Genetic polymorphisms of pharmacogenomic VIP variants in Li nationality of southern China

Environmental Toxicology and Pharmacology 42 (2016) 237–242

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Genetic polymorphisms of pharmacogenomic VIP variants in Li nationality of southern China Yipeng Ding a,∗,1 , Ping He a,1 , Na He b,c , Quanni Li a , Juan Sun a , Jinjian Yao a , Shengyang Yi a , Heping Xu a , Duoyi Wu a , Xiang Wang a , Tianbo Jin b,c,d,∗∗ a

Department of Emergency, People’s Hospital of Hainan Province, Haikou, Hainan 570311, China School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China c National Engineering Research Center for Miniaturized Detection Systems, Xi’an 710069, China d School of Life Sciences, Northwest University, Xi’an, Shaanxi 710069, China b

a r t i c l e

i n f o

Article history: Received 2 November 2015 Received in revised form 29 January 2016 Accepted 2 February 2016 Keywords: Genetic polymorphism Pharmacogenomics VIP variants Li ethnicity

a b s t r a c t Objectives: The present study aimed to screen members of the Li nationality in southern China for genotype frequencies of VIP variants and to determine differences between the Li ethnicity and global human population samples in HapMap. Methods: In this study, we genotyped 77 very important pharmacogenetic (VIP) variants selected from the pharmacogenomics knowledge base (PharmGKB) in members of the Li population and compared our data with other eleven populations from the HapMap data set. Results: Our results showed that VDR rs1540339, VKORC1 rs9934438, and MTHFR rs1801133 were most different in Li compared with most of the eleven populations from the HapMap data set. Furthermore, population structure and F-statistics (Fst) analysis also showed differences between the Li and other HapMap populations, and the results suggest that the Li are most genetically similar to the CHD population, and the least similar to the YRI in HapMap. Conclusions: The findings of our study complement the pharmacogenomics database with information on members of the Li ethnicity and provide a stronger scientific basis for safer drug administration, which may help clinicians to predict individual drug responses, thereby avoiding the risk of adverse effects and optimizing efficacy in this population. © 2016 Elsevier B.V. All rights reserved.

1. Introduction The concept of pharmacogenetics originated as a result of clinical observations that showed there were patients with very high or very low plasma or urinary drug concentrations, suggesting inherited variation in drug metabolism (Weinshilboum, 2003). It is estimated that genetics can account for 20–95% of the variability in drug disposition and effects (Kalow et al., 1998). Pharmacogenetics relates to the inheritance of individual variation in drug response, whilst pharmacogenomics, the convergence of pharmacogenetics and rapid advances in human genomics, with the aim to ultimately

∗ Corresponding author at: #19, Xiuhua Road, Haikou 570102, Hainan, China. ∗∗ Corresponding author at: #229 North Taibai Road, Xi’an 710069, Shaanxi, China. E-mail addresses: YipengDing [email protected] (Y. Ding), tianbo [email protected] (T. Jin). 1 These authors contribute equally to this work. http://dx.doi.org/10.1016/j.etap.2016.02.003 1382-6689/© 2016 Elsevier B.V. All rights reserved.

identify the right drug for the right patient, leading to personalized medicine (March, 2000; Weinshilboum, 2003). PharmGKB, the pharmacogenomics knowledge base (http:// www.pharmgkb.org) is devoted to the dissemination of information related to how genetic variation results in the variation of drug responses. It mainly describes the association between genes, drugs and diseases. PharmGKB delivers knowledge in a number of forms, including very important pharmacogene (VIP) summaries, drug pathway diagrams, and curated literature annotations (Owen et al., 2008). It currently contains information for more than 26,000 genes and 3000 drug, including 54 VIP summaries, 113 pharmacokinetic and pharmacodynamic pathways (Zhang et al., 2015). Recently, high-throughput experimental methods have provided a large quantity of genetic data that may permit us to improve pharmacogenomic research. These large-scale human data sets such as the HapMap Project, the Human Genome Diversity Project (HGDP) and the 1000 Genomes Project can be analyzed to discover sequence variants that affect common disease or drug responses. So this available information may be used

