difficult credible set. The query functions of CAUSALdb are very beneficial for cross-study and cross-trait comparisons of causality. For instance, rs17293632 shows notable PP of causality in multiple disease categories, including cardiovascular and autoimmune diseases, implying that this variant plays a role in pleiotropy. Furthermore, CAUSALdb provides fine-mapping results of the 95% credible set of all GWASs, which may be a useful resource for researchers in other fields, such as disease risk prediction and drug repositioning. The well-formatted summary statistics of each causal block are also downloadable, enabling specific downstream analysis such as Mendelian randomization and colocalization. Although we have established several novel online functions for trait causality investigation, there are still some points that can be further improved in future. Many complex genetic loci harbor multiple causal variants for a particular trait/disease (54,55); given the computational burden and relatively low accuracy of
