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. 2019 Jul 30;7(9):e884. doi: 10.1002/mgg3.884

Genetic polymorphisms analysis of pharmacogenomic VIP variants in Bai ethnic group from China

Wanlu Chen 1, Heng Ding 2, Yujing Cheng 1, Qi Li 1, Run Dai 1, Xin Yang 1, Chan Zhang 1,✉
PMCID: PMC6732286  PMID: 31361092

Abstract

Background

The pharmacogenomics study has been widely used for the study of very important pharmacogenetic (VIP) variants among different ethnic groups. However, there is little known about the pharmacogenomics information regarding Bai family. Our study aimed to screen the polymorphism of the VIP gene in Bai nationality.

Methods

We genotyped 81 VIP variants (selected from the PharmGKB database) in the Bai population and then compared them to the other 11 major HapMap populations by chi‐square test, structure and F‐statistics (Fst) analysis.

Results

Our results indicated that rs20417 (PTGS2), rs4148323 (UGT1A), and rs1131596 (SLC19A1) were most different in Bai compared with most of the 11 populations from the HapMap data set. Furthermore, population structure and F‐statistics (Fst) analysis also demonstrated that the Bai population has the closest genetic relationship with Han Chinese in Beijing, China (CHB), followed by Japanese in Tokyo, Japan (JPT), and the farthest population from the Yoruba in Ibadan, Nigeria (YRI).

Conclusions

Our study not only presented the genotype frequency difference between the selected population of the Bai population and the other 11 populations, but also showed that the Bai population is most similar to the CHB populations, followed by JPT. These findings would contribute to the development of individualized medicine for the Bai population.

Keywords: bai ethnic group, genetic polymorphisms, individualized medicine, pharmacogenomics, VIP variants

1. BACKGROUND

Personalized medicine, also known as precision medicine, refers to a customized medical model based on personal genomic information to design the best treatment for patients to achieve maximum therapeutic effect and minimize side effects (Jorgensen, 2015). Pharmacogenomics is an aspect of personalized medicine that is used to explore the impact of genetic variation on drug response (Yunus et al., 2013). Currently, most pharmacogenomics studies are focused on very important drug genes (VIPs) thought to be involved in the pharmacokinetics or pharmacodynamics of clinically relevant drugs (Jin, Shi, et al., 2016). These VIP genes play a crucial role in drug metabolism, transport, efficacy or drug response processes, and have been summarized in the pharmacogenetics and pharmaco‐genomics knowledge base in detail.

At present, the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB: http://www.pharmgkb.org) is the most comprehensive database, which have collected amounts of genotype and phenotypic information related to the pharmacogenomic genome and systematically classify for these information (Jin, Zhao, et al., 2016). It is dedicated to reveal the relationship between these genetic variants and drug responses, and then providing patients with the most appropriate drug type and accurate guidance for optimal doses, so as to improve the drug efficacy and safety (Zhang et al., 2014). The PharmGKB currently contains information on more than 4,654 drugs, 4,067 diseases and 27,007 genotypic variants, and its knowledge delivers in a variety of forms, including VIP summaries, drug pathway diagrams, and curated literature annotations (Jin, Xun, et al., 2015).

As we all know, there are 56 ethnic groups in China. The Bai nationality is one of the fifteen ethnic minorities in China, with a long history in the region and distinct culture and traditions. The 861,895 people (According to the 2010 census) of the Bai ethnic minority reside primarily in Dali Bai Autonomous Prefecture of Yunnan Province (Pei, Zhengwei, Yijuan, Chai, & Zhang, 2017). Additionally, there are also distributions in Sichuan and Chongqing provinces. In recent years, pharmacogenomics research on genotypes and drug metabolism among different ethnic groups has become more common (Jin, Aikemu, et al., 2015), For example, several articles have reported that individuals carrying CYP2C9*2 and * 3 allele variants have lower dose requirements or warfarin sensitivity in Brazilian populations (Fohner, Brackman, Giacomini, Altman, & Klein, 2017). Shi et al. researched the genetic polymorphism of VIP variation in the pharmacogenomic of the Himalayan Deng people in southeast Tibet (Shi et al., 2015). Li et al. investigated the genetic polymorphism of very important pharmacogenomic variants in the Zhuang ethnic group of Southwestern China (Li et al., 2018). However, no report has addressed studies of pharmacogenomics information regarding the Bai nationality. Here, we are the first to propose a systematic research on the genetic polymorphism of VIP variants in the Bai ethnic group, which is of great significance for understanding the Bai population and further helping to diagnose, prevent and treat specific diseases.

In this study, we randomly selected and genotyped 81 VIP variants from the PharmGKB database in 100 Dali Bai Autonomous Prefecture from Yunnan province, aimed to identify the allele frequencies of VIP variants in the Bai nationality and to determine the difference in allele frequencies between the Bai nationality and 11 populations from the HapMap data set. The results could not only expand our understanding of ethnic diversity and pharmacogenomics, but also provide a solid theoretical basis for safer administration of drugs and better individualized treatments among the Bai population.

2. METHODS

2.1. Ethics statement

All volunteers were informed about the procedures and purpose of the study, both orally and in writing. They also agreed to provide blood samples and signed informed consent forms. The clinical protocol was approved by the Ethics Committee of Yunnan First People's Hospital and was performed in accordance with the Declaration of Helsinki.

