The use of pharmacological agents in the treatment of diseases is becoming increasingly important in modern medicine. However, considerable variability in the efficacy and side effects of medications between different population groups and different individuals is well known from medical practice. The risk of non-standard reactions has increased as result of the production of new medicines and the increasing number of medications taken by patients. Almost 50% of cases of adverse drug reactions or lack of therapy efficacy is caused by the genetic characteristics of a patient [1].

The action of drugs in the human body is due to processes such as absorption, distribution (by organs, cells, and organelles), interaction with receptors, metabolism and excretion.

All stages of thepharmacokinetic process are carried out by means of specific and non-specific enzymes, and xenobiotic biotransformation proteins play a major role in these processes. Mutations in the genes encoding these enzymes may lead to a decrease or increase of their enzymatic activity, which affects the metabolic rate of the corresponding drugs.

Direct (phenotypic) determination of the concentration change rate of a drug or its metabolites in the blood is a time-consuming and complicated procedure, in addition to requiring the patient to take the relevant medication. Determining allelic variants (genotype) of biotransformation genes, however, allows for the identification of patients who are more likely to develop adverse drug reactions.

CYP2C9 gene

The CYP2C9 gene has high genetic polymorphism. The structural gene polymorphisms CYP2C9 - R144C (CYP2C9*2) and I359L (CYP2C9*3) are the most well-studied. The allele CYP2C9*2 reduces the warfarin dose by 40% for heterozygotes and 68% by homozygotes, and the CYP2C9*3 allele by 40 and 85%, respectively. This polymorphic variant explained 14.5% of the interindividual variability in the dose of warfarin together with non-genetic predictors, such as age, body weight and height [2].

VKORC1 gene

The VKORC1 gene on chromosome 16 is one of the major genes associated with effective doses of coumarin anticoagulants. Many mutations are associated with a deficiency in this enzyme. The allelic variant VKORC1 c.-1639G>A determines up to 30% of the variability in warfarin dosage [3-8].

CYP4F2 gene

The CYP4F2 enzyme of the cytochrome P450 superfamily is responsible for the synthesis of hydroxyeicosatetraenoic acid (20- HETE), which plays an important role in blood vessel function. Therefore, the production efficiency of 20-HETE may cause CYP4F2 to be associated with the warfarin dose. The CYP4F2 c. 1297G>A mutation, which leads to the V433M amino acid substitution, affects the activity of the enzyme, reducing 20-HETE synthesis to 60% [9]. In this case, the warfarin dosage should be increased.

GGCX gene

GGCX is a key factor in the regulation of blood clotting. Several mutations in the GGCX gene associated with a decrease in the clotting factor activity have been found. One of these mutations is rs11676382 c. 1913+45G>C, which was detected in intron 14. A study of 186 Caucasians who took thrombosis therapy using warfarin showed that a polymorphic locus accounts for approximately 2% of the variation in drug dosage [10].

CYP2D6 gene

The CYP2D6 enzyme is encoded by a gene on chromosome 22. Approximately 25% of all drugs are substrates of CYP2D6, including beta-blockers, tricyclic antidepressants, neuroleptics, derivatives of morphine and other drugs. At least 40 genetic variants of CYP2D6 can result in the reduced metabolic activity of this enzyme. However, the major variants of interest are the *1-6, *9, *10 and *17 alleles. Carriers with the CYP2D6*3 and CYP2D6*4 alleles have no enzyme activity. For example, a 25% dose reduction is recommended with amitriptyline therapy and for CYP2D6*4 allele carriers, therapy with other analgesics is recommended rather than using codeine [2].

CYP1A2 gene

The CYP1A subfamily in humans and other mammals consists of two enzymes; according to standard P450 nomenclature, they are denoted as CYP1A1 and CYP1A2. These enzymes have prompted significant interest because they are involved in the metabolic activation of many procarcinogens and are induced by compounds of interest to toxicology, including dioxins. More than 15 alleles of the CYP1A2 gene have been identified to date (*1B-*16), including several promoter variants [11].

The polymorphic variants g.28338G>A (CYP1A2*1C), c.-1635delT (*1D), -739T> G (*1E) and c.-9-154C>A (*1F) located in intron 1 are the most thoroughly studied alleles that lead to a change in the enzymatic activity of CYP1A2. A polymorphic variant CYP1A2*1F (c.-9-154C>A) in intron 1 leads to increased enzyme inducibility and increased drug metabolism. For example, increased enzyme inducibility during smoking, leads to low concentrations of clozapine levels in schizophrenic patients undergoing standard treatment. The risk of toxicity increased in allele C carriers during leflunomide and olanzapine therapy [12].

