Single Nucleotide Polymorphism Fragments Research Articles

HAHap: a read-based haplotyping method using hierarchical assembly.

BackgroundThe need for read-based phasing arises with advances in sequencing technologies. The minimum error correction (MEC) approach is the primary trend to resolve haplotypes by reducing conflicts in a single nucleotide polymorphism-fragment matrix. However, it is frequently observed that the solution with the optimal MEC might not be the real haplotypes, due to the fact that MEC methods consider all positions together and sometimes the conflicts in noisy regions might mislead the selection of corrections. To tackle this problem, we present a hierarchical assembly-based method designed to progressively resolve local conflicts.ResultsThis study presents HAHap, a new phasing algorithm based on hierarchical assembly. HAHap leverages high-confident variant pairs to build haplotypes progressively. The phasing results by HAHap on both real and simulated data, compared to other MEC-based methods, revealed better phasing error rates for constructing haplotypes using short reads from whole-genome sequencing. We compared the number of error corrections (ECs) on real data with other methods, and it reveals the ability of HAHap to predict haplotypes with a lower number of ECs. We also used simulated data to investigate the behavior of HAHap under different sequencing conditions, highlighting the applicability of HAHap in certain situations.

Association of Smoking and XPG, CYP1A1, OGG1, ERCC5, ERCC1, MMP2, and MMP9 Gene Polymorphisms with the early detection and occurrence of Laryngeal Squamous Carcinoma.

A total of 200 smoking-related laryngeal carcinoma patients with pathology confirmation from the Eye and ENT Hospital and 190 high-risk smokers were included in a survey. All of the participants had a smoking index greater than 400 (cigarettes/day*year.) We obtained data on clinical and baseline characteristics, and peripheral blood was obtained and subjected to DNA extraction to analyse the correlation between smoking and the occurrence of laryngeal carcinoma. We selected candidate genes and SNP fragments that were found to be closely associated with smoking-related tumours in preliminary studies. The selected candidate genes were XPG, CYP1A1, OGG1, ERCC5, ERCC1, MMP2, and MMP9. We then performed SNP sequencing using Sequenom SNP detection technology. Target genes and single nucleotide polymorphism (SNP) fragments were evaluated to analyse the correlation between genotype or allele and smoking-related laryngeal carcinoma and to identify susceptibility genes related to laryngeal carcinoma. The results included four main findings: (1) The smoking index differed significantly between laryngeal cancer patients and control subjects (P=0.0035). The risk of laryngeal cancer was increased among individuals with a smoking index greater than 600 cigarettes/day*year (P=0.03). (2) The smoking index was significantly correlated with the T, N and clinical stages (P<0.05). (3) The polymorphisms CYP1A1-rs1048943, rs4646421, and rs4646422 and MMP9-rs17577 were significantly associated with laryngeal squamous carcinoma (P<0.05). (4) After stratifying the subjects by smoking degree, the GT genotype of ERCC1-rs2298881 was associated with a significantly greater risk of laryngeal carcinoma among heavy smokers (P=0.04). The results suggest that smoking plays an important role in the occurrence and development of laryngeal squamous carcinoma; CYP1A1 and MMP9 might be susceptibility gene SNPs for smoking-related laryngeal carcinoma, and ERCC1 might play an important role in heavy smokers. The results of this study might help identify an early marker for the detection and prevention of laryngeal carcinoma.

Evidence of association of interleukin-23 receptor gene polymorphisms with Egyptian rheumatoid arthritis patients