2.2. Study participants

According to detailed recruitment and exclusion criteria, we recruited a random sample of 200 healthy, unrelated white people (100 men and 100 women) from Dali Bai Autonomous Prefecture in China's Yunnan province between July and October 2017. The incorporation criteria for all participants were as follows: (a) All individuals had exclusive Bai ancestry for at least the past three generations; (b) There was no genetic relationship among all participants; (c) All subjects were confirmed to be in good health through a routine medical history and physical examination and had no hereditary disease.

2.3. VIP loci selection and genotyping

We searched the PharmGKB database (https://www.pharmgkb.org/) and 81 random VIP variants of 40 genes were ultimately selected for our study according to available data on frequency, functionality, and linkage based on published research. The minor allele frequency of these SNPs sites in Chinese Han population was >0.05, which increase the statistical efficacy. According to the manufacturer's protocols, the genomic DNA was extracted from peripheral blood (5 ml) using a GoldMag‐ Mini Whole Blood Genomic DNA Purification Kit (GoldMag Co. Ltd., Xi'an, China). Then we measured the DNA concentration and purity with the NanoDrop spectrophotometer 2000 C (Thermo Scientific, Waltham, Massachusetts, USA). We designed polymerase chain reaction extended primers for these SNPS using the MassARRAY Assay Design 3.0 software. Agena MassARRAY RS1000 instrument (Shanghai, China) system was used for SNP genotyping analysis according to the standard scheme recommended by the manufacturer (Gabriel, Ziaugra, & Tabbaa, 2009). SNPs genotyping data were managed and analyzed by Agena Typer 4.0 software.

2.4. HapMap genotype data

The genotype data of the 11 populations were downloaded from the International HapMap Project web site (HapMap release127) at http://hapmap.ncbi.nlm.nih.gov.

The 11 populations are as follows: (a) African ancestry in Southwest USA (ASW); (b) Utah, USA residents with Northern and Western European ancestry from the CEPH collection (CEU); (c) Han Chinese in Beijing, China (CHB); (d) Punjabi in Lahore, Pakistan (PJL); (e) Gujarati Indians in Houston, Texas, USA (GIH); (f) Japanese in Tokyo, Japan (JPT); (g) Luhya in Webuye, Kenya (LWK);(h)Mexican Ancestry in Los Angeles, Colombia (MXL); (i) Peruvian in Lima, Peru (PEL); (j) Toscani in Italy (TSI); and (h) Yoruba in Ibadan, Nigeria (YRI).

2.5. Statistical analysis

We performed data processing and statistical analysis using Microsoft Excel (Redmond, WA, USA) and SPSS 17.0 statistical software package (SPSS, Chicago, IL, USA), including Hardy‐Weinberg equilibrium (HWE) analysis and χ 2 test. Accurate testing was used to determine whether the genotype frequency of each VIP variant in the Bai populations deviated from the HWE balance. The genotype frequencies of the Bai and 11 HapMap populations were calculated and compared using the χ 2 test. All p values were obtained two‐sided, and p < .05 were considered statistically significant before correction. In order to reduce the error detection rate of multiple tests, after Bonferroni correction, p < .05/(81*11) was indicated statistically significant. The structure (version 2.3.4) software (Excoffier, Laval, & Schneider, 2007) was used to analyze and compare the genetic structure of 12 populations. The value of Fst was calculated using Arlequin (version 3.1) software to infer the degree of genetic differentiation between populations (Evanno, Regnaut, & Goudet, 2005).

3. RESULTS

3.1. VIP variants identification

By searching the VIP variants listed in the PharmGKB database, we selected 81 VIP variants for study. The basic characteristics of the selected variants were listed in Table 1, including SNP, gene name, chromosome number and location, corresponding protein function, allelic variation, and genotype frequency.

Table 1.

Basic Characteristics of the selected VIP variants from the PharmGKB database and genotype frequencies in Bai population

