Introduction

Genetic variation has a significant impact on an individual’s metabolism and response to a wide range of medications. The cytochrome P450 family of enzymes are key determinants in the metabolism of multiple pharmacologic compounds. One such member, CYP2D6 is involved in the metabolism of a wide range of medications including drugs for pain management, cancer, mental health disorders, some cardiovascular symptoms, chorea, and Gaucher disease (1, 2). This enzyme is encoded by the CYP2D6 gene, which is highly polymorphic. The CYP2D6 alleles can be classified as having no function, decreased function, normal function or increased function. The combination of alleles present in an individual (also referred to as genotype or diplotype) allows for prediction of metabolizer phenotype ranging from poor metabolizer (PMs, no enzymatic activity) through ultrarapid metabolizer (UMs, increased enzyme activity). Poor metabolizers exhibit no enzyme activity, intermediate metabolizers (IMs) have decreased enzyme activity, and UM’s have increased enzyme activity relative to the normal metabolizer (NM) phenotype. Increased enzyme activity is most often due to gene copy number variation, namely, the presence of one or more functional gene copies.

As is commonly observed with genomic variation, the frequency of CYP2D6 alleles differs from one population to another. Knowing the frequency of specific alleles and metabolizer phenotypes among sub-populations of different ethnic, or geographical groups, or both, will enable clinicians to identify individuals who can most benefit from preemptive genetic testing for actionable pharmacogenetic variation. As the CYP2D6 locus has such a high degree of variation, there is an extensive field of literature regarding allele and phenotype frequencies for various populations. The purpose of this document is to provide a centralized resource for key information on the CYP2D6 gene, allele function, and phenotype prediction, as well as an overview of the recent literature reporting the allele and phenotype frequencies.

Gene: CYP2D6

The cytochrome P450 superfamily (CYP450) is a large and diverse group of enzymes that form the major system for metabolizing lipids, hormones, toxins, and drugs in the liver. The CYP genes involved in drug metabolism and disposition are highly polymorphic and contribute to the wide range of activity observed among individuals (no function to increased function).

The CYP2D6 enzyme is responsible for the metabolism of many commonly prescribed drugs, including antidepressants, antipsychotics, analgesics, and beta-blockers. Many of these medications have their own chapters within the Medical Genetics Summaries.

The CYP2D6 gene on chromosome 22q13.2 is one of the most variable among all CYP genes. Over 135 distinct star (*) alleles have been described and catalogued by the Pharmacogene Variation (PharmVar) Consortium, and each allele is associated with either normal, decreased, absent, or unknown enzyme function, as defined by the Clinical Pharmacogenomics Implementation Consortium (CPIC) (Table 1). (1, 3)

The combination of CYP2D6 alleles a person harbors determines their diplotype (often also referred to as genotype). Examples are CYP2D6*1/*2 or CYP2D6*4/*5. Based on function, each allele can be assigned a value to calculate the activity score of the diplotype, which in turn is often used to assign phenotype. Briefly, a value of 1 is assigned to each allele of a CYP2D6*1/*2 diplotype giving rise to an activity score of 2 (NM), while both alleles of the CYP2D6*4/*5 diplotype receive a value of 0, which results in an activity score of 0 (PM). If an individual has a gene duplication at the CYP2D6 locus, then the additional copy is also counted toward the total activity score. An individual who has no detected variants and is determined to have triplication of the CYP2D6 gene would thus be genotyped as CYP2D6*1x3 and have an activity score of 3. This CPIC-recommended genotype to phenotype translation method was developed to facilitate standardization (4), it is however, not consistently utilized across clinical laboratories for reporting CYP2D6 pharmacogenetic test results.

• Ultrarapid metabolizers (UM) have a diplotype with an activity score greater than 2.25
• Normal metabolizers (NM) have an activity score of 1.25 to 2.25
• Intermediate metabolizers (IM) have an activity score of 0.25 to 1
• Poor metabolizers (PM) have an activity score of 0

The CYP2D6*1 allele is considered the reference allele and is often inferred when no variants interrogated by a genotyping test are detected. The CYP2D6*1 allele confers normal enzyme activity and can be found in many individuals with a NM phenotype. Some groups refer to this phenotype as an “extensive” metabolizer. Other alleles including CYP2D6*2 and *35 (to name a few) have activities that are similar to that of *1 and are thus also classified by CPIC as having normal function.

Other CYP2D6 alleles have one or more single nucleotide polymorphisms (SNPs, indels) that obliterate function (for example, CYP2D6*3, *4, and *6) (5, 6, 7, 8) or cause decreased function (for example, *10, *17, *29, and *41) (9, 10, 11) (see Table 1) (3). Duplication of a no-function CYP2D6 allele has the same effect on the metabolizer phenotype as a single copy, however duplicated decreased-function alleles will contribute additively to the final activity score. Clinical testing does not always specify which allele is duplicated; thus, interpretation of these testing results may be ambiguous regarding the metabolizer phenotype.

The role of CYP2D6 in drug metabolism is well established. Whether a particular medication should be administered at lower doses or be avoided entirely by individuals with altered CYP2D6 metabolism will often be discussed on the regulatory agency-approved drug prescribing Information or label (namely, the Food and Drug Administration in the United States) and in published guidelines from professional societies (for example, the Clinical Pharmacogenetics Implementation Consortium, the Canadian Pharmacogenomics Network for Drug Safety, or the Dutch Pharmacogenetic Working Group). In addition to the information presented here, additional published reviews are available that discuss CYP2D6 gene variations and its role in personalized medicine (1, 2, 12).

Allele and Phenotype Frequencies

There are substantial differences for CYP2D6 allele frequencies not only among ethnic groups, but also among populations within the major ethnic groups. These differences are illustrated by CYP2D6*3, *4, *5, *6, and *41, which are more common in people of European descent, while CYP2D6*17 is more prevalent in Africans and their descendants, and CYP2D6*10 is more common in Asians (13, 14, 15). While the frequency of specific alleles is useful knowledge for the implementation of pharmacogenetics and genetic testing, clinical recommendations are typically based on an individual’s phenotype.

Globally, normal and IM phenotypes are the most common. Normal metabolizers comprise 43–67% of populations and IMs comprise an additional 10–44% (3). Poor and UMs are less common, but these individuals are at a higher risk for adverse reactions or treatment failure when treated with a drug that is metabolized or bioactivated by CYP2D6.

If there is any concern regarding an individual’s CYP2D6 metabolism, genetic testing provides valuable information to inform drug choice or dosage for the individual. We refer to the NIH Genetic Testing Registry (GTR) and Bousman et al (16) for more information regarding whether testing should be performed and available clinical testing options. The available CYP2D6 tests include targeted single-gene tests, as well as multi-gene panels and genome-wide sequencing tests (see comment below regarding genotyping methodology). In addition, variant CYP2D6 alleles to be included in clinical genotyping assays have been recommended by the Association for Molecular Pathology (AMP) (17). These alleles, their defining ‘core’ variants and reported frequencies in the major ethnic groups are summarized in Table 2.

Literature Summaries

Many studies have sought to determine allele frequencies of interest in specific ethnic populations or study cohorts across the globe with new data being published regularly. The sections below describe the summarized findings of many of these studies, organized by geographic region of the population studied. Upon review of many of these studies, Zhang and Finklestein noted that broad race classifications are not adequate to accurately capture the ethnicity-specific allele frequencies, and many of these classifications are applied differently by different author groups (18). Thus, we have grouped the literature references based on geographic location, rather than race or ethnicity, which is often self-reported by the study participants. When provided by the authors, racial and ethnic composition of the study population is noted. The geographic groupings used herein follow the Biogeographical Groups from PharmGKB (19, 20) where possible. Further regional or country-specific divisions have been provided for clarity. Some of the larger studies that include multi-ethnic populations provide ethnicity-specific allele frequencies. These data are presented herein as reported by the authors, and readers are encouraged to verify the population definitions from the original publication.

Readers are encouraged to review any articles of interest in more detail and search out additional literature if the information regarding a specific population is not adequately described below. The following literature summaries are not exhaustive for all CYP2D6 allele frequency reports. Rather, the most recent literature covering any specific region or people group has been included, along with landmark studies that are commonly cited by newer publications.

There are various genotyping methods utilized in the cited literature below and used in clinical testing, each with their own caveats for accuracy of final genotype and phenotype assignment. Targeted allele testing (by TaqMan or other probe-based technologies) for the core, defining SNPs (recommended by AMP or provided by PharmVar) will provide the highest specificity in identifying known alleles. However, these targeted tests alone may not detect copy number variants (CNVs), which may change the assigned phenotype. For example, an individual whose genotype is homozygous for the CYP2D6*2 variant SNPs may in fact have a gene duplication on one chromosome, thus they would be given a phenotype of UM rather than NM (AS of 3 for CYP2D6*2x3). Gene duplication testing may not include identification of which allele is duplicated in a heterozygous genotype. If the duplication is a decreased or no-function allele versus a normal-function allele, the resulting phenotype may be different. Additionally, some variants are shared among star alleles and not all author groups interrogate the frequency of the distinguishing variants. Thus, reporting the specific variant frequency overestimates the frequency of the most common allele, which is defined (partially) by that SNP. This is particularly common for the CYP2D6*10 allele and tests of SNP rs1065852. Genotyping for gene hybrids may be beyond the scope of some of the literature herein. As such, some star allele frequencies may be overestimated due to the combined counting of the core allele without regard to the potential for hybrid alleles (for example, CYP2D6*10+*36 hybrids may be only identified as CYP2D6*10). While the advances in next-generation sequencing (NGS), including whole exome (WES) and whole genome sequencing (WGS), have made this technology available to a broader range of laboratories and researchers, WES/WGS has its own limitations in accurate diplotype identification. Phasing of identified variants is difficult with short sequencing reads, thus obscuring which variants occur in cis or in trans. Other technical limitations of NGS approaches include potential interference of pseudogenes, limited information regarding structural variants, and difficulty with interpreting novel or rare haplotypes.

Global Population studies

I.

