Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants further investigation with independent samples.Although genome-wide association studies (GWAS) have identified a significant number of single-nucleotide polymorphisms (SNPs) associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene–gene interaction (G×G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU)-based GMDR program (named GWAS-GPU), which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D) from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes  During the past several years, searching susceptibility loci for various human diseases has been revolutionized by genome-wide association studies (GWAS). Although a significant number of single-nucleotide polymorphism (SNP) have been reported to be associated with various human complex traits To deal with the challenge of detecting G×G, much research is under way on improving both statistical and computational methodologies. A number of statistical methods and corresponding software packages have been developed, which range from simple exhaustive searches to data-mining and machine-learning approaches to Bayesian model selection CHRNA4 with CHRNB2NTRK2 with BDNFGABBR1 with GABBR2LEPR and ADRB2 in obesity HNF4A and KCNJ11 in type 2 diabetes (T2D) Regarding the multifactor dimensionality reduction (MDR) method per se, which is computational intensiveness and infeasibility in the original Java implementation of the algorithm. Thus, the primary objective of this study was to develop an effective software  that can run much more effectively in a more sophisticated computing system. As a demonstration of this newly developed GMDR-GPU program, we used it to analyze the type 2 diabetes (T2D) phenotype from the Wellcome Trust Case Control Consortium (WTCCC) study Although two general strategies, the filter approach and the stochastic search algorithm, have been proposed for scaling up the capability of MDR for analyzing GWAS data The GMDR-GPU software implements GMDR using standard C++ and CUDA 4.0 to make use of multiple graphics processing units (GPUs). The source code is cross-platform and can be built for Windows, Linux, or Mac OS X. As illustrated in score” statistic for each subject based on a generalized linear model under different distributions; i.e., normal, Poisson, and Bernoulli Compared with other MDR algorithms, one of the major advantages of GMDR K partitions of equal size for K-fold cross-validation, where K is a default to 10. Accordingly, K training sets are formed where each set consists of all but one of the K data partitions. Within each training set, the genotypes of all the SNP combinations are classified as high-risk or low-risk cells according to the genotype and score data; i.e., the justified phenotypic data; and all the SNP combinations are ranked by their training (classification) accuracies. Those combinations with the highest training accuracies are then selected as the input for step 3 to complete further testing. With the default option, in each training set, only the SNP combination with a rank of 1 is selected. However, users have the option of selecting multiple SNP combinations with the highest training accuracies from each training set. In the example provided in After appropriate justification of covariates, GMDR-GPU performs intensive computation with the goal of selecting those SNP combinations showing the strongest association signals as candidate G×G models. This is realized by a training step based on a cross-validation framework K/2)/K are outputted as possible G×G models. This step is the one in which the massively parallel computation technique of GPU and program performance optimization begin to be applied. The GMDR-GPU generates the SNP combinations in the CPU to take advantage of its fast sequential execution feature, while at the same time, it distributes the calculation of all the training accuracies for an SNP combination to one of the thousands of GPU threads using a round-robin scheduling approach This step also outputs the cross-validation consistency (CVC) statistics for the selected SNP combinations to indicate their accuracies as predictive SNP–SNP interaction models. The CVC statistic of an SNP combination is defined by the number of times it is selected from all the training sets. The higher the CVC, the more robust the SNP combination as a predictive interaction model. By default, in this step, only those SNP combinations with CVCs> driver.A detailed description of the WTCCC study sample can be found in the original paper 3 to 106 per subject  and 96 GB of RAM. Six Tesla C2070 GPUs on the same server were used to test the speed of GMDR-GPU. Performance tests were run on a simulated dataset containing 5,000 subjects with a number of SNPs ranging from 10 subject . We perfAs shown in P value <0.001. Following those filtering steps, 351,976 SNPs remained for each subject and were used in this study.To provide an example of GWAS data analysis with the GMDR-GPU software, we applied it to the WTCCC dataset for the T2D phenotype For the G×G analysis, we performed searches from two- to five-way interactions . To avoi7 permutations for each SNP combination based on our current computational capacity and the time required to complete each run. Specifically, for our report on the WTCCC T2D data, we removed those interactions whose cross-validation consistencies were <7/10 or whose P value was >10−7. After these filtering steps, the remaining best SNP combinations were considered to be significant interactions and are reported here.Because it was impractical to run three- or higher-dimensional searches among these 351,976 SNPs, exhaustive three- to five-way searches were run only among the SNPs in the best combinations generated from the exhaustive two-dimensional search, a commonly used approach in G×G analysis for large dataset like GWAS P values based on 107 permutated samples are shown in Following the two- to five-way interaction analyses for the T2D phenotype with GMDR-GPU, we generated an SNP-SNP interaction network with the goal of identifying core SNPs that were consistently detected by our G×G analyses at different dimensions . As showL3MBTL3) on chromosome 6; 8 in the transcription factor 7-like 2 gene (TCF7L2) on chromosome 10; 1 in the tetraspanin 8 gene (TSPAN8) on chromosome 12; 6 in the fat mass and obesity-associated gene (FTO) on chromosome 16; 2 in the CUGBP, Elav-like family member 4 gene (CELF4) on chromosome 16; and 5 in the runt-related transcription factor 1 gene (RUNX1) on chromosome 21. Of these six genes, TCF7L2, TSPAN8, and FTO have been previously reported to be associated with T2D L3MBTL3, CELF4, and RUNX1) have not; thus, they likely represent new susceptibility genes for T2D in WTCCC GWAS dataset.Further mapping and bioinformatics analysis of the 24 core SNPs revealed that they are located in six genes , with 2 −7 because our available computational capacity can perform only 107 permutations within a reasonable time. We acknowledge that using a p value of 10−7 as the threshold for pair-wise G×G analysis in a GWAS dataset is not as stringent as we wish for this type of analysis. However, it is our believe that such a threshold could easily be changed with the improvement of computational capacity. Finally, we performed G×G analysis only for all possible pair-wise SNP combinations but not for higher-order G×G analysis because of our limited computational capability now. Even though there existed the abovementioned limitations in our current study, our newly developed GMDR-GPU still represents one of the most advanced tools available in the field for performing G×G analysis in a GWAS dataset.As more and more GWAS studies are conducted throughout the world, efficient methodologies and computer programs for detecting G×G in the data have become essential and a challenge for many researchers TCF7L2TSPAN8FTOi.e., rs4506565 TCF7L2 have been reported to be associated with T2D, and SNP rs11196205 in TCF7L2 is associated with both T2D and coronary artery disease TSPAN8 gene, SNP rs1495377 has been reported to be associated with T2D FTO, three SNPs , with two SNPs in L3MBTL3 (rs10485400 and rs4897366), two in CELF4 (rs2852373 and rs608489), and five in RUNX1 . Whether the three genes are indeed involved in T2D and how they are involved awaits further replication in independent studies. Although these three genes have not been reported to be associated with T2D, they have been thought to be involved in other human diseases or complex traits. For example, rs6899976 in L3MBTL3 is associated with human adult height rs6569648 with birth height CELF4 is a family member of structurally related RNA-binding proteins involved in various aspects of RNA processing, including splicing and mRNA stability RUNX1 are associated with colon and rectal cancer Importantly, we identified three novel susceptibility genes for T2D  beta 1 gene (CTNNB1) CTNNB1 interacts with TCF7L2RUNX1 interacts with the core-binding factor beta subunit gene (CBFB) RUNX3RUNX3 interacts with TCF7L2RUNX3 also interacts with CTNNB1CELF4TCF7L2FTORUNX1RUNX3CTNNB1Our gene network investigation provides more evidence for the involvement of the identified six genes in T2D see . As descAlthough MDR or its derivatives have identified numerous G×G variants for many human diseases at the individual gene level TCF7L2, TSPAN8, and FTO variants with T2D) but also identified three more susceptibility genes  that have not been associated with T2D before. Since the association of the three genes with T2D has not been replicated in independent samples, we caution that these findings are tentative, and replication studies are warranted.In sum, by taking advantage of the massively parallel computing technology of GPU, our newly developed GMDR-GPU software is able to overcome the computational bottleneck of the original GMDR software and perform exhaustive searches of G×G on GWAS data. Following the development of GPU-based GMDR-GPU program, we analyzed WTCCC dataset for T2D phenotype and our obtained results not only confirmed some earlier findings (such as the well-documented associations of


