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Article

Population Pharmacogenomics in Croatia: Evaluating the PGx Allele Frequency and the Impact of Treatment Efficiency

by
Vid Matišić
1,
Petar Brlek
1,2,
Luka Bulić
3,
Vilim Molnar
1,
Marina Dasović
3 and
Dragan Primorac
1,2,4,5,6,7,8,9,10,11,*
1
St Catherine Specialty Hospital, 10000 Zagreb, Croatia
2
School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
3
School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
4
Medical School, University of Split, 21000 Split, Croatia
5
Department of Biochemistry & Molecular Biology, The Pennsylvania State University, State College, PA 16802, USA
6
The Henry C. Lee College of Criminal Justice and Forensic Sciences, University of New Haven, West Haven, CT 06516, USA
7
Medical School REGIOMED, 96450 Coburg, Germany
8
Medical School, University of Rijeka, 51000 Rijeka, Croatia
9
Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
10
Medical School, University of Mostar, 88000 Mostar, Bosnia and Herzegovina
11
National Forensic Sciences University, Gujarat 382007, India
 
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*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(17), 13498; https://doi.org/10.3390/ijms241713498
Submission received: 3 July 2023 / Revised: 24 August 2023 / Accepted: 28 August 2023 / Published: 31 August 2023
(This article belongs to the Special Issue Advances in Integration of Pharmacogenetics into Practice)
Versions Notes

Abstract

:
Background: Adverse drug reactions (ADRs) are a significant cause of mortality, and pharmacogenomics (PGx) offers the potential to optimize therapeutic efficacy while minimizing ADRs. However, there is a lack of data on the Croatian population, highlighting the need for investigating the most common alleles, genotypes, and phenotypes to establish national guidelines for drug use. Methods: A single-center retrospective cross-sectional study was performed to examine the allele, genotype, and phenotype frequencies of drug-metabolizing enzymes, receptors, and other proteins in a random sample of 522 patients from Croatia using a 28-gene PGx panel. Results: Allele frequencies, genotypes, and phenotypes for the investigated genes were determined. No statistically significant differences were found between the Croatian and European populations for most analyzed genes. The most common genotypes observed in the patients resulted in normal metabolism rates. However, some genes showed higher frequencies of altered metabolism rates. Conclusions: This study provides insights into the allele, genotype, and phenotype frequencies of drug-metabolizing enzymes, receptors, and other associated proteins in the Croatian population. The findings contribute to optimizing drug use guidelines, potentially reducing ADRs, and improving therapeutic efficacy. Further research is needed to tailor population-specific interventions based on these findings and their long-term benefits.

1. Introduction

Pharmacogenomics (PGx) is a field of research that focuses on genomic information and how it affects individual responses to drugs [1]. The field is constantly expanding as new interactions between certain genes and drugs are discovered [2,3,4]. By doing so, PGx allows the optimization of therapeutic efficacy and minimizes the likelihood of adverse drug reactions (ADRs), which are among the most common causes of death in Western countries [5,6,7]. In 2021, there were 9966 reported ADRs in Croatia, which is 148% more than the year prior, according to the Agency for Medicinal Products and Medical Devices of Croatia (HALMED) [8].
Due to insufficient data on the Croatian population, we have recognized the importance of investigating the most frequent alleles, genotypes, and phenotypes of the population to optimize national guidelines for drug use with the goal of reducing ADRs and increasing therapeutic efficacy. Such guidelines already exist in the USA—the Clinical Pharmacogenetics Implementation Consortium (CPIC), the Netherlands—the Dutch Pharmacogenetics Working Group (DPWG), Canada—the Canadian Pharmacogenomic Network for Drug Safety (CPNDS), and France—the French National Network (Réseau) of Pharmacogenetics (RNPGx) [9,10].
Research efforts have shown that a vast majority of individuals carry single nucleotide polymorphisms (SNPs) that are relevant for drug metabolism; such is also the case in the Republic of Croatia, where it was shown that actionable gene-drug pairs were present in 73.7% of patients at the time of pharmacogenomic testing [11]. It is, therefore, prudent to systematically report the population-specific frequencies of the most relevant SNPs, as it carries the potential benefit of tailoring population-specific interventions that may bring long-term health and economic benefits [12].
This retrospective cross-sectional study aimed to investigate the allele, genotype, and phenotype frequencies of drug-metabolizing enzymes and receptors in a random sample of the Croatian population using a commercially available 28-gene panel. The secondary aim of the study was to compare the observed allele frequencies with the available non-Finnish European population data from the GnomAD database.

2. Results

A total of 522 patients were included in the study. The population distribution by category is demonstrated below (Table 1). Regarding ethnicity, the population was predominantly white/caucasian, alongside one American Indian/Alaska native subject and one near/Middle Eastern subject. The distribution by sex demonstrated a higher number of female subjects (58 to 42 ratio). Where age is concerned, 81.8% of the analyzed population was within the 31–80 years-of-age range. A smaller number of subjects (14%) were younger than 31 years of age. Only 4.2% of subjects were 81 years of age or older.

