Screening for 392 polymorphisms in 141 pharmacogenes
- Authors:
- Published online on: April 30, 2014 https://doi.org/10.3892/br.2014.272
- Pages: 463-476
Abstract
Introduction
Inter-individual variation in drug response among patients is a major obstacle in medicine application, due to the different response of each patient to the same medication (1). Several cases of adverse drug reactions occurring in certain patients, such as renal/hepatic disorders, congestive heart failure and anemia, were previously reported (1,2). The variations in drug responses may result from disease determinants, genetics, environmental factors and idiosyncratic response, which collectively affect drug metabolism (3). The knowledge of variations in efficacy and toxicity caused by the same doses of medications may enhance the effectiveness of drug therapy (3).
The initial human genome sequencing identified ~1.42 million single-nucleotide polymorphisms (SNPs), including >60,000 SNPs in the exonic region of genes (4). Some of these SNPs have been suggested to be associated with considerable changes in drug disposition and metabolism or the effects of medication (5–7), whereas others are used for the diagnosis of clinical response (8). The interaction of several gene products is known to affect the pharmacokinetics and pharmacodynamics of medications. For example, inherited variations in drug targets, drug disposition and polygenic factors of drug effects are determinants of the majority of drug effects and have become increasingly important in pharmacogenomics (9).
Pharmacogenomics is the study of how genetic differences among individuals affect the variability in their response to medications (6,10). Pharmacogenomic research includes clinical and basic science regarding genetic variation and drug response. The field of pharmacogenomics has attracted significant attention along with the completion of the Human Genome Project (11). Consequently, there has been an increase in the number of pharmacogenomic studies published (11). Furthermore, the continuous development of genotyping technologies may allow pharmacogenomic applications to move into the mainstream of medicine and pharmacy practice (12,13).
Over the last few years, the available technologies for genomic analyses have increased significantly (14). For example, the TaqMan and the SNPlex assays from Applied Biosystems (Foster City, CA, USA), the GeneChip assay from Affymetrix (Santa Clara, CA, USA) and the Infinium and GoldenGate assays developed by Illumina (San Diego, CA, USA) are the most frequently used techniques in genome research (15). In addition, the SNaPshot technique has been tested on a small scale of multiplex for analysis of gene polymorphisms (16). In this study, we used the GoldenGate assay and the SNaPshot technique for genotyping to investigate the allele distribution of 392 SNPs from 141 pharmacogenes in 150 Korean subjects.
Materials and methods
Study subjects
DNA samples from a total of 150 Korean subjects was used for this study. The 150 unrelated Korean samples were provided by the Center for Genome Science, Korea Centers for Disease Control and Prevention. The protocol and consent forms of this study were reviewed and approved by the Institutional Review Board of Sogang University (no. 2010_690).
SNP selection and genotyping
We selected a total of 378 SNPs of 141 well-known pharmacogenes, based on the score assigned by the Illumina GoldenGate assay design tool (ADT; Illumina). The SNPs were genotyped using the GoldenGate assay with the VeraCode microbead (Illumina) (17,18), followed by a scan using the BeadExpress® system (Illumina). Normalized bead intensity data obtained for each sample were loaded into the GenomeStudio® software (Illumina), which converted fluorescent intensities into SNP genotypes. SNP clusters for genotype calling were examined for all SNPs using the GenomeStudio® software. The cluster plots were then visually assessed and SNPs with poor cluster quality were removed. The overall call rate for all SNPs was 99.99%. For quality control, SNPs that met the following criteria were retained: call rate ≥0.98% and no triplicate error after three repetition tests. Fourteen additional SNPs which did not pass ADT scoring were genotyped using the SNaPshot technique (Invitrogen Life Technologies, Carlsbad, CA, USA). The SNaPshot technique is single- extension-based method, which enables the simultaneous analysis of multiple SNPs (19). The information of SNaPshot primers used for the 14 SNPs are shown in Table I. The GoldenGate and SNaPshot assays were conducted three times in order to increase the accuracy of the test.
Statistical analysis
The Chi-square test was used to determine whether individual variants were in Hardy-Weinberg equilibrium (HWE) at each locus in a Korean population. Haplotypes of SNPs representing the star-alleles of each pharmacogene investigated in this study were defined using PHASE software (Stephen Laboratory, University of Chicago, Chicago, IL, USA).