BackgroundThe identification of additional genetic risk factor is an on-going process that will aid in the understanding of rheumatoid arthritis (RA) aetiology. A genome-wide association scan in Crohn’s (CD) disease highlighted the interleukin-23 receptor (IL23R) gene as a susceptibility factor. Since the IL-23/IL-17 pathway is known to associate with other autoimmune disease, including rheumatoid arthritis and systemic sclerosis, we hypothesised that IL23R could be a shared susceptibility gene. The rare allele of IL23R single nucleotide polymorphism (SNP) rs11209026 (Arg381Gln) confers strong protection against CD. Our aim was to analyse IL23R SNP (rs11209026, rs2201841, and rs10889677) and to detect its association with RA in Egyptian patients. MethodsA group of Egyptian patients with RA (n=120) and apparently healthy persons as controls (n=120) was genotyped for rs11209026, rs2201841 and rs10889677 by real time/polymerase chain reaction (real-time/PCR) for the first SNP and restriction fragment length polymorphism/PCR (RFLP/PCR) in the last two SNPs. ResultsOur data emphasise that the AA genotype of rs11209026 (Arg381Gln) was significantly associated with RA patients compared to the controls (P value=0.001).We did not find any significant association between either rs2201841 or rs10889677 and the development of rheumatoid arthritis (P value=1.000 & 0.562 respectively). ConclusionOur results suggest that IL23 receptor AA genotype variant of rs11209026 would contribute to RA aetiology; consequently, it might be a genetic marker for RA. We need to address the subgroup of patients who will benefit from the selective suppression of the IL23 signalling which would represent new perspectives toward a personalized therapy of RA patients by further studies.

Association of interleukin-23 receptor (IL-23R) gene polymorphisms (rs11209026, rs2201841 and rs10889677) with Egyptian rheumatoid arthritis patients

Aim of the workTo analyse interleukin 23 receptors (IL23R) single-nucleotide polymorphism (SNPs) (rs11209026, rs2201841, and rs10889677) and to detect their association with Egyptian rheumatoid arthritis (RA) patients. Patients and methodsThe study included 120 Egyptian RA patients and 120 healthy controls that were genotyped for the three SNPs by real time/polymerase chain reaction for the first SNP and restriction fragment length polymorphism/PCR (RFLP/PCR) in the last two SNPs. The disease activity score (DAS28) was assessed in the patients. ResultsThe studied patients had a mean age of 42.5±13.4years, a disease duration of 5.2±3.5years and consisted of 22 males and 98 females. Joint deformities were present in 35 and 66 patients had swollen joints. The rheumatoid factor (RF) was positive in 78.3% and the DAS28 was 3.2±1.2. Our data emphasize that the AA genotype of rs11209026 was significantly associated with RA patients compared to the controls (p=0.001). We did not find any significant association between either rs2201841 or rs10889677 and the development of RA (p=1, p=0.56 respectively). The AA allele in the 3 SNPs were remarkable frequent in those with deformities and positive RF. ConclusionOur results suggest that IL23 receptor AA genotype variant of rs11209026 contributes to the aetiology of RA and may be considered a genetic marker and shared the susceptibility gene. We need to address the subgroup of patients who will benefit from the selective suppression of the IL-23 signalling which would represent new perspectives towards a personalized therapy of RA patients by further studies.

A Study of single nucleotide polymorphism fragments of the aur gene metalloprotease strains of Straphylococcus aureus isolated from the skin of patients with atopic dermatitis

to study the heterogeneity of internal fragments of the aur gene coding a proteolytic enzyme aureolysin in strains of Staphylococcus aureus with different enzymatic activity, which were allocated with the affected skin of patients with atopic dermatitis. the study included 125 patients aged 1 to 35 years old with the diagnosis of atopic dermatitis. 100 clinical strains of Staphylococcus aureus were studied in monoculture. Identification of the strains of Staphylococcus aureus in skin was performed using standard classical methods. Specific biochemical microtests using a set of APIs Staph 20 (bioMerieux SA, France) were conducted to confirm the obtained results. Reference strains of S. aureus ATCC 29213, NCTC at 8.325 were used. Specific aur gene loci were detected by the method of multiplex polymerase chain reaction. Proteolytic activity was determined by the ability to split the substrate (human IgG, Sigma) into fragments using as inhibitors of matrix 0.01-0.1M solutions of sodium EDTA. The sequencing of the amplified areas of aur gene, including the preliminary stages of separation and purification of DNA, was performed by Syntol, Moscow. different strains of the conservative plot amplicon fragment of aur contains up to 9 polymorphic nucleotides. The index of the nucleotide diversity for proteolytic active strains was less (Pi = 0.04) than for proteolytically inactive (Pi = 0.07), p < 0.05. It should be noted that all proteolytically active strains were isolated from the skin of patients with high and moderate severity of atopic dermatitis (67.8 +/- 5.4 points on SCORAD scale) with erythematous form of the disease. The inactive strains were allocated in patients of older age with reduced severity of atopic dermatitis (with the dry form of skin lesions).

