Abstract
Background
The Chinese Hui population, as the second largest minority ethnic group in China, may have a different genetic background from Han people because of its unique demographic history. In this study, we aimed to identify genetic differences between Han and Hui Chinese from the Ningxia region of China by comparing eighteen single nucleotide polymorphisms in cancer-related genes.
Methods
DNA samples were collected from 99 Hui and 145 Han people from the Ningxia Hui Autonomous Region in China, and SNPs were detected using an improved multiplex ligase detection reaction method. Genotyping data from six 1000 Genomes Project population samples (99 Utah residents with northern and western European ancestry (CEU), 107 Toscani in Italy (TSI), 108 Yoruba in Ibadan (YRI), 61 of African ancestry in the southwestern US (ASW), 103 Han Chinese in Beijing (CHB), and 104 Japanese in Tokyo (JPT)) were also included in this study. Differences in the distribution of alleles among the populations were assessed using χ2 tests, and FST was used to measure the degree of population differentiation.
Results
We found that the genetic diversity of many SNPs in cancer-related genes in the Hui Chinese in Ningxia was different from that in the Han Chinese in Ningxia. For example, the allele frequencies of four SNPs (rs13361707, rs2274223, rs465498, and rs753955) showed different genetic distributions (p<0.05) between Chinese Ningxia Han and Chinese Ningxia Hui. Five SNPs (rs730506, rs13361707, rs2274223, rs465498 and rs753955) had different FST values (FST >0.000) between the Hui and Han populations.
Conclusions
These results suggest that some SNPs associated with cancer-related genes vary among different Chinese ethnic groups. We suggest that population differences should be carefully considered in evaluating cancer risk and prognosis as well as the efficacy of cancer therapy.
Introduction
Genetic studies have revealed that different populations have different genetic structures because of their complex demographic histories [1]. Therefore, genetic differences in cancer-related genes are expected to exist between different ethnic groups. This diversity in cancer-related genes may result in differences in cancer susceptibility, sensitivity to radiotherapy and chemotherapy as well as prognosis among different ethnic populations. For example, p53 is well known as the most commonly mutated gene in human cancer. Codon 72 of p53, localized in exon 4, is among the most intensively studied polymorphisms found in the coding region of TP53. Substitution of Arg (codon CGC) with Pro (codon CCC) at residue 72 (R72P) results in a structural change of the protein [2]. Banks et al. demonstrated the existence of biochemical and biological differences between the Arg and Pro isoforms of p53 [3]. Several groups have reported an association between the Arg p53 variant and increased risk of epithelial cancer such as gastric cancer [4]. However, other studies have shown the opposite correlation, with the Pro (lesser apoptotic) variant corresponding to an increased risk of other cancer types such as thyroid cancer [5]. Beckman et al. first noted a significant difference in the frequency of the Pro allele between a Nigerian population (African Black) and a Swedish population (Western European), with values of 17% and 63%, respectively [3]. The frequency of p53 codon 72 alleles and haplotypes differs across ethnicities, which may be the leading cause of the different effects of the p53 codon 72 polymorphism on cancer risk in different ethnicities [6].
There are 56 ethnic groups in China. Han is the largest ethnic population, comprising 98% of the total population in China. The populations of the other 55 minority groups vary from thousands to millions, and some of the minority groups differ substantially from Han Chinese in terms of morphological and genetic characteristics. For example, the Uyghur (UIG) population, the fifth largest minority group in China, differs significantly from the Han Chinese in terms of facial features and has an approximately 55% European genetic component [7]. Hui Chinese, behind only Mongolian Chinese, is the second largest ethnic minority with a population of more than 12 million. The majority of the Chinese Hui people live in Ningxia Hui Autonomous region (hereafter Ningxia) located in northwest China, accounting for one-third of the population in Ningxia [8]. It has been proposed that the Hui Chinese may have descended from Central Asian, Arab, and Persian merchants who came to China during the 7th century. Therefore, populations may differ from Han Chinese with respect to their genetic background. Single nucleotide polymorphisms (SNPs) have emerged as genetic markers of choice because of their high density and relatively even distribution across the human genome, and SNPs have been used for fine mapping of disease loci and for candidate gene association studies [9]. Several investigations have recently shown that differences existed in psychological stress susceptibility, cancer susceptibility and cancer prognosis between Chinese Hui and Chinese Han [10,11,12]. However, there are still a lot of works needed to be done to ultimately unveil the genetic differences between these two groups of people. In this study, we randomly selected eighteen SNPs in cancer-related genes to advance the understanding of the genetic differences between Hui and Han Chinese from the Ningxia region of China.
