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Investigating the Impact of CYP2D6 on Health Through Genetic Analysis

Author: Heidi Riihilahti
Supervisor: Juho Rousu
Advisors: Prof. Samuli Ripatti and Dr. Felix Vaura, Institute for Molecular Medicine Finland FIMM, University of Helsinki

Abstract: Drug efficacy and safety vary considerably between individuals, posing a major challenge for personalized medicine. One important contributor to this variability is genetic variation in drug metabolism, particularly through the cytochrome P450 (CYP450) family of enzymes. Among these, CYP2D6 is especially relevant, as it metabolizes approximately 25% of commonly prescribed drugs, including antidepressants, antipsychotics, beta blockers, and opioids. The gene displays a high degree of polymorphism, with over 100 known star alleles and structural variations (gene deletions and duplications), contributing to diverse metabolizer phenotypes including poor, intermediate, normal, and ultrarapid metabolizers. While CYP2D6 variation is well established in pharmacogenomics, its broader impact on general health outcomes remains less well characterized.

This thesis explores CYP2D6 genetic variation and its associations with health outcomes in the Finnish population using data from approximately 500,000 individuals in the FinnGen cohort. Star allele calling was performed alongside imputation of gene deletions and duplications. Phenome-wide association studies (PheWAS) were conducted to evaluate the relationship between copy number variations (CNVs), activity scores, and a wide range of disease and drug-related phenotypes.

The allele frequency distribution of CYP2D6 in FinnGen aligned well with previous research. However, no statistically significant associations were observed between CYP2D6 variation (including CNVs and activity scores) and health outcomes after correction for multiple testing. This likely reflects CYP2D6’s primary role in drug metabolism rather than direct disease risk, emphasizing the need for studies focused on treatment response and adverse drug effects. The use of drug purchase data to estimate dosage offers a useful, though imperfect, measure of drug exposure in large cohorts. Overall, these findings provide a foundation and point toward more targeted pharmacogenomic research that could better capture clinical relevance in future studies.