Genetic variation in the Estonian population: pharmacogenomics study of adverse drug effects using electronic health records

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.

Genetic insight into cytochrome P450 in Chinese from the Chinese Millionome Database

“…The diversity of drug responses in healthy participants in phase I clinical trials for one drug candidate could be minimized through sequencing the whole genome of patients and choosing the most homogenous ones. 8,29 The drug dosing scheme could be precise after it is marketed to be able to be adjusted to patients' genetic background. More interesting, the withdrawal or failed marketing of certain drugs is due to adverse drug reactions in patients with specific genetic variants, and NGS evidence would change the destiny of such drugs and have a cost-effectiveness benefit for the world.…”

Electronic Medical Records and Machine Learning in Approaches to Drug Development

“…An ADE study using Estonian EHR databases by Tasa et al demonstrates the database's ability to conduct high impact, translational research. The whole-genome sequencing (WGS) data of +2200 Estonian Biobank participants and the EHRs of the sequenced individuals were taken from Health Insurance Fund Treatment Bills, Tartu University Hospital and North Estonia Medical Center databases [127]. EHRs were mined using ICD codes to find ADE occurrences and a mixture of the ICD and manual verification methods was used to identify associations between genetic polymorphisms and ADEs [127].…”

“…The whole-genome sequencing (WGS) data of +2200 Estonian Biobank participants and the EHRs of the sequenced individuals were taken from Health Insurance Fund Treatment Bills, Tartu University Hospital and North Estonia Medical Center databases [127]. EHRs were mined using ICD codes to find ADE occurrences and a mixture of the ICD and manual verification methods was used to identify associations between genetic polymorphisms and ADEs [127]. Associations between genetic variations and drug responses are vital in advancing personalized drug treatment, which is also referred to as pharmacogenomics.…”

“…To priorities genetic analysis, Tasa et al compiled 1314 loss-of-function, missense, and putative high-impact variants in promoter regions of 64 pharmacogenes [127]. They reported that 80.3% of the variants were rare (MAF < 1%), and this high proportion suggests that gene variation is crucial in understanding pharmacogenomics [127]. Next, the study combined EHRs to the genetic data to extract 1187 participants with potential ADEs.…”

Genome-Wide Association Studies of Therapeutic Response: Addressing the Complexities

“…Another way of achieving large sample sizes may be to use electronic health records, although this requires linkage to genetic data. Genomic data on over 16,000 participants in the Estonian Biobank have recently been used to carry out pharmacogenomic studies of adverse drug effects by linking genomic data to health records and prescription information, with some success [12]. Extending this approach to studies of therapeutic response may be difficult since outcome measures may not be well recorded in routine clinical practice.…”