Subjects and Methods
2.1 Genetic Variation Data
All subjects were from the Ningxia Hui Autonomous region in China. A total of 99 Hui individuals were selected from the physical examination center of a county hospital located in the Ningxia Haiyuan region of China. A total of 145 Han individuals were selected from the physical examination center of the General Hospital of Ningxia Medical University. The inclusion criteria were: (1) All subjects were from Ningxia Han or Hui residents whose ancestral native living places were Ningxia Hui Autonomous Region and at least three generations of their families were also the same ethnic people. (2) All subjects were proved to be physically healthy by their history, physical examination, and clinical examination when their samples were collected. (3) All subjects were proved to be free of benign or malignant tumors, both previously and at present, by their history, physical examination, and clinical examination. Demographic data including age, gender, and alcohol and tobacco consumption were obtained using a survey. Blood samples were collected from all subjects for DNA isolation and genotyping of the eighteen SNPs. Signed informed conset was obtained from each participant. All procedures were approved by the Medical Ethics Review Committee of Ningxia Medical University (Ningxia Region, China).
2.2 Cancer-Related Genes and Selected SNPs
Eighteen SNPs were selected for the analysis. Among all the SNPs, ten including rs13042395, rs465498, rs753955, rs17728461, rs2274223, rs13361707, rs9841504, rs9485372, rs4488809, and rs9934948 were reported by GWAS to be associated with cancer risk. For example, four SNPs associated with lung cancer are located in the TP63 gene (rs4488809 at 3q28), TERT-CLPTM1 L gene (rs465498 at 5p15), MIPEP-TNFRSF19 gene (rs753955 at 13q12), and MTMR3-HORMAD2-LIF gene (rs17728461 at 22q12). Two SNPs related to esophageal cancer are located in the PLCE1 gene (rs2274223 at 10q23) and C20orf54 gene (rs13042395 at 20p13). Two SNPs related to breast cancer are located in the TAB2 gene (rs9485372 at 6q25) and LOC100506172 (rs9934948 at chromosome 16). Two SNPs with independent effects and significant gastric cancer associations are located in the PRKAA1 (rs13361707 at 5p13) and ZBTB20 genes (rs9841504 at 3q13).
Eight other SNPs, including rs1042522, rs3176320, rs3829964, rs762624, rs4135234, rs730506, rs3829963, and rs2395655, are in p53 or CDKN1A, both of which play a critical role in carcinogenesis in the p53 pathway. For example, only one SNP, rs1042522, is located in the p53 gene (at 17p13), which represents one of the most studied tumor suppressor genes in cancer biology. A great number of genetic association studies have reported that rs1042522 is a risk factor for human malignancies [3,13]. Seven of the remaining SNPs are located in the promoter region of CDKN1A gene (rs2395655, rs3176320, rs3829964, rs762624, rs4135234, rs730506, and rs3829963 at 6p21), six of which have been analyzed for their associations with longevity, esophageal squamous cell carcinoma, or lung cancer except rs3176320 [14,15,16,17].
2.3 DNA Extraction and Genotyping
Genomic DNA was extracted using Qiagen genomic DNA extraction kits (QIAGEN Inc., Valencia, CA, USA). SNP genotyping was performed using an improved multiplex ligase detection reaction method (iMLDR, Genesky Bio-Tech Cod., Ltd., Shanghai, China) as previously described [18]. The primers and probes of ten SNPs used in polymerase chain reactions (PCRs) and ligase detection reaction (LDR) were listed in S1 Table, and the primers and the probes for the remaining eight SNPs used in PCR and LDR were the same as previously described [19].