Determining the allelic variants of xenobiotic biotransformation genes is not only important for personalised drug prescription; the knowledge of the allele frequency distribution in different populations may be taken into account when the preferred medication regimen is selected. The frequency of CYP2C9, VKORC1, CYP4F2, GGCX, CYP2D6, and CYP1A2 gene polymorphisms has been studied thoroughly in worldwide populations, except for populations in Central Asia (Tables 1 and 2).

Table 1: Characteristic of studied allele variants of the CYP2C9, VKORC1, CYP4F2, GGCX, CYP2D6 and CYP1A2*1F genes.

Table 2: Evaluation of genotype frequency differences between the Kazakh and Russian populations.

Kazakhs are one of the Turkic peoples of Central Asia and comprise the majority of the population in Kazakhstan. According to the Agency of Statistics of the Republic of Kazakhstan, approximately 11 million Kazakhs live in Kazakhstan, and approximately 3.5 million Kazakhs live in regions neighbouring Kazakhstan and other regions (China, Russia, Uzbekistan, Turkmenistan, Kyrgyzstan, west Mongolia, Turkey) [13]. Kazakhs residing in the territory of Kazakhstan are internally divided into three large groups, the Elder, Middle and Lesser Zhuzes (or Hordes), which have historically demarcated territories. There are several tribes in each Zhuz [14]. Every Kazakh knows to which tribe and Zhuz they belong. Representatives of the same tribe are considered relatives because they are considered to be descended from a common ancestor. Marriages are expected to respect the “seven generations law”, that is, marriage between members of the same tribe is only possible when 7 generations from a common ancestor separate the individuals.

The purpose of this study was to determine the frequency of the allelic variants of CYP2C9 (CYP2C9*2, CYP2C9*3), VKORC1 (c. 173+1000C>T, c. 173+1369G>C), CYP4F2 (c. 1297G>A), GGCX (c. 1913+45G>C), CYP2D6 (CYP2D6*3, CYP2D6*4) and CYP1A2*1F (c. -9-154C>A) in Kazakh populations from different regions of Kazakhstan and in Russian populations from the West-Siberian region compared with published frequency data of other populations. The results of allele frequencies obtained by real-time PCR (RT-PCR) and direct sequencing methods from individuals from different regions of Kazakhstan were compared. Because no significant differences were found between these samples, the samples from the three regions were combined. The resulting distributions of allele and genotype frequency in the Kazakh population were compared with the frequencies in a Russian population, which was used as a control group.

Characteristics of the study populations

Kazakh and Russian populations were included (Table 3). The first group included people of Kazakh nationality. Each volunteer filled out a questionnaire in which they indicated their nationality and the nationality of their parents and grandparents, in addition to standard personal data. The concept of “Zhety ata” exists in the traditions of the Kazakh people. It means that each Kazakh should know 7 generations of their ancestors. Even today, this tradition is strongly maintained. Therefore, by conducting this survey, we were able to trace the ancestors of the participants to nearly 7 generations. As mentioned above, data on the participants’ ancestors to the 2nd generation were collected through the questionnaire. Additionally, a verbal survey of the volunteers was performed to establish the ethnicity their ancestors from the third to the seventh generation. When any ancestors were found to be of other (not Kazakh) ethnicity, blood samples of these volunteers were excluded from the study. In addition to ethnicity, tribal affiliations of volunteers were specified. Kazakhs residing in the territory of Kazakhstan have an internal division into three large groups, the Elder, Middle and Lesser Zhuzes, which have historically demarcated territories. There are several tribes in each Zhuz.

Table 3: Characteristics of the studied groups.

The study group included 450 healthy individuals of Kazakh nationality from different regions of Kazakhstan, and a written informed consent was obtained from all of the participants. The age of the participants ranged from 17 to 70 years (mean age, 40.6 ± 13.2 years) (Table 3). Venous blood samples (5 ml) in tubes with EDTA were collected at clinical bases in the cities of Astana and Kokshetau (North Kazakhstan), Pavlodar (Northeast Kazakhstan) and Taraz (South Kazakhstan). DNA from blood was extracted by the salting-out method [15].

The second group was used as the control and included Russian residents of the West-Siberian region (the Novosibirsk and Kemerovo regions and the Altai Krai) who donated blood in the regional blood transfusion station and passed the standard medical examination but were not permanent donors. The age of the participants ranged from 18 to 55 years (mean age, 33.5 ± 11 years) (Table 3). DNA was extracted from venous blood using the phenol-chloroform method [16].

