The development of new high-throughput genotyping technologies has allowed fast evaluation of single nucleotide polymorphisms (SNPs) on a genome-wide scale. Several recent genome-wide association studies employing these technologies suggest that panels of SNPs can be a useful tool for predicting cancer susceptibility and discovery of potentially important new disease loci.In the present paper we undertake a careful examination of the relative significance of genetics, environmental factors, and biases of the data analysis protocol that was used in a previously published genome-wide association study. That prior study reported a nearly perfect discrimination of esophageal cancer patients and healthy controls on the basis of only genetic information. On the other hand, our results strongly suggest that SNPs in this dataset are not statistically linked to the phenotype, while several environmental factors and especially family history of esophageal cancer  have only a modest association with the disease.The main component of the previously claimed strong discriminatory signal is due to several data analysis pitfalls that in combination led to the strongly optimistic results. Such pitfalls are preventable and should be avoided in future studies since they create misleading conclusions and generate many false leads for subsequent research. One of the promising methods for analysis of the human genome and identification of genes and genomic regions contributing to phenotypes is the use of single nucleotide polymorphisms (SNPs). SNPs make up more than 90% of all human genetic variation and have been extensively studied for functional relationships between genotype and phenotype. The advent of high-throughput genotyping technologies has allowed fast evaluation of SNPs on a genome-wide scale at a relatively low cost During the last two years several groups reported success in using SNP genotyping assays in association studies of cancer We identified two data analysis pitfalls in We conducted a re-analysis of the SNP and environmental data that corrects the above problems and found that the SNPs in this dataset are not statistically linked to esophageal cancer, while several environmental factors, especially family history of esophageal cancer , have a modest association with the disease. We quantified the contribution of each of the factors to cancer classification and provided unbiased classification performance estimates using established unbiased data analysis protocols. Given the insignificant contribution of SNPs to cancer classification, our findings suggest that the SNPs identified in In all data analyses in addition to replicating the methods of The data used in the present study is the same as used in the original paper The genotyping of venous blood samples for all subjects in the study was performed at the National Cancer Institute  as summarized below: The germ line DNA was extracted and purified. DNA samples were subsequently prepared and assayed according to Affymetrix GeneChip Mapping Assay protocol. The 10K SNP arrays with 11,555 SNPs distributed throughout human genome were scanned and genotype calls were assigned automatically by the Affymetrix GeneChip DNA Analysis software. Four genotype calls were defined in the data: AA, AB, BB, or “no call”. More details on biological specimen collection and processing, target preparation, scanning, and genotype generation are provided in For each subject, the following five variables were also recorded: age at interview (years), tobacco use (yes/no), alcohol consumption (yes/no), family history of esophageal cancer (yes/no), and consumption of pickled vegetables (yes/no).Before data analyses, we preprocessed the SNP array data following the approach described in the original paper Since some of the data analysis methods  require no missing data, we imputed missing genotypes in the SNP dataset and used it whenever these methods were employed. Specifically, we used the multivariate nonparametric nearest neighbor imputation technique of 0D of the null model without any predictor variables and the deviance 1D of the fitted model. The difference 0–D1D follows a chi-squared distribution with 3 degrees of freedom. Since the above procedure is applied to each SNP in the dataset, it is necessary to adjust for multiple comparisons to ensure that the desired proportion of false positives (0.05) is preserved. To this end, Bonferroni adjustment was performed to the significance level 0.05 of the test . We refer to the above method as “GLM1”. Finally, we note that Bonferroni adjustment often provides a conservative assessment of the statistical significance and assumes that all SNPs are independent, while there exist methods that are less conservative and can be applicable when the SNPs are dependent, e.g. First, we employed the SNP selection method described in D'0 of the model including family history of esophageal cancer and alcohol consumption and the deviance 1D. The resultant statistic D'0–D1 follows a chi-squared distribution with one degree of freedom, and it reflects the effect of the SNP that is being analyzed. We refer to this method as “GLM2” and show that it is indeed unbiased in the Results and Discussion section and in the Supporting Information Since the p-value of GLM1 reflects the combined effect of the three predictor variables, it tends to be small even if the SNP does not have any effect on esophageal cancer at all. To address this problem of the original analysis, we also applied the following unbiased SNP selection method: we proceed similarly as in GLM1 except that the p-value is based on the difference between the deviance Finally, when fitting support vector machines (see next section) to the data, we also applied the Recursive Feature Elimination (RFE) technique that is among the best performing variable selection methods for microarray gene expression data and other high-throughput molecular datasets First, we used the classification procedure described in www.csie.ntu.edu.tw/∼cjlin/libsvm/). We also experimented with the nonlinear SVM classifiers but they resulted in more complex models with similar classification performance.As a state-of-the-art alternative to the PCA-based classification procedure, we applied support vector machine (SVM) classifiers To assess the combined performance of SNPs and environmental factors (and/or family history), we used ensemble classification methods based on SVM classifiers. We present in this paper only results for the best ensembling technique that averages predictions of the two SVM classifiers for each subject: one based on SNP data and another one based on environmental factors (and/or family history). The description and results for the other ensembling techniques are provided in the Supporting Information Unlike the original study In order to obtain unbiased AUC estimates, the cancer classification models were built and evaluated by repeated 10-fold cross-validation procedure While the prior work reported 37 significant SNPs by applying method GLM1 to the esophageal cancer SNP array dataset Nevertheless, the application of the PCA-based classifier to the data of 226 significant SNPs reproduces the classification performance of the original study −3 . Because family history and alcohol consumption are strong risk factors for esophageal cancer, this p-value will be biased towards zero, even when the SNP has nothing to do with esophageal cancer. This bias can be demonstrated as follows: It is reasonable to assume the majority of the SNPs do not have any effect on esophageal cancer risk. For these SNPs, the p-values should follow a uniform distribution between 0 and 1. However, a vast majority of their p-values were <10−3 , which i−3 . A more −3 . TherefoSecond, both SNP selection by GLM1 and building of PCA-based classifier model were performed in To assess the contribution of SNPs and other variables to esophageal cancer classification, we performed several analyses that are summarized in The experiments presented in this paper involved SVM classifiers. As we mentioned, the choice of classifier was based on empirical evidence suggesting that SVMs have superior performance in different high-dimensional “omics” datasets In conclusion, our findings suggest that several data analysis pitfalls of File S1Demonstration of Bias in Computation of P-Values(0.08 MB DOC)Click here for additional data file.File S2Integrated Analysis of Multiple Data Types(0.09 MB DOC)Click here for additional data file.


Single nucleotide polymorphisms (SNPs) and genes that exhibit presence/absence variation have provided informative marker sets for bacterial and viral genotyping. Identification of marker sets optimised for these purposes has been based on maximal generalized discriminatory power as measured by Simpson's Index of Diversity, or on the ability to identify specific variants. Here we describe the Not-N algorithm, which is designed to identify small sets of genetic markers diagnostic for user-specified subsets of known genetic variants. The algorithm does not treat the user-specified subset and the remaining genetic variants equally. Rather Not-N analysis is designed to underpin assays that provide 0% false negatives, which is very important for e.g. diagnostic procedures for clinically significant subgroups within microbial species.Campylobacter jejuni, Yersinia enterocolitica and Clostridium difficile.The Not-N algorithm has been incorporated into the "Minimum SNPs" computer program and used to derive genetic markers diagnostic for multilocus sequence typing-defined clonal complexes, hepatitis C virus (HCV) subtypes, and phylogenetic clades defined by comparative genome hybridization (CGH) data for Not-N analysis is effective for identifying small sets of genetic markers diagnostic for microbial sub-groups. The best results to date have been obtained with CGH data from several bacterial species, and HCV sequence data. The last two decades have seen an exponential increase in the generation of comparative genetic data from within bacterial and viral species. Many of the bacterial data sets are derived from electrophoresis-based genotyping methods, such as pulsed-field gel electrophoresis, which has been used to develop the inter-laboratory PulseNet system for real-time monitoring of foodborne bacterial pathogens . More reThe extensive knowledge base of comparative genetic information can be exploited to develop rationally-designed