Genotyping data from six 1000 Genomes Project population samples (99 Utah residents with northern and western European ancestry (CEU), 107 Toscani in Italy (TSI), 108 Yoruba in Ibadan (YRI), 61 persons of African ancestry in the southwestern United States (ASW), 103 Han Chinese in Beijing (CHB), and 104 Japanese in Tokyo (JPT)) were included in this study. According to two references by Xu et al. and Hu et al, we downloaded the genotype data of individuals from six populations from the 1000 Genomes Project web site (www.1000genomes.org) as controls [20,21]. These individuals derive from three different population groups covering six subpopulations: CEU and TSI as European groups, YRI as representation of Africans, ASW as African American, and CHB and JPT as East Asian groups.
2.4 Statistical and Population Genetic Analyses
Genotype and allele frequencies were obtained by direct counting. Differences in the distribution of alleles among the populations were assessed using the χ2 test. All significance tests were two-tailed and were considered statistically significant at p<0.05. Statistical Package for Social Sciences (SPSS) version 17.0 statistical software was used for the statistical analyses. The FST value, originally defined by Wright, was introduced as the correlation between gametes chosen randomly from within the same subpopulation relative to the entire population. FST can be thought of either as the proportion of genetic diversity due to allele frequency differences among populations or as the correlations between alleles within populations relative to the entire population [22,23,24]. FST calculations were performed using Arlequin 3.5. The FST of SNPs were calculated following Weir and Cockerham [7]. The FST of an SNP was two-tailed and were considered statistically significant at p<0.05.
Results
We investigated a total of 244 subjects, including 99 Hui and 145 Han subjects. Table 1 shows the demographic characteristics, including age, gender, cigarettes smoking, and alcohol drinking. There were no significant differences in age, gender, and drinking consumptions. Compared with Hui people, however, there were more cigarette smokers in Han population than that in Hui population (32.4% vs 16.2%).
Table 1. Distributions of Selected Characterstics Between Chinese Ningxia Han and Hui.
| Ningxia Han(n = 145) | Ningxia Hui (n = 99) | ||||
|---|---|---|---|---|---|
| Variable | n % | n % | χ 2 /t | p value | |
| Gender | Male | 85(58.6) | 55(55.6) | 0.226 | 0.634 |
| Female | 60(41.4) | 44(44.4) | |||
| Age(mean±SD), years | 44.96±12.852 | 43.56±10.621 | -0.930 | 0.354 | |
| Cigarettes Smoking | Yes | 47(32.4) | 16(16.2) | 8.113 | 0.004 |
| No | 98(67.6) | 83(83.8) | |||
| Alcohol Drinking | Yes | 23(15.9) | 11(11.1) | 1.107 | 0.293 |
| No | 122(84.1) | 88(88.9) |
To identify cancer-related genes that are highly differentiated with respect to allele frequency among the six 1000 Genomes Project populations and the two populations from the Ningxia region of China, an FST value, a measure of genetic differentiation, was calculated for each SNP to quantify the differences among the different populations. As shown in Table 2, all of the SNPs had different FST values among the eight populations, varying from 0.013 to 0.192, and all p values were less than 0.05. This finding suggests that the cancer-related genes differ substantially among the studied populations.
Table 2. SNPs that Are Differentiated among CEU, TSI, YRI, ASW, JPT, CHB, Chinese Ningxia Hui, and Chinese Ningxia Han.