Gaedigk, et al. (2017) (14) PubMedCentral: PMC5292679. This publication is frequently cited as a point of reference for specific subpopulation studies. Gaedigk and colleagues performed a literature review and summarized the predicted allele and activity score based ‘phenotype’ frequencies for global major ethnic groups: African American, African, American, East Asian, European, Middle Eastern/Oceanian, S. Central Asian, Jewish.

Among these groups, PM’s were present in 5.9–5.4% of the Jewish and European populations, 2.3% of African Americans and 2.8% of Africans, and ~1% or less of the other ethnicities. Intermediate metabolizer^ were most prevalent in the Jewish population at 37.7%, followed by 38–34% of African, African American, East Asian and European populations, South Central Asians were predicted to have an IM phenotype prevalence of 28.6% and Middle Eastern and American populations had the lowest frequency of IM phenotype at 24–22%. Normal metabolizer phenotype^ was most common in South Central Asian, Middle Eastern and East Asian populations (64–68%), but least common in the Jewish population at 44.8%. Ultrarapid metabolizer phenotype was predicted to be highest in Jewish and Middle Eastern populations (~11%) and least common in East Asian (~1%). (^Note: The intermediate and NM frequencies are adjusted to the CPIC activity score definitions provided above, an update from the original publication.) Figure 1 shows the predicted phenotype based on genotype; frequencies are shown in percentage (%) based on the ethnicity. A corrigendum for this table is available from PubMedCentral.

Figure 1:

Global ethnicity-specific predicted phenotype frequencies based on genotype. AS : activity score. gPM: genetic poor metabolizer, gIM: genetic intermediate metabolizer, gNM: genetic normal metabolizer, gUM: genetic ultrarapid metabolizer. This article (more...)

II. Koopmans, et al. (2021) (15) PubMedCentral: PMC7904867. This review summarizes the phenotype frequencies among global populations from over 300 published articles and provides the non-NM (either poor, intermediate, or ultrarapid) phenotype frequencies for CYP2D6 and CYP2C19 to facilitate optimized dosing for psychiatric medications. The authors used allele frequency data and activity scores as described above to translate genotype to phenotype. The population with the highest prevalence of non-NMs was Algeria, and the lowest prevalence was in Gambia. The frequency of UM phenotype was nearly 40% in the Mozabite people, a Berber ethnic group in North Africa. Non-Austronesian Melanesians and Druze (from the Middle East) groups also were noted to have high frequencies of UM. The PM frequencies were highest in British (12.1%), Danish (10.6%) and Basque French (9.7%) populations. The global average for non-NM phenotypes was reported as 36.41%. It is important to note that some of the populations included in this study had small sample sizes (less than 50 individuals) and as such, the phenotype frequencies made be over- or underestimated.

III. Taliun, et al. (2021) (21) reported on the sequencing data from 53,831 diverse genomes in the National Heart Lung and Blood Institute’s (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program. The TOPMed program aims to combine more than 80 “omics” studies from a broad range of populations of varying race and ethnicity. The entire current set of ~155,000 selected participants consists of approximately 41% European ancestry (European, European American), 31% African ancestry (African, African American, African Caribbean), 15% Hispanic/Latino (including Mexican, Mexican American, Central American, South American, Cuban, Dominican, Puerto Rican), 9% Asian ancestry (Chinese, Taiwanese, Asian American, Pakistani) and 4% ‘Other’ (Samoan, Native American, multiple groups, or unknown). Among the data in the TOPMed analysis, the authors described the frequency of CYP2D6 star alleles as identified by WGS in the supplemental data from this report. The frequencies of 99 alleles (including known star alleles, duplications, deletions, hybrid alleles and novel alleles) are reported for individuals of European, African, Hispanic/Latino, Asian, Samoan, and Amish ancestry. In all populations, the CYP2D6*1 allele was most common, but this ranged from 29% in African ancestry up to 47% in Hispanic/Latino ancestry. The TOPMed data are available here. Detailed Tier 1 allele frequencies for AMP recommended alleles are shown in Table 3. As stated above, the identification of hybrid alleles and phasing of variants for accurate haplotype calls is difficult with NGS data. The authors cite methods using the program Stargazer to perform haplotype analysis.

American Allele Studies

European Allele Studies

Gene duplication events are more common in south-eastern Europe, while loss-of-function alleles are more common in north-western Europe. The frequency of UM phenotype in Denmark and the Netherlands are between 0–5% and PM frequency is between 4.2–11%. Lithuanian populations may have a lower frequency of the *5 allele as compared with the European average. Studies from the Czech Republic and Bosnia reported a higher frequency of the *10 allele among these populations and the Romani people living in the Czech Republic as compared with other Caucasian European groups.

I.

Petrovic, et al. (2019) (52) published a systematic review of CYP2C19 and CYP2D6 allele frequencies from multiple European countries and compared the relative frequencies of alleles. The article provides detailed summaries of the literature but further notes a trend of frequencies of CYP2D6 gene duplications formed a clear South-East to North-West gradient ranging from <1% in Sweden and Denmark to 6% in Greece and Turkey. The inverse trend was observed for the no-function CYP2D6*4 and CYP2D6*5 alleles (higher in the Nordic, north-west countries, lower in the south-east).

DENMARK

II. Lunenberg, et al. (2021) (53) studied 77,684 individuals from Denmark to determine pharmacogenetic genotype and phenotype frequencies in this population. A genotyping array was used to detect variants in pharmacogenes, this included 8 variants in CYP2D6 as well as deletions (CYP2D6*5 allele) and duplications at this locus. The frequency of NM was 62.4%, IM was 33.4%, and PM was 4.2%. The authors noted there was an absence of CYP2D6 gene deletions or duplications in their cohort, thus no UM predicted phenotypes were detected.

NETHERLANDS

III. Poulussen, et al. (2019) (54) reported on the allele and phenotype frequencies of 7 different CYP2D6 variants and gene duplications among 105 hospitalized individuals from the Netherlands who were being treated with metoprolol. The authors reported the frequency of PM was 11%, IM frequency was 45%, NM frequency was 39% and UM frequency was 5%. These frequencies were based on the Royal Dutch Pharmacist Association genotype-phenotype translation guidelines as of January 2019, before the publication of the above AS and phenotype harmonization report.

LITHUANIA

IV. Dlugauskas, et al. (2019) (55) studied 179 individuals from Lithuania for CYP2D6 polymorphisms. Variants were identified by Sanger sequencing of CYP2D6 exons 1, 2, 3–4, 5–6, 6–7, and 9; CNVs were detected by MLPA. The authors noted similar allele frequencies to other European populations, with the exception that the CYP2D6*5 allele was less common in Lithuania. A total of 9 star alleles were detected by sequencing as well as one individual with a duplication of the CYP2D6*2 allele. The predicted phenotype frequencies among the Lithuanian population are 1.15% for PM, 0.56% for UM, 4.47% for IM and the remainder were predicted NM. (Note: the phenotypes were predicted based on these activity score values: PM 0, IM 0.5, NM 1–2 and UM >2.)

SPAIN

V. Lopez de Frutos, et al. (2020) (56) reported on the frequency of genetic and phenotypic variation at the CYP2D6 locus in individuals from Spain who were diagnosed with type I Gaucher disease. Out of the 109 individuals enrolled in the study, 87 were predicted to be NM (80%), 14 were predicted to be IM (13%), 6 were predicted to be PM (5%), and only 2 individuals were predicted to be UM (1.8%). These frequencies were compared with previous reports from other Iberian populations, noting a higher incidence of IM phenotype in their report versus previous publications. The study utilized an xTAG assay on the Luminex platform that detects 19 different variants, most of which were previously associated with known star alleles, as well as duplications and deletions to characterize the metabolizer phenotypes.

VI. Barreda-Sanchez, et al. (2019) (57) studied a cohort of 50 individuals in southern Spain with acute intermittent porphyria. They performed targeted genotyping via TaqMan SNP or CNV assays at multiple CYP loci; at the CYP2D6 locus, they tested for the presence of the CYP2D6*4 and CYP2D6*5 alleles. The frequency of the CYP2D6*4 allele in this small cohort was 12% and the CYP2D6*5 allele was observed with a frequency of 1% of the cohort. Phenotype predictions were not given for metabolizer status.

ITALY

VII. Dagostino, et al. (2018) (58) reported on the utility of CYP2D6 genotyping for opioid safety in the treatment of chronic back pain among a cohort of 196 Italians in the PainOMICS study. They observed 79.6% of their study participants were predicted to be NM, 16.8% were predicted as either IM or PM, and 3.6% were predicted UM. They examined 10 different star alleles and also tested for duplication of the CYP2D6*1 and CYP2D6*2 alleles on the Luminex platform.

SERBIA

VIII. Skadric and Stojkovic (2020) (59) published a description of multiple cytochrome P450 gene variants among more than 7,000 DNA samples from Serbia. The authors divided the samples into 5 distinct groups based on geography, historic precedent, and ethnically distinguishable populations. These regions are north Serbia, west Serbia, east and south Serbia, central Serbia, and Belgrade. Based on the sample size and number of genes interrogated in this study, only 4 variants were tested at the CYP2D6 locus. In general, the data from the Serbian population closely followed other European allele frequencies, including 3 out of the 4 variants associated with altered CYP2D6 function. The authors noted that rs28371706 in CYP2D6 was very rare and thus may not be an informative variant in the Serbian population. This variant is associated with several no-function alleles. Because limited variants were genotyped at the CYP2D6 locus, accurate star allele calls are not feasible from this dataset.

BOSNIA

IX. Nefic (2018) (60) reported on the frequency of gene duplication and the frequency of the c.100C>T variant (commonly associated with the CYP2D6*10 decreased-function allele, among other known haplotypes) in 151 unrelated, healthy individuals from Bosnia. Gene duplications were observed at a frequency of 2.73%, similar to other Caucasian populations. The variant associated with the decreased-function allele was observed at a higher frequency of 15.56%, more commonly than was reported in other Caucasian groups, but less common than in Asian populations.

