Genetic associations of cardiovascular risk genes in European patients with coronary artery spasm.

BACKGROUND: Coronary artery spasm (CAS) is a frequent finding in patients presenting with angina pectoris. Although the pathogenesis of CAS is incompletely understood, previous studies suggested a genetic contribution. Our study aimed to elucidate genetic variants in a cohort of European patients with angina and unobstructed coronary arteries who underwent acetylcholine (ACh) provocation testing. METHODS: A candidate association analysis of 208 genes previously associated with cardiovascular conditions was performed using genotyped and imputed variants in patients grouped in epicardial (focal, diffuse) CAS (n = 119) and microvascular CAS (n = 87). Patients with a negative ACh test result (n = 45) served as controls. RESULTS: We found no association below the genome-wide significance threshold of p < 5 × 10-8, thus not confirming variants in ALDH2, NOS3, and ROCK2 previously reported in CAS patients of Asian ancestry. However, the analysis identified suggestive associations (p < 10-05) for the groups of focal epicardial CAS (CDH13) and diffuse epicardial CAS (HDAC9, EDN1). Downstream analysis of the potential EDN1 risk locus showed that CAS patients have significantly increased plasma endothelin-1 levels (ET-1) compared to controls. An EDN1 haplotype comprising rs9349379 and rs2070698 was significantly associated to ET-1 levels (p = 0.01). CONCLUSIONS: In summary, we suggest EDN1 as potential genetic risk loci for patients with diffuse epicardial CAS, and European ancestry. Plasma ET-1 levels may serve as a potential cardiac marker.

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia.

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

Consequences of Amyloid-ß Deficiency for the Liver.

The hepatic content of amyloid beta (Aß) decreases drastically in human and rodent cirrhosis highlighting the importance of understanding the consequences of Aß deficiency in the liver. This is especially relevant in view of recent advances in anti-Aß therapies for Alzheimer's disease (AD). Here, it is shown that partial hepatic loss of Aß in transgenic AD mice immunized with Aß antibody 3D6 and its absence in amyloid precursor protein (APP) knockout mice (APP-KO), as well as in human liver spheroids with APP knockdown upregulates classical hallmarks of fibrosis, smooth muscle alpha-actin, and collagen type I. Aß absence in APP-KO and deficiency in immunized mice lead to strong activation of transforming growth factor-ß (TGFß), alpha secretases, NOTCH pathway, inflammation, decreased permeability of liver sinusoids, and epithelial-mesenchymal transition. Inversely, increased systemic and intrahepatic levels of Aß42 in transgenic AD mice and neprilysin inhibitor LBQ657-treated wild-type mice protect the liver against carbon tetrachloride (CCl4)-induced injury. Transcriptomic analysis of CCl4-treated transgenic AD mouse livers uncovers the regulatory effects of Aß42 on mitochondrial function, lipid metabolism, and its onco-suppressive effects accompanied by reduced synthesis of extracellular matrix proteins. Combined, these data reveal Aß as an indispensable regulator of cell-cell interactions in healthy liver and a powerful protector against liver fibrosis.

Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes.

Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.

Nutrimetric Validation of Solanidine as Dietary-Derived CYP2D6 Activity Marker In Vivo.

CYP2D6 is involved in the metabolism of many drugs. Its activity is affected by pharmacogenetic variability leading to highly polymorphic phenotypes between individuals, affecting safety and efficacy of drugs. Recently, solanidine, a steroidal alkaloid from potatoes, and its metabolites, has been identified as a dietary-derived activity marker for CYP2D6. The intraday variability in plasma within individuals has not been studied yet in healthy subjects. As part of a CYP phenotyping cocktail study with 20 healthy participants, plasma concentrations of solanidine, 4-OH-solanidine and 3,4-secosolanidine-3,4-dioic acid (SSDA) were determined using a sensitive liquid chromatography-mass spectrometry method in urine and in plasma at timepoints 0, 2.5, 5, 8, and 24 hours after intake of test substances. The participants were phenotyped for CYP2D6 with oral metoprolol (12.5 mg) with 15 plasma sampling points over 24 hours (DRKS00028922). Metabolic ratios (MRs) of metabolite to parent plasma concentrations were formed from single timepoints and the area under the curve (AUC). All participants were genotyped for CYP2D6. The intra-individual variability of the CYP2D6 metabolite SSDA was highly stable with a median SD of 11.62% over 24 hours. MR SSDA/solanidine was more variable (median SD 31.90%) but correlated significantly at all measured timepoints with AUC MR α-OH-metoprolol/metoprolol. The AUC MR SSDA/solanidine showed a significant linear relationship with the genetically predicted CYP2D6 activity score. This study substantiates the MR SSDA/solanidine as CYP2D6 activity marker. The high correlation with metoprolol MR indicates a valid prediction of the CYP2D6 phenotype at any timepoint during the study day.

