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Bipolar disorder I (BD-I) and bipolar disorder II (BD-II) have specific characteristics and clear diagnostic criteria, but quite different treatment guidelines. In clinical practice, BD-II is commonly mistaken as a mild form of BD-I. This study uses data science technique to identify the important Single Nucleotide Polymorphisms (SNPs) significantly affecting the classifications of BD-I and BD-II, and develops a set of complementary diagnostic classifiers to enhance the diagnostic process. Screening assessments and SNP genotypes of 316 Han Chinese were performed with the Affymetrix Axiom Genome-Wide TWB Array Plate. The results show that the classifier constructed by 23 SNPs reached the area under curve of ROC (AUC) level of 0.939, while the classifier constructed by 42 SNPs reached the AUC level of 0.9574, which is a mere addition of 1.84 percent. The accuracy rate of classification increased by 3.46 percent. This study also uses Gene Ontology (GO) and Pathway to conduct a functional analysis and identify significant items, including calcium ion binding, GABA-A receptor activity, Rap1 signaling pathway, ECM proteoglycans, IL12-mediated signaling events, Nicotine addiction), and PI3K-Akt signaling pathway. The study can address time-consuming SNPs identification and also quantify the effect of SNP-SNP interactions.

Chromosomal abnormalities are a common cause of infertility in horses. However, they are difficult to detect using automated methods. Here, we propose a simple methodology based on single nucleotide polymorphism (SNP)-array data that allows us to detect the main chromosomal abnormalities in horses in a single procedure. As proof of concept, we were able to detect chromosomal abnormalities in 33 out of 268 individuals, including monosomies, chimerisms, and male and female sex-reversions, by analyzing the raw signal intensity produced by an SNP array-based genotyping platform. We also demonstrated that the procedure is not affected by the SNP density of the array employed or by the inbreeding level of the individuals. Finally, the methodology proposed in this study could be performed in an open bioinformatic environment, thus permitting its integration as a flexible screening tool in diagnostic laboratories and genomic breeding programs.

Russian sheep breeds traditionally raised in specific environments are valuable parts of sociocultural heritage and economic component of the regions. However, the import of commercial breeds negatively influences the population sizes of local sheep populations and might lead to biodiversity loss. Estimation of the runs of homozygosity (ROH) in local sheep genomes is an informative tool to address their current genetic state. In this work, we aimed to address the ROH distribution and to estimate genome inbreeding based on SNP data to evaluate genetic diversity in Russian local sheep breeds. Materials for this study included SNP-genotypes from twenty-seven Russian local sheep breeds which were generated using the Illumina OvineSNP50 BeadChip (n = 391) or the Illumina Ovine Infinium HD BeadChip (n = 315). A consecutive runs method was used to calculate ROH which were estimated for each animal and then categorized in the ROH length classes. The ROH were found in all breeds. The mean ROH length varied from 86 to 280 Mb, while the ROH number ranged from 37 to 123. The genomic inbreeding coefficient varied from 0.033 to 0.106. Our findings provide evidence of low to moderate genomic inbreeding in major local sheep populations.

Accurate SNP locus identifiers are essential for the correct matching and aggregation of multiple historical datasets. The National Center for Biotechnology Information (NCBI) issued unique “submitted SNP identification” numbers (ssIDs) for every published SNP submitted to its database. Discrepant ssID information was discovered for 78 SNPs for the 8K IRSC apple SNP array while attempting to collate multiple historical datasets, and a review of the archived SNP database from the NCBI led to their correction and a collation of SNP markers with no duplicate ssIDs. As the incorrect ssIDs were subsequently republished and uploaded to the Genome Database for Rosaceae, the aim of this short communication is to report the correct NCBI ssIDs for these 78 SNPs to correctly aggregate historical SNP data, prevent perpetuation of the misassigned ssIDs in further publications, and provide a citation for correcting the misassigned ssIDs in the Genome Database for Rosaceae (because the NCBI no longer supports storage of non-human SNP data).

Imaging genomics is an effective tool in the detection and causative factor analysis of Alzheimer's disease (AD). Based on single nucleotide polymorphism (SNP) and structural magnetic resonance imaging (MRI) data, we propose a novel AD analysis model using multi-modal data fusion and multi-detection model fusion. Firstly, based on the characteristics of SNP data, we fully extract highly representative features. Secondly, the fused data are used to construct fusion features. Finally, according to the characteristics of the fused data, we design a fused prediction model with multiple machine learning models. The model integrates four machine learning models and feeds them into the final XGBoost model for AD prediction. The experimental results show that the data fusion method and the fusion model of multiple machine learning models proposed in this paper can effectively improve the prediction accuracy. Our model can identify AD patients and detect abnormal brain regions and risk SNPs associated with AD, which can perform the association of risk SNPs with abnormal brain regions.

