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. 2016 May 28;17:70. doi: 10.1186/s12863-016-0379-0

Genetic polymorphisms of pharmacogenomic VIP variants in the Mongol of Northwestern China

Tianbo Jin 1,2,3,4,✉, Xugang Shi 1,2, Li Wang 1,2, Huijuan Wang 3,4, Tian Feng 3, Longli Kang 1,2,✉
PMCID: PMC4884435  PMID: 27233804

Abstract

Background

Within a population, the differences of pharmacogenomic variant frequencies may produce diversities in drug efficacy, safety, and the risk associated with adverse drug reactions. With the development of pharmacogenomics, widespread genetic research on drug metabolism has been conducted on major populations, but less is known about minorities.

Results

In this study, we recruited 100 unrelated, healthy Mongol adults from Xinjiang and genotyped 85 VIP variants from the PharmGKB database. We compared our data with eleven populations listed in 1000 genomes project and HapMap database. We used χ2 tests to identify significantly different loci between these populations. We downloaded SNP allele frequencies from the ALlele FREquency Database to observe the global genetic variation distribution for these specific loci. And then we used Structure software to perform the genetic structure analysis of 12 populations.

Conclusions

Our results demonstrated that different polymorphic allele frequencies exist between different nationalities,and indicated Mongol is most similar to Chinese populations, followed by JPT. This information on the Mongol population complements the existing pharmacogenomic data and provides a theoretical basis for screening and therapy in the different ethnic groups within Xinjiang.

Keywords: Pharmacogenomics, Genetic polymorphisms, Mongol, VIP variant

Background

It is well known that different individuals have different reactions to the same medications. Pharmacogenomics seeks to identify genetic markers that may influence a person’s response to pharmaceuticals. It will undoubtedly become an indispensable part of medical care in the future [1, 2]. Pharmacogenomic research seeks to identify single nucleotide polymorphisms (SNPs) or multiple gene signatures that are possibly associated with medication responses [3]. The goal of the research is to provide information for personalized medicine, i.e. give to the patient the optimal medication in optimal dose, and promote personalized therapeutics [4–6].

Numerous studies had shown that certain important genes and genetic variations affect critical functions during the drug reaction process. These genetic variations are called very important pharmacogenetic (VIP) variants and listed in the pharmacogenomics databases such as the Pharmacogenomics Knowledge Base (PharmGKB), the Pharmacogenetics of Membrane Transporters (PMT) database, and PharmaADME [6–8]. Currently, PharmGKB (http://www.pharmgkb.org) is the most comprehensive database and dedicates to propagating primary pharmacogenomic data and knowledge. They have extensively annotated the vital drug response genes and presented this information in VIP summaries, pathway diagrams, and curated literature [9].

In China, there are 56 different nationalities. Besides Han, the others account for approximately 100 million people. Due to the different genetic backgrounds and diverse environments of these minor populations, we distinguish them easily from the Han ethnicity. The Mongolian population represents one of the fifteen largest ethnic minorities in China [10]. They primarily live in the Inner Mongolia, Liaoning, Heilongjiang and the Xinjiang Uygur Autonomous Region. The areas are located in the grassland region of Northern China and significantly different with the Central Plains. Special living environments of the Mongol people shaped their unique gene distribution frequencies. An increasing number of studies suggest that genes related to drug response vary between different populations [11], so the pharmacogenomics population genetic studies of different population is valuable.

In this study, we random selected and genotyped 85 VIP variants from the PharmGKB VIP database in 100 Mongols from Xinjiang. We designed primers using MassARRAY Assay Design 3.0 Software [12]. We compared the Mongol’s allele frequencies with 11 populations from 1000 genomes project and the Mongol’s genotype frequencies and haplotype construction with 11 HapMap populations to identify the differences among them. The results will expand the current Mongol pharmacogenomic information and ethnic diversity. We aimed to provide new strategies for medical professionals through use genomic and molecular data to optimize drug administration and therapeutic treatment in the future.

Methods

Ethics statement

Blood samples and signed informed consent forms were obtained from all enrolls. All participants were informed both verbally and in writing of the procedures and purpose of the study, and signed informed consent documents. The clinical protocol was approved by the Clinical Research Ethics of Xizang Minzu University and Northwest University, and it is in compliance with Department of Health and Human Services (DHHS) regulations for human research subject protection.

Study participants

We recruited 100 random unrelated Mongol adults (50 males and 50 females, average age range 25-40 years) from the Xinjiang Region of China and collected blood samples. The detailed recruitment criteria are the sample have good health body and had at least three generations of exclusive ethnic ancestries. They rarely communicate with other ethnics in Xinjiang because they are still nomads which living on relatively limited pasture. They were determined to be a representative Mongol population sample with regard to both ancestry and environmental exposure.

Variant selection and genotyping

Using the PharmGKB database, we screened published genetic polymorphisms associated with VIP variants, and finally 85 genetic variant loci from 37 genes were randomly selected for our investigation. We extracted genomic DNA from whole blood using a GoldMag-Mini Whole Blood Genomic DNA Purification Kit (GoldMag Ltd. Xi’an, China) according to the manufacturer’s protocol. The genomic DNA concentration was measured by absorbance at 260 nm using a NanoDrop 2000C (Thermo Scientific, Waltham, Massachusetts, USA). We used the Sequenom MassARRAY Assay Design 3.0 software (San Diego, California, USA) to design multiplexed SNP MassEXTEND arrays [12]. We utilized a Sequenom MassARRAY RS1000 (San Diego, California, USA) to genotype the SNPs according to the manufacturer’s instructions. Sequenom Typer 4.0 Software was used for data collection and analysis as described previously [13].

Statistical analyses

We used Microsoft Excel and the SPSS 19.0 statistical package (SPSS, Chicago, IL) to perform a Hardy–Weinberg Equilibrium (HWE) analysis and χ2 tests. All p values calculated were two-sided and Bonferroni’s multiple adjustment was used to correction. The values were considered statistically significant when p < 0.05 and p < 0.05/(85 × 11), respectively [14]. We analyzed each variant frequency in Mongols using an exact test to identify those that departed from HWE. We downloaded the allele frequencies of 85 loci in eleven randomly population of 1000 genomes project, which are a population of African ancestry in the southwestern USA (ASW); a population of Chinese Dai in Xishuangbanna, China (CDX); a Utah residents population (CEPH) with North and Western European Ancestry (CEU); the Chinese Han in Beijing, China (CHB); the Gujarati Indians in Houston, Texas, USA (GIH); the Japanese population in Tokyo, Japan (JPT); the Luhya people in Webuye, Kenya (LWK); people of Mexican ancestry from Los Angeles, USA (MXL); a population of Puerto Ricans from Puerto Rico (PUR); the Tuscan people of Italy (TSI); and the Yoruba in Ibadan, Nigeria (YRI). We downloaded the genotype frequencies of 85 variation loci in eleven populations from the HapMap database that are ASW; a northwestern European population (CEU); CHB; a Chinese population of metropolitan Denver, Colorado, USA (CHD); GIH; JPT; LWK; people of Mexican ancestry living in Los Angeles, California, USA (MEX); the Maasai people in Kinyawa, Kenya (MKK); TSI; and YRI. We first compared the allele frequencies difference between Mongolian and 11 random 1000 genomes project popualtions and calculate the correlation coefficient (R2) among the minor different population, then compared and calculated the selected SNP’s variant frequencies between the Mongol people and eleven HapMap populations (data from the second phase of HapMap: http://hapmap.ncbi.nlm.nih.gov) using a χ2 test. Afterwards, we downloaded the SNP allele frequencies of selected loci from the ALlele FREquency Database (http://alfred.med.yale.edu, ALFRED) and analyzed the global genetic variation patterns. We used Haploview software package (4.2) to perform the linkage disequilibrium (LD) analysis constructed haplotype, and genetic association of significant polymorphism loci.