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance.

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

Structural variation of the coding and non-coding human pharmacogenome.

Genetic variants in drug targets and genes encoding factors involved in drug absorption, distribution, metabolism and excretion (ADME) can have pronounced impacts on drug pharmacokinetics, response, and toxicity. While the landscape of genetic variability at the level of single nucleotide variants (SNVs) has been extensively studied in these pharmacogenetic loci, their structural variation is only poorly understood. Thus, we systematically analyzed the genetic structural variability across 908 pharmacogenes (344 ADME genes and 564 drug targets) based on publicly available whole genome sequencing data from 10,847 unrelated individuals. Overall, we extracted 14,984 distinct structural variants (SVs) ranging in size from 50 bp to 106 Mb. Each individual harbored on average 10.3 and 1.5 SVs with putative functional effects that affected the coding regions of ADME genes and drug targets, respectively. In addition, by cross-referencing pharmacogenomic SVs with experimentally determined binding data of 224 transcription factors across 130 cell types, we identified 1276 non-coding SVs that overlapped with gene regulatory elements. Based on these data, we estimate that non-coding structural variants account for 22% of the genetically encoded pharmacogenomic variability. Combined, these analyses provide the first comprehensive map of structural variability across pharmacogenes, derive estimates for the functional impact of non-coding SVs and incentivize the incorporation of structural genomic data into personalized drug response predictions.

A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study.

BACKGROUND: The benefit of pharmacogenetic testing before starting drug therapy has been well documented for several single gene-drug combinations. However, the clinical utility of a pre-emptive genotyping strategy using a pharmacogenetic panel has not been rigorously assessed. METHODS: We conducted an open-label, multicentre, controlled, cluster-randomised, crossover implementation study of a 12-gene pharmacogenetic panel in 18 hospitals, nine community health centres, and 28 community pharmacies in seven European countries (Austria, Greece, Italy, the Netherlands, Slovenia, Spain, and the UK). Patients aged 18 years or older receiving a first prescription for a drug clinically recommended in the guidelines of the Dutch Pharmacogenetics Working Group (ie, the index drug) as part of routine care were eligible for inclusion. Exclusion criteria included previous genetic testing for a gene relevant to the index drug, a planned duration of treatment of less than 7 consecutive days, and severe renal or liver insufficiency. All patients gave written informed consent before taking part in the study. Participants were genotyped for 50 germline variants in 12 genes, and those with an actionable variant (ie, a drug-gene interaction test result for which the Dutch Pharmacogenetics Working Group [DPWG] recommended a change to standard-of-care drug treatment) were treated according to DPWG recommendations. Patients in the control group received standard treatment. To prepare clinicians for pre-emptive pharmacogenetic testing, local teams were educated during a site-initiation visit and online educational material was made available. The primary outcome was the occurrence of clinically relevant adverse drug reactions within the 12-week follow-up period. Analyses were irrespective of patient adherence to the DPWG guidelines. The primary analysis was done using a gatekeeping analysis, in which outcomes in people with an actionable drug-gene interaction in the study group versus the control group were compared, and only if the difference was statistically significant was an analysis done that included all of the patients in the study. Outcomes were compared between the study and control groups, both for patients with an actionable drug-gene interaction test result (ie, a result for which the DPWG recommended a change to standard-of-care drug treatment) and for all patients who received at least one dose of index drug. The safety analysis included all participants who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03093818 and is closed to new participants. FINDINGS: Between March 7, 2017, and June 30, 2020, 41 696 patients were assessed for eligibility and 6944 (51·4 % female, 48·6% male; 97·7% self-reported European, Mediterranean, or Middle Eastern ethnicity) were enrolled and assigned to receive genotype-guided drug treatment (n=3342) or standard care (n=3602). 99 patients (52 [1·6%] of the study group and 47 [1·3%] of the control group) withdrew consent after group assignment. 652 participants (367 [11·0%] in the study group and 285 [7·9%] in the control group) were lost to follow-up. In patients with an actionable test result for the index drug (n=1558), a clinically relevant adverse drug reaction occurred in 152 (21·0%) of 725 patients in the study group and 231 (27·7%) of 833 patients in the control group (odds ratio [OR] 0·70 [95% CI 0·54-0·91]; p=0·0075), whereas for all patients, the incidence was 628 (21·5%) of 2923 patients in the study group and 934 (28·6%) of 3270 patients in the control group (OR 0·70 [95% CI 0·61-0·79]; p <0·0001). INTERPRETATION: Genotype-guided treatment using a 12-gene pharmacogenetic panel significantly reduced the incidence of clinically relevant adverse drug reactions and was feasible across diverse European health-care system organisations and settings. Large-scale implementation could help to make drug therapy increasingly safe. FUNDING: European Union Horizon 2020.

