Parkinson’s Disease

Parkinson’s Disease / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 6452471 | 7 pages | https://doi.org/10.1155/2019/6452471

Variants in the 3′ End of SLC6A3 in Northwest Han Population with Parkinson’s

Academic Editor: Jan Aasly
Received29 Mar 2019
Revised06 Jun 2019
Accepted29 Jun 2019
Published03 Sep 2019

Abstract

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders in neurology. It is possible that multifactorial and genetic factors are related to its pathogenesis. Recently, there have been reports of SLC6A3 genetic variants leading to PD. However, the role of 3′ end of SLC6A3 in PD is less studied in different ethnic groups. To explore the roles of 3′ end of SLC6A3 in PD development, 17 SNP sites in 3′ end of SLC6A3 were analyzed in 360 PD patients and 392 normal controls of Han population residing in northwest of China. The significant difference of gene type and allele frequencies between the PD and control groups was detected only in rs40184 ( = 0.013 and 0.004, respectively; odds ratio 2.529, 95% confidence interval 1.325–4.827). The genotype and allele frequencies of the other 16 SNP sites were not found to be different between the PD group and the control group. rs2550936, rs3776510, and rs429699 were selected to construct the haplotypes; no significant difference was found in a frequency of 5 haplotypes between the PD group and the control group. These results suggest that the SLC6A3 variant in rs40184 A allele may increase the risk of PD in northwest Han population and may be a biomarker of PD.

1. Introduction

Parkinson’s disease (PD) is a neurodegenerative disease; its prevalence increases with age, and PD influences 1% of the population over 60 years [1, 2]. Due to the high disability rate and long course of illness, PD has seriously impacted the quality of life of middle-aged and elderly people. Worldwide, the burden of medical expenses on Parkinson’s disease has risen from 2.5 million patients in 1990 to 6.1 million patients in 2016, almost doubled in 26 years [3]. The detail mechanism of the etiology and pathogenesis of PD is still unknown. The evidence from most studies showed that PD was a complex multifactor disease influenced by environmental and genetic factors [4, 5], in which genetic factors play a critical role [68]. Up to now, the genetic susceptibility of Parkinson’s disease is mainly focused on the screening of new genes in the PD family and the exploring of susceptible genes in patients. The genetic susceptibility genes in patients are mainly concentrated in the dopamine metabolic system genes, including the catecholamine oxymethyltransferase gene, the monoamine oxidase gene, the dopamine receptor gene, and the dopamine transporter (DAT) gene (gene symbol: SLC6A3), in which the DAT gene plays an essential role in the pathogenesis of PD. DAT is a transmembrane protein which is expressed by the presynaptic dopamine neuron. The main function of DAT is to reuptake dopamine released into the synaptic space and to stop the transmission of information among nerve cells [9, 10]. In addition, neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) enters into the synapse cell through DAT. Finally, MPTP destroys the black dopaminergic neurons by oxidative stress.

Since PD is characterized by the selective loss of dopaminergic neurons, genes that affect the expression of dopaminergic neurons may become candidate genes for PD. The evidence from genetic studies revealed that variants in SLC6A3 were associated with PD [1113]. The most extensively studied polymorphism was the variable number of tandem repeats (VNTR) in the 3′untranslated region (UTR) of SLC6A3 gene. However, the results from a number of related studies have not yet reached a consensus on its genetic correlation with PD. Moreover, there are few related studies on other polymorphisms at the 3′ end of SLC6A3 and PD. In order to further investigate the correlation between SLC6A3 and PD in northwest Han population, we performed a variants study in 3′ end of SLC6A3 in 360 PD patients and 392 healthy controls.

