Bioinformatics Strategies for Genome-Wide Association Studies

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One promise of precision medicine for Alzheimer’s disease 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 in subjects ascertained with and without Alzheimer’s. 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 for Alzheimer’s 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 Alzheimer’s 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 Alzheimer’s clinicians (AIM 1). We will then develop a novel simulation approach that is able to generate artificial Alzheimer’s 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 Alzheimer’s 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 Alzheimer’s disease status for the specific patient will be tracked in real time. Finally, we provide a feature in the workbench that will allow the Alzheimer’s clinician to generate specific hypotheses about individual genetic variants that can then be validated using integrated Alzheimer’s 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 to the Alzheimer’s disease research community (AIM 5).