Combination Of Single Nucleotide Polymorphisms Research Articles

Association Studies Between Regulatory Regions of IRF6/TP63 Genes and Nonsyndromic Oral Clefts.

To evaluate genetic variants within the regulatory regions of interferon regulatory factor 6 (IRF6) and TP63 for the etiology of nonsyndromic oral clefts risk factors. We performed allelic transmission disequilibrium test analysis on 5 eligible single-nucleotide polymorphisms (SNPs) and SNP haplotypes using the Family-Based Association Test. The study sample consisted of 334 case-parent trios of nonsyndromic oral clefts from Taiwanese population, separated into nonsyndromic cleft lip/palate (NSCL/P) and nonsyndromic cleft palate only (NSCPO) groups. We found all 3 selected SNPs of the IRF6 gene show significant association with nonsyndromic oral clefts (rs2235371, P = 5.10E-07; rs642961, P = .00194; and rs77542756, P = 9.08E-07). Haplotype analyses identified 3 possible SNP combination haplotypes in the IRF6 gene and found that C-G-G showed significant undertransmission (P = .058), whereas 2 other haplotypes, T-G-A and C-A-G (P = 2.71E-06 and P = 5.00E-04, respectively), were significantly overtransmitted to the NSCL/P children but not to the NSCPO children. For the TP63 gene, we failed to detect evidence of nonsyndromic oral cleft association in the 2 SNPs within the TP63 large intron 1 region. We used a family-based analysis in 334 Taiwanese case-parent trios to evaluate selected SNPs of IRF6 genes and TP63 genes for a risk of orofacial clefting. This study provides additional evidence for an association between IRF6 and NSCL/P, including the genetic variants within the 5'-noncoding region of the gene. We also confirmed that NSCL/P and NSCPO individuals belong to different groups. For the TP63, our data did not favor the direct involvement of TAp63 isoforms during orofacial development.

Vulnerable combinations of functional dopaminergic polymorphisms to late-onset treatment resistant schizophrenia.

BackgroundA significant portion of patients with schizophrenia who respond to initial antipsychotic treatment acquire treatment resistance. One of the possible pathogeneses of treatment-resistant schizophrenia (TRS) is antipsychotic-induced dopamine supersensitivity psychosis (Ai-DSP). Patients with this disease progression might share some genetic vulnerabilities, and thus determining individuals with higher risks of developing Ai-DSP could contribute to preventing iatrogenic development of TRS. Therefore, we decided to examine whether combinations of functional single nucleotide polymorphisms (SNPs) known to affect dopaminergic functions are related to Ai-DSP development.MethodsIn this case-control study, 357 Japanese participants diagnosed with schizophrenia or schizoaffective disorder were recruited and divided into two groups, those with and without Ai-DSP. As functional SNPs, we examined rs10770141 of the tyrosine hydroxylase gene, rs4680 of the catechol-O-methyltransferase gene, and rs1799732 and rs1800497 of the DRD2 genes, which are known to possess strong directional ties to dopamine synthesis, dopamine degradation and post-synaptic DRD2 prevalence, respectively.ResultsAmong the 357 Japanese patients with schizophrenia or schizoaffective disorder, 130 were classified as Ai-DSP(+) and the other 227 as Ai-DSP(-). Significantly higher proportions of Ai-DSP(+) patients were found to have the SNP combinations of rs10770141/rs4680 (57.9%, OR2.654, 95%CI1.036–6.787, P = 0.048) and rs10770141/rs4680/ rs1800497 (64.3%, OR4.230, 95%CI1.306–13.619, P = 0.029). However, no single SNP was associated with Ai-DSP.ConclusionsWe preliminarily found that carrying particular combinations of functional SNPs, which are related to relatively higher dopamine synthesis and dopamine degradation and lower naïve DRD2, might indicate vulnerability to development of Ai-DSP. However, further studies are needed to validate the present results.

Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis.

Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping the SNPs underlying complex diseases, there is still difficulty in detecting epistatic interactions due to the lack of heuristic information to expedite the search process. In this study, a method EACO is proposed to detect epistatic interactions based on the ant colony optimization (ACO) algorithm, the highlights of which are the introduced heuristic information, fitness function, and a candidate solutions filtration strategy. The heuristic information multi-SURF* is introduced into EACO for identifying epistasis, which is incorporated into ant-decision rules to guide the search with linear time. Two functionally complementary fitness functions, mutual information and the Gini index, are combined to effectively evaluate the associations between SNP combinations and the phenotype. Furthermore, a strategy for candidate solutions filtration is provided to adaptively retain all optimal solutions which yields a more accurate way for epistasis searching. Experiments of EACO, as well as three ACO based methods (AntEpiSeeker, MACOED, and epiACO) and four commonly used methods (BOOST, SNPRuler, TEAM, and epiMODE) are performed on both simulation data sets and a real data set of age-related macular degeneration. Results indicate that EACO is promising in identifying epistasis.

Incorporating methylation genome information improves prediction accuracy for drug treatment responses

BackgroundAn accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data into models for disease prediction. However, it is still a challenge to integrate different types of omics data into prediction models, and the contribution of methylation information to prediction remains to be fully clarified.ResultsA stratified drug-response prediction model was built based on an artificial neural network to predict the change in the circulating triglyceride level after fenofibrate intervention. Associated single-nucleotide polymorphisms (SNPs), methylation of selected cytosine-phosphate-guanine (CpG) sites, age, sex, and smoking status, were included as predictors. The model with selected SNPs achieved a mean 5-fold cross-validation prediction error rate of 43.65%. After adding methylation information into the model, the error rate dropped to 41.92%. The combination of significant SNPs, CpG sites, age, sex, and smoking status, achieved the lowest prediction error rate of 41.54%.ConclusionsCompared to using SNP data only, adding methylation data in prediction models slightly improved the error rate; further prediction error reduction is achieved by a combination of genome, methylation genome, and environmental factors.

Methods to evaluate rare variants gene-age interaction for triglycerides

Triglycerides are an important measure of heart health. Although more than 90 genes have been found to be associated to lipids, they only explain 12 to 15% of the variance in lipid levels. Evidence suggests that age may interact with the genetic effect on lipid levels. Existing methods to detect the main effect of rare variants cannot be readily applied for testing the gene environment interaction effect of rare variants, as those methods either have unstable results or inflated Type I error rates when the main effect exists. To overcome these difficulties, we developed two statistical methods: testing of optimally weighted combination of single-nucleotide polymorphism (SNP) environment interaction (TOW-SE) and a variable weight TOW-SE (VW-TOW-SE) to test the gene environment interaction effect of rare variants by grouping SNPs into biologically meaningful SNP-sets (SNPs in a gene or pathway) to improve power and interpretability. The proposed methods can be applied to either continuous or binary environmental variables, and to either continuous or binary outcomes. Simulation studies show that Type I error rates of the proposed methods are under control. Comparing the two methods with the existing interaction sequence kernel association test (iSKAT), the VW-TOW-SE is the most powerful test and the TOW-SE is the second most powerful test when gene environment interaction effect exists for both rare and common variants. The three tests were applied to the GAW20 simulated data, among the five regions in which the main effect of common SNPs was simulated and the gene–age interaction effect was not included. As expected, none of the tests indicated positive results.

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies.

Detecting high-order epistasis in genome-wide association studies (GWASs) is of importance when characterizing complex human diseases. However, the enormous numbers of possible single-nucleotide polymorphism (SNP) combinations and the diversity among diseases presents a significant computational challenge. Herein, a fast method for detecting high-order epistasis based on an interaction weight (FDHE-IW) method is evaluated in the detection of SNP combinations associated with disease. First, the symmetrical uncertainty (SU) value for each SNP is calculated. Then, the top-k SNPs are isolated as guiders to identify 2-way SNP combinations with significant interaction weight values. Next, a forward search is employed to detect high-order SNP combinations with significant interaction weight values as candidates. Finally, the findings were statistically evaluated using a G-test to isolate true positives. The developed algorithm was used to evaluate 12 simulated datasets and an age-related macular degeneration (AMD) dataset and was shown to perform robustly in the detection of some high-order disease-causing models.