Ethical approval was received from the Ethics Committee of the National Centre for Biotechnology of the Republic of Kazakhstan, Astana, Kazakhstan (No. 10,14.02.2010).

Genotyping

Genotyping was performed using two methods: RT-PCR and direct sequencing. The characteristics of the studied allele variants of the CYP2C9, VKORC1, CYP4F2, GGCX, CYP2D6 and CYP1A2*1F genes are shown in Table 1. A small amount of DNA was genotyped by direct sequencing and RT-PCR. Thus, the obtained DNA sample genotypes were used as a control for the further genotyping by RTPCR. DNA samples were genotyped by direct sequencing if genotype determination by RT-PCR was difficult.

Genotyping by RT-PCR

RT-PCR genotyping was performed using TaqMan probes (Table 4). The composition of the PCR mixture was as follows: a buffer (65 mM Tris-HCl (pH 8.9); 0.05% Tween 20; 16 mM (NH₄)₂SO₄; 3.5 mM MgCl₂), 0.1-0.2 μM Taqman probe with FAM, 0.1-0.2 μM Taqman probe with R6G, 0.2 mM dNTPs, 0.3 μM primers and 0.5 units of thermally stable Taq polymerase. The reaction volume was 25.5 μl, and each reaction mixture was covered by an equal volume of mineral oil. The PCR conditions were as follows: denaturation at 96°C for 2 min and then 45 two-step cycles, which included 96°C for 30 s, 58-60°C for 40 s and 25°C for 10 s. The amplification was performed using the iCycler iQ5 PCR machine (Bio-Rad, United States).

Table 4: Primers and probes using for genotyping by real-time PCR.

Genotyping by the direct sequencing method

Fragments of the gene region containing the analysed SNPs were obtained by PCR using oligonucleotide pairs of primers (Table 5). The amplification was performed in a multichannel thermocycler “PTC- 0240 DNA EngineTetrad2 Cycler” (BioRad, Foster city, California, USA). The composition of the PCR mixture was as follows: a buffer (65 mM Tris-HCl (pH 8.9); 0.05% Tween 20, 16 mM (NH₄)₂SO₄; 2.5 mM MgCl₂), 0.2 mM dNTPs, 0.4 μM primers and 0.5 units of thermostable Taq-polymerase. The reaction volume was 25 ml. The PCR conditions were as follows: initial denaturation for 5 min at 95°C and then 35 cycles of three steps, 30 seconds of denaturation at 95°C, 30 seconds of primer annealing at 50-60°C and 30 seconds elongation at 72°C, and then 7 min at 72°C. The identification of SNPs was carried out based on DNA sequencing using the Big Dye Terminanor v3.1 Cycle Sequencing Kit (3730XL Genetic Analyzer, Applied Biosystems ABI) in accordance with the manufacturer’s protocol. Sequence Scanner v1.0 software was used for analysing data.

Table 5: The primers used to determine the nucleotide sequence by direct sequencing method.

Statistical analyses

The statistical analysis was performed using SPSS software (v16.0). The correspondence of the distribution of the genotype frequencies to the Hardy–Weinberg equilibrium was assessed using the χ² criterion (α=0.05, df=2). Significant differences in genotype frequencies between Kazakh and Russian populations were assessed according to the χ² criterion (α=0.05, df=2). The analysis of linkage disequilibrium of genes VKORC1, CYP2C9, and CYP2D6 was performed using the available program at http://www.oege.org/software/cubex.

Allele and genotype frequency data were obtained for 9 SNPs in the Kazakh and Russian populations. The allele frequencies (MAF) in the Kazakh population were as follows: CYP2C9*2, 0.02; CYP2C9*3, 0.03; VKORC1 (c. 173+1000C>T), 0.72; VKORC1 (c. 173+1369G>C), 0.72; CYP4F2 (c. 1297G>A), 0.31; GGCX (c. 1913+45G>C), 0.04; CYP2D6*3, 0.01; CYP2D6*4, 0.07; and CYP1A2*1F (c. -9-154C>A), 0.35. The allele and genotype frequencies of the Kazakh population are summarised in Table 6. All alleles were in Hardy-Weinberg equilibrium (p>0.05). DNA samples from individuals of Kazakh nationality were collected from different regions of Kazakhstan. Therefore, a comparative analysis of allele frequencies by regions using the statistical criterion χ² was assessed. Significant differences in the Kazakh population according to region (North, Northeast and South Kazakhstan) are presented in Table 7. A significant difference in the frequency of genotypes was not found (p>0.05). Thus, the DNA samples from different regions of Kazakhstan were combined into one group.