Analysis of population genetic structures

There are studies proved that the center of study which research human origins, DNA forensics and complex diseases is population genetic structure. It is also important to our study as a pharmacogenomics population study. Structure analysis is common in population genetic study. To further investigate variation at the VIP locus in terms of population structure we used STRUCTURE ver. 2.3.1 (Pritchard Lab, Stanford University,USA, http://pritchardlab.stanford.edu/structure.html) which based on the Bayesian clustering algorithmto assign the samples within a hypothetical K number of populations hypothesized by Pritchard et al [15]. We performed structure analysis using ancestry model with correlated allele frequencies among clusters. K = 2 to 8 is the range of possible numbers of clusters and 12 trials were run for each K. We performed the MCMC analyses for each structure analysis was run for 10,000 after an initial burn-in period of 10,000 for data collection. △K was calculated to identified the most likely number of clusters by STRUCTURE HARVESTER [16].

Results

We sequenced 85 VIP variants from 100 Mongols. The selected SNP PCR primers were designed using the Sequenom MassARRAY Assay Design 3.0 Software. Information regarding the selected VIP loci and their genotype frequencies is listed in Table 1, including the genes, their positions, the nucleotide change, the amino acid translation, the calculated allele frequencies, and the genotype frequencies for Mongols. Several variants, such as rs698, rs1695, rs5219, rs16974, rs20417, rs890293, rs2740574, and rs3211371, did not meet HWE with a 5 % significance level and were not included in the final 85 loci analyzed. We first compared the allele frequencies differences among the Mongols and the selected 11 groups from 1000 genomes project database (p < 0.05). We found that there are some loci have significantly different between them. In ASW population, there are 22 loci exist different with Mongol. The results of other groups are as follows: CDX, 14; CEU, 19; CHB,15; GIH,15; JPT,15; LWK,18; MXL,18; PUR, 22; TSI, 18; YRI, 18(Table 2), respectively. In Fig. 1, we selected CDX, CHB and JPT which are the minimum difference population compared with Mongol population to calculate the correlation coefficient, R2. From the allele frequencies difference comparison, we figure out one initial conclusion that the Mongolian is relatively close to CDX, followed by CHB and JPT.

Table 1.