CZECH REPUBLIC

X. Dlouhá, et al. (2020) (61) compared the frequencies of variants in multiple CYP enzymes between the Roma (302 individuals) and non-Roma (298 individuals) populations residing in the Czech Republic. Study participants were genotyped for 2 variants in CYP2D6 and one variant each in CYP1A2, CYP2A6, and CYP2B6. The authors reported a higher frequency of the variant that defines the no-function CYP2D6*4 allele in the Roma populations as compared with the Czech population (39.2% versus 38.2%). The variant commonly associated with the CYP2D6*10 allele was observed at a similar frequency in the 2 populations (24%). However, the frequency of the predicted CYP2D6*10/*10 IM genotype, was higher in the Czech population (9.1%) than the Roma population (6.5%). This study does not account for potential hybrid gene structures or other alleles incorporating the c.100C>T variant that may contribute to an overestimate of homozygosity for this variant.

RUSSIA

The prevalence of the no-function CYP2D6*4 allele ranges from 17.4–27.1% in the Russian population but is notably lower in the Nanai people group (1.4%), Tatar group (11.5%), and Mari group (8.98%). The predominant predicted phenotype is NM, with IM’s present at a frequency of 21.5–33%.

XI. Sychev, et al. (2017) (62) compared allele frequencies at several pharmacogenes between Russian and Nanai populations. The study enrolled 70 individuals of the Nanai people group and 642 individuals from the broader Russian population. At the CYP2D6 locus, they interrogated a variant associated with the no-function CYP2D6*4 allele. The CYP2D6*4 allele was more common the Russian population (17.4%) than the Nanai (1.4%), and consequently the authors predicted a higher frequency of NM phenotypes in the Nanai population. (Note: The Nanai population in Russia live in the eastern region of the country, an area that is technically part of the “East Asian” region defined by PharmVar. Because this study compares this ethnic group to the broader Russian population, it is grouped here with other Russian population studies.)

XII. Muradian, et al. (2021) (63) studied the effects of CYP2D6 and CYP2C9 variants on pain management with tramadol and ketorolac. A total of 107 individuals were genotyped for the CYP2D6*4 allele. The authors reported 21.5% of the individuals in the study were heterozygous or homozygous for the no-function CYP2D6*4 allele, resulting in either an IM or PM phenotype, depending upon the functional status of the other allele in heterozygotes.

XIII. Zastrozhin, et al. (2021) (64) analyzed the effect of the no-function CYP2D6*4 allele on the efficacy and safety of fluvoxamine in 96 males treated for major depressive disorder. They performed targeted variant genotyping for a defining SNP variant (rs3892097, G>A) and determined that 72.9% of the cohort were homozygous WT and 27.1% were heterozygous (CYP2D6*4 present for one allele, leading to a possible IM phenotype). No individuals in the study were determined to be homozygotes for the CYP2D6*4 allele.

XIV. Ivashchenko, et al. (2021) (65) studied the pharmacogenetics of CYP2D6, CYP3A4/5 and ABCB1 variants and the efficacy and safety of antipsychotics in adolescents with acute psychotic episodes. A total of 101 individuals were enrolled, aged 10–18, and targeted variant genotyping was performed to detect the CYP2D6*4, *9, and *10 alleles. Individuals with at least one decreased or no-function allele were assumed to be IMs, and those with 2 no-function alleles were phenotyped as PMs. Individuals with no variation detected were assumed to be NMs. The authors reported 68/101 individuals were NMs, 33/101 were IMs, and there were no PMs detected in the study cohort.

XV. Abdullaev, et al. (2020) (66) examined clinically relevant pharmacogenetics markers in Tatars and Balkars, 2 ethnic groups living in in the Volga and Caucasus regions of Russia. A total of 341 individuals were enrolled in the study. Targeted genotyping at 10 variants was performed, but only the CYP2D6*4 allele was tested at the CYP2D6 locus. The authors observed that 77% of the 141 Tatar subjects did not have the CYP2D6*4 allele and the remaining 23% of individuals were heterozygous or homozygous for the CYP2D6*4 variant. The CYP2D6*4 allele frequency in Europeans is similar to the Balkar group, but the Tatar group had a slightly lower allele frequency. The PM phenotype in Europeans is often associated with the CYP2D6*4 allele.

XVI. Mirazev, et al. (2020) (67) studied polymorphisms in several pharmacogenes in 845 healthy individuals from the Volga and northern Caucasus regions of Russia. These individuals were from multiple ethnic groups: 238 from the Chuvash ethnic group, 206 Mari, 157 Kabardins and 244 Ossetians. The only allele studied at the CYP2D6 locus was CYP2D6*4. The frequency of heterozygotes for the defining CYP2D6*4 variant was 22.69% in the Chuvash group, 17.96% in the Mari group, 30.74 in the Ossetians and 32.48% in the Kabardins. The Ossetians and Kabardins’ CYP2D6*4 allele frequencies were most similar to the overall Russian population (15–16% versus 18%), the frequency in the Chuvash and Mari groups was statistically significantly lower. These frequencies suggest the rates of PM and IM due to the presence of the CYP2D6*4 allele will be lower in the Chuvash and Mari as compared with the Ossetians, Kabardins, and Russian population at large.

Near Eastern Allele Studies

The prevalence of the decreased-function CYP2D6*41 allele may be higher in the United Arab Emirates, Saudi Arabia, and Turkey than other populations. The frequency of NM phenotype ranges between 54–82%.

I.

Khalaj, et al. (2019) (68) reported on the distribution of various CYP2D6 alleles across the Middle East. A total of 32 studies were reviewed, with the total number of individuals in each study ranging from 43–552. Overall, the most common allele was the CYP2D6*1, normal-function allele at 68% average frequency, though this may be overestimated due to default assignment when no variants are detected. The CYP2D6*3, *4, and *5 no-function alleles combined for an average frequency of 37.5%. As one might expect, the NM phenotype was most common in every country reported, ranging from a peak of ~82% in Saudi Arabia to 54% in Egypt. The UM phenotype was most commonly seen in Saudi Arabia (20%), Syria (15%), Jordan (14%), and Emirates (13%). Individuals with PM phenotype were most frequently observed in Egypt (19%) and Iran (9%) with other countries having PM frequencies fewer than 5%.

SAUDIA ARABIA

II. Almeman (2020) (69) reviewed CYP450 gene polymorphism in Saudi individuals from 10 different studies in this population. The number of individuals in each study ranged between 90 and 200, though not all reviewed studies examined CYP2D6 function or genotype. Overall, the author summarizes the findings of these studies and notes there are rare PM individuals and associated no-function allele frequencies were also low (with some alleles notably absent). Gene duplication at the CYP2D6 locus was noted to be frequent in the Saudi population. The author reported the frequency of the CYP2D6*41 decreased-function allele within the Saudi population was higher than in other populations, such as Chinese, Mexican, Caucasian and Ghanaians. Overall, the Saudi population demonstrates similar allele frequencies for the reported loci as other Middle Eastern populations.

TURKEY

III. Arici & Ozhan (2016) (70) reported on multiple CYP gene profiles and susceptibility to drug response in a Turkish population of 160 individuals. This study examined the frequency of variation leading to the decreased-function CYP2D6*9 and *41 alleles within the CYP2D6 locus. They observed a minor-allele frequency of 4% for the CYP2D6*9 allele (a 3-nucleotide deletion) and 15% for the CYP2D6*41 allele. Thus, the *9 decreased-function allele was more prevalent in Turkish individuals than in European or Asian populations. Similarly, the prevalence of the *41 allele was higher in the Turkish study cohort as compared with Caucasian or Chinese populations.

EGYPT

IV. Mutawi, et al. (2021) (71) reported on a genotyping study of 145 healthy Egyptian individuals with the goal of elucidating the frequency or major allelic variants at the CYP2D6 locus. From the variant genotyping data of 5 CYP2D6 alleles and CNVs (detected with CNV TaqMan assay), the authors concluded the NM phenotype was the most common among their cohort, with a frequency of 67.6%. They did not identify any study participants with 2 no-function alleles suggesting that PM phenotype in Egypt is rare. Gene duplications, however, were observed and the authors predict a frequency of UMs of 4.8% in this Egyptian cohort.

Sub-Saharan Africa Allele Studies

The IM phenotype has been reported as the most common phenotype in Kenya and Madagascar. The CYP2D6*17 and CYP2D6*29 decreased-function alleles both have frequency greater than 10) in multiple regions of Africa (Zimbabwe, Kenya, Ethiopia, and Madagascar for CYP2D6*17; Tanzania, Kenya for CYP2D6*29). Multiple studies report a very low frequency (<2%) of PMs.

I.

Rajman, et al. (2017) (72) performed a literature-based review (80 articles total) of cytochrome P450 variants across the continent of Africa. While results from multiple genes are presented, the CYP2D6 alleles discussed specifically are *3, *4, *9, *10, *17, and *29. The no-function CYP2D6*3 and CYP2D6*4 alleles were most common the San population from Zimbabwe (9% allele frequency). The decreased-function CYP2D6*17 and CYP2D6*29 alleles were notably higher in certain populations; CYP2D6*17 was seen at a frequency of 34% in Zimbabwe (Shona), and CYP2D6*29 was observed in 20% of the alleles in Tanzania. The CYP2D6*9 decreased-function allele was not present in any population reviewed, while the CYP2D6*10 decreased-function allele was most common in the South African Venda population (19%), but not present in multiple other populations. The other decreased-function allele, CYP2D6*29, was present in nearly 30% of the Igbo (Nigeria) population, but only 2% of the San population.

ETHIOPIA

II. Ahmed, et al. (2019) (73) studied the genotype and predicted phenotype of female individuals being treated for breast cancer with tamoxifen in Ethiopia. The authors reported the frequency of 5 specific star alleles detected via variant genotyping and the duplication of the CYP2D6*1 and CYP2D6*2 alleles, detected via TaqMan CNV assays. Among the 181 participants, 22.2% were predicted to be UM, roughly 60% were NM, approximately 16% had activity scores resulting in a current predicted phenotype of IM, and 1.2% were PM. The authors reported differences in tamoxifen metabolites among individuals with the same metabolizer phenotype but distinct diplotypes (for example, CYP2D6*1/*1 individuals had different endoxifen levels as compared with CYP2D6*2/*2 genotyped individuals).

