Screening for 392 polymorphisms in 141 pharmacogenes

  • Authors:
    • Jason Yongha Kim
    • Hyun Sub Cheong
    • Tae‑Joon Park
    • Hee Jung Shin
    • Doo Won Seo
    • Han Sung Na
    • Myeon Woo Chung
    • Hyoung Doo Shin
  • View Affiliations

  • Published online on: April 30, 2014
  • Pages:463-476
Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations


Pharmacogenomics is the study of the association between inter‑individual genetic differences and drug responses. Researches in pharmacogenomics have been performed in compliance with the use of several genotyping technologies. In this study, a total of 392 single‑nucleotide polymorphisms (SNPs) located in 141 pharmacogenes, including 21 phase I, 13 phase II, 18 transporter and 5 modifier genes, were selected and genotyped in 150 subjects using the GoldenGate assay or the SNaPshot technique. These variants were in Hardy‑Weinberg equilibrium (HWE) (P>0.05), except for 22 SNPs. Genotyping of the 392 SNPs revealed that the minor allele frequencies of 47 SNPs were <0.05, 105 SNPs were monomorphic and 22 variants were not in HWE. Also, based on previous studies, we predicted the association between the polymorphisms of certain pharmacogenes, such as cytochrome P450 2D6, cytochrome P450 2C9, vitamin K epoxide reductase complex, subunit 1, cytochrome P450 2C19, human leukocyte antigen, class I, B and thiopurine S‑methyltransferase, and drug efficacy. In conclusion, our study demonstrated the allele distribution of SNPs in 141 pharmacogenes as determined by high‑throughput screening. Our results may be helpful in developing personalized medicines by using pharmacogene polymorphisms.


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 (57), 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.

Table I

SNaPshot primer sequences for 14 single-nucleotide polymorphisms (SNPs).

Table I

SNaPshot primer sequences for 14 single-nucleotide polymorphisms (SNPs).


a The CYP2D6 SNPs share the same forward and reverse primers.

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 II

Minor allele frequency of 287 polymorphic single-nucleotide polymorphisms (SNPs) from 141 pharmacogenes in a Korean population (n=150).

Table II

Minor allele frequency of 287 polymorphic single-nucleotide polymorphisms (SNPs) from 141 pharmacogenes in a Korean population (n=150).

