The long-term goal of this project is to identify clinical and genetic features of retinopathy of prematurity (ROP) development, and to analyze their relationships. Although biomedical research data are being generated at an enormous pace, much less work has been done to integrate disparate scientific findings across the spectrum from genomics to imaging to clinical medicine. Our overall hypotheses are that genetic factors are involved in the initiation and modulation of ROP, and that analysis of relationships among clinical, imaging, and genetic findings in ROP using bioinformatics approaches will improve understanding of disease pathogenesis and diagnosis. These hypotheses will be tested using three Specific Aims: (1) Recruit, phenotype, and collect genetic material from ~600 additional premature infants at-risk for ROP from 8 centers. This renewal will continue work from the original project, to obtain a total cohort size of >1600 infants for genetic analysis. Demographic and clinical features from serial ophthalmic examinations will be ascertained fully, retinal images will be captured, rigorous reference standards will be established, and blood samples will be collected. (2) Perform imaging and informatics analysis of this cohort. This will: (a) Create quantitative indices for computer- based diagnosis of severe ROP. Features from automated image analysis that best correlate with severe ROP and plus disease will be identified, and quantitative indices will be defined and validated for clinical use. (b) Combine this into disease prediction models incorporating the effects of quantitative image traits, clinical features, and environmental risk factors on ROP susceptibility. (3) Perform genetic and bioinformatics analysis of this cohort. This will: (a) analyze exome sequence data (previously obtained) from 100 phenotypically extreme subjects that were also preferentially enriched as being dizygotic twin pairs, to identify rare variants; (b) perform and analyze genome-wide genotyping to test against clinical and imaging findings, testing specific candidate genes related to vascular pathology in the eye and the vasculature at large, followed by performing a pathway-based analysis; and (c) perform bioinformatics analysis to combine findings from (3a) and (3b) in relation to the clinical findings. Ultimately, these studies should improve understanding of neovascularization in ROP and related ocular diseases, and of normal vascular development in infants. In addition, this work should demonstrate a prototype for health information management which combines genotypic and phenotypic data. This project will be performed by a multi-disciplinary team of collaborative investigators with expertise in ophthalmology, biomedical informatics, computer science, computational biology, ophthalmic genetics, genetic analysis, and statistical genetics.