Since being developed in 2005, genome-wide association studies (GWAS) have identified 70,566,447 associations between 3,251,694 unique single-nucleotide polymorphisms (SNPs) and 1,451 unique diseases/traits, which provides researchers with new opportunities to study these diseases/traits. However, because most of these SNPs are in the noncoding region of the human genome, it is difficult to specify the causative functional SNPs (fSNPs) from all the disease-associated SNPs in linkage disequilibrium. Thus, researchers are limited to using these GWAS data, a huge public investment, for patient care and therapeutic interventions. While numerous in silico methods have been generated to identify fSNPs, these methods cannot avoid the fact that they can only compute a prior probability for each SNP, which represents its likelihood of having a causal effect on risk-gene expression. To overcome this difficulty, Li and colleagues have recently developed a sequential methodology with a set of novel techniques. By using these techniques, they can rapidly identify fSNPs in a high-throughput experimental fashion and characterize these fSNPs by identifying the fSNP-bound proteins that regulate risk-gene expression. In this talk, Li will present two examples to show how he used these techniques to translate GWAS data into biological mechanisms. One is to generate a disease-associated CD40 induced NF-kB-dependent signal transduction and transcriptional regulation network. The other is to dissect the CDKN2A/B locus associated with age-related pathologies by revealing the underlying mechanism regulating cellular senescence. In both cases, Li shows the potential to apply post-GWAS functional studies to identify targets for drug development.