KENYA

III. Rico, et al. (2020) (74) reported on the CYP2D6 genotype frequencies and functional characterization of novel variants found in the Ni-Vanuatu (Melanesia/Polynesia) and localized Kenyan populations. The 278 Ni-Vanuatu study participants were residents of 6 different islands from Melanesia or Polynesia. Within Kenya, the authors enrolled 195 individuals residing on islands or the shore of Lake Victoria in western Kenya. All study participants were healthy and unrelated. Eight variant star alleles were identified by PCR-based sequencing, allele fusions and duplications were also detected along with 6 novel variants. Detailed allele frequencies are presented in tables 1 and 2 within the publication. Among the Kenyan population, 34.4% of the individuals were predicted to be IM, 1% UM, and 0.5% PM.

MADAGASCAR

IV. Mehlotra, et al. (2021) (75) conducted a study of 211 individuals from 2 health regions in Madagascar to determine the frequency of CYP2D6 metabolizer phenotypes associated with primaquine therapy failure for Plasmodium vivax (P. vivax)-caused malaria. A total of 29 variants were tested, allowing for identification of 27 distinct star alleles. Duplications, deletions, and complex gene arrangements (hybrids or tandem genes) were detected by previously published multiplex methods. The authors predicted 51.2% of individuals in the study population were IM. The frequency of UM phenotype in the study population was 4.88% and NM phenotype comprised 43.9% of the study population. No PM genotypes were identified in the study population, but the predicted population-wide PM phenotype rate was 0.32%.

East Asian Allele Studies

The most common altered-function allele in this region of the world is the CYP2D6*10 allele, with frequencies near 40–60% in most countries. Notably, Japan has a slightly lower frequency of the CYP2D6*10 allele (36%). The IM phenotype is significantly enriched in populations from to this region. The no-function CYP2D6*5 allele (gene deletion) is rare, observed at a rate of 9% or less.

I.

Dorji, et al. (2019) (76) performed a systematic literature review of 86 studies of CYP gene allele frequencies in South-East and East Asian (SEEA) populations. Multiple tables in the original publication delineate allele frequencies for the specific CYP loci by specific population. In total, 8 variant CYP2D6 star alleles were commonly reported in the reviewed articles. Overall, the authors report that no-function alleles (namely CYP2D6*3, *4, and *6) are exceedingly rare (or absent) in Asian populations, whereas the CYP2D6*5 no-function allele is present at rates similar to other populations. The decreased-function CYP2D6*10 allele is present at a rate of up 50% in many SEEA populations, making that allele the major contributor to the IM phenotype in Asians. Specifically, the CYP2D6*5/*10 diplotype may be of clinical significance to individuals of Asian descent, given the prevalence of these alleles. The normal-function alleles that were most commonly seen were CYP2D6*1 and CYP2D6*2. Duplication of the CYP2D6*1 and CYP2D6*2 (or even *10 alleles) are rarely seen among SEEA populations, leading to a very low frequency of the UM phenotype.

SINGAPORE

II. Goh, et al. (2017) (77) reported on an analysis of CYP450 genes and allele frequencies among residents of Singapore. At the CYP2D6 locus, 12 different variants were examined for a total of 10 star alleles. A total of 506 individuals were enrolled: 126 Malays, 179 Indians, and 201 Chinese. Overall, the authors report that NM phenotype was most common, followed by IM. The frequency of PMs ranged from 0.7–3.4% but was not observed in any of the Chinese subjects. In fact, the highest frequency of UM phenotype was observed in Chinese study participants (11%), followed by Indian (5%), and Malay (4.8%).

III. Bakar (2021) (38) reviewed pharmacogenetic variation of common alleles, making a comparison between Singaporean/Malaysian populations and European populations. Special emphasis was given to the decreased-function alleles CYP2D6*4 and *10, as described in 3 of the reviewed studies. As in Chinese populations, the CYP2D6*10 allele is more common in Singapore and Malaysia than in European populations. The author reports that individuals from Asia who are homozygous for the CYP2D6*10 allele are more likely to have altered tamoxifen pharmacogenetics and have higher odds of developing metastatic cancer.

KOREA

IV. Byeon, et al. (2018) (78) studied the frequency of 4 CYP2D6 alleles and gene duplications in 3,417 individuals from Korea and compared their results to published frequencies from east Asian, Caucasian, and African populations. The authors report the CYP2D6*10 decreased-function allele was most prevalent at 46.2%, more common than the wild-type CYP2D6*1 allele (34.6% allele frequency). Among studies of east Asian populations, the CYP2D6*10 allele was observed with a frequency between 38–53%, notably higher than the 1.4% reported for Caucasians. The no-function CYP2D6*4 allele was also noted to be far less frequent in east Asians than Caucasians or Africans. Overall, the Korean population showed an NM phenotype in roughly 22% and only 7 out of the 3,417 (0.2%) individuals had a predicted PM phenotype.

V. Ryu, et al. (2017) (79) reported on the effects of CYP2C19 and CYP2D6 on individual responses to amitriptyline in healthy Koreans. A total of 53 volunteers enrolled in the study and were genotyped for the CYP2D6*10 and *5 alleles. The authors reported 12 individuals (22.6%) had 2 normal-function alleles, indicative of NM phenotype. There were 17 individuals with genotype combinations including one function allele, leading to IM phenotype (32%). The authors reported 24 additional individuals with decreased function diplotype associated with IM phenotype (45%). Thus, most study participants would be predicted to be IM.

VI. Han, et al. (2021) (80) reported on variation of multiple pharmacogenes among the Korean Genome and Epidemiology Study (KoGES) cohort, a total of 69,027 individuals were genotyped via SNP array and copy number variation (CNV) data was available from 947 individuals (614 individuals were both genotyped and tested for CNVs). Three variants at the CYP2D6 locus were reported. Variation at the CYP2D6 locus was notable for the SNP rs1065852, also called c.100C>T, which is associated with the decreased-function allele CYP2D6*10 (among other alleles). The frequency of the variant was 48.23% of all alleles genotyped. Variation at rs16947 (G>A) was observed in 25% of the alleles; this variant is associated with multiple CYP2D6 star alleles. Variation at rs1135840 (G>C) was observed in 46.3% of the tested alleles; this variation is associated with decreased and no-function star alleles. Based on the nature of the study and how the genomic data was obtained, there was no data regarding CNVs for the CYP2D6 locus, nor could specific star allele haplotypes be assigned to this cohort.

HONG KONG

VII. Chan, et al. (2018) (81) reported on the allele and phenotype frequencies from 800 individuals residing in Hong Kong who self-reported their ethnicity. The authors tested for 12 different star alleles via targeted variant genotyping. The vast majority identified as Asian descent, with the other members of the cohort either identifying as Caucasian or “mixed race.” Among the individuals of Asian descent, the UM phenotype was seen at a rate of 3.3%, NM at 49.9%, IM at 46.4%, and PM at only 0.4%. The frequencies of the specific alleles within the Asian study participants showed a frequency of 32% for the CYP2D6*36-*10 fusion allele (characterized by a tandem arrangement of the no-function CYP2D6*36 allele and the decreased-function CYP2D6*10 allele, see PharmVar’s page on structural variants for additional information).

CHINA

VIII. Liu, et al. (2020) (82) reported on the genetic variation in a cohort of 105 individuals from the Zhuang people group of southern China. Overall, among several “very important pharmacogenes” (VIPs), the genetic variation of the Zhuang population was notably similar to Han Chinese in Beijing, southern Han Chinese, and the Japanese population in Tokyo, Japan. Two variants in CYP2D6 were interrogated. The SNP rs1065852 in the CYP2D6 locus was a notable deviation between the Zhuang and other global people groups, including some Asian populations. The data suggests this variant may be present at a higher frequency in this subpopulation. This variant has been associated with the decreased-function CYP2D6*10 allele and the authors mention that variation at this position has been previously associated with altered responses to multiple drugs.

IX. Qi, et al. (2019) (83) reported on the frequency of specific CYP2D6 alleles in the Chinese Millinome WGS database. The database comprises data from 141,431 individuals from 31 different provinces in China. Note: the genotyping data is obtained from WGS, and as such there may be inaccuracies in calling of the specific haplotype frequencies. The frequency of the CYP2D6*2, normal-function allele was reported as 27% and the CYP2D6*10 decreased-function allele was present at 68%. Because the genotype calls were determined by WGS, the study examined known variants associated with star alleles as well as identified novel genomic variants with potential functional impact. The identified variants in CYP2D6 were not listed in full, but no novel variants were reported.

X. Huang, et al. (2021) (84) studied CYP2D6 genotype in 120 individuals living in the Yunnan province, China to identify correlation between genotype and malarial P. vivax infection relapse following chloroquine and primaquine therapy. The authors interrogated 12 variants at the CYP2D6 locus and 5 known star alleles. They observed the most common allele was the decreased-function CYP2D6*10 allele, present in 45.4% of all alleles tested. Additionally, 60 individuals carried at least one copy of the CYP2D6*10 allele, suggesting the IM phenotype was present in 50% of the study population.

XI. Lu, et al. (2021) (85) reported on CYP2D6 genotype in 76 individuals prescribed risperidone who were seen at a hospital in the Jiangsu province, China. Five common variants were tested, translating into 3 variant star alleles. The authors observed the CYP2D6*10, decreased-function allele was present in 81.6% of the individuals in the study. The authors reported that the CYP2D6*65 allele was present in 17.1% of the individuals in the study and the CYP2D6*2 allele was present in just 9.2% of the individuals. The functional classification of the CYP2D6*65 allele is uncertain (as reported by the authors and per CPIC (3)).

JAPAN

XII. Kisoi, et al. (2020) (86) reported on a method for genotyping variants and CNVs (CNVs) in a cohort of 216 healthy females from Japan. The specific star alleles studied were CYP2D6*2, *5, *10, *14, and *41. Similar to other studies from Asia, the most commonly observed variant allele in this cohort was the CYP2D6*10 decreased-function allele (36.3%). Diplotype combinations including the CYP2D6*10 allele, likely leading to an IM phenotype, were observed in over 60% of the study cohort. The NM-associated diplotype of 2 WT (CYP2D6*1) alleles was observed in 16.2% of the cohort.