The Affymetrix 6.0 and Illumina OmniExpress chip have similar genotyping accuracy and provide similar accuracy of imputed SNPs. The OmniExpress chip however provides better coverage of Asian HapMap SNPs, although its coverage of HapMap SNPs is moderate.Most genome-wide association studies to date have been performed in populations of European descent, but there is increasing interest in expanding these studies to other populations. The performance of genotyping chips in Asian populations is not well established. Therefore, we sought to test the performance of widely used fixed-marker, genome-wide association studies chips in the Han Chinese population. Non-HapMap Chinese samples (n = 396) were genotyped using the Illumina OmniExpress and Affymetrix 6.0 platforms, whereas a subset also were genotyped using the Immunochip. Genotyped markers from the Affymetrix 6.0 and Illumina OmniExpress were used for full genome imputation based on the HapMap 2 JPT+CHB  reference panel. The concordance between markers genotypes for the three platforms was very high whether directly genotyped or genotyped and imputed single nucleotide polymorphisms  were compared. The OmniExpress chip data enabled more SNPs to be imputed, particularly SNPs with minor allele frequency >5%. The OmniExpress chip achieved better coverage of HapMap SNPs than the Affymetrix 6.0 chip (73.6%  Genetic polymorphism is known to contribute to phenotypic variation, disease risks, and an individual’s response to pharmaceuticals and the environment. For the past 20 years, genetic linkage combined with positional cloning has achieved tremendous success for mapping the variations that underlie monogenic Mendelian diseases , GWAS hahttp://www.illumina.com) and Affymetrix (http://www.affymetrix.com) microarrays are the two most popular microarray platforms in the GWAS research area. The ability to identify associations depends heavily on the coverage of the genotyping chip used  selection on chips, marked increases in SNP density, the development of copy number variant tagging approaches, and improvements in chip throughput and reduction in per SNP genotyping costs. At the moment, Illumina  and Illumina Human OmniExpress Bead Chip (abbreviated as OmniExpress), in a Han Chinese population (n = 396). The Affymetrix 6.0 is designed to genotype more than 906,600 SNPs and 946,000 copy number variants. The OmniExpress is an Illumina Infinium HD BeadChip, which is designed to genotype 733,202 markers per sample. A subset of individuals also was genotyped using the Immunochip, an Illumina Infinium chip containing 196,524 polymorphisms for known immunogenetic loci . Chip peThis study was approved by the research ethics committee of the Second Military Medical University, China. Blood samples were obtained from patients attending outpatient clinics in Changzheng Hospital, Shanghai, China. All the study subjects provided signed informed consent. Subjects genotyped with the Affymetrix 6.0 platform were a subset of a larger cohort of non-HapMap Han Chinese individuals at Changzheng Hospital, which included rheumatoid arthritis cases (n = 51) and controls (n = 188). Only control subjects were genotyped further using OmniExpress or Immunochip. Genomic DNA was extracted from peripheral blood leukocytes using the AxyPrep Blood Genomic DNA Miniprep Kit  according to the manufacturer’s instructions. All genomic DNA was resuspended in TE Buffer for the following study.The concentration of genomic DNA was measured using the Quant-iT PicoGreen dsDNA assay , and concentrations were standardized to 50 ng/μL for genotyping. All samples were genotyped initially using the Illumina OmniExpress  and Affymetrix 6.0  chips. Correlation of genotypes was then checked with a further Illumina Infinium chip, the Immunochip (Illumina Inc.), which contains a mixture of common and rare variants . All plahttp://genepath.med.harvard.edu/∼reich/Software.htm) using the HapMap phase 3 Release 27 data (pngu.mgh.harvard.edu/∼purcell/plink/), and markers with call rates less than 95%, minor allele frequency (MAF) less than 1%, and Hardy-Weinberg Equilibrium P-values less than 10−6 as were excluded from further analysis (http://www.ebi.ac.uk/ega/) which is hosted at the European Bionformatics Institute, under accession number EGAS00000000131.Illumina genotype clustering was performed using Illumina’s BeadStudio software (Illumina Inc.). SNPs were reclustered using the study samples, low quality subjects were removed, and a subset of clustering results were manually inspected and verified. All SNPs with GenCall scores less than 0.15 were excluded. For Affymetrix chips, clustering was performed using the program Birdseed version 2, as implemented using Affymetrix Genotyper Console v4.0 (Affymetrix Inc.), using the default quality control (QC) thresholds. Population stratification was assessed with Eigenstrat and SmartPCA (both available from www.sph.umich.edu/csg/abecasis/MACH) with 50 rounds of model building  divided by the number of genotyped HapMap markers for the JPT+CHB reference panel at a given MAF. Corrected coverage was calculated using the formula presented in Imputations were carried out using MACH . Imputation efficacy was initially evaluated using the MACH default value of r2 =0.3 as in vs. 7.10% for the Affymetrix 6.0. For both data sets, there was a high prevalence of SNPs with MAF less than 1%, but this was greater for the Affymetrix 6.0 chip (8.42%) than the OmniExpress (4.72%).Information on imputation efficacy for HapMap SNPs using genotyped Affymetrix 6.0 and OmniExpress markers is presented in 2 = 0.80 to calculate coverage of HapMap CHB+JPT markers (www.illumina.com/products/human_omni_express.ilmn). Corresponding coverage estimates for HapMap SNPs with MAF greater than 1% were 76.1% (uncorrected) and 71.1%  markers with phasing information in the HapMap II CHB+JPT set  a heterozygote mis-called as a homozygote, (2) a homozygote mis-called as a heterozygote, and (3) a homozygote called as the opposite homozygote. For all chip types, homozygote mis-calls as heterozygotes were more common than the converse . Homozyg2, as well as to determine the types of discordance present. It has been previously reported that imputation accuracy is positively correlated with MAF, which implies that concordance between genotyped and imputed markers also should increase with increasing MAF (P < 0.001) and R2  were detected when comparing the imputed OmniExpress SNPs to the Affymetrix 6.0 genotyped SNPs (P < 0.001) for the imputed Affymetrix 6.0 markers compared with OmniExpress genotypes , but still quite low.Concordance was analyzed on a per-marker basis to explore correlations with MAF and Rsing MAF . Here, wet al. showed similarly high rates of concordance between the Affymetrix 6.0 and Illumina 550k platforms for genotyping in a German population  populations, with similarly good performance also in CHB+JPT (91% in HapMap). In our study, coverage was much lower for the OmniExpress platform (www.illumina.com/products/human_omni_express.ilmn). The use of more specialized reference panels with larger sample sizes, and genotyping platforms tailored to East Asian populations could improve coverage in future studies.Using HapMap data, Illumina reports that OmniExpress chips capture around 91% SNPs with MAF > 5% and rplatform . This deplatform . This rehttp://www.affymetrix.com), and are then required to ameliorate to universal hybridization conditions for adaptor ligation and subsequent inclusion on the array (Coverage for the Affymetrix 6.0 chip was lower than for the OmniExpress because a greater proportion of HapMap SNPs could be imputed using OmniExpress. Lower coverage of CEU, JPT+CHB, and YRI populations by Affymetrix arrays as compared with Illumina arrays has previously been reported and may be related to differences in SNP selection between the two platforms . The Affhe array .This study demonstrates that, in a real-world setting, both the Affymetrix 6.0 and OmniExpress chips achieve good coverage of genetic variation in Japanese and Chinese as defined by HapMap. From our results, we can conclude that despite the different mechanisms in chemistry, the Affymetrix 6.0 and OmniExpress platforms both give good call rates and similar genotype accuracy, in comparison with the Illumina Infinium Immunochip genotypes. Further, the imputation accuracy comparing SNPs imputed on one platform with SNPs genotyped on another platform was high, indicating that, with appropriate quality control, it is valid to combine imputed and genotyped SNPs in studies where samples have been genotyped either on the Affymetrix 6.0 or OmniExpress chips.