SNP ID Genes Chr Position Functional consequence Allele Allele frequencies
A B A (%) B (%)
rs1801131 MTHFR 1 11,794,419 Missense G T 0.25 0.75
rs1801133 MTHFR 1 11,796,321 Missense A G 0.25 0.75
rs890293 CYP2J2 1 59,926,822 Upstream variant 2KB A C 0.08 0.92
rs3918290 DPYD 1 97,450,058 Splice donor variant T C 0.00 1.00
rs1801159 DPYD 1 97,515,839 Intron variant, missense C T 0.18 0.82
rs1801265 DPYD 1 97,883,329 Intron variant, missense, nc transcript variant, utr variant 5 prime G A 0.26 0.74
rs6025 F5 1 169,549,811 Missense T C 0.01 0.99
rs5275 PTGS2 1 186,673,926 Utr variant 3 prime G A 0.40 0.60
rs20417 PTGS2 1 186,681,189 Nc transcript variant, upstream variant 2KB G C 0.20 0.80
rs689466 PTGS2 1 186,681,619 Downstream variant 500B, upstream variant 2KB C T 0.22 0.78
rs4124874 UGT1A1 2 233,757,013 Intron variant, upstream variant 2KB G T 0.42 0.58
rs10929302 UGT1A1 2 233,757,136 Intron variant, upstream variant 2KB A G 0.30 0.70
rs4148323 UGT1A1 2 233,760,498 Intron variant, missense A G 0.03 0.97
rs1805124 SCN5A 3 38,603,929 Missense C T 0.23 0.77
rs6791924 SCN5A 3 38,633,208 Missense A G 0.04 0.96
rs3814055 NR1I2 3 119,781,188 Upstream variant 2KB, utr variant 5 prime T C 0.32 0.68
rs2046934 P2RY12 3 151,339,854 Intron variant G A 0.13 0.87
rs1065776 P2RY1 3 152,835,839 Synonymous codon T C 0.11 0.89
rs701265 P2RY1 3 152,836,568 Synonymous codon G A 0.37 0.63
rs975833 ADH1A 4 99,280,582 Intron variant G C 0.40 0.60
rs2066702 ADH1B 4 99,307,860 Missense A G 0.05 0.95
rs698 ADH1C 4 99,339,632 Missense, nc transcript variant C T 0.21 0.79
rs17244841 HMGCR 5 75,347,030 Intron variant A T 0.04 0.96
rs3846662 HMGCR 5 75,355,259 Intron variant G A 0.38 0.62
rs1042713 ADRB2 5 148,826,877 Missense G A 0.48 0.52
rs1042714 ADRB2 5 148,826,910 Missense G C 0.20 0.80
rs1142345 TPMT 6 18,130,687 Missense T C 0.04 0.96
rs2066853 AHR 7 17,339,486 Missense A G 0.27 0.73
rs1045642 ABCB1 7 87,509,329 Synonymous codon A G 0.40 0.60
rs1128503 ABCB1 7 87,550,285 Synonymous codon G A 0.42 0.58
rs2740574 CYP3A4 7 99,784,473 Upstream variant 2KB C T 0.23 0.77
rs3807375 KCNH2 7 150,970,122 Intron variant C T 0.43 0.57
rs4646244 NAT2 8 18,390,208 Intron variant, upstream variant 2KB A T 0.26 0.74
rs4271002 NAT2 8 18,390,758 Intron variant, upstream variant 2KB C G 0.14 0.86
rs1801280 NAT2 8 18,400,344 Missense C T 0.29 0.71
rs1799929 NAT2 8 18,400,484 Synonymous codon T C 0.27 0.73
rs1208 NAT2 8 18,400,806 Missense G A 0.32 0.68
rs1799931 NAT2 8 18,400,860 Missense A G 0.08 0.92
rs12248560 CYP2C19 10 94,761,900 Upstream variant 2KB T C 0.15 0.85
rs4986893 CYP2C19 10 94,780,653 Stop gained A G 0.01 0.99
rs4244285 CYP2C19 10 94,781,859 Synonymous codon A G 0.22 0.78
rs1057910 CYP2C9 10 94,981,296 Missense C A 0.05 0.95
rs7909236 CYP2C8 10 95,069,673 Upstream variant 2KB T G 0.14 0.86
rs17110453 CYP2C8 10 95,069,772 Upstream variant 2KB C A 0.17 0.83
rs2070676 CYP2E1 10 133,537,633 Intron variant G C 0.31 0.69
rs1695 GSTP1 11 67,585,218 Missense G A 0.35 0.65
rs1138272 GSTP1 11 67,586,108 Missense T C 0.03 0.97
rs1800497 ANKK1 11 113,400,106 Missense A G 0.32 0.68
rs6277 DRD2 11 113,412,737 Synonymous codon A G 0.24 0.76
rs1801028 DRD2 11 113,412,762 Missense C G 0.03 0.97
rs4149015 SLCO1B1 12 21,130,388 Upstream variant 2KB A G 0.05 0.95
rs2306283 SLCO1B1 12 21,176,804 Missense A G 0.38 0.62
rs4149056 SLCO1B1 12 21,178,615 Missense C T 0.09 0.91
rs731236 VDR 12 47,844,974 Synonymous codon G A 0.28 0.72
rs7975232 VDR 12 47,845,054 Intron variant A C 0.48 0.52
rs1544410 VDR 12 47,846,052 Intron variant T C 0.30 0.70
rs2239185 VDR 12 47,850,776 Intron variant A G 0.50 0.50
rs1540339 VDR 12 47,863,543 Intron variant C T 0.39 0.61
rs2239179 VDR 12 47,863,983 Intron variant C T 0.36 0.64
rs3782905 VDR 12 47,872,384 Intron variant C G 0.24 0.76
rs4516035 VDR 12 47,906,043 Upstream variant 2KB C T 0.18 0.82
rs11568820 None 12 47,908,762 None T C 0.46 0.54
rs762551 CYP1A2 15 74,749,576 Intron variant C A 0.37 0.63
rs3760091 SULT1A1 16 28,609,479 Intron variant, upstream variant 2KB G C 0.36 0.64
rs7294 VKORC1 16 31,091,000 Upstream variant 2KB, utr variant 3 prime T C 0.42 0.58
rs9934438 VKORC1 16 31,093,557 Intron variant G A 0.36 0.64
rs1800566 NQO1 16 69,711,242 Missense A G 0.29 0.71
rs2108622 CYP4F2 19 15,879,621 Missense T C 0.24 0.76
rs8192726 CYP2A6 19 40,848,591 Intron variant A C 0.10 0.90
rs1801272 CYP2A6 19 40,848,628 Missense T A 0.01 0.99
rs28399433 CYP2A6 19 40,850,474 Upstream variant 2KB C A 0.13 0.87
rs3211371 CYP2B6 19 41,016,810 Downstream variant 500B, missense, utr variant 3 prime T C 0.05 0.95
rs5629 PTGIS 20 49,513,169 Stop gained, synonymous codon T G 0.22 0.78
rs1051298 SLC19A1 21 45,514,912 Intron variant, utr variant 3 prime A G 0.48 0.52
rs1051296 SLC19A1 21 45,514,947 Intron variant, utr variant 3 prime C A 0.49 0.51
rs1051266 SLC19A1 21 45,537,880 Missense, utr variant 5 prime T C 0.49 0.51
rs1131596 SLC19A1 21 45,538,002 Synonymous codon, utr variant 5 prime A G 0.48 0.52
rs4680 COMT 22 19,963,748 Missense, upstream variant 2KB A G 0.37 0.63
rs59421388 CYP2D6 22 42,127,608 Missense, synonymous codon, upstream variant 2KB T C 0.03 0.97
rs28371725 CYP2D6 22 42,127,803 Intron variant, upstream variant 2KB T C 0.06 0.94
rs61736512 CYP2D6 22 42,129,132 Intron variant, missense, upstream variant 2KB T C 0.03 0.97
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SNP, Single nucleotide polymorphism; Chr, Chromosome; A, reference allele; B, other allele.