Basic characteristics of the selected VIP variants from the PharmGKB database

SNP ID Genes Position Chr Categories Alleles Amino Acid Translation Mongol
Family Phase A B AA AB BB
rs1801131 MTHFR 11854476 1 Methylenetetrahydrofolate reductase family Phase I C A Glu429Ala 8 34 58
rs1801133 MTHFR 11856378 1 Phase I T C Ala222Val 9 39 52
rs890293 CYP2J2 60392494 1 Cytochrome P450 superfamily Phase I G T 0 95 5
rs3918290 DPYD 97915614 1 - PhaseI G A 100 0 0
rs6025 F5 169519049 1 - Others G A Arg534Gln 99 1 0
rs20417 PTGS2 186650321 1 - Phase I G C 97 0 3
rs689466 PTGS2 186650751 1 - Phase I A G 45 45 10
rs4124874 UGT1A1 234665659 2 UDP-glucuronosyltransferase family Phase II C A 10 46 44
rs10929302 UGT1A1 234665782 2 Phase II G A 65 30 5
rs4148323 UGT1A1 234669144 2 Phase II A G Gly71Arg 7 34 59
rs7626962 SCN5A 38620907 3 Sodium channel gene family Others G T Ser1103Tyr 100 0 0
rs1805124 SCN5A 38645420 3 Others G A Pro1090Leu 1 25 74
rs6791924 SCN5A 38674699 3 Others G A Arg34Cys 100 0 0
rs3814055 NR1I2 119500035 3 Nuclear receptor family Others C T 46 47 7
rs2046934 P2RY12 151057642 3 G-protein coupled receptor family Others T C 63 34 3
rs1065776 P2RY1 152553628 3 Others T C Ala19Ala 0 7 93
rs701265 P2RY1 152554357 3 Others G A Val262Val 9 52 39
rs975833 ADH1A 100201739 4 Alcohol dehydrogenase family Phase I G C 33 52 15
rs2066702 ADH1B 100229017 4 Phase I C T Arg370Cys 100 0 0
rs1229984 ADH1B 100239319 4 Phase I G A His48Arg 45 44 11
rs698 ADH1C 100260789 4 Phase I A G Ile350Val 67 25 7
rs17244841 HMGCR 74642855 5 - Phase I A T 99 1 0
rs3846662 HMGCR 74651084 5 - Phase I T C 30 52 18
rs17238540 HMGCR 74655498 5 - Phase I T G 100 0 0
rs1042713 ADRB2 148206440 5 Adrenergic receptors family Phase I G A Arg16Gly 26 53 21
rs1042714 ADRB2 148206473 5 Phase I G C Gln27Glu 6 42 52
rs1800888 ADRB2 148206885 5 Phase I C T Thr164Ile 99 0 0
rs1142345 TPMT 18130918 6 Methyltransferase superfamily Phase II G A Tyr240Cys 0 2 98
rs1800460 TPMT 18139228 6 Phase II A G Ala154Thr 0 1 99
rs2066853 AHR 17379110 7 - Others G A Arg554Lys 35 48 16
rs1045642 ABCB1 87138645 7 ATP-binding cassette (ABC) transporters superfamily Others T C Ile1145Ile 17 47 36
rs2032582 ABCB1 87160618 7 Others G T Ser893Ala 25 35 15
rs2032582 ABCB1 87160618 7 Others G A Ser893Thr 25 10 2
rs2032582 ABCB1 87160618 7 Others T A 15 13 2
rs1128503 ABCB1 87179601 7 Others T C Gly412Gly 37 48 13
rs10264272 CYP3A5 99262835 7 Cytochrome P450 superfamily Phase I C T Lys208Lys 100 0 0
rs776746 CYP3A5 99270539 7 Phase I G A 79 20 1
rs4986913 CYP3A4 99358459 7 Phase I C T Pro467Ser 100 0 0
rs4986910 CYP3A4 99358524 7 Phase I T C Met445Thr 100 0 0
rs4986909 CYP3A4 99359670 7 Phase I C T Pro416Leu 100 0 0
rs12721634 CYP3A4 99381661 7 Phase I T C Leu15Pro 100 0 0
rs2740574 CYP3A4 99382096 7 Phase I A G 97 2 1
rs3815459 KCNH2 150644394 7 Eag family Others A G 40 48 12
rs36210421 KCNH2 150644428 7 Others G T Arg707Leu 100 0 0
rs12720441 KCNH2 150647304 7 Others C T Arg444Trp 100 0 0
rs3807375 KCNH2 150667210 7 Others A G 56 37 7
rs4986893 CYP2C19 96540410 10 Cytochrome P450 superfamily Phase I G A Trp212null 88 11 1
rs4244285 CYP2C19 96541616 10 Phase I G A Pro227Pro 69 26 5
rs1799853 CYP2C9 96702047 10 Phase I C T Arg144Cys 100 0 0
rs1801252 ADRB1 115804036 10 Adrenergic receptors family Phase I G A Ser49Gly 5 26 69
rs1801253 ADRB1 115805056 10 Phase I C G Gly389Arg 69 26 4
rs5219 KCNJ11 17409572 11 Inward-rectifier potassium channel family Others C T Lys23Glu 39 54 7
rs1695 GSTP1 67352689 11 Glutathione S-transferase family Phase II A G Ile105Val 52 46 2
rs1138272 GSTP1 67353579 11 Phase II T C Ala114Val 0 3 97
rs1800497 ANKK1 113270828 11 Ser/Thr protein kinase family Phase I T C Glu713Lys 7 40 51
rs6277 DRD2 113283459 11 G-protein coupled receptor family Others C T Pro290Pro 79 19 2
rs4149056 SLCO1B1 21331549 12 Solute carrier family Others T C Val174Ala 71 28 1
rs7975232 VDR 48238837 12 Nuclear receptor family Others C A 42 51 7
rs1544410 VDR 48239835 12 Others G A 72 26 2
rs2239185 VDR 48244559 12 Others T C 7 51 42
rs1540339 VDR 48257326 12 Others G A 18 49 33
rs2239179 VDR 48257766 12 Others A G 46 43 11
rs3782905 VDR 48266167 12 Others C G 62 35 2
rs2228570 VDR 48272895 12 Others T C Met51Arg, Met51Lys, Met51Thr 9 51 40
rs10735810 VDR 48272895 12 Others C T 38 40 9
rs11568820 VDR 48302545 12 Others G A 55 36 7
rs1801030 SULT1A1 28617485 16 Sulfotransferase family Phase II A G Val223Met 100 0 0
rs3760091 SULT1A1 28620800 16 Phase II C G 32 43 18
rs7294 VKORC1 31102321 16 - Phase I G A 67 32 1
rs9934438 VKORC1 31104878 16 - Phase I G A 2 35 63
rs28399454 CYP2A6 41351267 19 Cytochrome P450 superfamily Phase I G A Val365Met 100 0 0
rs28399444 CYP2A6 41354190 19 Phase I AA - Glu197Ser, Glu197Arg 100 0 0
rs1801272 CYP2A6 41354533 19 Phase I T A Leu160His 95 3 0
rs28399433 CYP2A6 41356379 19 Phase I G T 1 20 79
rs3745274 CYP2B6 41512841 19 Phase I G T Gln172His 65 29 6
rs28399499 CYP2B6 41518221 19 Phase I T C Ile328Thr 99 1 0
rs3211371 CYP2B6 41522715 19 Phase I C T Arg487Cys 0 100 0
rs12659 SLC19A1 46951556 21 Solute carrier family Others C T Pro192Pro 19 51 30
rs1051266 SLC19A1 46957794 21 Others G A His27Arg 18 50 31
rs1131596 SLC19A1 46957916 21 Others T C 19 38 24
rs4680 COMT 19951271 22 - Phase II A G Val158Met 10 38 52
rs59421388 CYP2D6 42523610 22 Cytochrome P450 superfamily Phase I C T Val287Met 100 0 0
rs28371725 CYP2D6 42523805 22 Phase I G A 86 13 0
rs16947 CYP2D6 42523943 22 Phase I G A 44 35 0
rs61736512 CYP2D6 42525134 22 Phase I C A/G/T Val136Met 100 0 0
rs28371706 CYP2D6 42525772 22 Phase I C T Thr107Ile 100 0 0
rs5030656 CYP2D6 42524176:42524176 22 Phase I AAG - 100 0 0
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Table 2.

Significant VIP variants in Mongols compared with the eleven populations which selected from 1000 genomes project

