Extraction of information from rich and diverse datasets through summary-level statistics, as opposed to individual level data, can be appealing because of various practical and ethical considerations. In this talk, I will describe statistical methods for building complex models using summary-level information in two distinct applications. One involves assessment of genetic architecture of complex traits by modeling of effect-size distributions using association statistics available from large genome-wide association studies. The other involves development of a generalized meta-analysis framework for unified model building using information on parameters from disparate, but possibly overlapping, sub-models fitted to different studies. Implications for future precision medicine efforts towards disease prevention will be discussed for both applications.