Toll-like Receptor 4 Pathway Polymorphisms Interact with Pollution to Influence Asthma Diagnosis and Severity

Asthma is a common chronic lung disease, the incidence and severity of which may be influenced by gene-environment interactions. Our objective was to examine associations between single nucleotide polymorphisms (SNPs) and combinations of SNPs in the toll-like receptor 4 (TLR4) pathway, residential distance to roadway as a proxy for traffic-related air pollution exposure, and asthma diagnosis and exacerbations. We obtained individual-level data on genotype, residential address, and asthma diagnosis and exacerbations from the Environmental Polymorphisms Registry. Subjects (n = 2,704) were divided into three groups (hyper-responders, hypo-responders, and neither) based on SNP combinations in genes along the TLR4 pathway. We geocoded subjects and calculated distance, classified as <250 m or ≥250 m, between residence and nearest major road. Relationships between genotype, distance to road, and odds of asthma diagnosis and exacerbations were examined using logistic regression. Odds of an asthma diagnosis among hyper-responders <250 m from a major road was 2.37(0.97, 6.01) compared to the reference group (p < 0.10). Hypo-responders ≥250 m from the nearest road had lower odds of activity limitations (0.46 [0.21, 0.95]) and sleeplessness (0.36 [0.12, 0.91]) compared to neither-responders (p < 0.05). Specific genotype combinations when combined with an individual’s proximity to roadways, possibly due to traffic-related air pollution exposure, may affect the likelihood of asthma diagnosis and exacerbations.

Bioavailability of Fat-Soluble Vitamins and Phytochemicals in Humans: Effects of Genetic Variation.

Recent data have shown that interindividual variability in the bioavailability of vitamins A (β-carotene), D, and E, and carotenoids (lutein and lycopene), as well as that of phytosterols, is modulated by single nucleotide polymorphisms (SNPs). The identified SNPs are in or near genes involved in intestinal uptake or efflux of these compounds, as well as in genes involved in their metabolism and transport. The phenotypic effect of each SNP is usually low, but combinations of SNPs can explain a significant part of the variability. Nevertheless, results from these studies should be considered preliminary since they have not been validated in other cohorts. Guidelines for future studies are provided to ensure that sound associations are elucidated that can be used to build consolidated genetic scores that may allow recommended dietary allowances to be tailored to individuals or groups by taking into account the multiloci genotypic signature of people of different ethnic origin or even of individuals.

Fat Mass and Obesity-Associated (FTO) Gene Polymorphisms Are Associated with Risk of Intervertebral Disc Degeneration in Chinese Han Population: A Case Control Study.

BackgroundThe present study aimed to evaluate whether the fat mass and obesity-associated (FTO) gene polymorphisms are associated with risk of intervertebral disc degeneration (IDD) in a largest Chinese Han population.Material/MethodsThere were 502 IDD patients and 497 healthy controls enrolled in this study. Nineteen single nucleotide polymorphisms (SNPs) in the FTO gene were tested using the Sequenom MassARRAY platform. The Hardy-Weinberg equilibrium test, followed by allelic, genotypic, haplotypic association, and SNP interaction analyses were used for SNP evaluation. The Genotype-Tissue Expression (GTEx) database was used to evaluate expression quantitative trait loci (eQTL) value of polymorphism. Spearman rank correlation and logistic regression analyses were used for assessing the internal relation between genotypic changes and the risk of IDD.ResultsSeventeen SNPs survived the Hardy-Weinberg equilibrium test. Allelic analysis showed that allele T of SNP rs1121980 was a risk allele. Haplotypic and SNP interaction analyses suggested that 2 haplotypes and 5 SNP combinations were associated with the predisposition of IDD respectively. GTEx database revealed that the SNP rs1121980 might interfere with the expression of the FTO gene in the muscle-skeletal system. Through clinical statistics analysis, the different genotypes of rs1121980 can present different disease severity of IDD.ConclusionsOur study suggests that rs1121980 can become a biomarker for the screening and prognosis of IDD. The 2 haplotype blocks and 5 SNP-SNP combinations that we discovered might be indicative of the onset of IDD. Therefore, our study might serve as evidence for future IDD molecular diagnosis.

ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information.

Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach (called ClusterMI) to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilizes conditional mutual information to screen significant pairwise SNP combinations in each cluster. In this way, there is a higher probability of identifying significant two-locus combinations in each group, and the computational load for the follow-up search can be greatly reduced. In the search stage, two different search strategies (exhaustive search and improved ant colony optimization search) are provided to detect high-order SNP interactions based on the cardinality of significant two-locus combinations. Extensive simulation experiments show that ClusterMI has better performance than other related and competitive approaches. Experiments on two real case-control datasets from Wellcome Trust Case Control Consortium (WTCCC) also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data.

Clinical validation of molecular markers of macrocyclic lactone resistance in Dirofilaria immitis

Prophylaxis with macrocyclic lactone (ML) endectocides is the primary strategy for heartworm control. Recent evidence has confirmed that ML-resistant Dirofilaria immitis isolates have evolved. Comparison of genomes of ML-resistant isolates show they are genetically distinct from wild-type populations. Previously, we identified single nucleotide polymorphisms (SNPs) that are correlated with phenotypic ML resistance. Since reliable in vitro assays are not available to detect ML resistance in L3 or microfilarial stages, the failure to reduce microfilaraemia in infected dogs treated with an ML has been proposed as a surrogate clinical assay for this purpose. The goal of our study was to validate the genotype-phenotype correlation between SNPs associated with ML resistance and failure to reduce microfilaraemia following ML treatment and to identify a minimal number of SNPs that could be used to confirm ML resistance. In this study, 29 participating veterinary clinics received a total of 148 kits containing supplies for blood collection, dosing and prepaid shipping. Patients recruited after a diagnosis of heartworm infection were treated with a single standard dose of Advantage Multi® and a blood sample taken pre- and approximately 2–4 weeks post-treatment. Each sample was processed by performing a modified Knott's Test followed by isolation of microfilariae, genomic DNA extraction and MiSeq sequencing of regions encompassing 10 SNP sites highly correlated with ML resistance. We observed significant correlation of SNP loci frequencies with the ML microfilaricidal response phenotype. Although all predictive SNP combination models performed well, a 2-SNP model was superior to other models tested. The predictive ability of these markers for ML-resistant heartworms should be further evaluated in clinical and epidemiological contexts.

MTOR Pathway-Based Discovery of Genetic Susceptibility to L-DOPA-Induced Dyskinesia in Parkinson's Disease Patients.

Dyskinesia induced by L-DOPA administration (LID) is one of the most invalidating adverse effects of the gold standard treatment restoring dopamine transmission in Parkinson's disease (PD). However, LID manifestation in parkinsonian patients is variable and heterogeneous. Here, we performed a candidate genetic pathway analysis of the mTOR signaling cascade to elucidate a potential genetic contribution to LID susceptibility, since mTOR inhibition ameliorates LID in PD animal models. We screened 64 single nucleotide polymorphisms (SNPs) mapping to 57 genes of the mTOR pathway in a retrospective cohort of 401 PD cases treated with L-DOPA (70 PD with moderate/severe LID and 331 with no/mild LID). We performed classic allelic, genotypic, and epistatic analyses to evaluate the association of individual or combinations of SNPs with LID onset and with LID severity after initiation of L-DOPA treatment. As for the time to LID onset, we found significant associations with SNP rs1043098 in the EIF4EBP2 gene and also with an epistatic interaction involving EIF4EBP2 rs1043098, RICTOR rs2043112, and PRKCA rs4790904. For LID severity, we found significant association with HRAS rs12628 and PRKN rs1801582 and also with a four-loci epistatic combination involving RPS6KB1 rs1292034, HRAS rs12628, RPS6KA2 rs6456121, and FCHSD1 rs456998. These findings indicate that the mTOR pathway contributes genetically to LID susceptibility. Our study could help to identify the most susceptible PD patients to L-DOPA in order to prevent the appearance of early and/or severe LID in a future. This information could also be used to stratify PD patients in clinical trials in a more accurate way.

Type 2 Diabetes Mellitus: Integrative Analysis of Multiomics Data for Biomarker Discovery.