We report here some anomalies discovered in the minor allele frequencies (MAFs) and some likely mismappings found in our analyses of UK Biobank dataset (UKB) and several other databases. We compared the MAFs present in the UKB to those measured in two other UK studies, ALSPAC and TwinsUK, and found a large set of SNPs for which the UKB MAFs are inconsistent. Additionally, even after accounting for population structure effects and other possible causes of spurious correlations, we found many SNPs that appear to be in interchromosomal linkage. Analyzing these interchromosomal linkages carefully, we found that they are all associated with identical sequences on different chromosomes, implying that these SNPs are simply mismapped. Some (but certainly not all) of the MAF disagreements appear to be the result of these mismappings. Our results, including lists of SNPs with inconsistent MAFs and/or apparent interchromosomal linkage, are freely available to download at: http://kunertgraf.com/data/biobank.html

Genome-wide association studies (GWAS) are a valuable approach to identify single nucleotide polymorphisms (SNPs) associated with a phenotype of interest. There are now a variety of R-packages and command line tools available to perform GWAS. Here, we provide an example downloading and filtering SNP data, followed by GWAS analysis using the R-package rMVP.

Phylogenetic trees based on genome-wide single nucleotide polymorphisms (SNPs) among diverse inbreds could provide valuable and intuitive information for breeding and germplasm management in crops. As a result of sequencing technology developments, a huge amount of whole genome SNP data have become available and affordable for breeders. However, it is a challenge to perform quick and reliable plotting based on the huge amount of SNP data. To meet this goal, a visualization pipeline was developed and demonstrated based on publicly available SNP data from the current important maize inbred lines, including temperate, tropical, sweetcorn, and popcorn. The detailed phylogenetic tree plotted by our pipeline revealed the authentic genetic diversity of these inbreds, which was consistent with several previous reports and indicated that this straightforward pipeline is reliable and could potentially speed up advances in crop breeding.

Massively parallel sequencing (MPS) offers the ability to detect and analyse low quantity and quality DNA present on touched items. Here, we present MPS identity and intelligence SNP data generated from latent DNA using direct PCR and three different forensic panels: (1) the QIAGEN 140-SNP forensic identification multiplex (2) the 24 SNP HIrisplex System and (3) the 165 SNP Precision ID Ancestry Panel. The effect of individual and substrate type (wire, glass and zip-lock bag) on the recovery of SNPs and prediction accuracy was examined and compared across the three panels.

INTRODUCTION AND OBJECTIVES:Baseline PSA during midlife predicts lethal prostate cancer (PCa) with accuracy and can be used for risk-stratified screening. We sought to investigate how adjustment of baseline PSA levels for single nucleotide polymorphisms (SNPs) related to circulating PSA levels might improve prediction of aggressive PCa.METHODS:We used the Physicians Health Study (PHS) and the Southern Community Cohort Study (SCCS) to identify men aged 40-70 yrs who had baseline PSA and SNP data. In the PHS, there were 392 white men with maximum 30 yr follow-up consisting of 145 aggressive PCa cases (Gleason >6, metastatic or fatal) with 121 controls plus 126 non-aggressive PCa cases as comparison. In the SCCS, there were 319 black men with maximum 12 yr follow-up consisting of 211 PCa cases and 108 controls. Baseline PSA>10 ng/ml were excluded. Median and percentile PSA groupings were established from among controls as in prior publications. In PHS, median PSA was 1.10, 75th percentile was 2.2, and 90th w...

Although initial studies suggested that Denisovan ancestry was found only in modern human populations from island Southeast Asia and Oceania, more recent studies have suggested that Denisovan ancestry may be more widespread. However, the geographic extent of Denisovan ancestry has not been determined, and moreover the relationship between the Denisovan ancestry in Oceania and that elsewhere has not been studied. Here we analyze genome-wide single nucleotide polymorphism data from 2,493 individuals from 221 worldwide populations, and show that there is a widespread signal of a very low level of Denisovan ancestry across Eastern Eurasian and Native American (EE/NA) populations. We also verify a higher level of Denisovan ancestry in Oceania than that in EE/NA; the Denisovan ancestry in Oceania is correlated with the amount of New Guinea ancestry, but not the amount of Australian ancestry, indicating that recent gene flow from New Guinea likely accounts for signals of Denisovan ancestry across Oceania. However, Denisovan ancestry in EE/NA populations is equally correlated with their New Guinea or their Australian ancestry, suggesting a common source for the Denisovan ancestry in EE/NA and Oceanian populations. Our results suggest that Denisovan ancestry in EE/NA is derived either from common ancestry with, or gene flow from, the common ancestor of New Guineans and Australians, indicating a more complex history involving East Eurasians and Oceanians than previously suspected.