| rsID | Fst | p Value | Chr. | Position | Gene | MIM Number | Catagory |
|---|---|---|---|---|---|---|---|
| rs1042522 | 0.084 | 0.000 | 17 | 7579472 | TP53 | 191170 | exon4 |
| rs2395655 | 0.087 | 0.000 | 6 | 36645696 | CDKN1A | 116899 | 5'-flanking |
| rs3176320 | 0.058 | 0.000 | 6 | 36646788 | CDKN1A | 116899 | intron1 |
| rs3829963 | 0.071 | 0.000 | 6 | 36644386 | CDKN1A | 116899 | 5'-flanking |
| rs3829964 | 0.103 | 0.000 | 6 | 36644498 | CDKN1A | 116899 | 5'-flanking |
| rs4135234 | 0.013 | 0.003 | 6 | 36644221 | CDKN1A | 116899 | 5'-flanking |
| rs762624 | 0.094 | 0.000 | 6 | 36645588 | CDKN1A | 116899 | 5'-flanking |
| rs730506 | 0.035 | 0.000 | 6 | 36645968 | CDKN1A | 116899 | 5'-flanking |
| rs4488809 | 0.024 | 0.000 | 3 | 189356261 | TP63 | 603273 | N/A |
| rs13042395 | 0.113 | 0.000 | 20 | 754511 | C20orf54 | 613350 | N/A |
| rs13361707 | 0.057 | 0.000 | 5 | 40791884 | PRKAA1 | 602739 | N/A |
| rs17728461 | 0.035 | 0.000 | 22 | 30598552 | LIF downstream 38kb | N/A | N/A |
| rs2274223 | 0.049 | 0.000 | 10 | 96066341 | PLCE1 | 608414 | N/A |
| rs465498 | 0.192 | 0.000 | 5 | 1325803 | CLPTM1L | 612585 | N/A |
| rs753955 | 0.171 | 0.000 | 13 | 24293859 | MIPEP-TNFRSF19 | N/A | N/A |
| rs9485372 | 0.082 | 0.000 | 6 | 149608874 | TAB2 | 605101 | N/A |
| rs9841504 | 0.062 | 0.000 | 3 | 114362764 | ZBTB20 | 606025 | N/A |
| rs9934948 | 0.134 | 0.000 | 16 | 73439355 | LOC100506172 | N/A | N/A |
Abbreviations are as follows: CEU, Utah residents with Northern and Western European ancestry; TSI, Toscani in Italy; YRI, Yoruba in Ibadan; ASW, African Ancestry in Southwest US; JPT, Japanese in Tokyo; CHB, Han Chinese in Beijing; Chr., chromosome; MIM, Mendelian Inheritance in Man; N/A, not available.
The average FST value between each pair of subpopulations for the eighteen SNP sites was also calculated (Table 3). The FST values between CEU and TSI, YRI and ASW, and JPT and CHB varied from 0.00440 to 0.00970, showing that there was little genetic differentiation between any two European groups, two African groups, or two East Asian groups. The average FST value between CHB and Chinese Ningxia Han was 0.0000, which was less than the value between Ningxia Hui and Ningxia Han (0.00363). However, the average FST value between two different ethnic groups of people among the European groups, African groups, and two East Asian groups varied from 0.07961 to 0.16061, suggesting that there was considerable genetic differentiation. For example, the maximum average FST value was 0.16061 between CEU and YRI, and the minimum value was 0.07961 between ASW and JPT. We found that the average FST value between Chinese Ningxia Han and CEU showed the greatest differentiation (0.10627) between Chinese Ningxia Han and the other populations; furthermore, the average FST value between Chinese Ningxia Han and CHB showed the least differentiation (0.00000), and the value between Chinese Ningxia Han and Hui was between the maximum and minimum (0.00363). Similarly, regarding the differentiation between Chinese Ningxia Hui and the other populations, the average FST value between Chinese Ningxia Hui and YRI indicated the maximum differentiation (0.10129), and the average FST value between Chinese Ningxia Hui and CHB indicated the least differentiation (0.00108).
Table 3. Genetic Differentiation between Subpopulations.
| CEU | TSI | YRI | ASW | JPT | CHB | Chinese Ningxia Han | Chinese Ningxia Hui | |
|---|---|---|---|---|---|---|---|---|
| CEU | 0.00000 | 0.00592 | 0.16061 | 0.11947 | 0.11528 | 0.11038 | 0.10627 | 0.09615 |
| TSI | 0.00000 | 0.15190 | 0.11445 | 0.10791 | 0.09906 | 0.09690 | 0.09125 | |
| YRI | 0.00000 | 0.00440 | 0.09563 | 0.09941 | 0.08699 | 0.10129 | ||
| ASW | 0.00000 | 0.07961 | 0.08586 | 0.07391 | 0.08105 | |||
| JPT | 0.00000 | 0.00970 | 0.00920 | 0.01131 | ||||
| CHB | 0.00000 | 0.00000 | 0.00108 | |||||
| Chinese Ningxia Han | 0.00000 | 0.00363 | ||||||
| Chinese Ningxia Hui | 0.00000 |
Abbreviations are as follows: CEU, Utah residents with Northern and Western European ancestry; TSI, Toscani in Italy;YRI, Yoruba in Ibadan; ASW, African Ancestry in Southwest US; JPT, Japanese in Tokyo; CHB, Han Chinese in Beijing.
