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Sökning: onr:"swepub:oai:DiVA.org:uu-387930" > Translating genotyp...

Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations challenges and solutions

Reisberg, Sulev, (författare)
Univ Tartu, Inst Comp Sci, Tartu, Estonia;STACC, Tartu, Estonia;Quretec, Tartu, Estonia
Krebs, Kristi, (författare)
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia;Univ Tartu, Inst Mol & Cell Biol, Tartu, Estonia
Lepamets, Maarja, (författare)
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia;Univ Tartu, Inst Mol & Cell Biol, Tartu, Estonia
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Kals, Mart, (författare)
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia
Magi, Reedik, (författare)
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia
Metsalu, Kristjan, (författare)
Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia
Lauschke, Volker M., (författare)
Karolinska Inst, Dept Physiol & Pharmacol, Sect Pharmacogenet, Stockholm, Sweden
Vilo, Jaak, (författare)
Univ Tartu, Inst Comp Sci, Tartu, Estonia;STACC, Tartu, Estonia;Quretec, Tartu, Estonia
Milani, Lili, 1981- (författare)
Uppsala universitet, Molekylär medicin, Uppsala universitet, Science for Life Laboratory, SciLifeLab, Univ Tartu, Estonian Genome Ctr, Inst Genom, Tartu, Estonia
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Uppsala universitet Medicinska och farmaceutiska vetenskapsområdet. Medicinska fakulteten. Institutionen för medicinska vetenskaper. Molekylär medicin. (creator_code:org_t)
Uppsala universitet Science for Life Laboratory, SciLifeLab. (creator_code:org_t)
2019
Engelska.
Ingår i: Genetics in Medicine. - NATURE PUBLISHING GROUP. - 1098-3600 .- 1530-0366. ; 21:6, s. 1345-1354
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska grundvetenskaper -- Medicinsk genetik (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Medical Genetics (hsv//eng)

Nyckelord

pharmacogenetics
pharmacogenomics
biobank participants
preemptive pharmacogenetic testing
genotyping array

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