E. coli and Shigella genomes and a set of the same genomes to which have been added 47 assemblies and four “raw read” genomes of H104:H4 strains from the recent European E. coli outbreak that resulted in both bloody diarrhea and hemolytic uremic syndrome (HUS), and caused at least 50 deaths.Effective use of rapid and inexpensive whole genome sequencing for microbes requires fast, memory efficient bioinformatics tools for sequence comparison. The kSNP v2 software finds single nucleotide polymorphisms (SNPs) in whole genome data. kSNP v2 has numerous improvements over kSNP v1 including SNP gene annotation; better scaling for draft genomes available as assembled contigs or raw, unassembled reads; a tool to identify the optimal value of k; distribution of packages of executables for Linux and Mac OS X for ease of installation and user-friendly use; and a detailed User Guide. SNP discovery is based on k-mer analysis, and requires no multiple sequence alignment or the selection of a single reference genome. Most target sets with hundreds of genomes complete in minutes to hours. SNP phylogenies are built by maximum likelihood, parsimony, and distance, based on all SNPs, only core SNPs, or SNPs present in some intermediate user-specified fraction of targets. The SNP-based trees that result are consistent with known taxonomy. kSNP v2 can handle many gigabases of sequence in a single run, and if one or more annotated genomes are included in the target set, SNPs are annotated with protein coding and other information  from Genbank file(s). We demonstrate application of kSNP v2 on sets of viral and bacterial genomes, and discuss in detail analysis of a set of 68 finished  SNP phylogenetics play a role in outbreak tracking, forensic investigations, inferring lineage evolution, and identifying mutations linked to phenotypes like drug resistance. Next generation sequencing technologies enable whole genome sequencing of many isolates. kSNP software finds SNPs and builds phylogenies for large numbers of finished and draft sequences. It does not require a reference genome or multiple sequence alignment. Version 2 includes improvements from version 1 Usually SNP finding begins with a multiple sequence alignment or many pairwise sequence alignments of a set of target sequences. It is challenging, however, to find software to build accurate alignments for hundreds of microbial genomes in a feasible time frame, particularly if there are multiple distantly-related clades. Mauve https://sourceforge.net/projects/ksnp as executables for Mac OS X and for Linux 64-bit operating systems, and as source code; a complete User Guide is provided.Unlike other methods for SNP identification, kSNP v2 can analyze hundreds of bacterial or viral genomes in only a few hours. It can analyze together a set of finished genomes, genome assemblies, and genomes that are at the raw read stage. kSNP is a reference free method, which facilitates analyses of more genomes that are more distantly related than an alignment-based or reference-genome based approach to finding SNPs. kSNP builds Maximum Likelihood, Neighbor Joining, and parsimony phylogenetic trees based on all SNPs, only core SNPs, and SNPs present in at least a user-specified fraction of genomes. Finally, if some finished genomes are included in the data set kSNP annotates the identified SNPs based on the GenBank files of those genomes, which greatly speeds up the process of correlating SNPs with genetic functions. kSNP v2 is available at http://www.cbcb.umd.edu/software/jellyfish/, version 1.1.2) for k≤31 or sa for k>31 The user provides a fasta input file of the target genomes for SNP discovery, and specifies k, the length of the flanking sequence including the SNP; i.e. k = 13 means that each SNP will be flanked by 6 bases on each side which are conserved among at least two genomes. The SNP is at the central base of the k-mer, and the flanking (k-1)/2 bases either side of the SNP define the SNP locus. The user also specifies which (if any) genomes are finished, for which SNP positional information is desired. In step 8, SNP phylogenies are calculated from the SNP allele sequences using several alternative methods: parsimony as implemented by Parsimonator Trees are also calculated with Maximum Likelihood based on all SNPs, core SNPs only for which the loci are present in all the input genomes, and SNPs for which the loci are present in at least a user specified fraction of genomes. In step 9, SNP loci are mapped back onto each tree, and trees are rooted by taking as root the node resulting in the most SNPs mapping to nodes of the tree. Newick formatted tree files are created for importing to a graphics display tool such as Dendroscope In step 10, SNP loci are annotated by cross referencing the SNP positions in finished genomes with the annotations provided by Genbank files which are automatically downloaded from NCBI Each of the phylogenetic trees is given in four versions, each of which displays different information. (1) The basic tree, e.g. tree.ML.tre, includes no node labels. (2) The AlleleCounts tree, e.g. tree_AlleleCounts.ML.tre, labels the internal nodes with the number of SNPs that are shared exclusively by the descendants of that node. (3) The tipAlleleCounts tree, e.g. tree_tipAlleleCounts.ML.tre, in addition to labeling the internal nodes with the number of SNPs that are shared exclusively by the descendants of that node, also labels the strain names at the tips with the number of SNPs that are exclusive to that strain. (4) The AlleleCounts-NodeLabel tree, e.g. tree_AlleleCounts.ML.NodeLabel.tre, labels the internal nodes with the node number separated by an underscore from the number of SNPs that are shared exclusively by the descendants of that node.within a given genome. Even if such regions create allele conflicts within a subset of genomes, the SNP locus can still be detected as a SNP in other genomes without an allele conflict. This facilitates identification of SNPs on regions that may be duplicated or horizontally transferred, such as phage, plasmids, or other mobile elements, in those genomes for which the duplication does not create a SNP allele conflict. But the SNP will not be reported in the genomes with allele conflicts, which would require a longer value of k, i.e. more sequence context, in order to tell the duplicates apart. So running k with a longer value of k should be better at distinguishing loci in homologous regions by detecting some of the SNPs that would be considered allele conflicts with a shorter value of k. But the tradeoff is that a longer k will miss all those high density SNPs in which there is sequence variation within half k of the SNP.kSNP cannot find SNPs that are too close together . K is usually in the range of 13–31. For viruses, we have found that k = 13 or 15 works well, and for bacteria, k = 19 or 21, and have included the Kchooser script to assist the user in selecting an optimal value of k for a given data set. Repetitive elements like gene duplications can contain SNPs so long as the duplicate kmer locus does not create an allele conflict kSNP cannot distinguish true SNPs from sequencing errors. It is advised that for raw read data, some quality filters are imposed on the reads prior to running kSNP . kSNP v2 does not find indels. Indel sequencing errors that occur in the kmer sequence flanking a SNP will cause a SNP detection failure for that locus in that genome.Some unique features of kSNP v2 are that it scales better for large data sets  than other SNP finding approaches . It can https://sourceforge.net/projects/ksnp/files/).Parallel processes are used at many steps of the code, so multiple CPU are advantageous. The code is for linux/unix operating systems. It is written primarily in PERL and tcsh unix shell script, and requires several other software packages which do not need to be installed separately for the compiled versions of kSNP: MUMmer For better speed, v2 uses MUMmer instead of BLAST, jellyfish instead of sa (suffix array) for k-mers<32, and FastTreeMP and Parsimonator instead of RAxML In addition, SNPs are annotated with information from Genbank files, automatically retrieved from NCBI using the efetch utilities, regarding whether SNPs land on proteins, genes, regulatory regions, etc., and amino acids coded by SNP variants, and various summary files are created. Also, a script is included to create VCF files for a specified reference genome, for compatibility with other tools.To better handle genomes available only as unassembled raw reads with sequencing errors, the user specifies which genomes are not assembled. k-mers present only once (one may change this threshold in the run script) in the raw reads for a given genome can be removed prior to SNP finding, to avoid considering probable sequencing errors as SNPs or conflicting alleles.New files beginning with “ClusterInfo” are created for each phylogenetic tree showing, for each SNP, whether it is a core SNP and the node or cluster of genomes sharing alleles, according to the genome-cluster information in the files “Homoplasy_groups” and “Node_SNP_counts”. The ClusterInfo files are handy for parsing, for example, if you want to pull out the loci that map to a given node or homoplastic group or leaf SNPs. A script “select_node_annotations” is provided to pull out the annotated loci mapping to a particular node of a tree.Estimation of NJ trees is now optional. We noticed that when analyzing a large data set  it took 26 hours to build the distance matrix from which the NJ tree was computed. This was just over half of the total time for the analysis. Estimation of NJ trees is now optional, invoked by the command line argument –j.EvolveAGene3 http://bellinghamresearchinstitute.com/software/.Minimum spanning trees were estimated from all of the SNPs by MSTgold based on the equidistant method http://bellinghamresearchinstitute.com/software/index.html.Phylogenetic trees were compared by the program CompareTrees4, a later version of the program described in barryghall@gmail.com.Congruency between phylogenetic trees and their corresponding minimum spanning trees was determined by a perl script, Congruency.pl. Congruency is available as an executable for Mac OS X or Linux by request to BGH at http://www.ncbi.nlm.nih.gov/Traces/sra) and preprocessed with fastq-dump —split-spot —skip-technical –dumpbase , seqtk trimfq, andThe E. coli O104:H4 genomes in unassembled raw reads were downloaded from the NCBI Sequence Read Archive  before running the merge_fasta_reads script provided with kSNP. This pre-processing masked bases with quality below Q20 and eliminated reads with length shorter than 25 nt.seqtk seq -A -L 25 -l 0 -q 20 -n N . Mean branch length is the mean of all the branch lengths.For each simulated data set we determined the number of true SNPs from the True Alignment, and we used kSNP to estimate the number of SNPs. We define the kSNP efficiency as the number of SNPs identified by kSNP divided by the number of true SNPs.K, but genomes are anything but random. Some kmers will not be unique within a genome because of duplications and the possible presence of multiple copies of mobile elements within the genome. We are interested in identifying a threshold value of k for which non-unique kmers are the result of real duplication, not the result of chance. The program Kchooser chooses the median length genome from a data set, then tries as the initial value of k an odd integer that would make the chance of a kmer occurring twice <0.01 if the genome were a random sequence. It then counts all of the kmers and the number of times each