PARENT ABSTRACT The overall objective of the Center Pharmacogenomics of Statin Therapy (POST) is to apply genomic, transcriptomic, and metabolomic analyses, together with studies in cellular and animal models, and innovative informatic tools, to identify and validate biomarkers for efficacy of statin drugs in reducing risk of cardiovascular disease (CVD), and for adverse effects of statins, specifically myopathy and type 2 diabetes. This multidisciplinary approach is enabled by a team of investigators with expertise in genomics (human, mouse, and molecular), statistics and informatics, and clinical medicine and pharmacology. The Center is comprised of three Projects, two Research Cores, and an Administrative Core. A major aim of Project 1 is the identification of cellular transcriptomic and metabolomic markers for clinical efficacy and adverse effects of statins. This will be accomplished by analyses in statin-exposed lymphoblast cell lines derived from patients with major adverse coronary events, or onset of myopathy or type 2 diabetes on statin treatment, compared with unaffected statin-treated controls. In addition, using genome wide genotypes from these patients, DNA variants will be identified that are associated with statin-induced changes in the transcripts and metabolites that most strongly discriminate affected patients and controls. Project 2 will use a unique, well- characterized panel of 100 inbred mouse strains to discover genetic variation associated with statin- induced myopathy and dysglycemia. Mechanisms underlying these effects will be investigated, with emphasis on the role of dysregulation of autophagy by statin treatment. Projects 1 and 2 will also use relevant cellular and mouse models, respectively, to perform functional studies to validate effects of genes identified in all POST projects as strong candidates for modulating statin efficacy or adverse effects. In Project 3, information derived from genome-wide genotypes, electronic health records, and pharmacy data in a very large and diverse population-based patient cohort will be leveraged to identify and replicate genetic associations with statin efficacy (lipid lowering and CVD event reduction) and adverse effects (myopathy and type 2 diabetes), as well as to assess the overall heritability of these responses. The Clinical Core, based in Kaiser Permanente of Northern California, will provide the clinical information and biologic materials for both Projects 1 and 3. Investigators in the Informatics Core will optimize data analysis and integration of results across all projects. The Administrative Core will provide scientific leadership and management of the Center, and foster scientific interactions and training opportunities. Overall, the research program of this Center provides an innovative model for a systems approach to pharmacogenomics that incorporates complementary investigative tools to discover and validate genetically influenced determinants of drug response. Moreover, the findings have the potential for guiding more effective use of statins for reducing CVD risk and minimizing adverse effects, and identifying biomarkers of pathways that modulate the multiple actions of this widely used class of drugs. ADMINISTRATIVE SUPPLEMENT ABSTRACT In response to NOT-AG-18-008, our goal is to extend the validation and application of our data integration methodologies into Alzheimer?s disease research. This administrative supplement is designed to extend the work of the existing subaward to the University of Pennsylvania subcontract for the POST Informatics Core. The PGRN POST Informatics Core serves as the central hub for data sharing and coordination across the three POST projects in the PGRN P50 award. One of our jobs is annotating the extensive information that will be collected and providing analysis expertise to the projects as needed. However, to make great strides in scientific progress and ensure that the collective whole of the Center is greater than the sum of the parts, a key function of the Informatics Core is to serve as ?The Integrator? to combine these data and information. We and others have shown that integration of complementary omics-based data can provide emergent insights into biological processes compared to what can be learned through any single approach alone. The methods that we are developing to integrate data for statin pharmacogenomic phenotypes will be equally applicable in the area of Alzheimer?s disease. Additionally, recent emphasis on open data science by Alzheimer?s disease researchers provides ample data for us to interrogate our method. We have developed novel statistical analysis tools such as the Analysis Tool for Heritable and Environmental Network Associations (ATHENA), and data visualization tools, such as PhenoGram, both of which are designed to collect and combine information from diverse data sources. With these tools, we will leverage publicly available Alzheimer?s disease datasets to maximize the knowledge gleaned about disease risk for Alzheimer?s diseases. The methodologies that we have been developing as part of the PGRN POST award for the past 2.5 years are clearly applicable to the study of Alzheimer?s disease risk. An important validation step of the application of our methodologies is to apply them to different types of datasets and in different phenotypic areas. This administrative supplement focused on extended research into having an Alzheimer?s disease focus is a great mechanism to simultaneously allow us to validate our methodologies with different types of data and potentially identify important risk factors and pathways toward a better understanding of the etiology of Alzheimer?s disease. Finally, we may have the opportunity to identify cross biological implications due to the known pleiotropic relationships between Alzheimer?s disease and cardiovascular disease.
This administrative supplement is focused on extended our data integration research into Alzheimer?s disease. This funding mechanism will simultaneously allow us to validate our methodologies with different types of data and also potentially identify important risk factors and pathways toward a better understanding of the etiology of Alzheimer?s disease.
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