A single-nucleotide polymorphism (SNP) multiplex system: the association of five SNPs with human eye and hair color in the Slovenian population and comparison using a Bayesian network and logistic regression model

Aim

To analyze two phenotype characteristics – eye and hair color – using single-nucleotide polymorphisms (SNPs) and evaluate their prediction accuracy in Slovenian population.

Methods

Twelve SNPs (OCA2 – rs1667394, rs7170989, rs1800407, rs7495174; HERC2 – rs1129038, rs12913832; MC1R – rs1805005, rs1805008; TYR – rs1393350; SLC45A2 – rs16891982, rs26722; SLC24A5 – rs1426654) were used for the development of a single multiplex assay. The single multiplex assay was based on SNaPshot chemistry and capillary electrophoresis. In order to evaluate the accuracy of the prediction of eye and hair color, we used the logistic regression model and the Bayesian network model, and compared the parameters of both.

Results

The new single multiplex assay displayed high levels of genotyping sensitivity with complete profiles generated from as little as 62 pg of DNA. Based on a prior evaluation of all SNPs in a single multiplex, we focused on the five most statistically significant in our population in order to investigate the predictive value. The two prediction models performed reliably without prior ancestry information, and revealed very good accuracy for both eye and hair color. Both models determined the highest predictive value for rs12913832 (P < 0.0001), while the other four SNPs (rs1393350, rs1800407, rs1805008, and rs7495174) showed additional association for color prediction.

Conclusion

We developed a sensitive and reliable single multiplex genotyping assay. More samples from different populations should be analyzed before this assay could be used as one of the supplemental tools in tracing unknown individuals in more complicated crime investigations.Height, face structure, pigmentation of the eye, hair, and skin, the presence of freckles, and male baldness make up human externally visible characteristics (EVC). To be able to predict eye and hair color based solely on biological material left behind at a crime scene or obtained from dismembered missing persons, or even of disaster victims, is one of the major expectations from the routine forensic work in the near future (1). However, genetic understanding of human appearance is still in its infancy, mainly due to the fact that all EVCs are polygenic traits. This means that yields from a large number of different genes and the expression of these genes are further influenced by mutual interactions and environmental interactions (2). Above all, molecular mechanisms and functional protein assays must also be considered in order to really understand how allelic variation in pigmentation genes could result in such a diversity of phenotypes in different human populations (3). The human eye (iris) and hair color are one of the most highly polymorphic phenotypes in people of European origin. The non-brown iris colors and red hair are generally features of European origin resulting from positive selection in early European history. There are several hypotheses for positive selection that mainly occurred in the Baltic region and Northern Europe. These are most likely: UV exposure causing skin cancer, vitamin D deficiency, and even sexual selection (4,5). Most EVCs are complex traits with many genes and single nucleotide polymorphism (SNP) variations, so the right combination of SNPs is crucial for the correct prediction of eye and hair color. Several genome-wide association studies (GWAS) for pigmentation have revealed that SNPs within the HERC2, OCA2, MC1R, SLC24A5, SLC45A2, TYR, and ASIP (4,6-16) genes were most strongly associated with eye and hair color in European populations. The latest data have shown that the main iris color variation is associated with a highly evolutionarily preserved region in the HERC2 gene or within the short sequence between the HERC2 and OCA2 genes. It is assumed that these regions represent a regulatory region controlling the constitutive expression of OCA2 (4,11,12). As for iris color, it has also been explained that red hair color is mainly associated with polymorphisms in the MC1R gene (13,17). On the other hand, the variations of genes such as SLC24A5, SLC45A, HERC2, and ASIP seem to be responsible for influencing the shades of hair color from blond to black (18,19).In order to correctly predict human eye and hair color from genetic data for the Slovenian population, we compared two alternative prediction models that are nowadays used most often in this field of forensics – the Bayesian network model and the logistic regression model. These models were developed and compared on the basis of the informative SNPs selected from our single multiplex assay.     