genotyping methods for examining epidemiology, or inferring virulence potential, vaccine susceptibility or antimicrobial-antiviral resistance. One approach to discriminating known genotypes within a species is to interrogate every known genetic polymorphism. However, this approach is inefficient due to linkage of alleles, and may also provide more resolving power than is required . DespiteD), and are therefore optimized with respect to discriminating all variants from each other [Neisseria meningitidis and Staphylococcus aureus variants [D module has been used to extract D-maximized SNP sets from S. aureus and Campylobacter jejuni MLST databases [D-maximized binary gene sets (sets of genes that are present in some isolates but not others) from C. jejuni CGH data [The considerable volume of comparative genetic information now available renders computerized data-mining the only practical means of identifying sets of polymorphisms optimized for particular genotyping tasks. Our research group has previously developed and described the "Minimum SNPs" computer program, which extracts resolution-optimized single-nucleotide polymorphism (SNP) sets from complex DNA sequence alignments . Previouch other ,20. The variants , while tatabases -23, and,CGH data .D maximization module [Other bioinformatics programs that carry out similar functions to "Minimum SNPs" include a linkage disequilibrium-selection algorithm, which identifies SNPs diagnostic for haplotype blocks in mammalian genomes , and SNPn module . One funE. coli, two independent MLST schemes were examined. Using scheme 1, Not-N analysis successfully identified 15 SNPs that completely differentiated the 10 major CCs .The clonal complexes (CCs) defined by MLST data are emerging as important epidemiological or taxonomic units ,26. TherH. influenzae and E. coli, Not-N analysis was unable to identify high-confidence SNPs (≥ 98%) for four S. aureus and five C. jejuni CCs. These CCs were the largest in their respective databases. The difficulty in identifying SNPs diagnostic for the larger CCs was investigated further and was found to be due to CC members that have diverged from the CC founders by recombination rather than mutation. S. aureus exhibits a low recombination frequency, with approximately 10% of CC members arising by recombination [S. aureus SNPs are dimorphic, coupled with the effects of recombination and the small pool of available SNPs in S. aureus sequence data, there exist few SNPs unique to all members of a CC. Therefore, the probability of finding highly discriminatory SNP sets is low. In support of this, the Not-N algorithm was used to find sets of SNPs diagnostic for the 212 methicillin-resistant S. aureus (MRSA) STs in the MLST database [S. aureus MLST database, and therefore the CCs contain correspondingly fewer recombinants. This analysis yielded 22 SNPs that delineated the ten major MRSA CCs with 100% confidence (results not shown).In contrast to bination . While mdatabase . The MRSC. jejuni, the influence of recombination on Not-N performance is similar to S. aureus but is more extreme. The majority of STs in C. jejuni arise by recombination, at an estimated frequency of 50 times the rate of mutation [S. aureus CCs are more efficiently derived using the D maximization algorithm of "Minimum SNPs", while in C. jejuni, the high recombination rate renders the identification of small numbers of CC-specific SNPs with high discriminatory power and a low false-negative rate potentially impossible by any means. Other researchers have identified small numbers of C. jejuni CC SNPs characteristic of six major CCs of C. jejuni; ST-21, ST-45, ST-48, ST-61, ST-206 and ST-257 [In mutation , resultid ST-257 . HoweverD-optimized (diversity maximizing) SNP sets from the S. aureus and C. jejuni databases [S. aureus, the genotypes defined by approximately eight D-optimized SNPs correspond closely to the population structure as defined by eBURST analysis. Thus, the D-optimized SNP sets appear superior to the Not-N derived SNP sets for assigning an S. aureus isolate to a CC. In the case of C. jejuni, the correspondence between D-optimized SNP genotypes and population structure is less than in S. aureus because of the higher recombination frequency. However, adding the interrogation of more loci such as a hypervariable region (sequencing of the flagellin A short variable region) to the SNP-based genotyping reduced to insignificance the incidence of unrelated isolates failing to be discriminated, thus demonstrating the value of the D-optimized SNP set. In summary, for both species, Not-N-derived SNPs are generally not optimal for assigning isolates to larger CCs, but are highly effective for identifying the smaller CCs.The "Minimum SNPs" software has previously been used to derive atabases -23. In tC. jejuni, Y. enterocolitica and C. difficile enabled the identification of phylogenetic clades that can predict infection source or pathogenicity traits. Champion et al. [C. jejuni predictive of infection source, with one clade containing predominantly livestock isolates and the other non-livestock  isolates. Isolates identified from human infection were roughly evenly distributed between the two clades. In Y. enterocolitica, three clades corresponding to non-pathogenic (biotype 1A), low-pathogenicity (biotypes 2–5) and highly pathogenic (biotype 1B) were identified by the comparative phylogenomics approach [C. difficile, CGH phylogeny identified four clades comprising a hypervirulent clade, a toxin A-B+ clade, and two clades containing human and animal isolates [CGH allows the large-scale identification of genetic differences across a number of strains. Bayesian-based algorithms applied to the CGH data of n et al.  identifiapproach . For C. isolates .C. jejuni that were characteristic of the livestock clade, including the gene cluster Cj1321 to Cj1326 within the O-linked flagellin glycosylation locus. However, none of the 33 CDS identified by MacClade 4 analysis were 100% specific to the livestock clade [C. jejuni CGH data that separated, with 100% confidence, isolates from the livestock clade from those in the nonlivestock clade  and HIV-1 (1507 genomes) 8 genomes, providiCurrent HCV genotyping methods such as the line probe assay (INNO-LiPA) , the reaTo our knowledge, this is the first set of genotyping targets that enables the specific and accurate discrimination of the 13 major subtypes of HCV. Real-time PCR-based methods, such as allele-specific real-time PCR  or high-Bacillus anthracis [This study has shown that the Not-N algorithm provides a practical tool for identifying diagnostic polymorphisms that discriminate bacterial or viral populations of interest. Not-N analysis was particularly valuable with bacterial CGH and HCV genome sequence data, where the software identified genetic markers with superior performance to polymorphisms in current use. The ability of the algorithm to select SNPs diagnostic for MLST-defined CCs was dependent on CC size, with large numbers of SNPs required to delineate the larger CCs that have undergone extensive recombination. The purpose of the Not-N algorithm is conceptually similar to the identification of canonical phylogenetic SNPs, such as those previously described for nthracis , and indC. jejuni, S. aureus, and Haemophilus influenzae were downloaded from the MLST databases for these organisms [Escherichia coli, data was obtained from two MLST schemes using seven loci [S. aureus and C. jejuni, CCs were defined as STs sharing 6/7 loci with the ancestral clone, whereas with H. influenzae and E. coli, this parameter was set to 5/7 loci and 4/7 loci, respectively.The sequence type (ST) and allele files for rganisms . For Escven loci -46, eBURven loci . For S. Sequence data for HIV-1 and HCV were downloaded from the respective databases ,10. The C. jejuni, Y. enterocolitica and C. difficile was downloaded from BμG@s [C. jejuni, 93 Y. enterocolitica and 74 C. difficile strains [Y. enterocolitica strain 237_02, which was shown to group with the non-pathogenic clade following ClustalX phylogenetic analysis [CGH array data for om BμG@s  accessio strains  were ana strains . Isolateanalysis  and visuanalysis  of the fThe Not-N algorithm is designed to derive, from sequence alignments, sets of SNPs or binary genes that discriminate a user-defined subset of the isolates (the group of interest) from all the other genotypes in the alignment (the out-group). The fundamental principle of this algorithm is that it does not treat the group of interest and the out-group equally. A position in the alignment is only considered informative if one or more bases that are present at that position in the out-group are not present in any of the sequences in the group of interest. The resolving power of a position is the proportion of out-group sequences that contain the base(s) in common with the group of interest. The rationale for the algorithm design is twofold. Firstly, the derived SNP sets cannot give rise to false-negatives since SNPs are specifically selected to identify all members of a group of interest. This lack of false-negatives is important if the SNP sets are to form the bases of e.g. diagnostic procedures for identifying virulent subgroups within a species. Secondly, Not-N can accommodate polymorphisms within the group of interest; that is, the algorithm is not reliant on identifying only invariant sites within the group of interest. Therefore, Not-N efficiently uses the available sequence data. The algorithm is demonstrated in Table D, or maximization of the power to discriminate one user-selected sequence from all known sequences. The Not-N algorithm represents a third user-selectable performance criterion for SNP set assembly. The SNP sets are assembled one SNP at a time, with the SNP giving the highest informative power identified first, followed by the SNP that gives the highest informative power in combination with the previous SNP, and so forth. Where different SNPs have identical informative power, multiple SNP combinations are assembled, until either a pre-set level of discrimination, a pre-set number of SNPs, or 100% discrimination is reached. The software incorporates 'include' and 'exclude' functions that allow the operator to force the inclusion of one or more SNPs in the output SNP set, or to remove one or more SNPs from the analysis. This provides considerable flexibility in SNP set assembly, which can be of benefit when optimizing actual assays, and provides a means of protecting against "local minima" . In the example shown in Table The Not-N function has been incorporated into the "Minimum SNPs" version 2.043 software ,23. Prev. There is a requirement to agree to a click-wrap license that is applicable to non-commercial use only. The software is written using the Java Runtime Environment which makes is essentially platform independent. Users need to have the Java Runtime Environment installed on their computer. This is freeware that can be obtained from . Downloading of this also requires agreeing to a license."Minimum SNPs" version 2.043, together with documentation, may be obtained from The authors on this manuscript are inventors on patent applications describing this algorithm and its applications. They may in consequence be eligible for financial benefit if these patent applications are commercialised.EPP carried out most of the data analysis and drafted the manuscript. JI-B and FH carried out additional data analysis. VT wrote the source code of the Not-N algorithm for integration into the "Minimum SNPs" software. PG conceived of the study, and participated in its design and coordination and helped draft the manuscript. All authors read and approved the final manuscript.Single-nucleotide polymorphisms extracted from multilocus sequence typing data using the Not-N module of "Minimum SNPs".Click here for file