Motivation: Large-scale genotype datasets can help track the dispersal patterns of epidemiological outbreaks and predict the geographic origins of individuals. Such genetically-based geographic assignments also show a range of possible applications in forensics for profiling both victims and criminals, and in wildlife management, where poaching hotspot areas can be located. They, however, require fast and accurate statistical methods to handle the growing amount of genetic information made available from genotype arrays and next-generation sequencing technologies. Results: We introduce a novel statistical method for geopositioning individuals of unknown origin from genotypes. Our method is based on a geostatistical model trained with a dataset of georeferenced genotypes. Statistical inference under this model can be implemented within the theoretical framework of Integrated Nested Laplace Approximation, which represents one of the major recent breakthroughs in statistics, as it does not require Monte Carlo simulations. We compare the performance of our method and an alternative method for geospatial inference, SPA in a simulation framework. We highlight the accuracy and limits of continuous spatial assignment methods at various scales by analyzing genotype datasets from a diversity of species, including Florida Scrub-jay birds Aphelocoma coerulescens, Arabidopsis thaliana and humans, representing 41–197,146 SNPs. Our method appears to be best suited for the analysis of medium-sized datasets (a few tens of thousands of loci), such as reduced-representation sequencing data that become increasingly available in ecology. Availability and implementation: http://www2.imm.dtu.dk/∼gigu/Spasiba/ Contact: gilles.b.guillot@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Key message The number of SNPs required for QTL discovery is justified by the distance at which linkage disequilibrium has decayed. Simulations and real potato SNP data showed how to estimate and interpret LD decay. Abstract The magnitude of linkage disequilibrium (LD) and its decay with genetic distance determine the resolution of association mapping, and are useful for assessing the desired numbers of SNPs on arrays. To study LD and LD decay in tetraploid potato, we simulated autotetraploid genotypes and used it to explore the dependence on: (1) the number of haplotypes in the population (the amount of genetic variation) and (2) the percentage of haplotype specific SNPs (hs-SNPs). Several estimators for short-range LD were explored, such as the average r 2, median r 2, and other percentiles of r 2 (80, 90, and 95 %). For LD decay, we looked at LD½,90, the distance at which the short-range LD is halved when using the 90 % percentile of r 2 at short range, as estimator for LD. Simulations showed that the performance of various estimators for LD decay strongly depended on the number of haplotypes, although the real value of LD decay was not influenced very much by this number. The estimator LD½,90 was chosen to evaluate LD decay in 537 tetraploid varieties. LD½,90 values were 1.5 Mb for varieties released before 1945 and 0.6 Mb in varieties released after 2005. LD½,90 values within three different subpopulations ranged from 0.7 to 0.9 Mb. LD½,90 was 2.5 Mb for introgressed regions, indicating large haplotype blocks. In pericentromeric heterochromatin, LD decay was negligible. This study demonstrates that several related factors influencing LD decay could be disentangled, that no universal approach can be suggested, and that the estimation of LD decay has to be performed with great care and knowledge of the sampled material. Electronic supplementary material The online version of this article (doi:10.1007/s00122-016-2798-8) contains supplementary material, which is available to authorized users.

The aim of this study was to find out the influence of the genomic data on the showjumping horses performance evaluation for the purpose of genetic evaluation and subsequent estimation of breeding values of sport horses in Slovakia. For the evaluation of showjumping performance data of Global Champions Tour (season 2012) was used. The 4,729 individual competitions were recorded. The fourteen SNPs (single nucleotide polymorphisms) linked with showjumping performance were used for the simulation analyse. To explain the variability of horses ranking we used some different models. In the first model without SNP effect we are able to describe 53 % of performance (coefficient of determination R2 was 0.53). In linear model with some effects in form of quadratic regression we obtained R2 0.60. Highest R2 0.61 we obtained in the third model where one SNP effect was taken into account. Coefficient of determination R2 = 0.63 we obtained in the model where we used fourteen simulated SNP effects. It is possible to use simulated SNP data for evaluation of performance of showjumping horses. We observed increase of R2 in dependence on number of involved SNPs in the model. In case of fourteen simulated SNP effects used in the linear model, increase of R2 was 0.03 compared to the model without SNP effect.

Background The BRCA1/2 genes have emerged as critical biomarkers, particularly for patients with hereditary breast and ovarian cancer (HBOC). MH BRCA classifies variants based on ACMG guidelines. Clinical interpretation of NGS results by MH GUIDE provides clinicians with treatment recommendations to cancer based on expert curated biomarker knowledge. Methods MH BRCA was compared to the clinical interpretation of three Japanese HBOC studies. Population-frequency (PF) data from the 1000 Genomes Project (1000G) and the Japanese genome cohort of Tohoku Medical Megabank Organization (ToMMo) were analyzed. Based on functional evidence for DNA damage response, we correlated the ACMG index with the predicted response to PARP inhibition by MH GUIDE. Finally, BRCA classifications of 350 patients with breast and/or ovarian cancer sequenced at the Yamanashi Central Hospital (YPCH) were compared to MH BRCA. Results MH BRCA using the integrated ToMMo data revealed 40% increased accuracy in benign classification. The Japanese-cohort in 1000G represents Conclusion We enhanced the performance of MH BRCA for Japanese HBOC patients by integrating data from the ToMMo database. Our collaboration with YPCH revealed that MH BRCA supports a standardized process for assessment of pathogenicity. Contextualizing of MH BRCA classifications with MH GUIDE supports the treatment strategy of PARP inhibition with the benefit on fine-resolution of hypermorphic and hypomorphic BRCA mutations.

In this paper it is described how to analyze SNPs data by using bootstrap method. The bootstrap method is used in case of the chi-square Q2 statistics and the likelihood ratio statistics G2. It is described how to use the method in case-control association studies. Simulation study is used to explore the properties of the method. Based on the original table, it can be construct a new table and related statistical value of statistics. Figure 1(a) and Figure 1(b) presents the number B = 1.000.000 statistical value of chi-square test and likelihood ratio test, respectively. The bootstrap p-values for two (original) statistical tests are 1.2 × 10−5 and 1.9 × 10−5, respectively. The described method can also be applied to the whole genome case-control association studies which uses thousands SNPs.

