In this application, we propose a highly ambitious yet realistically attainable goal: to align existing expertise at UNC-CH into a center of excellence in order to develop as a resource and demonstrate the utility of the murine Collaborative Cross (CC) to delineate genetic and environmental determinants of complex phenotypes drawn from psychiatry, the most intractable set of problems in all of biomedicine. We propose a particularly challenging definition of success - we will identify high probability etiological models (which can be realistically complex) and then prove the predictive capacity of these models by generating novel strains of mice bred to be at either very low or very high risk of the phenotype. Once validated, these high confidence models can then be tested in subsequent human studies. The data collected at the UNC center would be a valuable resource to the wider scientific community and could be used to interrogate any number of biological problems. The development of sophisticated, user-interactive databases to access the large, complex datasets collected represents a key component of the project. Accomplishing this overarching goal requires an exceptional diversity of scientific expertise - psychiatry, human genetics, mouse phenotyping, mouse genetics, statistical genetics, computational biology, and systems biology. Experts in all of these disciplines were deeply involved in the preparation of this application and are committed to the projects described here. Moreover, successful integration of these diverse fields is non-trivial;however, we can document that all scientists on this application have a history of extensive interactions over the past five years, know now how to work together and have a working knowledge of their colleagues'expertise. UNC-Chapel Hill has a shown an intense commitment to promoting inter-disciplinary genomics research and is one of the most collegial biomedical research institutions in the US which provides a fertile backdrop for "Science 2.0" projects such as that proposed here.
Psychiatric disorders are a paradox-the associated morbidity, mortality, and societal costs are enormous and yet, despite over a century of scientific study, there are few hard facts about the etiology of the core diseases. Although GWAS meta-analyses are in progress, early results suggest that strong and replicable findings are elusive. Our proposal provides an alternative model approach to complement the study of fundamental psychiatric phenotypes.
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