FST values were also calculated for each SNP to quantify the differences among the CHB, Hui, and Han populations (Table 4). Eighteen SNPs showed low FST values (FST≤0.004) between the CHB and Chinese Ningxia Han samples. By contrast, the FST values of five SNPs between the Hui and Han populations ranged from 0.000 to 0.050, suggesting the existence of genetic differentiation between the two populations. The other thirteen SNPs had FST values ≤0.000 between the two populations (Table 4).
Table 4. Fst and Allele frequency of 18 SNPs from cancer-related genes in CHB, Chinese Ningxia Han, Chinese Ningxia Hui.
| rsID | Gene | Alleles | f_CHB | f_Chinese Ningxia Han | Fst | p value | χ2 | p value | f_Chinese Ningxia Han | f_Chinese Ningxia Hui | Fst | p value | χ2 | p value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rs1042522 | TP53 | C>G | 0.451 | 0.445 | -0.004 | 0.910 | 0.021 | 0.884 | 0.445 | 0.414 | -0.002 | 0.496 | 0.451 | 0.502 |
| rs2395655 | CDKN1A | G>A | 0.485 | 0.448 | -0.001 | 0.396 | 0.669 | 0.413 | 0.448 | 0.424 | -0.003 | 0.541 | 0.276 | 0.599 |
| rs3176320 | CDKN1A | A>G | 0.228 | 0.248 | -0.003 | 0.739 | 0.267 | 0.605 | 0.248 | 0.278 | -0.002 | 0.613 | 0.532 | 0.466 |
| rs3829963 | CDKN1A | C>A | 0.432 | 0.397 | -0.002 | 0.505 | 0.626 | 0.429 | 0.397 | 0.399 | -0.004 | 0.991 | 0.003 | 0.957 |
| rs3829964 | CDKN1A | C>T | 0.296 | 0.317 | -0.003 | 0.631 | 0.252 | 0.616 | 0.317 | 0.313 | -0.004 | 0.991 | 0.009 | 0.924 |
| rs4135234 | CDKN1A | G>A | 0.117 | 0.117 | -0.004 | 0.991 | 0.001 | 0.980 | 0.117 | 0.101 | -0.003 | 0.694 | 0.315 | 0.575 |
| rs762624 | CDKN1A | C>A | 0.388 | 0.403 | -0.004 | 0.784 | 0.115 | 0.735 | 0.403 | 0.384 | -0.003 | 0.694 | 0.189 | 0.664 |
| rs730506 | CDKN1A | G>C | 0.107 | 0.131 | -0.001 | 0.550 | 0.665 | 0.415 | 0.131 | 0.172 | 0.002 | 0.198 | 1.548 | 0.213 |
| rs4488809 | TP63 | T>C | 0.490 | 0.466 | -0.003 | 0.586 | 0.296 | 0.586 | 0.466 | 0.439 | -0.003 | 0.604 | 0.324 | 0.569 |
| rs13042395 | C20orf54 | C>T | 0.272 | 0.217 | 0.004 | 0.117 | 1.969 | 0.161 | 0.217 | 0.207 | -0.004 | 0.820 | 0.073 | 0.788 |
| rs13361707 | PRKAA1 | C>T | 0.505 | 0.545 | -0.001 | 0.369 | 0.772 | 0.380 | 0.545 | 0.414 | 0.029 | 0.000 | 8.040 | 0.005 |
| rs17728461 | LIF downstream 38kb | C>G | 0.228 | 0.252 | -0.003 | 0.604 | 0.365 | 0.546 | 0.252 | 0.222 | -0.002 | 0.468 | 0.562 | 0.454 |
| rs2274223 | PLCE1 | A>G | 0.189 | 0.207 | -0.003 | 0.622 | 0.233 | 0.629 | 0.207 | 0.131 | 0.015 | 0.045 | 4.630 | 0.031 |
| rs465498 | CLPTM1L | A>G | 0.165 | 0.190 | -0.002 | 0.577 | 0.495 | 0.482 | 0.190 | 0.121 | 0.013 | 0.054 | 4.062 | 0.044 |
| rs753955 | MIPEP-TNFRSF19 | A>G | 0.350 | 0.303 | 0.001 | 0.297 | 1.170 | 0.279 | 0.303 | 0.439 | 0.035 | 0.000 | 9.454 | 0.002 |
| rs9485372 | TAB2 | G>A | 0.432 | 0.417 | -0.004 | 0.775 | 0.108 | 0.742 | 0.417 | 0.449 | -0.002 | 0.468 | 0.499 | 0.480 |