Objective Several studies have shown how sets of single-nucleotide polymorphisms (SNPs) can help to classify subjects on the basis of their continental origins, with applications to case–control studies and population genetics. However, most of these studies use dimensionality-reduction methods, such as principal component analysis, or clustering methods that result in unipartite (either subjects or SNPs) representations of the data. Such analyses conceal important bipartite relationships, such as how subject and SNP clusters relate to each other, and the genotypes that determine their cluster memberships. Methods To overcome the limitations of current methods of analyzing SNP data, the authors used three bipartite analytical representations (bipartite network, heat map with dendrograms, and Circos ideogram) that enable the simultaneous visualization and analysis of subjects, SNPs, and subject attributes. Results The results demonstrate (1) novel insights into SNP data that are difficult to derive from purely unipartite views of the data, (2) the strengths and limitations of each method, revealing the role that each play in revealing novel insights, and (3) implications for how the methods can be used for the analysis of SNPs in genomic studies associated with disease. Conclusion The results suggest that bipartite representations can reveal new patterns in SNP data compared with existing unipartite representations. However, the novel insights require multiple representations to discover, verify, and comprehend the complex relationships. The results therefore motivate the need for a complementary visual analytical framework that guides the use of multiple bipartite representations to analyze complex relationships in SNP data.

We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.

Large numbers of dairy cattle are now routinely genotyped for dense single nucleotide polymorphism (SNP) arrays for the purpose of predicting genomic estimated breeding values. Such SNP arrays contain very good information for parentage assignment and pedigree reconstruction. The main challenge in using this information for parentage assignment and pedigree reconstruction is development of computationally efficient strategies that enable a candidate animal to be assigned its sire and dam with the large volume of data. Here we describe an efficient algorithm for parentage assignment with SNP data and demonstrate very accurate assignment with 50,000-SNP and 3,000-SNP panels. The computer code implementing the algorithm is given in the Appendix.

Despite decades of intensive research, NHL (non-Hodgkin lymphoma) still remains poorly understood and is largely incurable. Recent molecular studies suggest that genomic variants measured with SNPs (single nucleotide polymorphisms) in genes may have additional predictive power for NHL prognosis beyond clinical risk factors. We analyzed a genetic association study. The prognostic cohort consisted of 346 patients, among whom 138 had DLBCL (diffuse large B-cell lymphoma) and 101 had FL (follicular lymphoma). For DLBCL, we analyzed 1229 SNPs which represented 122 KEGG pathways. For FL, we analyzed 1228 SNPs which represented 122 KEGG pathways. Unlike in existing studies, we targeted at identifying pathways with significant additional predictive power beyond clinical factors. In addition, we accounted for the joint effects of multiple SNPs within pathways, whereas some existing studies drew pathway-level conclusions based on separate analysis of individual SNPs. For DLBCL, we identified four pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 2.535, 2.220, 2.094, 2.453, and 2.512, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 0.552. For FL, we identified two pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 4.320 and 3.532, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 1.212. For NHL overall, we identified three pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 5.722, 5.314, and 5.441, respective. As a comparison, the clinical factors had a median of the prediction logrank statistics around 4.411. The identified pathways have sound biological bases. In addition, they are different from those identified using existing approaches. They may provide further insights into the biological mechanisms underlying the prognosis of NHL.

The HapMap project has facilitated the selection of tagging single nucleotide polymorphisms (tagSNPs) for genome-wide association studies (GWAS) under the assumption that linkage disequilibrium (LD) in the HapMap populations is similar to the populations under investigation. Earlier reports support this assumption, although in most of these studies only a few loci were evaluated. We compared pair-wise LD and LD block structure across autosomes between the Dutch population and the CEU-HapMap reference panel. The impact of sampling distribution on the estimation of LD blocks was studied by bootstrapping. A high Pearson correlation (genome-wide; 0.93) between pair-wise r(2) for the Dutch and the CEU populations was found, indicating that tagSNPs from the CEU-HapMap panel capture common variation in the Dutch population. However, some genomic regions exhibited, significantly lower correlation than the genome-wide estimate. This might decrease the validity of HapMap tagSNPs in these regions and the power of GWAS. The LD block structure differed considerably between the Dutch and CEU-HapMap populations. This was not explained by demographic differences between the CEU and Dutch samples, as testing for population stratification was not significant. We also found that sampling variation had a large effect on the estimation of LD blocks, as shown by the bootstrapping analysis. Thus, in small samples, most of the observed differences in LD blocks between populations are most likely the result of sampling variation. This poor concordance in LD block structure suggests that large samples are required for robust estimations of local LD block structure in populations. European Journal of Human Genetics (2009) 17, 802-810; doi: 10.1038/ejhg.2008.248; published online 7 January 2009