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to identify the health-related aspects most influenced by ethnicity. Now, global human population samples in HapMap are as following (http://hapmap.ncbi.nlm.nih.gov/biomart/martview/ e4f42d4d0acde5ea6c35312381c1e461): African ancestry in southwest USA (ASW); Utah residents with Northern and Western European ancestry (CEU); Han Chinese in Beijing, China (CHB); Chinese in Metropolitan Denver, Colorado (CHD); Gujarati Indians in Houston, Texas (GIH); Japanese in Tokyo, Japan (JPT); Luhya in Webuye, Kenya (LWK); Mexican ancestry in Los Angeles, California (MEX); Maasai in Kinyawa, Kenya (MKK); Toscans in Italy (TSI); Yoruba in Ibadan, Nigeria (YRI). The Li ethnic group has traditionally lived on Hainan Island in China for approximately 5000 years, and has a population of more than 1.28 million (2010 Census). As an ethnic minority in China, the Li population is a unique group who live only on Hainan Island. In recent years, pharmacogenomic studies have been conducted in several ethnic groups in China, however, we are unable to found any pharmacogenomic information in the Li ethnic group. Herein, we present the first study that systematically screens the Li ethnic group for genetic polymorphisms of pharmacogenomic VIP variants. 2. Materials and methods 2.1. Study participants We randomly recruited participants in a healthy Li population of 100 unrelated subjects (50 males and 50 females) from Hainan province of China and determined their lineage and birthplace belong to the Li ethnicity. All subjects were healthy in terms of their medical history and physical examination. Besides, an explanation about the purpose and experimental procedures of the study were given to all individuals. Written informed consent was obtained from all subjects prior to sample donation, and the study protocol was performed in accordance with the Declaration of Helsinki and approved by the Human Research Committee of the People’s Hospital of Hainan Province and Xizang Minzu University for Approval of Research Involving Human Subjects. 2.2. Variant selection and genotyping We successfully genotyped 77 VIP variants selected from PharmGKB VIP in 100 Li population from Hainan. Genomic DNA was isolated from whole blood using the GoldMag nanoparticle method (GoldMag Ltd., Xi’an, China), in combination with liquid assistant, high performance composite magnetic particles can be specific adsorption of combined genomic DNA, and after elution can separate DNA. Compared with other traditional methods, the advantage lies in the extraction of genomic DNA fragments integrity, with high purity and high yield, and no protein and nucleic acid enzymes impurity pollution, can be directly for various downstream experiment, such as PCR and sequencing. DNA concentration was measured with the NanoDrop 2000C (Thermo Scientific, Waltham, MA, USA). The Sequenom MassARRAY Assay Design 3.0 Software (San Diego, CA, USA) was used to design Multiplexed SNP MassEXTEND assays (Gabriel et al., 2009). Single nucleotide polymorphism (SNP) genotyping was performed using the Sequenom MassARRAY RS1000 (San Diego, CA, USA) according to the standard protocol recommended. Sequenom Typer 4.0 Software (San Diego, CA, USA) was used to perform data management and analysis (Thomas et al., 2007). 2.3. Data analysis Statistical calculations were performed with the help of Microsoft Excel (Redmond, WA, USA) and SPSS 19.0 statistical