GeneSNPAlternative nameAllelesMAFHWE
CYP2D6 rs1065852*10, *36, *49, 100C>TC>T0.1630.551
rs16947*2, 2850C>TC>T0.1400.472
rs3892097*4, 1846G>AG>A0.0730.152
rs79738337*60, 2303C>TC>T0.0070.934
rs1058164*2, *4, *8, *10, 1661G>CG>C0.2170.645
rs1135840*2A, 4180G>CC>G0.4000.496
rs28371525*41, 2988G>AG>A0.0030.967
rs5030865*8, 1758G>TG>T0.0030.967
CYP2C9 rs1057910*3, 42614A>CA>C0.0430.579
rs4918758*1C, C1188TT>C0.4430.624
VKORC1 rs9934438C6484T, 1173C>TA>G0.0870.368
rs8050894*2, 1542G>C, 6853G>CG>C0.0830.964
rs2359612*2, 2255C>T, 7566C>TA>G0.0830.964
rs17708472*4, 6009C>T, 698C>TG>A0.0030.967
CYP2C19 rs12248560*17, −806C>TC>T0.0670.662
rs4244285*2, G681AG>A0.3130.011
rs4986893*3, G636AG>A0.0770.891
rs28399504*4, A1GA>G0.0030.967
rs17885098*2, *4, 99C>TC>T0.0370.641
rs3758580*2, 80160C>TC>T0.2400.872
rs11188072*17, −3402C>TC>T0.0130.869
DPYD rs1801159*5, I543V, A1627GA>G0.2770.311
rs1801265*9A, C29R, T85CT>C0.0530.490
rs2297595496A>G, Met166ValT>C0.0100.902
G6PD rs22300371311C>TC>T0.080 4.69×10−19
rs2472393IVS1+2955A/GC>T0.130 8.31×10−12
rs743544IVS1−773C/TC>T0.407 7.44×10−10
HLA-B*1502 rs3909184 HLA-B*1502C>G0.0530.354
rs2844682 HLA-B*1502C>T0.1570.674
NAT1 rs4986988*11, c.−344C>TC>T0.0070.934
rs4986989*11, c.−40A>TA>T0.0070.934
rs4986783*11, c.640T>G, p.S214AT>G0.0070.934
NAT2 rs1799929*11, *5BC>T0.0330.673
rs1041983*13, *5G, *6AC>T0.3130.011
rs1799930*5E, *6AG>A0.1730.153
rs1799931*7, G286E, *6IG>A0.1400.524
TPMT rs1800460*3BG>A0.0030.967
rs1142345*3C, C240Y, 18485A>GA>G0.0130.869
rs75543815*6, 15327A>TA>T0.0230.770
UGT1A1 rs4124874*60, −3263T>GA>C0.3000.560
rs4148323*6, Gly71ArgG>A0.1730.153
rs10929302*93, −3156G>AG>A0.1270.764
CYP2E1 rs2031920*5, −1053C>TC>T0.1270.299
rs6413432*6, 7632T>AT>A0.0700.357
rs3813867*5A, *5BG>C0.2070.484
rs2070673*7, −333T>AT>A0.4200.410
CYP3A4 rs28371759*18, L293P (T>C)T>C0.0300.015
CYP3A5 rs776746*3, 6986A>GG>A0.2230.475
rs55965422*5, 12952T>CA>G0.0070.934
CYP4B1 rs4646487Arg173TrpC>T0.1430.472
CYP4F2 rs2108622V433MC>T0.3230.387
CYP1A2 rs762551*1F, −163C>AA>C0.3630.259
rs2069526*K, *1E, −739T>GT>G0.0270.737
rs2470890*1B, 5347T>CC>T0.1530.740
CYP1B1 rs1056836*3, 4326C>G, L432VC>G0.1230.832
CYP2A6 rs28399433*13, *15, −48T>GT>G0.1730.391
CYP2B6 rs1042389-T>C0.2700.041
rs8192709*2, 64C>TC>T0.0200.803
CYP2C8 rs11572177-A>G0.0530.490
CYP2C18 rs12777823-G>A0.3130.011
ABCB1 rs10456423435C>TC>T0.3600.610
ABCC1 rs3784862-G>A0.3200.102
ABCC2 rs717620−24C>TG>A0.2430.696
ABCC4 rs17510343463 A>GT>C0.1770.132
rs9561778 c.3366+1243G>TG>T0.2430.068
ABCC6 rs2238472Arg1268GlnG>A0.1730.779
ABCG2 rs13120400-T>C0.0070.934
ABO rs8176746-C>A0.1600.481
ADM rs11042725−1923C>AC>A0.2730.187
ADRB2 rs1042713Arg16GlyA>G0.4370.259
AKT1 rs2494732-C>T0.3130.388
ANKK1 rs1800497-C>T0.3800.565
APOB rs1367117711C>TG>A0.1070.144
ARG1 rs2781659-A>G0.3200.376
ATP7A rs2227291Val767LeuG>C0.210 2.10×10−8
ATXN1 rs179997A-241GA>G0.1330.814
BAT3 rs750332-A>G0.1130.383
BDKRB1 rs12050217-A>G0.4000.089
BDKRB2 rs1799722C-58TT>C0.4330.542
C6orf10 rs3129900-T>G0.0200.803
CACNG2 rs2284017-C>T0.4670.229
CAT rs10836235c.66+78C>TC>T0.2100.096
KCNH2 rs3807375-A>G0.1930.172
KCNJ11rs5219Lys23Glu, E23KC>T0.3500.346
KNG1 rs4686799-C>T0.3970.892
LEMD2 rs2395402-T>C0.1770.858
LRP2 rs2075252-A>G0.4800.402
