With their introduction into practice over the last two decades, electronic medical records (EMRs) have become increasingly recognized as platforms to not only improve delivery of care to the individual but also to understand variability in domain such as disease presentation and outcomes or quality assurance. Coupling dense genomic information to EMRs in eMERGE has provided tools for both discovery and initial implementation in genomic medicine, while raising new challenges and opportunities for using genomic data in healthcare. These include developing and mining the large datasets necessary to identify groups of patients with extreme phenotypes or rare genotypes; identifying clinically-relevant subsets of common diseases; and identifying actionable genomic variants and determining how best to deploy these in a learning healthcare system. Building on our experience and contributions to eMERGE-I and eMERGE-II, we propose here three specific aims to address these challenges.
In Specific Aim 1, we will expand the network's phenotyping library by creating increasingly granular phenotype definitions that identify specific subsets of disease with predictable clinical courses or response to therapies. Genotype-phenotype relations will be studied by GWAS and advanced PheWAS methodology we have developed.
In Specific Aim 2, we will identify rare variants with strong associations with human traits by resequencing 100 genes in 2,500 subjects at our center as part of the eMERGE-III 25,000 patient cohort. We propose studying genes with variants known to affect human health and drug responses, and variants that our preliminary PheWAS analysis implicates as robust markers of important human phenotypes.
In Specific Aim 3, we will expand PREDICT, our pre-emptive pharmacogenomic implementation program, to develop a pipeline that will deliver actionable variants to patients and providers and to assess their response. We will collaborate across eMERGE to develop, implement, and assess tools to deliver new information, measuring impact to ensure optimal benefit to patients. By executing these discovery and implementation aims, our site and the eMERGE network will contribute importantly to advancing the vision of Genomic Medicine as a contributor to modern healthcare.
Collectively, investigators dedicated to implementing genomic medicine for improving health have made strides using medical records and DNA information to address associations between genotypes and phenotypes like disease susceptibility and responses to treatment. Ongoing challenges - that this proposal addresses - are 1) to identify clearly actionable variants and 2) to develop optimal methods to deliver them appropriately into the flow of healthcare. In our proposal to join the eMERGE-III network, we seek actionable variants by sequencing disease genes most likely to contain rare variants with strong human trait associations identified by strategically applying EMR data-based inquiry and analysis, and we engage patients, providers, and data security expertise to expand PREDICT, our pre-emptive pharmacogenomic implementation program, for delivering actionable variants, assessing patient and provider response, and feeding results back into clinical process.
|Bastarache, Lisa; Hughey, Jacob J; Hebbring, Scott et al. (2018) Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science 359:1233-1239|
|Kroncke, Brett M; Yang, Tao; Kannankeril, Prince et al. (2018) Exploiting ion channel structure to assess rare variant pathogenicity. Heart Rhythm 15:890-894|
|Volpi, Simona; Bult, Carol J; Chisholm, Rex L et al. (2018) Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects. Clin Pharmacol Ther 103:778-786|
|Robinson, Jamie R; Denny, Joshua C; Roden, Dan M et al. (2018) Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records. Clin Transl Sci 11:112-122|
|Roden, Dan M; Van Driest, Sara L; Wells, Quinn S et al. (2018) Opportunities and Challenges in Cardiovascular Pharmacogenomics: From Discovery to Implementation. Circ Res 122:1176-1190|
|Giudicessi, John R; Roden, Dan M; Wilde, Arthur A M et al. (2018) Classification and Reporting of Potentially Proarrhythmic Common Genetic Variation in Long QT Syndrome Genetic Testing. Circulation 137:619-630|
|Mosley, Jonathan D; Feng, QiPing; Wells, Quinn S et al. (2018) A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. Nat Commun 9:3522|
|Fossey, Robyn; Kochan, David; Winkler, Erin et al. (2018) Ethical Considerations Related to Return of Results from Genomic Medicine Projects: The eMERGE Network (Phase III) Experience. J Pers Med 8:|
|Denny, Joshua C; Van Driest, Sara L; Wei, Wei-Qi et al. (2018) The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development. Clin Pharmacol Ther 103:409-418|
|Jerome, Rebecca N; Pulley, Jill M; Roden, Dan M et al. (2018) Using Human 'Experiments of Nature' to Predict Drug Safety Issues: An Example with PCSK9 Inhibitors. Drug Saf 41:303-311|
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