The restriction fragment length polymorphism (RFLP) is a common laboratory method for the genotyping of single nucleotide polymorphisms (SNPs). Here, we describe a web-based software, named SNP-RFLPing, which provides the restriction enzyme for RFLP assays on a batch of SNPs and genes from the human, rat, and mouse genomes.Three user-friendly inputs are included: 1) NCBI dbSNP "rs" or "ss" IDs; 2) NCBI Entrez gene ID and HUGO gene name; 3) any formats of SNP-in-sequence, are allowed to perform the SNP-RFLPing assay. These inputs are auto-programmed to SNP-containing sequences and their complementary sequences for the selection of restriction enzymes. All SNPs with available RFLP restriction enzymes of each input genes are provided even if many SNPs exist. The SNP-RFLPing analysis provides the SNP contig position, heterozygosity, function, protein residue, and amino acid position for cSNPs, as well as commercial and non-commercial restriction enzymes..This web-based software solves the input format problems in similar softwares and greatly simplifies the procedure for providing the RFLP enzyme. Mixed free forms of input data are friendly to users who perform the SNP-RFLPing assay. SNP-RFLPing offers a time-saving application for association studies in personalized medicine and is freely available at  SNP genotyping is essential for association studies in personalized medicine. Although many high-throughput SNP genotyping methods have been reported, lots of researchers still report their SNP genotyping by restriction fragment length polymorphism (RFLP). NEBcutter  can provIn this paper, we present the web-based integrated system called SNP-RFLPing for SNP ID information and its availability for restriction enzymes. Users can input any formats of SNPs including NCBI dbSNP rs or ss ID, HUGO gene name and gene ID for Entrez gene in NCBI . Then, tSNP-RFLPing, a web-based interface, was designed and implemented under the SQL server database system. Java server pages and Java applets are used to input data and file processing between the users and the applications, and to parse the data, respectively. The workflow of SNP-RFLPing is illustrated in Figure Inputs of SNP-RFLPing are line fed through its web interface for the human, rat or mouse SNP-RFLP assay. The gene name (HUGO), gene ID (Entrez gene in NCBI), and SNP ID  from these species are accepted formats for SNP-RFLPing Figure . To provSNP related information is provided for the RFLP assay including the SNP ID, species, contig-position, heterozygosity, function, protein residue (P), codon position (C), and amino acid position (A) Figure . It may In this paper, we propose a web-based interface and a java-based program, SNP-RFLPing, to provide SNP ID-based (rs# and ss#), gene-based (gene name and ID) and SNP-in-sequence-based RFLP analysis from the REBASE and dbSNP database. In Table In the SNP-RFLPing server, more input items are provided, including: rs#, ss#, gene name, and ID for human, mouse and rat genomes. It is very convenient for a user to check the available restriction enzyme for each gene of interest, both online and per email. To our knowledge, SNP-RFLPing is the first software to link the gene name and its SNP-RFLP restriction enzyme. It's not necessary to search all SNPs of a certain gene from the NCBI dbSNP  before pIn addition to RFLP enzymes, RFLP genotyping also needs the primers for PCR-RFLP. Softwares like PIRA-PCR , SNP cutThe web-based software, SNP-RFLPing, can solve the input format problems inherent in similar software, and greatly simplify the procedure for providing the RFLP enzyme. A novel function of SNP-RFLPing is that it can accept any common input formats to check the RFLP availability in human, mouse, and rat genomes. In addition, the searching of SNP and RFLP information by gene name is a very powerful tool for association studies with a target gene. In conclusion, it is time-saving and user-friendly to use SNP-RFLPing for association studies in personalized medicine.Project name: SNP-RFLPing: restriction enzyme mining in genomes.Project home page: Operating system(s): Microsoft Windows XPProgramming language: JavaOther requirements: Java 1.5.0, Tomcat 5.5, SQL server 2000, MySQL 4.0License: none for academic users.For any restrictions regarding the use by non-academics please contact the corresponding author.SNP, single nucleotide polymorphismRFLP, restriction fragment length polymorphismNCBI, National Center for Biotechnology InformationHUGO, Human Genome OrganizationREBASE, The Restriction Enzyme DatabaseH-WC provided the biochemistry background, introduced the bioinformatics for SNP-RFLPing and wrote the manuscript. P-LC participated in the earlier development of the program. C-HY instructed Y-HC in writing and testing the algorithm of this software. L-YC coordinated and oversaw this study.


The authors discuss a new study showing that patients with type 1 DM are susceptible to nocturnal hypoglycemia and that there is a relationship between awakening and activation of the counterregulatory response to hypoglycemia. The Diabetes Control and Complications Trial in type 1 diabetes (T1DM) unequivocally showed the benefits of good glycemic control in preventing the complications of diabetes. Despite the treatment advances of the past decade, today iatrogenic hypoglycemia remains the major impediment to the appropriate control of blood glucose . The resHypoglycemia-associated autonomic failure (HAAF) describes a clinical syndrome apparently resulting from antecedent episodes of mild hypoglycemia that further degrade the counterregulatory response. As shown experimentally in people without diabetes, recurrent and/or recent episodes of hypoglycemia are associated with reduced sympathoadrenal (epinephrine and norepinephrine), symptomatic, and cognitive responses to subsequent hypoglycemia, impairing all of the defense mechanisms required for prevention and reversal of hypoglycemia . Since pA particularly important condition, observed mainly in T1DM, is nocturnal hypoglycemia, which may be asymptomatic and often is neither suspected nor recognized. Plasma glucose is rarely measured during sleep, and nocturnal hypoglycemia may therefore not be confirmed. Factors that contribute to the development of nocturnal hypoglycemia include preceding physical activity; imbalance between the insulin regimen and the amount, content, and timing of meals; and alcohol consumption. In addition, there may be enhanced sensitivity to insulin, and sleep per se is associated with a decrease in the autonomic response to hypoglycemia . AlthougAlthough the clinical problem of nocturnal hypoglycemia in T1DM is well recognized, less is known about the mechanisms responsible for awakening during episodes of nocturnal hypoglycemia, and their perturbation in T1DM. Understanding these mechanisms is particularly important given data on the “dead in bed” syndrome, hypothesized to be due to a combination of undetected autonomic dysfunction and nocturnal hypoglycemia that may predispose T1DM patients to fatal ventricular dysrhythmias . MoreoveBanarer et al.  have shoPLoS Medicine [In order to better define the relationship between nocturnal hypoglycemia and awakening from sleep, Bernd Schultes and colleagues studied 16 patients with T1DM and 16 healthy controls in two experimental conditions: (1) insulin-induced hypoglycemia (40 mg/dl) during sleep together with polysomnographic measurements and serial determination of the hormonal counterregulatory response, and (2) the same experimental protocol, but with maintenance of euglycemia. The researchers now report their findings in Medicine .The goal of the study was to determine the occurrence of awakening during the period of hypoglycemia and its correlation to the hormonal counterregulatory response. There were three important findings. First, only one of the 16 patients with T1DM had spontaneous awakening during hypoglycemia, whereas ten of the 16 healthy controls were awakened by hypoglycemia. Second, the hormonal counterregulatory response to hypoglycemia was (as expected) diminished in the patients with T1DM. Finally, in all cases of awakening during hypoglycemia, there was an associated increase in plasma epinephrine and cortisol, suggesting a relationship between the counterregulatory response  and awakening from sleep.Prevention of nocturnal hypoglycemia involves several strategies, including “fine tuning” of the insulin regimen, the use of “long-acting” bedtime snacks, routine monitoring of blood glucose at bedtime and before breakfast, and occasional monitoring of glucose at the time of maximum risk during sleep . The devSchultes and colleagues' study  stronglyAdditional studies are needed to examine whether the abnormal autonomic response in T1DM is further impaired during sleep, and whether a direct correlation can be established between the degree of counterregulation impairment (or HAAF) and nocturnal awakening during hypoglycemia; both factors could play important roles in the susceptibility to nocturnal hypoglycemia in T1DM.


Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide – adenine (A), thymine (T), cytosine (C) or guanine (G) – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Our laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). This mini-sequencing method is a powerful combination of a highly parallel microarray with distinctive Sanger-based dideoxy terminator sequencing chemistry. Using this microarray platform, our current genotype calling system (known as SNP Chart) is capable of calling single SNP genotypes by manual inspection of the APEX data, which is time-consuming and exposed to user subjectivity bias.Using a set of 32 Coriell DNA samples plus three negative PCR controls as a training data set, we have developed a fully-automated genotyping algorithm based on simple linear discriminant analysis (LDA) using dynamic variable selection. The algorithm combines separate analyses based on the multiple probe sets to give a final posterior probability for each candidate genotype. We have tested our algorithm on a completely independent data set of 270 DNA samples, with validated genotypes, from patients admitted to the intensive care unit (ICU) of St. Paul's Hospital (plus one negative PCR control sample). Our method achieves a concordance rate of 98.9% with a 99.6% call rate for a set of 96 SNPs. By adjusting the threshold value for the final posterior probability of the called genotype, the call rate reduces to 94.9% with a higher concordance rate of 99.6%. We also reversed the two independent data sets in their training and testing roles, achieving a concordance rate up to 99.8%.The strength of this APEX chemistry-based platform is its unique redundancy having multiple probes for a single SNP. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any 'bad data' corresponding to image artifacts on the microarray slide or failure of a specific chemistry. In this regard, our method is able to automatically select the probes which work well and reduce the effect of other so-called bad performing probes in a sample-specific manner, for any number of SNPs. Determination of the alleles at a specific single nucleotide polymorphism (SNP) site is called genotyping. An optimal genotyping technology should be capable of genotyping any number of SNPs for a large number of individuals satisfying the following criteria: 1. easy and quick development of an assay from the sequence information; 2. over-all low cost; 3. the data analysis must be simple, transparent, fully-automated and robustly give accurate genotype-calls for all kinds of samples; and 4. the study design must be flexible and scalable in all respects . Automated genotype calling is an essential part of such a system. A number of medium to high-throughput genotyping methods have been developed. Among these various techniques, TaqMan  corresponding to a single SNP . To our Commercial software called Genorama  can deteSNP Chart is a Java based visualization tool, developed by our research group . In thisWe built our genotyping model based on the training set of 32 Coriell DNA samples  and 3 neAn example of a data source for a single Coriell sample and a single SNP is given in Table In the second column of Table The data represented in Table In fact, this is a very good source of data, as all the signals corresponding to allele 2 (T in this case) are comparable to the level of background signals. Now suppose the true genotype is TT, then we should expect dominating signals only from the expected channels corresponding to all allele 2 probes. For a heterozygous CT genotype, we should expect dominating signals from all the expected channels corresponding to both allele 1 probes and allele 2 probes. These features of our redundant and robust platform can also be represented through our data visualization tool: SNP Chart . In FiguIn our study we use the 32 Coriell samples plus three negative PCR controls for model building. These 35 samples will be called the Coriell training set. To test the performance of the calling algorithm we also have a completely independent set of 270 SIRS (systematic inflammatory response syndrome) DNA samples from the ICU of St. Paul's hospital, plus one test negative control sample. This set of 271 samples will be called the SIRS test data. Note that the SIRS data are not used in model building and come from a separate study, so they provide a very rigorous test. For the training data, there are 123 SNPs on the microarray slide, but only 96 were usable: (1) 15 SNPs had PCR chemistry failure and (2) 12 SNPs had one of the three possible genotypes missing among the training set.Ideally, the genotype call could be solely based on just one of four sets of probes: 1) APEX_LEFT, (2) APEX_RIGHT, (3) ASO_1LEFT and ASO_2LEFT, and (4) ASO_1RIGHT and ASO_2RIGHT ; ;  and . They are derived from the signal intensities in rows 1–2, 3–4, 5–8, and 9–12, respectively, in data structures exemplified in Table Our main objective is to automatically select from these four sets of variables those pairs which give "good" signals for genotype calling. Moreover, the variables and hence the classifier(s) used will be chosen dynamically, i.e., for a specific SNP and sample. In this paper we use Fisher's  linear dFigure Conversely, problems with the samples or the chemistry may lead to overlap in the four clusters, making calling difficult. In Figure For each SNP we build four separate LDA classification models; the models are based on the pairs of explanatory variables in Table We also reverse the roles of the training and test data sets, leading to the second row of Table only the remaining data. The results are similar to those in row 1. For the SIRS data, row 4 reports analogous cross validation performance estimates, and there is very close agreement with row 2.Row 3 of Table For comparison, for each SNP we use the training data to build a single LDA classification model using all eight variables available in Table The results from simple linear discriminant analysis are given in Table As shown in rows 3 and 4 of Table We also tried classifiers based on different sets of variables. For example, we built an ASO classifier using the variables ASO.XL, ASO.YL, ASO.XR and ASO.YR and an APEX classifier using the variables APEX.XL, APEX.YL, APEX.XR and APEX.YR. The calls from the two classifiers were then combined using the dynamic variable methodology. Little improvement in concordance rate was found relative to eight-variable simple LDA. Similar results were obtained when combining left and right classifiers, based on the left variables  and the right variables , respectively.same probe sets are reliable from sample to sample.We have developed a robust automated genotype calling method based on an ASO and APEX microarray platform. Multiple, qualitatively different probes provide redundancies in the event that a probe does not provide a reliable signal. The dynamic-variable calling algorithm respects these redundancies, building up an overall call from classifiers based on subsets of variables, with more weight given to seemingly more reliable classifiers. The weights change from one test sample to another; it is in this sense that the method is dynamic. Standard methods of variable selection  as described by, for example, Hand, Mannila, and Smyth  or HastiFor a call rate of approximately 95%, we were able to achieve a concordance rate of 99.6% in a large, independent test set of validated genotypes. The probe data for those samples/SNPs that are not automatically called would be manually inspected within SNP Chart; unlike 100% manual inspection, this does not impose an unreasonable time burden. The method of combining classifiers is not specific to linear discriminant analysis; other statistical classifiers could be used. Similarly, the method could be applied to other microarray platforms with complex redundancies.Linear discriminant analysis (LDA), due to Fisher , is one LDA is applied to each SNP separately. It is assumed that the variables  used to classify have a multivariate normal distribution, with a within-class covariance matrix that is common to all classes (the genotypes and a negative control class) but within-class mean vectors that vary from one class to another. These quantities are estimated from the Coriell training data. For any test sample, the values of the same variables lead to posterior probabilities for the various classes. The genotype called is the class with the highest posterior probability. The method also requires the prior probabilities of belonging to the various classes. We assume priors based on observed frequencies in the training data. This basic LDA methodology is common to all the strategies we use.Simple LDA we train a single LDA genotyping model using the logarithms of all eight variables described in Table In c indexes one of the classes (genotypes) in the setFor each SNP we apply LDA to each pair of variables in Table C = {XX, XY, YY, NN}.Then the entropy for this probability distribution over classes is defined to beEntropy or uncertainty is maximized when all the less entropy (uncertainty). Thus, we define for the ASO.L classifier in row 1 of the table, for example,Entropy is computed for each of the four classifiers in Table EASO.R, EAPEX.L, and EAPEX.R in Table which is a quantity which is large if ASO.L's entropy is small compared to the maximum possible entropy. Analogous quantities are computed for WASO.R, WAPEX.L, and WAPEX.R. Note that the probabilities in Table with analogous computations for The weights for the four classifiers are applied to the posterior probabilities for each class (column) in Table with similar calculations for XY, YY, and NN. A sample is assigned to the class with maximum weighted probability.To increase the concordance with the validated test samples , a call is made if and only if the maximum probability across the classes exceeds a threshold. For instance, the results in the last two columns of the first row of Table Figure The posterior probabilities from dynamic-variable LDA corresponding to Table The author(s) declare that they have no competing interests.MP designed and developed the algorithms, performed the statistical analysis of the data sets and drafted the manuscript; all the authors contributed to the writing of the final version. WJW and RHZ supervised in developing the algorithms. SJT designed the APEX microarray chip and provided the data sets. All authors read and approved the final manuscript.