3.2. Statistical analyses of 81 loci among 11 populations

Compared the genotype frequency distribution between the Bai population and these 11 HapMap populations by χ 2 test combined with Bonferroni correction multiple hypotheses and multiple comparisons [p < .05/(81*11)]. The genotype frequencies of 81 loci in HapMap 11 populations were listed in Table 2. Without Bonferroni's correction (p < .05), there were 45, 51, 8, 49, 53, 18, 55, 40, 50, 51, and 50 variants that differed in frequency in the Bai population compared to the ASW, CEU, CHB, PJL, GIH, JPT, LWK, MXL, PEL, TSI, and YRI populations, respectively. After adjustment, the number of VIP variants has updated and were recorded as follows: 32, 35, 3, 35, 35, 3, 40, 24, 24, 35, and 43, which corresponds to the order illustrated before. However, compared with Bai, the YRI population contained the most different VIP variants loci after Bonferroni adjustment, indicating that YRI was the most different race from Bai. At the same time, compared with the other 11 populations, the rs20417 (PTGS2: OMIM: 600262), rs4148323 (UGT1A: OMIM: 191740), and rs1131596 (SLC19A1: 600424) locus presented the greatest number of significant differences in the Bai ethnic population. While the rs890293 (DPYD: OMIM: 612779), rs3918290 (DPYD), rs6025 (F5: OMIM: 612309), rs2046934 (P2RY12: OMIM: 600515), rs4646244 (NAT2: OMIM: 608490), rs4986893 (CYP2C19: OMIM:124020), rs1057910 (CYP2C9: OMIM: 601130), rs1800497 (ANKK1: OMIM: 608774), rs8192726 (CYP2A6: OMIM: 12270), rs5629 (PTGIS: OMIM: 601699), rs1051298 (SLC19A1), rs1051296S (LC19A1) has no significant genetic differences between Bai nationality and the 11 HapMap populations. The genotype counts of 81 loci in 11 HapMap populations listed in Table S1.

Table 2.