Motivation: Errors in nucleotide sequence and SNP genotyping data are problematic when inferring haplotypes. Previously published methods for error detection in haplotype data make use of pedigree information; however, for many samples, individuals are not related by pedigree. This article describes a method for detecting errors in haplotypes by considering the recombinational history implied by the patterns of variation, three SNPs at a time.Results: Coalescent simulations provide evidence that the method is robust to high levels of recombination as well as homologous gene conversion, indicating that patterns produced by both proximate and distant SNPs may be useful for detecting unlikely three-site haplotypes.Availability: The perl script implementing the described method is called EDUT (Error Detection Using Triplets) and is available on request from the authors.Contact: mclegg@uci.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Background: Human diversity, namely single nucleotide polymorphisms (SNPs), is becoming a focus of biomedical research. Despite the binary nature of SNP determination, the majority of genotyping assay data need a critical evaluation for genotype calling. We applied statistical models to improve the automated analysis of 2-dimensional SNP data. Methods: We derived several quantities in the framework of Gaussian mixture models that provide figures of merit to objectively measure the data quality. The accuracy of individual observations is scored as the probability of belonging to a certain genotype cluster, while the assay quality is measured by the overlap between the genotype clusters. Results: The approach was extensively tested with a dataset of 438 nonredundant SNP assays comprising >150 000 datapoints. The performance of our automatic scoring method was compared with manual assignments. The agreement for the overall assay quality is remarkably good, and individual observations were scored differently by man and machine in 2.6% of cases, when applying stringent probability threshold values. Conclusion: Our definition of bounds for the accuracy for complete assays in terms of misclassification probabilities goes beyond other proposed analysis methods. We expect the scoring method to minimise human intervention and provide a more objective error estimate in genotype calling.

JSNP is a repository of Japanese Single Nucleotide Polymorphism (SNP) data, begun in 2000 and developed through the Prime Minister's Millennium Project. The aim of this undertaking is to identify and collate up to 150 000 SNPs from the Japanese population, located in genes or in adjacent regions that might influence the coding sequence of the genes. The project has been carried out by a collaboration between the Human Genome Center (HGC) in the Institute of Medical Science (IMS) at the University of Tokyo and the Japan Science and Technology Corporation (JST). JSNP serves as both a storage site for the Japanese SNPs obtained from the ongoing project and as a facility for public dissemination to allow researchers access to high quality SNP data. A primary motivation of the project is the construction of a basic data set to identify relationships between polymorphisms and common diseases or the reaction to drugs. As such, emphasis has been placed on the identification of SNPs that lie in candidate regions which may affect phenotype but which would not necessarily directly cause disease. Unrestricted access to JSNP and any associated files is available at http://snp.ims.u-tokyo.ac.jp/.

Studies on SNP correlations have been focused on SNPs located on the same chromosome since SNPs on different chromosomes are expected to segregate randomly. Previous studies suggest that SNPs can be associated with each other over long distances and even across different chromosomes. To facilitate the study of SNP associations, our goal is to find SNPs that coexist in a significant number of samples regardless of their genomic distance, and subsequently to study the relationships among these associated SNPs and corresponding genes. This problem of mining co-occurrent SNP associations is computationally challenging and motivates us to design an efficient data mining algorithm FCIRC to mine SNP associations from massive SNP data. By applying our method on the original SNP data and random chromosome permutation data, we demonstrate that our method is able to find non-random SNP associations across multiple chromosomes. Among the large amount of associated SNPs identified by our method, many of them involve multiple chromosomes. Some SNP associations also suggest novel relationships among the corresponding genes, and some may imply biological and disease mechanisms related to corresponding genes.

The risk of some complex diseases are likely related to single nucleotide polymorphisms (SNPs), which are the most common form of DNA variations. Rapidly developing bioinformatics have made it possible to recognize a group of SNPs as the risk/protective factors of a specific disease, which are related to the possibility of the sample be infected. However, a particular algorithm to consider this kind of tendency information together is still in need. In this paper, inspired form the process that human beings to make a decision, we regard the risk/protect factor in the gene variations as the emotional of our nervous system. In this way, we regard these SNP combination factor as prior knowledge and use the emotional neural networks (ENN) to analysis the disease susceptibility. By sending this kind of information to ENN and using particle swarm optimization with hierarchical structure (PSO_HS) to train the parameters, we get a better result of susceptibility classification. The experimental results about real dataset shows that consider the risk/protect factor by emotional neural networks improve the performance of disease susceptibility analysis.

Summary: We extended the original easyLINKAGE program by enabling linkage analyses for large-scale SNP data in addition to those of microsatellites. We implemented new modules for Allegro, Merlin, SimWalk, GeneHunter Imprinting, GeneHunter TwoLocus, SuperLink and extended FastSLink by automatic loop breaking and new outputs. We added conditional linkage analyses as well as multipoint simulation studies, and extended error test routines by checking for Mendelian/non-Mendelian genotyping errors and for deviations from Hardy--Weinberg equilibrium. Data can be analyzed in sets of markers, in defined centimorgan intervals and by using different allele frequency algorithms. The outputs consist of genome-wide as well as chromosomal postscript plots of LOD scores, NPL scores, P-values and other parameters. Availability: http://www.uni-wuerzburg.de/nephrologie/molecular_genetics/molecular_genetics.htm Contact: tom.lindner@mail.uni-wuerzburg.de Supplementary information: Supplementary information is available on the website. The current version is v4.01beta.

