Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read – or, more likely, none – from a true singleton.Performing high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 : the approach takes into account sequencing errors and allows users to choose different priors. We also set up a pipeline which can simulate the coalescence process giving rise to the SNPs, the pooling procedure and the sequencing. We used it to compare the performance of snape to that of other packages.To improve upon existing theory and software packages, we have developed a Bayesian approach for minor allele frequency (MAF) computation and SNP calling in pools  artificial genomes and computed the effect of a pooled sequencing protocol, followed by SNP calling. In this setting, snape has better power and False Discovery Rate (FDR) than the comparable packages samtools, PoPoolation, Varscan : for N = 50 chromosomes, snape has power ≈ 35%and FDR ≈ 2.5%. snape is available athttp://code.google.com/p/snape-pooled/ (source code and precompiled binaries).We present a software which helps in calling SNPs in pooled samples: it has good power while retaining a low false discovery rate (FDR). The method also provides the posterior probability that a SNP is segregating and the full posterior distribution of  For large samples, the frequency could be any number between 0 and 1. For this reason SNP calling methods for individual sequencing can detect only segregating sites with intermediate frequency and cannot be immediately extended to pooled samples without losing rare alleles. Rare alleles, though, are particularly important in population genetics because they represent most of the variability in natural populations. In fact, under the standard neutral model, singletons  and rare alleles  are the most frequent variants because P(f) ∝ 1/f. Moreover, the relative fraction of rare variants increases with read depth and sample size.High throughput sequencing on samples pooled from different individuals is an efficient strategy to infer genetic variation in a population. In principle, single nucleotide polymorphisms (SNPs) and indels can be effectively detected and their frequency can be estimated with a variance not much higher than for individual sequencing-3. HowevThe detection of rare variants is also strongly affected by sequencing errors. While the impact of errors in individual sequencing can be reduced by setting a restriction on the minimum number of reads per allele, this would have a strong and undesired effect in pools because alleles at low frequency in the population often also appear at low frequency among the reads.P(f) that is (explicitly or implicitly) specified. While maximum entropy (i.e. complete ignorance) would favor a flat prior, the standard population genetics result for neutral sites and low mutation rate is P(f) = θ/f [θ is the genetic variability. Different choices could result in different calls in particular for low frequency SNPs. The need for a prior P(f) points to Bayesian methods to reconstruct the frequency spectrum. In this note we present a method for Bayesian estimation of posterior frequency distribution for SNPs in pooled samples. This method addresses the issues described above, and it can be naturally used to call SNPs from the posterior probability that alleles are actually segregating for each site.Furthermore, the process used to generate the reads from pooled samples has a critical effect, that is allele frequencies among the reads can be different from actual frequencies among individuals in the sample. If we assume that reads are randomly extracted from the sampled individuals, read counts will follow a binomial distribution with mean equal to the allele frequency in the sample times the read depth. This means that an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read – or none – from a true singleton. Finally, since the frequency of each allele is unknown, SNP calling would depend on the prior for the frequency spectrum f) = θ/f  where θ C to indicate the read depth, f  for the true alternative allele frequency, and nA for the number of symbols alternative to the reference allele in the pileup. Moreover N is the number of different chromosomes in the pool and θ is the nucleotide diversity. Note that N obviously needs not coincide with the number of individuals in the pool: for example when considering autosomes in diploid populations, the number of chromosomes is twice the number of individuals. There could be also experimental setups where N is not known a priori, however in these situations N is typically large and therefore its exact value is not relevant for the model in this section. In general, if N is not precisely known, a rough estimate of it should suffice for our purposes. The quantity we want to compute is P that is, the posterior distribution of allele frequency. From there, many quantities of interest  can be obtained. Now, via Bayes one has Our model presents two interesting, novel aspects when it comes to pooled SNP calling: it takes into account sequencing errors, and allows the user to specify different priors. We explain below both these features, but first we introduce the basic formulae here. In what follows we will use P(nA ∣ f) can be written as follows: nA depends on the probability of including in the pool k alleles with frequency f  in the population and on the probability of those alleles getting sequenced once they are in the pool .where, in the simplifying assumption that there are no sequencing errors,Ã which means that we observe “A” as an output of the sequencing machine, whereas A means that the symbol is actually present on the genome in the same position. Here we have effectively only two symbols to account for, those which coincide with the reference genome (“R”) and those which belong to alternative alleles (“A”). To each letter generated by the sequencing machine there is associated a quality score , i.e. a character with ASCII code 33 to 126. This represents the probability that a particular base has been wrongly sequenced, and can be translated to numbers using the Phred scale asc is the ASCII code. For each class of symbols we consider in our model  we compute the geometric mean of the respective error probabilities as they appear in the pileup, and we call it εR . Hence we translate the sequencing errors into probabilities as follows: A conspicuous feature of high throughput sequencing is the presence of a non-negligible error rate, hence we must allow for a difference between the nucleotide we observe as a result of the measurement and what is really present on the genome. To this purpose we introduce the notation Notice that once we set α wherep so that it allows for sequencing errors : we call this new probabilityfrom which we can inferεA,εR. But many of these errors can be filtered out at an earlier stage. PCR duplicates are typically eliminated just after mapping; variants which are observed on one strand only  can be excluded from the pileup; the precision of the mapping can also be refined before using snape. We focus here on the problem of how to deal with the pooling because the error filtering is tackled by other packages which can easily be used together with snape.We are aware of the fact that PHRED scores are useful only up to a point, and other kinds of errors can happen while sequencing which are not described by P(nA ∣ f) taking into account sequencing errors, we now focus on the choice of prior for the allele frequencies, P(f∣θ). First, we discuss the distribution of the frequencies of segregating alleles i.e. those which satisfy the condition 0 < f < 1. If we assume complete ignorance about allele frequencies, a flat prior P(f∣θ) = 1 is a possible choice. However, it is well known that frequency spectra from real populations exhibit an excess of rare alleles, due to the fact that new mutations are born at low frequency and only a few reach intermediate frequencies before going to fixation or extinction. To account for this effect, we use some results from population genetics. The site frequency spectrum under the standard neutral model  is equal to θ/f[θ is the nucleotide variability in the population.Having written al to θ/f-8, wherei.e. knowing which is the ancestral and derived allele). An informative prior can also be written in the folded case i.e. when the identity of the ancestral allele is not known : in this case, it will have to be invariant with respect to the transformation f → 1 − f. See Tablef = 0,1) is different since these frequencies correspond to fixed alleles in the population, i.e. to absorbing states of the dynamics of mutations and substitutions. The prior probability of f = 1 is naturally given by the genetic differentiation D between the outgroup sequence and the population studied, while the probability for f = 0 can be obtained by requiring the sum of all probabilities to be 1. Note that the continuum prior 1/f is improper since its integral diverges logarithmically; however we discretize the allele frequencies in the population as multiples of a minimum nonzero frequency 1/Nd, so the discretized priors and posteriors that we use are well defined. P(nA ∣ f) is computed for f  taking values in 0,0.01,…,0.99,1.We will call this the informative prior with unfolded spectrum  SNP data along a 1 Mb long DNA stretch for a single population with varying number of individuals  assuming nucleotide diversity 0.0005 and scaled recombination rate 0.0005 per site. For each resulting haplotype, the program ART[X was simulated for the whole pool in all cases, and reads were aligned with BWA[X and 40X. We compared Varscan[PoPoolation[samtools pileup[samtools mpileup[snape with flat and informative priors. In Varscan, behaviour depended largely on the significance level used : at low P-values power was very low, whereas at P-value 1, FDR was very high. Here we chose a P-value of 0.1 as a compromise. In samtools pileup we retained SNPs with quality >20, and samtools mpileup was run with default options. snape was run with flat and informative priors with options divergence 0.01, prior nucleotide diversity 0.001 and folded spectrum. We retained SNPs with posterior probability of segregation > 0.9 . Power was computed as the proportion of true SNPs in the population  located within regions of appropriate depth that were correctly recovered. False Discovery Rate (FDR) was obtained as the proportion of SNP calls that were incorrect. A total