VIETNAM

XIII. Nguyen, et al. (2019) (87) studied the frequency of single nucleotide variants and structural variations in a cohort of 136 Kinh Vietnamese unrelated individuals by Sanger sequencing the coding region and targeted copy number analysis of CYP2D6. The authors reported 7 novel variants in the sequencing data as well as 23 known variants. The normal-function alleles (CYP2D6*1 and *2) comprised less than 30% of the alleles identified in this study. The decreased-function CYP2D6*10 allele had a frequency of approximately 44%, the most common allele in the study. Consequently, the 3 most common diplotypes all involved the CYP2D6*10 allele, resulting in at least 50% of the cohort having allele combinations that would predict an IM phenotype. No individuals with increased copy number were detected in this cohort, suggesting an extreme rarity for the UM phenotype in this population.

THAILAND

XIV. Puaprasert, et al. (2018) (88) reported on targeted allele genotyping of 6 star alleles the CYP2D6 locus among members of the Karen population living in Tak, a western province of Thailand. The authors reported a high frequency of the decreased-function CYP2D6*10 allele (40%), with the next most common variant allele being the normal-function CYP2D6*2 allele (33%). However, only one variant (c.100C>T) was used to define the CYP2D6*10 allele, as this frequency may be indicative of multiple haplotypes. Duplication (1%) and deletion (CYP2D6*5 allele, 3%) alleles were relatively rare in this study cohort. Based on the high prevalence of the decreased-function allele, the authors advise the efficacy of the malaria medication primaquine may be affected in this population.

XV. Mauleekoonphairoj, et al. (2020) (89) performed WGS on 291 individuals of self-reported Thai ethnic origin. While several pharmacogenes were studied, the CYP2D6 sequencing revealed 20 distinct star alleles in the study cohort. There were 5 duplications (1.9% of the identified alleles), 1 deletion (CYP2D6*5 allele, 4.5% of the alleles), and 6 rearrangements (34.7% of the alleles). The most common decreased-function alleles in the cohort were the CYP2D6*36+*10 fusion and the CYP2D6*10 allele. Overall, 25% of the Thai cohort was found to have a “high risk” diplotype for at least one of the studied pharmacogenes. This study utilized WGS and Stargazer analysis to impute haplotypes and diplotypes.

CAMBODIA

XVI. Spring, et al. (2020) (90) reported the prevalence of 18 different CYP2D6 star alleles as well as gene duplications and reported the predicted phenotypes among 96 Cambodians at high risk for Malaria. Genotyping was performed using the Luminex xTAG kit, which detects specific variants and identifies copy number variants. The overall phenotype frequencies were 46% for NM, 52% for IM, and 1% for PM. The authors also provide a table comparing the specific allele frequencies in their study and other publications focused on the greater Mekong subregion.

Central/South Asian Allele Studies

The phenotype frequencies in India range from 1–3% for PMs, IMs were observed in 7.3%, and NMs were most common at 91.7%.

INDIA

XVII. Manoharan, et al. (2019) (91) published a study on the allele frequency of CYP2D6 variants in South India. The study cohort was comprised of 105 individuals with mild to moderate depression but no other noted medical conditions. Targeted sequencing of the CYP2D6 locus led to the identification of 18 distinct star alleles, and the CYP2D6*1 allele was assigned in the absence of variants of interest. Based on the predicted activity scores, the NM phenotype was most common at 91.7%, with IM frequency of 7.3%, and the PM phenotype was present in 1% of their cohort. The authors noted that the CYP2D6 *41 allele was the most common decreased-function allele in their cohort and reported on 4 novel missense variants that have been integrated into the star allele nomenclature. The CYP2D6*1 and *2 normal-function alleles were most common, with a combined frequency of approximately 70%.

XVIII. Dhuya, et al. (2020) (92) tested 97 individuals to assess their metabolism of dextromethorphan as a measure of CYP2D6 metabolizer status. In contrast to other recent publications, genotyping was not performed. Their data suggests 3/97 (~3%) individuals to be PM’s and the rest (94/97, ~97%) were NMs. The authors conclude the prevalence of CYP2D6 polymorphisms is similar in the region of east India to other regions in the country, with overall a low frequency of PMs.

PAKISTAN

XIX. Ahmed, et al. (2018) (93) reported on the frequency of variation in multiple pharmacogenes among 244 healthy individuals in an indigenous Pakistani population. Fifteen variants were genotyped via targeted sequencing and the frequencies were compared with published frequencies of these variants in other populations. The SNP rs16947, which defines the CYP2D6*41 allele but has been found in haplotypes for normal, decreased and no-function CYP2D6 alleles was observed in nearly 40% of the individuals in the study, but was slightly higher in the Azad Kashmir region. This specific variant genotype distribution varies notably from east Asians, admixed Americans, most Africans and Europeans, but was similar to other south Asians. Another SNP, rs1135840, was present at a lower rate (35.2%) in the Pakistani population compared with all other populations. This variant has also been associated with haplotypes of varying functional activity, though the authors conclude in this study that both this and the rs16947 SNP are responsible for a UM phenotype. The combination of these 2 variants defines the CYP2D6*2 normal-function allele. The authors did not interrogate CNVs, thus the presumption of a UM phenotype is dubious.

XX. Ullah, et al. (2020) (94) reported the frequency of the no-function CYP2D6*4 allele in 16 different ethnic groups from Pakistan. Over 900 individuals were enrolled in the study. The frequency of the CYP2D6*4 allele in these groups was highest at 13.64% in the Meo population, and lowest in the Kalash population at 3.73%. The frequency of homozygotes for the CYP2D6*4 allele, with a predicted PM phenotype, was highest in the Sindhi group (4.49%) and lowest in the Pathan group (0.83%).

BHUTAN

XXI. Dorji, et al. (2019) (95) reported a study of CYP allele frequencies in a Bhutanese population cohort. Specifically, the authors studied the CYP2D6*10 and presumed CYP2D6*1 allele frequencies via targeted analysis in 443 individuals. The CYP2D6*10 allele was observed at frequency of 21%, lower than other Asian populations (see Dorji et al., 2019 (64)) but more commonly than Caucasians and Africans. The genotyping method (PCR with restriction fragment polymorphism) may not accurately differentiate between the CYP2D6*10 allele and alleles with similar variants. The frequency of the CYP2D6*1 allele is overestimated here, due to limited testing for CYP2D6 variants.

Oceanian Allele Studies

Australian studies reported a frequency for UMs in this population to be around 2%, NMs were predicted in slightly more than 50% of the population, IM frequencies were more discordant and ranged between 11–30%. The frequency of PMs ranged between 5.7–19.7%. The no-function *4 allele was seen, on average, at a frequency of 20%.

AUSTRALIA

I.

Chen, et al. (2019) (96) studied CYP2D6 allele frequency among members of the Australian Defense Forces personnel who were deployed to regions with endemic malaria and were subsequently treated for P. vivax infection with primaquine. While the primary purpose was to examine whether CYP2D6 genotype correlated with malarial relapse, the authors reported the prevalence of 14 star alleles and duplication of the CYP2D6*1 allele within the 157 individuals enrolled in the study. The most common allele was the normal-function CYP2D6*1 allele (~32%), but the CYP2D6*4 no-function allele was also relatively common (24.5%). The authors found roughly 1.3% of the study participants were predicted to have a UM phenotype, 50.9% had an NM-associated diplotype, approximately 29% had an IM-associated diplotype, 19.7% had a PM-predicting diplotype and one individual had 2 novel, unknown function alleles.

II.

Mostafa, et al. (2019) (97) analyzed genotype and phenotype frequencies in 5,408 Australian individuals at several pharmacogene loci. Fifteen variant star alleles were studied via Sanger sequencing and CNV analysis was based on published methods (14). The CYP2D6*1 allele was assigned in the absence of detected variants. This large cohort was ethnically diverse and subsequent analysis of study participants with medication histories enabled further analysis of the frequency of phenoconversion in a subset of the cohort. The most common altered-function allele was CYP2D6*4, at 17.8%. Genotyping results led to the predicted prevalence of UMs at 2.8%, NMs at 53.2%, IM at 11.4% (with an additional 26.2% of “low normal metabolizer” diplotypes that would be reclassified as IM based on the current CPIC definition), PMs were observed at 5.7% and one individual could not be genotyped. When considering medications that may alter the predicted metabolizer phenotype, the frequency of CYP2D6 PMs increased from 5.4% in the sub-cohort to 24.7%, all the result of other metabolizer phenotype groups being decreased in predicted enzyme activity levels.

MELANESIA/POLYNESIA

III. Rico, et al. (2020) (74) reported on the CYP2D6 genotype frequencies and functional characterization of novel variants found in the Ni-Vanuatu (Melanesia/Polynesia) and localized Kenyan populations. The 278 Ni-Vanuatu study participants were residents of 6 different islands from Melanesia or Polynesia. Within Kenya, the authors enrolled 195 individuals residing on islands or the shore of Lake Victoria in western Kenya. All study participants were healthy and unrelated. Eight variant star alleles were identified by PCR-based sequencing, allele fusions and duplications were also detected along with 6 novel variants. Detailed allele frequencies are presented in tables 1 and 2 within the publication. Overall, the phenotype frequencies in the Ni-Vanuatu population were predominantly NM, with only 5.8% as predicted IM, 5% UM, and 0% PM.

Genetic Variation Frequency Resources

At the National Library of Medicine at the National Institutes of Health, the National Center for Biotechnology Information (NCBI) has several resources for learning more about genetic variation. These resources do not focus on established star allele haplotypes but are typically limited to the data available for single SNPs or other short variants. The dbSNP database is a repository of known “short” genetic variants, ranging from single base changes to small deletions or insertions and microsatellite repeats. In the nomenclature table below, the dbSNP-assigned identifiers are provided as a link to the entry for that specific variant. Within dbSNP, users can access allele frequency data from the Allele Frequency Aggregator (ALFA) project, explore the clinical significance data stored in ClinVar and access literature citations from PubMed for the variant of interest. Specific dbSNP identifiers, star alleles, and gene names can also be searched in the LitVar database. When available, dbSNP pages also display frequency data from other studies including the 1000Genomes project, the Exome Aggregation Consortium (ExAC), the genome Aggregation Database (gnomAD), and country-specific studies that have been submitted to the BioProject database at NCBI. Additionally, the data from TOPMed are available at the NHLBI website. These individual variant frequencies do not inherently translate to star allele frequencies.