LTC4S rs730012−444CA>C0.1670.096
METTL21A rs7569963-G>A0.0830.964
MICA rs2848716-C>G0.2930.223
MLH1 rs1800734−93A>G0.4330.782
MTHFR rs18011311298A>CA>C0.1630.232
NEFM rs1379357-G>C0.3330.391
NOS1AP rs10918594-G>C0.4870.877
NOS3 rs2070744−786T>CT>C0.1300.699
NQO1 rs1800566*2, c.558C>TC>T0.4370.426
NR1I2 rs1464603g.252A>GT>C0.4400.181
NTRK1 rs2768759-A>C0.0830.964
OPRM1 rs1799971A118GA>G0.3830.719
P2RY1 rs701265-A>G0.3630.672
P2RY12 rs2046934T744CT>C0.2330.027
PTGS1 rs3842787P17LC>T0.0900.226
RGS4 rs951439-C>T0.4130.256
SCN5A rs12053903-C>T0.4570.453
SLC10A1 rs2296651800C>TG>A0.0230.770
SLC10A2 rs2301159 c.*755C>TC>T0.2930.667
SLC19A1 rs1051266Arg27His, c.*746C>TA>G0.4970.022
SLC1A1 rs2228622-G>A0.2400.775
SLC22A16 rs714368146A>G, His49ArgA>G0.4430.405
SLC22A2 rs316019*4, A270SG>T0.1130.451
SLC28A2 rs241377516334845T>AA>T0.1530.354
SLCO1B1 rs2306283*1B, N130DC>T0.2370.470
rs4149056*5, c.521T>CT>C0.1600.264
rs4149081Intronic A/GG>A0.4530.296
rs11045879Intronic C/TT>C0.4530.296
SLCO1B3 rs11045585-A>G0.1700.440
SLCO2B1 rs12422149Arg312GlnG>A0.3900.334
SULT1C4 rs1402467p.Asp5GluC>G0.0800.965
TCF7L2 rs12255372-G>T0.0070.934
TNF rs1800629−308G>AG>A0.0530.354
TP53 rs1042522Arg72ProG>C0.3630.138
UGT1A7 rs7586110−57T>GT>G0.2200.282
UGT1A8 rs1042597*2, c.518C>G, Ala173GlyG>C0.4470.723
UGT2B15 rs1902023*2, Y85DT>G0.4870.630
UGT2B17 rs6552182*2, CNVC/T0.1630.999
ULK3 rs2290573-C>T0.1500.689
VDR rs1544410BsmIG>A0.0400.106
CBR1rs20572627C>T, A209AC>T0.2200.549
CBR3 rs1056892Val244MetG>A0.3800.642
CCND1 rs17852153870G>AA>G0.4600.567
CDA rs2072671Lys27Gln, K27QA>C0.1800.303
rs60369023c.208G>A, Ala70ThrG>A0.0070.934
CETP rs708272Taq1BC>T0.3400.224
CHST3 rs4148943 c.*1278C>TC>T0.1030.596
rs4148945 c.*1361C>TC>T0.0730.816
rs4148950 c.*3477G>AG>A0.0730.816
rs1871450 c.*3785G>AG>A0.0730.816
rs730720 c.*4533C>TG>A0.1030.596
rs12418 c.*4785G>AG>A0.0730.816
CNTF rs1800169FS63TERG>A0.1430.957
COMT rs737865-T>C0.2770.844
CRHR2 rs2267715-G>A0.3830.719
CYTSA rs5760410 g.4205975G>AA>G0.3530.060
DRD2 rs4436578-T>C0.4870.863
DRD3 rs167771-A>G0.1470.882
EGFR rs2227983R497KG>A0.2330.704
EPHX1 rs1051740Y113H, 337T>CT>C0.4470.760
rs2234922H139R, 416A>GA>G0.1430.957
ERBB2 rs1136201Ile655ValA>G0.1330.099
ERCC1 rs32129868092C>AG>T0.2770.833
rs1161519007T>C, Asn118AsnC>T0.2300.341
ERCC2rs131812251A>C, Lys751GlnT>G0.0430.579
FDPS rs2297480-C>A0.1870.677
FKBP5 rs1360780-C>T0.2500.057
GGCX rs6996648016G>AG>A0.3670.682
GGH rs11545078452C>TC>T0.0770.309
rs3780126 c.109+1307G>CC>T0.3130.783
GRIK4 rs1954787-C>T0.1230.333
GSK3B rs334558−50T>CG>A0.3500.622
rs13321783 IVS7+9227A>GG>A0.4200.246
rs2319398 IVS7+11660G>TT>G0.4300.362
rs6808874 IVS11+4251T>AA>T0.4930.252
GSTP1 rs1138272C341T, A114VC>T0.0800.965
rs1695*B, Ile105ValA>G0.1930.400
HLA-E rs1059510Asn98AsnG>A0.2930.252
HMGCR rs12654264-T>A0.4500.902
HSPA1L rs2227956-T>C0.0670.662
HTR2A rs9316233-C>G0.3230.623
rs7997012Intron 5, 2 variantG>A0.2030.919
HTR2C rs1414334-G>C0.013 1.53×10−9
rs518147−697G/CC>G0.140 6.36×10−14
rs3813928c.−995G>AG>A0.123 5.77×10−16
rs6318Cys23SerG>C0.013 1.53×10−9
rs3813929−759C>TC>T0.123 5.77×10−16
HTR3B rs2276307-A>G0.2230.486
HTR7 rs1935349-G>A0.2770.067
IL28B rs8099917-T>G0.0700.740
ITPA rs1127354P32TC>A0.1470.882
KCNH2 rs3815459-A>G0.1870.903