Cross-Ancestry Genome-Wide Association Study Defines the Extended CYP2D6 Locus as the Principal Genetic Determinant of Endoxifen Plasma Concentrations.

The therapeutic efficacy of tamoxifen is predominantly mediated by its active metabolites 4-hydroxy-tamoxifen and endoxifen, whose formation is catalyzed by the polymorphic cytochrome P450 2D6 (CYP2D6). Yet, known CYP2D6 polymorphisms only partially determine metabolite concentrations in vivo. We performed the first cross-ancestry genome-wide association study with well-characterized patients of European, Middle-Eastern, and Asian descent (n = 497) to identify genetic factors impacting active and parent metabolite formation. Genome-wide significant variants were functionally evaluated in an independent liver cohort (n = 149) and in silico. Metabolite prediction models were validated in two independent European breast cancer cohorts (n = 287, n = 189). Within a single 1-megabase (Mb) region of chromosome 22q13 encompassing the CYP2D6 gene, 589 variants were significantly associated with tamoxifen metabolite concentrations, particularly endoxifen and metabolic ratio (MR) endoxifen/N-desmethyltamoxifen (minimal P = 5.4E-35 and 2.5E-65, respectively). Previously suggested other loci were not confirmed. Functional analyses revealed 66% of associated, mostly intergenic variants to be significantly correlated with hepatic CYP2D6 activity or expression (ρ = 0.35 to -0.52), and six hotspot regions in the extended 22q13 locus impacting gene regulatory function. Machine learning models based on hotspot variants (n = 12) plus CYP2D6 activity score (AS) increased the explained variability (~ 9%) compared with AS alone, explaining up to 49% (median R2 ) and 72% of the variability in endoxifen and MR endoxifen/N-desmethyltamoxifen, respectively. Our findings suggest that the extended CYP2D6 locus at 22q13 is the principal genetic determinant of endoxifen plasma concentration. Long-distance haplotypes connecting CYP2D6 with adjacent regulatory sites and nongenetic factors may account for the unexplained portion of variability.

HTCA: a database with an in-depth characterization of the single-cell human transcriptome.

Single-cell RNA-sequencing (scRNA-seq) is one of the most used single-cell omics in recent decades. The exponential growth of single-cell data has immense potential for large-scale integration and in-depth explorations that are more representative of the study population. Efforts have been made to consolidate published data, yet extensive characterization is still lacking. Many focused on raw-data database constructions while others concentrate mainly on gene expression queries. Hereby, we present HTCA (www.htcatlas.org), an interactive database constructed based on ∼2.3 million high-quality cells from ∼3000 scRNA-seq samples and comprised in-depth phenotype profiles of 19 healthy adult and matching fetal tissues. HTCA provides a one-stop interactive query to gene signatures, transcription factor (TF) activities, TF motifs, receptor-ligand interactions, enriched gene ontology (GO) terms, etc. across cell types in adult and fetal tissues. At the same time, HTCA encompasses single-cell splicing variant profiles of 16 adult and fetal tissues, spatial transcriptomics profiles of 11 adult and fetal tissues, and single-cell ATAC-sequencing (scATAC-seq) profiles of 27 adult and fetal tissues. Besides, HTCA provides online analysis tools to perform major steps in a typical scRNA-seq analysis. Altogether, HTCA allows real-time explorations of multi-omics adult and fetal phenotypic profiles and provides tools for a flexible scRNA-seq analysis.