XbaI site (the restriction enzyme used in the Affymetrix platform), resulting in a fragment too long  to be amplified.The overlap of 94 single-nucleotide polymorphisms (SNP) among the 4,720 and 11,120 SNPs contained in the linkage panels of Illumina and Affymetrix, respectively, allows an assessment of the discrepancy rate produced by these two platforms. Although the no-call rate for the Affymetrix platform is approximately 8.6 times greater than for the Illumina platform, when both platforms make a genotypic call, the agreement is an impressive 99.85%. To determine if disputed genotypes can be resolved without sequencing, we studied recombination in the region of the discrepancy for the most discrepant SNP rs958883 (typed by Illumina) and tsc02060848 (typed by Affymetrix). We find that the number of inferred recombinants is substantially higher for the Affymetrix genotypes compared to the Illumina genotypes. We illustrate this with pedigree 10043, in which 3 of 7 versus 0 of 7 offspring must be double recombinants using the genotypes from the Affymetrix and the Illumina platforms, respectively. Of the 36 SNPs with one or more discrepancies, we identified a subset that appears to cluster in families. Some of this clustering may be due to the presence of a second segregating SNP that obliterates a  All markers are not created equal with respect to information content, ease of genotyping, or the frequency of miscalls.Genotyping errors are a source of concern in both linkage and association analyses. In a linkage study it is generally easier to detect genotyping errors for microsatellites than for single-nucleotide polymorphisms (SNPs) because microsatellite errors are more likely to give rise to Mendelian inheritance incompatibilities. However, because SNPs are primarily diallelic, genotyping errors often do not give rise to a Mendelian transmission inconsistency. Indeed, in some settings such as a linkage study of affected sibships, it is impossible to detect genotyping errors for individual SNP markers if parents are not genotyped.In this study we compare the genotypic calls reported by two competing SNP platforms: Illumina and Affymetrix. We identified 91 autosomal and 3 X-linked SNPs that are common to both datasets.Because the allele designations are arbitrary, we decided to use the Illumina designations as our template. In the Affymetrix data, 50 of the 94 SNPs used the same allele designations as Illumina. For the remaining 44 SNPs, we relabeled the alleles to achieve comparability.Table p < 0.0001). This patterning is not present for Illumina no-calls.The no-call rate is higher for the Affymetrix platform than for the Illumina platform. In the cleaned data for these 94 SNPs, the difference in the no-call rate is approximately 8.6-fold. There is, however, an interesting pattern in the distribution of no-calls. The Affymetrix no-call rate is 3.79% for genotypes that Illumina calls as homozygous and 6.53% for genotypes that Illumina calls heterozygous. The difference in call rates conditioned on Illumina's genotypes is highly significant  of the 94 SNPs . The second method, appropriate for family data, is to determine if double recombinants can be inferred over a short genomic region.p < 0.0001; tsc1018661, p = 0.014; tsc0039689, p = 0.023; tsc0280570, p = 0.022). None of these departures from HWE proportions is due to discrepancies in allele calling, however, because the number of discrepancies for tsc0546488, tsc1018661, tsc0039689, and tsc0280570 are only 1, 1, 3, and 1, respectively. Rather, the departures from HWE are due to differential no-calls in the Affymetrix platform. Compared to their Illumina counterparts , the number of no-calls for tsc0546488, tsc1018661, tsc0039689, and tsc0280570 are 51, 25, 18, and 28. For the first 3 SNPs all of the no-calls were for genotypes called heterozygous in the Illumina platform; for the fourth SNP, all of the no-calls were for genotypes called 1/1 by Illumina. Accordingly, it is the nonrandomness of the no-calls that leads to the departure from HWE for these 4 Affymetrix SNPs.We performed a HWE assessment of the 34 autosomal SNPs that showed one or more discrepancies between the two platforms. This assessment was performed on a panel of 245 unrelated Whites drawn from the COGA families. None of the 34 SNPs from the Illumina platform was significantly out of equilibrium. Four of the 34 SNPs from the Affymetrix platform were found not to be in HWE  significant departures from HWE in 32 SNPs that showed one or more discrepancy between the two platforms. The SNP that showed the largest number of discrepancies (rs958883 in the Illumina dataset and tsc0260848 in the Affymetrix dataset) was analyzed to determine if the presence of double recombinants could help resolve the differences. Analysis of recombination in 12 White families gave persuasive evidence of increased recombination using the Affymetrix genotypes but no increase using the Illumina genotypes. Analysis of a single family that contained 3 offspring with the disputed genotype identified 3 double recombinants using the Affymetrix genotypes but none with the Illumina genotypes.XbaI site in the vicinity of rs958883/tsc026848, thereby resulting in a fragment that is too large to amplify. The closest 5' and 3' XbaI sites are located 134 nucleotides upstream and 519 nucleotides downstream. When both of these XbaI sites are intact, the resulting fragment is within the size range  that can be amplified using common adapters. For rs958883/tsc0260848, NCBI's dbSNP (build 123) indicates that there is, indeed, another SNP (rs17150546) in the closest upstream XbaI site that changes the wild-type sequence (TCTAGA) to TCGAGA. When this XbaI site is obliterated, the resulting fragment is 3,617 nucleotides- clearly too long to be amplified. It is perhaps of interest that the SNP that gave the second highest number of discrepancies, rs768224/tsc0075731, also has a SNP (rs10977965) in its neighboring XbaI site. When the minor allele is present at this SNP, the resulting fragment is 2,978 nucleotides in length- again, too long to be amplified. Accordingly, these two known SNPs in XbaI sites would account for 32% of the 180 observed discrepancies. Of course, a definitive test of this hypothesis would require sequencing discrepant individuals at the respective XbaI sites.The clustering of discrepancies within families is consistent with the hypothesis that there is another SNP or insertion/deletion that obliterates an COGA: Collaborative Study on the Genetics of AlcoholismHWE: Hardy-Weinberg equilibriumSNP: Single-nucleotide polymorphismBKS, CT, and ADP conceived and executed the research. ALH, CT, and ADP identified the SNPs common to both datasets. ALH carried out the HWE analysis and the COUNT REC analysis. JSKK identified the 245 unrelated Whites used in the HWE analysis. CHJ carried out the haplotype analysis of pedigree 10043. BKS drafted the manuscript. SB, LJB, and GD participated in the design of the study and gave critical advice on earlier drafts of the manuscript. All authors have read and approved the final version of this manuscript.


Single nucleotide polymorphisms (SNPs) represent the most abundant type of DNA variation in the vertebrate genome, and their applications as genetic markers in numerous studies of molecular ecology and conservation of natural populations are emerging. Recent large-scale sequencing projects in several fish species have provided a vast amount of data in public databases, which can be utilized in novel SNP discovery in salmonids. However, the suggested duplicated nature of the salmonid genome may hamper SNP characterization if the primers designed in conserved gene regions amplify multiple loci.Salmo salar) and other salmonids. A total of 69 loci with differing priming strategies were screened in S. salar, and 27 of these produced ~13 kb of high-quality sequence data consisting of 19 SNPs or indels (one per 680 bp). The SNP frequency and the overall nucleotide diversity (3.99 × 10-4) in S. salar was lower than reported in a majority of other organisms, which may suggest a relative young population history for Atlantic salmon. A subset of primers used in cross-species analyses revealed considerable variation in the SNP frequencies and nucleotide diversities in other salmonids.Here we introduce a new intron-primed exon-crossing (IPEC) method in an attempt to overcome this duplication problem, and also evaluate different priming methods for SNP discovery in Atlantic salmon (Sequencing success was significantly higher with the new IPEC primers; thus the total number of loci to screen in order to identify one potential polymorphic site was six times less with this new strategy. Given that duplication may hamper SNP discovery in some species, the IPEC method reported here is an alternative way of identifying novel polymorphisms in such cases. The diversification of the variety of molecular markers available has been an important development in the field of genetics over the past two decades , with onet al. ], where K is the number of observed polymorphic sites, L is the total length of the sequence (in bp) and n is the total number of chromosomes screened. The formula corrects for different sequence lengths and variation in the number of gene copies analysed . Data provides detailed information of the observed polymorphic sites in the analyzed loci.Details of the polymorphic sites detected at the sequenced fragments in fifteen Atlantic salmon populations. Overlapping fragments from the same gene have been combined as one index. Population abbreviations are given in Figure Click here for file


We describe a hierarchical clustering algorithm for using Single Nucleotide Polymorphism (SNP) genetic data to assign individuals to populations. The method does not assume Hardy-Weinberg equilibrium and linkage equilibrium among loci in sample population individuals.We show that the algorithm can assign sample individuals highly accurately to their corresponding ethnic groups in our tests using HapMap SNP data and it is also robust to admixed populations when tested with Perlegen SNP data. Moreover, it can detect fine-scale population structure as subtle as that between Chinese and Japanese by using genome-wide high-diversity SNP loci.The algorithm provides an alternative approach to the popular STRUCTURE program, especially for fine-scale population structure detection in genome-wide association studies. This is the first successful separation of Chinese and Japanese samples using random SNP loci with high statistical support. Population structure analysis is important to genetic association studies and human evolutionary history investigations. Population structure may confound the population based genetic association studies, leading to false positive results and failure to detect true associations -4. In stTwo major approaches have been proposed to detect population structure: distance-based clustering methods and model-based methods. Distance-based approaches utilize the proportion of allele sharing as distances between individuals and are not computationally demanding. Since some of the genetic information in the data is ignored, such as allele frequencies, critics have suggested that distance-based methods are not suitable for detecting fine population structure, even when many SNP loci are used . ContrarSTRUCTURE is a popular model-based program using Markov chain Monte Carlo (MCMC) within a Bayesian framework . HoweverMany recent studies have been devoted to the human population structure analysis using DNA genetic markers. Bowcock et al. used 30 polymorphic microsatellites to construct trees of human inividuals that reflect their geographic origin with a neighbor-joining algorithm . MountaiAllele sharing distance and Ward's minimum variance hierarchical clustering), to assign individuals to populations based on multilocus genotype data. The method is different from previous distance-based approaches in many respects. Bowcock et al. used a neighbor-joining method to construct a human evolutionary tree [K, by cross-validation and the k-means algorithm in order to construct the hierarchical cluster [K, the correct number of populations. We view this as a variable that need not be determined in advance.  