It is now recognized that many visual diseases are influenced by complex interactions between multiple different genetic variants. As a result, our ability to predict susceptibility to visual diseases will depend critically on the computational, mathematical and statistical modeling methods and software that are available for making sense of high-dimensional genetic data. We propose here a bioinformatics research project to develop network modeling approaches for identifying combinations of genetic biomarkers associated with visual disease endpoints. Our working hypothesis is that a systems-based bioinformatics approach using network modeling will play a very important role in confronting the complexity of the relationship between genomic variation and visual diseases. We will first develop and evaluate modeling methods to infer large-scale genetic interaction networks from genome-wide association studies (AIM 1). We will then apply the modeling methods developed in AIM 1 to the inference of genetic interaction networks from genome-wide association data in subjects with and without visual diseases (AIM 2). Next, we will utilize the inferred genetic interaction networks to guide the development of predictive genetic models of visual diseases (AIM 3). Finally, all network modeling methods will be released to the vision research community as part of a popular user-friendly, freely available and open-source software package (AIM 4). We anticipate that the network modeling methods and software developed and distributed as part of this project will play an important role in the development of the genetic tests that will be necessary to identify those at risk for visual diseases.