2. Materials and Methods

2.1. Patients

A cohort of 360 Chinese Han PD patients (63.5 ± 10.4 years) were enrolled. All the patients came from the Inner Mongolia Medical University Affiliated Hospital, Hohhot, China, and Bayannaoer City Hospital, Bayannaoer, China, between 2015 and 2018. All patients live in northwest of China and were not related to each other. All patients were examined by experienced neurologists, and the diagnosis of PD was based on clinical criteria [14]. The control group consisted of 392 age- and sex-matched healthy persons (63.7 ± 9.7 years) from the same geographic areas. This study was approved by the Institutional Review Board of the Inner Mongolia Medical University, Hohhot, China. The study obtained the informed consent of all participants.

2.2. Selection of Single-Nucleotide Polymorphisms (SNPs)

TagSNPs were selected from the Chinese HapMap database (http://www.hapmap.org), which is based on pairwise r2 ≥ 0.8 and minor allele frequency (MAF) ≥0.1. In this study, we chose 17 tagSNPs (rs2270913, rs27048, rs2270914, rs2550936, rs11133767, rs3776510, rs429699, rs11564759, rs27047, rs6347, rs40184, rs37022, rs10036478, rs2652514, rs2981359, rs365663 and rs11133770) from the 3′ end of SLC6A3.

2.3. Genotyping

Genomic DNA was extracted from leukocytes in a peripheral blood sample using a blood DNA extraction kit (TIANamp Blood DNA kit; TIANGEN BIOTECH, Beijing, China), which was stored at −20°C. Gene typing was performed using the polymerase chain reaction (PCR)/ligase detection reaction assay. Primers were synthesized by HAYU Biological Engineering LTD in Shanghai; the information of the primers is shown in Table 1. The probe for each group of ligase detection reactions consists of one common probe and two discriminating probes for the two types, as shown in Table 2.


GeneUpstream primerDownstream primerPCR length

rs2270913GGTTCCCCTACCTGTGCTACAACAGCTTCATCTCGTTTCCG129
rs27048AAAGGCGGAGGAGGTGTTCCCAAACTGCGTTGACTTTTGG135
rs2270914GGTTCCCCTACCTGTGCTACAACAGCTTCATCTCGTTTCCG129
rs2550936CTCCCAAATAATCACGGGGCGCAGTTGGGTTCCTTCCACC124
rs11133767GAGAGGGTGAGCTCCTGAAGTGCTTTTTGTCACCTGCAGC139
rs3776510ACAGAGGAAGGGAGAAAGTGCGAGAGGGGCGTGGATTTCTC139
rs429699CCTCACGGAGCCTTTTTCAGTTTGGAGTGCTCATCGAAGC126
rs11564759AGCGCCCTTGGGAGTTCATGCACCCAGGGCAGATCTTCC131
rs27047ACAAATCACACACGTCCACACCCACGTCTAACCTCACGGG139
rs6347GGGTTCTGTTTCAGGGCCAGGATACCCAGGGTGAGCAGC118
rs40184TCTGATCAATACGCCCCAGAGCCAACACACCCTTGACAGG140
rs37022TGCTTGCTTTGACCTTTATGGCCAGCGCCCACTCTCAGTG140
rs10036478TCCCAGTTAGGAGCAGGGAGGAGCTAAAAGGCCATCCAGC134
rs2652514CCAGAACCCAGCCACAGAGATAGAGGCCAATGAGGGAGG138
rs2981359CAGAGTTTAGGAAAGGGAGGCCGCAGGCTGTTCTTTGGACC140
rs365663GTGAGACGCTGGCCATGTCTTGCCAACCCTGAGGAACAC138
rs11133770ACACCTCTGACCACAGTGTGAAGCCTGGGTTGTGGTCATC125