[i] MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium.

Table III

List of 105 monomorphic single-nucleotide polymorphisms (SNPs).

Table III

List of 105 monomorphic single-nucleotide polymorphisms (SNPs).

GeneSNPAlternative nameAlleles
CYP2D6 rs1135822*49, 1611T>AT>A
rs35742686*3, 2549delAIns>del
rs5030655*6, 1707delTIns>del
CYP2D6_2*60, 1887insTADel>ins
rs5030867*7, 2935A>CA>C
rs5030656*9, 2615_2617delAAGIns>del
CYP2C9 rs28371685*11, R335WC>T
rs9332239*12, 50338C>TC>T
rs72558187*13, 3276T>CT>C
rs72558190*15, 9100C>AC>A
rs72558193*18, 47391A>CA>C
rs1799853*2, Arg144CysC>T
rs72558188*25, 353_362delAGAAATGGAAIns>del
rs9332131*6, 818delAIns>del
VKORC1 rs7200749*3F, 3462C>T, 8773C>TG>A
CYP2C19 rs41291556*8, 12711T>C, W120RT>C
rs56337013*5A, 1297C>T, R433WC>T
DPYD rs3918290*2A, IVS14+1G>AG>A
rs1801268*10, 2983G>T, V995FG>T
rs72549309*7, 295delTCATA>T
rs1801266*8, R235WC>T
rs1801267*9B, 2657G>A, R886HG>A
G6PD rs1050828202G>AC>T
HLA-B*1502 rs3130690 HLA-B*1502C>A
HLA-B*5701 rs2395029 HLA-B*5701T>G
NAT1 rs4986990*11, c.459G>A, p.T153TG>A
rs5030839*15, c.559C>T, p.R187XC>T
rs56379106*17, c.190C>T, p.R64WC>T
rs56318881*19, c.97C>T, p.R33XC>T
rs56172717*22, c.752A>T, p.D251VA>T
rs55793712*5, c.884A>GA>G
rs72554612*5, c.976delAA>G
NAT2 rs1805158*19, 190C>T, R64WC>T
rs4986996 *12DG>A
TPMT rs1800462*2, 238G>C, A80PG>C
UGT1A1 rs28934877*38A>G
rs55750087*29, R367GC>G
CYP3A4 rs4987161*17, F189S, 670T>CT>C
rs2740574*1B, −392A>GA>G
CYP3A5 rs10264272*6, 14690G>AC>T
rs41303343*7, 27131_27132insTA>T
rs28383479*9, 19386G>AG>A
rs41279854*10, 29753T>CA>G
CYP1A2 rs72547513*11, F186L, 558C>AC>A
rs72547511*15, P42R, 125C>GC>G
rs72547515*16, R377Q, 2473G>AC>T
rs55889066*5, C406Y, 3497G>AG>A
rs28399424*6, R431W, 5090C>TC>T
rs72547517*8, R456H, 5166G>AG>A
CYP2A6 rs28399468*10, 6600G>TG>T
rs1809810*18, 5668A>TA>T
rs56256500*23, R203C, 607C>TC>T
rs28399444*20, 2141_2142delAAA>C
rs12721655*8, 415A>G, K192EA>G
rs34223104*22, −82C>TT>C
rs36079186*27, 593T>C, M198TT>C
rs34097093*28, 1132C>T, R378XC>T
rs3211371*1C, *5, *7, 1459C>T, Arg487CysT>C
rs28399499*18, 983T>C, I328TT>C
rs58425034 c.646-159G>CG>C
CYP2C8 rs11572103*2, I269F, A805TA>T
rs10509681*5, 2189delAIns>del
ABCB1 rs35810889M89TT>C
ABCC2 rs8187710Cys1515TyrG>A
ADRB2 rs1800888Thr164IleC>T
AOX1 rs55754655Asn1135SerA>G
BCHE rs1799807Asp70GlyA>G
CBR3 rs2835285Val93IleG>A
COMT rs9332377-C>T
EGFR rs121434568L858RT>G
F2 rs1799963-G>A
GRIK2 rs2518224-A>C
GSTM3 rs1799735Intron 6, 3-bp deletionG>T
KCNH2 rs12720441R784WC>T
SCN5A rs7626962S1103YG>T
SLC22A1 rs340595081393G>A, G465RG>A
rs12208357148C>T, R61CC>T
SLC22A2 rs8177517K432QA>C
rs8177516*7, R400CC>T
SLC28A3 rs115683881099G>AG>A
SLCO1B1 rs56199088*10, D655GA>G
rs56101265*2, F73LT>C
rs72559745*3, E156GA>G
rs56061388*3, V82AT>C
rs59502379*9, G488AG>C
ST6GAL1 rs10937275-G>A
UGT2B10 rs7657958Asp67Tyr taggingG>A

