Summary statistics from extensive genetic and -omic research provide valuable biological insights into human complex traits and diseases, but also introduce new statistical, logistical, and computational challenges. In this talk, I will share our recent work in statistical models and computing infrastructures designed to address these interdisciplinary challenges in analyzing genetic summary data. This includes leveraging random matrix theory to understand the behavior of reference panel-based estimators in high-dimensional summary statistics from genome-wide association studies. I will further discuss the self-training of summary statistics, with applications to develop large-scale proteomic imputation models. In addition, I will introduce the BIGA platform (http://bigagwas.org/), a website based on cloud computing that offers unified data analysis pipelines and centralized data resources for accessible genetic summary data analysis.