HapMap coverage for SNPs in the Japanese population

The coverage of human genomic variations is known to substantially affect the success of genome-wide association studies. We therefore assessed the SNP coverage in the HapMap database for a total of 1,304 subjects from the Japanese population by combining resequencing and high-density genotyping approaches. First, we resequenced 48 Japanese subjects in 86 genes (572 kb in total), and we then genotyped the subset of tag SNPs and also imputed genotypes for all of the detected SNPs in an additional panel of 1,256 subjects. Subsequently, we genotyped 555,352 tag SNPs selected from the HapMap in 72 Japanese subjects (from the panel of 1,256 subjects) and further imputed genotypes for all SNPs currently included in the HapMap. Of 738 common genic SNPs (1.3 per kb) that we detected by resequencing, 58% had already been genotyped in the HapMap, and 31% were not genotyped but had a proxy SNP in the HapMap with a linkage disequilibrium coefficient r ² ≥ 0.8, whereas 11% were not represented in the current HapMap database. Thus, the HapMap coverage appears to be high although not thorough for SNPs in the Japanese population as compared to its coverage reported in Caucasians, and this needs to be considered when we interpret association results. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Characterization of publicly available SNPs in the Korean population

Single-nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations and have a great potential for mapping studies of complex genetic traits. Currently a great deal of effort is invested in the identification of SNPs, and a large volume of data is already available through public databases (NCBI, NCI, WICGR, HGBASE). For an association mapping study, SNP allele frequencies in the population are critical. As an initial step toward construction of an SNP database of the Korean population, we have determined the allele frequencies of 300 cSNPs selected from the public database in 24 individuals. Among the tested markers, approximately 23% did not show polymorphism in the population. The results suggest that the ethnic and population based differences should be considered in the selection of SNPs for the study of complex diseases with association mapping methods.     

Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach

The identification of some important retinal anatomical regions is a prerequisite for the computer aided diagnosis of several retinal diseases. In this paper, we propose a new adaptive method for the automatic segmentation of the optic disk in digital color fundus images, using mathematical morphology. The proposed method has been designed to be robust under varying illumination and image acquisition conditions, common in eye fundus imaging. Our experimental results based on two publicly available eye fundus image databases are encouraging, and indicate that our approach potentially can achieve a better performance than other known methods proposed in the literature. Using the DRIVE database (which consists of 40 retinal images), our method achieves a success rate of 100% in the correct location of the optic disk, with 41.47% of mean overlap. In the DIARETDB1 database (which consists of 89 retinal images), the optic disk is correctly located in 97.75% of the images, with a mean overlap of 43.65%.     

眼皮肤白化病Ⅳ型基因MATP的SNPs与皮肤颜色

皮肤颜色差异是人类生物学中最易见、区分不同人群的差异之一,这种差异来自自然的选择,有几个候选基因可以解释其中差异,其中眼皮肤白化病Ⅳ型基因MATP就是其中之一。眼皮肤白化病Ⅳ型基因MATP至今已发现有11种单核苷酸多态性（SNPs）,这些SNPs中有些与皮肤颜色有显著相关,如p.Phe374Leu。     

Prediction of functional regulatory SNPs in monogenic and complex disease

Next-generation sequencing (NGS) technologies are yielding ever higher volumes of human genome sequence data. Given this large amount of data, it has become both a possibility and a priority to determine how disease-causing single nucleotide polymorphisms (SNPs) detected within gene regulatory regions (rSNPs) exert their effects on gene expression. Recently, several studies have explored whether disease-causing polymorphisms have attributes that can distinguish them from those that are neutral, attaining moderate success at discriminating between functional and putatively neutral regulatory SNPs. Here, we have extended this work by assessing the utility of both SNP-based features (those associated only with the polymorphism site and the surrounding DNA) and gene-based features (those derived from the associated gene in whose regulatory region the SNP lies) in the identification of functional regulatory polymorphisms involved in either monogenic or complex disease. Gene-based features were found to be capable of both augmenting and enhancing the utility of SNP-based features in the prediction of known regulatory mutations. Adopting this approach, we achieved an AUC of 0.903 for predicting regulatory SNPs. Finally, our tool predicted 225 new regulatory SNPs with a high degree of confidence, with 105 of the 225 falling into linkage disequilibrium blocks of reported disease-associated genome-wide association studies SNPs.     

Haplotype structure of five SNPs within the ACE gene in the Tunisian population

BACKGROUND: The Angiotensin-Converting Enzyme (ACE) is a candidate gene in the aetiology of several common diseases. The study of the haplotype structure of this gene is of interest in diagnosis and in pharmacogenomics. AIM: The study investigated the haplotype profile of single nucleotide polymorphisms (SNPs) within the ACE gene in the Tunisian population and compared it with other populations. SUBJECTS AND METHODS: Five SNPs (rs1800764, rs4291, rs4309, rs4331, rs4340) covering a region of 15.6 kb of the ACE gene were typed by PCR-digestion in a sample of 100 healthy subjects. RESULTS: All SNPs were polymorphic and in Hardy-Weinberg equilibrium. A total of 21 haplotypes were identified but only eight had a frequency of more than 1%. The four most common haplotypes had a cumulative frequency of 87.4%. The 'Yin-Yang' phenomenon (the two major haplotypes are complementary at all sites) was found. Linkage disequilibrium between all pairs of loci was highly significant (p<10-5). A simple and efficient statistical procedure was used to identify three important SNPs. CONCLUSION: The Tunisian population showed a different haplotype structure from the European one for the ACE gene and three important SNPs were identified. These will be very helpful in future association studies in the Tunisian and North African populations.     