Bai compared with 11 HapMap populations after Bonferroni's multiple adjustments

Gene SNP ID p < .05/(81*11) Different Populations
ASW CEU CHB PJL GIH JPT LWK MXL PEL TSI YRI
MTHFR rs1801131 — 7.05E−04 — 3.60E−08 2.00E−08 — — — 1.29E−02 2.04E−03 — 2
  rs1801133 2.70E−07 — — 1.10E−08 3.20E−08 — 3.20E−14 — — — 9.90E−12 5
CYP2J2 rs890293 — — 2.06E−02 — — 1.75E−03 — 3.43E−02 1.87E−04 — — 0
DPYD rs3918290 — — — — — — — — — — — 0
  rs1801159 — 4.51E−02 — 2.30E−05 1.06E−04 — — — 3.60E−08 3.98E−02 1.57E−02 2
  rs1801265 5.70E−18 — — 4.20E−08 3.90E−11 — 1.40E−21 7.40E−05 — 2.70E−04 4.90E−19 5
F5 rs6025 — 1.67E−02 — — — — — — — — — 0
PTGS2 rs5275 2.50E−15 1.02E−07 — 1.50E−11 1.90E−07 4.47E−02 2.90E−22 1.19E−04 5.60E−08 1.46E−03 3.50E−27 7
  rs20417 1.30E−26 3.16E−15 1.20E−05 1.10E−19 6.40E−16 1.06E−04 2.40E−24 9.80E−20 4.80E−17 1.40E−16 5.50E−33 10
  rs689466 7.20E−08 2.04E−07 — 8.40E−11 1.50E−11 — 5.60E−21 4.95E−03 1.39E−03 2.80E−07 9.70E−17 7
UGT1A1 rs4124874 3.60E−21 1.14E−03 — 6.50E−16 1.10E−12 — 1.80E−38 2.80E−06 5.60E−12 6.79E−04 4.70E−42 7
  rs10929302 3.30E−14 3.81E−13 — 1.30E−21 8.50E−21 1.50E−04 2.10E−17 1.10E−13 7.50E−23 4.80E−08 1.00E−16 9
  rs4148323 3.00E−11 1.29E−17 4.04E−02 9.20E−17 2.10E−15 1.50E−05 1.30E−17 2.40E−10 1.30E−15 9.80E−19 7.10E−19 10
SCN5A rs1805124 1.65E−02 — — 1.91E−02 — — 3.52E−04 — 4.78E−02 — 1.70E−05 1
  rs6791924 — — — — — — 8.20E−15 — — — 2.10E−08 2
NR1I2 rs3814055 — 2.81E−03 — 2.17E−02 7.30E−07 — 2.83E−02 9.86E−03 5.40E−05 3.15E−04 — 2
P2RY12 rs2046934 — — — — 3.27E−02 ‐ 3.49E−02 — 4.00E−03 — — 0
P2RY1 rs1065776 1.70E−05 — — 2.10E−03 — — 6.30E−07 — — 2.90E−02 2.00E−07 3
  rs701265 7.00E−12 7.34E−04 — — 2.54E−02 — 5.80E−26 — — 2.59E−04 2.30E−27 3
ADH1A rs975833 6.80E−22 5.66E−28 — 1.20E−14 1.20E−10 — 1.70E−32 2.40E−32 1.50E−38 1.00E−26 2.20E−24 9
ADH1B rs2066702 7.90E−18 — — — — — — — — — 1.20E−27 2
ADH1C rs698 — 5.61E−23 — 3.00E−11 2.40E−08 — — 2.90E−07 1.11E−02 2.30E−10 — 5
HMGCR rs17244841 — — — — — — 3.80E−08 — — 3.28E−03 7.20E−09 2
  rs3846662 1.20E−15 — — 6.40E−03 1.70E−06 2.12E−02 3.10E−32 — 2.39E−02 — 3.70E−32 4
ADRB2 rs1042713 — 3.79E−05 — 4.53E−02 2.26E−02 — — — 1.30E−02 1.15E−04 — 1
  rs1042714 — 2.11E−19 — 7.30E−05 7.10E−05 — 3.32E−04 — — 5.70E−16 — 2
TPMT rs1142345 — — — — — — 1.60E−08 — — — 2.46E−04 1
AHR rs2066853 — 3.92E−10 — 3.40E−06 7.50E−09 3.49E−02 5.83E−03 2.60E−05 9.92E−03 4.30E−10 — 5
ABCB1 rs1045642 5.90E−07 1.06E−02 — — 6.02E−03 — 2.00E−12 — — — 3.30E−15 3
  rs1128503 5.20E−23 2.37E−10 — 1.21E−04 1.08E−03 1.90E−02 2.60E−35 3.80E−06 2.10E−15 8.70E−11 4.70E−33 7
CYP3A4 rs2740574 1.70E−51 — — — — — 6.10E−60 — — — 4.70E−62 3
KCNH2 rs3807375 — 4.29E−22 — 1.50E−18 1.70E−19 — — 4.40E−06 — 3.80E−24 — 5
NAT2 rs4646244 — — — 4.19E−02 2.37E−03 — — 1.86E−02 1.58E−04 — — 0
  rs4271002 3.38E−02 3.49E−05 — — 4.73E−02 — 3.27E−04 — 3.58E−02 — 9.70E−05 1
  rs1801280 2.50E−16 4.31E−28 — 3.90E−28 3.80E−21 — 1.60E−24 3.20E−21 1.80E−17 3.10E−29 1.70E−13 9
  rs1799929 4.50E−13 5.20E−28 — 2.30E−25 1.20E−18 — 2.10E−21 2.00E−20 8.80E−17 3.10E−29 8.90E−08 9
  rs1208 3.80E−18 3.57E−25 — 2.40E−27 9.40E−20 — 9.00E−29 5.00E−24 4.10E−16 2.10E−28 1.50E−23 9
  rs1799931 0.000212 7.92E−11 — 3.30E−05 4.70E−06 9.10E−04 3.40E−10 — 0.008694 9.00E−10 8.70E−08 6
CYP2C19 rs12248560 1.10E−18 1.43E−21 — 6.20E−13 1.00E−12 — 1.90E−16 5.30E−10 — 1.80E−20 6.40E−22 8
  rs4986893 — — — — — — — — — — — 0
  rs4244285 4.52E−02 5.29E−03 7.60E−03 — — — — 1.59E−02 2.00E−06 4.00E−05 — 2
CYP2C9 rs1057910 — — — — 2.26E−03 — — — — — — 0
CYP2C8 rs7909236 — 7.21E−05 — 3.37E−02 1.24E−03 — 6.40E−05 2.80E−05 2.30E−09 — 4.60E−07 3