Challenges and opportunities associated with rare-variant pharmacogenomics.

Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.

Hepatic Expression of the Na+-Taurocholate Cotransporting Polypeptide Is Independent from Genetic Variation.

The hepatic Na+-taurocholate cotransporting polypeptide NTCP/SLC10A1 is important for the uptake of bile salts and selected drugs. Its inhibition results in increased systemic bile salt concentrations. NTCP is also the entry receptor for the hepatitis B/D virus. We investigated interindividual hepatic SLC10A1/NTCP expression using various omics technologies. SLC10A1/NTCP mRNA expression/protein abundance was quantified in well-characterized 143 human livers by real-time PCR and LC-MS/MS-based targeted proteomics. Genome-wide SNP arrays and SLC10A1 next-generation sequencing were used for genomic analyses. SLC10A1 DNA methylation was assessed through MALDI-TOF MS. Transcriptomics and untargeted metabolomics (UHPLC-Q-TOF-MS) were correlated to identify NTCP-related metabolic pathways. SLC10A1 mRNA and NTCP protein levels varied 44-fold and 10.4-fold, respectively. Non-genetic factors (e.g., smoking, alcohol consumption) influenced significantly NTCP expression. Genetic variants in SLC10A1 or other genes do not explain expression variability which was validated in livers (n = 50) from The Cancer Genome Atlas. The identified two missense SLC10A1 variants did not impair transport function in transfectants. Specific CpG sites in SLC10A1 as well as single metabolic alterations and pathways (e.g., peroxisomal and bile acid synthesis) were significantly associated with expression. Inter-individual variability of NTCP expression is multifactorial with the contribution of clinical factors, DNA methylation, transcriptional regulation as well as hepatic metabolism, but not genetic variation.

Mobility endpoints in marketing authorisation of drugs: what gets the European medicines agency moving?

BACKGROUND: Mobility is defined as the ability to independently move around the environment and is a key contributor to quality of life, especially in older age. The aim of this study was to evaluate the use of mobility as a decisive outcome for the marketing authorisation of drugs by the European Medicines Agency (EMA). METHODS: Fifteen therapeutic areas which commonly lead to relevant mobility impairments and alter the quantity and/or the quality of walking were selected: two systemic neurological diseases, four conditions primarily affecting exercise capacity, seven musculoskeletal diseases and two conditions representing sensory impairments. European Public Assessment Reports (EPARs) published by the EMA until September 2020 were examined for mobility endpoints included in their 'main studies'. Clinical study registries and primary scientific publications for these studies were also reviewed. RESULTS: Four hundred and eighty-four EPARs yielded 186 relevant documents with 402 'main studies'. The EPARs reported 153 primary and 584 secondary endpoints which considered mobility; 70 different assessment tools (38 patient-reported outcomes, 13 clinician-reported outcomes, 8 performance outcomes and 13 composite endpoints) were used. Only 15.7% of those tools distinctly informed on patients' mobility status. Out of 402, 105 (26.1%) of the 'main studies' did not have any mobility assessment. Furthermore, none of these studies included a digital mobility outcome. CONCLUSIONS: For conditions with a high impact on mobility, mobility assessment was given little consideration in the marketing authorisation of drugs by the EMA. Where mobility impairment was considered to be a relevant outcome, questionnaires or composite scores susceptible to reporting biases were predominantly used.

The Pediatric Precision Oncology INFORM Registry: Clinical Outcome and Benefit for Patients with Very High-Evidence Targets.

