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. 2019 Apr 5;7(5):e574. doi: 10.1002/mgg3.574

Genetic analysis of pharmacogenomic VIP variants in the Blang population from Yunnan Province of China

Chan Zhang 1,†, Weiwei Guo 2,†, Yujing Cheng 1, Qi Li 1, Xin Yang 1, Run Dai 1, Linhao Zhu 3,4,5, Wanlu Chen 1,✉
PMCID: PMC6503013  PMID: 30955239

Abstract

Background

Genetic polymorphisms in numerous pharmacogenetics studies were regarded as the essential factors involved in the response to or metabolism of drugs. These genetic variants called very important pharmacogenetic (VIP) variants played a role in drugs metabolism, which have been summarized in the PharmGKB database. In this study, we genotyped 80 VIP variants from the PharmGKB in 100 members of Blang volunteers from Yunnan province.

Methods

Based on the PharmGKB database, we genotyped 80 VIP variants loci located in 47 genes. We used χ2 tests to evaluate the significant loci between Blang and the other populations, including ASW, CEU, CHB, CHD, GIH, JPT, LWK, MEX, MKK, TSI, and YRI. The global variation distribution of the significant variants was observed from the ALlele FREquency Database. And then, we used F‐statistics (Fst), genetic structure, and phylogenetic tree analyses to ascertain the genetic affinity among 12 populations.

Results

Comparing the Blang with the other 11 populations from the HapMap Project, the statistical results revealed that rs3814055 (NC_000003.12:g.119781188C>T) of nuclear receptor subfamily 1 group I member 2 (NR1I2, OMIM# 603,065) was the most significant variant, followed by rs1540339 (NC_000012.12:g.47863543C>T) of vitamin D receptor (VDR, OMIM#601,769). Furthermore, we found that genotype frequency of rs3814055 in the Blang was closer to the populations distributed in Miao. And genetic structure and F‐statistics indicated that the Blangs had a relatively closer affinity with CHD, CHB, and JPT populations. In addition, the Han nationality in Shaanxi was closer to it.

Conclusions

Our results will complement the pharmacogenomics information of the Blang ethnic group and provide a theoretical basis for safer drug administration for Blang.

Keywords: Blang, genetic polymorphism, pharmacogenomics, VIP variants

1. INTRODUCTION

Personalized medicine (Jain, 2009) simply means selection of a best treatment suited for a person on a comprehensive consideration of each patient's characteristics. Its scope is more wider, including pharmacogenetics, pharmacogenomics, and so forth. Pharmacogenomics, a crucial foundation for the development of personalized medicine and patient medication management, enables therapy more precisely.

Furthermore, the Pharmacogenomics Knowledge Base (PharmGKB: http://www.pharmgkb.org) is an extremely useful resource for explaining the gene–drug–disease relationships, more importantly, supporting personalized medicine projects. Recently, a large number of pharmacogenomics studies focused on genetic variations considered to be involved in response to or metabolism of drugs (Evans & McLeod, 2003). These genetic variations also called very important pharmacogenetic (VIP) variants (Peters & McLeod, 2008). At present, there were a total of 246 VIP variants located in 66 genes, which have been summarized in the PharmGKB database.

Numerous studies have elucidated that the importance of ethnicity is great in influencing the frequencies of gene variants. There are 56 ethnic minorities in China, including the Blang ethnic group. The Blang nationality has a population of 91,882 (the fifth national census statistics in 2000), most of whom live in Mount Blang, Xiding, Bada, Mengman, and Daluo areas of Menghai County in Xishuangbanna Dai Autonomous Prefecture of Yunnan province of Southwest China. The others distribute in ***Lincang, Simao, and Baoshan areas (Wang, Hu et al., 2008a). The areas they live in are mild climate and rich products. They are mainly engaged in agricultural production, especially tea planting, which is the origin of the famous Pu'er tea.

This study aims to determine the Blang's genotype and allele frequencies distribution of pharmacogenetic variants. And we compare Blang with the 11 HapMap populations and two national minorities to assess the differences in allele frequencies. The results will complement the database information of pharmacogenomics, better understand the Blang nationality, and provide them with more reasonable individualized health management in the future.

2. MATERIALS AND METHODS

2.1. Ethical compliance

All participants were informed both in writing and verbally to the procedures and purpose of the study and signed informed consent documents. The study protocol was approved by the Clinical Research Ethics Committee of Xizang Minzu University. It is in accordance with the Department of Health and Human Services (DHHS) regulations for human research subject protection.

2.2. Study participants

We randomly recruited about 100 unrelated, healthy Blang people from the Yunnan Province of China. Each participant has undergone rigorous screening criteria. None of the subjects had any diseases including self‐reported cancer history and other diseases. Moreover, despite the influence of the Han and Dai people whose economy and culture development are relatively rapid, they still maintain the characteristics of the nation. They can be seen as representatives of the Blang population.

2.3. Variant selection and genotyping

We chose 80 VIP variants loci located in 47 genes from the PharmGKB database. Genomic DNA was extracted from peripheral blood sample using the GoldMag‐Mini Whole Blood Genomic DNA Purification Kit (GoldMag Ltd. Xi'an, China) according to the manufacturer's protocol. NanoDrop 2000C spectrophotometer (Thermo Scientific, Waltham, MA) was used to measure the DNA concentration. We utilized the Sequenom MassARRAY Assay Design 3.0 Software (San Diego, CA) to design Multiplexed SNP MassEXTEND assays (Gabriel, Ziaugra, & Tabbaa, 2009) and genotyped the variants using Sequenom MassARRAY RS1000 (San Diego, CA). Based on the Sequenom Typer 4.0 software (San Diego, CA) used in previous research (He et al., 2015; Jin, Aikemu et al., 2015a; Jin, Yang et al., 2015b; Thomas et al., 2007), we completed data management and analyses.