Table 6: Allele frequency and genotype distribution in the Kazakh population

Table 7: Evaluation of allele and genotype frequency differences according to region (North, Northeast and South Kazakhstan).

DNA samples from a Russian population were used as the control. The allele frequencies (MAF) in the Russian population were as follows: CYP2C9*2, 0.08; CYP2C9*3, 0.08; VKORC1 (c. 173+1000C>T), 0.40; VKORC1 (c. 173+1369G>C), 0.41; CYP4F2 (c. 1297G>A), 0.24; GGCX (c. 1913+45G>C), 0.08; CYP2D6*3, 0.15; CYP2D6*4, 0.22; and CYP1A2*1F (c. -9-154C>A), 0.31. The allele and genotype frequencies of the Russian population are summarised in Table 8. All alleles except GGCX (p=0.04) were in Hardy-Weinberg equilibrium (p>0.05) in the Russian population.

Table 8: Allele frequency and genotype distribution in the Russian population.

Significant differences in genotype frequencies between Kazakh and Russian populations were assessed according to the χ² criterion. The overall difference (between Kazakh and Russian populations) in allele frequencies were highly significant (p<0.05), except for CYP1A2*1F (p=0.17) (Table 2).

Furthermore, the linkage disequilibrium of the VKORC1, CYP2C9 and CYP2D6 genes in the Kazakh population was analysed. We identified the linkage disequilibrium of the VKORC1 c. 173+1369G>C and VKORC1 c. 173+1000C>T (D’=0.96) allele polymorphisms (Table 9).

Table 9: Evaluation of linkage disequilibrium of the VKORC1, CYP2C9 and CYP2D6 genes in the Kazakh population.

Kazakhs residing in Kazakhstan are internally divided into three large groups, the Elder, Middle and Lesser Zhuzes, which have historically demarcated territories. There are several tribes in each Zhuz.

The metabolism of xenobiotics may vary depending on the individual’s ethnicity and race. Therefore, we compared allele frequencies from the different regions of Kazakhstan. Blood samples were collected from the northern and southern part of Kazakhstan because the climatic conditions differ from north to south. Northern and Northeastern Kazakhstan are traditionally the territory of the Middle Zhuze, and Southern Kazakhstan is traditionally the territory of the Elder Zhuze. However, significant differences between genotype frequencies were not detected (p>0.05) (Table 7).

Tribes within Zhuzes are considered related to each other, and marriage between Zhuzes is encouraged. Therefore, the distribution of allele frequencies between all three Zhuzes was nearly equal, which allowed the DNA samples from different regions of Kazakhstan to be combined into one group, representative of the Kazakh population.

The previous literature has examined Kazakh populations living in areas other than those included here. For example, Wang et al. studied Kazakhs living in China [17]; ethnicity was only tracked until the third generation. Thus, we could not use these data to assess differences. Magalon et al. studied only 26 DNA samples and did not indicate whether they monitored the ethnicity of the participants’ ancestors, which does not allow an adequate comparative analysis [18]. Tarlykov et al. investigated the hypervariable segment I of mitochondrial DNA and Y-STR loci in DNA samples of individuals living in isolation in the same area and who were members of the same tribe. However, there was no comparative analysis with other groups living in other regions of Kazakhstan or who were representatives of other tribes and Zhuzes [19].

A Russian population was used as a control group. A comparative analysis of the allele frequency of the Russian population between the studied samples and published data was carried out. The findings correspond to published data (Table 10).

Table 10: A comparative table of frequencies of genes in different populations.

Furthermore, a comparative analysis of the allele frequency of the Kazakh population between studied samples and published data from the other populations was conducted (Table 10).

CYP2C9 gene

Wide inter-ethnic variability was observed for the CYP2C9 gene polymorphic variants. On average, the frequency of the CYP2C9*2 allele was 11-15% in the Caucasian population and 2-3% in the African and Asian populations, and for the CYP2C9*3 allele, the averages were 5-7% and 2-4%, respectively [3,4,20]. Thus, the *2 and *3 alleles of the CYP2C9 gene are much less common among individuals in Asia and Africa than among Caucasians.

The allele frequency of CYP2C9*2 (0.02) and CYP2C9*3 (0.03) in the Kazakh population is near the frequency of these polymorphic loci in Asian populations (p=0.27 and p=0.30) and significantly different from that of Caucasian populations (p=0.00).