Results and Discussion
A total of 392 SNPs located in 141 pharmacogenes (including 21 phase I, 13 phase II, 18 transporter and 5 modifier genes) were successfully genotyped in 150 Korean subjects and their minor allele frequencies (MAFs) were calculated (Table II). These variants were in HWE (P>0.05), except for 22 SNPs. Among the SNPs, 47 variants exhibited a MAF<0.05, while 105 variants exhibited a monomorphic allele distribution in the Korean population (Table III). In addition, there were 22 SNPs with HWE<0.05, 9 of which exhibited significantly lower HWE compared to others [rs2230037, rs2472393, rs743544 [all in the glucose-6-phosphate dehydrogenase (G6PD) gene], rs2227291 [copper-transporting ATPase 1 (ATP7A) gene], rs1414334, rs518147, rs3813928, rs6318 and rs3813929 [all in the 5-hydroxytryptamine receptor 2C (HTR2C) gene]. The 3 genes were all located in the X chromosome, although located far away from each other (G6PD at Xq28, ATP7A at Xq21.1 and HTR2C at Xq24). The classifications and SNP numbers of each pharmacogene investigated in this study are listed in Table IV. Among these, cytochrome P450 2D6 (CYP2D6), cytochrome P450 2C19 (CYP2C19), thiopurine S-methyltransferase (TPMT), cytochrome P450 2C9 (CYP2C9), vitamin K epoxide reductase complex, subunit 1 (VKORC1) and human leukocyte antigen, class I, B (HLA-B) are of great significance, due to their association with well-known drugs. Therefore, we investigated the respective genes and their effects on enzyme activity or associated drugs below.
 | Table IIMinor allele frequency of 287 polymorphic single-nucleotide polymorphisms (SNPs) from 141 pharmacogenes in a Korean population (n=150). |
First, 14 CYP2D6 SNPs (rs1065852, rs16947, rs1135822, rs35742686, rs3892097, rs5030655, rs79738337, rs1058164, rs1135840, rs28371525, CYP2D6_2, rs5030867, rs5030865 and rs5030656) were used for the investigation of 17 star-alleles (*1, *2, *3, *4, *5, *6, *7, *8, *9, *10, *14A, *14B, *34, *36, *41, *49 and *60). CYP2D6 is one of the most important pharmacogenes involved in the metabolism of foreign substances in the body. Overall, the genotypes associated with decreased activity or a non-functional enzyme were ~20% of all the investigated genotypes (Table V). Specifically, screening of polymorphisms such as rs3892097, rs1065852, rs16947 and rs1135840 may be useful for detecting the enzyme activity level of CYP2D6. CYP2C19 is clinically important for the metabolism of drugs including clopidogrel (20), which is an inhibitor of adenosine diphosphate-induced platelet aggregation (21–23). To investigate the association between CYP2C19 and clopidogrel response, 15 CYP2C19 SNPs were used for the investigation of seven alleles (*1, *2, *3, *4, *5A, *8 and *17) (Table V). In general, over half of the subjects were found to have genotypes that reduced the efficacy of clopidogrel, while 11.5% of the subjects carried genotypes which enhanced the efficacy of clopidogrel. This information may be used to adjust the dose of clopidogrel depending on the patient genotypes of the investigated polymorphisms.
| Table VFrequencies and effects of CYPD6, CYP2C19, TPMT, DPYD and IL28B genotypes on enzyme activity and drug toxicity. |
TPMT encodes an enzyme involved in the detoxification of azathioprine, mercaptopurine and thioguanine, which are immunosuppressive drugs used in organ transplantation (24–27). Five SNPs (rs75543815, rs1142345, rs1800460, rs1800462 and rs1800584) were used to investigate the frequency of five alleles (*2, *3B, *3C, *4 and *6) in this study (Table V). The results suggested that the majority of Korean subjects have TPMT genotypes, which render them more prone to the toxicity of the aforementioned drugs. The dihydropyrimidine dehydrogenase gene (DPYD) encodes an enzyme catabolizing 5-fluorouracil (5-FU), which is commonly used for the treatment of solid carcinomas (28,29). A decrease in enzyme activity involved in 5-FU catabolism due to the mutational variants in DPYD may lead to an increase in the half-life of 5-FU and an increased risk of dose-dependent toxicity (28,30,31). In our study, all the Korean subjects were found to carry the DPYD genotypes associated with normal enzyme activity (*1/*5, *5/*5, *1/*9A, or c.496A>G; Table V). A polymorphism of interleukin 28B (IL28B), rs8099917 has been reported to be associated with the virologic response to peginterferon-α (PEG-IFNα) and ribavirin (RBV) combination therapy in hepatitis C virus-infected patients (32–34), whereas the GT and GG genotypes of rs8099917 have been suggested to be less responsive to treatment compared to TT (32). In this study, subjects carrying the TT genotype were the most common (86.7%), whereas the GT and GG genotypes were significantly less frequent (12.7 and 0.7%, respectively) in the Korean population (Table V). These results may be used to identify patients with reduced responsiveness to PEG-IFNα/RBV combined therapy and adjust the amount accordingly.