2.4. Statistical analyses

We performed χ2 tests and Hardy–Weinberg equilibrium (HWE) analysis by the Microsoft Excel (Redmond, WA) and SPSS 19.0 statistical software platform (SPSS, Chicago, IL). The genotype frequencies of 80 variants in the Blang population were separately compared with those of the other populations, including the Chinese Han in Beijing, China (CHB); the Chinese of metropolitan Denver, Colorado, USA (CHD); the Japanese in Tokyo, Japan (JPT); a residents population in Utah with Northern and Western European Ancestry (CEU); the Gujarati Indians in Houston, Texas, USA (GIH); people with Mexican ancestry living in Los Angeles, California, USA (MEX); the Tuscan people of Italy (TSI); a population of African ancestry in the southwestern USA (ASW); the Luhya people in Webuye, Kenya (LWK); the Maasai people in Kinyawa, Kenya (MKK); and the Yoruba in Ibadan, Nigeria (YRI). All p values of less than 0.05 obtained in this study were two‐sided and Bonferroni's multiple tests were used to calculate the level of significance. After Bonferroni's multiple adjustment, we attempted to discover significantly different sites (p < [0.05/(80 × 11)]). Subsequently, we downloaded significant SNP allele frequencies from the ALlele FREquency Database (http://alfred.med.yale.edu, ALFRED) and analyzed the global genetic variation patterns from the HapMap database (Gibbs et al., 2003).

2.5. Population genetic structures analysis

In view of the genetic structure of human populations, we used Structure 2.3.4 (Pritchard Lab, Stanford University, USA) (http://pritchardlab.stanford.edu/software/structure_v.2.3.4.html) to observe the variation of the selected VIP variants. On the basis of the Bayesian clustering algorithm approach, we performed structural analysis to assign the samples within a hypothetical K number of populations hypothesized by Pritchard, Stephens, and Donnelly (2000). The MCMC analyses for each structure analysis (K = 3–10) was run for 10,000 steps after an initial burn‐in period of 10,000 steps. And we used △K to calculate to identify the most likely number of clusters by STRUCTURE HARVESTER (Evanno, Regnaut, & Goudet, 2005). Moreover, Wright's F‐statistics is the most widely used descriptive statistics in population and evolutionary genetics. (Wright, 1931). We used the program Arlequin version 3.1 to calculate the Fst values to deduce the pairwise distance between populations. Besides, neighbor‐joining method was used to group them in several clusters based on the genetic distance.

3. RESULTS

3.1. Basic information of the VIP variants

We selected 80 VIP variants from PharmGKB database in 100 members of the Blang population.

The selected single‐nucleotide polymorphisms (SNPs) of PCR primers (listed in Table S1) were designed by the Sequenom MassARRAY Assay Design 3.0 Software. The basic information of the selected variants has been shown in Table 1, including the genes name, their positions, the nucleotide change, the amino acid translation, the allele frequencies, and the genotype frequencies of Blang and the like.

Table 1.

