Biobank Scale Pharmacogenomics Informs the Genetic Underpinnings of Simvastatin Use

Abstract

Studying drug‐metabolizing enzymes, encoded by pharmacogenes, may inform biological mechanisms underlying the diseases for which a medication is prescribed. Until recently, pharmacogenes could not be studied at biobank scale. In 7,649 unrelated African‐ancestry (AFR) and 326,214 unrelated European‐ancestry (EUR) participants from the UK Biobank, we associated pharmacogene haplotypes from 50 genes with 265 (EUR) and 17 (AFR) medication use phenotypes using generalized linear models. In EUR, N‐acetyltransferase 2 (NAT2) metabolizer phenotype and activity score were associated with simvastatin use. The dose of NAT2*1 was associated with simvastatin use when compared with NAT2*5 (the most common haplotype). This association was robust to effects of low‐density lipoprotein cholesterol (LDL‐C) concentration (NAT2*1 odds ratio (OR) = 1.07, 95% CI: 1.05–1.09, P = 1.14 × 10−8) and polygenic risk for LDL‐C concentration (NAT2*1 OR = 1.09, 95% CI: 1.04–1.14, P = 2.26 × 10−4). Interactive effects between NAT2*1 and simvastatin use on LDL‐C concentration (OR = 0.957, 95% CI: 0.916–0.998, P = 0.045) were replicated in the electronic Medical Records and Genomics Pharmacogenetic Sequencing Pilot (eMERGE‐PGx) cohort (OR = 0.987, 95% CI: 0.976–0.998, P = 0.029). We used biobank‐scale data to uncover and replicate an association between NAT2 locus variation and better response to statin therapy. Testing NAT2 alleles may be useful for making clinical decisions regarding the potential benefit (e.g., absolute risk reduction) in LDL‐C concentration prior to statin treatment.

Pharmacogenomics Variability of Lipid-Lowering Therapies in Familial Hypercholesterolemia

“…A considerable variation in the statin pharmacokinetics was reported in NAT2-rs1208 polymorphism carriers [60]. Interestingly, a wide pharmacogenomic investigation revealed an association between the NAT2*1 SNP and a significant LDL-C decrease in response to simvastatin [61]. These findings could be potentially used to guide medical decision-makers to improve the therapeutic plan for FH patients.…”

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward

“…Similarly, 99.8% of the 44,000 participants in the Estonian Biobank had genotypes associated with increased risk for at least one medication, affecting over 50 daily drug doses per 1,000 Estonians (89). In addition, large-scale genotype data coupled to electronic health records have facilitated the discovery of novel pharmacogenetic biomarkers for a multitude of medications, including penicillin, simvastatin, and nonsteroidal anti-inflammatory oxicams (90)(91)(92)(93), as well as for the identification of new drug targets and for drug repurposing (94). Complementary to genotype data, mining of biobanked sequencing data facilitates pharmacogenomic evaluations of rare and novel variations that go beyond variants included in GWAS arrays (95).…”