Summary Based on pairwise identity-by-state (IBS) distances and whole-genome SNP data, kinship was investigated in the Israeli Holstein population. A total of 789 bulls, including most of the artificial insemination sires in service since 1987, were genotyped by the BovineSNP50 BeadChip. This sample included up to five generations. For each bull-by-bull combination, three states are possible for each marker: no match, a single match and both alleles match. Summing over all markers, the 932 598 IBS scores (three match frequencies*310 866 bull-by-bull combinations) were visualized using three-dimensional coordinates that corresponded to the frequencies of the three possible states. Results were reduced to two dimensions using the transformations x’ = 0.7071(1 + freq1−freq2) and y’ = 1.2247freq0. Bull-by-bull pairs were grouped according to their level of kinship, and canonical scores were calculated using discriminant analysis and the x’ and y’ features. Of the 474 pairs of recorded maternal grandsire–grandson with both individuals genotyped, the probability for 28 pairs to belong to this level of kinship was low (P

Current microarray technologies are able to assay thousands of samples over million of SNPs simultaneously. Computational approaches have been developed to analyse a huge amount of data from microarray chips to understand sophisticated human genomes. The data from microarray chips might contain errors due to bad samples or bad SNPs. In this paper, we propose a method to detect bad SNPs from the probe intensities data of Illumina Beadchips. This approach measures the difference among results determined by three software Illuminus, GenoSNP and Gencall to detect the unstable SNPs. Experiment with SNP data in chromosome 20 of Kenyan people demonstrates the usefulness of our method. This approach reduces the number of SNPs that are needed to check manually. Furthermore, it has the ability in detecting bad SNPs that have not been recognized by other criteria.

Single Nucleotide Polymorphisms (SNP) is a DNA sequence variation that occurs when a single nucleotide differs between members of a biological species or paired chromosomes in an individual. Studies on SNP data are important for scientists to shed light on identifying genetic variations underlying complex diseases. While recent advances in high-throughput genotyping technologies are resulting in data accumulation at large scales, the need for collection and service of this data under a standard format and globally normalized and structured metadata that houses the structured SNP data is becoming essential. Here, we present iSNP that is a regularly updated, machine curated database that holds SNP and its associated metadata from publicly available databases under a structured standard format that can be efficiently utilized within different applications.

Single-nucleotide polymorphisms (SNPs), believed to determine human differences, are widely used to predict risk of diseases. Typically, clinical samples are limited and/or the sampling cost is high. Thus, it is essential to determine an adequate sample size needed to build a classifier based on SNPs. Such a classifier would facilitate correct classifications, while keeping the sample size to a minimum, thereby making the studies cost-effective. For coded SNP data from 2 classes, an optimal classifier and an approximation to its probability of correct classification (PCC) are derived. A linear classifier is constructed and an approximation to its PCC is also derived. These approximations are validated through a variety of Monte Carlo simulations. A sample size determination algorithm based on the criterion, which ensures that the difference between the 2 approximate PCCs is below a threshold, is given and its effectiveness is illustrated via simulations. For the HapMap data on Chinese and Japanese populations, a linear classifier is built using 51 independent SNPs, and the required total sample sizes are determined using our algorithm, as the threshold varies. For example, when the threshold value is 0.05, our algorithm determines a total sample size of 166 (83 for Chinese and 83 for Japanese) that satisfies the criterion.

We study the link between two quality measures of SNP (single nucleotide polymorphism) data in genome-wide association (GWA) studies, that is, per SNP call rates (CR) and p-values for testing Hardy-Weinberg equilibrium (HWE). The aim is to improve these measures by applying methods based on realized randomized p-values, the false discovery rate and estimates for the proportion of false hypotheses. While exact non-randomized conditional p-values for testing HWE cannot be recommended for estimating the proportion of false hypotheses, their realized randomized counterparts should be used. P-values corresponding to the asymptotic unconditional chi-square test lead to reasonable estimates only if SNPs with low minor allele frequency are excluded. We provide an algorithm to compute the probability that SNPs violate HWE given the observed CR, which yields an improved measure of data quality. The proposed methods are applied to SNP data from the KORA (Cooperative Health Research in the Region of Augsburg, Southern Germany) 500 K project, a GWA study in a population-based sample genotyped by Affymetrix GeneChip 500 K arrays using the calling algorithm BRLMM 1.4.0. We show that all SNPs with CR = 100 per cent are nearly in perfect HWE which militates in favor of the population to meet the conditions required for HWE at least for these SNPs. Moreover, we show that the proportion of SNPs not being in HWE increases with decreasing CR. We conclude that using a single threshold for judging HWE p-values without taking the CR into account is problematic. Instead we recommend a stratified analysis with respect to CR.

In this study, the SNPs located in the chicken pre-microRNAs were performed a genome-wide analysis. The result showed that pre-microRNA SNPs are negatively selected in respect to SNPs distribution between the pre-microRNA and their flanking regions. Interestingly, the seed regions show a higher SNP density than both the MIR and the other MIR regions excluding seed regions. Furthermore, the SNPs in the seed regions were found show fewer effects on the stability of the pre-microRNA secondary structure. The data suggest that there is a rapid evolution has occurred in the seed regions and these SNPs are tend to be more deleterious , being good candidates for causal phenotypic variability among chickens. These findings will improve our understanding of the functions, evolution of chicken pre-microRNAs

Here, we raised some concerns about the identification of RNA editing sites at transcriptomic level which might be interfered by widespread SNPs (single nucleotide polymorphisms) at genomic level. This commentary will remind the RNA biologists that the potential RNA editing sites discovered by high-throughput RNA sequencing without supportive evidences obtained from genome resequencing may be at risk of false positive candidates. In this regard, more efforts are required for genome-wide resequencing-based SNP data collection and in-depth mechanical studies on RNA editing.