SNP ID p < 0.05
ASW CDX CEU CHB GIH JPT LWK MXL PUR TSI YRI
rs10264272 1.42E-05 - - - - - 1.01E-01 1.35E-10 3.35E-06 7.27E-48 3.30E-02
rs1042713 1.90E-01 2.05E-01 2.09E-01 1.90E-01 1.74E-01 1.70E-01 1.63E-01 1.57E-01 1.84E-01 2.00E-01 1.83E-01
rs1042714 1.29E-01 1.31E-01 2.37E-01 1.30E-01 1.17E-01 1.32E-01 1.19E-01 1.27E-01 1.95E-01 1.87E-01 1.29E-01
rs1045642 2.25E-01 1.59E-01 2.35E-01 1.58E-01 2.36E-01 1.88E-01 2.43E-01 1.86E-01 1.61E-01 1.81E-01 2.49E-01
rs1051266 1.54E-01 1.67E-01 2.22E-01 1.98E-01 2.41E-01 1.64E-01 2.01E-01 2.65E-01 2.20E-01 2.12E-01 1.93E-01
rs1065776 7.08E-02 1.10E-05 8.68E-04 1.09E-04 8.98E-03 2.49E-03 7.70E-02 4.22E-04 4.99E-03 9.58E-05 8.60E-02
rs10735810 4.49E-01 2.18E-01 2.79E-01 2.83E-01 3.77E-01 3.30E-01 4.69E-01 2.42E-01 3.16E-01 3.10E-01 4.55E-01
rs10929302 1.62E-01 7.71E-02 1.47E-01 7.55E-02 2.47E-01 7.73E-02 1.85E-01 1.74E-01 1.61E-01 1.00E-01 1.87E-01
rs1128503 4.27E-01 1.45E-01 2.53E-01 1.68E-01 1.73E-01 1.56E-01 4.73E-01 2.29E-01 2.69E-01 2.59E-01 4.48E-01
rs1131596 1.94E-01 1.59E-01 2.04E-01 1.81E-01 2.21E-01 1.61E-01 2.56E-01 2.42E-01 2.00E-01 1.94E-01 2.37E-01
rs1138272 4.98E-07 6.87E-16 4.54E-03 6.87E-16 2.20E-03 6.87E-16 6.75E-09 2.13E-04 6.74E-06 1.43E-04 6.87E-16
rs1142345 4.41E-03 1.55E-06 8.22E-07 2.81E-16 7.94E-08 5.19E-09 9.34E-03 4.20E-05 3.05E-03 9.93E-11 2.63E-04
rs11568820 4.50E-01 1.56E-01 1.09E-01 2.10E-01 1.89E-01 2.38E-01 5.72E-01 1.15E-01 1.13E-01 1.06E-01 6.86E-01
rs1229984 2.02E-01 3.25E-01 2.00E-01 3.79E-01 1.98E-01 3.96E-01 2.02E-01 1.88E-01 1.92E-01 1.94E-01 2.02E-01
rs12659 2.31E-01 1.65E-01 2.21E-01 1.88E-01 2.51E-01 1.60E-01 1.66E-01 2.62E-01 2.19E-01 2.09E-01 1.90E-01
rs12720441 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00 1.00E + 00
rs1540339 3.35E-01 1.98E-01 2.90E-01 2.11E-01 2.72E-01 2.17E-01 4.10E-01 2.53E-01 2.82E-01 2.69E-01 3.65E-01
rs1544410 1.27E-01 3.56E-02 2.98E-01 3.26E-02 2.59E-01 4.48E-02 1.13E-01 7.41E-02 2.43E-01 2.49E-01 1.47E-01
rs16947 2.23E-01 9.14E-02 1.54E-01 9.11E-02 2.41E-01 9.15E-02 4.12E-01 1.10E-01 2.01E-01 1.90E-01 3.38E-01
rs1695 2.42E-01 1.06E-01 1.95E-01 1.09E-01 1.39E-01 1.14E-01 2.81E-01 3.22E-01 1.79E-01 1.29E-01 1.98E-01
rs17238540 3.20E-03 - 1.07E-15 - - - 2.03E-03 9.22E-07 1.14E-12 6.91E-08 2.99E-03
rs17244841 3.79E-03 4.44E-45 2.57E-12 4.44E-45 4.44E-45 1.82E-10 1.81E-03 3.30E-06 8.88E-13 4.29E-07 2.75E-03
rs1799853 1.71E-06 - 2.34E-02 - 1.22E-05 - - 3.82E-03 1.69E-02 2.50E-02 -
rs1800460 9.41E-09 4.44E-45 1.66E-07 4.44E-45 4.44E-45 4.44E-45 4.44E-45 3.30E-06 2.57E-05 5.05E-13 4.44E-45
rs1800497 2.14E-01 2.22E-01 1.24E-01 2.16E-01 1.20E-01 1.84E-01 1.77E-01 2.16E-01 1.19E-01 1.22E-01 1.86E-01
rs1800888 - - 1.07E-15 - - - - - - 6.14E-17 -
rs1801131 1.07E-01 1.26E-01 1.51E-01 1.05E-01 2.12E-01 1.10E-01 1.09E-01 1.11E-01 1.06E-01 1.41E-01 1.13E-01
rs1801133 1.40E-01 1.40E-01 1.25E-01 2.32E-01 1.38E-01 1.74E-01 1.46E-01 2.34E-01 2.19E-01 2.33E-01 1.43E-01
rs1801252 6.55E-02 6.41E-02 6.28E-02 6.45E-02 6.35E-02 6.43E-02 1.47E-01 1.15E-01 8.73E-02 5.96E-02 8.24E-02
rs1801253 2.01E-01 8.00E-02 1.68E-01 8.58E-02 9.53E-02 6.70E-02 1.53E-01 6.09E-02 9.70E-02 1.82E-01 2.94E-01
rs1801272 9.73E-01 9.83E-01 9.43E-01 9.83E-01 9.72E-01 9.83E-01 9.83E-01 9.65E-01 9.77E-01 9.30E-01 9.83E-01
rs1805124 1.22E-01 2.93E-02 6.49E-02 3.75E-02 7.93E-02 3.85E-02 1.55E-01 5.42E-02 1.20E-01 1.00E-01 1.77E-01
rs20417 6.15E-01 9.18E-01 7.73E-01 9.06E-01 7.81E-01 9.17E-01 6.48E-01 7.29E-01 7.28E-01 8.22E-01 5.42E-01
rs2046934 7.68E-02 7.56E-02 7.82E-02 7.93E-02 7.48E-02 7.71E-02 7.48E-02 7.54E-02 7.44E-02 7.61E-02 7.73E-02
rs2066702 6.37E-02 - - - - - 1.79E-02 3.41E-08 2.32E-10 - 1.45E-01
rs2066853 1.69E-01 1.99E-01 2.60E-01 1.58E-01 2.51E-01 1.79E-01 1.94E-01 2.45E-01 2.39E-01 2.58E-01 1.75E-01
rs2228570 1.75E-01 2.40E-01 1.84E-01 1.80E-01 1.51E-01 1.43E-01 1.82E-01 2.17E-01 1.53E-01 1.58E-01 1.77E-01
rs2239179 1.37E-01 1.44E-01 2.39E-01 1.54E-01 2.39E-01 1.59E-01 1.47E-01 1.38E-01 1.99E-01 1.80E-01 1.39E-01
rs2239185 2.69E-01 1.33E-01 3.05E-01 1.41E-01 2.42E-01 1.32E-01 3.30E-01 1.82E-01 2.94E-01 2.96E-01 2.92E-01
