Set Of Single Nucleotide Polymorphisms Research Articles

Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case–control association studies

Crohn's disease is an inflammatory bowel disease. Because of strong heritability, it is possible to deploy the pattern of DNA variations, such as single nucleotide polymorphisms (SNPs), to accurately predict the state of this disease. However, there are many possible SNP subsets, which make finding a best set of SNPs to achieve the highest prediction accuracy impossible in one patient's lifetime. In this paper, a new technique is proposed that relies on chromosomes of various lengths with significant order feature selection, a new cross-over approach, and new mutation operations. Our method can find a chromosome of appropriate length with useful features. The Crohn's disease data that were gathered from case–control association studies were used to demonstrate the effectiveness of our proposed algorithm. In terms of the prediction accuracy, the proposed SNP prediction framework outperformed previously proposed techniques, including the optimum random forest (ORF), the univariate marginal distribution algorithm and support vector machine (USVM), the complimentary greedy search-based prediction algorithm (CGSP), the combinatorial search-based prediction algorithm (CSP), and discretized network flow (DNF). The performance of our framework, when tested against this real data set with a 5-fold cross-validation, was 90.4% accuracy with 87.5% sensitivity and 92.2% specificity.

Precise Dissection of an Escherichia coli O157:H7 Outbreak by Single Nucleotide Polymorphism Analysis

The current pathogen-typing methods have suboptimal sensitivities and specificities. DNA sequencing offers an opportunity to type pathogens with greater degrees of discrimination using single nucleotide polymorphisms (SNPs) than with pulsed-field gel electrophoresis (PFGE) and other methodologies. In a recent cluster of Escherichia coli O157:H7 infections attributed to salad bar exposures and romaine lettuce, a subset of cases denied exposure to either source, although PFGE and multiple-locus variable-number tandem-repeat analysis (MLVA) suggested that all isolates had the same recent progenitor. Interrogation of a preselected set of 3,442,673 nucleotides in backbone open reading frames (ORFs) identified only 1 or 2 single nucleotide differences in 3 of 12 isolates from the cases who denied exposure. The backbone DNAs of 9 of 9 and 3 of 3 cases who reported or were unsure about exposure, respectively, were isogenic. Backbone ORF SNP set sequencing offers pathogen differentiation capabilities that exceed those of PFGE and MLVA.

Signatures of adaptation and genetic structure among the mainland populations of Pinus radiata (D. Don) inferred from SNP loci

Insights into the relative contributions of locus specific and genome-wide effects on population genetic diversity can be gained through separation of their resulting genetic signals. Here we explore patterns of adaptive and neutral genetic diversity in the disjunct natural populations of Pinus radiata (D. Don) from mainland California. A first-generation common garden of 447 individuals revealed significant differentiation of wood phenotypes among populations (P ST), possibly reflecting local adaptation in response to environment. We subsequently screened all trees for genetic diversity at 149 candidate gene single nucleotide polymorphism (SNP) loci for signatures of adaptation. Ten loci were identified as being possible targets of diversifying selection following F ST outlier tests. Multivariate canonical correlation performed on a data set of 444 individuals identified significant covariance between environment, adaptive phenotypes and outlier SNP diversity, lending support to the case for local adaptation suggested from F ST and P ST tests. Covariation among discrete sets of outlier SNPs and adaptive phenotypes (inferred from multivariate loadings) with environment are supported by existing studies of candidate gene function and genotype–phenotype association. Canonical analyses failed to detect significant correlations between environment and 139 non-outlier SNP loci, which were applied to estimate neutral patterns of genetic differentiation among populations (F ST 4.3 %). Using this data set, significant hierarchical structure was detected, indicating three populations on the mainland. The hierarchical relationships based on neutral SNP markers (and SSR) were in contrast with those inferred from putatively adaptive loci, potentially highlighting the independent action of selection and demography in shaping genetic structure in this species.

Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms

Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

Acute chest syndrome is associated with single nucleotide polymorphism‐defined beta globin cluster haplotype in children with sickle cell anaemia

Genetic diversity at the human β-globin locus has been implicated as a modifier of sickle cell anaemia (SCA) severity. However, haplotypes defined by restriction fragment length polymorphism sites across the β-globin locus have not been consistently associated with clinical phenotypes. To define the genetic structure at the β-globin locus more thoroughly, we performed high-density single nucleotide polymorphism (SNP) mapping in 820 children who were homozygous for the sickle cell mutation (HbSS). Genotyping results revealed very high linkage disequilibrium across a large region spanning the locus control region and the HBB (β-globin gene) cluster. We identified three predominant haplotypes accounting for 96% of the β(S) -carrying chromosomes in this population that could be distinguished using a minimal set of common SNPs. Consistent with previous studies, fetal haemoglobin level was significantly associated with β(S) -haplotypes. After controlling for covariates, an association was detected between haplotype and rate of hospitalization for acute chest syndrome (ACS) (incidence rate ratio 0·51, 95% confidence interval 0·29-0·89) but not incidence rate of vaso-occlusive pain or presence of silent cerebral infarct (SCI). Our results suggest that these SNP-defined β(S) -haplotypes may be associated with ACS, but not pain or SCI in a study population of children with SCA.