2.1. Allele Frequencies

Allele frequencies for the investigated genes are shown in the table below (Table 2). Select alleles were compared with the frequencies in the gnomAD database. No statistically significant discrepancy was found for the CYP2C cluster, COMT, NUDT15, DRD2, OPRM1, or F II genes. Minor discrepancies were found for the CYP4F2, GRIK4, HTR2A, HTR2C, IFNL4, and F V genes. A statistically significant difference in allele frequencies between the Croatian and European populations (gnomAD) was not established.

2.2. Genotype and Phenotype Frequencies of Enzyme-Coding Genes

Genotype frequencies and their respective phenotypes for the investigated enzyme-coding genes are shown in the table below (Table 3). The most commonly observed genotypes of the analyzed genes in our patients resulted in normal metabolism rate/no additional gene-drug risk phenotypes. Those were observed in CYP2B6 (51.5%), the CYP2C9 (57.9%), CYP2C cluster (72%), CYP3A4 (93.9%), COMT (intermediate activity 51.7%), DPYD (98.7%), NUDT15 (99.2%), and TPMT (95%). CYP2D6 genotypes mostly resulted in normal metabolizer phenotype (39.1%), followed by intermediate metabolizer phenotype (28.4%), with poor, rapid, and ultrarapid metabolizer phenotypes observed in 5.4%, 0.8%, and 2.7%, respectively. CYP1A2 rapid phenotype was observed in 88.5%. The CYP2C19 phenotype was found to be normal in 35.8%, rapid in 30%, and intermediate in 18%. Poor metabolizer phenotypes were most common in CYP3A5 (88.1%) and CYP4F2 (51.1%). UGT1A1 genotypes were predominantly associated with increased and high risk for ADRs, with frequencies of 45.4% and 16.3%, respectively. The VKORC1 rs9923231 G/A genotype associated with intermediate activity was present in 50.6% of patients, whereas rs9923231 A/A was present in 18.4% of patients. Combined rs1801133 and rs1801131 genotypes resulted in decreased enzyme activity in 89.1% of patients, with almost half having severely decreased activity phenotypes.

2.3. Genotype and Phenotype Frequencies of Receptor-Coding Genes

Genotype frequencies and their respective phenotypes for the investigated enzyme-coding genes are shown in the table above (Table 3). Normal metabolism rates and no additional risk phenotypes were most commonly observed in genes DRD2 (87%), GRIK4 (79.7%), HTR2C (72.8%), and OPRM1 (76.4%). The HTR2A rs7997012 A/A genotype, which is linked to reduced venlafaxine therapeutic response, was present in 21.1%. IFNL4 rs12979860 C/C normal response genotype to antiviral efficacy was most common with 47.5%, followed by the rs12979860 C/T reduced response phenotype.

2.4. Genotype and Phenotype Frequencies of Uncategorized Genes

Genotype frequencies and their respective phenotypes for the investigated enzyme-coding genes are shown in the table below (Table 3). Normal metabolism rates without additional risk phenotypes were most commonly observed in the genes HLA-A (96.4%), HLA-B (92.9%), SLCO1B1 (57.5%), F II (96.9%), and F V (96.7%). IFNL4 rs12979860 C/C normal response genotype to antiviral efficacy was most common with 47.5%, followed by the rs12979860 C/T reduced response phenotype.

3. Discussion

With the growing implementation of pharmacogenomics worldwide, there are a growing number of studies on population-specific differences in allelic and genotype frequencies of drug-metabolizing enzymes [13,14,15]. Public databases such as GnomAD aggregate this data for future research; however, not all populations are always represented. As a European Caucasian population, the Croatian population did not show major differences in allelic variants when compared to other similar populations in previous studies [16,17]. The results of the present study are in line with these observations. No significant difference was observed between the allele frequencies of the study population and those of the European population for the analyzed alleles that are represented in the database. Regarding the previously published studies that investigated the allele frequencies in the Croatian population, we observed mainly concordant results. However, the previously observed allele frequency for CYP2C19*1 was 85% and for CYP2D6*1 was 76.5%; this was not the case in our patient group, where the wild-type *1 allele frequencies for both enzymes were 59.8% and 37.4%, respectively [16]. This discrepancy suggests greater potential for altered substrate metabolism, as the here detected genotypes coded for normal metabolizing phenotypes in 35.8% of CYP2C19 and 39.1% of CYP2D6. Expectedly, the substrates of CYP2C19, proton pump inhibitors, are one of the most commonly prescribed pharmacologic agents in Croatia. It is well established that, when present, genotype information should be considered for therapy guidance [18,19,20]. However, a recent study observed a marked difference in allelic frequencies for CYP2B6*4 (24.3% vs. 9.3% in Europe), VKORC1*2 (40.1% vs. 34.9% in Europe), and CYP2C9*2 (14.7% vs. 12.3% in Europe) [21]. In the present study, this was not the case for CYP2B6*4, which was reported at 2.4%. VKORC1*2 (referred to as rs9923231 A in the present study) was found to be even more frequent at 43.7%, which is closer to the European average.