Probe nameSequence (5′–3′)LDR length

rs2270913_modifyP-GGGGGTCTAGGGCAGCCGTTTTTTTTTTTTTTTTTTTTTT-FAM
rs2270913_ATTTTTTTTTTTTTTTTTTCTCCCTGAGCATGCTGGCCGGGT81
rs2270913_GTTTTTTTTTTTTTTTTTTTTCTCCCTGAGCATGCTGGCCGGGC83
rs27048_modifyP-AGTCTGCCTGCTGGTAGCAGTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs27048_CTTTTTTTTTTTTTTTTTTTTTTCTCGTCCCCTCCACCTCCATCCG89
rs27048_TTTTTTTTTTTTTTTTTTTTTTTTTCTCGTCCCCTCCACCTCCATCCA91
rs2270914_modifyP-GACCCCCGCCCGGCCAGCATTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs2270914_GTTTTTTTTTTTTTTTTTTTTTTTTTTGCCTTGGCCCCGGCTGCCCCTAC95
rs2550936_modifyP-ACGGCCCCCAGACCTCCTGTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs2550936_ATTTTTTTTTTTTTTTTTTTTTTTTTTAGGCAAGATCCCTGGGCTCACGT97
rs2550936_CTTTTTTTTTTTTTTTTTTTTTTTTTTTTAGGCAAGATCCCTGGGCTCACGG99
rs11133767_modifyP-GCTGCGGCAGCTCCTGGGGCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs11133767_ATTTTTTTTTTTTTTTTTTTTTTTTTTTTAACGTGCCTTCCTTCCACTGCCT101
rs11133767_GTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTAACGTGCCTTCCTTCCACTGCCC103
rs3776510_modifyP-GCTTCTCCCCATCTCCCGTGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs3776510_CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAGGTGCAGGTCGCCAGGGCCG105
rs3776510_TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAGGTGCAGGTCGCCAGGGCCA107
rs429699_modifyP-CCCCCGGACTCACCATAGAATTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs429699_ATTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAGGGTGCCGGCTTGGCTGCCTT109
rs429699_GTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAGGGTGCCGGCTTGGCTGCCTC111
rs11564759_modifyP-GGTCTCATGGGGTCTCGGGGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs11564759_CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTAAGATGCAGATCCTGACTGGGCG113
rs11564759_TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTAAGATGCAGATCCTGACTGGGCA115
rs27047_modifyP-TGTGCCTGGAAGGCGGAGGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs27047_CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGTGGGAGGACCTCAGCTTCCTCG117
rs27047_TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGTGGGAGGACCTCAGCTTCCTCA119
rs6347_modifyP-GAGGACAGAGGGAGCGTGGCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs6347_ATTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAAGAAGACCACGGCCCAGGCT121
rs6347_GTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAAGAAGACCACGGCCCAGGCC123
rs2652514_TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAAGGGATCACCAATGTTCTTGGACA172
rs2981359_modifyP-AAACAGGAGGCAGAGCCAAGCTGCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs2981359_CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCTTTCCAAAGCGAAGATAGCCTCTGG175
rs2981359_GTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCTTTCCAAAGCGAAGATAGCCTCTGC177
rs365663_modifyP-TTAGTGGGGCAGCTCAGCAGTCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs365663_CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTATTCATGGCACATGGAGGAAGCACCG180
rs365663_TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTATTCATGGCACATGGAGGAAGCACCA182
rs11133770_modifyP-TGATGGGATCAGTGAGGTGCTTAGCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-FAM
rs11133770_ATTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGGGGAGAGGCTTGGCACTGGTCCCTT185
rs11133770_CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGGGGAGAGGCTTGGCACTGGTCCCTG187

The multiplex PCR methods were used to amplify the target DNA sequences. The final volume of PCRs for each subject was 20 μl, which contained 1X PCR buffer, 3.0 mM/L·MgCl2, 2.0 mM/L deoxynucleotide triphosphate, 0.5 μmol/μl primer mix, 5 U/μl Qiagen HotStarTaq Polymerase (QIAGEN, Shenzhen, China), 1X Q-solution, and 50 ng/μl genomic DNA. The thermal cycle was carried out in GeneAmp PCR system 9600 (Norwalk, CT.06859, USA). The initial denaturation was 2 min at 95°C, followed by 40 cycles of denaturation at 94°C for 30 s, annealing at 56°C for 90 s, and extension at 72°C for 1 min. The final extension of 72°C is 10 min.