rs2740574 6.05E-01 3.29E-12 8.71E-08 3.29E-12 1.99E-03 3.29E-12 7.90E-01 1.22E-03 5.35E-02 4.58E-06 7.11E-01
rs28371706 2.11E-02 - - - - - 5.24E-02 - 3.96E-24 - 1.14E-01
rs28371725 7.01E-04 7.46E-03 2.18E-02 1.65E-03 3.43E-02 3.16E-04 1.41E-03 6.68E-04 2.13E-02 3.39E-02 4.43E-04
rs28399433 2.33E-02 6.01E-02 1.44E-02 1.27E-01 6.80E-02 1.37E-01 2.10E-02 2.26E-02 2.25E-02 1.69E-02 2.27E-02
rs28399454 5.13E-04 - - - - - 4.87E-05 - 3.96E-24 - 1.45E-02
rs28399499 3.79E-03 4.44E-45 4.44E-45 4.44E-45 4.44E-45 4.44E-45 1.77E-04 9.37E-19 1.82E-10 4.44E-45 8.27E-03
rs3211371 3.94E-01 4.14E-01 3.57E-01 4.14E-01 3.56E-01 4.08E-01 4.14E-01 3.63E-01 3.57E-01 3.50E-01 4.08E-01
rs36210421 - - 6.00E-06 - 5.70E-46 6.74E-47 - 2.15E-29 - 6.14E-17 -
rs3745274 1.79E-01 1.61E-01 1.26E-01 8.04E-02 2.18E-01 8.94E-02 1.83E-01 1.50E-01 1.74E-01 1.43E-01 2.17E-01
rs3760091 1.69E-01 1.79E-01 1.60E-01 1.64E-01 2.31E-01 1.65E-01 1.82E-01 1.78E-01 1.68E-01 1.85E-01 1.79E-01
rs3782905 5.54E-01 6.44E-01 4.26E-01 6.28E-01 5.24E-01 6.82E-01 5.48E-01 5.66E-01 4.68E-01 4.46E-01 5.58E-01
rs3807375 1.14E-01 1.07E-01 3.91E-01 1.06E-01 3.59E-01 1.11E-01 1.11E-01 2.19E-01 3.31E-01 3.97E-01 1.08E-01
rs3814055 1.27E-01 1.53E-01 1.43E-01 1.31E-01 1.91E-01 1.35E-01 1.25E-01 1.42E-01 1.89E-01 1.56E-01 1.31E-01
rs3815459 2.70E-01 1.82E-01 4.25E-01 1.61E-01 3.22E-01 1.87E-01 2.89E-01 2.83E-01 3.54E-01 3.97E-01 3.27E-01
rs3846662 3.96E-01 1.86E-01 1.60E-01 1.90E-01 2.68E-01 1.97E-01 4.66E-01 1.55E-01 1.85E-01 1.58E-01 4.58E-01
rs3918290 5.03E-28 - 3.18E-44 - 2.72E-16 - - - - 7.27E-48 -
rs4124874 4.33E-01 2.03E-01 1.92E-01 1.47E-01 3.05E-01 1.37E-01 5.27E-01 2.44E-01 2.24E-01 1.98E-01 5.38E-01
rs4148323 9.94E-02 1.05E-01 9.82E-02 9.89E-02 1.01E-01 1.05E-01 9.82E-02 1.01E-01 9.82E-02 9.82E-02 9.82E-02
rs4149056 3.68E-02 4.68E-02 4.74E-02 4.64E-02 2.73E-02 4.48E-02 2.74E-02 3.90E-02 4.48E-02 8.70E-02 2.48E-02
rs4244285 6.40E-02 1.19E-01 6.35E-02 1.72E-01 1.68E-01 1.62E-01 8.47E-02 6.31E-02 6.34E-02 6.01E-02 6.53E-02
rs4680 1.23E-01 1.23E-01 2.29E-01 1.34E-01 2.10E-01 1.20E-01 1.20E-01 1.85E-01 1.85E-01 1.54E-01 1.54E-01
rs4986893 1.93E-04 6.14E-03 1.93E-04 2.27E-03 2.96E-04 5.46E-03 4.39E-04 1.93E-04 1.93E-04 1.93E-04 1.93E-04
rs4986910 5.03E-28 - 1.07E-15 - - - - - 6.74E-47 - -
rs4986913 - - - - 5.70E-46 - - - - - -
rs5219 1.85E-01 1.64E-01 1.59E-01 1.57E-01 1.79E-01 1.38E-01 2.13E-01 1.72E-01 1.49E-01 1.49E-01 2.14E-01
rs59421388 1.71E-06 - - - - - 3.66E-02 - 6.74E-47 - 4.90E-03
rs6025 4.44E-45 4.44E-45 1.42E-08 4.44E-45 7.17E-24 4.44E-45 4.44E-45 9.37E-19 1.36E-16 2.56E-24 4.44E-45
rs61736512 1.71E-06 - - - - - 3.66E-02 - 6.74E-47 - 6.07E-03
rs6277 4.28E-02 1.69E-02 3.46E-01 1.42E-02 1.87E-01 2.09E-02 9.87E-03 1.66E-01 2.93E-01 4.45E-01 1.22E-02
rs6791924 6.55E-05 - - - - - 2.96E-02 1.35E-10 3.96E-24 - 1.66E-03
rs689466 1.74E-01 2.50E-01 1.61E-01 2.17E-01 1.75E-01 1.99E-01 1.92E-01 1.47E-01 1.50E-01 1.60E-01 1.85E-01
rs698 7.48E-02 7.35E-02 2.80E-01 6.65E-02 1.29E-01 6.98E-02 7.49E-02 1.29E-01 1.91E-01 1.48E-01 6.94E-02
rs701265 3.38E-01 1.81E-01 1.87E-01 1.55E-01 1.76E-01 1.58E-01 4.38E-01 1.77E-01 1.65E-01 1.88E-01 4.39E-01
rs7294 2.93E-01 5.96E-02 1.57E-01 4.55E-02 4.69E-01 5.39E-02 2.53E-01 1.88E-01 1.79E-01 1.75E-01 3.28E-01
rs7626962 6.55E-05 - - - - - 1.98E-08 - 1.68E-16 - 1.18E-03
rs776746 5.43E-01 1.66E-01 1.26E-02 1.66E-01 1.36E-01 1.17E-01 7.44E-01 1.01E-01 1.25E-01 1.45E-02 6.95E-01
rs7975232 3.26E-01 1.42E-01 3.01E-01 1.41E-01 2.46E-01 1.37E-01 3.90E-01 1.77E-01 2.94E-01 2.96E-01 3.05E-01
rs890293 3.42E-01 4.34E-01 4.12E-01 4.16E-01 4.10E-01 4.29E-01 3.58E-01 4.24E-01 4.04E-01 4.11E-01 3.48E-01
rs975833 2.05E-01 3.46E-01 2.09E-01 3.72E-01 1.91E-01 3.80E-01 2.28E-01 2.50E-01 2.17E-01 1.99E-01 1.88E-01
rs9934438 6.28E-01 7.43E-02 3.64E-01 6.42E-02 6.01E-01 7.08E-02 7.79E-01 3.29E-01 3.99E-01 3.22E-01 7.49E-01
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aItalics indicated that after adjustment p < 0.05 the locus has statistically significant