3.1. Antiviral Therapy Considerations

CYP2B6 is included in the CPIC guidelines for efavirenz genotype-based prescribing, noting that careful dose titration should be performed for poor metabolizers or when certain combinations with the CYP2C19 phenotype are detected [22]. The observed combined frequencies of poor and poor-to-intermediate metabolizer phenotypes in the observed population were 6.5%; for rapid and ultrarapid metabolizers, the combined frequencies were 4%—those results indicate a considerable number of patients that may require dose adjustment based on genotype alone. When considering the CPIC guideline for efavirenz dosing, the intermediate metabolizer phenotype becomes considerable, as it states that a lower initiating dose should be used compared to normal and rapid phenotypes, with an even lower dose for poor metabolizers [22]. The observed frequency of intermediate metabolizers was 37.4%, suggesting that a third of patients would benefit from this recommendation. UGT1A1 inhibition is a known side-effect of atazanavir therapy, used in antiretroviral therapy; therefore, it is included in the panel as it is an important metabolizer of bilirubin and other substrates [23]. Our results indicate that the majority of the population has an increased risk of atazanavir-related toxicities due to the UGT1A1 genotype, as the normal risk *1/*1 genotype was observed only in 38.3%; we also noted a relatively high proportion of the *28/*28 genotype, which is associated with Gilbert syndrome. Another gene important for antiviral therapy, namely the anti-hepatitis C virus, is IFNL4 [24]. IFNL4 rs12979860 T variants are associated with a reduced likelihood of a sustained virologic response to peginterferon-containing regimens; this allele was detected in 31.5% of the population, with a normal genotype being present in almost half of the population (47.5%) [25].

3.2. NSAID and Opioid Analgetic Considerations

Considering the guideline for non-steroid anti-inflammatory drugs (NSAIDs) recommends lower starting doses for intermediate and poor CYP2C9 metabolizers, as they have an increased risk of ADRs, it is worth noting that in our population, poor metabolizing phenotype was detected in 2 patients (0.4%), poor to intermediate in 1.9%, and intermediate in 17.4% [26]. This should remind clinicians that, although popular and often available over-the-counter, NSAIDs may still cause ADRs in a considerable proportion of our patients, especially knowing their ATK group had DDD/1000/day of 56.61 in the latest report of the national regulator agency [20].
Opioid analgesics, on the other hand, are mainly metabolized by CYP2D6, and their effect is also modulated by OPRM1 and COMT, all of which are analyzed by the panel that was used in our institution and are included in the CPIC guideline for opioid therapy [27,28]. It should be noted that the latest version of the guideline explicitly states the dosing recommendation for codeine, tramadol, and hydrocodone based on the CYP2D6 phenotype. In contrast, the genotypes of COMT and OPRM1, even though linked to altered responses to opioid therapy, did not reach the level of evidence for a definite recommendation. Ultrarapid and poor CYP2D6 metabolizers should avoid using both codeine and tramadol due to a risk of toxicity or a lack of therapeutic response, respectively. CYP2D6 genotypes in the Croatian population we analyzed show that a combined 14.5% are at risk for either of these adverse events, as they were categorized as either poor, rapid, or ultrarapid metabolizers. This is particularly interesting because tramadol was one of the most prescribed pharmacologic agents for non-inpatient use in the Republic of Croatia in the latest report, with a DDD/1000/day of 12.89.

3.3. Warfarin and Coagulation Factor Considerations

CPIC guidelines include recommendations for warfarin dosing, which was considered in the Croatian population in the previous studies, as discussed in the previous section. However, CYP2C9 and VKORC1, which were studied, are not the sole genetic factors for altered clearance. CYP2C cluster variant rs12777823 A was represented in 14.9%. Interestingly, this SNP was found to be relevant for warfarin clearance in patients of African descent combined with CYP2C9, CYP4F2, and VKORC1 genotypes [29]. This finding shows that similar studies should be performed in other ethnic groups, as the observed allele frequency cannot be overlooked. Although the most commonly observed allele for CYP4F2 was *1 wild-type allele (69.4%), the resulting phenotypes in our population were predominantly reduced activity (51.1%).
The VKORC1 wild-type allele was the most commonly observed (56.1%), but like with CYP4F2, the most commonly observed phenotype was intermediate activity (50.6%), followed by normal (30.8%) and poor activity (18.4%).
Considering the combined observed frequencies of CYP2C9, CYP2C cluster, CYP4F2, and VKORC1 phenotype combinations in our population, the results point to a much-needed precaution when prescribing warfarin due to a highly possible gene-drug interaction occurrence altering the patient’s response to warfarin. This has been stressed by previous research from Croatian authors and is now further established by adding the observed frequencies of the CYP2C cluster and CYP4F2 gene [21,30]. It is also worth noting that ethnic differences were previously observed for CYP2C9, CYP4F2, and VKORC1, suggesting it may be beneficial to add CYP4F2 testing to algorithms for genotype-based warfarin dosing [31]. This may be true for our population due to the relatively high percentage with reduced activity CYP4F2 phenotype.
A relatively low-prevalence finding was the variant in F II and F V, which was found in only 3% of both genes. Both of those genes are included in the panel, as their variants predispose patients to hypercoagulability, which increases the risk of thrombosis for patients on hormone contraceptives.

