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. 2018 Dec 28;97(52):e13674. doi: 10.1097/MD.0000000000013674

Population genetic difference of pharmacogenomic VIP gene variants in the Lisu population from Yunnan Province

Chan Zhang a, Xiaochun Jiang b, Wanlu Chen a, Qi Li a, Fubin Yun a, Xin Yang a, Run Dai a, Yujing Cheng a,∗
Editor: Ziyuan Zhou
PMCID: PMC6314765  PMID: 30593137

Abstract

Individual differences in drug clinical response are related to pharmacogenomics. The genetic variation of drug-metabolizing enzymes, drug receptors, and their downstream protein genes is the main factor causing individual differences in drug response. The genetic backgrounds among different ethnic groups are quite different. In this study, we aimed to detect the distribution difference of genotype frequency in very important pharmacogenetic (VIP) gene variants in the Lisu.

Using the chi-squared test, we compared the genotype frequencies of the VIP variants in 105 Lisu people with those in 26 populations from the 1000 Genome project separately. Bonferroni's multiple adjustment was also conducted (P < .05/(26∗49)). Moreover, Arlequin v3.5 and Structure v2.3.4 software were used to analyze the genetic distance and genetic structure.

There were 9, 9, 11, 12, 11, 11, 9, 17, 13, 13, 16, 5, 3, 5, 3, 4, 17, 14, 16, 17, 16, 10, 13, 12, 10, and 9 single nucleotide polymorphisms that differed in frequency distribution, when Lisu people compared with the 26 populations separately. Only CYP2E1 rs2070676 was different in the Lisu population compared with the 26 groups from the 1000 Genome project. PTGS2 rs5275 and CYP2D6 rs1065852 were different in the Lisu population compared with most of the populations. Additionally, genetic backgrounds of Lisu and Han Chinese in Beijing were closest according to the lowest F-statistics value and resemblance in genetic structures.

Our results complete the information of the Lisu population in pharmacogenomics database.

Keywords: Lisu population, population genetic, very important pharmacogenetic

1. Introduction

The Pharmacogenetics and Pharmacogenomics knowledgebase (PharmGKB) is a national research alliance, which examines how variations in genes lead to individual differences in drug response. Pharmacogenomics Knowledge Base (PharmGKB: http://www.pharmgkb.org) has as many as 621 drugs with PGx-related gene polymorphism annotation information, and up to 128 Pathways involving pharmacokinetics and pharmacodynamics. There are 65 very important pharmacogenetics (VIP) involved in this database. VIP summaries provide an overview of a significant gene involved in metabolism or response to one or several drugs. VIPs play roles in the metabolism of many drugs, and contain variants, which potentially contribute to a severe drug response.

Comparing the effects of drugs among different races has become a major direction of pharmacogenomics research. Cytochrome P450 is the main family enzyme system in the drug-metabolism enzyme system in human body. The difference in the ability of metabolizing substrates among individuals in different populations will lead to differences in individual clinical treatment effects and disease susceptibility. Studies have shown that the vast majority of statins metabolism is closely related to Cytochrome P450 Family 3 Subfamily A Member 4 (CYP3A4),[1] Cytochrome P450 Family 2 Subfamily C Member 9 (CYP2C9),[2] and Cytochrome P450 Family 2 Subfamily D Member 6 (CYP2D6)[3] single nucleotide polymorphisms (SNPs). The presence of these SNPs may affect drug efficacy or cause adverse drug reactions.

As a result, different races and their subpopulations have different effects on the same dose of the same drug. Therefore, the study of differences in pharmacogenomics among different populations can provide valuable theoretical basis for individualized drug treatment based on different populations. At present, there are studies on population genetics of genes related to drug-metabolism enzymes in ethnic minorities in Yunnan Province (Zhuang population[4] and Yi population[5]), Tibet Autonomous Region (Lhoba population,[6] Deng population,[7] Sherpa population,[8] and Tibetan population[9]), Xinjiang Uygur Autonomous Region (Uygur population,[10] Tajik ethnic population,[11] and Kyrgyz population),[12] and Guizhou province (Miao people[13]). The Lisu originates from the ancient Diqiang family and has the origin relationship with the Yi people. The national language belongs to the Sino-Tibetan Tibetan-Burmese language group. The Lisu ethnic group mainly distributes along the Nu River and the Kaijiang River (Irrawaddy River Branch) basin areas, which are the border area of Yunnan, Tibet, and Burma Kachin.[14,15] The rest are scattered in the other regions of Yunnan, the east and the north of India, and the border between Thailand and Burma.