Genome-wide association study of lung function phenotypes in a founder population

Lung function is a long-term predictor of mortality and morbidity. We sought to identify single nucleotide polymorphisms (SNPs) associated with lung function. We performed a genome-wide association study (GWAS) of FEV1, forced vital capacity (FVC), and FEV1/FVC in 1144 Hutterites aged 6 to 89 years, who are members of a founder population of European descent. We performed least absolute shrinkage and selection operation regression to select the minimum set of SNPs that best predict FEV1/FVC in the Hutterites and used the GRAIL algorithm to mine the Gene Ontology database for evidence of functional connections between genes near the predictive SNPs. Our GWAS identified significant associations between FEV1/FVC and SNPs at the THSD4-UACA-TLE3 locus on chromosome 15q23 (P = 5.7 × 10(-8) to 3.4 × 10(-9)). Nine SNPs at or near 4 additional loci had P < 10(-5) with FEV1/FVC. Only 2 SNPs were found with P < 10(-5) for FEV1 or FVC. We found nominal levels of significance with SNPs at 9 of the 27 previously reported loci associated with lung function measures. Among a predictive set of 80 SNPs, 6 loci were identified that had a significant degree of functional connectivity (GRAIL P < .05), including 3 clusters of β-defensin genes, 2 chemokine genes (CCL18 and CXCL12), and TNFRSF13B. This study identifies genome-wide significant associations and replicates results of previous GWASs. Multimarker modeling implicated for the first time common variation in genes involved in antimicrobial immunity in airway mucosa that influences lung function.

High predictive value of CYP2B6 SNPs for steady-state plasma efavirenz levels in South African HIV/AIDS patients

Efavirenz is primarily metabolized by CYP2B6, with a minor contribution from CYP1A2, CYP2A6, CYP3A4 and CYP3A5. Genetic variability in these genes contributes towards differences in plasma efavirenz concentration, which ultimately leads to either development of adverse drug events or emergence of virus resistance. However, the clinical utility or validity of introducing genotype-assisted dosing is not known. The aim of this study was therefore to evaluate the effects of 14 single-nucleotide polymorphisms (SNPs) in five drug-metabolizing enzyme genes on steady-state plasma efavirenz levels in South African HIV/AIDS patients as well as their clinical validity. HIV/AIDS patients were recruited from Themba Lethu Clinic, at Helen Joseph Hospital, Johannesburg. Blood samples for plasma drug levels and DNA extraction were obtained from each participant. PCR/RFLP and SNaPshot genotyping were used for SNPs in CYP1A2, CYP2A6, CYP2B6, CYP3A4 and CYP3A5 among 464 Bantu-speaking South Africans. Plasma efavirenz concentrations were measured using LC/MS/MS. Genotypes and plasma efavirenz levels were used to calculate predictive values. Multivariate analysis was used to select the minimal set of SNPs with significant clinical validity. Qualitative and quantitative differences in allele frequencies were observed when comparing South Africans with African, Caucasian and Asian populations. CYP2B6 516T and 785G (*6) and CYP2B6 983C (*18) alleles were significantly associated with high plasma efavirenz levels. CYP2B6 A-G-A-C-C and A-T-G-T-C haplotypes (with respect to CYP2B6 136A>G; CYP2B6 516G>T; CYP2B6 785A>G; CYP2B6 983T>C; and CYP2B6 1459C>T) were associated with higher levels of efavirenz, whereas G-G-A-T-C and A-G-A-T-C haplotypes showed significantly lower levels of efavirenz. The CYP2B6*1/*6 genotype was significantly associated with an increased risk of loss to follow-up. The sensitivity, specificity and positive predictive values for the CYP2B6*6/*6 genotype in predicting efavirenz levels above 4 µg/ml were 46, 97 and 88%, respectively. However, these values improved to 49, 100 and 100%, respectively, when either the CYP1A2 -163A (*1F) allele or the NR1I3 8784C/C genotype was present. Screening for CYP2B6 516G>T SNP has a high specificity and positive predictive value for efavirenz levels above 4 µg/ml and could be used in deciding on efavirenz dosage among individuals homozygous for this variant, which could lead to better precision medication.