3.4. Fluoropyrimidine and Thiopurine Considerations with Respect to DPYD, NUDT15 and TPMT

Dihydropyrimidine dehydrogenase (DPYD) polymorphisms are a potential cause of severe adverse drug reactions in oncologic patients undergoing fluoropyrimidine therapy [32]. The DPYD wild-type allele was the predominantly detected variant (99%) in the present study. This number is higher than what is considered average for European populations [32].
Thiopurine toxicity can be reduced with proactive NUDT15 and TPMT screening, as stated in the respective CPIC guidelines [33]. Both of these genes were predominately of normal-risk phenotype (95% for TPMT and 99.2% for NUDT15) in the analyzed population.

3.5. CYP3A Family and HLA

The CYP3A family is one of the most important families of cytochrome P-450 enzymes considering its presence in the liver and intestine, with CYP3A4 and CYP3A5 being the most prominent members [34]. The vast majority of the analyzed population was carrying the wild-type *1 allele for CYP3A4 (93.9%), whereas the most commonly observed allele for CYP3A5 was *3 (93.9%), resulting in decreased enzymatic activity. This observation is in line with other Caucasian populations, where the allele frequencies were determined to be between 82 and 95%, as noted by the PharmGKB [35]. HLA-A and HLA-B polymorphisms may prone patients to adverse drug reactions with carbamazepine, oxcarbazepine, abacavir, and allopurinol. In this study, the increased risk variants were present in 3.6% of HLA-A and 7.1% of HLA-B, with 3.3% of the patients being positive for increased allopurinol risk [36].

3.6. Statin Therapy Implications

As an important hepatic transporter protein, SLCO1B1 activity is responsible for the metabolization of many drugs [37]. Most importantly, according to the available guidelines, statins. Decreased transporter activity due to SNPs in SLCO1B1 is recognized as an important factor in statin-related ADRs, namely myopathy [38]. Also, CYP2C9 intermediate and poor metabolizers should initiate fluvastatin therapy at lower starting doses. Normal activity of SLCO1B1 was observed in 41.4% of the patients, which is highly important as it puts over half of the patients in the at-risk group who would benefit from proactive testing prior to the start of statin therapy. Moreover, knowing that statins are included in the first and second lines of prevention for major coronary events, they are one of the most commonly prescribed medications, with 58 DDD/1000/day for atorvastatin and 35.02 DDD/1000/day for rosuvastatin in the Republic of Croatia [20]. A recent randomized controlled trial demonstrated the superiority of genotype-guided statin dosing to usual care with an increase in statin initiation and lower LDL cholesterol, further reinforcing the implementation of proactive pharmacogenomic testing in the setting of cardiovascular disease [39].

3.7. Clopidogrel Therapy Implications

Another finding of the present study that merits further attention regarding cardiovascular drugs is the prevalence of polymorphisms in CYP2C19 that interact with clopidogrel metabolism. Patients who are poor metabolizers are at risk of decreased therapeutic response as the concentration of their active metabolite is lower. This, in turn, puts the patients at risk of a lack of therapeutic response [40]. In the present study, the CYP2C19 poor metabolizer phenotype was present only in 2.1%, while the rapid and ultrarapid metabolizer phenotypes were present in 30% and 6.9%, respectively. Furthermore, knowing that adherence to the guideline on genotype-based dosing for clopidogrel was found to be non-inferior to the standard approach, patients in our population could benefit from CYP2C19 testing [41].

3.8. Beta-Blocker Implications

Beta-blockers were found to be one of the most commonly used drugs in the therapy of patients who reported for pharmacogenetic counseling in our previous study [11]. The DPWG guideline includes recommendations for metoprolol dosing based on genotype, where a decrease in dose and titration are recommended for both intermediate and poor metabolizers [42]. It should be noted, from a clinical perspective, that other beta-blockers such as carvedilol, propranolol, nebivolol, and timolol are all metabolized to a lesser extent by CYP2D6, but no genotype-based dosing guideline is provided by CPIC or DPWG [43]. A recent study demonstrated a statistically significant difference in the incidence of bradycardia in poor metabolizers compared to normal metabolizers and phenoconverted patients [44]. In the present study, intermediate metabolizer phenotypes were present in 25.8% and poor metabolizer phenotypes in 11.1%.
These results show a significant number of patients at risk for suboptimal drug therapy considering cardiovascular medications; therefore, pharmacogenetic testing should be considered for the Croatian population, especially in cases of secondary prevention, where the majority of the abovementioned drugs find their use.