The ethnic composition of various regions in China is complex, and the genetic backgrounds among different ethnic groups are quite different. There are few population genetic studies on the drug-metabolism genes of the Lisu population. The incidence of drug efficacy and adverse reactions in minority populations are not optimistic.[16] So, in this study, we want to detect the allele frequencies of 49 VIP variants in the Lisu population, and further determine the allele frequency differences between the Lisu and 26 populations reported in the 1000 Human Genome Project. We hope that the results of this study will extend our understanding of ethnic diversity, pharmacogenomics, and enable medical professionals to use genomic and molecular data to effectively implement personalized medicine in the future.

2. Materials and methods

2.1. Ethical statement

All procedures involving human participants were in accordance with the ethical standards of the First People's Hospital of Yunnan Province, and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from each individual who participated in the study.

2.2. Study participants

A total of 105 healthy, randomly selected individuals from Lisu ethnic group were enrolled in this study, and had exclusively Lisu ancestry for at least the last 3 generations. One hundred five blood samples were collected from the First People's Hospital of Yunnan Province.

2.3. Variant selection and genotyping

From the Pharmacogenetics and Pharmacogenomics knowledgebase, we selected 25 genes, which previously reported to be related to drug metabolism. And, based on the frequency of minor allele >0.05 in the global population from the 1000 Human Genome Project, we selected 49 SNPs. Genomic DNA was extracted from the peripheral blood of the participants using the GoldMag whole blood genomic DNA purification kit (GoldMag Co Ltd, Xi’an, China), as recommended by the manufacturer's instructions. DNA concentration was determined using a NanoDrop 2000C spectrophotometer (Thermo Scientific, Waltham, MA). The Agena MassARRAY Assay Design 3.0 Software (Agena Bioscience, Inc., San Diego, CA) was used to design Multiplexed SNPMassEXTEND assays.[17] SNP genotyping analysis was performed using the standard protocol recommended by the manufacturer with an Agena MassARRAY RS1000 (San Diego, CA). Agena Typer 4.0 Software (San Diego, CA) was used to manage and analyze the SNP genotyping data as described in a previous report.[18]

2.4. 1000 Human Genome Project genotype data

The 1000 Genomes Project ran between 2008 and 2015, creating the largest public catalog of human variation and genotype data. The genotype data of individuals from 26 populations were downloaded from the 1000 Genomes Project website (http://www.internationalgenome.org/data). The following table lists these populations. The 26 populations comprised Han Chinese in Beijing, China (CHB), Japanese in Tokyo, Japan (JPT), Southern Han Chinese (CHS), Chinese Dai in Xishuangbanna, China (CDX), Kinh in Ho Chi Minh City, Vietnam (KHV), Utah Residents (CEPH) with Northern and Western European Ancestry (CEU), Toscani in Italia (TSI), Finnish in Finland (FIN), British in England and Scotland (GBR), Iberian Population in Spain (IBS), Yoruba in Ibadan, Nigeria (YRI), Luhya in Webuye, Kenya (LWK), Gambian in Western Divisions in the Gambia (GWD), Mende in Sierra Leone (MSL), Esan in Nigeria (ESN), Americans of African Ancestry in southwest United States (ASW), African Caribbean in Barbados (ACB), Mexican Ancestry from Los Angeles, United States (MXL), Puerto Ricans from Puerto Rico (PUR), Colombians from Medellin, Colombia (CLM), Peruvians from Lima, Peru (PEL), Gujarati Indian from Houston, Texas (GIH), Punjabi from Lahore, Pakistan (PJL), Bengali from Bangladesh (BEB), Sri Lankan Tamil from the United Kingdom (STU), and Indian Telugu from the United Kingdom (ITU).