  rs17110453 7.70E−08 1.11E−04 1.74E−02 — — 4.16E−04 1.10E−12 1.15E−02 1.10E−06 4.92E−04 6.20E−13 4
CYP2E1 rs2070676 8.80E−13 4.30E−02 — — — — 8.50E−30 — 1.46E−02 — 1.40E−22 3
GSTP1 rs1695 8.10E−07 9.74E−07 — — 1.06E−02 1.03E−02 4.40E−13 1.00E−11 1.30E−21 1.45E−02 1.60E−06 6
  rs1138272 — — — 1.30E−08 — — — 6.80E−05 — — — 1
ANKK1 rs1800497 — 2.04E−04 — 3.78E−02 — — — — — 6.56E−04 — 0
DRD2 rs6277 8.80E−05 1.73E−36 — 2.70E−19 3.40E−20 2.17E−02 — 1.20E−14 3.00E−05 1.70E−44 — 6
  rs1801028 4.39E−02 — — — 2.70E−05 — 2.17E−02 — 4.19E−02 — 4.22E−03 1
SLCO1B1 rs4149015 8.64E−03 — — 1.81E−03 — — 3.11E−02 4.65E−02 9.74E−04 — 3.30E−05 1
  rs2306283 — 4.07E−14 — 8.72E−11 8.03E−06 — 1.72E−02 6.10E−13 1.70E−09 9.70E−17 — 6
  rs4149056 — 1.26E−02 — — 2.34E−02 — 3.00E−02 — 3.86E−02 2.00E−06 2.64E−03 1
VDR rs731236 1.70E−07 2.06E−23 — 1.00E−11 4.80E−14 4.74E−02 2.50E−10 5.65E−05 — 2.90E−21 6.40E−14 7
  rs7975232 4.90E−08 4.09E−08 — 3.40E−06 6.90E−06 — 2.20E−15 — 3.29E−02 5.20E−08 1.00E−08 7
  rs1544410 2.10E−10 1.04E−24 — 1.40E−23 2.70E−25 — 9.70E−11 1.70E−05 1.73E−02 1.50E−22 4.40E−13 8
  rs2239185 1.95E−04 2.35E−08 — 4.50E−06 1.00E−05 — 8.90E−11 — 2.73E−02 6.60E−08 8.50E−08 6
  rs1540339 7.90E−17 3.19E−16 — 6.50E−08 2.20E−14 — 3.50E−32 7.30E−10 9.40E−10 3.80E−14 8.00E−27 9
  rs2239179 2.42E−02 6.60E−11 — 1.20E−05 8.80E−10 — 1.21E−02 — — 5.18E−05 — 3
  rs3782905 2.21E−02 2.84E−08 — 1.45E−03 1.91E−03 — 5.37E−03 — — 4.90E−08 2.65E−02 2
  rs4516035 — 1.38E−17 — 6.40E−08 1.90E−05 2.95E−03 — 2.20E−08 3.10E−05 1.30E−22 6.85E−03 6
  rs11568820 1.90E−07 2.73E−05 — 4.79E−02 — — 9.60E−20 3.50E−07 2.70E−12 2.21E−04 1.30E−35 6
CYP1A2 rs762551 — — — — 2.32E−02 — 0.000892 — 1.30E−06 — — 1
SULT1A1 rs3760091 — — — — 2.90E−05 — — — 6.00E−08 5.65E−04 — 2
VKORC1 rs7294 2.80E−20 8.82E−13 — 2.50E−39 6.00E−43 — 3.80E−22 7.10E−13 7.80E−29 4.90E−15 1.40E−27 9
  rs9934438 2.40E−42 6.02E−29 — 1.10E−44 1.90E−49 — 3.70E−59 3.80E−24 1.20E−31 8.10E−28 6.40E−62 9
NQO1 rs1800566 1.10E−06 2.30E−08 — 2.89E−02 — — 5.60E−09 — — 4.10E−05 1.20E−08 5
CYP4F2 rs2108622 6.40E−05 — — — 2.96E−03 — 1.20E−05 — 8.50E−05 — 1.30E−10 2
CYP2A6 rs8192726 — — 1.67E−02 — — 1.02E−02 — — — — — 0
  rs1801272 — 5.73E−04 — — — — — 3.68E−02 — 5.00E−05 — 1
  rs28399433 2.41E−02 — 2.80E−05 2.53E−04 2.02E−03 2.30E−08 6.02E−03 — — — 4.26E−04 2
  rs3211371 — 9.79E−50 — — — — — — — 1.10E−48 — 2
PTGIS rs5629 — — — — — — 5.39E−04 — — 1.73E−02 — 0
SLC19A1 rs1051298 — — — — — — — 1.87E−02 8.11E−02 — — 0
  rs1051296 — — — — — — — 1.08E−02 2.95E−02 — — 0
  rs1051266 — — — — — — 3.90E−07 3.29E−02 — — 2.30E−06 2
  rs1131596 4.10E−14 7.89E−22 5.70E−16 9.30E−14 5.30E−19 3.00E−14 4.10E−12 1.10E−09 3.20E−14 5.80E−16 4.70E−13 11
COMT rs4680 — 1.25E−06 — 2.40E−08 2.30E−05 — — 1.62E−03 1.39E−03 7.40E−07 — 4
CYP2D6 rs59421388 1.28E−03 — — — — — 8.20E−15 — — — 3.00E−10 2
  rs28371725 — 1.43E−03 — 7.70E−05 2.42E−03 9.15E−03 — — 2.47E−02 1.80E−06 1.83E−02 1
  rs61736512 1.28E−03 — —     — 8.16E−15 — — — 2.97E−10 2
Different SNPs   3.20E + 01 35 3 35 35 3 40 24 24 35 43  
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ASW, African ancestry in southwestern USA; CEU, Utah residents with Northern and Western European ancestry; CHB, Han Chinese in Beijing, China; PJL, Punjabi in Lahore, Pakistan; GIH, Gujarati Indians in Houston, Texas, USA; JPT, Japanese in Tokyo, Japan; LWK, Luhya people in Webuye, Kenya; MXL, Mexican Ancestry in Los Angeles, Colombia; PEL, Peruvian in Lima, Peru; TSI, Toscans in Italy; YRI, Yoruba in Ibadan, Nigeria.