3.9. Selective Serotonin Reuptake Inhibitors (SSRI) and Tricyclic Antidepressants (TCA) Implications

Treatment outcomes of SSRI and TCA therapy are greatly influenced by polymorphisms in multiple genes [4]. The gene-drug interactions are influenced by polymorphisms in CYP2D6 (paroxetine, fluvoxamine, venlafaxine, vortioxetine, and both tertiary and secondary amine TCA), CYP2C19 (citalopram, escitalopram, sertraline, and tertiary amine TCA), and CYP2B6 (sertraline), as pointed out in CPIC guidelines [19,45]. As previously stated for other groups of medications, the combined poor, rapid, and ultrarapid CYP2D6 metabolizer phenotype was present in 14.5% of patients, whereas the combined poor, rapid, and ultrarapid CYP2C19 and CYP2B6 phenotypes were present in 39% and 10.5%, respectively.
Although included in the panel, HTR2A and SLC6A4 polymorphisms did not reach a high enough level of clinical evidence to be included in the SSRI guideline. It should be noted that if future guidelines include these genes in their recommendations.
The high combined variability of detected metabolizing phenotypes should stand to support proactive testing for psychiatric patients, as they are at an increased risk of both ADRs and a lack of therapeutic response, which too often leads to a trial-and-error prescribing approach in the clinical setting. Furthermore, a meta-analysis showed better therapeutic outcomes for patients with major depressive disorder when utilizing genotype-guided prescribing [46].
The main strength of this study is the scope of the analyzed SNPs in 28 genes. In the current study, the comparison with the European population averages from GnomAD revealed no significant differences, indicating a common guideline/protocol may be implemented with our population. We believe this data may be used as a reference point for future studies on allelic and genotype frequencies of drug-metabolizing enzymes, especially if the Croatian population is studied.
The limitations of the present study include the focus on specific SNPs. Although comprehensive, the panel we used targeted loci with a proven gene-drug effect, possibly missing those not included in the panel. This approach, however, has the added benefit that each of the analyzed loci is causal and leads to a change in drug metabolism. Another limitation is based on the retrospective nature of the study protocol. The patients were not asked to identify with respect to their nationality or relatedness at any point during their outpatient hospital visit; therefore, a minor but possible confounder in the dataset includes patients of other nationalities and patients related to one another.
We hope that the results of this study will not only add data for a specific Caucasian population but also serve as a stepping stone for a broader application of pharmacogenomics in Croatian healthcare.

4. Materials and Methods

4.1. Target Population and Genes

The analysis of the target genes was performed by OneOme, LLC. The isolated DNA was analyzed using PCR probe-based methods to discover potential variant locations. Haplotypes, or inherited variants, are designated based on the legacy nomenclature. The target genes were: CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2C cluster, CYP2D6, CYP3A4, CYP3A5, CYP4F2, COMT, DPYD, DRD2, GRIK4, HLA-A, HLA-B, HTR2A, HTR2C, IFNL4, NUDT15, OPRM1, SLC6A4, SLCO1B1, TPMT, UGT1A1, VKORC1, F II, F V, and MTHFR.
In the case of the CYP2D6 gene, the test can detect deletions, duplications/multiplications, and hybrid alleles, but it cannot differentiate duplications that are coupled with deletions. Variants are detected with an accuracy of >99.9%. PCR interference due to reaction inhibitors or compromised DNA quality can occur, but typically produces negative results rather than false-positive results. Results can be inaccurate in cases of non-autologous blood transfusions and organ transplant therapies. Finally, results can extremely rarely be impacted by laboratory errors.
This study retrospectively analyzed the results of DNA samples from 522 patients from a single center in Croatia using the described method. The included patients reported to St. Catherine Specialty Hospital for pharmacogenetic counseling from January 2018 until March 2023. Patients of both sexes and of all age groups were included in the study. No exclusion criterion was used with respect to the patient’s race. The patients’ data was accessed retrospectively through the hospital records system for all the patients. A database was created from the findings, showing each patient’s alleles for each gene. Allele frequency for each gene was determined by dividing the total count of that allele by the total number of alleles in the patient pool for the respective gene. Genotype frequency was determined for each gene by dividing the total count of each unique allele combination by the total number of genotypes in the patient pool for the respective gene. Finally, each genotype was associated with its phenotype in the context of drug metabolization, in accordance with the findings. The genotypes were then grouped by phenotype, and phenotype frequency was determined by dividing the total count of each unique phenotype by the total number of phenotypes.

4.2. Comparison with the GnomAD Database

Comparison of our data with data for the general European population was done using the gnomAD database (https://gnomad.broadinstitute.org/, accessed on 4 June 2023). GnomAD is an online resource used for the large-scale gathering of exome and genome sequencing data. For comparison, we used the v2.1.1 data set, which is in line with the GRCh37 reference human genome. The comparison was done only for pharmacogenetic genotypes determined by a single variant. The selected population, as relevant to our population, was European (non-Finnish). The relevant extracted data were the total number of sequences, the number of sequences in which the variant was discovered, and the frequency of the variant. The frequencies were then compared to the corresponding frequencies provided by our data set.