Single Nucleotide Polymorphism Genotyping for Breeding and Genetics Applications in Chickpea and Pigeonpea using the BeadXpress Platform

Single nucleotide polymorphisms (SNPs) are ideal molecular markers due to their higher abundance. Although several types of genotyping platforms for assaying large number of SNPs are available, in cases such as marker‐assisted selection, where few markers are required for genotyping a set of potential lines, high‐throughput SNP genotyping platforms (e.g., iScan or Infinium) may not be cost effective. In this scenario, GoldenGate assays based on VeraCode technology using Illumina BeadXpress seems to be the most cost‐effective platform. The objective of this study was to develop cost‐effective SNP genotyping platforms in chickpea (Cicer arietinum L.) and pigeonpea (Cajanus cajan L.). Two sets of SNPs, one each for chickpea (96 SNPs) and pigeonpea (48 SNPs), were developed and tested by genotyping 288 diverse genotypes from respective reference sets. The SNPs selected for the oligo pool assays had high transferability to crop wild relative species. The mean polymorphism information content value of assayed SNP markers was 0.31 and 0.32 in chickpea and pigeonpea, respectively. No unique pattern was observed in the chickpea reference set whereas two major groups were observed in the case of the pigeonpea reference set. The Illumina BeadXpress platform assays developed for chickpea and pigeonpea are highly informative and cost effective for undertaking genetic studies in these legume species.

Impact of HDL genetic risk scores on coronary artery calcified plaque and mortality in individuals with type 2 diabetes from the Diabetes Heart Study

BackgroundPatients with type 2 diabetes (T2D) are at elevated risk for cardiovascular disease (CVD) events and mortality. Recent studies have assessed the impact of genetic variants affecting high-density lipoprotein cholesterol (HDL) concentrations on CVD risk in the general population. This study examined the utility of HDL-associated single nucleotide polymorphisms (SNPs) for CVD risk prediction in European Americans with T2D enrolled in the Diabetes Heart Study (DHS).MethodsGenetic risk scores (GRS) of HDL-associated SNPs were constructed and evaluated for potential associations with mortality and with coronary artery calcified atherosclerotic plaque (CAC), a measure of subclinical CVD strongly associated with CVD events and mortality. Two sets of SNPs were used to construct GRS; while all SNPs were selected primarily for their impacts on HDL, one set of SNPs had pleiotropic effects on other lipid parameters, while the other set lacked effects on low-density lipoprotein cholesterol (LDL) or triglyceride concentrations.ResultsThe GRS were specifically associated with HDL concentrations (4.90 × 10-7 < p < 0.02) in models adjusted for age, sex, and body mass index (BMI), but were not associated with LDL or triglycerides. Cox proportional hazards regression analysis suggested the HDL-associated GRS had no impact on risk of CVD-mortality (0.48 < p < 0.99) in models adjusted for other known CVD risk factors. However, associations between several of the GRS and CAC were observed (3.85 × 10-4 < p < 0.03) in models adjusted for other known CVD risk factors.ConclusionsThe GRS analyzed in this study provide a tool for assessment of HDL-associated SNPs and their impact on CVD risk in T2D. The observed associations between several of the GRS and CAC suggest a potential role for HDL-associated SNPs on subclinical CVD risk in patients with T2D.

Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p=1.64×10−6). The sMRI component showed a significant group difference in loading parameters between patients and controls (p=1.33×10−15), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p=0.04) and was predominantly contributed to by 1030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size.

An omnibus permutation test on ensembles of two-locus analyses can detect pure epistasis and genetic heterogeneity in genome-wide association studies.