bThe results has not the mathematics sense

Fig. 1.

Fig. 1

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Pairwise comparisons of difference in correlation coefficient value R2

We used χ2 analyses to compare differences in the variants’ genotype frequency distributions among the Mongols and eleven HapMap populations (without adjustment, p < 0.05; adjustment, p < 0.05/85 × 11). There were a number of loci had significantly different distribution frequencies among Mongols and the 11 HapMap populations that listed in Table 3 Without adjustment the ASW population had 20 different loci; CEU, 27; CHB, 19; CHD, 10; GIH, 24; JPT, 17; LWK, 24; MEX, 17; MKK, 22; TSI, 14; and YRI, 38. Once the data underwent multiple comparison adjustment, the numbers of significantly different loci variants were revised to 13, 13, 6, 1, 8, 5, 18, 3, 18, 6 and 26, respectively.

Table 3.

Significant VIP variants in Mongols compared with the eleven HapMap populations after Bonferroni’s multiple adjustment

SNP ID p < 0.05/(85 × 11)
ASW CEU CHB CHD GIH JPT LWK MEX MKK TSI YRI
rs10264272 - - - - - - 1.44532E-12 a -b 8.82E-08 - 8.95E-09
rs1042713 0.27747316 0.04538453 0.20742869 0.12421983 0.47741241 0.58439074 0.54885721 0.90201422 0.32444254 0.01651791 0.32052007
rs1042714 - 8.50E-05 0.0164067 - - 0.00044539 - - - - 0.07513803
rs1045642 4.92E-09 0.00116183 0.54603257 0.29771668 0.00162633 0.28558754 - 0.45392674 1.61E-08 0.50240513 2.41E-10
rs1051266 0.2807523 0.01941254 0.33010486 0.12007109 0.00246504 0.95743171 0.00188424 0.00059997 1.36E-05 0.07011082 0.01298312
rs1065776 - - - - - - - - - - -
rs10735810 0.01884334 0.19825318 0.35306461 0.01687974 0.42789254 0.16219036 0.00044495 0.00959988 0.00592383 0.43490023 0.00575742
rs10929302 - 0.2213663 0.06968442 - - 0.08008041 - - - - 0.00749071
rs1128503 3.62E-11 0.00100994 0.18626034 0.47475329 0.86444934 0.72437343 9.47E-20 0.0231531 1.73E-21 0.00083246 2.91E-21
rs1131596 - - - - - - - - - - -
rs1138272 - - - - - - - 0.03123952 - - -
rs1142345 - - - - - - 0.00012785 0.01252136 - - 0.1353563
rs11568820 8.07E-12 0.39713354 0.00088999 0.06850479 0.00681425 0.00038384 3.81E-24 0.46841122 8.28E-23 0.75222572 9.16E-37
rs1229984 - 1.20E-11 5.49E-10 - - 7.68E-09 - - - - 1.69E-11
rs12659 - - - - - - - - - - -
rs12720441 - - - - - - - - - - -
rs12721634 - - - - - - - - - - -
rs1540339 6.30E-07 0.000116 0.02314118 0.00276558 5.31E-05 0.0048091 1.48E-15 0.01115187 7.82E-16 0.00014778 1.52E-12
rs1544410 0.06188271 1.69E-08 0.00273242 0.00242682 3.33E-08 0.39727054 0.04804346 0.14153371 2.63E-06 2.71E-07 0.00553292
rs16947 - - - - - - - - - - -
rs1695 0.00017138 0.0007169 0.21785447 0.42376541 0.07372284 6.16E-05 2.60E-07 6.20E-08 0.00864594 0.15360318 0.00135975
rs17238540 - - - - - - - - - - -
rs17244841 - - - - - - - - - - -
rs1799853 - - - - - - - - - - -
rs1800460 - - - - - - - - - - -
rs1800497 0.05635435 0.10573707 0.01289027 0.00481115 0.55312005 0.02909995 0.16955696 0.04539287 0.16542808 0.29503104 0.01419286
rs1800888 - - - - - - - - - - -
rs1801030 - - - - - - - - - - -
rs1801131 0.29838721 0.10171883 0.44379999 0.64433884 0.0183119 0.20392711 0.2737931 0.64086028 0.91251575 0.36340669 0.00184881
rs1801133 0.00034783 0.72955982 0.00135396 0.49992159 0.0319354 0.23529674 1.68E-05 0.05835697 1.52E-07 0.00349079 4.10E-06
rs1801252 - - 0.0001403 - - 0.0001681 - - - - 4.90E-05
rs1801253 - 0.01885371 0.22338349 - - 0.79204456 - - - - 2.70E-05
rs1801272 - 2.63E-33 1.46E-31 - - 2.42E-30 - - - - 4.91E-35
rs1805124 0.02658648 0.36484631 0.54483156 0.22673707 0.35376474 0.6491555 0.00245978 0.86877609 1.87E-06 0.04053952 0.00013843
rs2032582 2.07E-07 0.46831684 0.02691764 0.0899833 0.00032517 0.11450139 5.66E-17 0.70811234 6.28E-14 0.96126181 -
rs2032582 - - - - - - - - - - -
rs2032582 - - - - - - - - - - -
rs20417 - 1.04E-29 2.19E-29 - - 3.82E-29 - - - - 2.24E-24
rs2046934 - 0.84184132 0.87995183 - - 0.97034167 - - - - 0.77148622
rs2066702 1.06E-10 - - - - - 4.08E-07 - - - 5.65E-15
rs2066853 0.65615202 1.66E-11 0.94214103 0.94774007 2.18E-09 0.49230972 0.2116787 5.48E-06 0.41238266 9.43E-11 0.62463192
rs2228570 - - - - - - - - - - -
rs2239179 0.19236008 0.00467976 0.16879391 0.0735274 0.00371014 0.07153574 4.11E-08 0.71139635 0.04517466 0.17315647 0.71742841
rs2239185 - - 0.11123339 - - 0.45274301 - - - - 0.0001787
rs2740574 - - - - - - - - - - -
rs28371706 - - - - - - - - - - -
rs28371725 - - - - - - - - - - -
rs28399433 - - - - - - - - - - -
rs28399444 - - - - - - - - - - -
rs28399454 - - - - - - - - - - -
rs28399499 0.00193936 - - - - - - - 0.43590356 - 9.04E-06
rs3211371 - - - - - - - - - - -
rs36210421 - - - - - - - - - - -
rs3745274 0.12627303 0.2056118 0.70303194 0.34150665 0.00017843 0.84757978 0.04657355 0.04107996 0.00024687 0.1559596 2.11E-05
rs3760091 - - - - - - - - - - -
rs3782905 - 4.47E-15 4.87E-18 - - 1.49E-22 - - - - 4.87E-19
rs3807375 0.43616954 2.01E-12 0.86962796 0.83584673 8.18E-11 0.12181871 0.12372447 0.01752245 0.5934107 2.36E-12 0.57322879
rs3814055 0.83102212 0.52973507 0.45387793 0.20572098 0.03703383 0.18923006 0.29073405 0.7548396 0.05268438 0.26051487 0.09961755
rs3815459 - - 0.3754275 - - 0.00773141 - - - - 8.65E-06
rs3846662 8.13E-12 0.6759055 0.19583564 0.0971148 0.00018701 0.15042586 3.46E-24 0.45840601 2.29E-17 0.72844286 2.62E-26
rs3918290 - - - - - - - - - - -
rs4124874 1.52E-11 0.0357679 0.20056719 0.8982781 4.02E-07 0.89376825 3.20E-22 0.00572364 6.38E-23 0.14836683 3.23E-27
rs4148323 - 6.57E-08 0.11727653 1.15E-20 3.34E-08 0.02782333 - 7.78E-05 - - 6.57E-08
rs4149056 0.01202321 0.99516204 0.98870047 0.36757664 6.12E-05 0.19624085 1.43E-05 0.19712962 0.36973702 0.20946995 1.34E-07
rs4244285 - 0.75228568 0.00660126 - - 0.09009154 - - - - 0.73387157
rs4680 0.48647969 0.00082072 0.98587091 0.14463711 0.04170753 0.66470326 0.78746668 0.23399959 0.94982091 0.00298993 0.57419215
rs4986893 - - - - - - - - - - -
rs4986909 - - - - - - - - - - -
rs4986910 - - - - - - - - - - -
rs4986913 - - - - - - - - - - -
rs5030656 - - - - - - - - - - -
rs5219 - - - - - - - - - - -
rs59421388 - - - - - - - - - - -
rs6025 - 0.42877395 - - - - - - - - -
rs61736512 - - - - - - - - - - -
rs6277 - 1.03E-13 0.29571877 - - 0.24782281 - - - - 0.052309
rs6791924 - - - - - - - - - - -
rs689466 0.00012294 0.00040262 0.00388527 0.02658023 0.00036352 0.031278 9.46E-12 0.34461035 1.46E-16 0.00281474 3.77E-08
rs698 0.67209493 3.15E-08 0.00098605 0.00686794 0.18843861 0.00282244 0.51518149 0.14164572 0.31894381 0.05936975 0.00044968
rs701265 1.10E-07 0.00028496 0.22430801 0.08574165 0.01840946 0.09399066 1.48E-16 0.00740647 6.36E-16 4.43E-05 3.39E-18
rs7294 3.00E-09 3.54E-05 0.00145857 0.01140667 2.58E-21 0.1101385 3.98E-08 0.00317685 1.58E-11 0.00047099 3.28E-12
rs7626962 - - - - - - - - - - 0.0012409
rs776746 3.55E-17 0.01253767 5.28E-05 0.00537234 0.00294358 0.00027677 6.11E-31 0.00270059 2.59E-17 0.10945504 2.12E-34
rs7975232 5.21E-07 6.63E-07 0.17255518 0.39866127 0.00031086 0.49657642 1.18E-13 0.06431886 8.52E-13 3.91E-07 3.28E-09
rs890293 - - - - - - - - - - -
rs975833 - 0.0999674 5.65E-11 - - 4.30E-09 - - - - 0.02956016
rs9934438 1.86E-24 9.17E-14 0.0001892 0.00092503 1.98E-24 0.02360989 5.38E-33 1.64E-08 1.98E-32 1.07E-09 1.88E-41
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a Italics indicated that after adjustment p < 0.05/(85*11) the locus has statistically significant