Increased fasting plasma glucose (FPG) is an independent risk factor for type 2 diabetes mellitus (T2DM). The development of systems biology technologies for integration of multiomics data is crucial for predicting increased FPG levels. In this case-control study, immunoglobulin (Ig) G glycosylation profiling and genome-wide association analyses were performed on 511 participants, and among them 76 had increased FPG (aged 47.6 ± 6.14 years), and 435 had decreased or fluctuant FPG (aged 47.9 ± 6.08 years). We identified nine single nucleotide polymorphisms (SNPs) in five genes (RPL7AP27, SNX30, SLC39A12, BACE2, and IGFL2) that were significantly associated with increased FPG (odds ratios 1.937-2.393). Moreover, of the 24 glycan peaks (GPs), GPs 3, 8, and 11 presented positive trends with increased FPG levels, whereas GPs 4 and 14 presented negative trends. A significant improvement of predictive power was observed when adding 24 IgG GPs to 9 SNPs with the area under the curve increased from 0.75 to 0.81. This report shows that the combination of candidate SNPs with IgG glycomics offers biomarker potentials for T2DM. The substantial predictive power obtained from integrating genomics and glycomics biomarkers suggests the feasibility of applying such multiomics strategies to enable predictive, preventive, and personalized medicine for T2DM.

Genetic Diversity and Drug Susceptibility of Mycobacterium tuberculosis Isolates in a Remote Mountain Area of China

ObjectiveWe determined the genetic diversity of Mycobacterium tuberculosis (MTB) in a remote mountainous area of southwest China and evaluated the resolving ability of single nucleotide polymorphism (SNP) genotyping combined with variable number tandem repeat (VNTR) genotyping for Beijing family strains in association with drug resistance status. MethodsThree hundred thirty-one MTB strains were isolated from patients living in mountainous regions of southwest China, and 8-loci SNP, VNTR-15 genotyping assays, and drug susceptibility testing of 9 drugs were performed. ResultsA total of 183 [55.29% (183/331)] strains were classified into the Beijing family. Of the 183 strains, 111 (60.66%) were defined as modern Beijing strains. The most predominant modern Beijing sub-lineage and ancient Beijing sub-lineage were Bmyc10 [39.34% (72/183)] and Bmyc25 [20.77% (38/183)], respectively. Of the isolates, 19.64% (65/331) were resistant to at least 1 of the 9 anti-TB drugs and 17 [4.98% (17/331)] MTB isolates were multi-drug resistant tuberculosis (MDR-TB). Two hundred sixty-one isolates showed a clustering rate of 14.18% (37/261) and a discriminatory index of 0.9990. The Beijing lineage exhibited a significantly higher prevalence of MDR-TB, as well as resistance to isoniazid (INH), rifampin (RIF), and para-aminosalicylic acid (PAS) when analyzed independently (P = 0.005, P = 0.017, P = 0.014, and P = 0.006 respectively). The Beijing lineage was not associated with genetic clustering or resistance to any drug. In addition, genetic clustering was not associated with drug resistance. ConclusionMTB strains demonstrate high genetic diversity in remote mountainous areas of southwest China. Beijing strains, especially modern Beijing strains, are predominant in remote mountainous area of China. The combination of 8-loci SNPs and VNTR-15 genotyping is a useful tool to study the molecular epidemiology of MTB strains in this area.

Applicability of Precision Medicine Approaches to Managing Hypertension in Rural Populations.

As part of the Heart Healthy Lenoir Project, we developed a practice level intervention to improve blood pressure control. The goal of this study was: (i) to determine if single nucleotide polymorphisms (SNPs) that associate with blood pressure variation, identified in large studies, are applicable to blood pressure control in subjects from a rural population; (ii) to measure the association of these SNPs with subjects’ responsiveness to the hypertension intervention; and (iii) to identify other SNPs that may help understand patient-specific responses to an intervention. We used a combination of candidate SNPs and genome-wide analyses to test associations with either baseline systolic blood pressure (SBP) or change in systolic blood pressure one year after the intervention in two genetically defined ancestral groups: African Americans (AA) and Caucasian Americans (CAU). Of the 48 candidate SNPs, 13 SNPs associated with baseline SBP in our study; however, one candidate SNP, rs592582, also associated with a change in SBP after one year. Using our study data, we identified 4 and 15 additional loci that associated with a change in SBP in the AA and CAU groups, respectively. Our analysis of gene-age interactions identified genotypes associated with SBP improvement within different age groups of our populations. Moreover, our integrative analysis identified AQP4-AS1 and PADI2 as genes whose expression levels may contribute to the pleiotropy of complex traits involved in cardiovascular health and blood pressure regulation in response to an intervention targeting hypertension. In conclusion, the identification of SNPs associated with the success of a hypertension treatment intervention suggests that genetic factors in combination with age may contribute to an individual’s success in lowering SBP. If these findings prove to be applicable to other populations, the use of this genetic variation in making patient-specific interventions may help providers with making decisions to improve patient outcomes. Further investigation is required to determine the role of this genetic variance with respect to the management of hypertension such that more precise treatment recommendations may be made in the future as part of personalized medicine.