Genetic variation data from the CYP2D6 locus is also available in databases such as Ensembl and gnomAD, though these data do not specifically address pharmacogenetics.

Other resources specific to pharmacogenomics data include PharmVar, PharmGKB, and CPIC. The Allele Frequency Table maintained by CPIC and available from PharmGKB, is an excellent resource for star allele frequency data. These tables are periodically updated by these pharmacogenomic expert communities based on peer-reviewed scientific publications. Furthermore, the PharmVar Consortium provides data on the known star alleles for multiple pharmacogenes. Data on CYP2D6 includes the key variants that define each star allele, the assigned CPIC clinical function category, citations for the initial reports of the specific alleles, and a detailed description of the more complex structural variants at the CYP2D6 locus. The PharmGKB database provides a wide range of data and resources, including specific variant annotations, clinical annotations, and drug label annotations for the CYP2D6 gene, as well as multiple pharmacogenes. It should be noted that PharmGKB is a resource for drug-specific data, as well. The Clinical Pharmacogenetics Implementation Consortium (CPIC) is an authoritative source for clinical recommendations based on drug-gene interactions. The CPIC page for CYP2D6 guidelines links to their specific guidelines and each guideline further provides information on allele definitions, functionality, frequency data and diplotype-phenotype translation tables. It should be noted that PharmVar, PharmGKB, and CPIC coordinate much of their data and cooperatively update their shared resources.

Resources from additional publications described in detail above include:

REFARGEN (Brazil)

Helix DNA project (USA)

TOPMed (USA; NHLBI, NIH)

Nomenclature for Selected CYP2D6 Alleles

Acknowledgments

The author would like to thank Andrea Gaedigk, PhD, Director, Pharmacogenetics Core Laboratory, Children's Mercy Kansas City, Director, Pharmacogene Variation Consortium (PharmVar), Professor of Pediatrics, University of Missouri-Kansas City, Adjunct Associate Professor of Clinical Laboratory Sciences, University of Kansas Medical Center, Kansas City, MO, USA; Michelle Whirl-Carrillo, PhD, Director, PharmGKB, Standford University, Stanford, CA, USA; and Houda Hachad, PharmD MRes, Vice President of Clinical Operations, AccessDx Laboratory, Seattle, WA, USA for reviewing this summary.

References

1.

Nofziger C., Turner A.J., Sangkuhl K., Whirl-Carrillo M., et al. PharmVar GeneFocus: CYP2D6. Clin Pharmacol Ther. 2020;107(1):154–170. [PMC free article: PMC6925641] [PubMed: 31544239]

2.

Taylor C., Crosby I., Yip V., Maguire P., et al. A Review of the Important Role of CYP2D6 in Pharmacogenomics. Genes (Basel). 2020;11(11) [PMC free article: PMC7692531] [PubMed: 33143137]

3.

CYP2D6 allele functionality table [Cited 4 October 2021]. Available from: https://api​.pharmgkb​.org/v1/download/file​/attachment/CYP2D6_allele​_functionality_reference.xlsx.

4.

Caudle K.E., Sangkuhl K., Whirl-Carrillo M., Swen J.J., et al. Standardizing CYP2D6 Genotype to Phenotype Translation: Consensus Recommendations from the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group. Clin Transl Sci. 2020;13(1):116–124. [PMC free article: PMC6951851] [PubMed: 31647186]

5.

Yokota H., Tamura S., Furuya H., Kimura S., et al. Evidence for a new variant CYP2D6 allele CYP2D6J in a Japanese population associated with lower in vivo rates of sparteine metabolism. Pharmacogenetics. 1993;3(5):256–63. [PubMed: 8287064]

6.

PharmGKB [Internet]. Palo Alto (CA): Stanford University. Codeine and Morphine Pathway, Pharmacokinetics [Cited 2012 July 24]. Available from: http://www​.pharmgkb.org​/pathway/PA146123006.

7.

Ingelman-Sundberg M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J. 2005;5(1):6–13. [PubMed: 15492763]

8.

PharmGKB [Internet]. Palo Alto (CA): Stanford University. Haplotype CYP2D6*1 [Cited 2020 June 11]. Available from: http://www​.pharmgkb.org​/haplotype/PA165816576.

9.

PharmGKB [Internet]. Palo Alto (CA): Stanford University. Haplotype CYP2D6*4 [Cited 2012 July 24]. Available from: http://www​.pharmgkb.org​/haplotype/PA165816579.

10.

PharmGKB [Internet]. Palo Alto (CA): Stanford University. Haplotype CYP2D6*6 [Cited 2012 July 24]. Available from: http://www​.pharmgkb.org​/haplotype/PA165816581.

11.

PharmGKB [Internet]. Palo Alto (CA): Stanford University. Haplotype CYP2D6*10 [Cited 2012 July 24]. Available from: http://www​.pharmgkb.org​/haplotype/PA165816582.

12.

Owen R.P., Sangkuhl K., Klein T.E., Altman R.B. Cytochrome P450 2D6. Pharmacogenet Genomics. 2009;19(7):559–62. [PMC free article: PMC4373606] [PubMed: 19512959]

13.

Bradford L.D. CYP2D6 allele frequency in European Caucasians, Asians, Africans and their descendants. Pharmacogenomics. 2002;3(2):229–43. [PubMed: 11972444]

14.

Gaedigk A., Sangkuhl K., Whirl-Carrillo M., Klein T., et al. Prediction of CYP2D6 phenotype from genotype across world populations. Genet Med. 2017;19(1):69–76. [PMC free article: PMC5292679] [PubMed: 27388693]

15.

Koopmans A.B., Braakman M.H., Vinkers D.J., Hoek H.W., et al. Meta-analysis of probability estimates of worldwide variation of CYP2D6 and CYP2C19. Transl Psychiatry. 2021;11(1):141. [PMC free article: PMC7904867] [PubMed: 33627619]

16.

Bousman C.A., Zierhut H., Muller D.J. Navigating the Labyrinth of Pharmacogenetic Testing: A Guide to Test Selection. Clin Pharmacol Ther. 2019;106(2):309–312. [PubMed: 31004441]

17.

Pratt V.M., Cavallari L.H., Del Tredici A.L., Gaedigk A., et al. Recommendations for Clinical CYP2D6 Genotyping Allele Selection: A Joint Consensus Recommendation of the Association for Molecular Pathology, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, and European Society for Pharmacogenomics and Personalized Therapy. J Mol Diagn. 2021 [PMC free article: PMC8579245] [PubMed: 34118403]

18.

Zhang F., Finkelstein J. Inconsistency in race and ethnic classification in pharmacogenetics studies and its potential clinical implications. Pharmgenomics Pers Med. 2019;12:107–123. [PMC free article: PMC6612983] [PubMed: 31308725]

19.

PharmGKB. PharmGKB Biogeographical Groups. 5 October 2021; Available from: https://www​.pharmgkb​.org/page/biogeographicalGroups.

20.

Huddart R., Fohner A.E., Whirl-Carrillo M., Wojcik G.L., et al. Standardized Biogeographic Grouping System for Annotating Populations in Pharmacogenetic Research. Clin Pharmacol Ther. 2019;105(5):1256–1262. [PMC free article: PMC6465129] [PubMed: 30506572]

21.

Taliun D., Harris D.N., Kessler M.D., Carlson J., et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature. 2021;590(7845):290–299. [PMC free article: PMC7875770] [PubMed: 33568819]

22.

Del Tredici A.L., Malhotra A., Dedek M., Espin F., et al. Frequency of CYP2D6 Alleles Including Structural Variants in the United States. Front Pharmacol. 2018;9:305. [PMC free article: PMC5895772] [PubMed: 29674966]

23.

Chanfreau-Coffinier C., Hull L.E., Lynch J.A., DuVall S.L., et al. Projected Prevalence of Actionable Pharmacogenetic Variants and Level A Drugs Prescribed Among US Veterans Health Administration Pharmacy Users. JAMA Netw Open. 2019;2(6):e195345. p. [PMC free article: PMC6563578] [PubMed: 31173123]

24.

Dalton R., Lee S.B., Claw K.G., Prasad B., et al. Interrogation of CYP2D6 Structural Variant Alleles Improves the Correlation Between CYP2D6 Genotype and CYP2D6-Mediated Metabolic Activity. Clin Transl Sci. 2020;13(1):147–156. [PMC free article: PMC6951848] [PubMed: 31536170]

25.

Qiao W., Martis S., Mendiratta G., Shi L., et al. Integrated CYP2D6 interrogation for multiethnic copy number and tandem allele detection. Pharmacogenomics. 2019;20(1):9–20. [PMC free article: PMC6563015] [PubMed: 30730286]

26.

GitHub: helix-pgxdb [Cited 15 September 2021]. Available from: https://github​.com/myhelix​/helix-pgxdb/blob​/main/star_allele_frequencies.tsv.

27.

Bielinski S.J., Olson J.E., Pathak J., Weinshilboum R.M., et al. Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol. Mayo Clin Proc. 2014;89(1):25–33. [PMC free article: PMC3932754] [PubMed: 24388019]

28.

Gulilat M., Lamb T., Teft W.A., Wang J., et al. Targeted next generation sequencing as a tool for precision medicine. BMC Med Genomics. 2019;12(1):81. [PMC free article: PMC6547602] [PubMed: 31159795]

29.

Jurima-Romet M., Foster B.C., Casley W.L., Rode A., et al. CYP2D6-related oxidation polymorphism in a Canadian Inuit population. Can J Physiol Pharmacol. 1997;75(3):165–72. [PubMed: 9164697]

30.

Nowak M.P., Tyndale R.F., Sellers E.M. CYP2D6 phenotype and genotype in a Canadian Native Indian population. Pharmacogenetics. 1997;7(2):145–8. [PubMed: 9170152]

31.