package (SPSS, Chicago, IL, USA). All statistical tests were two sided. After Bonferroni correction, p ≤ 5.9 × 10−5 (0.05/77 × 11) was indicated statistical significance. Genotype frequencies of Li and 11 HapMap populations were compared using the chi-squared test. Structure (version 2.3.4) and Arlequin (version 3.1) software were used to analyze the genetic structure within a hypothetical K number and F-statistics (Fst) in 12 populations, respectively. 3. Results Table 1 lists the basic information of the selected variants in the selected study subjects of Li ethnicity. We successfully genotyped 77 VIP variants selected from PharmGKB VIP in 100 members of the Li population. Table 2 displays only the significantly different loci (p ≤ 5.9 × 10−5 ) between Li ethnicity and the 11 HapMap population groups. We used chi-squared test to compare differences between Li people and the 11 HapMap population groups in genotype frequency distribution of variants. The results showed there were only 1, 2, 2 and 3 of the selected VIP variant genotype frequencies in the Li that differed from those of the CHD, CHB, JPT and MEX, respectively. Differences among the selected Li group and GIH, TSI, LWK, ASW were found in 10, 10, 11 and 12 of the genotypes, respectively. When Li population was compared with the MKK, CEU, YRI groups, there were 15, 16, 19 different loci in frequency distribution, respectively. These variants were located in 16 genes, and the genotypes of VDR, ABCB1, MTHFR, PGTS2 and VKORC1 were significantly different between the Li and 11 other populations. We also found that the rs1540339 was most significantly different loci between the Li and 8 other populations. The structure analysis of the genetic relationship among 12 populations is shown in Fig. 1, most suitable K was observed at K = 3, where the proportion of each ancestral component in a single individual is represented by a vertical bar divided into three colors, the results show that the Li population is most similar to the CHD, CHB and JPT populations. The results are in good agreement with those presented in Table 2. On the basis of genetic structure, we further measured the genetic relationship throughout the population by using genetic distance, and calculated the distribution of pairwise Fst distances among the Li and all HapMap populations (Table 3). The Fst statistic is a measure of population differentiation, and the results showed that slight differences were observed between the Li group and CHB, CHD and JPT populations (Fst = 0.02488, 0.01363 and 0.02365, respectively). The minimum Fst distance was obtained between the Li and CHD (Fst = 0.01363), showed that the Li population is most similar to CHD, and least similar to the YRI populations in HapMap. 4. Discussion Individual differences in drug metabolism and effects are due to polymorphisms in genes that encode drug metabolizing enzymes, drug transporters and drug targets (Evans and Johnson, 2001). It should be mentioned that to our knowledge, information on polymorphic distribution of pharmacogenomics VIP variants among the Li population is null. It is generally appreciated that VDR gene binds the active form of vitamin D to modulate many of the neural, immune, and endocrine systems, containing calcium, phosphorous homeostasis, apoptosis and cell differentiation (Bikle, 2011; Hewison, 2010; Poon et al., 2012). Several reports have shown that the variant is associated with susceptibility to many diseases, such as type 1 diabetes mellitus (T1DM) (Wang et al., 2014), colorectal cancer (Wang et al., 2008) and asthma (Saadi et al., 2009). A Brazilian study demonstrated that rs1540339(C>T) was associated with T1DM (De Azevedo Silva et al.,

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Table 1 Basic characteristic of selected variants and allele frequencies in the Li ethnicity. SNP ID

Genes

Chr

Position

Category

Allele

Allele frequencies

Family

Phase

A

B

A (%)

B (%)

rs1801131 rs1801133 rs3918290 rs6025 rs20417 rs689466 rs4124874 rs10929302 rs4148323 rs7626962 rs1805124 rs6791924 rs3814055 rs2046934 rs1065776 rs701265 rs975833 rs2066702 rs1229984 rs17244841 rs3846662 rs17238540 rs1042713 rs1042714 rs1800888 rs1142345 rs1800460 rs2066853 rs1045642 rs1128503 rs10264272 rs4986913 rs4986910 rs4986909 rs12721634 rs2740574 rs3815459 rs36210421 rs12720441 rs3807375 rs4986893 rs4244285 rs1799853 rs1801252 rs5219 rs1695 rs1138272 rs1800497 rs6277 rs4149056 rs7975232 rs1544410 rs2239185 rs1540339 rs2239179 rs3782905 rs2228570 rs10735810 rs11568820 rs1801030 rs7294 rs9934438 rs28399454 rs1801272 rs28399433 rs3745274 rs28399499 rs3211371 rs12659 rs1051266 rs4680 rs59421388 rs28371725 rs16947 rs61736512 rs28371706 rs5030656

MTHFR MTHFR DPYD F5 PTGS2 PTGS2 UGT1A10 UGT1A10 UGT1A10 SCN5A SCN5A SCN5A NR1I2 P2RY12 P2RY1 P2RY1 ADH1A ADH1B ADH1B HMGCR HMGCR HMGCR ADRB2 ADRB2 ADRB2 TPMT TPMT AHR ABCB1 ABCB1 CYP3A5 CYP3A4 CYP3A4 CYP3A4 CYP3A4 CYP3A4 KCNH2 KCNH2 KCNH2 KCNH2 CYP2C19 CYP2C19 CYP2C9 ADRB1 KCNJ11 GSTP1 GSTP1 ANKK1 DRD2 SLCO1B1 VDR VDR VDR VDR VDR VDR VDR VDR VDR SULT1A1 VKORC1 VKORC1 CYP2A6 CYP2A6 CYP2A6 CYP2B6 CYP2B6 CYP2B6 SLC19A1 SLC19A1 COMT CYP2D6 CYP2D6 CYP2D6 CYP2D6 CYP2D6 CYP2D6