4.3. Statistical Analysis

We used the Hardy-Weinberg equilibrium for the initial genotype data quality check. The statistical analysis of the obtained data was performed using the software package IBM SPSS Statistics 24.0 (SPSS, Chicago, IL, USA). The normality of the distribution of individual parameters was tested using the Kolmogorov–Smirnov test of normality. The Chi-square test and Fisher’s exact test were used to assess whether a statistically significant difference in allele frequencies exists between the Croatian and European populations.

Author Contributions

Conceptualization, V.M. (Vid Matišić) and D.P.; methodology, V.M. (Vid Matišić) and P.B.; formal analysis, P.B. and L.B.; investigation, V.M. (Vid Matišić), P.B., L.B. and V.M. (Vilim Molnar); resources, D.P.; data curation, L.B., M.D. and V.M. (Vilim Molnar); writing—original draft preparation, V.M. (Vid Matišić), L.B. and M.D.; writing—review and editing, P.B., V.M., (Vilim Molnar) and D.P.; visualization, V.M. (Vid Matišić) and V.M. (Vilim Molnar); supervision, D.P. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by the International Society for Applied Biological Sciences.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of St. Catherine Specialty Hospital (23/10-I, 9 May 2023).

Informed Consent Statement

Patient consent was waived due to the study’s retrospective design. No research data can be traced back to the patients, as the dataset was anonymized by the lead author.