개선된 분기한정 알고리즘을 이용한 인간 유전체의 일배체형 조합문제 해결

인간의 한쪽 염색체상에 나타나는 SNP의 서열인 일배체형을 식별해내면 효과적인 유전질병 연관검사를 할 수 있다. 주어진 SNP 단편들로부터 계산적인 방법으로 한 쌍의 일배체형을 조합하기 위해 제시된 모델 중 하나인 최소오류수정 모델은 단편에 손실이 없는 경우조차 NP-hard임이 증명되었다. 기존의 분기한정 알고리즘은 많은 계산시간을 요구함에 따라 실제 응용에 사용하기 어려웠다. 그러나 최근에 개선된 분기한정 알고리즘이 제시되었고, 꿀벌(Apis mellifera)의 유전자형 데이터를 대상으로 성능을 분석해봄으로써 개선된 알고리즘이 기존 분기한정 알고리즘보다 효율적임을 보였다. 본 논문에서는 인간의 유전자형 데이터를 대상으로 개선된 분기한정 알고리즘을 적용해 일배체형 조합문제를 수행한다. 실험을 통한 성능분석 결과, 개선된 분기한정 알고리즘이 인간 유전체에 대해서도 성공적으로 적용됨을 확인함으로써 다양한 생명체의 일배체형 조합문제에 적용 가능함을 보인다. The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform haplotype-based association tests with diseases. Minimum Error Correction model, one of models to computationally assemble a pair of haplotypes for a given organism from Single Nucleotide Polymorphism fragments, has been known to be NP-hard even for gapless cases. In the previous work, an improved branch and bound algorithm was suggested and showed that it is more efficient than naive branch and bound algorithm by performing experiments for Apis mellifera (honeybee) data set. In this paper, to show the extensibility of the algorithm to other organisms we apply the improved branch and bound algorithm to the human data set and confirm the efficiency of the algorithm.

A heuristic algorithm for haplotype reconstruction from aligned weighted SNP fragments

Haplotyping problem has been the subject of extensive research in recent years, and is one of the hottest areas of Computational Biology today. In this paper, we study the single individual Single Nucleotide Polymorphism (SNP) haplotype reconstruction problem. A heuristic algorithm is proposed for assembling a pair of haplotypes from a set of aligned weighted SNP fragments. Computational experiments indicate that the algorithm is a good solution for the Weighted Minimum Letter Flips (WMLF) model, and gets better performance than previous works.

Neural network-based approaches, solving haplotype reconstruction in MEC and MEC/GI models

Single nucleotide polymorphism (SNP) in human genomes is considered to be highly associated with complex genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of recent studies on human genomics. The two sequences of SNPs in diploid human organisms are called haplotypes. In this paper, the problem of haplotype reconstruction from SNP fragments with and without genotype information is studied. Minimum error correction (MEC) is an important model for this problem but only effective when the error rate of the fragments is low. MEC/GI, as an extension to MEC model, employs the related genotype information besides the SNP fragments and, therefore, results in a more accurate inference. We introduce algorithmic neural network-based approaches and experimentally prove that our methods are fast and accurate. Particularly, our approach is faster, more accurate, and also compatible for solving MEC model, in comparison with a feed-forward (and back propagation like) neural network.