Basic information of selected VIP variants

SNP Gene Full name Chr Allele Position Amino Acid Translation Function Allele A Allele B Blang HWE
A B AA AB BB
rs1045642 ABCB1 ATP binding cassette subfamily B member 1 chr7 A G 87,138,645 Ile1145Ile Synonymous 0.335 0.665 12 43 45 0.941
rs1128503 ABCB1 ATP binding cassette subfamily B member 1 chr7 A G 87,179,601 Gly412Gly Synonymous 0.590 0.410 36 46 18 0.886
rs2032582 ABCB1 ATP binding cassette subfamily B member 1 chr7 A C 87,160,618 Ser893Ala Missense 0.378 0.622 11 43 32 0.841
rs975833 ADH1A alcohol dehydrogenase 1A (class I), alpha polypeptide chr4 G C 100,201,739 — Intronic 0.365 0.635 11 51 38 0.605
rs1229984 ADH1B alcohol dehydrogenase 1B (class I), beta polypeptide chr4 T C 100,239,319 His48Arg Missense 0.035 0.965 0 7 93 0.936
rs2066702 ADH1B alcohol dehydrogenase 1B (class I), beta polypeptide chr4 G A 100,229,017 Arg370Cys Missense 1.000 0.000 100 0 0 —
rs1801253 ADRB1 adrenoceptor beta 1 chr10 G C 115,805,056 Gly389Arg Missense 0.350 0.650 14 40 43 0.65
rs1042713 ADRB2 adrenoceptor beta 2 chr5 G A 148,206,440 Arg16Gly Missense 0.395 0.605 10 59 31 0.064
rs1042714 ADRB2 adrenoceptor beta 2 chr5 G C 148,206,473 Gln27Glu Missense 0.050 0.950 0 10 90 0.87
rs1800888 ADRB2 adrenoceptor beta 2 chr5 C T 148,206,885 Thr164Ile Missense 1.000 0.000 100 0 0 —
rs2066853 AHR aryl hydrocarbon receptor chr7 G A 17,379,110 Arg554Lys Missense 0.845 0.155 73 23 4 0.475
rs6151031 ALDH1A1 aldehyde dehydrogenase 1 family member A1 chr9 — CTGGTGAGG
AGAGAACC 		72,953,467 — — 0.953 0.047 87 9 0 0.89
rs1800497 ANKK1 ankyrin repeat and kinase domain containing 1 chr11 G A 113,270,828 Glu713Lys Missense 0.720 0.280 54 36 10 0.563
rs4680 COMT catechol‐O‐methyltransferase chr22 G A 19,951,271 Val158Met Missense 0.860 0.140 72 28 0 0.266
rs1801272 CYP2A6 cytochrome P450 family 2 subfamily A member 6 chr19 A T 41,354,533 Leu160His Missense 0.000 1.000 0 0 100 —
rs28399433 CYP2A6 cytochrome P450 family 2 subfamily A member 6 chr19 G T 41,356,379 — — 0.200 0.800 4 32 64 1
rs28399444 CYP2A6 cytochrome P450 family 2 subfamily A member 6 chr19 G A 41,354,190 — Frameshift 0.000 1.000 0 0 100 —
rs28399454 CYP2A6 cytochrome P450 family 2 subfamily A member 6 chr19 C T 41,351,267 Val365Met Missense 1.000 0.000 100 0 0 —
rs28399499 CYP2B6 cytochrome P450 family 2 subfamily B member 6 chr19 T C 41,518,221 Ile328Thr Missense 1.000 0.000 100 0 0 —
rs3745274 CYP2B6 cytochrome P450 family 2 subfamily B member 6 chr19 G T 41,512,841 Gln172His Missense 0.485 0.515 21 55 24 0.601
rs4986893 CYP2C19 cytochrome P450 family 2 subfamily C member 19 chr10 A G 96,540,410 Trp212null Stop Codon 0.025 0.975 0 5 95 0.968
rs1799853 CYP2C9 cytochrome P450 family 2 subfamily C member 9 chr10 C T 96,702,047 Arg144Cys Missense 1.000 0.000 100 0 0 —
rs16947 CYP2D6 cytochrome P450 family 2 subfamily D member 6 chr22 A G 42,523,943 Arg296Cys Missense 0.210 0.790 0 42 58 0.029
rs28371706 CYP2D6 cytochrome P450 family 2 subfamily D member 6 chr22 G A 42,525,772 Thr107Ile Missense 1.000 0.000 100 0 0 —
rs28371725 CYP2D6 cytochrome P450 family 2 subfamily D member 6 chr22 A G 42,523,805 — Intronic 0.130 0.870 1 24 75 0.83
rs5030656 CYP2D6 cytochrome P450 family 2 subfamily D member 6 chr22 — AAG 42,128,174 deletes K281 Non‐synonymous 0.000 1.000 0 0 100 —
rs59421388 CYP2D6 cytochrome P450 family 2 subfamily D member 6 chr22 C T 42,523,610 Val338Met Missense 1.000 0.000 100 0 0 —
rs61736512 CYP2D6 cytochrome P450 family 2 subfamily D member 6 chr22 C T 42,525,134 Val136Met Missense 1.000 0.000 100 0 0 —
rs12721634 CYP3A4 cytochrome P450 family 3 subfamily A member 4 chr7 C T 99,381,661 Leu15Pro Missense 0.000 1.000 0 0 100 —
rs2740574 CYP3A4 cytochrome P450 family 3 subfamily A member 4 chr7 A G 99,382,096 — — 1.000 0.000 100 0 0 —
rs4986909 CYP3A4 cytochrome P450 family 3 subfamily A member 4 chr7 G A 99,359,670 Pro415Leu Missense 1.000 0.000 100 0 0 —
rs4986910 CYP3A4 cytochrome P450 family 3 subfamily A member 4 chr7 A G 99,358,524 Met444Thr Missense 1.000 0.000 100 0 0 —
rs4986913 CYP3A4 cytochrome P450 family 3 subfamily A member 4 chr7 G A 99,358,459 Pro466Ser Missense 1.000 0.000 100 0 0 —
rs10264272 CYP3A5 cytochrome P450 family 3 subfamily A member 5 chr7 C T 99,262,835 Lys208Lys Synonymous 1.000 0.000 100 0 0 —
rs3918290 DPYD dihydropyrimidine dehydrogenase chr1 C T 97,915,614 — Splice acceptor 1.000 0.000 100 0 0 —
rs6277 DRD2 dopamine receptor D2 chr11 G A 113,283,459 Pro319Pro Synonymous 0.975 0.025 95 5 0 0.968
rs1138272 GSTP1 glutathione S‐transferase pi 1 chr11 C T 67,353,579 Ala114Val Missense 1.000 0.000 100 0 0 —
rs1695 GSTP1 glutathione S‐transferase pi 1 chr11 A G 67,352,689 Ile105Val Missense 0.740 0.260 55 38 7 0.992
rs17238540 HMGCR 3‐hydroxy−3‐methylglutaryl‐CoA reductase chr5 G T 74,655,498 — Splice acceptor 0.000 1.000 0 0 100 —
rs17244841 HMGCR 3‐hydroxy−3‐methylglutaryl‐CoA reductase chr5 A T 74,642,855 — Intronic 0.929 0.071 87 8 3 0.001
rs3846662 HMGCR 3‐hydroxy−3‐methylglutaryl‐CoA reductase chr5 A G 74,651,084 — Intronic 0.465 0.535 22 48 29 0.968
rs12720441 KCNH2 potassium voltage‐gated channel subfamily H member 2 chr7 G A 150,647,304 Arg784Trp Missense 1.000 0.000 100 0 0 —
rs36210421 KCNH2 potassium voltage‐gated channel subfamily H member 2 chr7 G T 150,644,428 Arg1047Leu Missense 1.000 0.000 99 0 0 —
