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. 2018 Nov 12;27(3):442–454. doi: 10.1038/s41431-018-0300-6

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Overview of genetic variation, drug consumption, and adverse drug effect (ADE) data in electronic health records (EHRs). a Outline of pharmacogenomic variation, high-risk drug prescriptions, and ADEs. Drug prescriptions and medical histories in EHRs were combined with whole-genome sequencing data and imputed genotypes to investigate effects of genetic variation in 64 pharmacogenetically important genes on prevalence of ADEs among people with specific drug prescriptions. b Numbers of Estonian Biobank participants with variations in pharmacogenes (light gray bars), filled prescriptions of high-risk drugs with known genetic associations (dark gray bars), and diagnosed ADEs (black bars). c Flowchart visualizing co-occurrences of genetic variants, drug prescriptions, and ADEs among Estonian Biobank participants. Line thickness reflects the number of individuals with a given feature (minimum n = 10)