Table IV

Summary of 141 pharmacogenes investigated in a Korean population (n=150).

Table IV

Summary of 141 pharmacogenes investigated in a Korean population (n=150).

ClassGene (no. of SNPs)
Phase ICYP2D6 (14)
CYP2C9 (13)
CYP2C19 (15)
DPYD (16)
CYP2E1 (6)
CYP3A4 (3)
CYP3A5 (6)
CYP4B1 (1)
CYP4F2 (1)
CYP19A1 (2)
CYP1A2 (14)
CYP1B1 (1)
CYP2A6 (5)
CYP2B6 (10)
CYP2C8 (5)
CYP2C18 (1)
AOX1 (1)
CBR1 (2)
CBR3 (2)
EPHX1 (2)
NOS3 (1)
Phase IINAT1 (10)
NAT2 (10)
TPMT (6)
UGT1A1 (9)
CHST3 (6)
GSTM3 (1)
GSTP1 (2)
SULT1C4 (1)
UGT1A7 (1)
UGT1A8 (1)
UGT2B10 (1)
UGT2B15 (1)
UGT2B17 (1)
TransporterABCB1 (16)
ABCC1 (11)
ABCC2 (6)
ABCC4 (2)
ABCC6 (1)
ABCG2 (4)
SLC10A1 (1)
SLC10A2 (1)
SLC19A1 (1)
SLC1A1 (3)
SLC22A1 (2)
SLC22A16 (1)
SLC22A2 (4)
SLC28A2 (1)
SLC28A3 (1)
SLCO1B1 (9)
SLCO1B3 (1)
SLCO2B1 (1)
ATP7A (1)
CAT (1)
ModifierCDA (3)
KCNJ11 (1)
NR1I2 (1)
VKORC1 (6)
G6PD (6)
Others HLA-B*1502 (3)
HLA-B*5701 (1)
ABO (2)
ACE (1)
ADM (1)
ADRB2 (2)
ADRB3 (1)
AGTR1 (1)
AKT1 (1)
OthersANKK1 (1)
APOB (1)
APOC3 (2)
ARG1 (1)
ATM (1)
ATXN1 (1)
BAT3 (1)
BCHE (3)
BDKRB1 (1)
BDKRB2 (1)
C6orf10 (1)
CACNG2 (3)
CCND1 (1)
CETP (1)
CNTF (1)
COMT (4)
CRHR2 (3)
DRD2 (4)
DRD3 (2)
EGFR (2)
ERBB2 (1)
ERCC1 (2)
ERCC2 (1)
F2 (1)
FDPS (1)
FKBP5 (2)
GGCX (1)
GGH (3)
GNB3 (1)
GRIK2 (1)
GRIK4 (1)
GSK3B (4)
HLA-E (1)
OthersHMGCR (2)
HSPA1L (2)
HTR1A (3)
HTR2A (5)
HTR2C (5)
HTR3B (1)
HTR7 (1)
IL1B (1)
IL28B (5)
ITGB3 (1)
ITPA (1)
KCNH2 (3)
KNG1 (3)
LDLR (1)
LEMD2 (1)
LRP2 (1)
LTC4S (1)
METTL21A (2)
MICA (1)
MLH1 (1)
NEFM (1)
NOS1AP (2)
NPPA (1)
NQO1 (1)
NTRK1 (1)
OPRM1 (1)
P2RY1 (2)
P2RY12 (1)
PTGS1 (1)
PTGS2 (1)
RGS4 (3)
SCN5A (3)
ST6GAL1 (1)
OthersTCF7L2 (1)
TNF (1)
TP53 (1)
ULK3 (1)
VDR (1)