This article presents the ability of an omnibus permutation test on ensembles of two-locus analyses (2LOmb) to detect pure epistasis in the presence of genetic heterogeneity. The performance of 2LOmb is evaluated in various simulation scenarios covering two independent causes of complex disease where each cause is governed by a purely epistatic interaction. Different scenarios are set up by varying the number of available single nucleotide polymorphisms (SNPs) in data, number of causative SNPs and ratio of case samples from two affected groups. The simulation results indicate that 2LOmb outperforms multifactor dimensionality reduction (MDR) and random forest (RF) techniques in terms of a low number of output SNPs and a high number of correctly-identified causative SNPs. Moreover, 2LOmb is capable of identifying the number of independent interactions in tractable computational time and can be used in genome-wide association studies. 2LOmb is subsequently applied to a type 1 diabetes mellitus (T1D) data set, which is collected from a UK population by the Wellcome Trust Case Control Consortium (WTCCC). After screening for SNPs that locate within or near genes and exhibit no marginal single-locus effects, the T1D data set is reduced to 95,991 SNPs from 12,146 genes. The 2LOmb search in the reduced T1D data set reveals that 12 SNPs, which can be divided into two independent sets, are associated with the disease. The first SNP set consists of three SNPs from MUC21 (mucin 21, cell surface associated), three SNPs from MUC22 (mucin 22), two SNPs from PSORS1C1 (psoriasis susceptibility 1 candidate 1) and one SNP from TCF19 (transcription factor 19). A four-locus interaction between these four genes is also detected. The second SNP set consists of three SNPs from ATAD1 (ATPase family, AAA domain containing 1). Overall, the findings indicate the detection of pure epistasis in the presence of genetic heterogeneity and provide an alternative explanation for the aetiology of T1D in the UK population.

Impact of Inherited Genetic Variants Associated With Lipid Profile, Hypertension, and Coronary Artery Disease on the Risk of Intracranial and Abdominal Aortic Aneurysms

Epidemiological studies show that an unfavorable lipid profile and coronary artery disease (CAD) are risk traits for abdominal aortic aneurysms (AAAs) but not for intracranial aneurysms (IAs), and that hypertension is a main risk trait for IAs but not for AAAs. To evaluate these observations, we investigated single-nucleotide polymorphisms associated with serum lipid levels, hypertension, and CAD and tested their contribution to AAA and IA risk. We defined sets of single-nucleotide polymorphisms previously reported to be associated with serum lipid levels, CAD, and blood pressure. From previously collected genome-wide data, we extracted genotypes for these single-nucleotide polymorphism sets in 709 IA cases and 2692 controls and 807 AAA cases and 1905 controls (all of Dutch origin). We computed genetic scores for each individual by summing the observed number of risk alleles weighted by their previously published effect size. Using logistic regression, we tested the genetic scores for association with IAs and AAAs and found significant associations for genetic scores of total cholesterol (P=3.6×10⁻⁶), low-density lipoprotein-cholesterol (P=5.7×10⁻⁷), and CAD (P=0.0014) with AAAs and for the blood pressure score with IAs (P=0.0030). We demonstrate that genetic risk profiles of lipid factors and CAD are associated with AAAs but not with IAs, and the genetic risk profile of blood pressure is associated with IAs but not with AAAs. These findings are consistent with epidemiological observations.

SNP Set Association Analysis for Genome-Wide Association Studies

Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population

Estimation of variance components for carcass traits in Japanese Black cattle using 50K SNP genotype data

Genomic selection using high-density single nucleotide polymorphism (SNP) genotype data may accelerate genetic improvements in livestock animals. In this study, we attempted to estimate the variance components of six carcass traits in fattened Japanese Black steers using SNP genotype data. Six hundred and seventy-three steers were genotyped using an Illumina Bovine SNP50 BeadChip and phenotyped for cold carcass weight, ribeye area, rib thickness, subcutaneous fat thickness, estimated yield percent and marbling score. Additive polygenic variance and the variance attributable to a set of SNPs that had statistically significant effects on the trait were estimated via Gibbs sampling with two models: (i) a model with the chosen SNPs and the additive polygenic effects; and (ii) a model with the polygenic effects alone. The proportion of the estimated variance attributable to the SNPs became higher as the number of SNP effects that fit increased. High correlations between breeding values estimated with the model containing the polygenic effect alone and those estimated by chosen SNPs were obtained. No fraction of the total genetic variance was explained by SNPs associated with the trait at P ≥ 0.1. Our results suggest that for the carcass traits of Japanese Black cattle, a maximum of half of the total additive genetic variance may be explained by SNPs between 100 several tens to several 100s.

Joint High‐Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis

We describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. To search the high-dimensional model space, where both the number of predictors and the number of possibly correlated responses can be larger than the sample size, we demonstrate that a marginalization-based collapsed Gibbs sampler, in combination with spike and slab type of priors, offers a computationally feasible and efficient solution. As an example, we apply our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets of SNPs and possibly correlated genetic transcripts. Our method also allows for inference on the sparse interaction network of the transcripts (response variables) after accounting for the effect of the SNPs (predictor variables). We exploit properties of Gaussian graphical models to make statements concerning conditional independence of the responses. Our method compares favorably to existing Bayesian approaches developed for this purpose.