| rs9841504 | ZBTB20 | C>G | 0.107 | 0.131 | -0.001 | 0.496 | 0.665 | 0.415 | 0.131 | 0.101 | 0.000 | 0.324 | 1.013 | 0.314 |
| rs9934948 | LOC100506172 | C>T | 0.519 | 0.541 | -0.003 | 0.595 | 0.233 | 0.629 | 0.541 | 0.495 | 0.000 | 0.360 | 1.017 | 0.313 |
Abbreviations are as follows: CHB, Han Chinese in Beijing.
The allele distribution information of eighteen SNPs among the eight populations was statistically significant (p<0.05), as shown in Table 5. For example, the p53 codon 72 (rs1042522) G allele showed a distribution of 24.2% in CEU, 27.6% in TSI, 63.9% in YRI, 59.8% in ASW, 31.7% in JPT, 45.1% in CHB, 41.4% in Chinese Ningxia Hui, and 44.5% in Chinese Ningxia Han.
Table 5. Allele frequency of 18 SNPs from cancer-related genes in study.
| rsID | Gene | Alleles | f_CEU | f_TSI | f_YRI | f_ASW | f_JPT | f_CHB | f_Chinese Ningxia Han | f_Chinese Ningxia Hui | χ2 | p value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rs1042522 | TP53 | C>G | 0.242 | 0.276 | 0.639 | 0.598 | 0.317 | 0.451 | 0.445 | 0.414 | 113.125 | 0.000 |
| rs2395655 | CDKN1A | G>A | 0.586 | 0.593 | 0.194 | 0.246 | 0.346 | 0.485 | 0.448 | 0.424 | 117.872 | 0.000 |
| rs3176320 | CDKN1A | A>G | 0.389 | 0.336 | 0.495 | 0.582 | 0.389 | 0.228 | 0.248 | 0.278 | 82.716 | 0.000 |
| rs3829963 | CDKN1A | C>A | 0.076 | 0.220 | 0.417 | 0.311 | 0.385 | 0.432 | 0.397 | 0.399 | 98.903 | 0.000 |
| rs3829964 | CDKN1A | C>T | 0.535 | 0.435 | 0.093 | 0.139 | 0.212 | 0.296 | 0.317 | 0.313 | 138.358 | 0.000 |
| rs4135234 | CDKN1A | G>A | 0.071 | 0.131 | 0.056 | 0.082 | 0.188 | 0.117 | 0.117 | 0.101 | 24.843 | 0.001 |
| rs762624 | CDKN1A | C>A | 0.702 | 0.752 | 0.431 | 0.451 | 0.462 | 0.388 | 0.403 | 0.384 | 124.997 | 0.000 |
| rs730506 | CDKN1A | G>C | 0.263 | 0.154 | 0.273 | 0.303 | 0.125 | 0.107 | 0.131 | 0.172 | 51.383 | 0.000 |
| rs4488809 | TP63 | T>C | 0.485 | 0.472 | 0.343 | 0.270 | 0.543 | 0.490 | 0.466 | 0.439 | 36.377 | 0.000 |
| rs13042395 | C20orf54 | C>T | 0.051 | 0.061 | 0.009 | 0.016 | 0.284 | 0.272 | 0.217 | 0.207 | 148.727 | 0.000 |
| rs13361707 | PRKAA1 | C>T | 0.268 | 0.290 | 0.361 | 0.328 | 0.548 | 0.505 | 0.545 | 0.414 | 79.840 | 0.000 |
| rs17728461 | LIF downstream 38kb | C>G | 0.258 | 0.313 | 0.060 | 0.156 | 0.192 | 0.228 | 0.252 | 0.222 | 50.880 | 0.000 |
| rs2274223 | PLCE1 | A>G | 0.313 | 0.393 | 0.356 | 0.369 | 0.207 | 0.189 | 0.207 | 0.131 | 69.307 | 0.000 |
| rs465498 | CLPTM1L | A>G | 0.429 | 0.439 | 0.606 | 0.549 | 0.130 | 0.165 | 0.190 | 0.121 | 254.324 | 0.000 |
| rs753955 | MIPEP-TNFRSF19 | A>G | 0.657 | 0.645 | 0.083 | 0.230 | 0.370 | 0.350 | 0.303 | 0.439 | 229.810 | 0.000 |
| rs9485372 | TAB2 | G>A | 0.187 | 0.140 | 0.269 | 0.230 | 0.457 | 0.432 | 0.417 | 0.449 | 109.866 | 0.000 |
| rs9841504 | ZBTB20 | C>G | 0.126 | 0.056 | 0.315 | 0.279 | 0.216 | 0.107 | 0.131 | 0.101 | 86.454 | 0.000 |