Individual haplotype assembly of Apis mellifera (honeybee) using a practical branch and bound algorithm

A haplotype is a single nucleotide polymorphism (SNP) sequence and a representative genetic marker describing the diversity of biological organs. Haplotypes have a wide range of applications such as pharmacology and medical applications. In particular, as a highly social species, haplotypes of the Apis mellifera (honeybee) benefit human health and medicine in diverse areas, including venom toxicology, infectious disease, and allergic disease. For this reason, assembling a pair of haplotypes from individual SNP fragments drives research and generates various computational models for this problem. The minimum error correction (MEC) model is an important computational model for an individual haplotype assembly problem. However, the MEC model has been proved to be NP-hard; therefore, no efficient algorithm is available to address this problem. In this study, we propose an improved version of a branch and bound algorithm that can assemble a pair of haplotypes with an optimal solution from SNP fragments of a honeybee specimen in practical time bound. First, we designed a local search algorithm to calculate the good initial upper bound of feasible solutions for enhancing the efficiency of the branch and bound algorithm. Furthermore, to accelerate the speed of the algorithm, we made use of the recursive property of the bounding function together with a lookup table. After conducting extensive experiments over honeybee SNP data released by the Human Genome Sequencing Center, we showed that our method is highly accurate and efficient for assembling haplotypes.

The human lactase persistence-associated SNP -13910*T enables in vivo functional persistence of lactase promoter-reporter transgene expression.

Lactase is the intestinal enzyme responsible for digestion of the milk sugar lactose. Lactase gene expression declines dramatically upon weaning in mammals and during early childhood in humans (lactase nonpersistence). In various ethnic groups, however, lactase persists in high levels throughout adulthood (lactase persistence). Genetic association studies have identified that lactase persistence in northern Europeans is strongly associated with a single nucleotide polymorphism (SNP) located 14 kb upstream of the lactase gene: -13910*C/T. To determine whether the -13910*T SNP can function in vivo to mediate lactase persistence, we generated transgenic mice harboring human DNA fragments with the -13910*T SNP or the ancestral -13910*C SNP cloned upstream of a 2-kb rat lactase gene promoter in a luciferase reporter construct. We previously reported that the 2-kb rat lactase promoter directs a post-weaning decline of luciferase transgene expression similar to that of the endogenous lactase gene. In the present study, the post-weaning decline directed by the rat lactase promoter is impeded by addition of the -13910*T SNP human DNA fragment, but not by addition of the -13910*C ancestral SNP fragment. Persistence of transgene expression associated with the -13910*T SNP represents the first in vivo data in support of a functional role for the -13910*T SNP in mediating the human lactase persistence phenotype.

A Practical Exact Algorithm for the Individual Haplotyping Problem MEC/GI

Haplotypes play an important role in genetic association studies of complex diseases. Recently, computational techniques helping to determine human haplotypes were studied extensively. Given the genotype and the aligned single nucleotide polymorphism (SNP) fragments of an individual, Minimum Error Correction with Genotype Information (MEC/GI) is an important computational model to infer a pair of haplotypes compatible with the genotype by correcting minimum number of SNPs in the given SNP fragments. The MEC/GI problem has been proven NP-hard, for which there is no practical exact algorithm. Despite the rapid advances in molecular biological techniques, modern high-throughput sequencers cannot sequence directly a DNA fragment that contains more than 1200 nucleotide bases. With low SNP density, current available data reveal that the number k of SNP sites that a DNA fragment covers is usually smaller than 10. Based on the above fact, we develop a new dynamic programming algorithm with running time O(mk2 k +mlog m+mk), where m is the number of fragments. Since k is small in real biological applications, the algorithm is practical and efficient.

A parthenogenetic algorithm for single individual SNP haplotyping

The minimum error correction (MEC) model is one of the important computational models for single individual single nucleotide polymorphism (SNP) haplotyping. Due to the NP-hardness of the model, Qian et al. presented a particle swarm optimization (PSO) algorithm to solve it, and the particle code length is equal to the number of SNP fragments. However, there are hundreds and thousands of SNP fragments in practical applications. The PSO algorithm based on this kind of long particle code cannot obtain high reconstruction rate efficiently. In this paper, a practical heuristic algorithm PGA-MEC based on parthenogenetic algorithm (PGA) is presented to solve the model. A kind of short chromosome code and an effective recombination operator are designed for the algorithm. The reconstruction rate of PGA-MEC algorithm is higher than that of PSO algorithm and the running time of PGA-MEC algorithm is shorter than that of PSO algorithm, which are proved by a number of experiments.

