Structural variants including copy number variations (CNV) have gained widespread attention, especially in pharmacogenomics but for several genes functional relevance and clinical evidence are still lacking. Detection of CNVs in next-generation sequencing data is challenging but offers widespread applications. We developed a cohort-based CNV detection workflow to extract CNVs from read counts of targeted NGS of 340 genes involved in absorption, distribution, metabolism and excretion (ADME) of drugs. We applied our method to 150 human liver tissue samples and correlated identified CNVs to mRNA expression levels. In total, we identified 445 deletions (73%) and 167 duplications (27%) in 36 pharmacogenes including all well-known CNVs of CYPs, GSTs, SULTs, UGTs, numerous described rare CNVs of CYP2E1, SLC16A3 or UGT2B15 as well as novel observations, e.g., for SLC22A12, SLC22A17 and GPS2 (G Protein Pathway Suppressor 2). We were able to fine-map complex CNVs of CYP2A6 and CYP2D6 with exon resolution. Correlation analysis confirmed known expression patterns for common CNVs and suggested an influence on expression variability for some rare CNVs. Our straightforward CNV detection workflow can be easily applied to any NGS coverage data and helped to analyze CNVs in an ADME-NGS panel of 340 pharmacogenes to improve genotype-phenotype correlations.