1 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 4 4 4 5 5 5 5 5 5 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 10 10 10 10 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 16 16 16 19 19 19 19 19 19 21 21 22 22 22 22 22 22 22

11854476 11856378 97915614 169519049 186650320 186650750 234665659 234665782 234669144 38620907 38645420 38674699 119500034 151057642 152553628 152554357 100201739 100229017 100239319 74607099 74615328 74619742 148206440 148206473 148206885 18130918 18139228 17379110 87138645 87179601 99262835 99358459 99358524 99359670 99381661 99382096 150644394 150644428 150647304 150667210 96540410 96541616 96702047 115804036 17409572 67352689 67353579 113270828 113283459 21331549 48238837 48239835 48244559 48257326 48257766 48266167 48272895 48272895 48302545 28617485 31102321 31104878 41351267 41354533 41356379 41512841 41518221 41522715 46951555 46957794 19951271 42523610 42523805 42523943 42525134 42525772 42524175

methylenetetrahydrofolate reductase methylenetetrahydrofolate reductase DPYD F5 PTGS2 PTGS2 UDP-glucuronosyltransferase UDP-glucuronosyltransferase UDP-glucuronosyltransferase sodium channel gene sodium channel gene sodium channel gene nuclear receptor G-protein coupled receptor G-protein coupled receptor G-protein coupled receptor alcohol dehydrogenase alcohol dehydrogenase alcohol dehydrogenase HMGCR HMGCR HMGCR adrenergic receptors adrenergic receptors adrenergic receptors methyltransferase methyltransferase AHR ATP-binding cassette (ABC) transporters ATP-binding cassette (ABC) transporters cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 eag eag eag eag cytochrome P450 cytochrome P450 cytochrome P450 adrenergic receptors inward-rectifier potassium channel glutathione S-transferase glutathione S-transferase Ser/Thr protein kinase G-protein coupled receptor solute carrier nuclear receptor nuclear receptor nuclear receptor nuclear receptor nuclear receptor nuclear receptor nuclear receptor nuclear receptor nuclear receptor sulfotransferase VKORC1 VKORC1 cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 solute carrier solute carrier COMT cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450 cytochrome P450

Phase I Phase I PhaseI Others Phase I Phase I Phase II Phase II Phase II Others Others Others Others Others Others Others Phase I Phase I Phase I Phase I Phase I Phase I Phase I Phase I Phase I Phase II Phase II Others Others Others Phase I Phase I Phase I Phase I Phase I Phase I Others Others Others Others Phase I Phase I Phase I Phase I Others Phase II Phase II Phase I Others Others Others Others Others Others Others Others Others Others Others Phase II Phase I Phase I Phase I Phase I Phase I Phase I Phase I Phase I Others Others Phase II Phase I Phase I Phase I Phase I Phase I Phase I

A C G G G A A A A G A A C C C A C C A A C T A C C A G A C C C C T C T A A G C A A A C A C A C C C T A A C A A C C C A A A A G T G G T T C A A C A G C C AAG

C T / / / G C G G / G G T T T G G / G T T / G G / G / G T T / / / / / / G / / G G G / G T G / T T C C G T G G G T T G / G G / / T T / C T G G / G A / / /

69 88.8 100 100 100 59.6 67 8 7 100 94 1 80.5 9.5 99 75.5 78 100 73 99 45.9 100 59 94.5 100 99 100 30.8 57.5 30 100 100 100 100 100 100 62.6 100 100 73.7 0.5 33.5 100 78.1 75.8 79.5 100 54 95.5 83.5 36 7.5 71.5 73.5 73.5 83.5 50 50 30.6 100 24.7 73.2 100 100 17.3 71 100 50 50.5 51.5 34.3 100 7 90.4 100 100 100