Warfarin is an anticoagulant used for the prevention of thromboembolic events and stroke (35). CYP2C9*2 variants, *3 variants (36–40) and VKORC1 polymorphism rs8050894 (41) were reported to be significantly associated with warfarin dose in European or African populations. In this study, we evaluated the variability of warfarin dose according to the CYP2C9 and VKORC1 genotypes using CYP2C9*2, *3 and rs8050894, based on previous reports (Table VI). Combinations of the CG or CC genotype of rs8050894 and CYP2C9 wild-type (*1/*1) yielded the highest warfarin dose requirement (5–7 mg/day), whereas a combination of the GG genotype of rs8050894 and CYP2C9*1/*1, demanding 3–4 mg/day of warfarin, was the most frequent (76.0%) in the Korean population. Furthermore, the warfarin dose requirement in CYP2C9 wild-type (*1/*1) was higher compared to that in *2 or *3 variant allele-containing genotypes (*1/*2, *1/*3, *2/*2, *2/*3 and *3/*3), which was also observed in European and African-American populations (42). As regards rs8050894 in VKORC1, an overall higher warfarin dose requirement in CG (heterozygote) or CC (minor homozygote) compared to that in GG (major homozygote) was observed. However, in European and African-American populations, GG exhibited a higher warfarin dose requirement compared to the CG or CC genotype (42). Further investigations may be required to verify the different genetic effect on warfarin response in various ethnic groups.
HLA-B encodes for a protein which is an important part of the human immune system and its polymorphisms have been associated with various drug reactions (43–45). Carbamazepine, which is often used for treatment of chronic pain, bipolar disorder, seizure disorder and trigeminal neuralgia, is one of the most common causes of drug hypersensitivity reactions (46). The star-allele HLA-B*1502 is associated with various toxic events resulting from carbamazepine, such as cutaneous adverse drug reactions or Stevens-Johnson syndrome in Asian populations (47–51). In this study, two tagging SNPs of HLA-B*1502, rs3909184 and rs2844682, were used for the evaluation of the HLA-B*1502 frequency in the Korean population (Table VII). No subject was identified as carrying one or two copies of HLA-B*1502, which is associated with increased risk of adverse reactions to carbamazepine. Thus, the Korean population may have a relatively low risk of adverse reactions to carbamazepine.
| Table VIIFrequencies and combined effects of rs2844682 and rs3909184 (tagging HLA-B*1502) on adverse reactions to carbamazepine. |
HLA-B*5701, which is in linkage disequilibrium with rs2395029 in HCP5, was reported to be a predictive marker of abacavir hypersensitivity (52). Abacavir is an inhibitor of nucleoside reverse-transcriptase and is used as an anti-retroviral agent for human immunodeficiency virus treatment (53). All 150 Korean subjects were found to carry the combination of the TT genotype of rs2395029 and HLA-B*5701-negative type, which is associated with a low risk of hypersensitivity to abacavir (Table VIII). The genotype frequencies of TPMT and catechol O-methyltransferase (COMT) polymorphisms, which are associated with the risk of hearing loss due to cisplatin toxicity (54), were also estimated in the Korean population (Table VIII) and the combination of the AA genotype of rs1142345 (TPMT) and the CC genotype of rs9332377 (COMT), which are associated with a lower risk of cisplatin ototoxicity (54), exhibited the highest frequency (93.7%) in the Korean population.
| Table VIIIFrequency and effects of HCP5/HLA-B*5701 and TPMT/COMT polymorphisms on adverse drug reaction. |
In conclusion, we conducted extensive analyses of the distribution of various pharmacogene polymorphisms in 150 Korean subjects and identified the genotype frequencies of important pharmacogene polymorphisms, such as CYP2D6, CYP2C9, VKORC1, CYP2C19, HLA-B and TPMT among others, which may affect the efficacy and side effects of various drugs, including warfarin, clopidogrel, carbamazepine, azathioprine and others. To the best of our knowledge, our study was the first to simultaneously investigate a large number of pharmacogene polymorphisms in multiple samples in a Korean population. The findings from the present study may be helpful in developing personalized medicines for Korean patients. Moreover, the methods used in the present study may also be applied in other populations in order to study their unique pharmacogenomics.
Acknowledgements
This study was supported by a grant from the Ministry of Food and Drug Safety, Republic of Korea, in 2011 (no. 10182MFDS572).
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