occurs. On the reasonable assumption that less than 1% of a genome is likely to be duplicated, if the fraction of unique kmers in that genome is <0.99 it increments k by 2 and tries again. If the fraction of unique kmers is >0.99, to err on the side of caution, the optimum value of k is chosen as k+2. Kchooser reports the fraction of unique kmers at each value of k that is tested.With real data sets we cannot use alignments to determine the amount of sequence variation, but we reasoned that the greater the amount of sequence variation the smaller would be the number of kmers that are present in every genome (core kmers) at any particular value of k. We also reasoned that as k gets smaller the proportion of core kmers would increase. As k decreases, at some point the number of occurrences of each kmer within one genome will increase as the result of chance matches. Assuming that a genome is a random sequence of length L, the number of times any particular kmer is expected to occur in the same genome by chance alone is L/4Kchooser picks the shortest genome in the data set, identifies all of the kmers in that genome, and determines for each of a random 1000 of those kmers whether it is a core kmer; i.e. is present in all genomes. The shortest genome is selected as the exemplar because all other genomes will include more kmers that are absent in some genomes because the target genome is smaller. Kchooser reports the fraction of kmers that are core kmers at the optimum k.At that optimum value of k, E. coli-Shigella finished genomes. When that assumption is invalid Kchooser continues incrementing k until the maximum value of k = 31 is reached . When that happens we have observed that the fraction of unique kmers reaches a plateau at some lower value of k. So far the lowest fraction of unique kmers that we have observed has been 0.96. We therefore provide an option to specify a lower fraction of unique kmers on the command line in order to permit evaluating the fraction of core kmers in the data set at an appropriate value of k.The assumption that less than 1% of a genome is actually duplicated holds well for most data sets that we have considered see  and 2, iKchooser was used to determine the optimum value of k and the fraction of core kmers for each of the simulated data sets in Acinetobacter genomes, at the optimum value of k the fraction of core kmers is well below 0.1, suggesting that a substantial fraction of the SNPs have not been detected. However, Kchooser indicates that using a lower value of k is likely to result in identifying false SNPs.When the fraction of core kmers is below 0.1, there is a risk of missing a significant fraction of the SNPs. The importance of that risk depends upon the intended use of the SNPs that are identified. If the purpose is SNP association studies then it must be recognized that some SNPs that are associated with a phenotype may well have been missed. If the purpose is to estimate a phylogenetic tree, it appears to be the case that a sample of SNPs will serve as well as all (or most) of the SNPs. We compared the topologies of the ML trees estimated by kSNP at k = 13 and at k = 25 with the topology of the true tree. In every case, even when kSNP efficiency was only 0.068, the topology of the ML tree was identical with the topology of the true tree. We conclude that phylogenetic tree estimation is very robust with respect to kSNP efficiency and that there is therefore little risk associated with using the optimum k value for tree estimation purposes even when the fraction of core SNPs suggests that the efficiency of SNP finding is likely to be low.We have evaluated the time and computational demands of kSNP v2 on two different platforms : a 12-coEscherichia coli and Shigella finished genomes in the unrooted radial phylogram format with numbers at the nodes indicating the number of alleles that are shared exclusively by the descendants of each node or that are exclusive to each genome. We point out that all of the trees estimated by kSNP v2 are unrooted trees. GenBank accession numbers and literature citations for phenotypes are in Supporting Information E. coli-Shigella group k was set to 19. homoplasies, may arise from convergence, genetic recombination, etc. Homoplasies contribute to a loss of phylogenetic signal and can lead to errors in the topology of a phylogenetic tree. kSNP records the number of homoplastic SNPs on each of the phylogenetic trees. On the ML tree 37.6% of the SNPs are homoplastic.Phylogenetic analysis is the most widely used tool to estimate biological relationships, but it is often not the most appropriate tool to estimate relationships among bacterial genomes because, as the result of frequent genetic exchange, different parts of the genome can have different evolutionary histories. Phylogenetic analysis assumes that characters that are shared by a pair of individuals are identical by descent. Deviations from that assumption, called E. coli- Shigella genomes based on all of the SNPs. There is generally good agreement between the MST based on SNPs and an MST of most of the same strains based on the presence/absence of DNA fragments An alternative analysis, by Minimum Spanning Trees (MST), only assumes identity by state; i.e. relationships among genomes are based only upon their similarity, not upon relationships to hypothetical ancestors. An MST is therefore not a different kind of phylogenetic tree, it is an entirely different way of representing relationships among genomes based only on their similarities. On a phylogenetic tree a cluster is defined as a bipartition that arises by removing a branch. Each of the resulting groups is a cluster. On an MST a cluster is defined as a subgroup such that there is a path from any member of the cluster to any other member without passing through a node that is not a member of the cluster. For instance, removing the branch indicated by the blue arrow on We can quantify the agreement between a phylogenetic tree and its corresponding MST as a “congruency score” that is the fraction of the clusters on the phylogenetic tree that are also clusters on the MST. The congruency score for the phylogenetic tree in E. coli that resulted in 50 deaths, over 800 cases of hemolytic uremic syndrome, and around 4000 cases of bloody diarrhea E. coli strains E. coli and Shigella finished genomes by SNP analysis.In the summer of 2011 Europe suffered an outbreak of enteroaggregative (EAEC) shiga-toxin producing (STEC) O104:H4 k was set to 19. The O104:H4 strains from 2001 and the Ontario Canada 2010 strains cluster with Eco55989. Then the 2004 and 2009_7901 strains branch off together, but do share 87 SNP alleles with the branch leading to the 2011-12 European outbreak strains. The 2009EL strains from Georgia as well as EC12_0465 and EC11_9450 branch off next (Node A), and uniquely share 524 SNPs with the other European outbreak strains. Below that branch on the tree, the other European outbreak E. coli's share a smaller number of SNPs at some nodes, but nowhere near the main divergence point with 524 SNPs.th assembly of the TY2482 genome from BGI (ftp.genomics.org.cn). We used the O104:H4 strains, but omitted the 3 outgroup strains Eco55989, EcoSE11, and EcoIAI1, and we included both the draft assembly for TY2482 from NCBI and the completed assembly from BGI. All the SNPs from Grad et al. We compared the SNPs found in On Nodes A, B, C, and D in The genomes available as unassembled raw reads had 201-669 genome-specific allele calls, many more than the finished O104:H4 genomes  but overlapping the range of the assembled draft genomes from 2011-12 (0-448) genome-specific allele calls). The 2010 strain from Ontario, Canada was very divergent with 2056 strain-specific alleles, and the 2001, 2004, and 2009 O104:H4 strains had 355, 237, and 164, respectively. kSNP cannot distinguish sequencing errors from true SNPs, and errors likely comprise a fraction of the SNPs in draft and unassembled sequences, and potentially a small fraction of SNPs in finished genomes. The SNPs that map to leaves are more likely to be errors than those SNPs shared among multiple strains, either those that map to nodes or those which are homoplastic.E. coli strains that include the strains from k was set to 19. kSNP performs rapid SNP discovery, phylogeny, and annotation of finished, draft, and unassembled genomes. It can take as input hundreds of microbial genomes, and return results in minutes to hours on a desktop or small cluster. This contrasts with a multiple sequence alignment approach to SNP finding that could require substantially more time and RAM for sequence alignment, or identification of a reference genome which must contain the SNPs reported. Automated SNP annotation as to genes and amino acids make it straightforward to select SNPs on particular genes, identify genes which are SNP hotspots, and list the genes containing SNPs in a particular cluster of genomes.Sequencing errors cannot be distinguished from true SNPs with kSNP, so predicted SNPs must be verified with other methods, particularly the allele calls unique to one genome. By using all SNPs to build a phylogeny, the effect of sequencing errors should be minimized since the majority of SNPs are expected to be correctly sequenced. Sequencing errors would have a larger effect on trees created from a small subset of SNPs, like those present on only a few genes or a few canonical SNPs, so in that regard kSNP is advantageous.Kchooser to determine the appropriate length of k for their data sets.The optimal length of k differs depending on the level of divergence of the target set. If k is short, within-genome allele conflicts eliminate SNP candidates for repeated, nonhomologous instances of a k-mer with different central bases, or nonhomologous regions may be mis-identified as SNPs simply because they share a short k-mer. If k is long, SNPs in proximity may not be detected. Users are urged to use Choosing among the trees generated by kSNP also depends on the divergence of the target set. Divergent viruses often work best with NJ or parsimony, while ML trees are usually better for bacteria. We generally select the tree that results in the fewest homoplastic SNPs. The core tree usually gives poor resolution of closely related strains, particularly if the analysis includes many draft genomes. Using the majority tree (-m option) is sometimes a good compromise to allow decent resolution of closely related strains while ignoring the SNP loci present in only a small fraction of genomes, which stand a greater likelihood of being a result of sequencing errors.kSNP serves a role for fast initial analysis of many genomes to rapidly create a SNP phylogeny from whole genomes. It could be followed for higher accuracy SNP finding by sequence alignment of small clades whose relatedness is determined from kSNP, or by selecting a small number of genomes for analysis chosen to represent the major phylogenetic branches identified by kSNP.https://sourceforge.net/projects/ksnp.kSNP is available at Figure S1Maximum RAM required by kSNP versus input data size, as required by the runs reported in this paper on the linux cluster.(PDF)Click here for additional data file.Table S1GenBank accession numbers and literature citations for phenotypes of finished E.coli and Shigella genomes in (DOCX)Click here for additional data file.Table S2SNP annotations mapping to the nodes indicated in (XLSX)Click here for additional data file.Table S3SNP annotations mapping to the node with 397 SNP alleles uniquely shared by the European 2011–2012 outbreak in (XLSX)Click here for additional data file.