rs3807375 KCNH2 potassium voltage‐gated channel subfamily H member 2 chr7 C T 150,667,210 — Intronic 0.200 0.800 6 28 66 0.458
rs1801131 MTHFR methylenetetrahydrofolate reductase chr1 T G 11,854,476 Glu429Ala Missense 0.795 0.205 65 29 6 0.544
rs1801133 MTHFR methylenetetrahydrofolate reductase chr1 G A 11,856,378 Ala222Val Missense 0.770 0.230 62 30 8 0.31
rs1800566 NQO1 NAD(P)H quinone dehydrogenase 1 chr16 G A 69,711,242 Pro187Ser Missense 0.595 0.405 32 55 13 0.369
rs3814055 NR1I2 nuclear receptor subfamily 1 group I member 2 chr3 C T 119,500,035 — 5'‐UTR 0.940 0.060 88 12 0 0.816
rs1065776 P2RY1 purinergic receptor P2Y1 chr3 C T 152,553,628 Ala19Ala Synonymous 0.875 0.125 64 19 1 0.953
rs701265 P2RY1 purinergic receptor P2Y1 chr3 A G 152,554,357 Val262Val Synonymous 0.695 0.305 45 49 6 0.297
rs2046934 P2RY12 purinergic receptor P2Y12 chr3 G A 151,057,642 — Intronic 0.085 0.915 0 17 83 0.65
rs5629 PTGIS prostaglandin I2 synthase chr20 G T 48,129,706 Arg373Arg Synonymous 0.885 0.115 77 23 0 0.43
rs689466 PTGS2 prostaglandin‐endoperoxide synthase 2 chr1 T C 186,650,751 — — 0.596 0.404 36 46 17 0.941
rs1805124 SCN5A sodium voltage‐gated channel alpha subunit 5 chr3 T C 38,645,420 His558Arg Missense 0.890 0.110 81 16 3 0.188
rs6791924 SCN5A sodium voltage‐gated channel alpha subunit 5 chr3 G A 38,674,699 Arg34Cys Missense 1.000 0.000 100 0 0 —
rs7626962 SCN5A sodium voltage‐gated channel alpha subunit 5 chr3 T G 38,620,907 Ser1103Tyr Missense 0.000 1.000 0 0 100 —
rs1051266 SLC19A1 solute carrier family 19 member 1 chr21 T C 46,957,794 His27Arg Missense 0.436 0.564 13 56 25 0.123
rs12659 SLC19A1 solute carrier family 19 member 1 chr21 C T 46,951,556 Pro232Pro Synonymous 0.556 0.444 25 59 14 0.094
rs4149056 SLCO1B1 solute carrier organic anion transporter family member 1B1 chr12 T C 21,331,549 Val174Ala Missense 0.965 0.035 93 7 0 0.936
rs1801030 SULT1A1 sulfotransferase family 1A member 1 chr16 C T 28,617,485 Val 223Met Missense 0.000 1.000 0 0 100 —
rs3760091 SULT1A1 sulfotransferase family 1A member 1 chr16 G C 28,609,479 — Intronic 0.355 0.645 6 59 35 0.016
rs1142345 TPMT thiopurine S‐methyltransferase chr6 T C 18,130,918 Tyr240Cys Missense 0.985 0.015 95 3 0 0.988
rs1800460 TPMT thiopurine S‐methyltransferase chr6 A G 18,139,228 Ala154Thr Missense 0.000 1.000 0 0 100 —
rs1800462 TPMT thiopurine S‐methyltransferase chr6 C G 18,143,955 Ala80Pro Missense 0.000 1.000 0 0 98 —
rs34489327 TS thymidylate synthetase chr18 Del — 3'‐UTR 1.000 0.000 100 0 0 —
rs10929302 UGT1A1 UDP glucuronosyltransferase family chr2 G A 234,665,782 — Intronic 0.880 0.120 78 20 2 0.869
rs4124874 UGT1A1 UDP glucuronosyltransferase family 1 member A1 chr2 T G 234,665,659 — Intronic 0.530 0.470 32 42 26 0.292
rs4148323 UGT1A1 UDP glucuronosyltransferase family 1 member A1 chr2 G A 234,669,144 Gly71Arg Missense 0.845 0.155 71 27 2 0.954
rs10735810 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 A G 48,272,895 Met1Thr Missense 0.571 0.429 28 57 14 0.22
rs11568820 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 C T 48,302,545 — — 0.196 0.804 4 21 49 0.694
rs1540339 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 C T 48,257,326 — Intronic 0.340 0.660 13 42 45 0.814
rs1544410 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 C T 48,239,835 — Intronic 0.975 0.025 94 5 0 0.967
rs2228570 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 T C 48,272,895 Met1Thr Missense 0.575 0.425 29 57 14 0.251
rs2239179 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 T C 48,257,766 — Intronic 0.000 0.000 0 0 0 —
rs2239185 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 G A 48,244,559 — Intronic 0.695 0.305 43 53 4 0.044
rs731236 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 A G 48,238,757 Ile352Ile Synonymous 0.975 0.025 95 5 0 0.968
rs7975232 VDR vitamin D (1,25‐dihydroxyvitamin D3) receptor chr12 C A 48,238,837 — Intronic 0.695 0.305 43 53 4 0.044
rs7294 VKORC1 vitamin K epoxide reductase complex subunit 1 chr16 C T 31,102,321 — 3'‐UTR 0.874 0.126 75 23 1 0.87
rs9923231 VKORC1 vitamin K epoxide reductase complex subunit 1 chr16 A C 31,096,368 — — 1.000 0.000 100 0 0 —
rs9934438 VKORC1 vitamin K epoxide reductase complex subunit 1 chr16 G A 31,104,878 — Intronic 0.125 0.875 1 23 76 0.876
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SNP: single‐nucleotide polymorphism; HWE: Hardy–Weinberg equilibrium. The GenBank reference of the above genes were as follows: ABCB1 (NC_000007.14), ADH1A (NC_000004.12), ADH1B (NC_000004.12), ADRB1 (NC_000010.11), ADRB2 (NC_000005.10), AHR (NC_000007.14), ALDH1A1 (NC_000009.12), ANKK1 (NC_000011.10), COMT (NC_000022.11), CYP2A6 (NC_000019.10), CYP2B6 (NC_000019.10), CYP2C19 (NC_000010.11), CYP2C9 (NC_000010.11), CYP2D6 (NC_000022.11), CYP3A4 (NC_000007.14), CYP3A5 (NC_000007.14), DPYD (NC_000001.11), DRD2 (NC_000011.10), GSTP1 (NC_000011.10), HMGCR (NC_000005.10), KCNH2 (NC_000007.14), MTHFR (NC_000001.11), NQO1 (NC_000016.10), NR1I2 (NC_000003.12), P2RY1 (NC_000003.12), P2RY12 (NC_000003.12), PTGIS (NC_000020.11), PTGS2 (NC_000001.11), SCN5A (NC_000003.12), SLC19A1 (NC_000021.9), SLCO1B1 (NC_000012.12), SULT1A1 (NC_000016.10), TPMT (NC_000006.12), TS (NC_000018.10), UGT1A1 (NC_000002.12), VDR (NC_000012.12), VKORC1 (NC_000016.10).

