Investigating the genetic factors of medication dosing using biobank-linked drug purchase data

Despite significant pharmacogenomic (PGx) insights into key biological pathways influencing drug response, the polygenic contribution to dose variability and the potential of electronic health records (EHRs) for maintenance dose estimation remain largely unexplored. To address this gap, we leveraged longitudinal drug purchase data from the Estonian Biobank to derive medication dose metrics and investigate genetic associations using polygenic scores (PGS) and genome-wide association studies (GWAS). Daily doses per purchase as well as median and maximum doses as consolidated metrics across purchases were derived for statins, warfarin, metoprolol, antidepressants, and antipsychotics. Sample sizes ranged from 684 (antipsychotics) to 20,642 (statins), with median doses reflecting typical maintenance doses. The PGS for the indicator trait was significant for the daily dose of statins (coronary heart disease PGS, β = 0.02, P = 5.9×10 ^{− 10} ) and metoprolol (systolic blood pressure PGS, β = 0.03, P = 7.5×10 ⁻¹³ ). The PGS for body mass index was linked to daily doses of statins (β = 0.02, P = 6.4×10 ⁻⁷ ), metoprolol (β = 0.03, P = 1.4×10 ⁻¹⁴ ), and warfarin (β = 0.03, P = 0.001), whereas the PGS for educational attainment showed opposing associations with statins (β = –0.01, P = 5.9×10 ⁻⁴ ) and antidepressants (β = 0.01, P = 0.002). Median and maximum doses had similar but weaker associations. GWAS confirmed signals for metoprolol ( CYP2D6 , P = 1.1×10 ⁻²⁰ ) and warfarin ( CYP2C9 , P = 8.9×10 ⁻⁶⁰ ; VKORC1 , P = 4.2×10 ⁻¹⁴⁸ ), as well as enrichment of PGx signals for individual statins (P = 0.02 for simvastatin, P = 0.03 for atorvastatin). Altogether, these findings illustrate the feasibility and value of deriving medication doses from EHRs and highlight the role of polygenic liability and PGx factors in dose variability.