INFORM is a prospective, multinational registry gathering clinical and molecular data of relapsed, progressive, or high-risk pediatric patients with cancer. This report describes long-term follow-up of 519 patients in whom molecular alterations were evaluated according to a predefined seven-scale target prioritization algorithm. Mean turnaround time from sample receipt to report was 25.4 days. The highest target priority level was observed in 42 patients (8.1%). Of these, 20 patients received matched targeted treatment with a median progression-free survival of 204 days [95% confidence interval (CI), 99-not applicable], compared with 117 days (95% CI, 106-143; P = 0.011) in all other patients. The respective molecular targets were shown to be predictive for matched treatment response and not prognostic surrogates for improved outcome. Hereditary cancer predisposition syndromes were identified in 7.5% of patients, half of which were newly identified through the study. Integrated molecular analyses resulted in a change or refinement of diagnoses in 8.2% of cases. SIGNIFICANCE: The pediatric precision oncology INFORM registry prospectively tested a target prioritization algorithm in a real-world, multinational setting and identified subgroups of patients benefiting from matched targeted treatment with improved progression-free survival, refinement of diagnosis, and identification of hereditary cancer predisposition syndromes.See related commentary by Eggermont et al., p. 2677.This article is highlighted in the In This Issue feature, p. 2659.

[How to manage polypharmacia?] / Praxisrelevante pharmakogenetische Diagnostik zur Verbesserung der Arzneimitteltherapie.

Pharmacogenomics (PGx) is a key component of personalized medicine to improve clinical outcome of drug therapy and/or to avoid adverse drug reactions. Major efforts are currently spent internationally to implement PGx diagnostics into clinical practice. Evidence-based recommendations for dose-adjusted treatment which are established by international expert groups covering clinical and pharmacological expertise are publicly available. Clinical relevant examples for PGx diagnostics such as genetic testing for dihydropyrimidin-dehydrogenase and thiopurin-S-methyltransferase, as well as for various cytochrome P450 enzymes are summarized to promote the clinical implementation process of PGx in Germany.

Antifibrotic Effects of Amyloid-Beta and Its Loss in Cirrhotic Liver.

The function and regulation of amyloid-beta (Aß) in healthy and diseased liver remains unexplored. Because Aß reduces the integrity of the blood-brain barrier we have examined its potential role in regulating the sinusoidal permeability of normal and cirrhotic liver. Aß and key proteins that generate (beta-secretase 1 and presenilin-1) and degrade it (neprilysin and myelin basic protein) were decreased in human cirrhotic liver. In culture, activated hepatic stellate cells (HSC) internalized Aß more efficiently than astrocytes and HSC degraded Aß leading to suppressed expression of α-smooth muscle actin (α-SMA), collagen 1 and transforming growth factor ß (TGFß). Aß also upregulated sinusoidal permeability marker endothelial NO synthase (eNOS) and decreased TGFß in cultured human liver sinusoidal endothelial cells (hLSEC). Liver Aß levels also correlate with the expression of eNOS in transgenic Alzheimer's disease mice and in human and rodent cirrhosis/fibrosis. These findings suggest a previously unexplored role of Aß in the maintenance of liver sinusoidal permeability and in protection against cirrhosis/fibrosis via attenuation of HSC activation.

α2-Adrenergic Receptor in Liver Fibrosis: Implications for the Adrenoblocker Mesedin.

The noradrenergic system is proposed to play a prominent role in the pathogenesis of liver fibrosis. While α1- and ß-adrenergic receptors (ARs) are suggested to be involved in a multitude of profibrogenic actions, little is known about α2-AR-mediated effects and their expression pattern during liver fibrosis and cirrhosis. We explored the expression of α2-AR in two models of experimental liver fibrosis. We further evaluated the capacity of the α2-AR blocker mesedin to deactivate hepatic stellate cells (HSCs) and to increase the permeability of human liver sinusoidal endothelial cells (hLSECs). The mRNA of α2a-, α2b-, and α2c-AR subtypes was uniformly upregulated in carbon tetrachloride-treated mice vs the controls, while in bile duct-ligated mice, only α2b-AR increased in response to liver injury. In murine HSCs, mesedin led to a decrease in α-smooth muscle actin, transforming growth factor-ß and α2a-AR expression, which was indicated by RT-qPCR, immunocytochemistry, and Western blot analyses. In a hLSEC line, an increased expression of endothelial nitric oxide synthase was detected along with downregulated transforming growth factor-ß. In conclusion, we suggest that the α2-AR blockade alleviates the activation of HSCs and may increase the permeability of liver sinusoids during liver injury.

Copy number variation profiling in pharmacogenes using panel-based exome resequencing and correlation to human liver expression.

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.