The Pharmacogenomics of Statin Therapy (POST) Center has developed three projects to discover and validate novel genomic markers (genes, transcripts, metabolites or SNPs) associated with statin effects. To facilitate scientific progress and synergize the Center such that the collective whole is greater than the sum of the parts, we have developed an Informatics Core to integrate the extensive data that will be collected, provide analysis expertise to the projects, and develop novel methods to integrate and visualize data across the three projects which include transcriptomic and metabolomic studies in human lymphoblastoid cell lines, genetic and functional studies in murine models, and extensive genetic epidemiology studies in a large population sample. A key function of the Informatics Core is to integrate information obtained across these diverse yet complementary platforms, thus fully leveraging each dataset to maximize our understanding of the factors underlying variation in statin response. The overall objective of the Informatics Core is to facilitate the maximal level of analysis and coordination of data and results across the three projects. This Core will function as the central hub for data sharing across the three POST projects, as well as functioning as `The Integrator' for combining the data/results from the three projects to extract new knowledge about statin response, not realized from one project alone. We will achieve these goals through the following three specific aims:
Aim 1) Assemble and integrate datasets from Projects 1-3 to maximize collaborations across the three projects.
Aim 2) Develop and apply a bioinformatics tool to integrate results from different experimental strategies (from Projects 1-3) to identify genes, metabolites, networks and/or DNA variants associated with statin efficacy or adverse effects using machine learning and powerful biological annotations.
Aim 3) Develop an interactive data visualization tool to combine results from different experimental strategies (from Projects 1-3). In addition to these Aims, the Core will also work closely with individual Project investigators to perform higher-order statistical analyses to serve the specific needs of each Project. Through these activities, the Informatics Core will not only aid in the identification of markers that may be used in the development of precision medicine guidelines for statin treatment, but will also develop novel statistical tools that may be generally applied to pharmacogenomics research.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center (P50)
Project #
5P50GM115318-03
Application #
9326330
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
Budget Start
2017-09-01
Budget End
2018-08-31
Support Year
3
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Children's Hospital & Res Ctr at Oakland
Department
Type
DUNS #
076536184
City
Oakland
State
CA
Country
United States
Zip Code
94609
Lee, Seung-Been; Wheeler, Marsha M; Patterson, Karynne et al. (2018) Stargazer: a software tool for calling star alleles from next-generation sequencing data using CYP2D6 as a model. Genet Med :
Kim, Dongwook; Shivakumar, Manu; Han, Seonggyun et al. (2018) Population-dependent Intron Retention and DNA Methylation in Breast Cancer. Mol Cancer Res 16:461-469
Miller, Jason E; Shivakumar, Manu K; Risacher, Shannon L et al. (2018) Codon bias among synonymous rare variants is associated with Alzheimer's disease imaging biomarker. Pac Symp Biocomput 23:365-376
Oni-Orisan, Akinyemi; Hoffmann, Thomas J; Ranatunga, Dilrini et al. (2018) Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Using Electronic Health Records in a Large Population-Based Cohort. Circ Genom Precis Med 11:e002043
Orozco, Luz D; Farrell, Colin; Hale, Christopher et al. (2018) Epigenome-wide association in adipose tissue from the METSIM cohort. Hum Mol Genet 27:1830-1846
Kim, Kyungpil; Theusch, Elizabeth; Kuang, Yu-Lin et al. (2018) ZNF542P is a pseudogene associated with LDL response to simvastatin treatment. Sci Rep 8:12443
Veturi, Yogasudha; Ritchie, Marylyn D (2018) How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures? Pac Symp Biocomput 23:228-239
Lee, Younghee; Han, Seonggyun; Kim, Dongwook et al. (2018) Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease. AMIA Jt Summits Transl Sci Proc 2017:124-131
Hoffmann, Thomas J; Theusch, Elizabeth; Haldar, Tanushree et al. (2018) A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet 50:401-413
El-Manzalawy, Yasser; Hsieh, Tsung-Yu; Shivakumar, Manu et al. (2018) Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data. BMC Med Genomics 11:71

Showing the most recent 10 out of 21 publications