The total volume of the ligase detection reaction is 10 μl for each subject, which contains 1X NEB Taq DNA ligase buffer 1 μl, 2 pmol of each probe mix 1 μl, 5 U/μl Taq DNA ligase 0.05 μl (BIOWING, Jiangsu, China), and 4 μl multi-PCR product. A total of 40 ligase detection reaction cycles were performed under conditions of 92°C for 2 min, 94°C for 15 s, and 50°C for 25 s. The fluorescent products of ligase detection reaction were identified by PRISM 3730 (ABI). The experimental methods are similar to Chang et al. [15].

2.4. Statistical Analysis

Statistical analysis was carried out by the Statistical Program for Social Sciences (SPSS version 11.0). Hardy-Weinberg equilibrium of each group was determined by using the chi-squared test. Allele and genotype frequencies between groups were studied using SHEsis software [16]. We use SHEsis software to calculate the coefficient D′ of linkage disequilibrium (LD) and to build haplotypes. A haplotype with a frequency of less than 3% is considered rare and ignored. When D′ is more than 0.8, it is considered that there is a strong linkage disequilibrium. A value of 0.05 was considered to have statistical significance.

3. Results

3.1. Association Study of TagSNPs and PD

The genotype and allele frequencies of the 3′ end of SLC6A3 are summarized in Table 3. No deviation from Hardy–Weinberg equilibrium was evident in the PD and control groups (). Statistically significant differences in genotype and allele frequencies were found in the SLC6A3 variant rs40184 between PD (AA 5.6%, AG 47.2%, and GG 47.2%) and controls (AA 2.0%, AG 24.0%, and GG 74.0%). The frequency of the minor A allele was 29.2% in patients and 14.0% in controls. There was no significant difference in genotype and allele frequencies for other 16 SNP polymorphism sites in the 3′ end of SLC6A3.


GeneAllele/genotypePD (n)Control (n)χ2OR95% CI

rs2270913C698 (1.000)768 (1.000)
C/C349 (1.000)384 (1.000)

rs27048C620 (0.912)652 (0.867)0.9390.3320.5850.620–4.0483
T60 (0.088)100 (0.133)
C/C280 (0.824)276 (0.734)
C/T60 (0.176)100 (0.266)1.0890.2971.6910.626–4.566

rs2270914G700 (1.000)768 (1.000)
G/G350 (1.000)384 (1.000)

rs2550936A651 (0.930)712 (0.908)
C49 (0.070)72 (0.092)0.2710.6021.3150.469–3.685
A/A301 (0.860)320 (0.816)
A/C49 (0.140)72 (0.184)0.3010.5840.3500.460–3.960

rs11133767A31 (0.043)36 (0.046)
G669 (0.956)748 (0.954)0.0110.9140.9300.245–3.539
A/G31 (0.089)36 (0.092)
G/G319 (0.911)356 (0.908)0.0120.9140.9270.236–3.640

rs3776510C668 (0.954)744 (0.949)
T32 (0.046)40 (0.051)0.0740.7861.2010.321–4.495
C/C318 (0.909)352 (0.898)
C/T32 (0.091)40 (0.102)0.0780.7801.2120.314–4.686

rs429699C500 (0.714)592 (0.755)
T200 (0.286)192 (0.245)0.4520.5020.8110.440–1.495
C/C190 (0.543)220 (0.561)
C/T120 (0.343)152 (0.388)
T/T40 (0.114)20 (0.051)1.6790.43

rs11564759C403 (0.576)443 (0.589)
T297 (0.424)309 (0.411)0.0760.7830.9240.531–1.611
C/C131 (0.374)116 (0.309)
C/T141 (0.403)211 (0.561)
T/T78 (0.223)49 (0.130)3.3030.192