3.2. Analyses of 80 loci among 12 populations

The average variants call rate of the results was over 95%. All selected loci meet the HWE. Using chi‐square test, we compared the Blangs and the 11 populations of the genotype frequencies distribution of 80 loci. Before adjustment (p < 0.05), we found that some loci were different (not shown). When compared to the 11 groups (ASW, CEU, CHB, CHD, GIH, JPT, LWK, MEX, MKK, TSI, and YRI) and Blang without adjustment, the number of significantly different variants in the Blang population was 23, 30, 17, 30, 30, 21, 26, 21, 25, 22, and 35, respectively (data no shown). After adjustment (p < [0.05/(80 × 11)], listed in Table 2), there were 15, 20, 6, 25, 25, 7, 19, 7, 20, 15, and 26 loci of significant differences between Blang and the 11 populations, respectively. While there were contrasts in the two sets of data, there were also similarities. It was also noteworthy that the different loci between CHB and the Blang were the least.

Table 2.

Significant VIP variants in the Blangs compared with the 11 populations after Bonferroni's multiple adjustment

SNP ID Gene p < 0.05/（80*11）
ASW CEU CHB CHD GIH JPT LWK MEX MKK TSI YRI
rs1045642 ABCB1 0.059 2.873E−06 0.277 0.024 0.292 0.022 — 0.076 2.808E−05 0.042 4.723E−07
rs1128503 ABCB1 2.072E−09 0.005 0.072 — 2.890E−10 0.974 2.319E−17 0.070 5.876E−19 0.009 7.463E−19
rs2032582 ABCB1 1.486E−06 0.161 0.001 — — 0.003 5.000E−16 0.668 2.232E−12 0.465 —
rs975833 ADH1A — 3.544E−08 0.001 — — 0.011 — — — — 9.068E−09
rs1229984 ADH1B — — 3.393E−25 — — 9.317E−25 — — — — —
rs2066702 ADH1B 1.065E−10 — — 1.056E−19 2.444E−05 — 4.081E−07 — — — 5.646E−15
rs1801253 ADRB1 — 0.785 0.217 — — 0.004 — — — — 0.365
rs1042713 ADRB2 0.258 4.559E−07 0.481 — — 0.001 0.028 0.011 0.097 1.166E−07 0.003
rs1042714 ADRB2 — 1.530E−12 — 8.438E−14 9.243E−23 0.305 — — — — 0.002
rs1800888 ADRB2 — — — — — — — — — — —
rs2066853 AHR 1.696E−05 0.181 1.202 E−06 — — 5.580E−09 1.516E−10 0.358 2.034E−06 0.085 5.504E−09
rs6151031 ALDH1A1 — — — — — — — — — — —
rs1800497 ANKK1 0.107 0.144 0.025 0.000 0.077 0.031 0.129 0.024 0.115 0.261 0.012
rs4680 COMT 0.002 2.205E−11 0.000 — — 0.001 0.000 2.862E−06 0.001 2.593E−10 0.000
rs1801272 CYP2A6 — 1.805E−35 — 6.726E−41 7.698E−40 5.380E−32 — — — — —
rs28399433 CYP2A6 — — — — — — — — — — —
rs28399444 CYP2A6 — — — — — — — — — — —
rs28399454 CYP2A6 — — — — — — — — — — —
rs28399499 CYP2B6 0.000 — — — — — — — 0.168 — 2.037E−06
rs3745274 CYP2B6 0.000 1.314E−06 1.166E−10 — — 2.002E−10 0.000 0.001 0.007 2.326E−05 0.100
rs4986893 CYP2C19 — — — — — — — — — — —
rs1799853 CYP2C9 — — — — — — — — — — —
rs16947 CYP2D6 — — — 0.248 0.003 — — — — — —
rs28371706 CYP2D6 — — — — — — — — — — —
rs28371725 CYP2D6 — — — — — — — — — — —
rs5030656 CYP2D6 — — — 2.373E−13 7.203E−13 — — — — — —
rs59421388 CYP2D6 — — — — — — — — — — —
rs61736512 CYP2D6 — — — — — — — — — — —
rs12721634 CYP3A4 — — — 9.801E−37 3.439E−16 — — — — — —
rs2740574 CYP3A4 — — — — — — — — — — —
rs4986909 CYP3A4 — — — — — — — — — — —
rs4986910 CYP3A4 — — — — — — — — — — —
rs4986913 CYP3A4 — — — — — — — — — — —
rs10264272 CYP3A5 — — — 7.700E−31 2.008E−21 — 1.445E−12 — 8.823E−08 — 8.948E−09
rs3918290 DPYD — — — 3.132E−18 1.273E−33 — — — — — —
rs6277 DRD2 — 1.663E−22 — — — — — — — — —
rs1138272 GSTP1 — — — — — — — 0.001 — — —
rs1695 GSTP1 0.003 0.003 0.231 — — 0.000 1.935E−06 1.304E−05 0.079 0.514 0.014
rs17238540 HMGCR — — — — — — — — — — —
rs17244841 HMGCR — — — — — — — — — — —
rs3846662 HMGCR 1.111E−07 0.084 0.994 — 1.222E−23 0.607 3.482E−18 0.030 2.452E−11 0.257 8.429E−20
rs12720441 KCNH2 — — — — — — — — — — —
rs36210421 KCNH2 — — — 2.249E−07 2.963E−20 — — — — — —
rs3807375 KCNH2 0.042 5.814E−16 0.172 3.093E−18 1.580E−11 0.165 0.619 0.001 0.048231 1.349E−15 0.627
rs1801131 MTHFR 0.458 0.006 0.439 0.013 0.448 0.440 0.688 0.753 0.308 0.066 0.035
rs1801133 MTHFR 0.013 0.076 1.559E−05 — — 0.009 0.002 0.002 0.000 0.000 0.001
rs1800566 NQO1 0.001 2.084E−06 0.135 — — 0.403 3.384E−06 0.218 3.058E−08 0.000 4.510E−06