High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other “omics” approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens. Genetic hallmarks of glioblastoma are deletion of p16INK4a, mutation of p53 and PTEN, loss of heterozygosity of chromosome 10q , and gene amplifications including EGFR amplified in 37.5% of GBM . Additional amplification are frequently found at 12q13-21 including CDK4, KUB3 or CYP27B1. The gene amplification have been associated with the overall survival of GBM patients . Besides these common features, a manifold of additional aberrations have been identified for GBM with high-throughput whole genome and whole transcriptome analysis tools, including amplicons detected by aCGH data Thus, SNPs in the TERT, RTEL1 and CDKN2B regions were strongly associated with high-grade glioma risk. While chromosomal imbalances and aberrant mRNA and miRNA expression profiles can be identified in tumor cells after tumor formation, putative SNPs that might be associated with an increased risk for developing glioblastoma can be identified prior to tumor formation in DNA from normal samples of a patient. These genetic predispositions offer essential screening tools to identify high-risk populations for specific cancers. Furthermore, the knowledge of such predispositions and of the resulting molecular changes in cellular processes will further our understanding of these tumors and will contribute to a personalized medicine.Glioblastoma (GBM) is the most frequent and most malignant human primary brain tumor that is characterized by a rapid and invasive growth pattern, high level of cellular heterogeneity and poor prognosis For decades, Sanger sequencing was the only reliable method available to sequence DNA fragments with high accuracy. However, the throughput was rather low and sequencing of several hundreds of genes was very expensive. For genome wide identification of DNA sequence variations, new sequencing technologies had to be developed. The three next generation sequencing (NGS) systems that are available up to date are based on different principles, including pyrosequencing Searching for novel SNPs and variants in genes associated with glioblastoma, we combined the HybSelect sequence capture technology of febit Finally, we analyzed the identified SNPs for an association with 21 autoantigens, suggested triggering a humoral immune response in glioblastoma patients.6 50 bp paired-end reads per lane. After excluding artifacts as described in Material and Methods, an average of 16.7*106 paired-end reads remained. After mapping the data to the human genome and excluding reads that map to multiple locations, we obtained on average 3.1*106 reads allowing a 308 fold coverage and a target coverage of approximately 87%. We obtained an up to 844 fold enrichment as compared to a random sequencing. An example of a plot with an exonic part of the SART1 gene and a coverage for the four samples is shown in To enrich the genomic DNA for the 132 glioma-associated genes, we applied a microarray based approach as detailed in the Methods section and previously described The method that is based on hybridization of DNA sequences to complementary oligonucleotides, allows the enrichment of DNA sequences of the target genes, but does not necessarily exclude all sequences similar to the target genes. Therefore, we detected also sequences of genes other than the targeted ones. In total we detected 2753 SNPs or Indels in leukocyte DNA of patient A, 2346 in tumor DNA of patient A, 1527 in leukocyte DNA of patient B, and 2356 in tumor DNA of patient B , including 1317 that mapped to the 132 target genes and 4879 that mapped to the remaining 1030 genes. For an in-depth SNP and Indel analysis, the data was remapped using NextGENe . Paired-end reads were mapped against the human genome NCI build 36.1 using NextGENe. atient B . ConsidePerforming a pairwise overlap of the four data sets we found more overlaps between the samples of each patient e.g. tumor versus leukocytes, than between the patients. In detail, we found 19.4% and 25.9% overlap for the comparison of tumor versus leukocytes of patient A and patient B, respectively. The overlaps between the blood samples of the two patients were only 18.5%, and between the tumor samples of the two patients 17.3% .To identify SNPs that might indicate tumor specific genetic changes we searched for SNPs that show a different genotype in the leukocytes as compared to the tumor DNA. We found several SNPs with a coverage above 50 including SNP rs715930 in the gene B4GALNT1. For this SNP that is annotated as ‘C’ in the wild type, we found a heterozygous ‘AC’ status in leukocytes of patient A, and an ‘A’ allele in the tumor of this patient. This might either indicate a homozygous ‘AA’ status or potentially a hemizygous status due to loss of heterozygocity in the tumor. SNP rs12172263 in gene sequence LOC643266 is annotated as ‘G’ the reference genome. Likewise, we found a heterozygous ‘TG’ status in the leukocytes of patient A and a ‘T’ allele in the tumor of this patient. Further examples for a heterozygous status in the leukocytes as compared to a hemizygous or homozygous in the according tumor were found for the SNP rs1805414 mapping in PARP1 gene, SNP rs935037 in PAXIP1 gene and SNP rs962976 in MDM1 gene .To further elucidate a disease association of the identified SNPs, we computed the overlap between the detected SNPs and the 2967 SNPs reported in the genome-wide association study (GWAS) catalog. We found two SNPs, namely rs6265 that was associated with smoking behavior SNP rs660118 that was found homozygous in both blood and tumor of the two patients, was recently associated with breast cancer For verification purposes we carried out Sanger sequencing of the exons containing the four SNPs. We confirmed the “1100” haplotype in all four samples. This haplotype has not been detected with increased frequency in ten blood samples of glioblastoma patients as compared to ten blood samples of healthy controls. However, including the two samples originally used for SNP identification we found an equal distribution of the SNP rs660118 between glioblastoma patients with homozygous minor allele (C) status, heterozygous (G/C) status, and homozygous major allele (G) status, whereas in the healthy controls we found the wildtype allele in 70% of cases, the rare allele in 10% and the heterozygous status in 20% of cases. Thus the SNP rs660118 that is essential for the “1100” haplotype was correlated with glioblastoma. Sanger Traces are shown in Beside the tumor association, we analyzed the identified SNPs for an association with autoantigens. An association between SNPs and immunogenicity of autoantigens was previously suggested We found five SNPs that were homozygous for alleles that were different from the reference sequence. These homozygous alleles were found in the two patients both in blood and tumor. Specifically, rs4715630 and rs4715631 map within the DST gene, rs176924 in the KIF5B gene, rs2301263 in the GPI gene, and rs41427849 in the ZNF594 gene. We also found five SNPs that were homozygous for the minor alleles in three of the four samples. Specifically, rs1800606 maps within the APBB1 gene, rs4283892 and rs4715626 in DST gene, rs10418774 in the ZNF324 gene and rs4764506 in ING4 gene. In four out of these five SNPs, the leukocytes of at least one patient did not show alleles different from the reference sequence . These rWith our approach, we could not completely avoid detecting sequences of genes other than the target genes. This might partially be due to sequence similarities between target and non-target genes, e.g. in gene families. An additional limitation is the small number of samples sequenced. However, even this limited number of samples indicates that additional important SNPs are likely to be found in a larger group of patients.For four glioblastoma samples we performed targeted resequencing that is increasingly recognized as valuable screening tool for an in-depth analysis of genomic regions while avoiding problems attached to storage and handling of a large collection data that result from whole genome sequencing. We detected several hundred SNPs mapping to 132 genes in the glioblastoma samples. For several SNPs we found a heterozygous situation in the leukocyte DNA and a homozygous or hemizygous situation in the tumor DNA of the same patient. Unfortunately cytogenetic information on these specific tumor tissues was not available. In general, glioblastoma are genetically highly heterogeneous with multiple chromosomal aberrations including loss of heterozygosity. Losses of chromosomal material occur frequently for chromosomes 6, 9p, 11p, 19q, 22q For several of the identified SNPs there is evidence for an association with cancer. The SNP rs660118 maps within the SART1 (squamous cell carcinoma antigen recognized by T cells) gene that is also known as tumor-rejection antigen in brain tumors Based on the reported evidence that SART1 is a tumor antigen we reviewed our own data on antigens that are reactive with tumor sera In summary, our results provide evidence that targeted resequencing is well suited to detect single nucleotide polymorphisms that may correlate with disease risks. The approach may be especially applicable in personalized medicine to assess the relevant genotype of a patient at reasonable cost. Moreover, combined with multiplexing and customized SNP array design, targeted resequencing also offers the option to validate SNPs detected by GWAS studies in independent sample cohorts.The major aim of our study was the identification of novel SNPs in genes already associated with glioblastoma using a combination of specific target selection and next generation sequencing. We selected a total of 132 genes including 21 genes associated with an anti-tumor humoral immune response in glioma , 55 genes associated with an immune response in glioma or other brain tumors , and 53 genes shown to be amplified or abnormally expressed in glioblastoma. . 