31 11.2 0 0 0 40.4 33 92 93 0 6 99 19.5 90.5 1 24.5 22 0 27 1 54.1 0 41 5.5 0 1 0 69.2 42.5 70 0 0 0 0 0 0 37.4 0 0 26.3 99.5 66.5 0 21.9 24.2 20.5 0 46 4.5 16.5 64 92.5 28.5 26.5 26.5 16.5 50 50 69.4 0 75.3 26.8 0 0 82.7 29 0 50 49.5 48.5 65.7 0 93 9.6 0 0 0

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Table 2 Significant variants between Li ethnicity and 11 HapMap population groups. SNP ID

Genes

CHD

CHB

JPT

MEX

GIH

TSI

LWK

ASW

MKK

CEU

YRI

rs1801133 rs3782905 rs1540339 rs1695 rs10735810 rs3807375 rs3814055 rs4124874 rs9934438 rs1544410 rs2239179 rs689466 rs7294 rs1042713 rs1128503 rs7975232 rs1800497 rs11568820 rs701265 rs3846662 rs1045642 rs1051266 rs1805124 rs1042714 rs1229984 rs20417 rs6277 rs975833 rs10929302 rs3815459 rs2239185

MTHFR VDR VDR GSTP1 VDR KCNH2 NR1I2 UGT1A10 VKORC1 VDR VDR PTGS2 VKORC1 ADRB2 ABCB1 VDR DRD2 VDR P2RY1 HMGCR ABCB1 SLC19A1 SCN5A ADRB2 ADH1B PTGS2 DRD2 ADH1A UGT1A10 KCNH2 VDR

2.05E−06 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

8.89E−12 3.24E−17 – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

1.70E−07 4.31E−21 – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

3.61E−08 – 6.28E−07 3.71E−08 – – – – – – – – – – – – – – – – – – – – – – – – – – –

– – 7.17E−11 – 5.34E−05 2.47E−10 8.89E−06 4.09E−07 5.88E−19 6.75E−14 4.87E−05 1.02E−06 6.04E−16 – – – – – – – – – – – – – – – – – –

1.27E−10 – 1.07E−10 – – 7.27E−12 – – 8.55E−06 1.94E−12 – 1.20E−05 – 1.72E−05 2.75E−06 5.68E−05 7.29E−06 – – – – – – – – – – – – – –

– – 1.88E−22 3.23E−09 9.35E−12 – – 9.96E−22 4.47E−27 – – 4.42E−15 – – 1.10E−22 8.45E−11 – 4.52E−22 1.82E−22 1.43E−21 – – – – – – – – – – –

– – 1.49E−12 3.11E−05 1.02E−07 – – 3.17E−11 1.19E−18 – 1.04E−06 3.22E−05 – – 8.41E−14 4.68E−05 – 6.77E−10 1.27E−12 4.27E−10 – – – – – – – – – – –

– – 1.06E−24 – 6.14E−11 – – 1.53E−22 2.92E−26 1.31E−11 – 5.04E−21 5.99E−07 – 1.66E−25 2.71E−10 – 1.06E−19 5.79E−23 2.37E−15 7.33E−10 2.99E−07 5.52E−12 – – – – – – – –

7.12E−06 4.64E−15 3.82E−11 1.94E−05 – 6.48E−12 – – 4.88E−09 8.64E−14 – 1.17E−06 – 1.93E−05 5.63E−07 – 2.97E−07 – – – – – – 3.28E−12 3.22E−28 2.59E−32 5.79E−20 1.69E−13 – – –

– 1.49E−18 1.57E−20 – 1.73E−10 – – 1.21E−26 2.92E−35 – – 1.31E−11 1.39E−07 – 8.09E−25 5.34E−07 – 3.47E−35 2.06E−24 1.51E−23 8.15E−12 – – – 5.49E−28 8.56E−27 – 3.01E−13 1.04E−07 1.86E−05 5.28E−05

Fig. 1. Structure analysis of the genetic relationship between 12 selected populations. Most suitable K was observed at K = 3, where the proportion of each ancestral component in a single individual is represented by a vertical bar divided into 3 colors. East Asian, European, and African populations belong to cluster 1, 2, and 3, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 3 Distribution of pairwise Fst distances among the Li and all HapMap populations.