rs3814055 NR1I2 1.029E−07 9.604E−11 2.269E−07 1.593E−19 1.270E−25 1.087E−06 1.235E−07 1.475E−07 3.575E−06 8.035E−13 1.283E−07
rs1065776 P2RY1 — — — 2.296E−11 2.388E−19 — — — — — —
rs701265 P2RY1 1.620E−09 0.007 0.293 5.222E−08 3.034E−13 0.266 6.247E−19 0.052 8.827E−19 0.001 1.022E−20
rs2046934 P2RY12 — 0.001 0.010 — — 0.020 — — — — 0.004
rs5629 PTGIS 0.124 0.006 0.003 — — 0.008 — 0.001 0.701 9.651E−07 0.408
rs689466 PTGS2 1.039E−06 1.169E−06 0.175 0.007 0.001 0.339 4.422E−15 0.029 5.0037E−21 1.203E−05 1.306E−11
rs1805124 SCN5A 0.003 0.008 0.194 1.046E−15 1.110E−07 0.082 0.000 0.334 2.198E−08 0.004 1.258E−06
rs6791924 SCN5A — — — 2.606E−22 1.626E−07 — — — — — —
rs7626962 SCN5A — — — 4.880E−15 7.055E−29 — — — — — 0.001
rs1051266 SLC19A1 0.531 0.818 0.056 — — 0.018 8.244E−09 0.059 9.767E−13 0.181 1.011E−07
rs12659 SLC19A1 — — — — — — — — — — —
rs4149056 SLCO1B1 0.382 0.000 0.000 4.768E−12 3.391E−15 0.024 — — 0.007 4.259E−07 —
rs1801030 SULT1A1 — — — 5.982E−36 3.565E−30 — — — — — —
rs3760091 SULT1A1 — — — 1.349E−12 0.255 — — — — — —
rs1142345 TPMT — — — 1.031E−15 8.286E−19 — 0.001 0.034 — — 0.269
rs1800460 TPMT — — — — — — — — — — —
rs1800462 TPMT — — — 2.714E−22 1.166E−12 — — — — — —
rs34489327 TS — — — — — — — — — — —
rs10929302 UGT1A1 — 0.002 0.592 2.608E−05 4.574E−09 0.610 — — — — 1.298E−05
rs4124874 UGT1A1 8.170E−06 0.630 0.0002 2.226E−07 3.725E−18 0.029 1.136E−13 0.832 1.947E−13 0.277 1.265E−17
rs4148323 UGT1A1 — 2.428E−05 0.108 0.536 0.000 0.769 — 0.007 — — 2.428E−05
rs10735810 VDR 1.259E−10 0.001 0.003 — — 4.321E−07 1.916E−15 0.299 3.465E−15 0.000 2.414E−14
rs11568820 VDR 0.133 2.969E−22 4.962E−08 — — 1.086E−07 0.061 8.376E−15 0.618 7.171E−18 5.433E−07
rs1540339 VDR 1.172E−09 1.135E−07 0.520 6.552E−26 1.452E−05 0.284 4.490E−19 0.000 2.537E−20 1.931E−07 1.484E−16
rs1544410 VDR — 9.321E−19 — — — — 1.521E−09 3.563E−08 1.478E−16 2.207E−17 1.580E−11
rs2228570 VDR — — — 1.814E−11 0.110 — — — — — —
rs2239179 VDR — — — — — — — — — — —
rs2239185 VDR — — 0.027 — — 0.161 — — — — 8.001E−06
rs731236 VDR 3.289E−08 6.439E−19 — — — — 1.059E−09 7.603E−09 4.993E−24 1.560E−17 1.221E−12
rs7975232 VDR 1.511E−08 1.887E−08 0.040 — — 0.127 1.767E−15 0.016 1.807E−14 1.129E−08 9.107E−11
rs7294 VKORC1 4.496E−11 2.436E−07 0.051 0.014 0.000 0.553 1.328E−10 0.000 3.526E−14 4.204E−06 9.144E−15
rs9923231 VKORC1 — — — 1.109E−40 1.328E−10 — — — — — —
rs9934438 VKORC1 1.112E−25 6.971E−18 0.055 — — 0.551 1.212E−34 1.623E−11 6.354E−37 9.903E−14 1.557E−42
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SNP: single‐nucleotide polymorphism; HWE: Hardy–Weinberg equilibrium. ASW, a population of African ancestry in the southwestern USA; CEU, a residents population in Utah with Northern and Western European Ancestry; CHB, the Chinese Han in Beijing, China; CHD, the population of metropolitan Denver, Colorado, USA; GIH, the Gujarati Indians in Houston, Texas, USA; JPT, the Japanese population in Tokyo, Japan; LWK, the Chinese living in Luhya in Webuye, Kenya; MEX, people with Mexican ancestry living in Los Angeles, California, USA; MKK, the Maasai people in Kinyawa, Kenya; TSI, the Tuscan people of Italy; YRI, the Yoruba in Ibadan, Nigeria. The GenBank reference of the above genes were as follows: ABCB1 (NC_000007.14), ADH1A (NC_000004.12), ADH1B (NC_000004.12), ADRB1 (NC_000010.11), ADRB2 (NC_000005.10), AHR (NC_000007.14), ALDH1A1 (NC_000009.12), ANKK1 (NC_000011.10), COMT (NC_000022.11), CYP2A6 (NC_000019.10), CYP2B6 (NC_000019.10), CYP2C19 (NC_000010.11), CYP2C9 (NC_000010.11), CYP2D6 (NC_000022.11), CYP3A4 (NC_000007.14), CYP3A5 (NC_000007.14), DPYD (NC_000001.11), DRD2 (NC_000011.10), GSTP1 (NC_000011.10), HMGCR (NC_000005.10), KCNH2 (NC_000007.14), MTHFR (NC_000001.11), NQO1 (NC_000016.10), NR1I2 (NC_000003.12), P2RY1 (NC_000003.12), P2RY12 (NC_000003.12), PTGIS (NC_000020.11), PTGS2 (NC_000001.11), SCN5A (NC_000003.12), SLC19A1 (NC_000021.9), SLCO1B1 (NC_000012.12), SULT1A1 (NC_000016.10), TPMT (NC_000006.12), TS (NC_000018.10), UGT1A1 (NC_000002.12), VDR (NC_000012.12), VKORC1 (NC_000016.10).