5 µl were controlled on a 2% agarose gel for the correct fragment size distribution. Preparation of the paired end adaptor-ligated gDNA library ready for sequencing on an Illumina/Solexa Genome Analyzer II  was performed according to the manufactures standard protocol. The size fraction of 300–500 bp for gDNA was excised from an agarose gel after the adaptor ligation step and the sample was analyzed by Bioanalyzer 2100 analysis (Agilent), quantified by Nanodrop 1000 UV measurement  and stored in EB buffer at −20°C until use.For four arrays, 6 µg adaptor-ligated gDNA library were dried in a speed-vac together with 64 µL febit hybridization booster mix. The dried pellet was dissolved in 100 µl febit Hybmix-4, heated to 95°C for 5 min and placed on ice. The sample mixture was split into four equal parts, placed into the sample loading station of the Geniom RT Analyzer and automatically injected into the microfluidic channels of the biochip. Sample was denatured within the biochip at 80°C for 10 min and hybridized for 16 h at 42°C with active movement of the sample. After hybridization, each array was automatically washed with 6× SSPE at room temperature and 0.5× SSPE at 45°C. Each array was subsequently washed with SSPE-based febit stringent wash buffers 1 and 2 at room temperature. All protocol steps were carried out in a completely automated fashion by the Geniom RT Analyzer instrument without manual interference.For elution of the enriched samples, arrays were filled with 15 µL of 90% formamide in water each and incubated at 70°C for 30 min. Solution was manually transferred into an reaction tube and dried by vacuum centrifugation in a Speed-Vac at 65°C. After an amplification step according to the Illumina library preparation procedure using primers PE Adapter 1.1 and PE Adapter 2.1 for 30 cycles, the sample was treated like the original library and subjected to a second round of enrichment under the same conditions as before, using a newly synthesized biochip.Eluted samples were subjected to 10 cycles of PCR according to the conditions described in the Illumina library preparation kit using paired end primers and purified by a Qiagen MinElute PCR purification . Quantification of samples was done by the Quant-It Picogreen assay  using the Nanodrop 3300 instrument .Paired-end sequencing on an Illumina Genome Analyzer II was carried out as described in the manufacturer's manual by Prognosys Inc .Paired-end reads (50 bp in length) were first filtered by removing reads with ambiguous nucleotide calls (3 or more N). Reads from File 1 and File 2 of the two paired end sequences were aligned with target genes by using razerS, which is part of SeqAn, an open source C++ library of efficient algorithms and data structures for the analysis of biological sequences. The parameters used were “-gn 1 -f -r -i 94 -rr 100 -m 10” which allows up to 3 mismatches. The output alignment files were matched for each pair of reads: the two reads were mapped to opposite strands and in correct orientation and the length between the two reads was within 100–500 bp. The fold coverage for each base within the probe regions was calculated for unique reads.Called SNPs with coverage of higher 50 have are considered as SNPs with high confidence, SNPs with coverage of smaller 10 reads have been removed from the consideration.Initial data analysis was carried out with Eland. Re-mapping of the raw reads files was additionally done with NextGENe. The default parameters were used for format conversion and the subsequent alignment, allowing up to two mismatches. Subsequent data analysis, including coverage plots, has been carried out using the freely available R 5′-ACTGCGGGGACGGGGT-3′, reverse1-5′-CTTCTCGCCACTGTCTCGCA-3′ (rs660118), respectively: forward2-5′-TCTCTGCAGGAACACCATCA-3′, reverse 2-5′-CGTCCCATCCTTCACACCT-3′ .and carried out Sanger sequencing using the following primers for amplification and sequencing: forward1- Traces have been evaluated with the freely available tool “Mac Sequence View” and alignments have been computed using clustalW.Four SNPs were exemplarily verified with Sanger sequencing. We amplified exon 12 of the SART1 gene containing the four SNPs described by Olson et al. Table S1Complete list of the glioblastoma associated genes incuding selection criteria and summary of the detected SNPs in all four samples.(XLS)Click here for additional data file.Table S2Detected SNPs, homozygous for minor allele in genes triggering humoral autoimmune response in GBM patients.(DOC)Click here for additional data file.


Currently, six commercial whole-genome SNP chips are available for cattle genotyping, produced by two different genotyping platforms. Technical issues need to be addressed to combine data that originates from the different platforms, or different versions of the same array generated by the manufacturer. For example: i) genome coordinates for SNPs may refer to different genome assemblies; ii) reference genome sequences are updated over time changing the positions, or even removing sequences which contain SNPs; iii) not all commercial SNP ID’s are searchable within public databases; iv) SNPs can be coded using different formats and referencing different strands ; v) Due to new information being discovered, higher density chips do not necessarily include all the SNPs present in the lower density chips; and, vi) SNP IDs may not be consistent across chips and platforms. Most researchers and breed associations manage SNP data in real-time and thus require tools to standardise data in a user-friendly manner.Here we present SNPchiMp, a MySQL database linked to an open access web-based interface. Features of this interface include, but are not limited to, the following functions: 1) referencing the SNP mapping information to the latest genome assembly, 2) extraction of information contained in dbSNP for SNPs present in all commercially available bovine chips, and 3) identification of SNPs in common between two or more bovine chips (e.g. for SNP imputation from lower to higher density). In addition, SNPchiMp can retrieve this information on subsets of SNPs, accessing such data either via physical position on a supported assembly, or by a list of SNP IDs, rs or ss identifiers.This tool combines many different sources of information, that otherwise are time consuming to obtain and difficult to integrate. The SNPchiMp not only provides the information in a user-friendly format, but also enables researchers to perform a large number of operations with a few clicks of the mouse. This significantly reduces the time needed to execute the large number of operations required to manage SNP data. SNP) markers distributed across the genome has led to a new approach for genetic studies and applications [The public availability of many thousands of single nucleotide polymorphism : Golden Gate Bovine3K BeadChip (Bovine3k), Infinium BovineLD BeadChip (BovineLD), Infinium BovineSNP50 v.1 BeadChip (BovineSNP50v.1), Infinium BovineSNP50 v.2 BeadChip (BovineSNP50v.2), Infinium BovineHD BeadChip (BovineHD), containing 2,900, 6,909, 54,001, 54,609 and 777,962 SNPs, respectively. Affymetrix has recently produced the Axiom Genome-Wide BOS 1 Bovine Array, containing 648,875 SNP probes (Axiom Bos 1). Considering the different chips and densities, and that data is routinely exchanged between projects and countries [Ambiguities in retrieving SNP information hinder the efficient use of data from different SNP panels in genomic applications. Researchers and breed associations routinely need to address several problems in order to manage data from different platforms, or even different versions of the same assay. For example, original reference genome sequences are updated over time, thus requiring updates to the genome coordinates for the SNPs on the chip(s); commercial SNP IDs might not be consistent within and across platforms; or SNP alleles might be coded using different formats and referencing strands.Here we present SNPchiMp, a MySQL database linked to an open access web-based interface to manage bovine and eventually multi-species SNP chip data in a straightforward, flexible, consistent and user-friendly manner.http://www.ncbi.nlm.nih.gov/snp/) from June 2012 onward were downloaded to a local server (builds 136 and 137). The 29 bovine autosomal, sexual, mitochondrial, and unassigned chromosomes files were downloaded (~120 Gb). Second, Illumina SNP chip data were collected from manifest files of the Illumina GenomeStudio® software, and from data obtained directly from the manufacturer. The vast majority of this information is available directly from the Illumina website, “Products and Support files” section (http://support.illumina.com/array/downloads.ilmn). A total of 788,048 unique commercial SNP IDs (and linked information) were stored in the database, corresponding to all SNP IDs present in the five commercially available SNP chips plus an additional 4,275 cross-reference SNP IDs (e.g. same SNPs with different IDs). Associations between commercial SNP IDs and dbSNP rs/ss IDs for Illumina SNPs were first obtained by merging SNP IDs against the “Submitter ID” section in the previously downloaded dbSNP database. To confirm such associations, random sampled sequences were blasted against UMD3.1 assembly, and re-checked independently. The third source of information, the Affymetrix Axiom® Genome-Wide BOS 1 Array Plate (version 33), was obtained from the Affymetrix website support section (http://www.affymetrix.com/support/support_result.affx). This file contained 648,875 SNP probes, including 360,274 associations between SNP IDs and official rs IDs, thus no other integration of information was needed.SNP data were collected from three different sources. First, all bovine dbSNP database builds (http://www.jruby.org) was used as Ruby language interpreter and Ruby on Rails (RoR) version 3.2.11 (http://www.rubyonrails.org) was chosen as a web framework for a rapid development of the web interface and to handle the connection with the database. Finally, Torque Box (http://www.torquebox.org) was used as the application server to deploy the web service.A MySQL relational database management system (RDBMS) was used to build and manage the underlying database in the SNPchiMp tool. The entity-relationship (E/R) diagram of the database is presented in Figure The base structure of the graphical interface was built using RoR base commands. A graphical template was designed using HTML and CSS languages, creating a common graphical layout for all the web pages in the graphical user interface (GUI). End users are able to interrogate the database in two dedicated sections: “Download” and “Browse”. Queries of the underlying database are facilitated through option selections using “check”, “select” and “input” boxes. Once the end user has selected the required options, the interrogation of the database is divided into two steps. First, a preview page displays the first 100 elements of the query. Only then, a second query allows the required data to be downloaded if the user selects the corresponding options. All files to be downloaded are automatically compressed, using a “tar” (i.e. tape archive) compression tool, in order to reduce download time.The SNPchiMp tool has six user menus: “Home”, “Manual”, “Download”, “Browse”, “About us” and “FAQs”. “Home” provides general information about the tool, “Manual”, contains a downloadable version of the manual, “About us” contains a short description of the people involved in the development of this tool, and “FAQs” are a list of frequently asked questions to solve specific issues not covered in the manual.In the “Download” menu the user can download the information available in the database for one or more SNP chips. This menu is divided into two further sections: “Detailed SNP information” and “Across SNPchip table”. One of the main features of the “Detailed SNP information” menu allows conversion of mapping information (e.g. chromosome and position in base pairs) for one or more SNP chips to any of the available genome assemblies. Currently