Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network

Bush, William S.; Crosslin, David R.; Owusu-Obeng, Aniwaa; Wallace, John R.; Almoguera, Berta; Basford, Melissa A.; Bielinski, Suzette J.; Carrell, David; Connolly, John J.; Crawford, Dana C.; Doheny, Kimberly F.; Gallego, Carlos J.; Gordon, Adam S.; Keating, Brendan J.; Kirby, Jacqueline; Kitchner, Terrie; Manzi, Shannon; Mejia, Ana R.; Pan, Vivian; Perry, Cassandra; Peterson, Josh F.; Prows, Cynthia A.; Ralston, James D.; Scott, Stuart A.; Scrol, Aaron; Smith, Maureen E.; Stallings, Sarah; Veldhuizen, Tamra L.; Wolf, Wendy A.; Volpi, Simona; Wiley, Ken; Li, R.; Manolio, Teri A.; Böttinger, Erwin P.; Brilliant, Murray H.; Carey, David J.; Chisholm, Rex L.; Chute, Christopher G.; Haines, Jonathan L.; Hákonarson, Hákon; Harley, John B.; Holm, Ingrid A.; Kullo, Iftikhar J.; Jarvik, Gail P.; Larson, Eric B.; McCarty, Catherine A.; Williams, Marc S.; Denny, Joshua C.; Rasmussen‐Torvik, Laura J.; Roden, Dan M.; Ritchie, Marylyn D.

doi:10.1002/cpt.350

Cited by 166 publications

(152 citation statements)

References 41 publications

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“…29 Preliminary results from 2 clinical studies, 1 that recruited from a hospital system and the other from a long-term care facility, showed actionable genotypes for dosage changes or contraindication for the patients' current medications in 24% and 50% of patients, respectively. 30,31 Given our finding that 97% of patients had at least 1 actionable pharmacogenetic variant, and the results of a 5000-patient US study in which 96% of participants had actionable pharmacogenetic variants, 32 it is likely that future studies will show similar numbers. The high proportion of patients with actionable genotypes, coupled with the fact that 11% of Canadians aged 45-64 years and 30% of those aged 65-79 years take at least 5 prescription drugs concurrently, 33 indicates that preemptive administration of a pharmacogenetic test has enormous potential.…”

Section: Discussionmentioning

confidence: 90%

Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study

et al. 2016

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Background: Inappropriate prescribing increases patient illness and death owing to adverse drug events. The inclusion of genetic information into primary care medication practices is one solution. Our aim was to assess the ability to obtain and genotype saliva samples and to determine the levels of use of a decision support tool that creates medication options adjusted for patient characteristics, drug-drug interactions and pharmacogenetics. Methods:We conducted a cohort study in 6 primary care settings (5 family practices and 1 pharmacy), enrolling 191 adults with at least 1 of 10 common diseases. Saliva samples were obtained in the physician's office or pharmacy and sent to our laboratory, where DNA was extracted and genotyped and reports were generated. The reports were sent directly to the family physician/pharmacist and linked to an evidence-based prescribing decision support system. The primary outcome was ability to obtain and genotype samples. The secondary outcomes were yield and purity of DNA samples, ability to link results to decision support software and use of the decision support software. Results:Genotyping resulted in linking of 189 patients (99%) with pharmacogenetic reports to the decision support program. A total of 96.8% of samples had at least 1 actionable genotype for medications included in the decision support system. The medication support system was used by the physicians and pharmacists 236 times over 3 months.Interpretation: Physicians and pharmacists can collect saliva samples of sufficient quantity and quality for DNA extraction, purification and genotyping. A clinical decision support system with integrated data from pharmacogenetic tests may enable personalized prescribing within primary care. Trial registration: ClinicalTrials.gov, NCT02383290. AbstractResearch Research CMAJ OPENCMAJ OPEN, 4(3) E529fective at changing prescribing decisions, 13 whereas information given within a physician's workflow has been found to be effective in reducing inappropriate orders for imaging. 14 A medication decision support system (MDSS) is a health information technology system that is designed to provide health care professionals with clinical decision support with medication decision-making tasks. These systems assess whether a drug is safe and effective for the patient, taking into account other medications, diseases and the patient's physical state. 15 We have developed a patient-centred MDSS that assesses the potential drug-drug, drug-condition, druggene and drug-drug-gene interactions and produces a list of drug options least likely to cause harm and most likely to be effective.We conducted a study to assess the DNA collection processes, investigate a panel of pharmacogenetic tests relevant to primary care patients and assess the use of an MDSS. The value of the MDSS will be assessed once feasibility of all processes has been shown. MethodsOver an 18-month period before the start of the trial, we developed a pharmacogenetic panel, a pharmacogenetic report and an MDSS for use in primary...

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Section: Discussionmentioning

confidence: 90%

Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study

et al. 2016

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“…The previous studies prove that EHRs provide a new avenue for reusing already existing genotypes by studying their relationships with different phenotypes. In addition to disease and trait genetics, EHRs are also a precious resource for investigating pharmacogenetic traits [69] and developing reverse genetics approaches such as phenome-wide association studies (PheWAS) [70]. PheWAS allow generating hypotheses by testing a selected group of single nucleotide polymorphisms (SNPs) and many phenotypes.…”

Section: B Secondary Use Of Patient Data For Genomic Research and Tomentioning

confidence: 99%

Secondary Use and Analysis of Big Data Collected for Patient Care

Martín-Sánchez¹,

Aguiar-Pulido²,

Lopez-Campos³

et al. 2017

Yearb Med Inform

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SummaryObjectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

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“…Recent years has seen the expansion of precision medicine, with the collection of full genomic sequence data for pharmacogenomics studies (Bush et al 2016;Rasmussen-Torvik et al 2014). Given the dramatic and rapid expansion of knowledge in this area, it is now widely accepted that physicians cannot digest the literature fast enough to implement research findings in clinical practice (Johansen Taber and Dickinson 2014).…”

Section: Introductionmentioning

confidence: 99%

Integrating Community-Level Data Resources for Precision Medicine Research

et al. 2017

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Precision Medicine focuses on collecting and using individual-level data to improve healthcare outcomes. To date, research efforts have been motivated by molecular-scale measurements, such as incorporating genomic data into clinical use. In many cases however, environmental, social, and economic factors are much more predictive of health outcomes, yet are not systematically used in clinical practice due to the difficulties in measurement and quantification. Advances in both the availability of electronic health information, environmental exposure data, and the more systematic use of geo-coding now provide ways to systematically assess community-level indicators of health, and link these factors to electronic health records for evaluating their influence on disease outcomes. In this workshop, we discuss new electronic sources of community-level data, and provide insight into their utility and validity when compared with gold-standard data collection approaches.

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Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network

Cited by 166 publications

References 41 publications

Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study

Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study

Secondary Use and Analysis of Big Data Collected for Patient Care

Integrating Community-Level Data Resources for Precision Medicine Research

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