Bold italics indicates that after adjustment p < .05/(80*11) the locus has statistically significant.

3.3. Analyses of population genetic structures

The genetic differentiation degree of allele frequencies between Bai and other 11 populations was compared using Fst statistics. An Fst value of less than 0.15 indicates that there is no significant genetic difference between the two populations. And pairwise FST values between the Bai population and the other 11 HapMap populations ranged from 0.0157 to 0.2213 (Table 3 ). Comparing other populations, the lowest level of differentiation was observed between the Bai and CHB populations (FST = 0.0157), followed by the JPT (FST = 0.0203), whereas the greatest divergence was found in the YRI population (FST = 0.2213).

Table 3.

Distribution of pairwise Fst distances among the Bai and all HapMap populations

Population Bai CHB JPT GIH PJL CEU TSI MXL PEL ASW LWK YRI
Bai 0.000                      
CHB 0.016 0.000                    
JPT 0.020 0.004 0.000                  
GIH 0.129 0.128 0.116 0.000                
PJL 0.138 0.137 0.124 0.002 0.000              
CEU 0.146 0.149 0.140 0.038 0.030 0.000            
TSI 0.132 0.133 0.125 0.040 0.031 0.004 0.000          
MXL 0.109 0.108 0.107 0.049 0.040 0.030 0.026 0.000        
PEL 0.125 0.121 0.120 0.081 0.078 0.082 0.079 0.022 0.000      
ASW 0.174 0.178 0.164 0.088 0.085 0.115 0.112 0.098 0.110 0.000    
LWK 0.236 0.243 0.226 0.144 0.142 0.177 0.174 0.167 0.176 0.013 0.000  
YRI 0.221 0.227 0.209 0.139 0.137 0.179 0.175 0.165 0.170 0.009 0.008 0.000
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ASW, African ancestry in southwestern USA; CEU, Utah residents with Northern and Western European ancestry; CHB, Han Chinese in Beijing, China; PJL, Punjabi in Lahore, Pakistan; GIH, Gujarati Indians in Houston, Texas, USA; JPT, Japanese in Tokyo, Japan; LWK, Luhya people in Webuye, Kenya; MXL, Mexican Ancestry in Los Angeles, Colombia; PEL, Peruvian in Lima, Peru; TSI, Toscans in Italy; YRI, Yoruba in Ibadan, Nigeria.

The Bayesian‐based structure analysis of the genetic relationship among 12 populations was shown in Figure 1, most suitable K was observed at K = 6, where the each individual was represented by a vertical column partitioned into different color segments. The results revealed that the Bai population was most similar to the CHB and JPT populations, which was consistent with the results in Table 3.

Figure 1.

Figure 1

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Results of structure clustering analysis (K = 6) for Bai and HapMap populations

4. DISCUSSION

Today, the rapidly advancing pharmacogenetics is increasingly focused on the interethnic or interracial differences in drug metabolism to identify the genetic backgrounds of these variations. For the first time, our study genotyped the variants related to drug reactions in the Bai ethnic group, and compared the genotype frequencies with the other 11 HapMap populations. Our results suggested that the expression of many VIP variants were significantly different between the Bai population and other populations. Among these variants, rs20417, rs4148323, and rs1131596 were significantly differentially expressed in the Bai population, compared to the 11 populations. We also found that the genetic backgrounds of the Bai and CHB populations were similar, but significantly different from the YRI populations.

Rs20417 is a significant variant of the prostaglandin‐endoperoxide synthase 2 (PTGS2) gene (Hung et al., 2017). PTGS2 gene also known as cyclooxygenase 2 (COX‐2), located on chromosome 1, is the key enzyme in prostaglandin biosynthesis that can converts arachidonate to prostaglandin H2 (PGH2) (Lucido, Orlando, Vecchio, & Malkowski, 2016). It has been widely reported that the polymorphism of rs20417 is associated with many diseases, such as myocardial infarction or stroke (Lemaitre et al., 2009). Previous pharmacogenomics studies have revealed that PTGS2 was the targets of nonsteroidal anti‐inflammatory drugs (NSAIDs) including aspirin and ibuprofen (Orlando & Malkowski, 2016). Studies by Yun‐Sil Lee DDS et al. (Y. S. Lee, Kim, Wu, Wang, & Dionne, 2006) showed that CC genotype patients tend to increase pain relief when treated with rofecoxib compared to genotype GG + CG, but reduce pain relief with ibuprofen. Lee C R et al. (C. R. Lee et al., 2008) also reported that patients with a CC genotype may have an increased risk of coronary artery disease when treated with aspirin compared to patients with a GG or CG genotype. Rozenn et al. (Lemaitre et al., 2009) studies have shown that that variation in TBXAS1 and PTGIS may influence Myocardial Infarction (MI) risk and carriers of rs20417 C allele might derive greater benefits from aspirin use in primary prevention in comparison with non‐carriers. In our study, the C allele frequency of the Bai nationality was as high as 80%, indicating that the Bai population increased the risk of coronary artery disease when treated with aspirin. At the same time, when using aspirin to prevent MI risk, the Bai population can get more benefits.

