Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the ‘unstable’ results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance.χ2-test and odds ratio methods in terms of retaining gene-gene interactions.We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional  The advancement of high-throughput genome-wide association (GWA) studies has tremendously improved our understanding of the genetic basis of many common complex diseases . Under tcombinatorial methods. Popular combinatorial methods include random forests based algorithms . D(.) is the difference function that calculates the difference between any two samples as and bs for a given gene g:where G(.) denotes the genotype of SNP g for sample s. The nearest neighbors to a sample are determined by the distance function, MD(.), between the pairs of samples (denoted as as and bs) which is also based on the difference function (Eq. 2):where Algorithm 1.Using pseudocode, we can outline the ReliefF algorithm in The ReliefF algorithm calculates the distance between different samples using the genotype information of all SNPs. However, such a procedure is sensitive to noise in the dataset.Algorithm 1 ReliefFforj = 1 to Ndo1: W(jg));2: initiate(end for3: fori = 1 to Mdo4: i =s randomSelect(sampleSize);5: H = findHitNeighbours; (h1...K ∈ Hh )6: M = findMissNeighbours; (m1...K ∈ Mm)7: forj = 1 to Ndo8: fork = 1 to Kdo9: W(jg) = W(jg) – D/M + D/M10: end for11: end for12: end for13: R, it removes the N/R lowest ranking  SNPs in each iteration, where N is the total number of SNPs. The pseudocode for TuRF is shown in Algorithm 2.TuRF  aims to Algorithm 2 TuRFfori = 1 to Rdo1: M,K);2: apply ReliefF;4: removeLowSNP is adopted, K = 10 of nearest neighbors is used, and 10 iterations (R = 10) for TuRF is applied.We follow the same configuration in previous studies ,19,25, iWe find that the ReliefF algorithm is sensitive to the order of samples used to calculate the SNP ranking score (Eq. 1). That is, running these algorithms on the same dataset in which the order of the samples is permuted (while maintaining the sample-class label association), leads to different SNP ranking results.Algorithm 1). Since K nearest neighbors are calculated by comparing the distance between each sample in the dataset  and the target sample (is in Algorithm 1), a tie occurs when more than K samples have a distance equal or less than the thK nearest neighbor of is. We can show that the sample order dependency can be caused by using any tie breaking procedure which forces exactly K samples out of all possible candidates to be the nearest neighbors of is, which causes a different assignment of “hit” and “miss” of nearest neighbors when the sample order is permuted.Such a sample order dependency is related to the assignment of “hit” and “miss” nearest neighbors of each sample  preserves the general algorithmic principle of ReliefF, and (2) make use of all the information embedded in all the tied samples when a tie-breaking situation occurs. To achieve this, we use a rank score aggregation approach that adhere to the general principle of ensemble learning . From ouL copies of the input SNP dataset Dl  by randomly permuting the order of the samples. ReliefF-E calculates the weight for each SNP jg by averaging multiple rankings rank(lh(jg), Dl) each from a permuted dataset as follows:Formally, our ensemble of ReliefF  produces ensembleW(.) is the ensemble weight and lh(.) is the hypothesis of a filter algorithm obtained from the permuted dataset Dl.where Similarly, the ensemble of TuRF  performs multiple runs of TuRF, and aggregates the ranking scores of each SNP produced in each iteration of TuRF using Eq. 4.We performed a set of analysis on simulated and real-world SNP datasets to 1) demonstrate the sample order dependency in ReliefF and TuRF, (2) show that ReliefF-E and TuRF-E can indeed largely eliminate such a sample order dependency, and (3) investigate whether our ensemble approaches can effectively retain functional SNP pairs. We used a set of simulated datasets generated by  in our a demonstrA GWA study dataset generated from case-control design of age-related macular degeneration (AMD) samples  is also r, is calculated using the rank of all SNPs generated from the two runs. A SNP filtering algorithm that obey sample order invariance should produce the same SNP ranking regardless of the sample order .To demonstrate the sample order dependency, a dataset is analyzed by each of the four filtering algorithms  twice in which a different permutation of sample order is used for each run of the algorithm . A Pearson correlation coefficient, χ2-test and odds ratio; ReliefF-based filters: ReliefF, TuRF, and SURFTuRF; and two ensemble filters: ReliefF-E and TuRF-E. Specifically, we apply these seven filtering algorithms to the eight simulated models and compare the success rate of each method in filtering 100 datasets of each model. The