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 (2123). 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 V

Frequencies and effects of CYPD6, CYP2C19, TPMT, DPYD and IL28B genotypes on enzyme activity and drug toxicity.

Table V

Frequencies and effects of CYPD6, CYP2C19, TPMT, DPYD and IL28B genotypes on enzyme activity and drug toxicity.

GeneStar-allele genotype Star-allele-defining SNPs (genotype of each SNP)No.Freq. (%)Enzyme activityaClopidogrel efficacybAdverse reactions of azathioprine, mercaptopurine and thioguaninec5-FU toxicitydTreatment outcome for hepatitis Ce
CYP2D6 *1/*1Wild-type9563.3NormalN/AN/AN/AN/A
*1/*2rs16947 (CT), rs1135840 (GG)64NormalN/AN/AN/AN/A
*2/*2rs16947 (TT), rs1135840 (GG)21.3NormalN/AN/AN/AN/A
*1/*4rs3892097 (AG)1812DecreasedN/AN/AN/AN/A
*4/*4rs3892097 (AA)21.3NoneN/AN/AN/AN/A
*1/*10rs1065852 (CT or TT), rs1135840 (GG or CG)74.7NormalN/AN/AN/AN/A
*1/*14Ars1065852 (CT or TT), rs16947 (CT or TT), rs1135840 (GG or CG)128DecreasedN/AN/AN/AN/A
*1/*34rs16947 (CT)3422.7NormalN/AN/AN/AN/A
*34/*34rs16947 (TT)42.7NormalN/AN/AN/AN/A
*1/*36rs1065852 (CT or TT), rs1135840 (GG or CG)74.7NormalN/AN/AN/AN/A
*1/*60 CYP2D6_2f (ins/del or del/del), rs79738337 (TT or CT)21.3NormalN/AN/AN/AN/A
CYP2C19 *1/*1Wild-type4525.9NormalTypicalN/AN/AN/A
*1/*2rs4244285 (AG)7844.8DecreasedReducedN/AN/AN/A
*2/*2rs4244285 (AA)84.6DecreasedGreatly reducedN/AN/AN/A
*1/*3rs4986893 (AG)2112.1DecreasedReducedN/AN/AN/A
*3/*3rs4986893 (AA)10.6DecreasedGreatly reducedN/AN/AN/A
*1/*4rs28399504 (AG)10.6DecreasedReducedN/AN/AN/A
*1/*17rs11188072 (CC or CT), rs12248560 (CC or CT)2011.5IncreasedEnhancedN/AN/AN/A
TPMT *1/*3Brs1800460 (AG)10.7DecreasedN/AIncreased drug toxicityN/AN/A
*1/*3Crs1142345 (AG)42.7DecreasedN/AIncreased drug toxicityN/AN/A
*1/*6rs75543815 (AA)13992.7DecreasedN/AIncreased drug toxicityN/AN/A
*6/*6rs75543815 (AT)74.7DecreasedN/AIncreased drug toxicityN/AN/A
DPYD *1/*1rs3918290 (GG)150100-N/AN/ALow riskN/A
*1/*5rs1801159 (AG)6543.3NormalN/AN/A-N/A
*5/*5rs1801159 (GG)96NormalN/AN/A-N/A
*1/*9Ars1801265 (CT)1610.7NormalN/AN/A-N/A
c.496A>Grs2297595 (AG)32NormalN/AN/A-N/A
IL28B-rs8099917 (TT)13086.7N/AN/AN/AN/ANormal
-rs8099917 (GT)1912.7N/AN/A
N/A1.64 times less likely to respond
-rs8099917 (GG)10.7N/AN/A
N/A2.39 times less likely to respond

a Enzyme activity based on previous studies [CYP2D6 (21,76–80), CYP2C19 (81), TPMT (82,83), DPYD (84)].

b The efficacy of clopidogrel was estimated based on a previous study (81).

c The adverse reactions to azathioprine, mercaptopurine and thioguanine were estimated based on previous studies (8).

d 5-Fluorouracil (5-FU) toxicity estimation was based on a previous study (84).

e Odds of responding to peginterferon-α and ribavirin (PEG-IFNα/RBV) treatment for hepatitis C. The outcome estimation was based on previous studies (74,85).

f TA insertion at position 1887. N/A, not applicable.