Linkage disequilibrium (LD) is an important measure used in the analysis of single-nucleotide polymorphism (SNP) data. We used the Genetic Analysis Workshop 16 (GAW16) Framingham Heart Study 500 k SNP data to explore the effect of sampling methods on estimating of LD for SNP data. Method and data We found 332 trios in the GAW16 Framingham SNP data. Repeated random samples without replacement, of different sizes of trios and independent individuals, are drawn from these 332 trios. For each sample, the LD is calculated using the Haploview program for the chromosome 1 SNP data. Percents of D' > 0.8 and r2 > 0.8 are calculated for different distance bins based on the Haploview output. The results are summarized by sample size and sampling methods to give us an overall view of the effect of sample size and sampling methods on the LD estimation. Results Trios design gave stable estimates. A sample of 30 to 40 trios gave estimates of percent of LD > 0.8 very close to those from 332 trios. When independent individuals are used, the estimates are less stable and are different from those obtained from the 332 trios for both D' and r2, with larger differences for D'. Conclusion Our results suggest that trio design gives a stable estimate of LD. Therefore it may be more suitable for LD analysis than using independent individuals. We must be cautious when comparing the LD estimates from trios, and those from independent individuals.

13 páginas, 4 figuras, 3 tablas., Different signatures of natural selection persist over varying time scales in our genome, revealing possible episodes of adaptative evolution during human history. Here, we identify genes showing signatures of ancestral positive selection in the human lineage and investigate whether some of those genes have been evolving adaptatively in extant human populations. Specifically, we compared more than 11,000 human genes with their orthologs in chimpanzee, mouse, rat, and dog and applied a branch-site likelihood method to test for positive selection on the human lineage. Among the significant cases, a robust set of 11 genes was then further explored for signatures of recent positive selection using single nucleotide polymorphism (SNP) data. We genotyped 223 SNPs in 39 worldwide populations from the HGDP-CEPH diversity panel and supplemented this information with available genotypes for up to 4,814 SNPs distributed along 2 Mb centered on each gene. After exploring the allele frequency spectrum, population differentiation and the maintenance of long unbroken haplotypes, we found signals of recent adaptative phenomena in only one of the 11 candidate gene regions. However, the signal of recent selection in this region may come from a different, neighboring gene (CD5) rather than from the candidate gene itself (VPS37C). For this set of positively selected genes in the human lineage, we find no indication that these genes maintained their rapid evolutionary pace among human populations. Based on these data, it therefore appears that adaptation for human-specific and for population-specific traits may have involved different genes., This work was supported by Dirección General de Investigación, Ministerio de Educación y Ciencia, Spain (BFU2005-00243 and BFU2008-010406/BMC to E.B.), and by the Direcció General de Recerca, Generalitat de Catalunya (2005SGR00608 to J.B). A Moreno-Estrada was supported by a fellowship from the Consejo Nacional de Ciencia y Tecnología (CONACyT), México, and received a travel grant from the Boehringer Ingelheim Foundation, Germany.

International audience; Background: The HapMap samples were collected for medical-genetic studies, but are also widely used in populationgenetic and evolutionary investigations. Yet the ascertainment of the samples differs from most population-genetic studies which collect individuals who live in the same local region as their ancestors. What effects could this non-standard ascertainment have on the interpretation of HapMap results? Methodology/Principal Findings: We compared the HapMap samples with more conventionally-ascertained samples used in population-and forensic-genetic studies, including the HGDP-CEPH panel, making use of published genome-wide autosomal SNP data and Y-STR haplotypes, as well as producing new Y-STR data. We found that the HapMap samples were representative of their broad geographical regions of ancestry according to all tests applied. The YRI and JPT were indistinguishable from independent samples of Yoruba and Japanese in all ways investigated. However, both the CHB and the CEU were distinguishable from all other HGDP-CEPH populations with autosomal markers, and both showed Y-STR similarities to unusually large numbers of populations, perhaps reflecting their admixed origins. Conclusions/Significance: The CHB and JPT are readily distinguished from one another with both autosomal and Ychromosomal markers, and results obtained after combining them into a single sample should be interpreted with caution. The CEU are better described as being of Western European ancestry than of Northern European ancestry as often reported. Both the CHB and CEU show subtle but detectable signs of admixture. Thus the YRI and JPT samples are well-suited to standard population-genetic studies, but the CHB and CEU less so.

An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.

For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data. Availability: A web interface and source code are offered at https://webtools.imbs.uni-luebeck.de/snptogo Contact: schwarz@imbs.uni-luebeck.de

SKOKLOSTER, SWEDEN-- During the past year, researchers, both public and private, have scrambled to accumulate the single-base variations in the human genetic code called single-nucleotide polymorphisms, or SNPs, because they believed that SNPs would be extremely helpful in tracking disease genes and understanding human genetic diversity. But new results presented here earlier this month at the first international meeting devoted to SNPs suggest that using SNPs to achieve these goals could prove more difficult than initially thought. In some cases, SNPs might fail to pick up disease genes, or researchers will need to have many more SNPs located in and around the suspected disease gene to make their case than first anticipated. Other work suggests that researchers will also need more information about the history of the people being studied, such as their migration patterns, to make sense of their SNP data.