Allowing K to vary gives researchers an opportunity to effectively define the resolution of their investigation in response to the particular problem they are addressing. If only rough distinctions need to be made between populations, the resulting tree can be examined at a high level. If the study requires highly refined distinctions, the tree can be examined at a low level. Once we had determined the number of SNP loci needed to differentiate populations, we used Tibshirani et al.'s gap statistic [K. In this paper we describe a distance-based algorithm, AW-clust , the mean ± standard deviation for the Classification Error Rates (CERs – see the Methods section for more details) were 0.01 ± 0.01, 0.05 ± 0.03 and 0.12 ± 0.05 using 200, 100 and 50 SNP loci respectively. For t = 0.05, the mean ± standard deviation for the CERs were 0.05 ± 0.03, 0.12 ± 0.05 and 0.22 ± 0.07 using 200, 100 and 50 SNP loci respectively. When the generations since isolation were relatively short, several hundred SNP loci may be insufficient to guarantee a low CER. When t = 0.015, the mean ± standard deviation for CER were 0.03 ± 0.02, 0.08 ± 0.03 and 0.17 ± 0.05 using 2000, 1000 and 500 SNP loci respectively. Therefore, the quality of separation is likely to be a function of t, the generations since isolation, and the number of SNP loci used. When t is long, a few loci are sufficient to guarantee a good separation. But when t is relatively short, a large number of SNP loci is required.We found that both the number of SNP loci and the length of generations since isolation have a strong impact on the clustering results. When two populations had been isolated for time, We used the AW-clust algorithm to assign individuals to populations using HapMap data. A particular clustering result using 200 random SNP loci for the 209 unrelated sample individuals is plotted in Figure What is the variation due to choice of SNP loci? What is the number of SNP loci needed to get a good clustering assignment? In order to answer these questions, we randomly selected 50–1,000 SNP loci from across the 22 autosomal chromosomes, and ran the AW-clust algorithm as described in the methods section. A dendrogram tree was cut at depth 2 to generate three clusters. We replicated each number of SNP loci tested 100 times to check the variation due to SNP sampling. The quality of clustering was plotted using boxplots in Figure We then determined whether CHB and JPT samples could be differentiated. The cluster plot in Figure We also combined the 45 CHBs and 44 JPTs together and ran the AW-clust algorithm on the pooled sample. The variation due to SNP selection was also checked with 100 replications. The quality of clustering was plotted using boxplots in Figure We also applied the AW-clust algorithm to the Perlegen SNP data set, which contains the admixed population African American (AA). We wanted to check the performance of the algorithm in the presence of admixed populations. A particular clustering result using 200 random SNP loci from the 71 unrelated sample individuals is shown in Figure The variation due to SNP sampling in the Perlegen SNP data set was also examined. We randomly selected 50–1,000 SNP loci from the 22 autosomal chromosomes, and ran the AW-clust algorithm to separate individuals into different clusters. A dendrogram tree was cut at depth 2 to generate three clusters. We replicated each number of SNP loci tested 100 times to check the variation due to SNP sampling. The quality of clustering was plotted by boxplots in Figure K, was identified from the major clusters in the hierarchical cluster plot. These values for K were then objectively verified using the gap statistic, in which three scenarios were considered. First, we considered three major populations in the HapMap data, CHB, YRI and CHB+JPT (CHB and JPT being indistinguishable), using 1,000 random genome-wide SNP loci. Second, only CHB and JPT individuals were included using 30,000 random SNP loci. Third, the three populations in the Perlegen data were considered using 1,000 random SNP loci. The gap statistic plots for each scenario are shown in Figure Wk) estimations and gap curves are plotted in the left  are indicated in red and blue and marked by O and E separately for each K ranges from 1 to 6. The gap curved is plotted in the format of Gap (k) ± standard deviation of log (Wk). The optimal K is the elbow point in the observed log (Wk) plot, which corresponds to the maximizing point in the gap curve. From the plots, the estimated optimal K for each scenario is 3, 2 and 3, respectively. Multiple runs gave similar results. Therefore, the observation of K from the major clusters in the hierarchical cluster plot is reasonable and consistent with the gap statistic estimation.In all empirical studies the estimation of correct number of populations, t Figure  and right Figure , respectδ), expected heterozygosity, F statistics (FST), informativeness for assignment (In) and informativeness for ancestry coefficients (Ia) [To distinguish populations, the ideal loci are those that have an allele fixed in one population and absent in the other populations -21. For nts (Ia) . We did There are two key parts in the implementation of the AW-clust algorithm. The first part is the ASD distance matrix between all pairs of individuals. This distance was chosen because it can be shown using Balding and Nichols's DNA profile match probability theory  that theThe advantage of our algorithm is that, relative to model-based methods, it is fast. Therefore, it can be applied to data sets with large numbers of individuals and SNPs, such as occur in genome-wide association studies. It took less than one minute on a desktop computer (P4 3.0G CPU with 1 GB memory) to separate CHB and JPT using 20K random SNP loci. Kruglyak (1999) estimated that approximately 500,000 uniformly distributed markers are required for genome-wide association studies. It would be valuable if we can make full use of these SNP markers in association studies for population structure protection. The AW-clust algorithm provides a possible solution to this scenerio. The AW-clust algorithm does not assume Hardy-Weinberg equilibrium or linkage equilibrium in the sample population individuals, no special marker selection criterion is required, and the algorithm is robust to relatively small sample sizes.K clusters from the data units. However, Ward's solution is generally very good even if it is not optimal on this criterion [cmdscale  function. Distance-based methods are criticized for being more suited to data exploration than to statistical inference [K, and it is no different for the AW-clust algorithm. We view K as a variable instead of a fixed number and let researchers determine the most appropriate level of separation. For example, CHB and JPT are often grouped together for data analysis [K should be defined to fit the researcher's interests and as the data permits. One possible alternative to the subjective definition of K, is to define it using the gap statistic. It should be noted, however, estimating K is still more art than science, and depends on many factors, such as population distance, number of individuals in each population, number of markers, random replicates and the method used.One drawback with distance-based methods is that the distance measure and the clustering algorithm are somewhat arbitrary. If another definition of distance or clustering method are used, the clustering results may change, which may be a reason, in addition to relatively small number of SNP loci used, why some authors did not see a separation of Chinese and Japanese sample individuals in previous studies ,10. A prriterion . We founriterion  can alsonference . Howeveranalysis ,27. HoweK. STRUCTURE easily found the correct K = 3 with 5,000 burn-in followed by 1,000 iterations when we tested it on CEU (60), YRI (60) and CHB (45) + JPT (44) using 200 random SNP loci. However, when we reduced the CHB sample size to 10 (CEU (60), YRI (60) and CHB (10)), we needed to set 10,000 burn-in and 10,000 iterations for STRUCTURE in order to get the correct K using 200 random SNP loci. Another challenging situation for STRUCTURE is fine-scale population structure detection, such as that between Chinese and Japanese. STRUCTURE assumes that marker loci are in linkage equilibrium within subpopulations [K. But when we reduced the sample size for JPT from 44 to 20, STRUCTURE failed to identify the correct K, even with 10,000 iterations after a burn-in period of 10,000 were used. The AW-clust identified two major clusters for CHB and JPT in the above situations. Neither of the two methods worked well when we reduced the sample size for JPT from 44 to 10 individuals using 5,000 random SNP loci. However, AW-clust created two discrete clusters for CHB (45) and JPT (10) when we increased the number of SNP loci to 20,000. STRUCTURE may or may not find the correct number of clusters, K, in the fine scale population structure situations using random SNP loci especially when a population has a relatively small sample size, such as 5 or 10 individuals, in addition to the considerable computing time consumed. If accurate AIMs are available, a likelihood approach should work well when predicting an individual's ethnicity [It is easy to separate genetically distant ethnic groups, such as CEU (60), YRI (60) and CHB (45) + JPT (44) in the HapMap data, AA (23), EA (24) and HC (24) in the Perlegen data (sample size is in parenthesis). Both STRUCTURE (version 2.1) and the AW-clust algorithm gave very good classification in these situations using several hundred of random SNP loci. But the burn-in period and number of iterations in STRUCTURE may not be easily decided. Different authors used different settings ,20,21,28ulations , which tthnicity .Q, is considered an advantage of STRUCTURE. However, in our test on the Perlgen data set (AA is an admixture population), the inferred ancestry of individuals, K, which depend on many factors, such as the length of burn-in, iterations, convergence, and number of populations in the sample.STRUCTURE usually requires multiple runs to check the convergence of MCMC  which requires substantial computing time when a large number of individuals and SNP loci are used. The admixture proportions for each individual, In summary, the AW-clust algorithm provides efficient calculation and visually appealing results. It can produce highly accurate clustering and assign individuals correctly to populations. This algorithm successfully differentiated the CEU, YRI, and CHB+JPT sample individuals in the HapMap data set. It is also robust to the admixed population AA in the Perlegen data set, which covers AA, EA and HC sample individuals. Moreover, it can detect fine-scale population structure as subtle as that between CHB and JPT. We anticipate that other closely related populations can also be separated by similar approaches. Our method combined with SNP markers has considerable power in population structure analysis and it is not necessary to estimate allele frequencies in order to differentiate populations.SNP data were drawn from the HapMap project Phase I and the Perlegen SNP data sets. In the HapMap Phase I SNP data, about 1.1 million SNPs were genotyped genome-wide from 269 individuals from four ethnic populations: 90 individuals 30 trios) from Yoruba in Ibadan, Nigeria (YRI), 90 individuals (30 trios) from CEPH in Utah residents with ancestry from northern and western Europe (CEU), 45 unrelated Han Chinese from Beijing, China (CHB), and 44 Japanese from Tokyo, Japan (JPT), among which there are 209 unrelated individuals ,30. The  trios frAllele sharing distance and Ward's minimum variance hierarchical clustering) method consists of two stages. In the first stage, a distance matrix between all pairs of individuals is constructed. The distance between individuals i and j was defined asThe AW-clust , where Wk is the pooled within-cluster sum of squares, is farthest below its null reference distribution curve. The gap statistic is defined as Gap (k) = E* {log (Wk)} - log (Wk), where E* denotes the expectation from the null reference distribution, in which a uniform distribution was used.Once we determined number of SNP loci needed to differentiate populations, we then confirmed the correct number of populations, tatistic . If K raA sample was considered correctly assigned if the cluster with the major fraction of ancestry was the same as the ethnic group to which that individual was known to belong . The clun = 50 individuals drawn from two random mating populations, each of size N = 10, 000, that had split from a single ancestral population at t generations in the past. The mutation rate at each simulated locus was assumed to be negligible. Given the SNP mutation rate is about 10-8 per locus per generation [n individuals from each population were generated by random pairing of 2n alleles. We retained only those loci for which polymorphisms remained in the two populations to the current sampling time, t = 0.To evaluate the performance of the clustering algorithm in situations where the classifications are known, we simulated SNP data using the standard coalescent approach . We consneration , this asn simulated individuals. Then we used the Ward's minium variance method to construct a tree from these distances. The quality of separation was evaluated by CER for the 2n individuals. For each set of parameters, we performed 100 simulations.We estimated the pair-wise distance between individuals from the set of genotypes for the 2XG designed the study, performed the statistical analysis, and drafted the manuscript. JS helped to draft the manuscript. All authors read and approved the final manuscript.This figure shows the full image of Figure 1.Click here for fileThis figure shows the full image of Figure 3 (a). Click here for fileThis figure shows the full image of Figure 3 (b). Click here for file