b The results has not the mathematics sense

When p < 0.05, rs1540339 locus (46489G > A) which located in an intron region of VDR (1, 25- dihydroxyvitamin D3 receptor), showed the greatest number of significant differences between Mongol and 11 HapMap populations; the SNP rs776746 (12083G > A) is a SNP of CYP3A5 which located in an intron region and a significant locus that observed in these populations except TSI. After Bonferroni’s multiple adjustment (p < 0.05/(85 × 11)), the number of HapMap populations with a significantly different rs1540339 locus changed very large which included CEU, CHB, CHD, JPT, MEX and TRI. The rs776746 locus also changed very large which except TSI added CEU, CHD, GIH, JPT and MEX.

Of the 85 variants analyzed, 74 could be classified as part of a superfamily. When the gene superfamily categories were tallied, the number of the associated variants with significantly different frequencies between the Mongols and the eleven HapMap populations were as follows: ASW, 10; CEU, 9; CHB, 5; CHD, 1; GIH, 5; JPT, 4; LWK, 14; MEX, 1; MKK, 14; TSI, 4; and YRI, 21 (Table 4). A number of distinct loci were significantly different and included several pharmacogenomic superfamilies such as the nuclear receptor family, the sodium channel gene family, and the methylenetetrahydrofolate reductase family.

Table 4.

The VIP variants in Mongols compared with eleven HapMap groups according to the gene superfamily classification

ASW CEU CHB CHD GIH JPT LWK MEX MKK TSI YRI
rs1045642 rs1229984 rs1229984 rs4148323 rs1540339 rs1229984 rs10264272 rs1695 rs10264272 rs1544410 rs10264272
rs1128503 rs1544410 rs1801272 rs1544410 rs1801272 rs1128503 rs1045642 rs3807375 rs1045642
rs11568820 rs1801272 rs3782905 rs3807375 rs3782905 rs11568820 rs1051266 rs701265 rs1128503
rs1540339 rs3782905 rs776746 rs4124874 rs975833 rs1540339 rs1128503 rs7975232 rs11568820
rs2032582 rs3807375 rs975833 rs4148323 rs1695 rs11568820 rs1229984
rs2066702 rs4148323 rs1801133 rs1540339 rs1540339
rs4124874 rs6277 rs2032582 rs1544410 rs1801133
rs701265 rs698 rs2066702 rs1801133 rs1801252
rs776746 rs7975232 rs2239179 rs1805124 rs1801253
rs7975232 rs4124874 rs2032582 rs1801272
rs4149056 rs4124874 rs2066702
rs701265 rs701265 rs28399499
rs776746 rs776746 rs3745274
rs7975232 rs7975232 rs3782905
rs3815459
rs4124874
rs4148323
rs4149056
rs701265
rs776746
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To further verify the ubiquitous differences between different groups through research the difference of maximum and minimum of two SNPs, we selected two variants, the most significantly different variants -- rs1540339, rs1801131 which is one of the least significantly loci distributed in all 12 populations, and downloaded the population data from the ALFRED database. Combining the new data, we carried out a global analysis. Figure 2 shows the global frequency data of rs1801131 and Fig. 3, the rs1540339 data. From the two figures, we only found that the frequency of Mongol is relatively close to the populations distributed in East Asia.

Fig. 2.

Fig. 2

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The global frequency distribution of rs1801131. NA, North America; SA, South America

Fig. 3.

Fig. 3

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Rs1540339 frequencies in various global populations. EAsia, East Asia; NA, North America; SA, South America

Meanwhile, we focused on rs1540339 to explore the difference of the haplotypes. We performed the LD analysis to define blocks and haplotypes of VDR gene which include rs1540339, rs7975232, rs1544410, rs2239179, rs10735810 and rs11568820 by Haploview. The six SNPs selected from our lists and all of them have the HapMap data. Figure 4 shown that Mongol and CHB has only one block which consisted by rs1540339 and rs2239179, others has obviously different blocks compared with Mongol.

Fig. 4.

Fig. 4

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Linkage disequilibrium analysis of the VDR in each of the twelve populations. LD is displayed by standard color schemes with bright red for very strong LD (LOD > 2, D ′ =1), pink red (LOD > 2, D ′ <1), blue (LOD < 2, D ′ = 1) for intermediate LD, and white (LOD < 2, D′ <1) for no LD

For further clarified the genetic structure of Mongol and different populations, we used Structure 2.3.1 performed the population genetic structure comparisons by which works well for 85 loci (K = 2–8). The results are indicated by K = 3–5 (Fig. 5), which based on the Estimated Ln Prob of Data and other recommendations of the STRUCTURE software manual, When k = 3, individuals were divided in three affinity groups (subgroups 1: Mongol, CHD, JPT, CHB; subgroup 2: MEX, TSI, GIH, CEU; subgroup 3: MKK, ASW, LWK, YRI.) which used relative majority of likelihood assignment of individuals to subgroup. Followed by more K value to run STRUCTURE and then displayed the results in bar plots. From the image when k = 4 and 5, we easily found Mongol is closest to CHD, followed by CHB, JPT, and existed significant genetic structure differences with GIH and MEX.

Fig. 5.

Fig. 5

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Structure analysis of the genetic relationship between 12 populations. K is the possible numbers of parental population clusters. One color represents one parental population cluster. Each individual is represented by a vertical column partitioned into different color segments. Most suitable K was observed at K = 5, where the proportion of each ancestral component in a single individual is represented by a vertical bar divided into 5 colors

Discussion

Personalized or stratified healthcare is an important goal for medicine in the 21st century. It ensures that the treatments of patients are safe and efficacious [17]. With the rapid development of pharmacogenetics, serious attention has been paid to interethnic or interracial differences in drug responses with the intent to identify the genetic backgrounds of these variations [18]. Our study analyzed the distribution of these VIP variant allele and genotype frequencies to seek out which are altered among the different human populations [19], and found that even the SNP of smallest difference also had significant diversity between different groups. Through the comprehensive analysis, we revealed that Mongol and Chinese populations have the minimum difference.