available assemblies are: native platform ; BTAU4.2 (map information extracted from dbSNP build 136); UMD3.1 and BTAU4.6 (map information extracted from dbSNP build 137). A “dummy” set of coordinates was assigned (e.g. chromosome 99 and position 0), for those loci where there was no commercial SNP ID - rs ID association , which contain specific SNP information. It should be noted that the information displayed in the external pages might be related to different genome assemblies than the version chosen by the user within the SNPchiMp tool. All the information from within SNPchiMP can be downloaded.All the data used for building this tool are publicly available. Although this database includes information from commercial SNP chip platforms, these data can be freely obtained from the manufacturers publicly available web sites. The information in the database is not updated in real-time, but an in-house bioinformatics pipeline has been developed to ensure quick updates of the data when new releases of dbSNP and commercial chips become available.The database was built using MySQL RDBMS as this is one of the most reliable open source database systems. Optimisation strategies and best practice translation rules were followed in order to enhance query performance of the web based GUI. The choice of JVM, the industry standard platform for web and applications development, combined with the flexibility of JRuby and Torque Box facilitated improvement in the performance of the SNPchiMp tool service (e.g. client–server request-answer time). RoR was chosen among other available options (e.g. PHP) as it allows fast development cycles and integration between Ruby code and html/javascript, increasing the flexibility of current and future integration of the database with other bioinformatic tools.User interrogation of the database is carried out by selecting options from “check”, “select” or “input” boxes. This solution was chosen to enhance user-friendliness of the SNPchiMp tool. For this same reason, a three-step guided selection of options is displayed to the end user in the “Download” menu. This extra feature was added for clarity and to avoid input errors from the user. In order to avoid excessive downloads of large datasets, we employed a preview step of the first 100 elements of the query, only then allowing a second full query for download. This was an inexpensive solution as the wall-time required for previewing the data is negligible.Ease of updating the map information for multiple SNP chips is one of the main features of this tool and allows users to readily update their chromosome and position information to any of the desired assemblies. Although this might seem trivial, it is in fact not a simple process for any of the non-model organisms and was one of the factors motivating the creation of this resource. In addition, this tool will be valuable to update SNP information as new genome assemblies are released. A centralized resource such as the SNPchiMp allows for the remapping, integration and updating of information to be carried out once and then easily accessed by the entire community. This resource will enable the community to use a standardized set of SNP information and minimize duplication of effort between labs. The other options, such as commercial SNP IDs and rs/ss IDs linked information can be used, for example, to merge genotypes of SNP chips of the same density but coded in different formats. This information is important for the integration (e.g. imputation) of high density SNP chip data to sequence data. The across SNP chip information was included to help users perform imputation of SNP data from lower to higher density panels and across platforms. In addition, combining this information with the most recent assembly (e.g. using the “Detailed SNP information” sub-section) can increase the accuracy of the imputation by reducing map errors.The “Browse” menu allows researchers to browse genomic regions near specified SNPs, for example, significant SNPs from genome-wide association or selection sweep analyses. Although there is no direct link between this database and other publicly available databases, external links have been added in the preview menu to allow browsing for extra information using external resources.Dealing with whole genome SNP information currently requires that large amount of information are considered, coming from many different sources. The centralized database described herein gathers all this data and will hence reduce errors when integrating genomic information. The SNPChiMp database contains all commercial SNP panel information and rs ID (and ss ID) associations for all SNP chips available. In addition, the information contained in SNPchiMp can be integrated with other online databases, including those specific to livestock, such as SNAT  .The current implementation of SNPchiMp was focused on integrating SNP information for cattle due to the number of assays currently available and multiple versions of genome assemblies. However, we recognized the utility of this tool across species and have built it in such a manner to allow inclusion of virtually any species with the same underlying data available. Both Illumina and Affymetrix SNP chip data are available for other species, including livestock, fish and companion animals; therefore, the database will be extended for these species in the future.http://bioinformatics.tecnoparco.org/SNPchimp. This tool is completely open source and multiplatform (accessible from any browser in any operating system). The information in this database is not updated in real-time. Considering that bovine SNP data is routinely updated, the Authors rely on feedback from the community to remain current.SNPchiMp is freely available at SNP: Single nucleotide polymorphism; Bovine3k: Golden gate Bovine3K BeadChip; BovineLD: Illumina Infinium BovineLD BeadChip; BovineSNP50v.1: Illumina Infinium BovineSNP50 v.1 BeadChip; BovineSNP50v.2: Illumina infinium BovineSNP50 v.2 BeadChip; BovineHD: Illumina Infinium BovineHD BeadChip; Axiom Bos 1: Affymetrix Genome-Wide BOS 1 Bovine Array; RDBMS: Relational database management system; E/R: Entity-relationship; JVM: Java virtual machine; RoR: Ruby on Rails; GUI: Graphical user interface.Authors declare that they have no competing interests.ELN, FS, AS conceived the study. ELN and MP built the database and the web-tool. RDS, CL, AP, FB provided the producer’s data, helped in the revision of the manuscript and helped in the improvement of functionality of the tool. RDS re-checked the database independently. ELN drafted the manuscript. All authors read and approved the final manuscript.


Populus, because this genus has a relatively small genome and is emerging as a target for population genomic studies. We estimate the proportions and patterns of genomic sampling by examining the Populus trichocarpa genome , and demonstrate a pronounced bias towards coding regions when using the methylation-sensitive ApeKI restriction enzyme in this species. Using population-level data from a closely related species (P. tremuloides), we also investigate various approaches for filtering GBS data to retain high-depth, informative SNPs that can be used for population genetic analyses. We find a data filter that includes the designation of ambiguous alleles resulted in metrics of population structure and Hardy-Weinberg equilibrium that were most consistent with previous studies of the same populations based on other genetic markers. Analyses of the filtered data  also resulted in patterns of heterozygosity and population structure similar to a previous study using microsatellites. Our application demonstrates that technically and analytically simple approaches can readily be developed for population genomics of natural populations.Continuing advances in nucleotide sequencing technology are inspiring a suite of genomic approaches in studies of natural populations. Researchers are faced with data management and analytical scales that are increasing by orders of magnitude. With such dramatic advances comes a need to understand biases and error rates, which can be propagated and magnified in large-scale data acquisition and processing. Here we assess genomic sampling biases and the effects of various population-level data filtering strategies in a genotyping-by-sequencing (GBS) protocol. We focus on data from two species of  Genetic studies of natural populations have traditionally relied on low numbers of loci to make inferences about evolutionary and demographic processes at various temporal and spatial scales. However, recent advances in sequencing chemistry, sequencing platforms, data storage, and computational processing are enabling the efficient collection of data from thousands to hundreds of thousands of loci from multiple individuals One of the most exciting developments in population genomics is the development of various reduced-representation protocols, collectively referred to as Genotyping-by-Sequencing (GBS), which allow sequencing of a subset of the genome through selective amplification of restriction fragments ApeKI). The Elshire et al. ApeKI in a eudicot. We performed this analysis using tissue from the Populus trichocarpa clone ‘Nisqually-1’; the individual clone used to assemble the first complete annotated genome in a forest tree (genome size 485 million bp) ApeKI following the Elshire et al. One potentially problematic issue with GBS analyses is the large proportion of repetitive elements in complex plant and animal genomes. Sequences from repetitive regions can consume sequencing capacity without increasing information content in population analyses. The GBS approach described by Elshire et al. Another issue with GBS analyses is accounting for variation in coverage depth and sequence read quality in individual genotyping and population allele frequency estimation Populus tremuloides) because of its close relationship to P. trichocarpaPopulus tremuloides consists of two major genetic clusters: one in Canada and the northern U.S. (N cluster) and another in the western U.S. (SW cluster) To assess the impact of various data filtering strategies, we chose trembling aspen , HSPQ , MI , SFQ , and two populations from the SW cluster: KFO , and WWA  For assessment of gene sampling in GBS, we obtained leaf material from a clone of Nisqually-1, the P. trichocarpa  and P. tremuloides samples, genomic libraries were prepared following Elshire et al. ApeKI and custom adapters and barcodes. For P. trichocarpa, 16 uniquely barcoded GBS genomic libraries (replicates) were constructed using the same DNA extraction. For P. tremuloides, uniquely barcoded ApeKI libraries consisted of 152 samples representing 107 individuals. Forty-five of the samples were replicates: 18 individuals run in duplicate, and three individuals run in triplicate, all from common DNA extractions. Illumina high-throughput sequencing was performed at the Vanderbilt University Medical Center, on an Illumina HiSeq 2000 using 100 bp single-end