rs27047C450 (0.643)488 (0.622)
T250 (0.357)296 (0.378)0.0920.7621.0920.619–1.926
C/C150 (0.429)160 (0.408)
C/T150 (0.429)168 (0.429)
T/T50 (0.143)64 (0.163)0.0940.954

rs6347A641 (0.914)771 (0.920)
G59 (0.086)67 (0.080)0.0010.9791.0130.383–2.682
A/A291 (0.831)325 (0.829)
A/G59 (0.169)67 (0.171)0.0010.9781.0140.365–2.821

rs40184A210 (0.292)112 (0.140)
G510 (0.708)688 (0.860)8.2450.0042.5291.325–4.827
A/A20 (0.056)8 (0.020)
A/G170 (0.472)96 (0.240)
G/G170 (0.472)296 (0.740)8.7600.013

rs37022A314 (0.431)402 (0.502)
T406 (0.569)398 (0.498)1.3370.2460.7260.422–1.250
A/A73 (0.194)93 (0.232)
A/T168 (0.472)216 (0.540)
T/T119 (0.333)91 (0.228)1.830.340

rs10036478C614 (0.853)704 (0.880)
T106 (0.147)96 (0.120)0.5070.4760.7560.3499–1.634
C/C262 (0.728)309 (0.773)
C/T90 (0.250)86 (0.215)
T/T8 (0.028)5 (0.012)0.7530.686

rs2652514C577 (0.824)693 (0.866)
T123 (0.176)107 (0.134)0.7360.3920.7220.343–1.523
C/C238 (0.680)299 (0.748)
C/T101 (0.289)95 (0.238)
T/T11 (0.031)6 (0.015)0.9650.617

rs2981359C383 (0.532)388 (0.485)
G337 (0.468)412 (0.515)0.6800.4101.2550.731–2.154
C/C96 (0.267)104 (0.260)
C/G191 (0.530)180 (0.450)
G/G73 (0.203)116 (0.290)1.2850.526

rs365663C420 (0.583)517 (0.646)
T300 (0.417)283 (0.354)0.8620.3530.7710.444–1.337
C/C110 (0.306)165 (0.412)
C/T200 (0.556)187 (0.468)
T/T50 (0.139)48 (0.120)1.2240.542

rs11133770A658 (0.914)737 (0.921)
C62 (0.086)63 (0.079)0.0520.8200.8920.332–2.395
A/A298 (0.828)337 (0.843)
A/C62 (0.172)63 (0.157)0.0560.8120.8820.314–2.482

OR = odds ratio; CI = confidence interval; χ2 = Pearson chi-square. .
3.2. Haplotypes of TagSNPs

LD plots of the SLC6A3 in the study are shown in Figure 1. The LD was measured among the tagSNPs by the Lewontin standardized disequilibrium coefficient D′ [17]. Adjacent SNPs in strong LD (D′ > 0.8), rs2550936, rs3776510, and rs429699 have strong LD (D′ > 0.99), which were chosen to build the haplotypes for subsequent analyses. A total of 5 haplotypes were formed, and the frequencies of haplotypes are shown in Table 4. No significant difference was found in frequencies of 5 haplotypes between PD and control groups in the Han population.


HaplotypePD (N%)Control (N%)χ2OR95% CI

A C C450.10 (0.643)519.79 (0.663)0.0940.7590.9140.516∼1.620
A C T200.20 (0.286)192.08 (0.245)0.4500.5021.2330.668∼2.275
C C C19.59 (0.028)32.14 (0.041)0.2140.6440.6910.143∼3.336
C T C30.10 (0.043)39.98 (0.051)0.0750.7840.8310.222∼3.117
C T T0.01 (0.000)0.01 (0.000)
Globe χ20.64
Fisher P0.89

OR = odds ratio; CI = confidence interval; χ2 = Pearson chi-square. .