of 100 replicates per case were simulated, and average power and FDR were plotted. Besides, we also plotted power and FDR as functions of actual depth per site, and of minor allele frequency (MAF).In order to test the performance of our method, we developed a pipeline. A 1 Mb sequence obtained by randomly sampling nucleotides for each position was used both as the ancestral sequence of the population simulated by ms and as twith BWA. We usedd Varscan, PoPoolaPoolation, samtoolls pileup, samtools mpileup, and snaX read depth used in the simulations described above, and plotted the resulting folded SFS excluding non-polymorphic sites. To compare the performance of snape and samtools in recovering the SFS, we estimated the SFS obtained by snape and samtools for the same set of simulations. For each software, the SFS was calculated using the SNPs identified by each software that were covered by exactly 20 reads, and taking as estimate for the frequency of each SNP the raw frequency of reads carrying the alternative allele. We restricted the estimate at depth 20X simply for estimates of SFS to be comparable. Note that the interpretation of SFS is much more complicated if a mixture of depths is analyzed, e.g., for depth 4X the folded frequency can only be 0.25 and 0.50, whereas the range is smoother at higher depths. While the approach of using raw read frequency as the estimate of allele frequency is not optimal, it allows us to compare easily samtools and snape. Note that, in contrast to snape, samtools does not output the posterior distribution of allele frequency f when using pooled samples.Given that the SNP calling process produces a bias against alleles found at low frequencies, we wished to study its effect on the site frequency spectrum (SFS). This is important because most real SNPs will be singletons or low frequency sites. We performed 100 coalescent simulations using the same settings as above with 100 chromosomes. For each simulation we subsampled, with replacement, 20 chromosomes in order to mimic the 20Average power and FDR are shown in FigurePoPoolation exhibits the highest power overall, however this comes at the cost of a very high FDR, a behaviour which is not unexpected as this software was not conceived as a SNP caller . Other than PoPoolation, snape exhibited the largest power, whereas the samtools mpileup function turned out to perform much better than the deprecated pileup function. Adding the prior information on the site frequency spectrum improved power by ≈ 15% in snape while not affecting FDR. This increase in power was observed across the range of parameters considered.N, the number of chromosomes in the pool. This occurs because the number of SNPs increases as well with N , and the increase is proportional to Ewen’s constantNote that power decreases with Two main factors affect the accuracy of SNP calling in pools: depth and minimum allele frequency, although their effect varied according to the algorithm used Figure.PoPoolation’s FDR increased dramatically with depth because, as mentioned, it is not conceived as a SNP caller and does not correct properly for sequencing errors. For the rest of the algorithms, FDR did not depend on depth. In contrast, power increased with depth, although it reached a plateau after 30X approximately except in PoPoolation and Varscan.snape performs better than other SNP callers (when keeping in account both power and FDR). Note that power of different methods tend to converge at intermediate frequencies, simply because an intermediate allele frequency is equivalent to that in a diploid heterozygous individual, and SNP callers usually assume diploid individuals. For instance, for N = 20,0.96 and 0.95 of SNPs for MAF > 0.40 are correctly called with snape and mpileup, respectively. For SNPs with MAF < 0.15, those numbers become 0.22 and 0.14 respectively.For what regards the influence of allele frequencies, it is precisely at low frequency that Two effects are worth mentioning : the reduction in power consequent to pooling, and what happens if samples are represented unevenly in the pool. For what regards the first, simple theoretical analyses show that the site frequency spectrum is highly distorted for moderate or low depths (< 20), even if SNP calling were perfect. Pérez-Enciso and Ferretti also shoe.g., the extreme case of a pool containing only contributions from 2-3 chromosomes: while rare alleles remain rare, frequent SNPs will be detected even at higher power than in a balanced pool. On the other hand, the mean allele frequency is the same as for balanced pools, although the variance in the estimation of population allele frequencies will increase. This increase in variance cannot be captured by any available method. FigureN = 50). The figure confirms previous arguments whereby average power and FDR remain approximately constant, whereas variance increases slightly, as can be seen from the wider distribution of power values than that of balanced pooling.When it comes to the second concern, it has to be noted that it is difficult to develop methods that account for uneven sampling in pools without knowing the actual distribution of individual contributions. Besides, there is no direct way to detect it from the data without extra assumptions about the demographic / evolutionary pattern of the data . Nevertheless, the really important question is: how strong is the impact of uneven sampling on the method? SNP calling should not be strongly affected because unequal sampling of chromosomes often results in a shift towards intermediate frequencies. Consider, The frequency spectra of SNPs called with informative snape and mpileup approaches are shown in Figuresnape obtains a less biased spectrum than other methods because a higher percentage of rare allele SNPs is called. The bias towards high MAF sites decreased if we lowered the posterior probability threshold for a SNP to be called , P = 0.50 instead of P = 0.90, but at the price of increasing FDR (results not shown). All in all, missing low MAF SNPs is inherent to all methods but the approach proposed here performs better than standard tools. Also, snape computes the complete posterior probability of the allele frequency f , which is of interest to develop new statistics that consider the whole uncertainty on f , rather than point estimates.The true spectrum is the black line. As expected from previous figures, which show a reduced power at low frequency alleles, singletons and low frequency alleles are clearly under represented with both methods. Yet, snape-pooled that reads a file in pileup (http://samtools.sourceforge.net/pileup.shtml) format as input and a number of command line options specifying the total number of chromosomes being sequenced, θ , D , the prior type (which can be flat or informative) and whether the reference allele can be taken as ancestral one or not . The output file contains P(f∣nA), together with other useful indicators .We developed a software called f  for every SNP. This should allow us to develop more reliable tests based on site frequency spectrum compared to those that merely employ point estimates.Using standard tools for calling SNPs in pools is not appropriate because many true variants will be confounded with sequencing errors and therefore will not be called. Here, we present a Bayesian method that overcomes this difficulty while retaining a low FDR. It also provides the posterior probability that a SNP is segregating and the full binned posterior distribution of http://code.google.com/p/snape-pooled/.The software (source code included) is available atThe authors declare that they have no competing interests.ER, LF, MP developed the mathematical method. ER wrote the software. BN wrote the simulation pipeline. AE and BN tested the software and ran the comparison against other packages. SH took part in discussions and testing. All authors read and approved the final manuscript.


ApeKI for GBS library preparation and sequencing on an Illumina GAIIx machine, we obtained 5.5 M reads and these were processed using our pipeline. A total of 10,120 high quality SNPs were obtained and the distribution of these SNPs mirrored closely the distribution of gene-rich regions in the soybean genome. A total of 39.5% of the SNPs were present in genic regions and 52.5% of these were located in the coding sequence. Validation of over 400 genotypes at a set of randomly selected SNPs using Sanger sequencing showed a 98% success rate. We then explored the use of selective primers to achieve a greater complexity reduction during GBS library preparation. The number of SNP calls could be increased by almost 40% and their depth of coverage was more than doubled, thus opening the door to an increase in the throughput and a significant decrease in the per sample cost. The approach to obtain high quality SNPs developed here will be helpful for marker assisted genomics as well as assessment of available genetic resources for effective utilisation in a wide number of species.Highly parallel SNP genotyping platforms have been developed for some important crop species, but these platforms typically carry a high cost per sample for first-time or small-scale users. In contrast, recently developed genotyping by sequencing (GBS) approaches offer a highly cost effective alternative for simultaneous SNP discovery and genotyping. In the present investigation, we have explored the use of GBS in soybean. In addition to developing a novel analysis pipeline to call SNPs and indels from the resulting sequence reads, we have devised a modified library preparation protocol to alter the degree of complexity reduction. We used a set of eight diverse soybean genotypes to conduct a pilot scale test of the protocol and pipeline. Using  Molecular markers are extremely useful in plant as well as animal genetics and genomics. Markers are prerequisite for mapping and tagging of genes/quantitative trait loci (QTLs), segregation analysis, genetic diagnosis, forensic examination, phylogenetic analysis, and numerous molecular biology applications PstI/MspI) that provides a greater degree of complexity reduction than the original protocol using ApeKI High-throughput next generation sequencing (NGS) technologies have opened the way to novel approaches in this area because of their capacity to produce sequence information on an unparalleled scale compared to Sanger sequencing ApeKI enzyme for GBS library preparation in soybean and