Bold type indicates that the locus has statistically significant.

However, through a comparison of before and after adjustment, the distribution of rs1801133 (HGVS: NM_001330358.1:c.788C>T) and rs4680 (HGVS: NM_000754.3:c.472G>A) in populations has changed. After correction for multiple tests, rs1801133 became less significant in ASW, JPT, LWK, MEX, MKK, TSI, YRI, except CHB. Besides, rs4680 were detected significant differences between CEU, MEX, TSI, and Blang. In the populations of ASW, CHB, JPT, LWK, MKK, and YRI, its differences disappeared. Nonetheless, some variants varied little, not even a bit, such as rs11568820, rs1544410, and so forth.

After analysis of Table 2, significant variants in some genes were distributed in every population, such as VDR and NR1I2. There were rs10735810, rs11568820, rs1540339, rs1544410, rs2228570, rs2239179, rs2239185, rs731236, and rs7975232 distributed in VDR (vitamin D receptor), which encodes the nuclear hormone receptor for vitamin D3. Although failing to make amino acid changed, rs1540339 was also very significant among the nine populations except CHB, JPT, and MEX. Although rs2228570 (HGVS: NM_000376.2:c.2 T>G) was, the only one SNP changing amino acid, located in exon 2 of VDR, it was still prominent in the CHD.

Although rs3814055 in NR1I2 changed little, significant differences still existed. We downloaded the associated data of rs3814055 from the website (http://alfred.med.yale.edu). As seen from the Figures 1 and 2, the frequency of the Blangs was closer to the populations distributed in East Asia, especially Miao. On the whole, the frequencies of the allele C of rs3814055, ranged from 67% to 94%, were higher in East Asia than the other populations. The Blang population was the highest among them, so attention should be paid to its allele C.

Figure 1.

Figure 1

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The frequencies of rs3814055 in the different populations. NA, North America; SA, South America; S, Siberia; O, Oceania

Figure 2.

Figure 2

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Rs3814055 frequencies in different populations of the world. NA, North America; SA, South America; S, Siberia; O, Oceania

3.3. The relationship between 23 populations

We used Structure 2.3.1 Software to analyze the genetic structure of the 23 populations in order to further identify the relationships between them throughout the world. Different K values ranging from 2 to 10 were hypothetically in structure analysis. And, the results of K = 2,3 among global populations and the results of K = 3,4 ethnic groups from China were shown in Figure 3. The cluster analysis indicated that when K = 3, the group was divided into three subgroups (subgroups 1: Blang, CHB, CHD, JPT, SX Han; subgroups 2: CEU, GIH, MEX, TSI, Deng, Sherpa, Lhoba, Kyrgyz, Tajik, Uygur; subgroups 3: ASW, LWK, MKK, YRI, Miao, Li, Tibet, Mongol) based on relative majority of likelihood to assign individuals to subgroups. The results illustrated that Blang had a relatively closer affinity with CHB, CHD, and JPT. In accordance with the Table 2, the results were confirmed. Likewise, when comparing ethnic groups within China, we found that Blang was closer to SX Han.

Figure 3.

Figure 3

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Analysis the genetic structure between Blang and the 23 populations. K denotes the possible numbers of parental population clusters. Each vertical bar represents a person, dividing into color sections. K = 2, 3 were used to assess the genetic relationship between Blang and 11 global populations. And the genetic relationship between 11 ethnic groups from China and Blang were evaluated by K = 3, 4. ASW: ASW: a population of African ancestry in southwestern USA; CEU: a residents population in Utah with Northern and Western European Ancestry; CHB: the Chinese Han in Beijing, China; CHD: Chinese in Metropolitan Denver, Colorado, USA; GIH: Gujarati Indians in Houston, Texas, USA; JPT: Japanese in Tokyo, Japan; LWK: Luhya people in Webuye, Kenya; MEX: people with Mexican ancestry in Los Angeles, California, USA; MKK: Maasai people in Kinyawa, Kenya; TSI: Toscans in Italy; YRI: Yoruba in Ibadan, Nigeria; SX Han, Shaanxi Han. A: Comparing the Blangs with the other 11 populations from the International HapMap Project, Blang was closer to CHB, CHD, and JPT. B: The Han nationality in Shaanxi was very close to the Blangs within China

Based on genetic structure, we further assessed the genetic relationship among 12 populations by using pairwise Fst values (Table 3). As mentioned in it, it was clear that the differences between CHB, CHD, JPT, and Blang (Fst = 0.04728, 0.04259, and 0.04914, respectively) were smaller. The smaller the Fst value, the more similar they were. The results indicated that the Blang and the other three groups had a relatively closer affinity, followed by MEX. As presented by the phylogenetic tree (Figure 4) about 12 populations in the same Fst‐based way, the results were verified again.

Table 3.