Data Availability Statement

All of the research data is included in the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Distribution of subjects by sex, age and ethnicity.
Table 1. Distribution of subjects by sex, age and ethnicity.
CategoryNumberPercentage (%)
ETHNICITY
White or Caucasian52099.6
American Indian or Alaska Native10.2
Near/Middle Eastern10.2
SEX
Male21741.6
Female30558.4
AGE
1–10112.1
11–20234.4
21–30397.5
31–407814.9
41–508115.5
51–609818.8
61–708716.7
71–808315.9
81–90214.0
91–10010.2
Table 2. Allele numbers and frequencies in comparison to available gnomAD database data.
Table 2. Allele numbers and frequencies in comparison to available gnomAD database data.
GeneAlleleAllele NumberFrequencyGnomAD Frequency *
ENZYMESCYP1A2*1A3230.309
*1F6490.622
*1D110.011
*1V150.014
*1L180.017
*1K60.006
*1W160.015
*1J60.006
CYP2B6*16440.617
*4250.024
*51210.116
*62540.245
CYP2C9*18030.769
*21420.136
*3960.092
*1130.003
CYP2C19*16240.598
*21540.148
*172660.255
CYP2C clusterrs12777823 G8880.8510.8540
rs12777823 A1560.1490.1460
CYP2D6*13900.374
*1x290.009
*210.001
*2x220.002
*2 + *1310.001
*2A1630.156
*2Ax2120.011
*2A + *1360.006
*3130.012
*41000.096
*4x230.003
*4 + *4N90.009
*4 + *68610.058
*4 + *68xN20.002
*5250.024
*670.007
*9210.020
*10160.015
*1330.003
*13 + *2A10.001
*1410.001
*35730.070
*3920.002
*411100.105
*41x210.001
*41x310.001
*59110.011
CYP3A4*19800.939
*1B310.030
*22330.032
CYP3A5*1630.060
*39800.939
*710.001
CYP4F2*17250.6940.7134
*33190.3060.2866
COMTrs4680 G5020.4810.4802
rs4680 A5420.5190.5198
DPYD*110340.990
*2A40.004
rs67376798 T30.003
rs67376798 A30.003
NUDT15rs116855232 C10400.9960.99649
rs116855232 T40.0040.003510
TPMT*110180.975
*3A240.023
*3C20.002
UGT1A1*16370.610
*640.004
*284030.386
VKORC1rs9923231 G5860.561
rs9923231 A4560.437
rs9923231 G/G
resistance allele		20.002
MTHFRrs1801133 C6740.658
rs1801133 T3500.342
rs1801131 A6920.676
rs1801131 C3320.324
RECEPTORSDRD2rs1799978 A9760.9350.93961
rs1799978 G680.0650.06039
GRIK4rs1954787 T4730.4530.4467
rs1954787 C5710.5470.5533
HTR2Ars7997012 A4850.4650.4546
rs7997012 G5590.5350.5454
HTR2Crs3813929 C8480.8120.8294
rs3813929 T1960.1880.1706
OPRM1rs1799971 A9140.8750.8743
rs1799971 G1300.1250.1257
OTHERHLA-ANegative5030.964
Positive *31:01190.036
HLA-BNegative4850.929
Positive *57:01200.038
Positive *58:01170.033
IFNL4rs12979860 C7150.6850.6793
rs12979860 T3290.3150.3207
SLC6A4La5440.541
Lg700.070
Sa3920.390
SLCO1B1*1170.013
*1A4430.348
*1B4270.335
*51440.113
*151270.100
*17400.031
*21760.060
F IIrs1799963 G10280.9850.98755
rs1799963 A160.0150.01245
F Vrs6025 G10270.9840.9704
rs6025 A170.0160.0296
For the European population (non-Finnish).
Table 3. Genotype numbers and frequencies with respective phenotypes.
Table 3. Genotype numbers and frequencies with respective phenotypes.
GeneGenotypeGenotype NumberFrequencyPhenotype
ENZYMESCYP1A2*1A/*1F2060.395rapid
*1F/*1F2020.387rapid
*1A/*1A540.103normal
*1F/*1W160.031rapid
*1A/*1L90.017rapid
*1F/*1V90.017rapid
*1F/*1L90.017rapid
*1D/*1J60.011rapid
*1K/*1V60.011intermediate to normal
*1D/*1F50.010rapid
CYP2B6*1/*11890.362normal
*1/*61690.324intermediate
*1/*5800.153normal
*6/*6280.054poor to intermediate
*5/*6260.050intermediate
*1/*4170.033rapid
*5/*560.011poor
*4/*530.006rapid
*4/*630.006intermediate to normal
*4/*410.002ultrarapid
CYP2C9*1/*13020.579normal
*1/*21140.218intermediate to normal
*1/*3820.157intermediate
*2/*3100.019poor to intermediate
*2/*290.017intermediate
*1/*1130.006intermediate to normal
*3/*320.004poor
CYP2C19*1/*11870.358normal
*1/*171560.300rapid
*1/*2940.180intermediate
*2/*17380.073intermediate to normal
*17/*17360.069ultrarapid
*2/*2110.021poor
CYP2C clusterrs12777823 G/G3760.720normal
rs12777823 G/A1360.261variant present
rs12777823 A/A100.019variant present
CYP2D6*1/*1770.148normal
*1/*2A570.109normal
*1/*41410.079intermediate to normal
*1/*35320.061normal
*1/*4300.057intermediate
*1/*4 + *68240.046intermediate
*2A/*41190.036intermediate to normal
*2A/*4170.033intermediate
*2A/*2A140.027normal
*2A/*4 + *68110.021intermediate
*2A/*35110.021normal
*1/*9100.019intermediate to normal
*4/*41100.019poor to intermediate
*4/*4 + *6880.015poor
*1/*370.013intermediate
*4 + *68/*3570.013intermediate
*41/*4170.013intermediate
*1/*1060.011intermediate to normal
*2A/*560.011intermediate
*4/*460.011poor
*4/*3560.011intermediate
*35/*4160.011intermediate to normal
*1/*550.010intermediate
*1/*5950.010intermediate to normal
*4/*1050.010poor to intermediate
*4 + *68/*4150.010poor to intermediate
*1/*2A + *1340.008normal
*1/*640.008intermediate
*9/*3540.008intermediate to normal
*1/*2Ax230.006ultrarapid
*1/*4 + *4N30.006intermediate
*2A/*2Ax230.006ultrarapid
*4/*530.006poor
*5/*4130.006poor to intermediate
*10/*4130.006intermediate
*1/*1x220.004ultrarapid
*1x2/*2A20.004ultrarapid
*1x2/*2Ax220.004ultrarapid
*1x2/*4120.004rapid
*2A/*620.004intermediate
*2A/*1020.004normal
*3/*420.004poor
*4/*920.004poor to intermediate
*4 + *4N/*3520.004intermediate
*4 + *68/*520.004poor
*5/*520.004poor
*9/*4120.004intermediate
*13/*3920.004intermediate
*1/*2x210.002ultrarapid
*1/*1410.002intermediate
*1/*41x310.002rapid