2.5. Statistical analyses

We compared the genotype frequencies of the VIP in the Lisu people with those in the 26 populations separately using the chi-squared test. All P values obtained in this study were 2-sided and Bonferroni's multiple adjustment was applied to improve the significance, which was set at P < .05/(26∗49). With the chi-squared test, we wanted to find some significant different SNPs.

The values of Fst and P were calculated by Arlequin v3.5 software.[19] The population genetic differentiation factor (Fst) reflects the degree of difference between the average heterozygosity of each subpopulation and the heterozygosity of the total population. The Fst value is 0 to 1, the larger the value, the more obvious the genetic differentiation among the subgroups.

A Bayesian model based on allelic frequency correlation among populations was used to estimate the K of population classification by Structure v2.3.4 software.[20] According to the recommendation of the Structure software manual, the K value was 5 to 8, each K value was repeated 3 times, and the Markov Chain Monte Carlo reaction times was set to 100,000, and the subsequent burn-in reaction times was set to 10,000. When software running to complete and getting results, we drew bar chart through drawing software.

3. Results

The basic information of the 49 selected variants is shown in Table 1, including the position, allele, alternative amino acids, and minor allele frequency of the selected SNPs.

Table 1.

Basic characteristic of selected variants and the MAF in Lisu people.

3.

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Through the chi-squared test, the differences in genotype frequencies of the 49 variants between the Lisu population and the 26 individuals from the 1000 Genomes Project were compared, and further Bonferroni adjustment was performed. The results showed that there were 9, 9, 11, 12, 11, 11, 9, 17, 13, 13, 16, 5, 3, 5, 3, 4, 17, 14, 16, 17, 16, 10, 13, 12, 10, and 9 SNPs that different in the frequency distribution, when Lisu people compared with the ACB, ASW, ESN, GWD, LWK, MSL, YRI, CLM, MXL, PEL, PUR, CDX, CHB, CHS, JPT, KHV, CEU, FIN, GBR, IBS, TSI, BEB, GIH, ITU, PJL, and STU populations, respectively (Table 2; Fig. 1). Two SNPs (CYP3A5 rs776746 and CYP2E1 rs2070676) were different in the Lisu population compared with East Asian population; 11 SNPs (PTGS2 rs5275, CYP3A5 rs776746, NAT2 rs1801280, NAT2 rs1799929, NAT2 rs1208, ALOX5 rs2115819, CYP2C19 rs12248560, CYP2E1 rs2070676, SLCO1B1 rs2306283, VDR rs731236, and VDR rs4516035) were different in the Lisu population compared with European population; 7 loci (PTGS2 rs5275, NAT2 rs1208, ALOX5 rs2115819, CYP2C19 rs12248560, CYP2C8 rs17110453, CYP2E1 rs2070676, and VDR rs731236) were different in the Lisu population compared with African population; 8 SNPs (PTGS2 rs5275, CYP3A5 rs776746, NAT2 rs1801280, CYP2C8 rs17110453, CYP2E1 rs2070676, VDR rs731236, VDR rs4516035, and CYP2D6 rs1065852) were different in the Lisu population compared with Ad Mixed American population; 8 SNPs (PTGS2 rs5275, NAT2 rs1801280, NAT2 rs1208, ALOX5 rs2115819, CYP2E1 rs2070676, VDR rs731236, VDR rs4516035, and CYP2D6 rs1065852) were different in the Lisu population compared with South Asian population. Only CYP2E1 rs2070676 was different in the Lisu population compared with 26 individuals from the 1000 Genomes Project. PTGS2 rs5275 and CYP2D6 rs1065852 were different in the Lisu population compared with most of the populations.

Table 2.

Significant variants in Lisu people compared with the 26 populations from 1000 genomes project (phase 3) by chi-squared test.

3.

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Figure 1.

Figure 1

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Number of variants significantly differed with 26 populations after multiple adjustment.

We used a model-based clustering approach, as implemented in STRUCTURE, to infer population structures among the 27 populations. When the K value runs 5, the genetic structure of Lisu people was much similar to the Chinese Dai in Xishuangbanna (CDX), CHB, and CHS (Fig. 2). Pairwise Fst values were calculated for all population comparisons. The Fst value is 0 to 1, the larger the value, the more obvious the genetic differentiation among the subgroups. From Table 3, we found that the value of Fst (Lisu and CHB) was 0.047, which was the smallest. Therefore, we considered that the genetic backgrounds of Lisu and CHB were similar. We speculated that the genetic backgrounds of Lisu and CHB were resemblance.