describe a novel pipeline for calling SNPs starting from the sequence reads. Following an in-depth characterization of the SNPs obtained via this pipeline, we describe a modified library preparation protocol in which selective amplification is used to increase both the number of SNPs called and their depth of coverage. The resulting increase in efficiency is shown to allow an important reduction in per sample cost.In the present investigation, we demonstrate the effective use of the A set of eight diverse soybean genotypes (Set A) of different origins and maturity groups (MG) was used for the identification of SNPs. The set included three cultivars from eastern Canada , two cultivars from the United States , as well as two genotypes from Africa , and one from Brazil . Williams 82 has been completely sequenced and was used as a reference. A second set of eight genotypes from Canada  were used for SNP validation by Sanger sequencing along with Set A. Finally, Maple Donovan and OAC Bayfield were used for the optimization of GBS using primers with selective bases.DNA was extracted from 100 mg fresh young leaves using the DNeasy 96 Plant kit  following the manufacturer’s protocol. DNA was quantified using Quant-iT™ PicoGreen® as well as Thermo Scientific Nanodrop 8000 spectrophotometer instrument (Fisher Scientific). DNA concentrations were normalized to 10 ng/µl and subsequently used for library preparation. Sequencing libraries were prepared according to the GBS protocol as per Elshire et al. except for the use of selective primers described below CAGCXY-3′). During the PCR amplification step performed on pooled adapter-ligated restriction fragments, 18 cycles were performed with such a modified 3′ primer and the standard 5′ primer (5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGC-TCTTCCGATCT-3′).A scalable complexity reduction was achieved by using longer 3′ primers that cover the entire common adapter, the 3′ restriction site and extend 1 or 2 bases into the insert (5′-CAAGCAGA-AGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCTIn silico digestion of the soybean genome sequence (http://phytozome.net) was performed for different restriction enzymes. The resultant fragments were localized on pseudo-molecules and the frequency distribution was calculated for each chromosome. The frequency distribution of fragment size was also evaluated to determine the proportion of fragments in the preferred size range (100–400 bp). In addition, the frequency of predicted protein coding genes and repetitive elements like Gypsy, Copia and other minor classes of transposons were calculated. The information of transposable elements was retrieved from a soybean transposon database ‘soytedb’ (http://soybase.org) and co-localized with SNPs and predicted genes on the soybean genome.A pipeline implemented in perl programming language was developed for the processing of Illumina sequence read data . The steps involved in the pipeline were executed in separate shell scripts. This pipeline uses different publicly available software tools  as well as some in-house tools and a flow diagram of the process is presented in www.dnastar.com).A subset of 24 randomly selected SNPs identified using the IGST-GBS pipeline was used for validation by Sanger sequencing. Primers were designed to obtain amplicons of 500–1,000 bases containing at least one putative SNP. PCR reactions of 10 µl volume containing 20 ng of genomic DNA, 0.5 µM each of forward and reverse primers, 0.1 mM dNTPs, 1x PCR buffer and 0.2 unit of Taq DNA polymerase was performed in a thermal cycler. The cycling conditions involved initial denaturation at 94°C for 4 min, followed by 35 cycles of denaturation at 94°C for 1 min, primer annealing at 55–60°C for 1 min, and primer extension at 72°C for 1 min. A final extension at 72°C for 7 min was performed and products stored at 4°C until electrophoresis. The PCR products were resolved by electrophoresis in 2% agarose gels in 1x TBE buffer and visualized by ethidium bromide staining. The PCR amplicons were sequenced on an Applied Biosystems 3130XL at the Plateforme d’analyses génomiques . All the sequences along with reference sequence were assembled using the SeqMan tool available in DNAstar software package (www.phytozome.com). The soybean genome annotation provided predicted gene structure and verified exon/intron boundaries using the ESTs and cDNA data. Therefore we have co-located all the SNPs with gene models and predicted their structural and functional relevance in the genome. A SnpEff v3.0 open source program was also used for variant annotation and effect prediction of SNPs (http://snpeff.sourceforge.net/) Soybean genome annotation information in GFF3 format was retrieved from phytozome (http://evolution.gs.washington.edu/phylip.html). Subsequently, a consensus neighbour-joining tree was constructed after 1,000 bootstrap replications using PHYLIP and a graphical representation of the resulting tree was produced using MEGA5 (www.megasoftware.net).SNP data for the eight diverse soybean cultivars were used to generate a phylogenetic tree. The matrix of genetic distances between the cultivars was obtained using the PHYLIP 3.69 package  previously used in soybean and other plant species. We evaluated the number of expected restriction sites and the size distribution of predicted fragments. MseI was predicted to generate up to 9.5 million fragments, ApeKI 800 K fragments and Pst1 100 K fragments in the soybean genome. These represent maximal values, as methylation would be expected to impede digestion at some sites. Nonetheless, these large differences in the number of restriction sites translated into marked differences in the expected distribution of fragment sizes. As illustrated in ApeKI. MseI produced a large majority of very small fragments not expected to perform well on the Illumina sequencing platform during bridge amplification. PstI, even if used in combination with a frequent cutter such as MspI in a two-enzyme strategy, imposed a fairly low ceiling on the number of genomic regions potentially interrogated (<100 K).Choosing the appropriate restriction enzyme is a critical step in developing a GBS protocol for an organism. In the absence of a size selection step during library preparation, it is important to maximize the proportion of predicted restriction fragments that fall within the desired size range (100–400 bp) for sequencing. We performed ApeKI library that included DNA from five other species of plants and animals. A total of 5.54 million raw sequence reads were obtained by single-end sequencing of the eight soybean genotypes on an Illumina Genome Analyzer II (Eight diverse soybean genotypes (Set A) were used in a 48-plex lyzer II . Sortinglyzer II . In addilyzer II . Processlyzer II . An analUsing the IGST-GBS pipeline that we have developed, a total of 33,553 polymorphisms (SNPs and indels) were identified, with indels representing 5% of the total. Subsequently, filtering of SNPs on the basis of read depth  yielded a total of 10,120 high quality SNPs. The average depth of coverage was 7.8 reads/SNP locus, thus ensuring a low level of missing data (8%). Among the called genotypes, the vast majority were homozygous for the reference (53.3%) or an alternate allele (33.6%), whereas 4.9% were heterozygous and 8.1% were missing.2 = 0.36) with the physical length of chromosome.The frequency of SNPs on the twenty soybean chromosomes averaged 10 SNPs/Mb. The distribution of SNPs was similar for all chromosomes and is illustrated for chromosome 5 in A set of 24 randomly selected SNP loci identified based on the standard GBS library preparation protocol were amplified in 16 genotypes (sets A and B) and sequenced by Sanger sequencing. The PCR amplification of all the loci showed a single sharp band after agarose gel electrophoresis . SubsequThe structural and functional relevance of SNPs were investigated by comparing the location of the 10,120 SNPs with the coordinates of predicted gene structure. It revealed that 60.57% of these SNPs resided in intergenic regions, of which 17.88% were within 5 kb immediately upstream and 22.83% within 5 kb immediately downstream of an open reading frame (ORF) . The remAs a further genome-wide validation of the quality of the data, we produced a phylogenetic tree of the eight genotypes of set A with the full set of 10,120 SNPs . The resAnalysis of pairwise SNP polymorphism  showed tApeKI restriction fragments. This selectively amplified subset would present a higher mean depth of coverage and a higher percentage of the interrogated loci would lead to a successful genotype call. We therefore produced GBS libraries in which the final amplification step was performed with a selective primer extending across the 3′-ApeKI site and 1 or 2 bases into the insert  were unique, i.e. they were seen only once. As our pipeline required a mean coverage of two reads per locus per individual for a SNP to be called with high confidence, we hypothesized that it might be preferable to amplify only a subset of all possible e insert .ApeKI protocol), 1 (A or C) or 2  selective bases at the 3′ end, we obtained a significant improvement in both the number and the depth of coverage of called SNPs. As can be seen in Using the same number of reads (2 M) obtained for each of two genotypes (Maple Donovan and OAC Bayfield) following library construction with a common primer having 0 (i.e. the standard ApeKI protocol (0 selective base). At 384-plex, a slight decrease in the number of called SNPs  and depth of coverage (10.0) was suffered in exchange for a 4-fold increase in the throughput of the analysis.The resulting increase (over 2-fold) in depth of coverage suggested that it should be possible to increase the degree of multiplexing without seeing a concomitant decrease in the number of SNP calls. To simulate such an increase in multiplexing, we reduced the number of reads per sample from 2 M to 1 M and then 0.5 M. Assuming that 200 M reads can be obtained per lane on the Illumina HiSeq, these numbers correspond to the average number of reads/sample expected for 96-, 192- and 384-plex libraries. As can be seen in The simultaneous identification and genotyping of SNPs is now possible because of the important recent advances in sequencing ApeK1 restriction sites was observed following in silico digestion of the soybean genome and, more importantly, a good proportion of the resultant fragments were short enough