4. Discussion

PD is a complex disease caused by age and environmental and genetic factors. Finding the genetic susceptibility factors for PD may help identify the individuals at risk and design more specific prevention or treatment options for them. SLC6A3 gene encodes DAT, which comprises 15 exons spanning 60 kb on chromosome 5p15.32 [18]. The studies of the coding region of SLC6A3 have shown that the gene was highly conservative [19]; therefore, the researchers turned the perspective to the noncoding area. The correlation between 40-bp VNTR polymorphisms in the 3′ UTR of SLC6A3 and PD has been studied extensively because VNTR polymorphisms may regulate gene transcription and affect the reuptake of dopamine in the synaptic cleft [20]. The variable numbers of the 40-bp repeat range from 3 to 13 copies; the most common alleles in human beings are the 9- and 10-repeat allele. A lot of studies have shown the association between VNTR polymorphisms of the SLC6A3 and PD in different populations, but the results are inconsistent [2124]. Two variants, rs28363170 and rs3836790 in SLC6A3, were found to be significantly correlated with PD patients in French population [25], while the variation of these two sites were not related to PD in Han population [26].

Here, we conducted a case-control study of 752 participants in the northwest Han population to further investigate the role of the 3′ end of SLC6A3 in the development of PD. We found that only the SLC6A3 variant rs40184 had statistically significant differences in genotypes and alleles, which may be related to PD. The minor A allele of rs40184 may lead to an increased risk of PD in northwest Han Chinese, and it may be a marker of PD. However, the other 16 polymorphic sites were not found to be related to PD. The reason why the other 16 sites had negative results may be the insufficient number of samples in this study and that these 16 sites may be rare genotypes or alleles in the Han population. Haplotype analysis is thought to be much more powerful than single-nucleotide polymorphism sites in correlation studies [27]. Haplotype analysis greatly reduces the number of test samples and control type I errors although this method will increase the incidence of unavoidable type II errors [28, 29]. Few studies have reported associations between haplotypes of the 3′ end of SLC6A3 and PD; we built the haplotypes among 3 strong LD tagSNPs, while no difference was found between the PD and the control group.

At present, more than 1,500 genes are known to be closely related to the occurrence of diseases. For a long time, coding areas have been a major research direction of genetic diseases and only a small part of noncoding area has been proved to be a useful component that can help genes to be turned on and off to regulate gene expression. The normal expression of genes cannot be separated from the participation of regulatory elements. Abnormalities in certain regulatory components can also lead to mutations in the corresponding gene-coding regions. Therefore, we can assume that the variant rs40184 in the noncoding area of SLC6A3 may lead to differences in susceptibility to PD in our study.

In summary, our findings showed a link between the 3′ end of SLC6A3 gene variant rs40184 and PD in northwest Han population. Given that different populations are genetically heterogeneous and mutations have a specific population frequency, larger sample size studies are needed to confirm the correlation between the 3′ end of SLC6A3 variant and PD in independent larger cohorts and in different geographical origins. Moreover, functional studies of the 3′ end of SLC6A3 need to be carried out to further understand the role of SLC6A3 in PD. Further research is likely to find genetic variations that are risky or protective of PD, which is important for the prevention and treatment of PD.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Peiye Chang, Yongwang Fu, and Ping Zhao are the co-first authors.

Acknowledgments

We thank all the participants and investigators involved in this study. This work was supported by the National Natural Science Foundation of Regional Project of China (81760319) and Inner Mongolia Natural Science Foundation Project (2016MS0825 and 2018LH08007).