| rs9934948 | LOC100506172 | C>T | 0.141 | 0.145 | 0.421 | 0.451 | 0.558 | 0.519 | 0.541 | 0.495 | 177.218 | 0.000 |
Abbreviations are as follows: CEU, Utah residents with Northern and Western European ancestry; TSI, Toscani in Italy; YRI, Yoruba in Ibadan; ASW, African Ancestry in Southwest US; JPT, Japanese in Tokyo; CHB, Han Chinese in Beijing.
The allele frequency and relative physical coordinates of the eighteen SNPs are shown in Table 4. The allele frequencies of all eighteen SNPs were found to be highly similar between the Chinese Ningxia Han and CHB samples, showing no significant difference between the two populations (p>0.05). However, four SNPs showed significantly different genetic distributions between Chinese Ningxia Hui and Chinese Ningxia Han (p<0.05). For example, the frequencies of the rs13361707 T, rs2274223 G, and rs465498 G alleles in Chinese Ningxia Hui (0.414, 0.131, and 0.121, respectively) were significantly less than those in Chinese Ningxia Han (0.545, 0.207, and 0.190, respectively). The frequency of the rs753955 G allele in Chinese Ningxia Hui (0.439) was significantly greater than that in Chinese Ningxia Han (0.303), and the frequencies of the other fourteen SNPs showed no significant differences between Chinese Ningxia Hui and Chinese Ningxia Han (p>0.05).
Discussion
Molecular genetics studies in the last few decades have provided the basis for ancestral analysis and analysis of the geographic origins of human populations using genetic data. Starting approximately 100,000 years ago, anatomically modern humans migrated out of East Africa and gradually spread to South Asia, Australia, Europe, East Asia, and eventually the Americas. All people living today are direct descendants of these earlier humans. Populations living in different parts of the world today exhibit a small number of genetic differences due to migration, mutation, genetic drift, natural selection, and reproductive isolation [25].
In this study, we sought to investigate the diversity pattern of cancer-related genes between Hui and Han Chinese from the Ningxia region of China to explore the hereditary differences between the two populations. We selected eighteen SNPs from cancer-related genes for the analysis. We first used FST to measure the degree of population differentiation [26], and our results suggested that all SNPs of the cancer-related genes differ substantially among the eight populations. In addition, all FST results were consistent with the allele frequency comparisons among the different populations.
We then used average FST values to investigate the diversity pattern of cancer-related genes among eight subpopulations with respect to the eighteen SNP sites. We found that there was little genetic differentiation between any two European, African, or East Asian groups, although there was remarkable genetic differentiation between each pair of the aforementioned ethnic groups.
