Evidence map›Paper›PMID 30327539›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019

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

Sulev Reisberg, Kristi Krebs, Maarja Lepamets, Mart Kals, Reedik Mägi, Kristjan Metsalu, Volker M Lauschke, Jaak Vilo, Lili Milani

Abstract read
In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sulev ReisbergInstitute of Computer Science, University of Tartu, Tartu, Estonia.
Kristi KrebsEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
Maarja LepametsEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
Mart KalsEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
Reedik MägiEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
Kristjan MetsaluEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
Volker M LauschkeDepartment of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, Stockholm, Sweden.
Jaak ViloInstitute of Computer Science, University of Tartu, Tartu, Estonia.
Lili MilaniEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia. lili.milani@ut.ee.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBiomedical 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.

methodsWe 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.

resultsOur 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.

conclusionWe 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.

Indexed as

AlgorithmsBiological Specimen BanksDatabases, FactualElectronic Health RecordsEstoniaGenetic TestingGenotypeHumansOligonucleotide Array Sequence AnalysisPharmacogeneticsPharmacogenomic TestingPharmacogenomic VariantsPhenotypePrecision MedicineSequence Analysis, DNAbiobank participantsgenotyping arraypharmacogeneticspharmacogenomicspreemptive pharmacogenetic testing

Identifiers

PMID30327539
PMCPMC6752278

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.