References

  1. K. R. Chaudhuri and A. H. Schapira, “Non-motor symptoms of Parkinson’s disease: dopaminergic pathophysiology and treatment,” The Lancet Neurology, vol. 8, no. 5, pp. 464–474, 2009. View at: Publisher Site | Google Scholar
  2. O.-B. Tysnes and A. Storstein, “Epidemiology of Parkinson’s disease,” Journal of Neural Transmission, vol. 124, no. 8, pp. 901–905, 2017. View at: Publisher Site | Google Scholar
  3. W. A. Rocca, “The burden of Parkinson’s disease: a worldwide perspective,” The Lancet Neurology, vol. 17, no. 11, pp. 928-929, 2018. View at: Publisher Site | Google Scholar
  4. A. Delamarre and W. G. Meissner, “Epidemiology, environmental risk factors and genetics of Parkinson’s disease,” La Presse Médicale, vol. 46, no. 2, pp. 175–181, 2017. View at: Publisher Site | Google Scholar
  5. F. N. Emamzadeh and A. Surguchov, “Parkinson’s disease: biomarkers, treatment, and risk factors,” Frontiers in Neuroscience, vol. 12, p. 612, 2018. View at: Publisher Site | Google Scholar
  6. J. Clarimón and J. Kulisevsky, “Parkinson’s disease: from genetics to clinical practice,” Current Genomics, vol. 14, no. 8, pp. 560–567, 2013. View at: Publisher Site | Google Scholar
  7. A. McInerney-Leo, “Genetic testing in Parkinson’s disease,” Movement Disorders, vol. 20, no. 7, pp. 908-909, 2005. View at: Publisher Site | Google Scholar
  8. C. M. Lill, “Genetics of Parkinson’s disease,” Molecular and Cellular Probes, vol. 30, no. 6, pp. 386–396, 2016. View at: Publisher Site | Google Scholar
  9. K. M. Lohr, S. T. Masoud, A. Salahpour, and G. W. Miller, “Membrane transporters as mediators of synaptic dopamine dynamics: implications for disease,” European Journal of Neuroscience, vol. 45, no. 1, pp. 20–33, 2017. View at: Publisher Site | Google Scholar
  10. R. R. Gainetdinov, M. G. Caron, J.-M. Beaulieu, and T. D. Sotnikova, “Molecular biology, pharmacology and functional role of the plasma membrane dopamine transporter,” CNS & Neurological Disorders—Drug Targets, vol. 5, no. 1, pp. 45–56, 2006. View at: Publisher Site | Google Scholar
  11. E. M. van de Giessen, M. L. de Win, M. W. T. Tanck, W. van den Brink, F. Baas, and J. Booij, “Striatal dopamine transporter availability associated with polymorphisms in the dopamine transporter gene SLC6A3,” Journal of Nuclear Medicine, vol. 50, no. 1, pp. 45–52, 2008. View at: Publisher Site | Google Scholar
  12. C. H. van Dyck, R. T. Malison, L. K. Jacobsen et al., “Increased dopamine transporter availability associated with the 9-repeat allele of the SLC6A3 gene,” Journal of Nuclear Medicine, vol. 46, no. 5, pp. 745–751, 2005. View at: Google Scholar
  13. S. V. Faraone, T. J. Spencer, B. K. Madras, Y. Zhang-James, and J. Biederman, “Functional effects of dopamine transporter gene genotypes on in vivo dopamine transporter functioning: a meta-analysis,” Molecular Psychiatry, vol. 19, no. 8, pp. 880–889, 2014. View at: Publisher Site | Google Scholar
  14. R. B. Postuma, D. Berg, M. Stern et al., “MDS clinical diagnostic criteria for Parkinson’s disease,” Movement Disorders, vol. 30, no. 12, pp. 1591–1601, 2015. View at: Publisher Site | Google Scholar
  15. P. Y. Chang, L. Qin, P. Zhao, and Z. Y. Liu, “Association of regulator of G protein signaling (RGS5) gene variants and essential hypertension in Mongolian and Han populations,” Genetics and Molecular Research, vol. 14, no. 4, pp. 17641–17650, 2015. View at: Publisher Site | Google Scholar