for effective amplification and sequencing on the Illumina platform , as the highest density of SNPs had been reported in the centromeric and pericentromeric regions The availability of an excellent reference genome for soybean provided us with a solid basis for the selection of a good restriction enzyme for GBS in this crop platform . HoweverOur first innovation regards the GBS library preparation process. We demonstrate how selective primers can be used to amplify and sequence only a subset of the restriction fragments obtained following digestion. As anticipated, this results in an increase in the depth of coverage of the SNPs as a given sequencing effort is focused on a smaller set of amplicons. Somewhat counterintuitive, however, is the observed increase in the number of SNP calls using certain selective primers. As was explained above, we hypothesize that this as a consequence of the large pool of restriction fragments that are sequenced only once and that do not offer sufficient depth of coverage to meet our criteria for the calling of high-quality SNPs with the regular protocol. An immediate benefit of the new protocol is that a greater level of multiplexing can be supported without any reduction in the number or depth of coverage of SNPs. We have demonstrated that, using a primer with 2 selective bases (AC), a 2-fold increase in the level of multiplexing still results in an increased number of SNPs (+9.5%) and depth of coverage (+37%). Also, even after increasing 4-fold the number of lines analyzed, the number of SNPs obtained is reduced by less than 20%  while depth of coverage is reduced by less than 15% (10.0/11.7).ApeKI for soybean) could be amenable for a wider range of applications. For example, in cases where a lower SNP density is sufficient , one could use the appropriate primers to obtain the desired number of SNPs instead of resorting to a different, less frequent-cutting enzyme with all the required sets of barcoded adapters and primers that this would call for. The resulting increase in the depth of coverage would allow for an increase in the multiplexing of the libraries, without reducing the number of SNPs that can be called successfully, thus reducing the per-sample cost.If we follow the same logic, one can anticipate that using primers with even more selective bases will continue to increase the depth of coverage of the SNPs but will eventually also lead to a decrease in the number of called SNPs. This has some important implications for the versatility of use of a given enzyme used for making GBS libraries. If using a range of selective primers allows one to adjust at will the number of SNPs obtained, a single enzyme with suitable distribution in the genome  files. Although the GBS analysis pipeline developed by the Cornell team and integrated in TASSEL uses a similar overall approach, it differs in some important respects. Firstly, in the TASSEL pipeline, the length of the reads used to call SNPs is limited to a maximum of 64 bases. Although this reduces the computation time, it also reduces the potential to identify SNPs in a read, as the average length of our reads after removal of the barcode and adapter sequence was ∼80 bases. Secondly, the output of our pipeline is in variant call format (.vcf), one that is widely used in SNP data analysis and on which a broad range of filtration tools can be applied, for example using VCFtools. Depending on the skill of the user, there are lots of options to modify the SNP calling parameters. In contrast, in the Hapmap format produced by the TASSEL pipeline, there are fewer possibilities for a user to modify the stringency of the SNP calling aspect of the pipeline. In side-by-side comparisons (data not shown), both pipelines produced a very similar tally of SNPs and a majority of these (57%) were called in identical fashion. An examination of the differences in SNP calls between the two pipelines suggests that most of the SNPs that are called by the IGST pipeline and not the TASSEL pipeline reside in the bases beyond the 64-base limit imposed in the TASSEL pipeline. As for SNPs called by the TASSEL pipeline and not the IGST pipeline, these were mostly due to a more permissive set of SNP calling parameters.Despite the fact that the IGST pipeline uses stringent SNP calling parameters, as many as a third of the heterozygous calls were not validated by Sanger sequencing. As it is probable that such erroneous calls could be due to sequence divergence at paralogous loci, we systematically removed these so as not to introduce incorrect data.We conclude that the improved GBS protocol described in this work allows one to identify large numbers of high quality SNPs in soybean in a very cost-effective manner. The improvements we propose to the GBS library making protocol could be applied to any species of interest, starting from a protocol anchored on a suitable enzyme. Similarly, the IGST-GBS pipeline is suited to the analysis of any species for which a reference genome is available. As it offers a wide suite of options for filtering SNPs, it can be optimized for various types of populations and analyses.Figure S1Frequency distribution of SNPs identified in eight soybean cultivars using the GBS approach. Frequency was calculated for each 1 Mb bin on all 20 soybean chromosomes(TIF)Click here for additional data file.Figure S2SNP confirmed using re-sequencing of loci using Sanger sequencing method. a) Agarose gel showing PCR amplification of SNP loci in different soybean cultivars. b) Sequence alignment showing SNP alleles in soybean cultivars.(TIF)Click here for additional data file.Table S1Functional annotation of SNPs. Annotation was performed using snpEff v.3 for a collection of SNPS obtained by Illumina sequencing of a GBS library prepared from eight soybean genotypes.(XLSX)Click here for additional data file.Table S2Number of SNPs between pairs of soybean genotypes.(DOCX)Click here for additional data file.


Glycine max) breeding involves improving commercially grown varieties by introgressing important agronomic traits from poor yielding accessions and/or wild relatives of soybean while minimizing the associated yield drag. Molecular markers associated with these traits are instrumental in increasing the efficiency of producing such crosses and Single Nucleotide Polymorphisms (SNPs) are particularly well suited for this task, owing to high density in the non-genic regions and thus increased likelihood of finding a tightly linked marker to a given trait. A rapid method to develop SNP markers that can differentiate specific loci between any two parents in soybean is thus highly desirable. In this study we investigate such a protocol for developing SNP markers between multiple soybean accessions and the reference Williams 82 genome. To restrict sampling frequency reduced representation libraries (RRLs) of genomic DNA were generated by restriction digestion followed by library construction. We chose to sequence four accessions Dowling (PI 548663), Dwight (PI 597386), Komata (PI200492) and PI 594538A for their agronomic importance as well as Williams 82 as a control.Soybean  were found by aligning Williams 82 reads to the reference assembly (Glyma1) suggesting limited genetic variation within the Williams 82 line. The SNP data allowed us to estimate genetic diversity between the four lines and Williams 82. Restriction digestion of soybean genomic DNA with MseI followed by high throughput sequencing provides a rapid and reproducible method for generating SNP markers. Glycine max) lines grown in the US were originally introduced from East Asia. A wide range of cultivars are grown in east Asia and have been selected over centuries for yield and suitability to local environment. Earlier domestication of wild soybean involved selection for larger seed and improved nutritional quality. After introduction into the US, commercially grown cultivars were selected for improved yield and biotic/abiotic stress tolerance traits. Studies on diversity of soybean germplasm in the United States have suggested that the introduction and multitude of selection steps may have served as a genetic bottleneck and reduced the genetic diversity within the elite germplasm in the US Soybean  with similar yield properties to the elite line and the added trait from the target locus. Molecular markers allow a breeder to rapidly screen a large number of lines for markers associated with the trait, allowing the selection of the molecular marker and thus specific introgression of a single genomic locus. Fine mapping the locus with molecular markers allows the amount of target DNA that will be integrated into the NIL to be reduced. This reduction in linkage drag can also allow reduction in the yield drag often associated with introgression. Therefore the availability of a large number of markers, spread more or less evenly over the genome of a specific exotic line targeted for introgression of a trait, is very valuable. Although fine mapping a locus with tightly linked markers is cost and labor intensive, it might need be done only once per allele of interest. Such an association will, potentially, be applicable in crosses between a different set of parents. The first generation molecular markers were restriction fragment length polymorphism (RFLP) In this study we chose four cultivars of soybean (based on the presence of useful resistance traits) to explore a method for exploiting the stated advantage of short read sequencing in generating SNP markers. Dowling is a low-yielding southern US accession of soybean that has proven useful as a source for the Rag1 allele, which provides soybean aphid resistance Sequencing technologies such as Illumina genome analyzer and ABI SOLiD offer the ideal combination of depth of coverage and frequency of sampling to generate a large set of SNP markers. The approach used in this work is to generate a large number of short reads from genomic DNA of these cultivars and align them to the reference genome. Numerous methods of SNP calling from sequence read data have been developed, each differing in the details of implementation and confidence measures used for calling SNPs. SNPs detected by high throughput sequencing of genomic DNA and called by these programs have the potential to provide very fine resolution of the genomic differences between the cultivar sequenced and the reference assembly, here the Glyma1 assembly G.max genome, estimated to be n = 1.1 gigabases (Gb) G.max is estimated to produce a large proportion of reads from the repetitive fraction of the genome. The proportion of reads sampled from a repeated region is directly proportional to the fraction of the genome representing repeats. Up to 60% of the G.max genome is estimated to be composed of moderately to highly repetitive elements G.max genome (Williams 82) Assuming a minimum requirement of three reads covering the base in question to call a SNP and given the size of the Choosing an enzyme that does not cleave in any highly repetitive region , the average distance between polymorphisms within regions sequenced in this experiment. This was calculated by computing the number of bases covered by at least three reads and dividing that by the number of SNPs, called at high confidence relative to the reference genome, without accounting for the base quality. Since the average quality for bases is comparable between the 5 libraries (data not shown) we estimate that any local biases in mapping quality will even out over the genome. After normalization the MDBS is almost even between the Dowling, Komata and PI 594538A lines but shows a marked increase in Dwight and is very large in Williams 82, as expected . Even thThe SNPs most informative from a molecular breeding perspective are those that are polymorphic between the lines of interest. To assess the number of informative SNPs between the various accessions sequenced here, the predicted genotype of SNP loci in all pairwise comparisons were studied. SNP loci that were successfully genotyped in both accessions being compared were classified into 3 categories: 1. SNP shared in these two accessions when compared to the reference, 2. SNP is polymorphic between the First accession and reference but not the second and 3. SNP is polymorphic between the Second accession and reference but not the first . The uniTo test the predicted high-confidence SNPs, we independently sequenced the SNP loci using traditional Sanger sequencing. Twenty SNP loci, with good quality flanking sequence, that are polymorphic between Komata and the reference genome were chosen for confirmation. Sequence around this region was extracted from the Glyma1 assembly, and used for primer design. Of these twenty regions, two failed to amplify with the designed primers. The eighteen other primer sets amplified a single region, as evidenced by a single band on a gel. Using Sanger sequencing, we confirmed the predicted SNP in these eighteen loci. In two cases additional SNPs in the vicinity that had not passed the high confidence SNP filter were also confirmed, implying that the SNP density estimate we arrived at is likely to be a conservative estimate of the true variation between these lines. A further 15 SNP loci that are polymorphic between Dowling and the reference were chosen randomly. Nine primer sets, designed from the reference genome, amplified a fragment from Dowling genomic DNA which was then submitted for sequencing by sanger method. Eight of the nine predicted SNPs were validated in this set implying that our overall false SNP discovery rate is less than 5%.Williams 82 was created by crossing Williams and Kingwa accessions of soybean In parallel with our study, Haun et al. The SNP positions for each accession were plotted on the twenty soybean chromosomes to visualize their distribution . Large gA number of reads from each library mapped to known transposable elements (TEs) in soybean. Reads were assigned to TE families by mapping them to the elements listed at soyTEdb Deep sequencing of reduced representation libraries from genomic DNA provides a rapid and relatively inexpensive method of generating markers in lines of agronomic interest. Restriction digestion of genomic DNA offers an excellent way of creating reduced representation libraries. Typically, the restriction enzyme used for digestion is chosen using a general strategy. One such strategy is to use a methylation sensitive enzyme Glycine max the enzyme MseI works exceptionally well at reducing the genome complexity sufficiently to allow SNP discovery, while also preferentially targeting intergenic DNA as a result of its lower GC content. A recent study used CviRI digested DNA, based on an in silico analysis of the draft genome and annotated repeat elements, to identify SNPs between Forrest and Williams 82 In the case of With the falling costs of short read sequencing and coupled increases in the number of sequencing reads produced per run, such a deep sequencing strategy is likely to be the most rapid and, perhaps, even the more economical method to generate a large amount of SNP markers for any new accession of interest to plant breeders. A single lane of Illumina sequencing, at the time of this manuscript being written, will likely tag the vast majority of MseI sites in the genome with sufficient depth to allow high confidence SNP detection and provide a very high density SNP map for several accessions using barcoded libraries, yet would still likely be insufficient for full whole-genome resequencing of a single line. In such an experiment the cost per accession is expected to fall further. At the estimated SNP density of 600 bp such genotyping should allow fine mapping a trait of interest down to a very small interval. In specific regions of interest, where a higher density of SNPs is needed, the SNP filter stringency can be lowered accordingly at the cost of increasing the false positive rate . Selection based on such markers will facilitate high throughput genotyping of progeny to select for traits of interest. High resolution mapping will also allow reduction of the yield drag often introduced in such crosses by allowing genotyping to select the progeny with least amount of DNA from the lower yielding parent.There was a noticeable increase in the number of sites sampled in two libraries out of the five. This difference can most likely be ascribed to small differences in the efficiency of digestion between the different DNA samples or in the fraction of genome obtained during the size selection from gel. While this variation suggests that great care must be taken during those steps, the result still provided sufficient sampling from all libraries to enable SNP calling.Our survey also detected significant residual variation between different sources of the cultivar Williams 82 (the source used for the reference genome sequencing and the soybean germplasm collection). This is a violation of a perhaps unrealistic assumption made historically by many crop biologists, that varieties of an inbred selfing crop such as soybean should be almost completely homogeneous and homozygous. Our results on variation within Williams 82 confirm those of Haun et al. We conclude that individual plant lines must be closely examined using this or a similar genotyping technique to ensure that within-cultivar variation does not cause errors in experiments involving genetic comparison . For important crop species with large germplasm collections or novel plants being introduced into agriculture this method provides a rapid, economical and easy protocol to catalog diversity and generate molecular markers. The advent of longer sequence reads makes this technology also potentially applicable to organisms with unsequenced genomes.Survey sequencing data Seed for each soybean line described in the text (Dowling (PI 548663), Dwight (PI 597386), Komata (PI200492), PI 594538A and Williams 82 (PI 518671)) was obtained from the USDA soybean germplasm collection. Plants were grown in pots in a temperature- and light-controlled greenhouse in long day (18 hr) light conditions for 4–12 weeks. Young leaf and stem tissue, tips of branches with at least two visible leaves, was collected from four to six individuals for each line.10–20 After library construction for Illumina sequencing, each library was loaded onto one lane of the sequencing flow cell. Sequencing was done on the Illumina GAI genome analyzer system, performed for 35 cycles and bases called. Each library was sequenced twice, except Dwight (sequenced three times), to satisfy quality criteria. Sequence and quality data was obtained in fastq format. All raw data from the sequencing runs was deposited into the SRA at NCBI. The individual runs can be accessed from NCBI using the accessions SRR111923–SRR111933.Reads were aligned to the Glyma1 version of the soybean genome assembly. Maq (v 0.7.1) was used to align the reads to the genome using the maq.pl script with easyrun option.SNP calling was done as part of the easyrun option for maq. Additional stringency levels were tested by running maq.pl with the SNPfilter option and varying the cut off parameters for minimum mapping quality at SNP position and in a 6 bp window around it. These changes did not change the number of SNP calls appreciably. Increasing the minimum depth required to call a SNP decreased the number of SNP calls substantially, especially in the Komata and PI 594538A lines. The SNPs reported in this report were obtained at the default maq parameters of d = 3, -n = 20, -q = 20, -w = 5, and N = 2. All high confidence SNP loci and allelic information were deposited in NCBI's dbSNP database See .Primers for SNP verification were designed using an in-house script. Komata genomic DNA extraction was performed as described earlier. PCR was performed with Ex Taq  according to the product menu. The reactions were first heated at 94C for 5 min, followed by 35 cycles using a 30 seconds denaturation step at 94C, a 30 seconds annealing step at 58C, and a 1-min extension step at 72C. An additional 7-min extension step at 72C was added after 35 cycles. PCR products were analyzed on a 1% agarose gel. In all cases that the PCR product showed as a single band in the gel, the Qiagen PCR purification kit  was used to purify the PCR product for sequencing. Sanger sequencing reaction of the PCR product was performed with BigDye Terminator v3.1 Cycle Sequencing Kit  according to the manual with the forward primer used in PCR reaction. BigDye reactions were submitted to Keck Center, UIUC for purification and capillary electrophoresis. Sequences were analyzed and compared to Glyma1 genome sequence using Sequencher .Table S1List of SNPs from each line sequenced with their genomic position according the soybean reference assembly (Glyma 1.09).(XLS)Click here for additional data file.Table S2dbSNP accession numbers for all SNP loci.(TXT)Click here for additional data file.


