Recent studies have shown that many disease phenotypes elevate the risk of future multimorbidities, particularly chronic cardiometabolic disorders. However, our understanding of these disease connections is still severely limited, and it is unclear how much of a role is played by genetics. To decipher the underpinnings of these prolonged disease complications, a holistic approach encapsulating insights from interconnected diseases and genetic associations is essential. A disease-disease network (DDN), a graph where nodes represent diseases and edges represent associations between diseases, can provide an intuitive way of visualizing connections between phenotypes across the landscape of all inheritable diseases. More specifically, a DDN that represents shared genetic variants with its edges can be used to study the genetic associations between phenotypes. Electronic health record (EHR)-linked biobanks, repositories of both phenotypic and genetic information for large populations of patients, serve as invaluable data sources for these networks. Applying a phenome-wide association study (PheWAS) to an EHR-linked biobank will identify genetic associations for all diseases in the dataset. The results of this PheWAS can then be used to create a corresponding genetics-based, biobank-driven DDN, where edges represent shared single nucleotide polymorphisms (SNPs). In this dissertation, we use DDNs to study the genetic architecture of cross-phenotype associations through 1) the development of a biobank-driven DDN for the evaluation of genetic similarity between phenotypes, 2) the generation and comparison of DDNs across different populations and genetic components to identify health disparities, and 3) the creation of multi-layer networks for downstream applications relevant to individual patients. Our endeavors bridge network medicine with precision medicine, aiming to spotlight key genetic drivers of long-term disease complications and pleiotropy while developing tools tailored for personalized disease prediction. We envision that our network-based approaches will be implemented for a variety of complex phenotypes to facilitate multiomic data integration and advance the field of precision medicine.