In‑silico identification of cardiovascular disease‑related SNPs affecting predicted microRNA target sites

MicroRNAs (miRNAs) are small RNAs that play an important role in the regulation of gene expression. miRNA dysregulation has been associated with phenotypic changes, including cardiovascular diseases (CVDs). The aim of the study was to obtain a list of single nucleotide polymorphisms (SNPs) related to CVDs, with computationally predicted effect on miRNA binding sites, which would verify the hypothesis that miRNA dysregulation can lead to the development of CVDs. SNPs, CVDs, and miRNAs were the 3 factors subjected to analysis. Based on the publicly available databases, we created a set of SNPs associated with the phenotype of interest and of SNPs located in known miRNA binding sites. We then merged the records assigned by the same SNP, which allowed us to indicate miRNA target sites, whose variants may be associated with CVDs. The results were supplemented with the additional data such as miRNA and mRNA coexpression, differences in the expression between various tissues, and Expression Quantitative Trait Locus analysis. Only in-silico methods, on the basis of publically available information tools and databases, were used. We obtained a list of 47 entries, constituting unique miRNA-SNP allele-phenotype linkages. Computational approach supports the hypothesis of the linkage between alterations in miRNA function and numerous CVDs. Given the high frequency of SNP incidence, this pathomechanism may be common in the population. Although the obtained results need to be further experimentally validated, limiting the number of interactions to the most probable ones will facilitate the identification of clinically significant associations.

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

BackgroundDue to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.MethodsWe aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA.ResultsA T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration.ConclusionsWe propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.

Discovering motifs that induce sequencing errors

BackgroundElevated sequencing error rates are the most predominant obstacle in single-nucleotide polymorphism (SNP) detection, which is a major goal in the bulk of current studies using next-generation sequencing (NGS). Beyond routinely handled generic sources of errors, certain base calling errors relate to specific sequence patterns. Statistically principled ways to associate sequence patterns with base calling errors have not been previously described. Extant approaches either incur decisive losses in power, due to relating errors with individual genomic positions rather than motifs, or do not properly distinguish between motif-induced and sequence-unspecific sources of errors.ResultsHere, for the first time, we describe a statistically rigorous framework for the discovery of motifs that induce sequencing errors. We apply our method to several datasets from Illumina GA IIx, HiSeq 2000, and MiSeq sequencers. We confirm previously known error-causing sequence contexts and report new more specific ones.ConclusionsChecking for error-inducing motifs should be included into SNP calling pipelines to avoid false positives. To facilitate filtering of sets of putative SNPs, we provide tracks of error-prone genomic positions (in BED format).Availabilityhttp://discovering-cse.googlecode.com

SNPflow: A Lightweight Application for the Processing, Storing and Automatic Quality Checking of Genotyping Assays

Single nucleotide polymorphisms (SNPs) play a prominent role in modern genetics. Current genotyping technologies such as Sequenom iPLEX, ABI TaqMan and KBioscience KASPar made the genotyping of huge SNP sets in large populations straightforward and allow the generation of hundreds of thousands of genotypes even in medium sized labs. While data generation is straightforward, the subsequent data conversion, storage and quality control steps are time-consuming, error-prone and require extensive bioinformatic support. In order to ease this tedious process, we developed SNPflow. SNPflow is a lightweight, intuitive and easily deployable application, which processes genotype data from Sequenom MassARRAY (iPLEX) and ABI 7900HT (TaqMan, KASPar) systems and is extendible to other genotyping methods as well. SNPflow automatically converts the raw output files to ready-to-use genotype lists, calculates all standard quality control values such as call rate, expected and real amount of replicates, minor allele frequency, absolute number of discordant replicates, discordance rate and the p-value of the HWE test, checks the plausibility of the observed genotype frequencies by comparing them to HapMap/1000-Genomes, provides a module for the processing of SNPs, which allow sex determination for DNA quality control purposes and, finally, stores all data in a relational database. SNPflow runs on all common operating systems and comes as both stand-alone version and multi-user version for laboratory-wide use. The software, a user manual, screenshots and a screencast illustrating the main features are available at http://genepi-snpflow.i-med.ac.at.

Effect of genetic regions on the correlation between single point mutation variability and morbidity

Cross-species sequence comparisons have suggested that cross-species sequence variability is correlated with functionality. The goal of this study was to extend this observation at different genetic regions, focusing on the morbidity of Single Nucleotide Polymorphisms (SNPs). A set of deleterious SNPs was compared to a set of neutral SNPs. Both samples were stratified according to the location of the SNPs at different genetic regions. Deleterious SNPs were observed to be less variable across species than neutral SNPs, these differences being significant for missense mutations (p=1.1×10−9), SNPs in introns (p=6.4×10−3), and SNPs in unknown regions (p=8×10−4).