Most of the disease-associated variants detected by genome-wide association studies (GWAS) are located in genomic non-coding regions, posing a significant challenge for interpreting the underlying molecular mechanism. The emergence of molecular quantitative trait loci (xQTL), such as expression QTL (eQTL), has been demonstrated as a powerful way of linking non-coding variants to disease phenotype. However, identifying these disease-associated xQTLs demands extremely complex computational strategies, which raise the bar for clinicians and experimental biologists. To maximize the value of xQTL data and lower the barrier for post-GWAS studies, a flexible and user-friendly tool is urgently needed. We thus developed an R package xQTLbiolinks as the first computational tool that enables the integrative analysis of molecular QTL and GWAS summary statistics data with guided workflow. We also applied xQTLbiolinks to the prostate cancer dataset and revealed several known and novel prostate cancer susceptible genes.