*1x2/*410.002normal
*2/*410.002intermediate
*2x2/*4110.002rapid
*2 + *13/*410.002intermediate
*2A/*4x210.002intermediate
*2A/*4 + *4N10.002intermediate
*2A/*910.002intermediate to normal
*2A/*13 + *2A10.002normal
*2A/*5910.002intermediate to normal
*2Ax2/*410.002normal
*2Ax2/*4 + *6810.002normal
*2Ax2/*4 + *68xN10.002normal
*2Ax2/*4110.002ultrarapid
*2A + *13/*3510.002normal
*2A + *13/*4110.002intermediate to normal
*3/*4 + *6810.002poor
*3/*4 + *68xN10.002poor
*3/*510.002poor
*3/*3510.002intermediate
*4/*5910.002poor to intermediate
*4x2/*4 + *4N10.002poor
*4x2/*3510.002intermediate
*4 + *4N/*910.002poor to intermediate
*4 + *4N/*4110.002poor to intermediate
*4 + *68/*4 + *6810.002poor
*5/*3510.002intermediate
*6/*4110.002intermediate
*9/*1310.002poor to intermediate
*35/*5910.002normal
*41x2/*5910.002intermediate to normal
*59/*5910.002intermediate
CYP3A4*1/*14590.879normal
*1/*1B310.059normal
*1/*22310.059intermediate to normal
*22/*2210.002intermediate
CYP3A5*3/*34590.879poor
*1/*3610.117intermediate
*1/*110.002normal
CYP4F2*1/*12550.489normal
*1/*32150.412reduced activity
*3/*3520.010reduced activity
COMTrs4680 G/A2700.517intermediate activity
rs4680 A/A1360.261low activity
rs4680 G/G1160.222high activity
DPYD*1/*15150.987normal risk
*1/*2A40.008increased risk (DPD score = 1)
rs67376798 T/A30.006increased risk (DPD score = 1,5)
NUDT15rs116855232 C/C5180.992normal metabolizer
rs116855232 C/T40.008increased risk
TPMT*1/*14960.950normal risk
*1/*3A240.046increased risk
*1/*3C20.004increased risk
UGT1A1*1/*12000.383normal risk
*1/*620.004increased risk
*1/*282350.450increased risk
*6/*2820.004high risk
*28/*28830.159high risk
VKORC1rs9923231 A/A960.184low activity
rs9923231 G/A2640.506intermediate activity
rs9923231 G/G1610.308normal activity
rs9923231 G/G
resistance allele 		10.002resistance allele(s)
MTHFRrs1801133 C/C–
rs1801131 A/A		560.109normal activity
rs1801133 C/C–
rs1801131 A/C		1090.213decreased activity
rs1801133 C/C–
rs1801131 C/C		560.109severely decreased activity
rs1801133 C/T–
rs1801131 A/A		1210.236decreased activity
rs1801133 C/T–rs1801131 A/C1110.217severely decreased activity
rs1801133 T/T–
rs1801131 A/A		590.115severely decreased activity
RECEPTORSDRD2rs1799978 A/A4540.870normal response
rs1799978 A/G680.130reduced response
GRIK4rs1952787 T/C2610.500normal response
rs1952787 C/C1550.297normal response
rs1952787 T/T1060.203risk of reduced response
HTR2Ars7997012 A/G2650.508intron 2 genotype AG
rs7997012 G/G1470.282intron 2 genotype GG
rs7997012 A/A1100.211intron 2 genotype AA
HTR2Crs3813929 C/C3800.728normal risk
rs3813929 C/T880.169protective effect
rs3813929 T/T540.103protective effect
OPRM1rs1799971 A/A3990.764Asn/Asn isoform
rs1799971 A/G1160.222Asn/Asp isoform
rs1799971 G/G70.013Asp/Asp isoform
OTHERHLA-ANegative5030.964normal risk
Positive *31:01190.036increased risk
HLA-BNegative4850.929normal risk
Positive *57:01200.038increased risk with abacavir and pazopanib
Positive *58:01170.033increased risk with allopurinol
IFNL4rs12979860 C/C2480.475normal response
rs12979860 C/T2190.420reduced response
rs12979860 T/T550.105reduced response
SLC6A4La/Sa1980.394typical to reduced expression
La/La1510.300typical to increased expression
Sa/Sa840.167reduced expression
La/Lg440.087likely typical to reduced expression
Lg/Sa260.052likely reduced expression
La/Sa1980.394typical to reduced expression
SLCO1B1*1/*2170.013reduced response
*1/*17 OR *5/*21100.019increased risk
*1A/*1A840.161normal risk
*1A/*1B1220.234normal risk
*1A/*5280.054increased risk
*1A/*21280.054reduced response
*1A/*15
OR *1B/*5		800.153increased risk
*1A/*17
OR *5/*21		170.033increased risk
*1B/*1B940.180normal risk
*1B/*15270.052increased risk
*1B/*2120.004reduced response
*1B/*17
OR *15/*21		80.015decreased function
*5/*530.006increased risk
*5/*1520.004increased risk
*5/*1710.002increased risk
*15/*1550.010poor function
*17/*1710.002increased risk
*17/*2120.004increased risk
*21/*2110.002reduced response
F IIrs1799963 G/A160.031increased risk
rs1799963 G/G5060.969normal risk
F Vrs6025 G/A170.033increased risk
rs6025 G/G5050.967normal risk
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MDPI and ACS Style

Matišić, V.; Brlek, P.; Bulić, L.; Molnar, V.; Dasović, M.; Primorac, D. Population Pharmacogenomics in Croatia: Evaluating the PGx Allele Frequency and the Impact of Treatment Efficiency. Int. J. Mol. Sci. 2023, 24, 13498. https://doi.org/10.3390/ijms241713498

AMA Style

Matišić V, Brlek P, Bulić L, Molnar V, Dasović M, Primorac D. Population Pharmacogenomics in Croatia: Evaluating the PGx Allele Frequency and the Impact of Treatment Efficiency. International Journal of Molecular Sciences. 2023; 24(17):13498. https://doi.org/10.3390/ijms241713498

Chicago/Turabian Style

Matišić, Vid, Petar Brlek, Luka Bulić, Vilim Molnar, Marina Dasović, and Dragan Primorac. 2023. "Population Pharmacogenomics in Croatia: Evaluating the PGx Allele Frequency and the Impact of Treatment Efficiency" International Journal of Molecular Sciences 24, no. 17: 13498. https://doi.org/10.3390/ijms241713498

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