Gonzalez-Covarrubias V., Morales-Franco M., Cruz-Correa O.F., Martinez-Hernandez A., et al. Variation in Actionable Pharmacogenetic Markers in Natives and Mestizos From Mexico. Front Pharmacol. 2019;10:1169. [PMC free article: PMC6796793] [PubMed: 31649539]

32.

Sosa-Macias M., Llerena A. Cytochrome P450 genetic polymorphisms of Mexican indigenous populations. Drug Metabol Drug Interact. 2013;28(4):193–208. [PubMed: 24145057]

33.

Luo S., Jiang R., Grzymski J.J., Lee W., et al. Comprehensive Allele Genotyping in Critical Pharmacogenes Reduces Residual Clinical Risk in Diverse Populations. Clin Pharmacol Ther. 2021;110(3):759–767. [PMC free article: PMC8453755] [PubMed: 33930192]

34.

Zhu Y., Lopes G.S., Bielinski S.J., Borah B.J., et al. Impact of Pharmacogenomic Information on Values of Care and Quality of Life Associated with Codeine and Tramadol-Related Adverse Drug Events. Mayo Clin Proc Innov Qual Outcomes. 2021;5(1):35–45. [PMC free article: PMC7930862] [PubMed: 33718782]

35.

Salyakina D., Roy S., Wang W., Oliva M., et al. Results and challenges of Cytochrome P450 2D6 (CYP2D6) testing in an ethnically diverse South Florida population. Mol Genet Genomic Med. 2019;7(9):e922. p. [PMC free article: PMC6732280] [PubMed: 31389673]

36.

Oshikoya K.A., Neely K.M., Carroll R.J., Aka I.T., et al. CYP2D6 genotype and adverse events to risperidone in children and adolescents. Pediatr Res. 2019;85(5):602–606. [PMC free article: PMC6435416] [PubMed: 30661084]

37.

Rossow K.M., Oshikoya K.A., Aka I.T., Maxwell-Horn A.C., et al. Evidence for Pharmacogenomic Effects on Risperidone Outcomes in Pediatrics. J Dev Behav Pediatr. 2021;42(3):205–212. [PMC free article: PMC7995603] [PubMed: 33759847]

38.

Davis B.H., Williams K., Absher D., Korf B., et al. Evaluation of population-level pharmacogenetic actionability in Alabama. Clin Transl Sci. 2021 [PMC free article: PMC8604228] [PubMed: 34121327]

39.

Leitao L.P.C., Souza T.P., Rodrigues J.C.G., Fernandes M.R., et al. The Metabolization Profile of the CYP2D6 Gene in Amerindian Populations: A Review. Genes (Basel). 2020;11(3) [PMC free article: PMC7140882] [PubMed: 32121156]

40.

Henderson L.M., Claw K.G., Woodahl E.L., Robinson R.F., et al. P450 Pharmacogenetics in Indigenous North American Populations. J Pers Med. 2018;8(1) [PMC free article: PMC5872083] [PubMed: 29389890]

41.

Naranjo M.G., Rodrigues-Soares F., Penas-Lledo E.M., Tarazona-Santos E., et al. Interethnic Variability in CYP2D6, CYP2C9, and CYP2C19 Genes and Predicted Drug Metabolism Phenotypes Among 6060 Ibero- and Native Americans: RIBEF-CEIBA Consortium Report on Population Pharmacogenomics. OMICS. 2018;22(9):575–588. [PubMed: 30183544]

42.

De Ameida Melo M., De Vasconcelos-Valenca R.J., Neto F.M., Borges R.S., et al. CYP2D6 gene polymorphisms in Brazilian patients with breast cancer treated with adjuvant tamoxifen and its association with disease recurrence. Biomed Rep. 2016;5(5):574–578. [PMC free article: PMC5103674] [PubMed: 27882219]

43.

Suarez-Kurtz G. Pharmacogenomics in admixed populations: the Brazilian pharmacogenetics/pharmacogenomics network--REFARGEN. Pharmacogenomics J. 2004;4(6):347–8. [PubMed: 15549130]

44.

Suarez-Kurtz G. Pharmacogenetic testing in oncology: a Brazilian perspective. Clinics (Sao Paulo). 2018;73 suppl 1:e565s. p. [PMC free article: PMC6157069] [PubMed: 30328952]

45.

Salles P.F., Perce-da-Silva D.S., Rossi A.D., Raposo L.R., et al. CYP2D6 Allele Frequency in Five Malaria Vivax Endemic Areas From Brazilian Amazon Region. Front Pharmacol. 2021;12:542342. p. [PMC free article: PMC8343396] [PubMed: 34366834]

46.

Silvino A.C.R., Kano F.S., Costa M.A., Fontes C.J.F., et al. Novel Insights into Plasmodium vivax Therapeutic Failure: CYP2D6 Activity and Time of Exposure to Malaria Modulate the Risk of Recurrence. Antimicrob Agents Chemother. 2020;64(5) [PMC free article: PMC7179649] [PubMed: 32122891]

47.

Chiurillo M.A. Genomic biomarkers related to drug response in Venezuelan populations. Drug Metab Pers Ther. 2015;30(1):33–41. [PubMed: 25252750]

48.

Munoz S., Vollrath V., Vallejos M.P., Miquel J.F., et al. Genetic polymorphisms of CYP2D6, CYP1A1 and CYP2E1 in the South-Amerindian population of Chile. Pharmacogenetics. 1998;8(4):343–51. [PubMed: 9731721]

49.

Varela N., Quinones L.A., Stojanova J., Garay J., et al. Characterization of the CYP2D6 drug metabolizing phenotypes of the Chilean mestizo population through polymorphism analyses. Pharmacol Res. 2015;101:124–9. [PubMed: 26211952]

50.

A, P.S., P. Dorado, A. Borbon, F. de Andres, et al., High prevalence of CYP2D6 ultrarapid metabolizers in a mestizo Colombian population in relation to Hispanic mestizo populations. Pharmacogenomics, 2020. 21(17): p. 1227-1236. [PubMed: 33124522]

51.

Isaza C.A., Henao J., Lopez A.M., Cacabelos R. Isolation, sequence and genotyping of the drug metabolizer CYP2D6 gene in the Colombian population. Methods Find Exp Clin Pharmacol. 2000;22(9):695–705. [PubMed: 11294012]

52.

Petrovic J., Pesic V., Lauschke V.M. Frequencies of clinically important CYP2C19 and CYP2D6 alleles are graded across Europe. Eur J Hum Genet. 2020;28(1):88–94. [PMC free article: PMC6906321] [PubMed: 31358955]

53.

Lunenburg C., Thirstrup J.P., Bybjerg-Grauholm J., Baekvad-Hansen M., et al. Pharmacogenetic genotype and phenotype frequencies in a large Danish population-based case-cohort sample. Transl Psychiatry. 2021;11(1):294. [PMC free article: PMC8131614] [PubMed: 34006849]

54.

Poulussen F.C.P., Peters B.J., Hua K.H., Houthuizen P., et al. The effect of the CYP2D6 genotype on the maintenance dose of metoprolol in a chronic Dutch patient population. Pharmacogenet Genomics. 2019;29(7):179–182. [PubMed: 31107373]

55.

Dlugauskas E., Strumila R., Lengvenyte A., Ambrozaityte L., et al. Analysis of Lithuanian CYP2D6 polymorphism and its relevance to psychiatric care of the local population. Nord J Psychiatry. 2019;73(1):31–35. [PubMed: 30661435]

56.

Lopez de Frutos L., Alfonso P., Lahoz C., Irun P., et al. Allelic and phenotypic characterization of CYP2D6 and its encoded P450 cytochrome enzyme in a serie of Spanish type 1 Gaucher disease patients. Med Clin (Barc). 2020;155(12):529–534. [PubMed: 32466973]

57.

Barreda-Sanchez M., Buendia-Martinez J., Glover-Lopez G., Carazo-Diaz C., et al. High penetrance of acute intermittent porphyria in a Spanish founder mutation population and CYP2D6 genotype as a susceptibility factor. Orphanet J Rare Dis. 2019;14(1):59. [PMC free article: PMC6390611] [PubMed: 30808393]

58.

Dagostino C., Allegri M., Napolioni V., D'Agnelli S., et al. CYP2D6 genotype can help to predict effectiveness and safety during opioid treatment for chronic low back pain: results from a retrospective study in an Italian cohort. Pharmgenomics Pers Med. 2018;11:179–191. [PMC free article: PMC6205525] [PubMed: 30425549]

59.

Skadric I., Stojkovic O. Defining screening panel of functional variants of CYP1A1, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 genes in Serbian population. Int J Legal Med. 2020;134(2):433–439. [PubMed: 31858263]

60.

Nefic H. The Genetic Variation of CYP2D6 Gene in the Bosnian Population. Med Arch. 2018;72(6):396–400. [PMC free article: PMC6340619] [PubMed: 30814768]

61.

Dlouha L., Adamkova V., Sedova L., Olisarova V., et al. Five genetic polymorphisms of cytochrome P450 enzymes in the Czech non-Roma and Czech Roma population samples. Drug Metab Pers Ther. 2020;35(2) [PubMed: 32681777]

62.

Sychev D.A., Shuev G.N., Suleymanov S.S., Ryzhikova K.A., et al. Comparison of CYP2C9, CYP2C19, CYP2D6, ABCB1, and SLCO1B1 gene-polymorphism frequency in Russian and Nanai populations. Pharmgenomics Pers Med. 2017;10:93–99. [PMC free article: PMC5386602] [PubMed: 28435307]

63.

Muradian A.A., Sychev D.A., Blagovestnov D.A., Sozaeva Z.A., et al. The effect of CYP2D6 and CYP2C9 gene polymorphisms on the efficacy and safety of the combination of tramadol and ketorolac used for postoperative pain management in patients after video laparoscopic cholecystectomy. Drug Metab Pers Ther. 2021 [PubMed: 35385894]

64.

Zastrozhin M., Skryabin V., Smirnov V., Zastrozhina A., et al. Effect of Genetic Polymorphism of the CYP2D6 Gene on the Efficacy and Safety of Fluvoxamine in Major Depressive Disorder. Am J Ther. 2021 [PubMed: 34117140]

65.