Mus musculus species or the M. spretus species. Unexpected heterozgyosity in CAST/Ei strain was observed in two out of 1,167 mouse SNPs. The second study identified 11,241 candidate SNPs in five ENCODE regions of the human genome covering 2.5 Mb of genomic sequence. Approximately 50% of the candidate SNPs were selected for experimental genotyping; the validation rate exceeded 95%. The third study detected ENU-induced mutations  in 64,896 traces of 1,236 zebra fish. Our analysis of three large and diverse test datasets demonstrated that SNPdetector is an effective tool for genome-scale research and for large-sample clinical studies. SNPdetector runs on Unix/Linux platform and is available publicly (http://lpg.nci.nih.gov).Identification of single nucleotide polymorphisms (SNPs) and mutations is important for the discovery of genetic predisposition to complex diseases. PCR resequencing is the method of choice for de novo SNP discovery. However, manual curation of putative SNPs has been a major bottleneck in the application of this method to high-throughput screening. Therefore it is critical to develop a more sensitive and accurate computational method for automated SNP detection. We developed a software tool, SNPdetector, for automated identification of SNPs and mutations in fluorescence-based resequencing reads. SNPdetector was designed to model the process of human visual inspection and has a very low false positive and false negative rate. We demonstrate the superior performance of SNPdetector in SNP and mutation analysis by comparing its results with those derived by human inspection, PolyPhred (a popular SNP detection tool), and independent genotype assays in three large-scale investigations. The first study identified and validated inter- and intra-subspecies variations in 4,650 traces of 25 inbred mouse strains that belong to either the  Single nucleotide polymorphisms (SNPs) are an abundant and important class of heritable genetic variations, and many of them contribute to genetic diseases. Accurate and automated detection of SNPs as heterozygous alleles in fluorescence-based sequencing traces from diploid DNA samples is challenging because of the low signal-to-noise ratio in the data, and because of sequencing artifacts associated with the various DNA sequencing chemistries.The authors of this publication have developed a new computer program, SNPdetector, that improves upon existing software tools. The main design principle of SNPdetector was to model the process of human visual inspection of experienced analysts. The new tool is able to cut down significantly on both false positive and false negative discovery rates. Good performance can be achieved, without the need for retraining, in substantially different datasets such as SNP discovery in human resequencing data, mutation discovery in zebra fish candidate genes, and discovery of inter- and intra-subspecies variations in inbred mouse strains. The results demonstrate that this software tool is suitable for the automation of SNP discovery in diploid sequencing traces, and permits a substantial reduction of costly and laborious visual data analysis. Identification of genetic variations and mutations is important for the discovery of genetic predisposition to complex diseases. Although a wide variety of methods are available for de novo single nucleotide polymorphism (SNP) discovery , DNA seqDirect sequencing of PCR-amplified genomic fragments from diploid samples results in mixed sequencing templates. Therefore, one of the most challenging issues in SNP discovery by this method is to distinguish bona fide heterozygous allelic variations from sequencing artifacts, which can give rise to two overlapping fluorescence peaks similar to true heterozygotes. Currently, PolyPhred  is the mTo provide a sensitive and accurate method for SNP detection in fluorescence-based resequencing, we developed a new software tool, SNPdetector, aiming to “computerize” the manual review process. We report SNPdetector's application in three large-scale genetic variation studies and compare its results with those obtained by human inspection, by PolyPhred, and by experimental validation. In the first study, resequencing was used to validate mouse SNPs discovered by whole-genome shotgun sequencing. The second study identifies novel SNPs in the ENCODE regions of the human genome , and theSNPdetector processes one PCR amplicon at a time with the following four main steps . (1) RunDuring the development of SNPdetector, the mouse resequencing data and a subset of the zebra fish resequencing data were used as training data for developing filters for false positive calls and to determine the lower bound of the signal thresholds for identifying true positive variation. The human resequencing data in the ENCODE regions were not included in training but were used as an independent testing dataset to evaluate the accuracy of the software after training had taken place. The results of the three studies presented here were obtained using the same software configuration.In this investigation we attempted to validate 151 mouse SNPs on Chromosome 16 that were originally discovered by shotgun sequencing of seven laboratory inbred strains ,14. We dDuring the development of SNPdetector, this dataset was used as the primary training data for identifying sequencing artifacts that are likely to produce false positive SNP calls. The mouse data were chosen for the following reasons. (1) Mouse inbred strains are expected to be either completely homozygous or to have an extremely low rate of heterozygosity as a result of their breeding history. This expectation has been confirmed experimentally by intra-strain variation analysis . TherefoWhen using the option to include all sequence reads, SNPdetector identified a total of 1,178 SNPs in all 25 strains. For each SNP, the assembled trace data were manually reviewed using the program Consed . Manual We ran the same dataset using the Phred/Phrap/PolyPhred 5.0.2 package and manually reviewed 187 putative SNPs with score ≥ 30 that were found only by PolyPhred. In the alignments produced by SIM and Phrap, gap locations can vary if they reside in polynucleotide repeats or tandem repeats. As a result, SNPdetector and PolyPhred may produce slightly different locations for a substitution variation adjacent to an insertion/deletion (indel) polymorphism. These discrepancies were manually resolved by comparing the alignments of Phrap and SIM. There were 34 additional valid SNPs in the PolyPhred output, which makes a total of 1,201 valid SNPs when combined with the valid SNPs found by SNPdetector.Using the 1,201 valid SNPs as a benchmark of the total number of valid SNPs in the mouse resequencing data, we analyzed the error rate for SNPdetector and PolyPhred 5.0.2. The results are summarized in EphA6 gene, and the two wild-derived inbred strains, MOLF/Ei and SPRET/Ei, are heterozygous at three putative SNP sites. The other locus encodes Bach1, a heme-binding transcription factor. CAST/Ei, a wild-derived inbred strain of the Mus. mus. castaneus subspecies, was heterozygous at two putative SNP sites located at the 3′ UTR of Bach1. None of the five markers has homozygous minor alleles.In the mouse sequencing data described above, SNPdetector identified 11 putative SNPs with heterozygous genotypes in at least one of the 25 inbred strains. As part of the quality assurance process, we manually reviewed all of these SNPs. Six of the putative SNPs were false positives arising from background noise. However, manual inspection detected no sequencing artifact in the remaining five markers. The markers are located at two genomic loci. One of the two loci encodes the EphA6 locus turned out to be an artifact of genomic duplication coupled with polymorphisms of MOLF/Ei and SPRET/Ei strains at the sequencing primer binding sites. However, no genomic duplication was found for the Bach1 locus using the current mouse assembly (March 2005 release of NCBI build 34). Genotypes of the four CAST/Ei animals are summarized in To determine the validity of the unexpected heterozygosity in the inbred strains, we redesigned sequencing primers to assay DNA samples of the original animals as well as additional animals of CAST/Ei , MOLF/Ei , and SPRET/Ei  strains. The observed heterozygosity at the http://locus.umdnj.edu/nigms/products/hapmap.html).We used SNPdetector for SNP discovery as part of the HapMap project . In the A total of 11,241 candidate SNPs were found across all regions, of which approximately half (51.9%) were novel, compared to data in build 121 of dbSNP. In all, 80% of the SNPs had a minor allele frequency greater than 0.05.Nearly 6,000 of the SNPs identified by SNPdetector were selected for genotyping in expanded assay panels using a variety of commercial and academic genotyping platforms as part of the International HapMap Project. The larger panels consisted of the following samples: 90 from the Centre d'Etude du Polymorphisme Humain collection; 90 from Yoruba individuals from Ibadan, Nigeria; 45 from Han Chinese individuals from Beijing, China; and 45 from Japanese individuals from Tokoyo, Japan; the 16 individuals used in the SNP discovery phase were a subset of the genotype panel. Candidate SNPs that turned out to be monomorphic across all populations were considered to be false positives. The false positive rate ranged from 2% on Chromosome 12 to 5.8% on Chromosome 7 . The oveTo compare the performance of SNPdetector with the other SNP detection programs, we reanalyzed a subset of ENCODE data (61 amplicons on Chromosome 18) using PolyPhred 5.0.2 and NovoSNP  . At such a low threshold, the majority of the variations identified are expected to be false positives.We analyzed the sequence coverage to estimate the overall false negative rate resulting from rejection of low-quality bases by SNPdetector. Sequence coverage refers to the percentage of the total bases that are accepted for SNP identification by the program. In this analysis, each aligned base was subjected to the acceptability test employed by SNPdetector, which evaluates its short-range and long-range quality score distribution as well as secondary peak profile  in all three datasets each sample was sequenced in both orientations; (2) SNPdetector analyzes both the forward and reverse reads from the same sample to obtain the genotype; and (3) sequencing artifacts such as stutter have a complementary pattern in the forward and the reverse reads, e.g., stutters in one orientation usually have non-stutter bases in the opposite orientation.We have demonstrated the ability of SNPdetector to accurately call SNPs in resequencing reads from PCR templates with very low false negative rates (2%–6%) and acceptable false positive rates (1%–9%). In the test data analyzed here, the error rate of SNPdetector is much lower than that of the two alternative methods: PolyPhred (version 5.0.2) and NovoSNP see . InSNP  is anothSNPdetector is able to find SNPs or mutations of very low frequency because it does not rely on multiple instances of a minor allele to evaluate SNP validity. In our experience of manual SNP review, we have observed that sequencing artifacts such as stuttering, bubbling, and spilling usually occur in multiple samples at the same locus . In the Bach1 were heterozygous in CAST/Ei strain while the remaining 