Li ASW CEU CHB CHD GIH JPT LWK MEX MKK TSI YRI

Li

ASW

CEU

CHB

CHD

GIH

JPT

LWK

MEX

MKK

TSI

YRI

0.00000 0.17597 0.08912 0.02488 0.01363 0.11848 0.02365 0.24833 0.06192 0.20337 0.09029 0.25128

0.00000 0.12871 0.20459 0.20298 0.08781 0.18453 0.01791 0.12065 0.01637 0.13214 0.01653

0.00000 0.12321 0.11941 0.03154 0.11657 0.19757 0.01986 0.14804 −0.00056 0.20596

0.00000 −0.00117 0.16664 0.00382 0.27509 0.08754 0.23403 0.10979 0.27886

0.00000 0.16250 0.00567 0.27634 0.08085 0.23610 0.11096 0.28123

0.00000 0.15531 0.15472 0.05857 0.10978 0.04081 0.15420

0.00000 0.25087 0.08746 0.20816 0.10380 0.25267

0.00000 0.19717 0.01235 0.20149 0.00422

0.00000 0.15986 0.02391 0.20691

0.00000 0.15255 0.01360

0.00000 0.21212

0.00000

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2013), and a separate study showed that the C>T polymorphism was protective against T1DM (Mory et al., 2009). We found that the C and T allele frequencies of rs1540339 in the Li were 26.5% and 73.5% respectively, suggesting that the Li may have decreased susceptibility to T1DM, this is consistent with results from the CHD population (data not shown), and these two groups can be regarded as genetically close populations, meaning that, Li patients who are taking medications can refer to the CHD populations. The SNP rs9934438 is located in intron of vitamin K epoxide reductase complex 1 (VKORC1) gene. The VKORC1 genetic polymorphisms have been increasingly acknowledged as one of major contributory factors of enhanced warfarin sensitivity (Chen et al., 2014). Warfarin is extensively used in clinical practice as an antithrombotic for the prevention and treatment of thromboembolism disorder (Hirsh et al., 1998). Several reports have found that the VKORC1-TT genotype was associated with a significantly lower effective dose of warfarin when compared to the CC and CT genotype (D’Andrea et al., 2005; El Din et al., 2012; Limdi et al., 2008; Yang et al., 2011). Especially one study showed that patients who carried CC genotype required the warfarin dose (6.2 mg) significantly higher than those carrying the CT (4.8 mg) or TT (3.5 mg) genotype (D’Andrea et al., 2005). Our data showed that 54% of Li population carried VKORC1-TT genotype, suggesting that patients with this genotype will require a lower dose of warfarin. The Methyleneterahydrofolate reductase (MTHFR) gene is a key enzyme in folate metabolism pathway and is involved in DNA synthesis, repair and methylation (Sameer et al., 2011). Rs1801133 (Ala222Val), located in MTHFR gene, was found to be a significant variant when compared CHB, CHD and JPT populations (Table 2). Interestingly, this result was quite inconsistent with our overall results. The Li nationality speaks their own Hlai language, a member of the Tai–Kadai language family, which has great difference compared to the languages spoken by North-East Asian populations. We assume that this case stem from the different genetic structure within East Asian populations. As far as we know, genetic affinities were found among populations in East Asia within linguistic family, despite prevalent gene flow among populations (Abdulla et al., 2009). Meanwhile, we cannot rule out two possible reasons: small sample size or relatively isolated consanguinity through different geography and environment of this population. In order to validate our results, future studies should investigate these loci in a larger population.

5. Conclusions Our results will complement the pharmacogenomics database, and provide further information about the Li ethnicity, leading to the development of tools to modify current treatments for various diseases. Furthermore, this will help physicians to personalize therapies for certain drugs, and select the optimal dosing pattern for each patient in this population. Further studies are planned with the aim of identifying VIP variants in a larger sample size of the Li population.

Conflict of interest The authors declare no conflict of interest to report.

Transparency document The Transparency document associated with this article can be found in the online version.

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Acknowledgments This work is supported by Special Project of Science and Technology for Hainan Province Social Development (No. SF201402) and Applied Technology Research and Development and Demonstration Projects of Hainan Province (No. ZDXM2014119). We would also like to thank all participants for this manuscript. 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