Fst values among 12 populations

BuL CHB CHD JPT CEU GIH MEX TSI ASW LWK MKK YRI
BuL 0
CHB 0.04728 0
CHD 0.04259 −0.00161 0
JPT 0.04914 0.00586 0.00761 0
CEU 0.14462 0.13026 0.12708 0.11499 0
GIH 0.15465 0.15697 0.15321 0.14338 0.03311 0
MEX 0.09721 0.08424 0.07821 0.08033 0.02248 0.05258 0
TSI 0.14058 0.11524 0.11626 0.10172 0.00012 0.04047 0.02447 0
ASW 0.17273 0.1955 0.19394 0.17125 0.12124 0.08173 0.11144 0.12461 0
LWK 0.23967 0.26654 0.26764 0.23703 0.18539 0.14618 0.18563 0.19061 0.01719 0
MKK 0.20378 0.23189 0.23406 0.19985 0.13638 0.10553 0.15181 0.14253 0.01888 0.01336 0
YRI 0.23439 0.26827 0.27045 0.23703 0.19138 0.14351 0.19235 0.1978 0.01513 0.00383 0.01359 0
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ASW, a population of African ancestry in the southwestern USA; CEU, a residents population in Utah with Northern and Western European Ancestry; CHB, the Chinese Han in Beijing, China; CHD, the population of metropolitan Denver, Colorado, USA; GIH, the Gujarati Indians in Houston, Texas, USA; JPT, the Japanese population in Tokyo, Japan; LWK, the Chinese living in Luhya in Webuye, Kenya; MEX, people with Mexican ancestry living in Los Angeles, California, USA; MKK, the Maasai people in Kinyawa, Kenya; TSI, the Tuscan people of Italy; YRI, the Yoruba in Ibadan, Nigeria.

Figure 4.

Figure 4

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The phylogenetic tree was reconstructed by the neighboring‐joining method among 12 populations

4. DISCUSSION

There is increasing interested in personalized medicine, because of genetic variations leading to each person's different metabolism of and reactions to some drugs. In our results, we genotyped the pharmacogenomic VIP variants in the Blang population. The conclusion was that that NR1I2 rs3814055 was the most significant variant among the 12 selected populations, followed by VDR rs1540339. Using genetic structure analysis and Fst values, we also concluded that the genetic backgrounds of the Blang were similar to CHB.

Pregnane X, encoded by the gene NR1I2, belongs to the nuclear hormone receptor superfamily, whose major role is to promote the detoxification and clearance of drugs and toxic xenobiotics from the body as a transcription factor (Bertilsson et al., 1998). And some CYPs (Ding et al., 2015; Jin, Zhang, Shi et al., 2016a; Jin, Zhang, Geng et al., 2016b; Shan et al., 2016; Zhang et al., 2016) regulated by PXR/NR1I2 were associated with phase I metabolism in human. Moreover, some studies (Lown et al., 1997; Shimada, Yamazaki, Mimura, Inui, & Guengerich, 1994) illustrated that SNPs in PXR may be a main reason to the differences in drug reactions and the induction of CYP3A4. Rs3814055, localized in the 5’ untranslated region (UTR) of NR1I2, has already attracted the attention of many researchers, for both disease risk and pharmacogenomics impact. Numerous studies showed that the frequency of rs3814055 in the NR1I2 gene varied according to different populations. The frequency of this variation in a Chinese Han population was 0.218 (Wang et al., 2007), 0.39 for Caucasians (Zhang et al., 2013), 0.21 for Asians (King et al., 2007), 0.50 for Europeans (King et al., 2007), 0.36 for the Dutch (Bosch et al., 2006), and 0.34 for African Americans (Thomas et al., 2007). In our previous studies, the frequency of the rs3814055 SNP variant in the Lhoba population and in the Miao population were 0.101 and 0.09 (He et al., 2015; Jin, Aikemu et al., 2015a), respectively. In our study about the Blangs, the allele T frequency of rs3814055 was 0.06 (Figures 1 and 2). In a Chinese Han Population, upregulated CYP3A4 expression was due to the frequency of rs3814055 (−25,385 T) (Zhang et al., 2001), demonstrating that it was similar to that of Lhoba and Miao. Yet it was still lower than the other populations. Additionally, another report has shown that the allele C linked to Inflammatory Bowel Diseases (IBD) in a European population (Martínez et al., 2010). However, the haplotype TCC of rs3814055/rs6784598/rs2276707 functioned as a whole in risk assessment for ulcerative colitis (UC) in Spanish population. In addition, Kurzawski M et al revealed that there were significant differences in tacrolimus concentrations between patients with different NR1I2 rs3814055: C > T genotypes (Kurzawski, Malinowski, Dziewanowski, & Drozdzik, 2017). And Zazuli et al. (2015) found that, in Indonesian patients with tuberculosis, the TT genotype of rs3814055 had a significantly greater risk of antituberculosis drug‐induced liver injury than those of CC genotype.

The SNP rs1540339 is situated in the intron region of VDR. Previous studies have demonstrated that rs1540339 was related to the susceptibility of type 1 diabetes mellitus (T1DM) (Wang et al., 2014), colorectal cancer (Wang, Li, & Zhou2008b), and so on. The other study drew the same conclusion that the variant involved in T1DM prevention (Wang, Li et al., 2008b). Jin TB et al. reported that the frequency of rs1540339 T in the Li population was higher than the allele C, indicating that the Li group had lower sensitivity to T1DM. In our study, the allele frequencies of rs1540339 C/T in the Blang were 34% and 66%, respectively. So we guess that the Blang may have lower susceptibility to T1DM.

Considering the above results, ethnicity is an important factor for the frequency distribution and the genotype of rs3814055 can be used as a marker for detecting IBD and UC. And the Blang may have a lower susceptibility to T1DM. Although rs1540339 has not been found to be relevant in the Blang, it is noteworthy in future studies. At present, there are more teams, including Jin TB et al., devoted to disease research of SNPs (Du et al., 2016; Duan et al., 2015; Hu et al., 2014; Wang et al., 2015; Yang et al., 2016), and we hope that our data will complement the pharmacogenomics database and provide some help for the development of personalized medicine.

DISCLOSURE

The authors have no conflicts of interest to declare.

Supporting information

 

Click here for additional data file. (31.1KB, docx)

ACKNOWLEDGMENTS

This work is supported by the study of genetic background difference about cytochrome P450 enzyme (CYP450) in Wa and Blang populations from Yunnan province (No. 2017FE468(‐125)). We are very grateful to those who participated in this manuscript, including personnel and related facilities of the First People’s Hospital of Yunnan Province.

Zhang C, Guo W, Cheng Y, et al. Genetic analysis of pharmacogenomic VIP variants in the Blang population from Yunnan Province of China. Mol Genet Genomic Med. 2019;7:e574 10.1002/mgg3.574

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