Figure 2.

Figure 2

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Bayesian clustering of cod sampled from 27 populations. Each vertical bar denotes an individual, whilst colors denote inferred clusters.

Table 3.

Fst values between population pairs.

3.

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4. Discussion

The emergence of pharmacogenomics has promoted correlation analysis of drug-metabolism, target-related genes, and various adverse drug reactions. Meanwhile, a number of genetic polymorphisms have gradually gained attention and laid the foundation for clinical individualized medication. In this study, we detected the allele frequencies of 49 VIP variants in the Lisu population, and further determined the allele frequency differences with populations reported in the 1000 Genome Project. In the end, we found that only CYP2E1 rs2070676 was different in the Lisu population compared with 26 individuals from the 1000 genome project. PTGS2 rs5275 and CYP2D6 rs1065852 were different in the Lisu population compared with most of the populations. And genetic backgrounds of Lisu and CHB were similar through the Structure and the Fst analysis.

Cytochrome P450 2E1 (CYP2E1) is an important phase I metabolizing enzyme in the body. It is mainly located in liver microsomes and can metabolize many low-molecular weight potentially carcinogenic chemicals including benzene. This enzyme can be induced by ethanol and other compounds. CYP2E1 affects the metabolism and efficacy of various drugs, such as cisplatin[21] and antituberculosis drug.[22] There are substantial differences among different populations in their reaction to drugs. Besides, CYP2E1 also affects the occurrence of diseases. Chang et al researched the association between CYP2E1 gene polymorphisms and formation of Coronary Artery Lesions in Kawasaki Disease, and the results pointed that the GG genotype of rs2070676 was strongly associated with the risk of coronary artery lesions formation in Kawasaki disease patients.[23] Shahabi et al presented significantly results indicating an association between the rs2070676 polymorphism and Parkinson disease.[24] The allele frequencies of the variants are different in populations. In our research, the frequency of minor allele (G) in rs2070676 is 0.30. From the ALelle FREquency Database, we found that the frequency range of minor allele (G) was 0.030 to 0.426 in Asian population, while in African population, was 0.500 to 0.811.[25] The same rule was found in the 1000 Genome project. There was very wide variation in population allele frequency of this variant because several African populations were reported to have the G variant as the major allele, whereas European and Asian populations to have the G variant as the minor allele.

Prostaglandin-endoperoxide synthase 2 (PTGS2) gene is a paralog of PTGS1, which located on 1q31.1. PTGS2 gene encodes the prostaglandin-endoperoxide synthase, regulates prostaglandin levels at the level of cyclooxygenase, and catalyzes the first 2 steps in the metabolism of arachidonic acid. The PTGS2 gene is associated with the efficacy of various drugs, such as anti-inflammatory agents, nonsteroids, rofecoxib, ibuprofen, Cobra drugs, aspirin, and other drugs. The PTGS2 gene also has a close relationship with a variety of cancers and diseases. PTGS2 rs5275 (8473 T>C) variant is located in the 3′-UTR in which it may stabilize the mRNA. One research found that rs5275 TT genotype was associated with better progression-free survival and overall survival in patients with advanced colorectal cancer treated with XELOX (capecitabine and oxaliplatin) chemotherapy.[26] The TT genotype was also associated with lower risk for severe pain in lung cancer patients.[27] The C variant occurs at a frequency of 0.355 in Caucasians, 0.435 in Native American/Hispanic, and 0.667 in African/African American sample sets in the SNP500Cancer control sample set from the Coriell collection.[28] The C variant is also observed at a frequency of 0.291 in German Caucasians.[29] In our research, the frequency of minor allele (T) of rs5275 is 0.00.