Two of the variants were identified, rs1801133 (C677T) and rs1801131 (A1298C), included one of the least significant locus in our data, they are located in the same gene -- methylenetetrahydrofolate reductase (MTHFR) gene. MTHFR is located on chromosome 1p36.3 in human which is an important regulatory enzyme that involved in the folate pathway. It catalyzes the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate [20, 21]. Thymidylate synthesis required a lower 5,10-methylenetetrahydrofolate levels which leading to misincorporation of uracil into DNA, increasing chromosome damage frequency. A lower levels of 5-methyltetrahydrofolate may decrease the methylation process of homocysteine to methionine which could lead to hyperhomocysteinemia and DNA hypomethylation. Severe MTHFR enzyme deficiency is the most common inherited folate metabolism disorder which leads to hyperhomocysteinemia and homocystinuria that eventually destroy the central nervous system and vascular system [22]. Several studies revealed that the C677T and A1298C mutations reduce MTHFR enzyme activity [20–25]. In Caucasians, the C677T of TT and CT carriers had 70 % and 35 % reduced MTHFR enzyme activity, respectively, compared to CC carriers [26]. In Mongolians, CT and TT carriers had a frequency about 0.39 and 0.09. We should pay more attention on capecitabine, cisplatin, pemetrexed, cyanocobalamin and related agents in the Mongolian. Research of this mutation in other populations had not been performed. The enzyme activity reduction extent of different A1298C carriers had not been researched, but the study would play a large role in clinical treatment when one medication cure different patient who carriers different A1298C genotype.

We randomly selected one of the middle significantly different variants in Mongols -- the non-synonymous SNP rs1805124 (A1673G-H558R), which is located in exon 12 of SCN5A [27]. SCN5A encodes the integral membrane protein, voltage-dependent sodium channel α-subunit. It primarily traffics sodium in human heart muscle cells [28, 29]. SCN5A can cause fast depolarization during the upstroke phase of cardiac action potentials, that is the reason as a molecular antiarrhythmic drug target [30]. Amounts of Studies reveals SCN5A is associated with various cardiac diseases including long-QT syndrome (LQTS), Brugada syndrome (Brs), progressive cardiac conduction defect, atrial fibrillation (AF), dilated cardiomyopathy, and overlapping syndromes [27–31]. SCN5A-H558R has been shown to generate moderate electrophysiological functions that can regulate the phenotypic expression of cardiac conduction. It is associated with the mechanism of atrial fibrillation [30, 32] and can modify QTc duration in people with LQTS [33]. Studies of different genotype frequencies in various populations related to SCN5A-H558R function have not yet been performed, but SY Nikulina.et.al already found that AG genotype of the H558R (rs1805124) polymorphism of the SCN5A gene is a genetic predictor of idiopathic disorders of atrioventricular and intraventricular conduction [34] We can carry out the prevention and early treatment of these diseases by gene sequencing.

Among Mongols and others global populations, numerous important genetic variants play critical roles in drug response and this information should directly applied to clinical guidelines. For instance rs1540339 (46489G > A), the most significant locus in our data, is associated with bronchodilator responsiveness [35]. Studies have been performed on the correlation between asthma and rs1540339; however, evaluation of this polymorphism in a clinical setting is not yet routine [36, 37].

Beyond the genetic factor, we also determined that long-term survival in different environments affects genetic adaption. Environmental pressures shape genotype distributions towards specific functions, particularly in pharmacogenetic genes. Studies by Janha et al., Sabbagh et al., and Fuselli et al. directly demonstrated that the different genotype frequencies of CYP2C19, NAT2, and CYP2D6 significantly differed between populations race, subsistence modes, and dietary habits also play a role in the evolutionary trajectory [38–40].

Conclusions

Different populations exists different genetic distribute frequencies. The drug dosage and usage of different genotype carriers is difference. Identifying genotype distribution and VIP variant frequencies in different populations to determine what medications might be most effective may provide a theoretical foundation for safe drug administration and improved curative effects. Besides, we figured out the minimum allele difference between Mongol and CDX. We also preliminary supplemented the pharmacogenomic data on the Mongol ethnic group and illustrated the differences between Mongols and other populations, and finally found Mongol and Chinese populations have the minimum difference. To the study, the sample size is relatively small and further investigation using a larger cohort of Mongols is needed to verify the generalizability of our results, and would be help us to establish a more reasonable and effective individualized treatment plan.

Abbreviations

ALFRED, the ALlele FREquency Database; ASW, a population of African ancestry in the southwestern USA; CEU, a northwestern European population; CHB, the Han Chinese in Beijing, China; CHD, the population of metropolitan Denver, Colorado, USA; GIH, the Gujarati Indians in Houston, Texas, USA; HWE, Hardy–Weinberg Equilibrium; JPT, the Japanese population in Tokyo, Japan; LWK, the Chinese living in Luhya in Webuye, Kenya; MEX, people of Mexican ancestry living in Los Angeles, California, USA; MKK, the Maasai people in Kinyawa, Kenya; MTHFR, methylenetetrahydrofolate reductase; PharmGKB, the Pharmacogenomics Knowledge Base; PMT, the Pharmacogenetics of Membrane Transporters database; PUR, a population of Puerto Ricans from Puerto Rico; TSI, the Tuscan people of Italy; VIP, very important pharmacogenetic; YRI, the Yoruba in Ibadan, Nigeria

Acknowledgments

Authors thank BioScience Writers for assistance in the manuscript preparation. We also appreciate the reviewers and editors for their patience to the work.

Funding

The work is supported by Natural Science Foundation of Xizang (Tibet) Autonomous Region (20152R-13-11), Major science and technology research projects of Xizang (Tibet) Autonomous Region (2015XZ01G23) and National Natural Science Foundation of China (No. 81560516).

Availability of data and material

All relevant data are available within the manuscript.

Authors’ contributions

TJ and XS drafted the manuscript. TJ, TF, and XS performed the statistical analyses and LW helped edit the manuscript. LW and HW performed the genotyping. TF and XS reviewed all the genotypes. TJ assisted in the study design. TJ and LK conceived the study and co-supervised the work. All the authors have read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interest.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study was approved by the Clinical Research Ethics of Xizang Minzu University and Northwest University, and it is in compliance with Department of Health and Human Services (DHHS) regulations for human research subject protection. Written informed consent was received from all of the study participants.

Footnotes

Tianbo Jin and Xugang Shi are joint first authors.

Contributor Information

Tianbo Jin, Phone: +86-29-33755000, FAX: +86-29-33755000, Email: jintianbo@gmail.com.

Longli Kang, Phone: +86-29-33755000, FAX: +86-29-33755000, Email: kanglongli6211@163.com.

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