Abstract
Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel loss-of-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery.
Highlights
These authors contributed : Tõnis Tasa and Kristi KrebsElectronic supplementary material The online version of this article contains supplementary material, which is available to authorized users.Variability in drug response constitutes a major public health concern, accounting for 2.5–10.6% of all hospital admissions [1]
Star allele—nomenclature for pharmacogenes, discrete star allele represents either a single genetic variant or a haplotype adverse drug effects (ADEs) adverse drug effect, OR odds ratio, 95% CI 95% confidence intervals, LD linkage disequilibrium, AA alternative allele, NA not available, NS not significant (a) Significantly replicated results from previously reported variant-drug associations (b) Significant new pharmacogenomic gene variants (n = 1314) in previously reported gene-drug associations (c) Variants that became insignificant after correction with previously reported variants in a gene-drug association
This study is the first to combine electronic health record (EHR) and whole-genome sequencing (WGS) data to investigate ADEs on a population scale. In this proof-ofconcept approach, we overlapped three independent sources of data to test the effects of genetic polymorphisms on ADEs among subjects taking specific drugs
Summary
Variability in drug response constitutes a major public health concern, accounting for 2.5–10.6% of all hospital admissions [1]. Around 30% of novel therapeutics will eventually be affected by ADEs that are not identified in clinical trials [3]. Mechanistic associations between drug response and pharmacogenetic variants in genomic coding regions are well understood, but sparse functional information is available for noncoding regions, with studies failing to identify or replicate significant results [5]. Uncovering associations between pharmacogenes and drugs increasingly relies on large-scale initiatives that organize and produce knowledge of variants in different populations and highlight actionable variants that can be clinically implemented to improve health outcomes [6,7,8].
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