Differences in allele frequencies of autosomal dominant hypercholesterolemia SNPs in the Malaysian population

Hypercholesterolemia is caused by different interactions of lifestyle and genetic determinants. At the genetic level, it can be attributed to the interactions of multiple polymorphisms, or as in the example of familial hypercholesterolemia (FH), it can be the result of a single mutation. A large number of genetic markers, mostly single nucleotide polymorphisms (SNP) or mutations in three genes, implicated in autosomal dominant hypercholesterolemia (ADH), viz APOB (apolipoprotein B), LDLR (low density lipoprotein receptor) and PCSK9 (proprotein convertase subtilisin/kexin type-9), have been identified and characterized. However, such studies have been insufficiently undertaken specifically in Malaysia and Southeast Asia in general. The main objective of this study was to identify ADH variants, specifically ADH-causing mutations and hypercholesterolemia-associated polymorphisms in multiethnic Malaysian population. We aimed to evaluate published SNPs in ADH causing genes, in this population and to report any unusual trends. We examined a large number of selected SNPs from previous studies of APOB, LDLR, PCSK9 and other genes, in clinically diagnosed ADH patients (n=141) and healthy control subjects (n=111). Selection of SNPs was initiated by searching within genes reported to be associated with ADH from known databases. The important finding was 137 mono-allelic markers (44.1%) and 173 polymorphic markers (55.8%) in both subject groups. By comparing to publicly available data, out of the 137 mono-allelic markers, 23 markers showed significant differences in allele frequency among Malaysians, European Whites, Han Chinese, Yoruba and Gujarati Indians. Our data can serve as reference for others in related fields of study during the planning of their experiments.     

Verification of 525 coding SNPs in 179 hypertension candidate genes in the Japanese population: identification of 159 SNPs in 93 genes

 Single-nucleotide polymorphisms (SNPs) located in coding regions (coding SNPs; cSNPs) with amino acid substitution can potentially alter protein function. Therefore, identification of the nonsynonymous cSNPs of the genes of common diseases is valuable in tests of association with phenotypes. In this study, we validated 525 candidate cSNPs from 179 hypertension candidate genes deposited in the publicly available database dbSNP by DNA sequencing of samples from 32 Japanese individuals. We identified a total of 143 SNPs (27%) in 93 hypertension candidate genes. We also identified 16 new SNPs, for a total of 159 SNPs. Of the 159 SNPs thus identified, 104 were nonsynonymous. We estimate that approximately 20% of the SNPs deposited in dbSNP database showed a minor allele frequency of over 5%. The candidate SNPs for hypertension identified in this study would be valuable for association studies with hypertension to accelerate the identification of hypertension genes. Received: March 14, 2002 / Accepted: April 15, 2002     

Determination of population origin: a comparison of autosomal SNPs,Y-chromosomal and mtDNA haplogroups using a Malagasy population as example

Y-chromosomal and mitochondrial DNA (mtDNA) polymorphisms have been used for population studies for a long time. However, there is another possibility to define the origin of a population: autosomal single-nucleotide polymorphisms (SNPs) whose allele frequencies differ considerably in different populations. In an attempt to compare the usefulness of these approaches we studied a population from Madagascar using all the three mentioned approaches. Former investigations of Malagasy maternal (mtDNA) and paternal (Y chromosome) lineages have led to the assumption that the Malagasy are an admixed population with an African and Asian-Indonesian heritage. Our additional study demonstrated that more than two-third of the Malagasy investigated showed clearly a West African genotype regarding only the autosomal SNPs despite the fact that 64% had an Asian mtDNA and more than 70% demonstrated an Asian-Indonesian heritage in either mtDNA or Y-chromosomal haplogroup or both. Nonetheless, the admixture of the Malagasy could be confirmed. A clear African or Asian-Indonesian heritage according to all the three DNA approaches investigated was only found in 14% and 1% of male samples, respectively. Not even the European or Northern African influences, detected in 9% of males (Y-chromosomal analysis) and 11% of samples (autosomal SNPs) were consistent. No Malagasy in our samples showed a European or Northern African origin in both categories. So, the analysis of autosomal SNPs could confirm the admixed character of the Malagasy population, even if it pointed to a greater African influence as detectable by Y-chromosomal or mtDNA analysis.     

