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Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions
Univ Tartu, Inst Comp Sci, Tartu, Estonia;STACC, Tartu, Estonia;Quretec, Tartu, Estonia.
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia;Univ Tartu, Inst Mol & Cell Biol, Tartu, Estonia.
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia;Univ Tartu, Inst Mol & Cell Biol, Tartu, Estonia.
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia.
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2019 (English)In: Genetics in Medicine, ISSN 1098-3600, E-ISSN 1530-0366, Vol. 21, no 6, p. 1345-1354Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2019. Vol. 21, no 6, p. 1345-1354
Keywords [en]
pharmacogenetics, pharmacogenomics, biobank participants, preemptive pharmacogenetic testing, genotyping array
National Category
Medical Genetics
Identifiers
URN: urn:nbn:se:uu:diva-387930DOI: 10.1038/s41436-018-0337-5ISI: 000470079700013PubMedID: 30327539OAI: oai:DiVA.org:uu-387930DiVA, id: diva2:1331885
Funder
EU, Horizon 2020, 692145EU, Horizon 2020, 668353Swedish Research Council, 016-01153Swedish Research Council, 2016-01154Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-06-27Bibliographically approved

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Milani, Lili

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