Ivashchenko D.V., Yudelevich D.A., Buromskaya N.I., Shimanov P.V., et al. CYP2D6 phenotype and ABCB1 haplotypes are associated with antipsychotic safety in adolescents experiencing acute psychotic episodes. Drug Metab Pers Ther. 2021 [PubMed: 34388331]

66.

Abdullaev S.P., Mirzaev K.B., Burashnikova I.S., Shikaleva A.A., et al. Clinically relevant pharmacogenetic markers in Tatars and Balkars. Mol Biol Rep. 2020;47(5):3377–3387. [PubMed: 32303955]

67.

Mirzaev K., Abdullaev S., Akmalova K., Sozaeva J., et al. Interethnic differences in the prevalence of main cardiovascular pharmacogenetic biomarkers. Pharmacogenomics. 2020;21(10):677–694. [PubMed: 32539557]

68.

Khalaj Z., Baratieh Z., Nikpour P., Khanahmad H., et al. Distribution of CYP2D6 polymorphism in the Middle Eastern region. J Res Med Sci. 2019;24:61. [PMC free article: PMC6670283] [PubMed: 31523247]

69.

Almeman A.A. Major CYP450 polymorphism Among Saudi Patients. Drug Metab Lett. 2020 [PubMed: 32703145]

70.

Arici M., Ozhan G. CYP2C9, CYPC19 and CYP2D6 gene profiles and gene susceptibility to drug response and toxicity in Turkish population. Saudi Pharm J. 2017;25(3):376–380. [PMC free article: PMC5357098] [PubMed: 28344492]

71.

Mutawi T.M., Zedan M.M., Yahya R.S., Zakria M.M., et al. Genetic variability of CYP2D6, CYP3A4 and CYP3A5 among the Egyptian population. Pharmacogenomics. 2021;22(6):323–334. [PubMed: 33789449]

72.

Rajman I., Knapp L., Morgan T., Masimirembwa C. African Genetic Diversity: Implications for Cytochrome P450-mediated Drug Metabolism and Drug Development. EBioMedicine. 2017;17:67–74. [PMC free article: PMC5360579] [PubMed: 28237373]

73.

Ahmed J.H., Makonnen E., Fotoohi A., Aseffa A., et al. CYP2D6 Genotype Predicts Plasma Concentrations of Tamoxifen Metabolites in Ethiopian Breast Cancer Patients. Cancers (Basel). 2019;11(9) [PMC free article: PMC6770728] [PubMed: 31547390]

74.

Gutierrez Rico E.M., Kikuchi A., Saito T., Kumondai M., et al. CYP2D6 genotyping analysis and functional characterization of novel allelic variants in a Ni-Vanuatu and Kenyan population by assessing dextromethorphan O-demethylation activity. Drug Metab Pharmacokinet. 2020;35(1):89–101. [PubMed: 32037159]

75.

Mehlotra R.K., Gaedigk A., Howes R.E., Rakotomanga T.A., et al. CYP2D6 Genetic Variation and Its Implication for Vivax Malaria Treatment in Madagascar. Front Pharmacol. 2021;12:654054. p. [PMC free article: PMC8093859] [PubMed: 33959023]

76.

Dorji P.W., Tshering G., Na-Bangchang K. CYP2C9, CYP2C19, CYP2D6 and CYP3A5 polymorphisms in South-East and East Asian populations: A systematic review. J Clin Pharm Ther. 2019;44(4):508–524. [PubMed: 30980418]

77.

Goh L.L., Lim C.W., Sim W.C., Toh L.X., et al. Analysis of Genetic Variation in CYP450 Genes for Clinical Implementation. PLoS One. 2017;12(1):e0169233. p. [PMC free article: PMC5207784] [PubMed: 28046094]

78.

Byeon J.Y., Kim Y.H., Lee C.M., Kim S.H., et al. CYP2D6 allele frequencies in Korean population, comparison with East Asian, Caucasian and African populations, and the comparison of metabolic activity of CYP2D6 genotypes. Arch Pharm Res. 2018;41(9):921–930. [PubMed: 30191460]

79.

Ryu S., Park S., Lee J.H., Kim Y.R., et al. A Study on CYP2C19 and CYP2D6 Polymorphic Effects on Pharmacokinetics and Pharmacodynamics of Amitriptyline in Healthy Koreans. Clin Transl Sci. 2017;10(2):93–101. [PMC free article: PMC5355968] [PubMed: 28296334]

80.

Han N., Oh J.M., Kim I.W. Combination of Genome-Wide Polymorphisms and Copy Number Variations of Pharmacogenes in Koreans. J Pers Med. 2021;11(1) [PMC free article: PMC7825650] [PubMed: 33430289]

81.

Chan W., Li M.S., Sundaram S.K., Tomlinson B., et al. CYP2D6 allele frequencies, copy number variants, and tandems in the population of Hong Kong. J Clin Lab Anal. 2019;33(1):e22634. p. [PMC free article: PMC6430334] [PubMed: 30069923]

82.

Liu Y., Li H., Cao K., Liu J., et al. Genetic variation of pharmacogenomic VIP variants in Zhuang nationality of southern China. Pharmacogenomics J. 2021;21(1):60–68. [PubMed: 32699276]

83.

Qi G., Han C., Sun Y., Zhou Y. Genetic insight into cytochrome P450 in Chinese from the Chinese Millionome Database. Basic Clin Pharmacol Toxicol. 2020;126(4):341–352. [PubMed: 31661191]

84.

Huang H., Dong Y., Xu Y., Deng Y., et al. The association of CYP2D6 gene polymorphisms in the full-length coding region with higher recurrence rate of vivax malaria in Yunnan Province, China. Malar J. 2021;20(1):160. [PMC free article: PMC7981985] [PubMed: 33743705]

85.

Lu J., Yang Y., Lu J., Wang Z., et al. Effect of CYP2D6 polymorphisms on plasma concentration and therapeutic effect of risperidone. BMC Psychiatry. 2021;21(1):70. [PMC free article: PMC7856706] [PubMed: 33535976]

86.

Kisoi M., Imai M., Yamamura M., Sakaguchi Y., et al. Unique Genotyping Protocol of CYP2D6 Allele Frequency Using Real Time Quantitative PCR from Japanese Healthy Women. Biol Pharm Bull. 2020;43(5):904–907. [PubMed: 32378566]

87.

Nguyen H.H., Ma T.T.H., Vu N.P., Bach Q.T.N., et al. Single nucleotide and structural variants of CYP2D6 gene in Kinh Vietnamese population. Medicine (Baltimore). 2019;98(22):e15891. p. [PMC free article: PMC6709254] [PubMed: 31145348]

88.

Puaprasert K., Chu C., Saralamba N., Day N.P.J., et al. Real time PCR detection of common CYP2D6 genetic variants and its application in a Karen population study. Malar J. 2018;17(1):427. [PMC free article: PMC6238304] [PubMed: 30442143]

89.

Mauleekoonphairoj J., Chamnanphon M., Khongphatthanayothin A., Sutjaporn B., et al. Phenotype prediction and characterization of 25 pharmacogenes in Thais from whole genome sequencing for clinical implementation. Sci Rep. 2020;10(1):18969. [PMC free article: PMC7641128] [PubMed: 33144648]

90.

Spring M.D., Lon C., Sok S., Sea D., et al. Prevalence of CYP2D6 Genotypes and Predicted Phenotypes in a Cohort of Cambodians at High Risk for Infections with Plasmodium vivax. Am J Trop Med Hyg. 2020;103(2):756–759. [PMC free article: PMC7410472] [PubMed: 32394887]

91.

Manoharan A., Shewade D.G., Ravindranath P.A., Rajkumar R.P., et al. Resequencing CYP2D6 gene in Indian population: CYP2D6*41 identified as the major reduced function allele. Pharmacogenomics. 2019;20(10):719–729. [PubMed: 31368850]

92.

Dhuya M., Pal M.M., Hazra A., Chatterjee S., et al. Cytochrome P450 2D6 polymorphism in eastern Indian population. Indian J Pharmacol. 2020;52(3):189–195. [PMC free article: PMC7446679] [PubMed: 32874001]

93.

Ahmed S., Zhou J., Zhou Z., Chen S.Q. Genetic Polymorphisms and In Silico Mutagenesis Analyses of CYP2C9, CYP2D6, and CYPOR Genes in the Pakistani Population. Genes (Basel). 2018;9(10) [PMC free article: PMC6211126] [PubMed: 30360443]

94.

Ullah A., Riaz S., Siddiqi S., Mazhar K., et al. REPORT - CYP2D6*4 null allele frequency in sixteen Pakistani ethnic groups. Pak J Pharm Sci. 2020;33(2):739–743. [PubMed: 32276921]

95.

Dorji P.W., Wangchuk S., Boonprasert K., Tarasuk M., et al. Pharmacogenetic relevant polymorphisms of CYP2C9, CYP2C19, CYP2D6, and CYP3A5 in Bhutanese population. Drug Metab Pers Ther. 2019;34(4) [PubMed: 32004143]

96.

Chen N., Dowd S., Gatton M.L., Auliff A., et al. Cytochrome P450 2D6 profiles and their relationship with outcomes of primaquine anti-relapse therapy in Australian Defence Force personnel deployed to Papua New Guinea and East Timor. Malar J. 2019;18(1):140. [PMC free article: PMC6471761] [PubMed: 30999967]

97.

Mostafa S., Kirkpatrick C.M.J., Byron K., Sheffield L. An analysis of allele, genotype and phenotype frequencies, actionable pharmacogenomic (PGx) variants and phenoconversion in 5408 Australian patients genotyped for CYP2D6, CYP2C19, CYP2C9 and VKORC1 genes. J Neural Transm (Vienna). 2019;126(1):5–18. [PubMed: 30191366]

98.

Kalman L.V., Agundez J., Appell M.L., Black J.L., et al. Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting. Clin Pharmacol Ther. 2016;99(2):172–85. [PMC free article: PMC4724253] [PubMed: 26479518]