Major genes of eye color and hair color linked to LU and SE

Eye color and hair color were studied in a large Danish family material, tested earlier for a comprehensive set of genetic marker systems. We found strong evidence for linkage of "green eye color" or GEY to the Lutheran-Secretor systems (combined lod score 9.19). This would appear to identify a major gene with influence towards "green eye color". We also found evidence for linkage of GEY to "brown hair color" or BRHC (lod score 5.06), which would appear to identify a second major gene influence in the same region of chromosome 19. Concerning the obvious association in the general population between eye color and hair color which could imitate linkage if it were reflected within sibships, we did not recognize any such intrafamilial association; in the pooled informative sibships GEY and BRHC were independently distributed (as were LU-SE and GEY).     

Accurate detection and genotyping of SNPs utilizing population sequencing data

Next-generation sequencing technologies have made it possible to sequence targeted regions of the human genome in hundreds of individuals. Deep sequencing represents a powerful approach for the discovery of the complete spectrum of DNA sequence variants in functionally important genomic intervals. Current methods for single nucleotide polymorphism (SNP) detection are designed to detect SNPs from single individual sequence data sets. Here, we describe a novel method SNIP-Seq (single nucleotide polymorphism identification from population sequence data) that leverages sequence data from a population of individuals to detect SNPs and assign genotypes to individuals. To evaluate our method, we utilized sequence data from a 200-kilobase (kb) region on chromosome 9p21 of the human genome. This region was sequenced in 48 individuals (five sequenced in duplicate) using the Illumina GA platform. Using this data set, we demonstrate that our method is highly accurate for detecting variants and can filter out false SNPs that are attributable to sequencing errors. The concordance of sequencing-based genotype assignments between duplicate samples was 98.8%. The 200-kb region was independently sequenced to a high depth of coverage using two sequence pools containing the 48 individuals. Many of the novel SNPs identified by SNIP-Seq from the individual sequencing were validated by the pooled sequencing data and were subsequently confirmed by Sanger sequencing. We estimate that SNIP-Seq achieves a low false-positive rate of ∼2%, improving upon the higher false-positive rate for existing methods that do not utilize population sequence data. Collectively, these results suggest that analysis of population sequencing data is a powerful approach for the accurate detection of SNPs and the assignment of genotypes to individual samples.With the availability of several next-generation sequencing platforms, the cost of DNA sequencing has dropped dramatically over the past few years and improvements in technology are expected to decrease the cost further (Shendure and Ji 2008). Next-generation sequencers, such as the Illumina Genome Analyzer (GA), can generate gigabases of nucleotides per day and have enabled the sequencing of complete individual human genomes (Bentley et al. 2008; Ley et al. 2008; Wang et al. 2008; Wheeler et al. 2008; McKernan et al. 2009). While the resequencing of complete human genomes still remains quite expensive, the targeted sequencing of specific genomic intervals in a large population of individuals is now feasible in an individual laboratory. Resequencing of coding sequences of genes in large populations has previously been shown to be useful for identifying multiple rare variants affecting quantitative traits (Cohen et al. 2004, 2006; Ji et al. 2008). Resequencing of genomic regions identified by genome-wide association studies in healthy and diseased populations represents a powerful strategy for assessing the contribution of rare variants to disease etiology. Nejentsev et al. (2009) have used this approach to identify four rare variants protective for type 1 diabetes.For harnessing the capacity of next-generation sequencers for deep population resequencing, the first challenge is to selectively capture DNA from the region of interest. Recently, Craig et al. (2008) used long-range PCR and DNA barcodes to sequence specific regions of the human regions in multiple samples simultaneously using the Illumina GA. Harismendy et al. (2009) also used long-range PCR to sequence targeted regions of the human genome using multiple sequencing platforms to evaluate the feasibility of targeted population sequencing and the concordancy of variant calling between the different platforms. However, traditional sequence capture methods, such as long-range polymerase chain reaction (LR-PCR), are not adequate for capturing thousands of noncontiguous regions of the genome, e.g., all exons, in a large number of samples. Several high-throughput target capture methods have been developed (Hodges et al. 2007; Okou et al. 2007; Porreca