Quantitative traits, such as disease resistance, are most often controlled by a set of genes involving a complex array of regulation. The dissection of genetic basis of quantitative traits requires large numbers of genetic markers with good genome coverage. The application of next-generation sequencing technologies has allowed discovery of over eight million SNPs in catfish, but the challenge remains as to how to efficiently and economically use such SNP resources for genetic analysis.In this work, we developed a catfish 250K SNP array using Affymetrix Axiom genotyping technology. The SNPs were obtained from multiple sources including gene-associated SNPs, anonymous genomic SNPs, and inter-specific SNPs. A set of 640K high-quality SNPs obtained following specific requirements of array design were submitted. A panel of 250,113 SNPs was finalized for inclusion on the array. The performance evaluated by genotyping individuals from wild populations and backcross families suggested the good utility of the catfish 250K SNP array.This is the first high-density SNP array for catfish. The array should be a valuable resource for genome-wide association studies (GWAS), fine QTL mapping, high-density linkage map construction, haplotype analysis, and whole genome-based selection. Catfish is the most important aquaculture species in the United States. In recent years, catfish industry encounters great challenges including devastating diseases which cause the largest economic loss. Improved brood stocks with a high level of disease resistance are desperately needed. Quantitative traits, such as disease resistance, are most often controlled by a set of genes involving a complex array of regulation -3. The dSingle nucleotide polymorphisms (SNPs) are now the markers of choice because they are the most abundant genetic variations widely distributed in the genome, and are generally bi-allelic polymorphisms that are amenable to automated genotyping . SNPs arUntil recently genome-scale SNP identification was a great challenge for most non-model species. The next-generation sequencing technologies enabled efficient identification of SNPs from genomes of various organisms . With thA variety of SNP array platforms are available, of which Illumina iSelect HD Custom BeadChip , the Sequenom MassArray  and Affymetrix GeneChip Custom Array  are widely used. More recently, Affymetrix adopted the Axiom genotyping technology that allows development of customized arrays containing 1,500 to 2.6 million SNPs . These pSNP arrays have been developed in several livestock species, including cattle , horse , pig 1919, sheepHigh-density SNP arrays have not been developed for aquaculture species. Only low to medium density arrays were used in several aquaculture species. The Illumina GoldenGate Assay was used to evaluate 384 rainbow trout SNPs, resulting in a validation rate of 48% for the tested SNPs . The GolSNP resources required for the development of a high-density SNP array have been developed in catfish. Over two million gene-associated SNPs were identified in channel catfish and blue catfish, respectively, using next-generation sequencing. Of these putative gene-associated SNPs, approximately 400,000-500,000 were of high quality . In a reOne of the most important goals of the SNP array development is to have a good coverage of the genome. The first task was to select a subset of SNPs from all identified SNPs, gene-associated as well as anonymous SNPs. From a large pool of the previously identified SNPs ,31, the in-silico analysis to assess the predicted performance on Axiom arrays. Both forward and reverse probes of each SNP were assigned with p-convert values, which were derived from a random forest model to predict the probability of the SNP conversion on the array. The model considers factors including probe sequence, binding energy and the expected degree of non-specific hybridization to multiple genomic regions . SNP probes with high p-convert values are expected to convert on the SNP array with high probability.The initial in house selection resulted in 641,489 SNPs that were submitted to Affymetrix for in-silico evaluation with various p-convert values, but the vast majority of SNPs had p-convert values greater than 0.5 . To assess the SNP distribution, the inter-SNP spacing was evaluated using this draft genome assembly. A total of 248,308 SNPs (99.3%) with flanking sequences were uniquely mapped to 11,017 genome scaffolds which span a total of 785.6 Mb, approximately 80% of the genome. As shown in Figure Due to the lack of a fully assembled genome sequence, the inter-marker spacing is probably underestimated. The current draft genome assembly had a total genome size of 830.5 Mb, but the catfish genome is approximately 950 Mb. In addition, the spacing from the most external SNPs of each scaffold to the next marker is not included in the assessment. Therefore, the overall marker spacing could have been slightly larger.The SNP distribution was also evaluated with regard to association with bacterial artificial chromosome (BAC) end sequences (BES). A total of 22,051 SNPs on the array are associated with 16,832 BES derived from 14,849 BAC clones. Accordingly, such SNPs are associated with 2,853  contigs from the catfish physical map developed by Xu et al. . Such BAThe catfish have a genome size of ~ 1 billion base pairs. With the 250,000 SNPs, theoretically, the average SNP marker intervals are about 4 kb in the catfish genome. We should acknowledge that it is too costly to develop a SNP array with millions of markers for catfish, like it has been done in human and other model species, since far less funding support is available for aquaculture species. For the same budget related reason, the number of samples genotyped for GWAS is limited for aquaculture species as well. Therefore, different strategies should be utilized when conducting genome-wide genetic analysis using the catfish 250K SNP array. The genome regions underlying production and performance traits can be first located through the whole genome scanning with the 250K SNPs genotyped from hundreds of samples. To further investigate the SNP effects, it’s cost-saving to use other SNP genotyping platforms such as Sequenom MassArray  to genotype thousands of individuals with denser SNPs from the targeted regions.Performance of the SNP array was examined by genotyping unrelated catfish samples from wild populations and catfish backcross families. As summarized in Table Although polymorphism ultimately dictates how useful the array, the results present herein is related to the testing samples used in this study. In the case of the catfish 250K SNP array, the situation is further complicated by inclusion of intra-specific as well as inter-specific SNPs. Inter-specific SNPs are not expected to be polymorphic within channel catfish or blue catfish, but are polymorphic in inter-specific hybrids. One obvious question we are interested to ask is how many of the 250K SNPs represented real SNPs . Here, a total of 137,459 SNPs were polymorphic in wild fish; 130,685 SNPs were polymorphic in BC1 fish, and 156,357 SNPs were polymorphic in BC3 fish. Taken together, of the 241,812 converted SNPs, a total of 200,860 SNPs (83.1%) were polymorphic in at least one testing population, demonstrating a high SNP validation rate.Comparisons of polymorphic and monomorphic SNPs among wild catfish, BC1 and BC3 catfish indicated that a large number of SNPs  were polymorphic in all three examined groups of fish  as SNPs are most often bi-allelic markers. In order to assess the informativeness of the SNPs on the array, the minor allele frequencies of the SNPs were determined in wild population. The genotypes of 137,459 polymorphic SNPs in wild catfish samples were used for the analysis. Distribution of minor allele frequencies with intervals of 0.05 was shown in Figure As the p-convert value is an important factor for the selection of SNPs, it is interesting to analyze its relationships with SNP performance. As shown in Figure On the catfish 250K SNP array, 179,116 SNPs were identified from channel catfish, of which 146,928 were anonymous SNPs while 32,188 were gene-associated SNPs. To compare the performance of gene-associated SNPs and anonymous SNPs, the conversion rates and percentages of polymorphic SNPs were analyzed. As shown in Figure The performances of 32,188 intra-specific SNPs in channel catfish, 31,392 intra-specific SNPs in blue catfish and 39,605 inter-specific SNPs between the two species were examined as shown in Figure Of the 250,113 SNPs on the array, 75.9% are transitions and 24.1% are transversions. Transition SNPs are more abundant than transversion SNPs, with an estimated ratio of 1.8-1.9 in catfish among gene-associated SNPs ,35. The Ictalurus furcatus), brown bullhead catfish (Ameiurus nebulosus) and white catfish (A. catus). As summarized in Table To assess the utility of the catfish 250K SNP array in the related catfish taxa, a set of DNA samples were tested from blue catfish  and high polymorphic rates (over 50%) can be obtained in all the examined samples. Technically, we showed that the Affymetrix design score  adequately predicted SNP probe performance and the inclusion of alternative probes greatly increased the SNP conversion rates, especially for SNPs with probes that had low design scores. The catfish 250K SNP array should be valuable for genome-wide association studies, fine QTL mapping, high-density linkage mapping, haplotype analysis, and whole genome-based selection.All procedures involving the handling and treatment of fish used during this study were approved by the Auburn University Institutional Animal Care and Use Committee (AU-IACUC) prior to initiation.Gene-associated SNPs were generated from Liu et al. . AnonymoAll SNPs passing the in house selection using the above criteria were submitted to Affymetrix for design score assessment, where a p-convert value was assigned to each of the two probes flanking a SNP, respectively. SNPs with probes of high p-convert values were more likely to be convertible. A p-convert value threshold was determined by excluding the tail of lowest performing probes to facilitate selection of final SNP list. For the SNPs with both probes passing the p-convert value threshold, only the probe with the greater value was selected. For the SNPs with both probes having low p-convert values, both probes were selected to ensure a high probability of conversion.To select well-spaced SNPs, at least one but no more than two SNPs per transcript contig were selected for gene-associated SNPs. At the time of the SNP selection, only the initial preliminary catfish genome assembly was available . The preliminary assembly was used to facilitate SNP selection according to contig length for the anonymous SNPs. One SNP per contig was selected from the contigs with lengths less than 4 kb. Two SNPs per contig were selected from the contigs with lengths greater than 4 kb. For the two SNPs selected from one contig, the SNPs with the largest distances were selected to ensure good spacing in the genome.In addition, A/T and C/G SNPs were not selected because the two alleles of these SNPs match the same dye, and additional distinct probes in different physical locations on the array are required to distinguish them. Non-polymorphic 31-mers were randomly picked from non-repetitive regions of the genome for data quality control (QC) probes. The QC probes along with the final list of SNPs were submitted to Affymetrix for production of Axiom genotyping arrays.To assess the genome distribution of SNPs on the array, all the 250,113 SNPs with 35-bp up and downstream flanking sequences  were aligned with the latest draft genome assembly now available  using BLAST to determine SNP positions. The inter-SNP spacing was determined based on SNP positions in the scaffolds. The distance between SNPs at the end of sequences and the next SNP was not included because it’s not possible to assess their inter-marker interval. Similarly, SNP flanking sequences were aligned with the catfish BAC end sequences (BES) ,37 to id1 hybrids  with a male channel catfish, and the 3rd generation of backcross hybrids were produced by backcrossing the 2nd generation of backcross hybrids with a male channel catfish.Three different sample sources were used for genotyping to assess the SNP array performance: 1) 192 unrelated channel catfish from wild populations; 2) 192 catfish hybrids from the 1st generation of backcrossing and 3) 192 catfish hybrids from the 3rd generation of backcrossing. Samples from wild populations were channel catfish collected for a previous study . The hybBlood samples (300-500μl) were collected in a 1-ml syringe and immediately expelled into a 15-ml tube containing 5 ml of cell lysis buffer  for DNA isolation. DNA was isolated as previously described ,40. PicoGenomic DNA samples were arranged in a 96-well microtiter plate, and normalized to a final concentration of 50 ng/μl with a final volume of 10 μl. Genotyping with the catfish 250K SNP array was outsourced to GeneSeek .http://www.animalgenome.org/repository) and are also available upon request.Raw data in the form of CEL files were imported into the Affymetrix Genotyping Console software (v4.1) for quality control analysis and genotype calling using Axiom GT1 algorithm (Affymetrix). Samples with dish quality control (QC) value of 0.82 or better and call rate >0.97 were considered to have passed the quality control assessment. Following genotyping, SNPolisher (Affymetrix), an R package, was used to process the genotyping results. The package calculates the QC metrics for each SNP/probe set to determine its quality, and classify SNPs into six categories  of Rio Grande strain (10) and D&B strain (10), 10 brown bullhead catfish (Ameiurus nebulosus) and 10 white catfish (A. catus). The DNA preparation, SNP genotyping and analysis were the same as mentioned above.A set of DNA samples were tested from other related catfish species, including blue catfish (The authors declare that they have no competing interests.SL conducted the major part of the research including SNP selection, array design, data analysis, and manuscript preparation. LS, YL, FS, YJ, YZ, JZ and JF were involved in data analysis. MK and HK are involved in DNA sample preparation. ZL supervised the entire study and provided assistance for data analysis and manuscript preparation. All authors read and approved the final manuscript.