We subsequently investigated the genetic differentiation between CHB and Han Chinese in Ningxia. Our data indicated that the allele frequencies of all eighteen SNPs were very similar between the two populations. The Han Chinese population is generally thought to be naturally divided by the Yangtze River into two groups: the Southern Han and Northern Han groups. A previous study showed that the difference between these two groups of Han Chinese is greater than that between a given subpopulation and ethnic minorities at the same location [27]. Because CHB and Ningxia Han Chinese are both located in northern China, the genetic difference between CHB and Ningxia Han Chinese should be smaller than that between the Southern Han and Northern Han groups.
Our study further showed that the allele frequencies of four SNPs differentiated Ningxia Hui Chinese from Ningxia Han Chinese, indicating that there was some genetic differentiation in the distribution of four SNPs from cancer-related genes between Chinese Ningxia Hui and Han. Among four SNPS, SNP rs753955 had been reported to be associated with lung cancer in Chinese Han population by GWAS. SNP rs753955 was also found to be related to non-cardia gastric cancer in Chinese Ningxia Han in our previous study [19]. However, their associations with cancer in Ningxia Hui people are still unclear. The Chinese Hui ethnic group descended from Arab and Persian Muslim immigrants who came to China and married local girls hundreds or even thousands of years ago [28]. However, Hui people adhere to Islamic principles [29]. To retain religious purity and group identity, most Hui people have always isolated themselves socially from other people in enclaves. Hui marriage practices tend toward endogamy in all respects, especially in the rural part of Ningxia. Therefore, the Hui population is religiously and culturally conservative [30]. Consequently, our results show that the allele frequency distribution of four SNPs in some cancer-related genes in the Ningxia Hui Chinese is different from that in Ningxia Han Chinese, indicating that hereditary differences exist between Hui and Han Chinese in Ningxia. Shuhua Xu et al. systemically investigated the influence of admixture on the diversity of absorption, distribution, metabolism, and excretion (ADME) genes responsible for drug absorption, distribution, metabolism and excretion in five northwestern Chinese minority populations, namely Tajik, Uyghur, Kazakh, Kirgiz and Hui. They found that northwestern Chinese populations exhibited substantial differences in some ADME genes compared with Han Chinese [31]. Therefore, both the work of Xu and our research indicate that Hui Chinese are different from the Han Chinese population with respect to their genetic background. Previous studies have shown that genetic background diversity might result in differences in disease spectrum. For example, in two large studies from Korea and China, Pro/Pro at p53 codon 72 (rs1042522) was found to be associated with colon cancer; the respective frequencies of the Pro allele in cases and controls were 34.0% and 36.4% in Koreans and 50.3% and 39.6% in Chinese [32]. In two larger studies, one with 442 cases and 904 controls in the United States, the frequency of the Pro allele in cases and controls was 27.4% and 25.5%, respectively [2]. Another study with 352 cases and 316 controls in Spain showed Pro allele frequencies in cases and controls of 24.0% and 21.0% and found no association between the p53 codon 72 polymorphism and the risk of colorectal cancer [33]. These results highlight that ethnicity is a critical factor in the distribution of allele frequencies, which may ultimately affect a person’s cancer spectrum. These results will have significant implications when evaluating cancer susceptibility, sensitivity to radiotherapy and chemotherapy, and prognosis in Hui and Han Chinese.
Conclusions
Our results showed for the first time that Hui Chinese in Ningxia exhibit differences in certain cancer-related genes compared with Han Chinese in Ningxia. Therefore, we suggest that population differences in cancer susceptibility, the efficacy of cancer therapy, and prognosis should be carefully considered. However, there are some limitations to our study, such as the small sample size, the small number of genetic markers, and the very limited geographic location. In addition, although we have investigated the associations between some of the SNPs and cancer in Ningxia Han population [19], we could not further compare the associations between the genetic polymorphisms and cancer among the Hui and Han populations due to a lack of cancer samples from Hui people, which would greatly strengthen this work. Therefore, additional research is needed to further identify genetic differences between the Hui and Han populations.
Supporting Information
(DOC)
Acknowledgments
We thank all of the participants of this study.
Data Availability
All relevant data are within the paper.
Funding Statement
This work was supported by the National Natural Science Foundation of China (grant numbers 81460434, 81160249 to WJY; http://www.nsfc.gov. cn/.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Data Availability Statement
All relevant data are within the paper.