In the Russian population, the frequency of CYP2C9*3 (0.08) is close to that of the Caucasian population (p=0.45) and significantly different from the frequency in the Asian and African-American populations (p=0.00).

Thus, in future pharmacogenetic studies in Kazakh populations, close attention should be paid to the polymorphic variants of the CYP2C9 gene, which affect the dose of warfarin and are characteristic of Asian populations.

VKORC1 gene

The distribution of VKORC1 allele polymorphisms in populations is interesting. The C. 173+1000C>T allele frequency is as high as 92% in Asian populations [4], whereas in Caucasian populations, it is approximately 42% [5]. The allele frequency in the Kazakh population (f=0.72, p=0.00) studied here was between that of Asian and Caucasian populations.

The allele frequency in the Russian population was 0.41 (p=0.09), which agrees with published data and corresponds to the Caucasoid populations.

According to Rieder et al., the VKORC1 c.-1639G>A mutation is linked to mutations in VKORC1 c. 173+1369G>C and VKORC1 c. 173+1000C>T [6]. We also identified a linkage disequilibrium between the VKORC1 c. 173+1369G>C and VKORC1 c. 173+1000C>T (D’=0.96) polymorphisms (Table 9).

CYP4F2 gene

The CYP4F2 c. 1297G>A allele frequency in both Asian and Caucasian populations is approximately 25% on average [7,8] and is 8% in African-Americans [7]. The allele frequency was 31% in the Kazakh population, which corresponds with the frequencies in Asian and Caucasian populations (p=0.25). The allele frequency in the Russian population was 0.24, which is near to that of the Japanese population (p=0.38).

GGCX gene

A mutant version of the GGCX gene (rs11676382) occurs at a frequency of 10% in Caucasian populations and nearly never occurs in the Asian and African-American populations [21]. The allele frequency in Kazakh population was 4%, which is between the Caucasoid and Asian populations.

The allele frequency in the Russian population was 8%, which corresponds with the Caucasian population (p=0.77).

CYP2D6 gene

Ethnic differences in the frequencies of polymorphisms of this gene are very high. The CYP2D6*3 and CYP2D6*4 alleles are almost never found in Asian and African populations. CYP2D6*3 occurs at a frequency of 0.01-0.02 and CYP2D6*4 at a frequency of 0.07-0.20 in the Caucasian population [22-24].

The frequency of CYP2D6*4 (0.07) in the Kazakh population is significantly different from that of the Caucasian population (p=0.00) and corresponds to the frequencies in Asian populations. At the same time, the frequency of CYP2D6*3 is closer to that of Caucasian populations (p=0.93).

In the studied Russian population, the frequency of CYP2D6*3 (0.15) is higher than in all analysed populations and does not correspond well with the literature data (p=0.00). The frequencies of CYP2D6*4 (0.22) found in our study are similar to those of similar populations reported in the literature (p=0.85).

CYP1A2 gene

The CYP1A2*1F allelic polymorphism frequency varies widely between populations, ranging from 22% in the Caucasian population to 61% in the Japanese population. The average frequency of the CYP1A2*1F allele is 31% in the Caucasian population, 37% in the Asian population, and reaches 46% in the African-American population [20,25]. In the Kazakh population, the frequency was 35%, which is between the Caucasian and Asian populations.

The allele frequency in the Russian population was 31%, which is typical for Caucasian populations (p=0.14). The allele frequency of the studied Kazakh and Russian population was nearly the same (p=0.17).

The Kazakh population exhibits allele frequencies at an intermediate level between Caucasian and Asian populations in nearly all of the studied gene allele variants. This distribution of frequencies can be explained by recent studies that have suggested that the Kazakh population was formed by the mixing of Asian and Caucasoid populations [14]. Currently, individuals exhibit distinctive Asian and/ or Caucasoid traits, depending on the region of Kazakhstan. Based on our data, we can conclude that further pharmacogenetic studies are required.

The datasets used in this article are included here.

The authors declare that they have no competing interests.

I.A.N. extracted DNA, performed SNP genotyping, summarised genotyping data, analysed data, carried out statistical analysis and drafted the manuscript. R.A.A. extracted DNA and performed SNP genotyping. V.E.N. helped edit the manuscript and developed the SNP typing methods (RT-PCR). S.N.S. carried out the statistical analysis. B.L.A. performed SNP genotyping for Russian populations. R.E.M. and F.M.L. helped edit the manuscript and made suggestions on study design. All authors read and approved the final manuscript.

This work was supported by grant No. 1.04.01 from the Ministry of Education and Science of the Republic of Kazakhstan.