The health benefit of drinking wine, expressed as capacity to defend the human organism from the free radicals action and thus reducing the oxidative stress, has already been demonstrated, and the results had been published in scientific literature. The aim of our study was to develop and assess a model able to estimate the antioxidant capacity (AC) of several samples of Romanian wines and to evaluate the AC dependency on the vintage (defined as the year in which wine was produced) and grape variety under presence of censored data. A contingency of two grape varieties from two different vineyards in Romania and five production years, with some missing experimental data, was used to conduct the analysis. The analysis showed that the antioxidant capacity of the investigated wines is linearly dependent on the vintage. Furthermore, an iterative algorithm was developed and applied to obtain the coefficients of the model and to estimate the missing experimental value. The contribution of wine source to the antioxidant capacity proved equal to 11%. The antioxidant capacity of food constituents and the role of antioxidants in human health found attention in the recent years . The antA series of food constituents with antioxidant capacities had been identified: tea (green tea leaves were found to have high phenolic content ), citrusThe antioxidant activity of wines and grapes was lately of interest for many researchers. Several antioxidant compounds such as flavanol, hydroxybenzoic acids, hydroxycinnamic acids, tartaric acid derivatives, proanthocyanidins, phenols, flavonols, anthocyanins, and resveratrols have been identified in wines and grapes . LachmanThe antioxidant capacity of wines produced in 1995, 2000, 2002, 2003, and 2005 in Romania had been previously determined . Two graS0 = baseline electron spin resonance spectroscopy (EPR) signal of the free radicals; S20 = EPR signal of the free radicals after 20 minutes following adding the extracts of wines.Seven samples of wine selected from Cabernet Sauvignon and Merlot varieties grown in Romania (Recaş vineyards in Timiş County and Miniş vineyards in Arad County) with different years were analyzed. The antioxidant content see  of the iThe experimental antioxidant content was summarized as a contingency of an ordinal variable (vintage years) and a categorical variable (variety of grapes and vineyard) see .X2 = 0.03; pχ2 = 0.86) [It had been previously proved that the hypothesis of independence between vintage year and vineyard as factors of antioxidant content could not be rejected for {2003,2005}×{CSI, TM1} subgroup ( = 0.86) . verify if the linearity between antioxidant content and vintage (year in which the investigated wine was produced) is true for experimental data included in the analysis. A significant linearity was identified when 8 experimental data were investigated  .If linearity exists, verify if the linearity between antioxidant content and wine age also exists; use the obtained mathematical model to estimate the antioxidant content for missing data based on available experimental data.  estimate the missing values , when 8 observations were investigated [The observed linearity is not significantly different by the one previous identified , the variable Year in the equation above contains a constant term (2010). Thus, a linear relationship between antioxidant content and wine age also exists and has the same statistical characteristics as the equation above:Considering the above linearity relationship, also the equation without the intercept is valid:X2 as function of iteration is presented X2 statistics cross the minimum value is detailed in The proposed estimation approach was applied on experimental data presented in X2 statistics did not converge to a global minimum. The local minimum is reached in the 7th iteration, and a slight increase in the values of X2 is observed after this iteration. A difference lower than 10−4 between consecutive X2 values led to the stop of the algorithm after the 59th iteration CSI (Cabernet Sauvignon from Recaş vineyard):(ii)CSII (Cabernet Sauvignon from Miniş vineyard):(iii)TMI (Merlot from Recaş vineyard):The regression models obtained for different investigated wines are as follows.The identified relationships are not significantly different from each other at a significance level of 5% since the 95% confidence intervals of coefficients overlap each other. As a result, the conclusions regarding a significant difference could not be sustained at a risk of error equal to 5%.The intercept provided a measure of the antioxidant quantity that can be obtained by wine ageing. According to this criterion, the descending classification of wine in regard of antioxidant content is Merlot-Cabernet Sauvignon-Miniş (distinct from TMI at risk to be in error of 91%)-Cabernet Sauvignon-Recaş .The slope gives a measure of speed of ageing. Merlot aged faster, and it is closely followed by Cabernet Sauvignon-Miniş  and it is followed by Cabernet Sauvignon-Recaş .P values ≥0.08). Furthermore, the antioxidant capacity is enriched annually with aging, and this enrichment is different for each brand.The investigated wines come with an original richness in antioxidants since all models that assumed that the amount of antioxidants is null in the year when the wine was produced are rejected  [Regression analysis of all data included in this study, analysis conducted using also the expected values, provided the following result:reported , 43, 44.he ring) , possiblhe ring) , and of he ring)  is embedX2 statistics and thus to minimize the risk of being in error. The equations obtained for antioxidant capacity showed small differences  in antioxidant capacity of wines from different varieties of grapes that allows obtaining an equation of antioxidant capacity as function of wine age for all samples included in the study.Our algorithm proved to be able to operate on contingency table with gaps (censored data), and the resulting solution is not a trivial solution in relation to minimizing the


Wireless sensor networks have attracted a lot of attention recently. In this paper, we develop a channel model based on the elliptical model for multipath components involving randomly placed scatterers in the scattering region with sensors deployed on a field. We verify that in a sensor network, the use of receive diversity techniques improves the performance of the system. Extensive performance analysis of the system is carried out for both single and multiple antennas with the applied receive diversity techniques. Performance analyses based on variations in receiver height, maximum multipath delay and transmit power have been performed considering different numbers of antenna elements present in the receiver array, Our results show that increasing the number of antenna elements for a wireless sensor network does indeed improve the BER rates that can be obtained. A WSN is a network of wirelessly interconnected devices, called sensor nodes, which are able to ubiquitously collect/retrieve data to be sent to a far receiver. Hundreds of nodes are scattered randomly throughout over a wide area, which assemble together, establish a routing topology, and transmit data back to a common collection point . The maiWireless communication involves entire environment related effects on the propagated signals between the transmitter and the receiver. Conventional communication systems suffer from multipath signals, Doppler spread and high propagation delays. Due to the irregular distribution of scatterers present in the environment, multipath signals arrive at the receiver from different directions at different times. All of these multipaths taken by the wireless signal possess different properties, and hence, each multipath signal has its own distinctive carrier phase shift, amplitude, angle of arrival, and time delay. A possible approach to address these issues is through the geometrical definition of the scattering region to calculate the above parameters. The geometry of the multipath propagation plays a vital role for communication systems to suppress multipath . A GBSBEIn this paper, we combine the Geometrically Based Single Bounce Elliptical Model (GBSBEM) with other aspects of fading channel and establish a vector channel model requirement for smart antennas employed at the receiver. Since we are using a cluster-based WSN deployment model, the sensor nodes do not face the reachback problem as they have to transmit the information over a shorter distance to the cluster head, hence they can be designed to work with comparatively lower power. There are benefits to incorporating receive diversity into wireless sensor networks . In addiThe rest of the paper is organized as follows. In Section 2, we discuss some of the related work. Section 3 describes the system model and the GBSBE channel model for the proposed system. In Section 4, we discuss the receiver structure exploiting receive diversity followed by various diversity combining techniques at the receiver. In Section 5 we present the simulation results and analyze the performance based on different variables for different number of receive antenna elements. Finally we present our conclusions in Section 6.2.Wireless sensor networks have attracted a lot of attention recently. In , the aveA distributed algorithm capable of computing linear signal expansions for a sensor broadcast protocol is presented in , where eOther than correct reception of data at the far end receiver and hence performance improvement, exploiting diversity techniques at the receiver can help in saving the energy substantially and leading to reduced battery consumption and subsequently increasing network lifetime. New relaying strategies based on Luby Transform Codes were presented in  by exploIn , energy 3.3.1.rN receive antennas is shown in ad-hoc network to disseminate the information among them via efficient flooding. The sensors pass on the information to the cluster head, where this information is filtered and modulated using BPSK and sent to the receiver. Another assumption is that the whole architecture is synchronous and the communication channel between the cluster head and the receiver is subjected to fading, multipath, and noise.The system model used in this paper for a cluster based WSN architecture for lα, time delay lτ, angle of arrival lθ, the power for the multipath components, and Doppler shift df of the thl multipath signal component. Accordingly, each of these signal parameters will be time-varying . These L samples of a random variable are described by the polar coordinates according to the following relationships The idea