Cytochrome P450 2D6 (CYP2D6) mediates several important metabolic pathways in the body and is a key enzyme in metabolism. As the most polymorphic enzyme system, approximate 55 mutants of CYP2D6 influence the final enzyme activity and quantity changes, resulting in considerable individualized differences in human response to drugs. The frequencies of CYP2D6 gene mutations are extremely different among races. In Asian populations, CYP2D6∗10 (rs1065852, Pro34Ser) is a common mutant with a mutation frequency of approximately 17.4% and an allelic mutation frequency of 45.7%.[30] Based on the data from the 1000 Genomes project, the frequency of minor allele (G) in Chinese population CHB, CDX, and CHS is 0.371 to 0.398, respectively, while the minor allele is A base in other races. In our research, the frequency of minor allele (G) was 0.31 in Lisu. The change of amino acid leads to decreased enzyme activity and metabolic conversion rate. A meta-analysis,[31] including 15 research with 1794 Asian breast cancer patients, revealed that the enzyme activity and metabolic conversion rate was low among the patients with CYP2D6∗10/∗10 (TT) genotype. A correlation study between CYP2D6 gene polymorphism and Tam and metabolite plasma concentrations revealed that the mean plasma concentration of Tamoxifen in poor metabolizers and intermediate metabolizers was only 25% and 55%, respectively, of the fast metabolizing type (extensive metabolizers + ultrarapid metabolizers).[32] Accordingly, female breast cancer patients with ∗10 variants should increase their Tamoxifen dose of >20 mg/d. In a study of the association between the CYP2D6 gene polymorphism and early onset preeclampsia patients and the effect of labetalol, the frequency of the CYP2D6 rs1065852 “G” allele in patients with ineffective labetalol is higher than that of the therapeutically effective one. Thus, the “G” allele of the rs1065852 may be associated with the therapeutic effect of labetalol.[33] The optimal drug dose should be based on the genotype of individual Lisu patients.

However, intrinsic limitations still exist in our study. Our sample size of Lisu is relative small, and further investigation in a larger cohort of Lisu is necessary to ascertain the generalizability and extrapolation of our results to these and other conditions in the Lisu population. In the follow-up study, we will conduct an in-depth study of the polymorphic sites with differences, and analyze the effects of mutations on the dose of different diseases.

In conclusion, CYP2E1 rs2070676 was different in the Lisu population compared with 26 individuals from the 1000 Genome project. PTGS2 rs5275 and CYP2D6 rs1065852 were different in the Lisu population compared with most of the populations. Due to the difference in genotype distribution frequency of SNPs in genes affecting drug metabolism, the appropriate drug dose should be chosen to ensure the safety and efficacy of the drug in certain group.

Acknowledgments

The authors are grateful to all people who participated in the study. The authors would also like to thank the clinicians and other hospital staff who contributed to the data collection for this study.

Author contributions

Conceptualization: Chan Zhang.

Data curation: Xiaochun Jiang, Wanlu Chen, Run Dai.

Formal analysis: Wanlu Chen.

Investigation: Fubin Yun.

Methodology: Qi Li, Fubin Yun, Run Dai.

Software: Qi Li, Xin Yang.

Supervision: Yujng Cheng.

Validation: Xiaochun Jiang, Xin Yang.

Writing – original draft: Chan Zhang.

Writing – review & editing: Yujing Cheng.

Footnotes

Abbreviations: ACB = African Caribbean in Barbados, ASW = Americans of African Ancestry in southwest United States, BEB = Bengali from Bangladesh, CDX = Chinese Dai in Xishuangbanna, China, CEU = Utah Residents (CEPH) with Northern and Western European Ancestry, CHB = Han Chinese in Beijing, China, CHS = Southern Han Chinese, CLM = Colombians from Medellin, Colombia, ESN = Esan in Nigeria, FIN = Finnish in Finland, GBR = British in England and Scotland, GIH = Gujarati Indian from Houston, Texas, GWD = Gambian in Western Divisions in the Gambia, IBS = Iberian Population in Spain, ITU = Indian Telugu from the United Kingdom, JPT = Japanese in Tokyo, Japan, KHV = Kinh in Ho Chi Minh City, Vietnam, LWK = Luhya in Webuye, Kenya, MSL = Mende in Sierra Leone, MXL = Mexican Ancestry from Los Angeles, United States, PEL = Peruvians from Lima, Peru, PJL = Punjabi from Lahore, Pakistan, PUR = Puerto Ricans from Puerto Rico, SNP = single nucleotide polymorphism, STU = Sri Lankan Tamil from the United Kingdom, TSI = Toscani in Italia, VIP = very important pharmacogenetic, YRI = Yoruba in Ibadan, Nigeria.