Rs4148323 is an intron variant of the UGT1A1 gene on human chromosome 2q37, which encodes a UDP‐glucuronosyltransferase, an enzyme of the glucuronidation pathway (Sugatani et al., 2001). It plays an important role in catalyzing the formation of bound bilirubin by unbound bilirubin (Liu, Lu, et al., 2017). Clinical studies have found that cancer patients carrying the GG genotype may reduce the risk of thrombocytopenia (Han, Lim, Park, Lee, & Lee, 2009) or diarrhea (Takekuma et al., 2006) when treated with irinotecan‐based regimens and may also increase tumor response, progression‐free survival Period or overall survival compared with patients with AA or AG genotype. In our study, we found that the G allele frequency of rs 4,148,323 in the Bai population was very high, indicating that the cancer patients in the Bai population can reduce the risk of thrombocytopenia or diarrhea. Other studies have demonstrated that patients with angina or heart failure carrying the G allele are more likely to increase glucuronidation of carvedilol than carriers of the A allele (Boyd et al., 2006). One study reported that patients with the G allele had a reduced risk of developing hyperbilirubinemia during treatment with indinavir, compared to HIV patients with the A allele (Bohanec Grabar et al., 2012). These findings pointed that rs4148323 polymorphism may be a useful pharmacogenomics point for providing rational and effectively tailored therapy for the Bai ethnic group.

The rs1131596 variant is located in the Solute Carrier Family 19 Member 1 (SLC19A1) gene, which encodes a folate transporter and is involved in the regulation of intracellular folate concentrations (Whetstine, Flatley, & Matherly, 2002). In our study, we found that the genotype frequency of rs1131596 was significantly different between the Bai and the other 11 races. One study reported that a linkage group (LD) rs1051266/rs11315962, which may influence the SLC19A1 function, such as changing the SLC19A1 splicing (Bohanec Grabar et al., 2012). Clinical evidence proposed that variants of rs1131596 and rs1051266 have protective effects against the risk of discontinuation of methotrexate toxicity (MTX) treatment due to toxicity and infection (Chatzikyriakidou et al., 2007). The allele G of rs1131596 can reduce the express‐

ion of SLC19A1 compared to allele A, but the allele G was not associated with response to methotrexate in people with arthritis, rheumatoid and in children with progenitor cell lymphoblastic leukemia‐lymphoma (Liu, Gao, et al., 2017). However, the pharmacoge‐ nomics information of the rs3807375 variant requires more in‐depth investigation.

Our results supplemented the pharmacogenomic information of the Bai population and shed light on the differences in selected genetic polymorphisms between the Bai population and 11 other populations around the world. In addition, these results provided a solid foundation for the Bai population to use drugs more rationally and safely. But our sample of the Bai population was relatively small and the results must be further validated in a larger sample set.

5. CONCLUSIONS

We identified the characteristics of 81 VIP variants of Bai population from southwestern China, and found that the genetic background of Bai population in Yunnan was closest to CHD population. This information helps Bai population to develop appropriate personalized treatment strategies, including appropriate drugs and the right dose.

6. THE INFLUENCE OF OUR RESULTS IN CLINICAL APPLICATION

Different populations may have different genotypes due to differences in ancestry, geographical location, lifestyle, etc., and different genotypes have certain differences in response to corresponding drugs. Our study found that the genotypes of some VIP sites of Bai population were different from those of 11 global representative groups, so this study is helpful for the individualized treatment of Bai population in clinical practice.

7. ETHICS APPROVAL AND CONSENT TO PARTICIPATE

All volunteers were informed the procedures and purpose of the study, both orally and in writing. They also agreed to provide blood samples and signed informed consent forms. The clinical protocol was approved by the Ethics Committee of Yunnan First People's Hospital and was performed in accordance with the Declaration of Helsinki.

8. CONSENT FOR PUBLICATION

Not applicable.

CONFLICT OF INTERESTS

The authors declare that they have no competing interests.

AUTHORS’ CONTRIBUTIONS

Wanlu Chen and Heng Ding: conceived and designed the experiments. Yujing Cheng and Qi Li: performed the experiments. Run Dai and Xin Yang: analyzed the data. Chan Zhang: contributed reagents/materials/analysis tools.

9.

Supporting information

 

Click here for additional data file. (35.9KB, docx)

ACKNOWLEDGMENTS

We are grateful to all individuals for participating in this study, and the clinicians and hospital staff who contributed to the sample and data collection.

Chen W, Ding H, Cheng Y, et al. Genetic polymorphisms analysis of pharmacogenomic VIP variants in Bai ethnic group from China. Mol Genet Genomic Med. 2019;7:e884 10.1002/mgg3.884

Wanlu Chen and Heng Ding are joint first authors.

Funding information

This study is supported by the Yunnan Science and Technology Plan Project (No. 2017FE468 (−125)).

DATA AVAILABILITY STATEMENT

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

 

Click here for additional data file. (35.9KB, docx)

Data Availability Statement

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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