The promise of precision medicine is to edit a patient?s DNA and/or administer therapeutics targeting etiologic molecules that prevent or reverse the disease process using a tailored design. All of this happens at the level of the individual and requires precision knowledge of that patient?s biology. In stark contrast, much of the knowledge we possess about genomic risk factors comes from statistical measures of association from human populations. The conceptual and practical disconnect between the populations we study and the individuals we want to treat is a major source of confusion about how to move forward in an era driven by genome technology. The primary goal of this proposal is to develop novel informatics methodology and software to facilitate precision medicine by connecting population and individual genomic phenomena. We propose here a Virtual Genomic Medicine (VGMed) workbench where clinicians can carry out thought experiments about the treatment of individual patients using models of disease risk derived from population-level studies. This will be accomplished by first developing a novel Genomics-guided Automated Machine Learning (GAML) algorithm for deriving risk models from real data that is accessible to clinicians (AIM 1). We will then develop a novel simulation approach that is able to generate artificial data that preserves the distribution of genetic effects observed in the real data while maintaining other characteristics such as genotype frequencies (AIM 2). This will generate open data allowing anyone to perform virtual interventions on patients derived from a population- level risk distribution. The workbench will allow editing of individual genotypes and simulate the administration of drugs by editing machine learning parameters in the simulation model (AIM 3). The change in risk and disease status for the specific patient will be tracked in real time. Finally, we provide a feature in the workbench that will allow the clinician to generate specific hypotheses about individual genetic variants that can then be validated using integrated knowledge sources that include databases such as PubMed and ClinVar thus giving the user immediate feedback (AIM 4). All methods and software will be provided as open-source (AIM 5).
Most genetic studies of common human diseases result in statistical summaries of risk derived from human populations. These statistical summaries are not that helpful for determining the health of an individual. This proposal will create new computer algorithms and software help clinicians and researchers connect population- level statistics with individual level genetic effects to advance our understanding of how to treat patients based on their own unique genetic makeup.
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