Dynamics of Associations Between Single Nucleotide Polymorphisms in Relation to Alzheimer's Disease Captured with a New Measure of Linkage Disequilibrium

Genome-Wide-Association-Studies have become a powerful method to link point mutations (e.g. single nucleotide polymorphisms (SNPs)) to a certain phenotype or a disease. However, their power to detect SNPs associated to polygenic diseases such as Alzheimer's Disease (AD) is limited, since they can only infer the pairwise relation of single SNPs to the phenotype and ignore possible effects of various SNP combinations. The common method to probe these possible complex genetic patterns is to compute a measure called linkage disequilibrium (LD). Despite the fact that several predictive patterns found with LD could successfully be applied to medical diagnosis, this measure still holds several drawbacks as for example the difficulty to confirm and replicate experimental results as well as its sensitivity to statistical biases. Here, we present the application of an alternative method, Linkage Probability (LP) for genetic pattern identification that provides the posterior probability of a relation between two categorical data sets and simultaneously considers potential biases from latent variables, such as the recombination rate or the genetic structure of a population. By applying the LP framework to data from the ADSP-Project, we show that changes of linkage patterns between SNPs can be associated to Alzheimer's disease. Common genomic relation measures still fail to extract this link.

Single nucleotide polymorphisms within Adducin 3 and Adducin 3 antisense RNA1 genes are associated with biliary atresia in Thai infants.

A genome-wide association study in East Asians suggested a genetic association between biliary atresia (BA) and a cluster of variants within the Adducin 3 (ADD3) and ADD3 antisense RNA1 (ADD3-AS1) genes. Another study in Thai neonates reported an association between BA and rs17095355. To validate those findings, this study aimed to analyze the BA association with single nucleotide polymorphisms (SNPs) and the additive influence of ADD3 and ADD3-AS1 in Thai neonates. DNAs from 56 BA cases and 166 controls were genotyped for rs2501577, rs11194981, rs12268910 (ADD3) and rs17095355 (ADD3-AS1), using TaqMan PCR. Genotype distributions were compared between the groups, and SNP-SNP interactions were analyzed by combination of allelotypes. The risk allele frequencies of rs2501577, rs11194981, and rs17095355 in the BA group were significantly higher than in the controls. Univariate analysis showed that recessive variants in the three SNPs were associated with BA risk at ORs of 1.81 (95% CI 1.32-2.50), 1.58 (95% CI 1.14-2.20) and 1.92 (95% CI 1.39-2.66), respectively. SNP-SNP interaction analysis showed that the SNP combination of the two genes rs17095355 and rs2501577 provided an additive increase in BA risk. ADD3 and ADD3-AS1 variants increased susceptibility to BA, suggesting that these genes may play an additive role in the pathogenesis of the disease. In addition, these interactions may give a clue to the overexpression of the ADD3 protein in the liver of BA patients.

Integrative DNA copy number detection and genotyping from sequencing and array-based platforms.

Copy number variations (CNVs) are gains and losses of DNA segments and have been associated with disease. Many large-scale genetic association studies are performing CNV analysis using whole exome sequencing (WES) and whole genome sequencing (WGS). In many of these studies, previous single-nucleotide polymorphism (SNP)-array data are available. An integrated cross-platform analysis is expected to improve resolution and accuracy, yet there is no tool for effectively combining data from sequencing and array platforms. The detection of CNVs using sequencing data alone can also be further improved by the utilization of allele-specific reads. We propose a statistical framework, integrated CNV (iCNV) detection algorithm, which can be applied to multiple study designs: WES only, WGS only, SNP array only, or any combination of SNP and sequencing data. iCNV applies platform-specific normalization, utilizes allele specific reads from sequencing and integrates matched NGS and SNP-array data by a hidden Markov model. We compare integrated two-platform CNV detection using iCNV to naïve intersection or union of platforms and show that iCNV increases sensitivity and robustness. We also assess the accuracy of iCNV on WGS data only and show that the utilization of allele-specific reads improve CNV detection accuracy compared to existing methods. https://github.com/zhouzilu/iCNV. Supplementary data are available at Bioinformatics online.