The digital SNP method has been proposed for identifying loss of heterozygosity (LOH) in tumour tissue and correlating it with patients' clinical characteristics. The method evaluates a tumour's allelic count at a single nucleotide polymorphism (SNP) for which the patient's normal tissue is heterozygous. The count is used to classify the tumour as positive or negative for LOH, using the sequential probability ratio test (SPRT). However, the SPRT was not developed for analysing digital SNP experiments. When applied to digital SNP data, the SPRT has several anomalies that can result in both loss of data and tumour misclassification. The anomalies are caused by discrepancies between the design of digital SNP experiments and the setting for which SPRT was developed. We propose an alternative classification scheme based on the false discovery rate, and show that it outperforms the SPRT when applied to Digital SNP data.

In the post genome era, considerable effort has been put into genetic association study with single nucleotide polymorphisms (SNPs) to investigate genes affecting traits, for example diseases and response to drugs. Although various software tools for SNP association study read plain text files as input data, their formats is not standardized. Manual data conversion may cause incorrect input. In addition, validity of analysis may be lost by experimental fault and by data not under assumption of analysis method. To detect various errors in input data, we implemented 19 rules as SNP data consulting program. The program can also infer first cause of error and then suggest how user should correct it. We demonstrate the program is effective not only for in-house data but also for published data where errors are expected to have been removed. With this program, biologist would be able to perform intended and valid analysis.

Summary: Forage is an application which uses two neural networks for detecting single nucleotide polymorphisms (SNPs). Potential SNP candidates are identified in multiple alignments. Each candidate is then represented by a vector of features, which is classified as SNP or monomorphic by the networks. A validated dataset of SNPs was constructed from experimentally verified SNP data and used for network training and method evalutation. Availability: The package is available at biobase.biotech.kth.se/forage/ Contact: fredrik@biotech.kth.se Supplementary information: Additional results and method description can be found at biobase.biotech.kth.se/forage/

Advances in modern genetics have brought a dramatic increase in the number of single nucleotide polymorphisms SNPs, as well as the fast accumulating genotype data in public databases. In response, many bioinformatics tools are explored to manage and extract valuable information from such large datasets. Here, we firstly review the methodological strategies and introduce some well-established programs in management and analyses of SNP data. Then, possible challenges and development in this field are discussed. It hopes that these applied tools will greatly assist in exploring susceptibility for complex diseases.

The Policy Forum article “No longer de-identified” by A. L. McGuire and R. A. Gibbs (21 Apr., p. 370) discusses the importance of protecting privacy in genomic research and informing subjects of the privacy risks associated with public data-sharing in the consent process. In particular, the authors propose adopting a stratified consent process presenting three levels of confidentiality based on the number of single-nucleotide polymorphisms (SNPs) to be released. It is necessary and crucial for all subjects to be fully informed about how their DNA data may be distributed, and to decide with whom they want their data shared. However, basing the decision to release data solely on the number of SNPs and their origin in single versus multiple gene loci is inadequate. The level of privacy risks posed by SNPs is also affected by many other factors, including linkage disequilibrium (LD) patterns among SNPs and frequencies of SNPs in the population. Modest numbers of SNPs, especially those statistically independent ones, are as identifiable as social security numbers ([1][1]). Twenty statistically independent SNPs from single gene loci could pose more of a privacy threat than 75 SNPs with high LD from multiple gene loci. Even releasing eight SNPs can be risky for individuals with rare alleles, particularly if they are associated with a known phenotype. Therefore, it would be misleading to use arbitrary numbers of SNPs as a confidentiality indicator in the consent process. Nevertheless, we agree with the authors that sharing SNP data requires sufficient safeguards. Further risk assessment and strategy discussion will be needed. 1. 1.[↵][2]1. Z. Lin, 2. A. B. Owen, 3. R. B. Altman , Science 305, 183 (2004). [OpenUrl][3][Abstract/FREE Full Text][4] [1]: #ref-1 [2]: #xref-ref-1-1 "View reference 1. in text" [3]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DLin%26rft.auinit1%253DZ.%26rft.volume%253D305%26rft.issue%253D5681%26rft.spage%253D183%26rft.epage%253D183%26rft.atitle%253DGENETICS%253A%2BGenomic%2BResearch%2Band%2BHuman%2BSubject%2BPrivacy%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.1095019%26rft_id%253Dinfo%253Apmid%252F15247459%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [4]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzMDUvNTY4MS8xODMiO3M6NDoiYXRvbSI7czoyNDoiL3NjaS8zMTMvNTc4Ni80NDEuMi5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=

To assess the relatedness and amount of genetic variation of wild and captive Mountain Bongo Tragelaphus eurycerus ssp. isaaci, both non-invasive and invasive samples were efficiently analyzed usin ...

Completeness of data sets is an important aspect of data quality as observed in biological domain such as Single Nucleotide Polymorphism (SNP). In order to decide on the acceptability of the data sets of concerned, biologists need to measure the completeness of the data sets. One type of data completeness measure is population-based completeness (PBC) that has been identified as relevant to deal with data completeness problem in this domain. In this paper, the implementation of PBC measurement will be presented as a system prototype involving real SNP data sets. The result of the analysis on the practical problems encountered during the implementation of PBC will also be presented.