A model of higher accuracy for the individual haplotyping problem based on weighted SNP fragments and genotype with errors.

Motivation: In genetic studies of complex diseases, haplotypes provide more information than genotypes. However, haplotyping is much more difficult than genotyping using biological techniques. Therefore effective computational techniques have been in demand. The individual haplotyping problem is the computational problem of inducing a pair of haplotypes from an individual's aligned SNP fragments. Based on various optimal criteria and including different extra information, many models for the problem have been proposed. Higher accuracy of the models has been an important issue in the study of haplotype reconstruction.Results: The current article proposes a highly accurate model for the single individual haplotyping problem based on weighted fragments and genotypes with errors. The model is proved to be NP-hard even with gapless fragments. Based on the characteristics of Single Nucleotide Polymorphism (SNP) fragments, a parameterized algorithm of time complexity O(nk22k2 + m log m + mk1) is developed, where m is the number of fragments, n is the number of SNP sites, k1 is the maximum number of SNP sites that a fragment covers (no more than n and usually smaller than 10) and k2 is the maximum number of the fragments covering a SNP site (usually no more than 19). Extensive experiments show that this model is more accurate in haplotype reconstruction than other models.Availability: The program of the parameterized algorithm can be obtained by sending an email to the corresponding author.Contact: jxwang@mail.csu.edu.cn

A clustering algorithm based on two distance functions for MEC model

Haplotype reconstruction, based on aligned single nucleotide polymorphism (SNP) fragments, is to infer a pair of haplotypes from localized polymorphism data gathered through short genome fragment assembly. This paper first presents two distance functions, which are used to measure the difference degree and similarity degree between SNP fragments. Based on the two distance functions, a clustering algorithm is proposed in order to solve MEC model. The algorithm involves two sections. One is to determine the initial haplotype pair, the other concerns with inferring true haplotype pair by re-clustering. The comparison results prove that our algorithm utilizing two distance functions is effective and feasible.

A Markov chain model for haplotype assembly from SNP fragments.

Single nucleotide polymorphism (SNP) is the most frequent form of human genetic variations and of importance for medical diagnosis and tracking disease genes. A haplotype is a sequence of SNPs from a single copy of a chromosome, and haplotype assembly from SNP fragments is based on DNA fragments with SNPs and the methodology of shotgun sequence assembly. In contrast to conventional combinatorial models which aim at different error types in SNP fragments, in this paper we propose a new statistical model - a Markov chain model for haplotype assembly based on information of SNP fragments. The main advantage of this model over combinatorial ones is that it requires no prior information on error types in data. In addition, unlike exact algorithms with the exponential-time computation complexity for most combinatorial models, the proposed model can be solved in polynomial time and thus is efficient for large-scale problems. Experiment results on several data sets illustrate the effectiveness of the new method.

Haplotype reconstruction from SNP fragments by minimum error correction

Haplotype reconstruction based on aligned single nucleotide polymorphism (SNP) fragments is to infer a pair of haplotypes from localized polymorphism data gathered through short genome fragment assembly. An important computational model of this problem is the minimum error correction (MEC) model, which has been mentioned in several literatures. The model retrieves a pair of haplotypes by correcting minimum number of SNPs in given genome fragments coming from an individual's DNA. In the first part of this paper, an exact algorithm for the MEC model is presented. Owing to the NP-hardness of the MEC model, we also design a genetic algorithm (GA). The designed GA is intended to solve large size problems and has very good performance. The strength and weakness of the MEC model are shown using experimental results on real data and simulation data. In the second part of this paper, to improve the MEC model for haplotype reconstruction, a new computational model is proposed, which simultaneously employs genotype information of an individual in the process of SNP correction, and is called MEC with genotype information (shortly, MEC/GI). Computational results on extensive datasets show that the new model has much higher accuracy in haplotype reconstruction than the pure MEC model.