  16. Y. Y. Shi and L. He, “SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci,” Cell Research, vol. 15, no. 2, pp. 97-98, 2005. View at: Publisher Site | Google Scholar
  17. M. Slatkin, “Linkage disequilibrium—understanding the evolutionary past and mapping the medical future,” Nature Reviews Genetics, vol. 9, no. 6, pp. 477–485, 2008. View at: Publisher Site | Google Scholar
  18. D. J. Vandenbergh, A. M. Persico, A. L. Hawkins et al., “Human dopamine transporter gene (DAT1) maps to chromosome 5p15.3 and displays a VNTR,” Genomics, vol. 14, no. 4, pp. 1104–1106, 1992. View at: Publisher Site | Google Scholar
  19. F. Grünhage, T. G. Schulze, D. J. Müller et al., “Systematic screening for DNA sequence variation in the coding region of the human dopamine transporter gene (DAT1),” Molecular Psychiatry, vol. 5, no. 3, pp. 275–282, 2000. View at: Publisher Site | Google Scholar
  20. D. Zhai, S. Li, Y. Zhao, and Z. Lin, “SLC6A3 is a risk factor for Parkinson’s disease: a meta-analysis of sixteen years’ studies,” Neuroscience Letters, vol. 564, pp. 99–104, 2014. View at: Publisher Site | Google Scholar
  21. B. R. Ritz, A. D. Manthripragada, S. Costello et al., “Dopamine transporter genetic variants and pesticides in Parkinson’s disease,” Environmental Health Perspectives, vol. 117, no. 6, pp. 964–969, 2009. View at: Publisher Site | Google Scholar
  22. J. J. Lin, K. C. Yueh, D. C. Chang, C. Y. Chang, Y. H. Yeh, and S. Z. Lin, “The homozygote 10-copy genotype of variable number tandem repeat dopamine transporter gene may confer protection against Parkinson’s disease for male, but not to female patients,” Journal of the Neurological Sciences, vol. 209, no. 1-2, pp. 87–92, 2003. View at: Publisher Site | Google Scholar
  23. P. W. Leighton, D. G. Le Couteur, C. C. P. Pang et al., “The dopamine transporter gene and Parkinson’s disease in a Chinese population,” Neurology, vol. 49, no. 6, pp. 1577–1579, 1997. View at: Publisher Site | Google Scholar
  24. G. Mercier, J. C. Turpin, and G. Lucotte, “Variable number tandem repeat dopamine transporter gene polymorphism and Parkinson’s disease: no association found,” Journal of Neurology, vol. 246, no. 1, pp. 45–47, 1999. View at: Publisher Site | Google Scholar
  25. C. Moreau, S. Meguig, J.-C. Corvol et al., “Polymorphism of the dopamine transporter type 1 gene modifies the treatment response in Parkinson’s disease,” Brain, vol. 138, no. 5, pp. 1271–1283, 2015. View at: Publisher Site | Google Scholar
  26. Q. Lu, Z. Song, X. Deng et al., “SLC6A3 rs28363170 and rs3836790 variants in Han Chinese patients with sporadic Parkinson’s disease,” Neuroscience Letters, vol. 629, pp. 48–51, 2016. View at: Publisher Site | Google Scholar
  27. K. Zhang, P. Calabrese, M. Nordborg, and F. Sun, “Haplotype block structure and its applications to association studies: power and study designs,” American Journal of Human Genetics, vol. 71, no. 6, pp. 1386–1394, 2002. View at: Publisher Site | Google Scholar
  28. A. G. Clark, “The role of haplotypes in candidate gene studies,” Genetic Epidemiology, vol. 27, no. 4, pp. 321–333, 2004. View at: Publisher Site | Google Scholar
  29. A. J. Lorenz, M. T. Hamblin, and J. L. Jannink, “Performance of single nucleotide polymorphisms versus haplotypes for genome-wide association analysis in barley,” PLoS One, vol. 5, no. 11, Article ID e14079, 2010. View at: Publisher Site | Google Scholar

Copyright © 2019 Peiye Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

402 Views | 262 Downloads | 0 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.