Journal
GENETICS IN MEDICINE
Volume 21, Issue 6, Pages 1345-1354Publisher
SPRINGERNATURE
DOI: 10.1038/s41436-018-0337-5
Keywords
pharmacogenetics; pharmacogenomics; biobank participants; preemptive pharmacogenetic testing; genotyping array
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Funding
- European Union's Horizon 2020 research and innovation program [692145, 668353]
- Estonian Research Council [PRG184, IUT20-60, IUT34-4, IUT34-11, IUT24-6]
- European Regional Development Fund for the Center of Excellence of Estonian ICT research (EXCITE)
- STACC [EU48684, 2014-2020.4.01.15-0012]
- Swedish Research Council [016-01153, 2016-01154]
- Uppsala University Strategic Research Grant as part of the Science for Life Laboratory fellowship program
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Purpose: Biomedical databases combining electronic medical records and phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations. Methods: We developed and tested algorithms for translation of preexisting genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by genome sequencing, exome sequencing, and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia. Results: Our most striking result was that the performance of genotyping arrays is similar to that of genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants. Conclusion: We find that microarrays are a cost-effective solution for creating preemptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.
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