et al. 2007; Turner et al. 2009).After millions of reads have been generated by the sequencer, the next challenge is to identify genetic variants by mapping the reads to a reference sequence. A variety of tools have been developed that can efficiently align hundreds of millions of short reads to a reference sequence even in the presence of multiple errors in the reads (Li et al. 2008; Langmead et al. 2009; Li and Durbin 2009; Li et al. 2009b). Each base mismatch in an aligned read represents either a sequencing error or a single nucleotide variant in the diploid individual. To compensate for the high sequencing error rates of next-generation sequencing platforms, one requires the presence of multiple overlapping reads, each with a base different from the reference base for single nucleotide polymorphism (SNP) calling. Base quality values—probability estimates of the correctness of a base call—are particularly useful for distinguishing sequencing errors from SNPs. The Illumina sequencing system generates a phred-like quality score for each base call using various predictors of the sequencing errors. SNP calling methods for Illumina sequence data utilize these base quality values to compute the likelihood of different genotypes at each position using Bayesian or statistical models (Li et al. 2008, 2009a). Positions for which the most likely genotype is different from the reference genotype and which satisfy additional filters on neighborhood sequence quality, read alignment quality, etc. are reported as SNPs. However, sequencing errors for the Illumina GA are not completely random and are dependent on the local sequence context of the base being read, the position of the base in the read, etc. (Dohm et al. 2008; Erlich et al. 2008). Therefore, assuming independence between multiple base calls, each with a non-reference base, results in overcalling of SNPs, i.e., increased number of false-positives. To reduce the number of false variant calls, the MAQ SNP caller (Li et al. 2008) uses a dependency model to estimate an average error rate using all base quality scores.MAQ and other SNP calling methods have enabled fairly accurate detection of SNPs from resequencing of individual human genomes (Bentley et al. 2008; Wang et al. 2008). However, there is potential for developing more accurate SNP detection methods, in particular, by taking advantage of sequence information from a population of sequenced individuals. Comparison of sequenced reads for a potential variant site across multiple individuals has the potential to differentiate systematic sequencing errors from real SNPs. Patterns of mismatched bases (bases not matching the reference base) resulting from systematic sequencing errors are likely to be shared across individuals. On the other hand, the profiles of mismatched bases between individuals with and without a SNP are likely to be distinct. Comparison of read alignments across multiple individuals also has the potential to filter out SNPs that are an artifact of inaccurate read alignments. We present a probabilistic model that leverages sequence data from a population of individuals, each sequenced separately, for detecting single nucleotide variants and also assigning genotypes to each individual in the population.³ Our method recalibrates each base quality value by adding a population error correction to the Illumina base error probability. This correction is computed using the distribution of mismatched bases across all sequenced individuals. The recalibrated base quality values are then used to compute genotype probabilities for each individual under a simple Bayesian model that assumes independence between base calls. Finally, positions in the sequence with one or more individuals showing evidence for harboring a non-reference allele are identified as SNPs. Craig et al. (2008) described a similar approach for SNP detection using sequence data from multiple individuals where they used Bayes factors to compare the fraction of reads with the alternate allele across multiple individuals. Sites at which one or more individuals have a fraction of reads with the alternate allele sufficiently greater than the average were identified as SNPs. Our model is much more general and can take advantage of the complete information about each base call, i.e., base quality value, position in the read containing the base, and the strand to which the read aligns to.To evaluate our population SNP detection method, we analyzed sequence data from a 200-kilobase (kb)-long region on chromosome 9p21 that was sequenced to a median depth of 45× in 48 individuals using the Illumina Genome Analyzer (O Harismendy, V Bansal, N Rahim, X Wang, N Heintzman, B Ren, EJ Topol, and KA Frazer, in prep.). We demonstrate that our method can accurately detect SNPs with a low false-positive rate (∼2%) and a low false-negative rate in comparison to SNP detection from individual sequence data using MAQ. By comparing genotype calls between replicate samples, we show a 98.8% accuracy for sequence-based genotyping using our method.     

Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model

Interleukin (IL)-18, an important proinflammatory cytokine, plays a potential pathological role in rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Studies on the relationship of IL-18 gene promoter rs1946518 (−607A/C) polymorphism, rs187238 (−137G/C) polymorphism with RA and SLE are inconclusive. The aim of this study was to get a more precise estimation of the relationship in Asian populations. Meta-analysis was conducted on the associations between the IL-18 (−607A/C and -137G/C) polymorphisms and RA and SLE, using; (1) allele contrast, (2) dominant, and (3) recessive models. A total of 11 studies were included in this study. For the relationship of IL-18 rs1946518 polymorphism with RA (additive model: OR=0.752, 95%CI=0.562-1.006; dominant model: OR=0.730, 95%CI =0.479-1.113; recessive model: OR=0.537, 95%CI=0.271-1.064) and SLE (additive model: OR=0.684, 95%CI=0.455-1.028; dominant model: OR=0.645, 95%CI=0.368-1.130; recessive model: OR=0.672, 95%CI =0.447-1.010), no significant association with RA and SLE risk can be found under all genetic models in Asian populations. However, significant associations were observed in Chinese population for both RA ((OR=0.688, 95%CI =0.532-0.889) and SLE (OR=0.606, 95%CI =0.396-0.930) under additive model. For the relationship between IL-18 rs187238 polymorphism and RA or SLE, there was no significant association detected in all genetic models, even in Chinese population. This meta-analysis indicates that the IL-18-607A/C polymorphism may confer susceptibility to RA and SLE in Chinese population, but not all Asians.     

Association between type 1 diabetes and GWAS SNPs in the southeast US Caucasian population

The present study was conducted to assess genetic associations for type 1 diabetes (T1D) reported in previous genome-wide association studies (GWAS). A total of 21 previously reported single-nucleotide polymorphisms (SNPs) were genotyped by TaqMan assays in 1434 Caucasian T1D patients and 1864 normal controls from Georgia. Analysis of the samples identified 18 SNPs (PTPN22, INS, IFIH1, SH2B3, ERBB3, CTLA4, C14orf181, CTSH, CLEC16A, CD69, ITPR3, C6orf173, SKAP2, PRKCQ, RNLS, IL27, SIRPG and CTRB2) with putative association.     

TG相互作用因子基因编码区域的单核苷酸多态在中国高度近视人群与正常人群中的分布

目的 筛查TG相互作用因子(TG interacting factor，TGIF)基因编码区域变异与中国高度近视人群的相关性及单核苷酸多态(single nucleotide polymorphism，SNP)在中国人群中的分布特征。方法 采用聚合酶链反应-单链构象多态性检测204例中国人高度近视先证者及112名正常人的TGIF基因所有编码外显子及两侧序列有无突变；对有突变的外显子区域进行克隆测序。结果 在TGIF基因3个外显子编码区域及两侧序列发现3个SNP和1个单核苷酸突变，分别是IVS-2 nt350G→T(36／204)、密码子140CCA→CCG，Prol40Pro、密码子163CCG→CTG，Pro163Leu及密码子126GTG→GCG，Vall26Ala(1／204)；其中密码子140CCA→CCG、密码子163 CCG→CTG两个SNP在中国人群中形成3个等位基因和5种基因型，符合Hardy-Weinberg平衡定律。结论 TGIF基因编码区域的变异与中国高度近视人群无相关性；TGIF基因编码区域密码子140和163的SNP的基因频率和基因型频率在中国人群的分布达到了遗传平衡．     

SNPs in axon guidance pathway genes and susceptibility for Parkinson's disease in the Korean population

Single-nucleotide polymorphisms (SNPs) in genes of the axon guidance pathway have been reported to be a possible susceptibility factor for Parkinson's disease (PD). This study investigated whether the genetic variability in the axon guidance pathway is a susceptibility factor in PD patients in the Korean population. A total of 373 patients and 384 healthy subjects were included. A set of 22 SNPs was analyzed, and the risk of PD was evaluated using odds ratios in an unconditional and conditional logistic regression models of age- and gender-matched subsets. A multidimensionality reduction (MDR) analysis was performed to explore potential gene-gene interactions. SNPs in the DCC, CHP, RRAS2 and EPHB1 genes of the axon guidance pathway showed significant associations with PD. The DCC rs17468382 and EPHB1 rs2030737 SNPs may be associated with increased PD risk, and the CHP rs6492998 and RRAS2 rs2970332 SNPs may be associated with reduced PD risk. However, no significant interactions for PD risk were found in the MDR analysis and logistic regression analysis using SNP interaction terms. This study supports that only four of the selected 22 SNPs are regulating factors associated with PD in the Korean population. However, no interactions were found among the SNPs, suggesting that the effect for the pathway as a whole is not greater than that for single genes in the Korean population. Further investigations involving populations of various ethnicities and other genetic markers and models are warranted.