Chan Zhang and Xiaochun Jiang have contributed equally to this work.

The authors have no funding and conflicts of interest to disclose.

References

  • [1].Sadee W. Gene-gene-environment interactions between drugs, transporters, receptors, and metabolizing enzymes: statins, SLCO1B1, and CYP3A4 as an example. J Pharm Sci 2013;102:2924–9. [DOI] [PubMed] [Google Scholar]
  • [2].Zhou Q, Chen QX, Ruan ZR, et al. CYP2C9∗3 (1075A>C), ABCB1 and SLCO1B1 genetic polymorphisms and gender are determinants of inter-subject variability in pitavastatin pharmacokinetics. Pharmazie 2013;68:187–94. [PubMed] [Google Scholar]
  • [3].Zuccaro P, Mombelli G, Calabresi L, et al. Tolerability of statins is not linked to CYP450 polymorphisms, but reduced CYP2D6 metabolism improves cholesteraemic response to simvastatin and fluvastatin. Pharmacol Res 2007;55:310–7. [DOI] [PubMed] [Google Scholar]
  • [4].Li J, Guo C, Yan M, et al. Genetic polymorphisms in very important pharmacogenomic variants in the Zhuang ethnic group of Southwestern China: a cohort study in the Zhuang population. Medicine 2018;97:e0559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Yan M, Li D, Zhao G, et al. Genetic polymorphisms of pharmacogenomic VIP variants in the Yi population from China. Gene 2018;648:54–62. [DOI] [PubMed] [Google Scholar]
  • [6].He Y, Yang H, Geng T, et al. Genetic polymorphisms of pharmacogenomic VIP variants in the Lhoba population of southwest China. Int J Clin Exp Pathol 2015;8:13293–303. [PMC free article] [PubMed] [Google Scholar]
  • [7].Shi X, Wang L, Du S, et al. Genetic polymorphism of pharmacogenomic VIP variants in the Deng people from the Himalayas in southeast Tibet. Biomarkers 2015;20:275–86. [DOI] [PubMed] [Google Scholar]
  • [8].Wang L, Ren Y, Shi X, et al. The population genetics of pharmacogenomics VIP variants in the Sherpa population. Drug Metab Pharmacokinet 2016;31:82–9. [DOI] [PubMed] [Google Scholar]
  • [9].Jin TB, Xun XJ, Shi XG, et al. Genetic polymorphisms in very important pharmacogenomic (VIP) variants in the Tibetan population. Genet Mol Res 2015;14:12497–504. [DOI] [PubMed] [Google Scholar]
  • [10].Wang L, Aikemu A, Yibulayin A, et al. Genetic polymorphisms of pharmacogenomic VIP variants in the Uygur population from northwestern China. BMC Genet 2015;16:66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Zhang J, Jin T, Yunus Z, et al. Genetic polymorphisms of VIP variants in the Tajik ethnic group of northwest China. BMC Genet 2014;15:102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Yunus Z, Liu L, Wang H, et al. Genetic polymorphisms of pharmacogenomic VIP variants in the Kyrgyz population from northwest China. Gene 2013;529:88–93. [DOI] [PubMed] [Google Scholar]
  • [13].Jin T, Aikemu A, Zhang M, et al. Genetic polymorphisms analysis of pharmacogenomic VIP variants in Miao ethnic group of southwest China. Med Sci Monit 2015;21:3769–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Dong ZM, Yao YF, Shi L, et al. Association between Alu insertion polymorphisms and HLA class I alleles in Chinese Lisu and Nu ethnic populations. Zhonghua Yi Xue Yi Chuan Xue Za Zhi 2012;29:222–8. [DOI] [PubMed] [Google Scholar]
  • [15].Chen S, Hu Q, Xie Y, et al. Origin of Tibeto-Burman speakers: evidence from HLA allele distribution in Lisu and Nu inhabiting Yunnan of China. Hum Immunol 2007;68:550–9. [DOI] [PubMed] [Google Scholar]
  • [16].Hu Y, Cantarero-Arevalo L. Ethnic differences in adverse drug reactions to asthma medications: a systematic review. J Asthma 2016;53:69–75. [DOI] [PubMed] [Google Scholar]