indexing runs. The samples were sequenced across two Illumina lanes. Base calling was performed in Casava v1.8 .For both P. tremuloides samples, index (barcode) deconvolution was done using a custom Perl script to sort each of the GBS barcoded samples into separate fastq files. The individual raw read files (fastq) were imported into CLC Genomics Workbench  and further processed for quality and length . The enzyme recognition sequence was removed from the 5′ end of each raw sequence read after quality trimming.For both the Nisqually-1 and P. trichocarpa v2 assembly  using CLC Genomics Workbench . The maximum gap and mismatch count were set to 2, and insertion and deletion costs were set to 3, with a minimum contig length of 200 bp. Length fraction and similarity parameters were set to 0.6 and 0.8, respectively. We used the above assembly to determine whether each GBS read was or was not within an annotated gene on the P. trichocarpa genome.Each of the 16 Nisqually-1 replicated libraries, consisting of single-end trimmed reads, was individually aligned to the ApeKI sites known to occur in each of the genomic scaffolds, we conducted a chi-square test comparing the number of observed vs. expected GBS loci. We calculated the expected number of GBS loci in each scaffold by multiplying the total in silico number of ApeKI-containing genes  by the overall proportion of these genes that was sampled by GBS . Only scaffolds containing more than five ApeK1 sites  were used in this analysis.To determine whether observed GBS loci occurred randomly across the P. trichocarpa  replicates, and counted the accumulating total number of sites sampled. We randomly ordered the replicates and averaged the effect over 1000 permutations. We acknowledge that accumulating replicates in this fashion is not technically equivalent to reducing the level of multiplexing in a single run, given that different sequencing runs have different error profiles. Our goal here was to estimate the effect of multiplexing level using a proxy.To explore the effect of increasing read depth on genome coverage, we sequentially added individual P. tremuloides samples, single nucleotide polymorphism (SNP) genotypes were assigned using the UNEAK  filter P. tremuloides. Using Illumina sequence reads, the network filter in UNEAK trimmed reads to 64 bp to minimize the effects of sequencing error, and enabled efficient storage of data in bit format. Identical reads are then collated as haplotypes (referred to as tags by Lu) For Prior to the application of alternative filtering strategies, we applied two functions that removed all SNPs (rows) and then samples (columns) containing 90% or more ‘N’ values . These Ns represent individuals where the allele cannot be called from the sequence reads. This is either because no read is available at this site  or the sequence quality is too low to call. Because we are attempting to retrieve SNPS for population genetic analysis, we apply this early filter to remove loci and individuals that contain very low levels of information prior to further filtering.https://github.com/schimar/gbs) to read the UNEAK output files and to apply three different filters , whereas ‘GG’ versus ‘AA’ is two differences (a double mismatch). Ambiguous alleles (coded as ‘?’) and unknown (‘N’) alleles were not included as mismatches. We report mismatch rates as the proportion of alleles differing among all alleles in the matrix . We anticipated that low read depths would contribute to mismatches by increasing allelic dropout and by increasing error rates in base calling. To test this hypothesis we compared read depths between replicated pairs in which one sample was a heterozygote and the other was a homozygote  and assessed the read depth difference between each pair using a Wilcoxon signed rank test. Further, we tested the read depth difference between mismatched homozygous samples and matched homozygous samples, using a Mann-Whitney test.For population analyses, we represented each replicated individual once, choosing the individual with the greatest overall read depth. For the unfiltered data, as well as those from all 3 filters, we used the Python HWE exact test ST values among populations, along with mean observed heterozygosity (Ho) and estimated heterozygosity (Hs) for each population, averaged across populations, using the R packages adegenet ST in SNPs and msat loci from the same populations.Using the output from each filter, we calculated Fhttp://dx.doi.org/10.5061/dryad.2cs4g.SNP data for this project are publicly available, in hapmap format, from the Dryad repository: P. trichocarpa genome to study the genomic patterns and distribution of regions captured by the GBS technique. There are 462,987 ApeKI in silico restriction sites in the P. trichocarpa  genome. Of those, 212,376 (45.9%) are within annotated gene boundaries. By contrast, in our 16 GBS replicates of P. trichocarpa, sequence reads were recovered from a mean of 125,022 ApeKI sites (loci) per replicate (27% of in silico restriction sites) , of which 87,202 (69.8%)  were within annotated genes. Thus, our use of ApeKI consistently captured loci within annotated genes more frequently than one would expect by chance alone (P<<0.005 according to a simple contingency test).We used the P. trichocarpa genome were represented in the GBS library. The number of annotated genes within genomic regions (scaffolds) sampled by GBS was not significantly different from a random subset of the genes present (P = 0.61), indicating that GBS sampled these regions evenly.Pooling results from all 16 replicates, we found that 84% of all annotated genes in the P. trichocarpa replicates, we detected 334,158 loci. On average, 26% of these loci were detected in only one of the 16 replicates, and 9.6% were detected in all 16 replicates. The cumulative distribution of locus numbers over replicates , and the mean number of reads retained by UNEAK per sample was 220,872 (s.e. 6674). After removing samples and SNPs with more than 90% ‘N’, the dataset consisted of 108,530 SNPs representing 101 individuals  and 39 replicates. This dataset was subjected to the three different filters . The MAFF dataset retained all 140 samples and 74,159 SNPs, whereas TF and AF datasets each retained 140 samples and 27,910 SNPs. The total numbers of ‘NN’ (unassigned) genotypes for each filter was 35628 (no filter and MAFF) and 11,929 (AF and TF).The proportions of homozygous and heterozygous genotypes at each SNP are shown for each filter in Mismatches across replicated samples at each SNP were calculated for each filtered data set . In the The distribution of population- and SNP-specific HWE probabilities, averaged across populations, is presented for each filtering approach . The difST value between pairs of populations was greater between previously described genetic groups (N and SW clusters) than within each of these groups. These patterns were consistent with results from the same populations using 8 nuclear microsatellite loci. A ten-fold difference in pairwise FST values between vs. within major groups was noted with microsatellite data, the unfiltered, the AF, and MAFF data , for example, has the broadest geographic range of any North American tree species P. tremuloides lends itself to studies of local adaptation and evolutionary history, tractable by GBS.The genus Populus species: the bias toward coding regions when using GBS with a methylation-sensitive restriction enzyme (in P. trichocarpa), and the effect of various data filtering strategies on population data (in P. tremuloides).Here we explored two aspects of using GBS in P. trichocarpa genome , we found that ApeKI increases sampling of annotated genes relative to that expected by chance alone. Previous studies have shown that ApeKI preferentially cuts at nonmethylated sites Populus genome is relatively small (410 Mb) ApeKI seems to be an ideal choice. Concomitantly, if questions require information about all genomic regions, irrespective of coding status, then a different endonuclease should be used.When we examined our data relative to the fully sequenced P. trichocarpa genome assembly suggests that the regions captured by the GBS technique are randomly distributed across the scaffolds, although the large size of many of the scaffolds may obscure some within-scaffold clustering. Thus, we have good reason to believe that SNP loci captured by GBS (using ApeKI) provide a representative sample of the genome.The distribution of GBS loci (regardless of the presence of SNPs) of the P. trichocarpa genome assembly ApeKI sites were captured by GBS. This value is 8% when pooling all 16 of our P. trichocarpa replicates, and 3% for any one replicate. These percentages would drop correspondingly for larger genomes, and they can be modified by controlling read depth via the level of multiplexing.Of the approximately 410 Mb in the P. tremuloides, the method of filtering had a large effect on some downstream analyses, particularly those influenced by homozygosity. Determining which filters are retaining useful data and which filters are generating artifacts can be difficult. Fortunately, we have independent data for P. tremuloides indicating that populations are generally in HWE In any research project that entails a sampling of variables, it is ideal to retain those variables that are informative for the questions at hand. In the case of population genetics based on allele frequency estimates, informative loci are those for which genotypes can be assigned most accurately. Thus, our goal in filtering was to retain as many of those SNP loci as possible. In our GBS study of P. tremuloides tended to show only low levels of differentiation  across SNPs, where maximizing loci is critical. However, for basic questions of population structure, filtering to retain informative data might be a more efficient strategy. Thus, depending on the organism and study question, it may well be worth assessing the optimal level of multiplexing for GBS, to control read depth and number of SNPs. Note that even for the small Populus genome, it appears that we sampled a small proportion of available SNPs with our level of multiplexing. Additional SNPs could be sampled if we reduced the level of multiplexing. However, the approach to an asymptote  of filtering systems. In general, we expect that filters that are more stringent will retain fewer usable SNPs, as we noted in our results . Buerklesymptote  suggestsApeKI results in a bias for coding regions, even in a species that is not closely related to the species in which the method was first developed (e.g. Zea mays) Several versions of NGS applications are becoming available for the study of population genomics, along with a variety of approaches to analyzing the resulting large-scale data. As these new techniques emerge, assessment of the impacts of various methods for sample processing, reduction and selection of genome representation, and compiling sequence data into population-scale genotypic data are becoming an important developmental phase