is first to define an ellipse corresponding to the maximum multipath delay, ld, and, the propagation delays, lτ, can be calculated as Thus, the multipath propagation distance, refP is the reference power measured at a distance refd from the transmitter using omni-directional antennas at the transmitter and the receiver. refP can be calculated using Friis’ free space propagation model given by:TP is the transmitted power and λ=c/f is the wavelength for a particular carrier frequency, f. The path loss exponent, n typically ranges from 3 to 4 in a microcell environment. tG (dθ) rand G (aθ) are the gains of the transmit and the receive antennas as functions of the angle of departure, dθ and the angle of arrival, aθ respectively. For the LOS component, dθ and aθare both zero. The power of each of the multipath component can be calculated as:rL is the path loss in dB. Assuming the phase of the multipath components, lγ, are uniformly distributed over the interval  the complex amplitudes of the multipath components are calculated as lα = 10lP−P0)/20( ljγe.The power of the direct path component (LOS) can be calculated as below:4.It can be generally supposed that the signal transmitted by the cluster head travels through several resolvable discrete multipaths and arrives at the receiver arrays, each multipath having its own independent DOA, time delay, and amplitude. For example, the sensors are deployed in an open field where they collect data and send to the cluster head. The collected data is sampled and modulated using BPSK modulation and converted into a serial bit stream. This data bit stream needs to be transmitted to the sink to be analyzed.t, s(t) is the transmitted signal across all links, then the transmitted signals are received over rN independent and identically distributed GBSB channels corrupted by complex Gaussian noise, the received signal y(t) can be represented as:s(t) is the input signal, n(t) is the additive white Gaussian noise, lα(t) is the attenuation factor for the signal received on the thl path. As per antenna array theory, each multipath signal brings multiple signals at the receiving array. The effect of every individual multipath signal on every element of the antenna array can be equalized to multiply by ka (lθ), known as the steering vector of antenna array where k represents the index of antenna array.Assuming that perfect channel state information (CSI) is available at the receiver, if at any time thi user can be expressed as:For an N-element linear antenna array the channel impulse response of the y(t) = Σl ih (t) s(t − lτ(t)) + n(t), and the N×1 array response vector or the steering vector a is defined as:d is the element spacing and Δi is the angle spread of the thi user.Thus, the output received at the sink is given by The noise on each diversity branch is assumed to be uncorrelated. The collection of independently fading signal branches can be combined in a variety of ways to improve the received SNR. Since the chance of having two deep fades from two uncorrelated signals at any instant is rare, combining them can reduce the effect of the fades. Diversity is a powerful communication receiver technique that provides wireless link improvement at relatively low cost. It exploits the random nature of radio propagation by finding independent signal paths for communication. In virtually all applications, diversity decisions are made by the receiver, and are unknown to the transmitter. The diversity concept can be explained simply. If one radio path undergoes a deep fade, another independent path may have a strong signal. By having more than one path to select from, both the instantaneous and average SNRs at the receiver may be improved. There are a variety of ways in which the independently fading signal branches can be combined; hence, the three most prevalent space diversity-combining techniques used in this paper are the Maximal Ratio Combining (MRC)  Equal GaFor example, the received signals are combined at the receiver using MRC to maximize the SNR and give the following expression:w, where w=Hh, the output x at the receiver is given by:In terms of the weight vector jh, the instantaneous SNR, γj, at thj receive antenna is given by:Hh, with N receive antennas, the effective SNR is given by:In the presence of channel s̃ (t).The received symbols are then passed through a maximum-likelihood detector to produce the estimate of transmitted signal 5.In this section we present simulation results to evaluate the performance of our system. We discuss the reliability and robustness of a cluster based WSN system by using smart antennas at the receiver.5.1.We used MATLAB to simulate the system. The proposed model has been simulated for a microcell environment. The focus of the model is to consider the scenario of local scattering giving rise to multipaths. These multipaths and the resulting fading are modeled as stochastic processes and channel characteristics like time-variation, amplitude, and angular spread are modeled using GBSBEM.We consider a cluster-based model with N sensor nodes randomly scattered over a large area. These nodes collect a common message and transmit it towards the cluster head. The information received at the cluster head is filtered and modulated and transmitted to the receiving station. The cluster head is located within a range of 2 meters from this receiving station. In this case both the cluster head and the receiver are surrounded by scatterers and the receiving antenna array is not well above the surrounding objects. The model parameters were chosen to fit the scenario.7. The whole sequence is divided into frames of length 100 symbols and the total number of frames are 105. The channel considered here is a quasi-static channel; i.e., the channel remains constant over the entire frame and changes from one frame to other.5.2.We have carried out the simulations where we have different combining schemes at the receiver. We have compared the performance of these schemes with different number of antennas at the receiver. We further analyze the performance of the system by varying the system parameters like receiver height, maximum multipath delay, and transmit power and compare the performance with: (i) no diversity, and (ii) MRC at the receiver.5.2.1.We present the performance analyses when we have multiple antennas at the receiver. We apply EGC, SC, and MRC at the receiver to exploit diversity. The performance of EGC is only marginally inferior to MRC. The implementation complexity for EGC is significantly less than the MRC because of the requirement of correct weighing factors. Hence, the basic idea of diversity reception is that, if two or more independent samples of a signal are taken, then these samples will fade in an uncorrelated manner. This means that the probability of all the samples being simultaneously below a given level is much less than the probability of any individual sample being below that level. Thus, a signal composed of a suitable combination of various samples will have much less severe fading properties than any individual sample alone.5.2.2.In this section we analyze the performance when there is a single antenna in the receive array. The simulations were carried with different varying parameters. First, we perform the simulations based on varying receiver height. If the receiver height is low, there is a possibility that it may suffer from deep fades due to dense environment surrounding the receiver hence degrading the performance of the system. On the other hand, if the receiver is mounted on a higher ground, it will be less susceptible to fading and hence will collect the signal more efficiently.max, therefore, as we change τmax, the geometry of the ellipse also changes. The results show that as τmax is decreased, the system gives better performance. It can be explained in terms of the geometry of the ellipse. As τmax increases, amand bm also increases, thus making the ellipse larger, and vice-versa. Larger ellipse means increase in the propagation delays, hence poorer performance. Smaller ellipse means lesser propagation delays, hence better performance.5.2.3.R and τmax when we have multiple antennas at the receiver. As seen from the table, MRC gives the best performance, thus, for our further simulations we have focused on the system model with MRC at the receiver only and the implementation of EGC and SC is straight forward. max and proves that BER improves with smaller τmax. The simulations have been performed with number of receive antennas up to four but it is not limited and can be extended for higher numbers of receive antennas.In this section, we repeat the simulations for different HThe BER values for multiple receive antennas at different receiver height with different maximum multipath delay have been summarized in 6.We analyze the problem from the overall performance of the system. The model presented in this paper has been developed for a microcell environment which has a quasi-static channel. A cluster based WSN architecture has been assumed at the transmission side. The cluster head is assumed to be surrounded by local scatterers giving rise to multipath and fading. At the receiver, receiving arrays are used to collect all the multipath components of the signal effectively. The advantage of using smart antennas in a cluster based WSN model has been demonstrated where performance improvements can be realized in terms of received SNR. The numerical simulations based on the variations in receiver height reveal that the performance of the system increases if the receiver height is increased above ground level. Also the numerical simulations based on maximum multipath delay shows that the semi-major and semi-minor axis of the ellipse changes with variations in the maximum multipath delay, hence affecting the performance of the overall system. The performance of the system is also improved as the transmission power increases. Since the cluster head is located very near to the sensor nodes, the sensor nodes do not require high transmission powers so they do not face the reachback problem. The paper justifies the use of receive diversity at the receiver for reliable communication between the cluster head and the receiving arrays and proves that MRC provides the best performance when applying receive diversity. We also quantify the fact that with the increase in the number of antenna elements, we are able to increase the reliability and robustness of the system. The number of antenna elements has been kept low while solving our problem. However, they can be extended to higher number of receive antennas for a large receiving array.