Mismatch repair single nucleotide polymorphisms and thyroid cancer susceptibility.

Thyroid cancer (TC) is the most common endocrine malignancy and its incidence continues to rise worldwide. Ionizing radiation exposure is the best established etiological factor. Heritability is high; however, despite valuable contribution from recent genome-wide association studies, the current understanding of genetic susceptibility to TC remains limited. Several studies suggest that altered function or expression of the DNA mismatch repair (MMR) system may contribute to TC pathogenesis. Therefore, the present study aimed to evaluate the potential role of a panel of MMR single nucleotide polymorphisms (SNPs) on the individual susceptibility to well-differentiated TC (DTC). A case-control study was performed involving 106 DTC patients and 212 age- and gender-matched controls, who were all Caucasian Portuguese. Six SNPs present in distinct MMR genes (MLH1 rs1799977, MSH3 rs26279, MSH4 rs5745325, PMS1 rs5742933, MLH3 rs175080 and MSH6 rs1042821) were genotyped through TaqMan® assays and genotype-associated risk estimates were calculated. An increased risk was observed in MSH6 rs1042821 variant homozygotes [adjusted odds ratio (OR)=3.42, 95% CI: 1.04-11.24, P=0.04, under the co-dominant model; adjusted OR=3.84, 95% CI: 1.18-12.44, P=0.03, under the recessive model]. The association was especially evident for the follicular histotype and female sex. The association was also apparent when MSH6 was analysed in combination with other MMR SNPs such as MSH3 rs26279. Interestingly, two other SNP combinations, both containing the MSH6 heterozygous genotype, were associated with a risk reduction, suggesting a protective effect for these genotype combinations. These data support the idea that MMR SNPs such as MSH6 rs1042821, alone or in combination, may contribute to DTC susceptibility. This is coherent with the limited evidence available. Nevertheless, further studies are needed to validate these findings and to establish the usefulness of these SNPs as genetic susceptibility biomarkers for DTC so that, in the near future, cancer prevention policies may be optimized under a personalized medicine perspective.

An In silico method to study structure, function, and regulatory role alteration mediated by single-nucleotide polymorphisms in gallbladder cancer

Introduction: Gallbladder cancer (GBC) is a fatal malignancy of gallbladder and bile ductswhich shows delayed symptoms and sometimes can be asymptomatic, being fatal. Reported globally, for a very low survival rate, it suffers from the lack of early diagnostic and prognostic markers. Single nucleotide polymorphisms (SNPs) have been reported to be associated in different cancers. Methods: In this study using in silico methods, we report for the first time a combination of SNPs from the coding and noncoding region leading to alteration in GBC. Different pipelines were designed for the study of SNPs. Regulatory role alteration of Synonymous and non-coding SNPs were studied using RegulomeDB, DeepSEA analysis and funcPred. Structural alteration and energy parameters for non-synonymous SNPs were studied by Swiss-PDB, Chimera and Gromacs. Protein stability analysis was done using MutPred, mCSM and I-mutant. Results: As a result, three potential variants from the coding region rs1042838, rs11887534, and rs700519 associated with progesterone receptor, ATP binding cassette subfamily G member 8 (ABCG8), and cytochrome P450 19A1, respectively, were predicted to be potentially damaging SNPs in GBC leading to structure and function alteration. Three noncoding SNPs (rs2978974, rs4633 and rs2830) and 1 missense SNP(rs523349) were shown to be associated with damaging effect in GBC, and one of these SNPs (rs2978974) showed significant chromatin feature alteration. Conclusion: Our study strongly shows that SNPs both in the coding and noncoding region may be exploited as a combination of potential biomarkers in early diagnosis of GBC due to structure function alteration by nonsynonymous SNPs and regulatory role alteration by noncoding SNPs.