TPMT encodes an enzyme involved in the detoxification of azathioprine, mercaptopurine and thioguanine, which are immunosuppressive drugs used in organ transplantation (2427). 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 (3234), 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 (3640) 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.

Table VI

Frequencies and effect of combined CYP2C9 and VKORC1 genotypes on the response to warfarin.

Table VI

Frequencies and effect of combined CYP2C9 and VKORC1 genotypes on the response to warfarin.

CYP2C9VKORC1 (rs8050894)


Star- allele genotypeStar-allele- defining SNPsGenotype in each SNPnFreq. (%)Warfarin dose (mg/day)anFreq. (%)Warfarin dose (mg/day)anFreq. (%)Warfarin dose (mg/day)a
*1/*1 rs1799853CC10.75–72214.75–711476.03–4
*1/*2 rs1799853CT005–7003–4003–4
*1/*3 rs1799853CC003–410.73–4128.00.5–2
*2/*2 rs1799853TT003–4003–4000.5–2
*2/*3 rs1799853CT or TT003–4000.5–2000.5–2
rs1057910AC or CC
*3/*3 rs1799853CC000.5–2000.5–2000.5–2

a Warfarin dose estimation was based on previous studies (52,86,87).

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 (4345). 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 (4751). 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 VII

Frequencies and combined effects of rs2844682 and rs3909184 (tagging HLA-B*1502) on adverse reactions to carbamazepine.

Table VII

Frequencies and combined effects of rs2844682 and rs3909184 (tagging HLA-B*1502) on adverse reactions to carbamazepine.

SNP/GenotypenFreq. (%) HLA-B*1502 typeAdverse reactions to carbamazepinea

rs2844682 rs3909184
CCCC9563.3NoneLow risk
CCCG106.7NoneLow risk
CCGG10.7NoneLow risk
CTCC3724.7NoneLow risk
CTCG42.7Unable to be determined-
CTGG00 *1502 (one copy)High risk
TTCC32.0NoneLow risk
TTCG00 *1502 (one copy)High risk
TTGG00 *1502 (two copies)High risk

a Reaction estimation based on a previous study (88).

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 VIII

Frequency and effects of HCP5/HLA-B*5701 and TPMT/COMT polymorphisms on adverse drug reaction.

Table VIII

Frequency and effects of HCP5/HLA-B*5701 and TPMT/COMT polymorphisms on adverse drug reaction.

SNP ID/GenotypenFreq. (%)Abacavir hypersensitivitya

rs2395029 (HCP5) HLA-B*5701
TTNone150100Low risk
TG*5701 (one copy)00High risk
GG*5701 (two copies)00High risk

rs1142345 (TPMT)rs9332377 (COMT)nFreq. (%)Adverse reactions to cisplatinb

AACC14693.7Low risk
AGCC42.7High risk
GGCC00High risk

a Estimated based on a previous study (89).

b Risk estimation of adverse reactions to cisplatin was based on a previous study (39).

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.


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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Kim, J.Y., Cheong, H.S., Park, T., Shin, H.J., Seo, D.W., Na, H.S. ... Shin, H.D. (2014). Screening for 392 polymorphisms in 141 pharmacogenes. Biomedical Reports, 2, 463-476.
Kim, J. Y., Cheong, H. S., Park, T., Shin, H. J., Seo, D. W., Na, H. S., Chung, M. W., Shin, H. D."Screening for 392 polymorphisms in 141 pharmacogenes". Biomedical Reports 2.4 (2014): 463-476.
Kim, J. Y., Cheong, H. S., Park, T., Shin, H. J., Seo, D. W., Na, H. S., Chung, M. W., Shin, H. D."Screening for 392 polymorphisms in 141 pharmacogenes". Biomedical Reports 2, no. 4 (2014): 463-476.