  • [17].Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Curr Protoc Hum Genet 2009;Chapter 2:Unit 2.12. [DOI] [PubMed] [Google Scholar]
  • [18].Thomas RK, Baker AC, Debiasi RM, et al. High-throughput oncogene mutation profiling in human cancer. Nat Genet 2007;39:347–51. [DOI] [PubMed] [Google Scholar]
  • [19].Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 2010;10:564–7. [DOI] [PubMed] [Google Scholar]
  • [20].Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000;155:945–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Khrunin A, Ivanova F, Moisseev A, et al. Pharmacogenomics of cisplatin-based chemotherapy in ovarian cancer patients of different ethnic origins. Pharmacogenomics 2012;13:171–8. [DOI] [PubMed] [Google Scholar]
  • [22].Sun F, Chen Y, Xiang Y, et al. Drug-metabolising enzyme polymorphisms and predisposition to anti-tuberculosis drug-induced liver injury: a meta-analysis. Int J Tuberc Lung Dis 2008;12:994–1002. [PubMed] [Google Scholar]
  • [23].Chang LS, Hsu YW, Lu CC, et al. CYP2E1 gene polymorphisms related to the formation of coronary artery lesions in Kawasaki disease. Pediatr Infect Dis J 2017;36:1039–43. [DOI] [PubMed] [Google Scholar]
  • [24].Shahabi HN, Westberg L, Melke J, et al. Cytochrome P450 2E1 gene polymorphisms/haplotypes and Parkinson's disease in a Swedish population. J Neural Transm (Vienna) 2009;116:567–73. [DOI] [PubMed] [Google Scholar]
  • [25].Lee MY, Mukherjee N, Pakstis AJ, et al. Global patterns of variation in allele and haplotype frequencies and linkage disequilibrium across the CYP2E1 gene. Pharmacogenomics J 2008;8:349–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Kim JG, Chae YS, Sohn SK, et al. Prostaglandin synthase 2/cyclooxygenase 2 (PTGS2/COX2) 8473T>C polymorphism associated with prognosis for patients with colorectal cancer treated with capecitabine and oxaliplatin. Cancer Chemother Pharmacol 2009;64:953–60. [DOI] [PubMed] [Google Scholar]
  • [27].Reyes-Gibby CC, Spitz MR, Yennurajalingam S, et al. Role of inflammation gene polymorphisms on pain severity in lung cancer patients. Cancer Epidemiol Biomarkers Prev 2009;18:2636–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Packer BR, Yeager M, Burdett L, et al. SNP500Cancer: a public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes. Nucleic Acids Res 2006;34:D617–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Skarke C, Schuss P, Kirchhof A, et al. Pyrosequencing of polymorphisms in the COX-2 gene (PTGS2) with reported clinical relevance. Pharmacogenomics 2007;8:1643–60. [DOI] [PubMed] [Google Scholar]
  • [30].Hertz DL, Deal A, Ibrahim JG, et al. Tamoxifen dose escalation in patients with diminished CYP2D6 activity normalizes endoxifen concentrations without increasing toxicity. Oncologist 2016;21:795–803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Lu J, Li H, Guo P, et al. The effect of CYP2D6 ∗10 polymorphism on adjuvant tamoxifen in Asian breast cancer patients: a meta-analysis. Onco Targets Ther 2017;10:5429–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Jin Y, Desta Z, Stearns V, et al. CYP2D6 genotype, antidepressant use, and tamoxifen metabolism during adjuvant breast cancer treatment. J Natl Cancer Inst 2005;97:30–9. [DOI] [PubMed] [Google Scholar]
  • [33].Sun CJ, Li L, Li XY, et al. Associations of polymorphisms of CYP2D6 and CYP2C9 with early onset severe pre-eclampsia and response to labetalol therapy. Arch Gynecol Obstet 2018;298:125–32. [DOI] [PubMed] [Google Scholar]

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