24 strains were homozygous. This discovery was confirmed by repeated sequencing of additional animals of CAST/Ei strain using a different pair of sequencing primers. All but one of the genotypes were heterozygous .At this level of accuracy, the success rate of the resequencing reactions is now the limiting factor in screens for identifying novel SNPs and mutations. SNPdetector runs on Unix and Linux and is publicly available by anonymous ftp (PCR amplicons were designed to tile five human genome ENCODE  regions http://www.hapmap.org/downloads/assay-design_protocols.html).PCR reactions were run in 6-fold multiplex reactions arranged so that consecutive amplicons were never in the same reaction. To the 5′ end of each primer pair were attached specific sequencing primers so the reaction mixture could be sequenced directly upon completion of the amplification. DNA sequences were tested for fidelity to the intended amplicon sequence by comparison to the human genome using BLAST. Sequencing reads that failed to make their best match to the genome between the sequencing primers were rejected from SNP analysis. In all, 258,909 sequences met this quality criterion and went on to SNPdetector. SNPdetector aligned the DNA sequences to the reference (NCBI build 34 of the human genome), and then the program called SNPs. From this analysis, 11,241 candidate SNPs were identified, including 1,571 homozygous indels. Approximately one-third of the candidate SNPs were not eligible for genotyping because they failed to meet criteria for assay design. These criteria exclude SNPs lying in palindromes, AT- or GC-rich regions, or repeated DNA  with high sequence quality  as well as high sequence identity (95%) to the reference sequence. We did not require high average quality across the entire sequence because such measures tend to exclude stuttered reads . The average quality score for a stuttered read is usually low but the bases upstream of the stutter can be of sufficient quality for SNP analysis. If a sequence has a high quality (defined previously), highly divergent (<70% identity to the reference sequence) subregion, then the read is considered a “misassembled” sequence not suitable for SNP detection.The parameters were derived from empirical analysis of genomic regions with high SNP density. However, they are adjustable in the pipeline, and the user can opt out of the low-quality/misassembly check.M × N multiple alignment by projecting insertions in subject sequences as deletions in the reference sequence. M corresponds to the alignment length; N corresponds to the number of subject sequences. A position in M is considered a putative variation site if it has more than two qualified alleles across N samples.Prior to heterozygote detection, we implemented a modified version of NQS to identify putative SNP and indel polymorphisms that have homozygous minor alleles in the resequencing sample. First, the many-to-one alignments computed by SIM  were conA residue is considered a qualified substitution allele if it and each base in its 4-bp flanking regions has Phred quality score ≥ 15. This minimum quality score is an adjustable parameter in the program. If a residue fails this standard NQS check but resides in a 9-bp window where the quality score of each base exceeds 25, then it is qualified by this expanded window NQS. The latter criterion ensures that a high-quality residue is included even if there is sequencing artifact on one side of the 4-bp flanking region.A higher-quality threshold is applied to qualify a putative indel because errors in base calling tend to generate false indel polymorphisms. The minimum quality score of NQS is 25 instead of 15. If a putative indel resides in a polynucleotide repeat, then the entire repeat as well as the 4-bp flanking region of the repeat is required to pass the NQS quality check because in a repeat polymorphism the gap location is arbitrary. The expanded window NQS is not used for indel allele qualification because alignment artifacts resulting from base calling errors often produce false indel alleles.If a sample has forward and reverse reads and its sequence in the opposite direction is identical in the 10-bp regions flanking either side of the putative allele, the quality scores from the two reads are combined for the NQS check. When there is a discrepancy in the forward and reverse reads of a putative indel allele, the indel allele is disqualified.Putative substitutions and indels identified by NQS are subjected to further evaluation in the process described below. Those that fail in subsequent test are not listed in the output.The zebra fish resequencing data had more than 1,200 subjects sequenced in both forward and reverse orientations. The large sample size in this study allowed us to inspect the distribution of secondary-to-primary-peak ratio as noise in “dirty” homozygotes  and as signal in true heterozygotes. We found that it was not uncommon for a homozygote to have a secondary peak approximately 20% of the primary peak height. On the other hand, it was uncommon for a true heterozygote to have a secondary peak less than 30% of the primary peak. Therefore, in the default setting we used the 30% threshold as the lower bound for detecting putative heterozygotes; a secondary peak below 20% of the primary peak was considered background noise in a dirty homozygote in the initial genotype assessment.The genotype of a sequence read is classified into one of the following six categories: high, med, low1, low2, low3, and reject. The quality class is determined by analyzing the Phred quality score of the variant site and its 4-bp flanking side. The threshold of each quality class is based on previous empirical analysis of NQS accuracy  → low3; score = 2 → low2; score =  → med; aThe initial genotype quality class can be turned into the class reject in horizontal or vertical scan analysis. In these scans, a putative heterozygote may also be reclassified as a dirty homozygote or vice versa. When a genotype changes its status, the quality class is also recalculated.p = (secondary_peak_area/primary_peak_area) × 100 . If each base in the flanking region passes the test of p = 0, p ≤ 10, or p ≤ 20, then the flanking region is considered to have no, little, or limited noise, respectively. A site with a p > 70 secondary peak in the flanking region is skipped to avoid penalizing a putative heterozygote in a SNP cluster  and long-range (>50 bp) features indicative of potential problems in a sequence read. These are (1) regions with low sequence similarity, (2) regions with low sequence quality, and (3) regions with a high secondary peak background. Long-range features, when occurring downstream of a STR (computed with the program Ptrfinder ) or a poTo model the vertical scan of a human inspector, SNPdetector first identifies high-quality homozygotes with no secondary peak  in the 10-bp flanking region. These “clean” homozygotes are then used to find “dirty” homozygotes. A dirty homozygote is determined to be present if one of the following conditions is true: (1) there is a discrepancy between forward and reverse reads of the same sample, e.g., a clean homozygote is found in sequence read of one orientation while the read in the opposite orientation has a secondary peak; or (2) a sequence read has a low secondary peak (secondary-to-primary-peak-area ratio < 10) and no reduction of its primary peak compared to that of a clean homozygote derived from the same orientation. To adjust for baseline differences in peak area measurements in different traces, we used a relative score, i.e., the ratio of the primary peak area at the putative site to that of its immediate homozygous neighbors. The unclassified traces are processed in ascending order of their secondary-to-primary-peak-area ratio, and each is compared to reads of clean or dirty homozygotes to determine (1) whether its secondary peak area is comparable to the secondary-to-primary-peak-area ratio found in dirty homozygotes, and (2) whether the reduction of its primary peak is comparable to those observed in dirty-to-clean homozygotes. A sequence read deemed indistinguishable from a classified homozygote is considered to represent a dirty homozygote and is included in the analysis of the remaining data. A more stringent threshold is used for reads with short-range features indicative of sequence artifacts identified during the horizontal scan. The genotype quality class and noise class are recomputed for putative heterozygotes reclassified as dirty homozygotes.The complete list of parameters used for the horizontal and the vertical scan is listed in Sequence coverage refers to the percentage of the total bases that are accepted for SNP detection by the software. Empty trace files, reads with unacceptable quality , and reads that fail to make their best match to the reference sequences are considered assay failures. They are not used as an input for SNPdetector and, as a result, are not included in the coverage analysis.The total number of resequenced bases in a read includes every base that is aligned to the genomic interval spanned by the forward and the reverse sequence primers. An aligned base is considered acceptable if it passes “horizontal scan,” described in the previous section. The numbers of Q20 bases in the total bases and the accepted bases are recorded. The number of bases rejected due to stuttering is also recorded.In all three datasets, each sample was sequenced in both the forward and the reverse orientations, giving a 2× redundancy in sequence coverage for each sample.In addition to the read-based coverage described above, we also analyzed the sample-based coverage, which combines the reads from both the forward and the reverse orientations to calculate the total bases and the accepted bases for each sample. At each position in the genomic interval spanned by the forward and the reverse primers, bases from all reads of the same sample are evaluated. An accepted base in one read always overwrites a rejected base in another. For bases with the same status, the higher quality is recorded for the Q20 analysis.M. spretus). Using the parameters “–repeat_stringency 0.55 –forcelevel 2,” we were able to obtain a single contig in 95% of the amplicons. Therefore, we used this setting in all datasets.We ran Phred/Phrap/PolyPhred using the template genomic sequences and traces derived from resequencing. Each template sequence was converted into a reference trace using the program SudoPhred. Each base of the reference template sequence was assigned a Phred quality score of 59. Using the default parameters of Phrap, an amplicon with a high SNP rate can be split into multiple contigs. For example, in the mouse resequencing data, 95% of the amplicons were assembled into multiple contigs even after excluding the traces derived from the strain SPRET/Ei .In the results generated by PolyPhred 5.0.2, approximately 50% of the high-quality SNPs (score = 99) were monomorphic in their genotype calls . We visually analyzed 30 such cases, and were able to confirm that they were all monomorphic sites. We developed a filter to remove these monomorphic “SNPs” from the PolyPhred output.We calculated false positive rate to measure the specificity of the three programs tested in this study using the following formula: the number of false positive SNPs divided by the number of SNPs discovered. This formula is referred to as the false discovery rate . We calcFigure S1All but the second animal Click here for additional data file.Figure S2T was only found in this individual. The top is a sequence of a homozgygote control. Both SNPdetector and PolyPhred found this SNP (PolyPhred score = 99). However, the genotype result is monomorphic at this site.The second and the third traces are the reverse and the forward reads from an individual identified as a heterozygote by visual analysis. The minor allele (104 KB PDF)Click here for additional data file.Table S1(113 KB PDF)Click here for additional data file.