success rate is defined as the number of times a give filtering algorithm is able to retain the interaction SNP pair in the dimension reduced subset in 100 datasets. The dimension of the dataset is divided in percentile. For a dataset with a SNP size of 1000, the percentile of 1 includes the top 10 SNPs while the percentile of 10 includes the top 100 SNPs. Therefore, if we reduce the dimension of the dataset to 100 SNPs , and the interaction SNP pair is within this 100 SNPs, we say the filter successfully retained the interaction SNP pair at the percentile of 10.These simulated datasets Table  are alsoaverage cumulative success rate which is computed as the sum of success rate from percentile 1 to 50 divided by 50.We use graph to present the success rate of each method from 1 to 50 percentile, and we quantify the overall success rate of each method by an r = 0.43 for the dataset with 400 samples and a r = 0.36 for the dataset with 800 samples. As we shall demonstrate later, such an instability caused by sample order dependency has led TuRF to perform suboptimally.We found that the SNP ranking generated by ReliefF and TuRF are sensitive to the order in which the samples are presented in the dataset. Figure r = 0.97 for the dataset with 400 samples and a r = 0.95 for the dataset with 800 samples.By using the ensemble approach , we are able to stabilize the ranking results of both ReliefF and TuRF. Especially, TuRF-E can significantly increase the stability of the SNP ranking results of TuRF, with a r = 0.99 for ReliefF and a r = 0.98 for TuRF.Similar results were obtained when the AMD dataset was analyzed Figure . The reshttp://sourceforge.net/projects/mdr/) to report the tie causing samples and remove them from the dataset. After removing all tie causing samples, we were able to obtain completely reproducible ranking results  with both ReliefF and TuRF . In fact, any tie-breaking procedure which chooses one sample out of all valid candidate samples will necessarily produce instability in its resulting ranking score.kNN criteria. Indeed, our evaluation (which is presented in later section) confirmed that SURF does not fully recover the SNP filtering capability. As discussed later in this paper, our ensemble approach, which rely on sample ranking instead of direct thresholding, gives consistently better results.Another way to solve such a sample order dependency can be achieved by defining nearest neighbors to a sample as the ones that are within a certain distance threshold of the target sample. A recently developed variant algorithm of ReliefF called SURF   employedAn important parameter in any ensemble method is the ensemble size. In our case, it is the number of times an algorithm is repeatedly applied on a dataset with reordered samples. It is important to estimate the minimum ensemble size that is sufficient to reduce sample order dependency. We estimate this value via repeating the correlation analysis on TuRF-E with an ensemble size of 10, 20, 30, 40, and 50 using the simulated datasets with 400 samples and 800 samples  using simulated datasets with 400 sample and 800 sample respectively. We then repeated ReliefF and its corresponding ReliefF-E, TuRF and its corresponding TuRF-E each five times to calculate the standard deviation in multiple runs. Table χ2-test and odds ratio give the lowest success rates. The superiority of TuRF-E is particularly noticeable in datasets simulated with low heritability or small number of samples. This implies that TuRF-E is applicable in even these “challenging” cases where other ReliefF-based algorithms fails to achieve high enough success rates.One motivation of using the proposed ensemble approach is to gain a more informative SNP scoring. Therefore, we investigated whether our ensemble scheme can improve the ability of ReliefF and TuRF to retain functional SNP pairs in SNP filtering. Figure ReliefF-E only has marginal improvement over ReliefF whereas TuRF-E achieves a significant improvement over TuRF. This is likely due to the fact that the TuRF algorithm executes ReliefF multiple times while removing low ranking SNPs in each iteration. Therefore, the ensemble approach could accumulate more information in each iteration. It is also observed that SURFTuRF does not improve on TuRF in analyzing datasets of 400 samples. This is consistent with our hypothesis that a predefined distance threshold in SURFTuRF may be sensitive to high SNP-to-sample ratio . Moreover, The standard deviations of ReliefF and TuRF are generally much larger than their ensemble version, indicating the sample order dependency also affecting the stability of the success rate of SNP-SNP filtering.We further investigated whether TuRF-E is simply “averaging” out the detection ability at different runs of TuRF. Figure The field of gene-gene and gene-environment interaction identification from GWAS data is still young and rapidly developing. One of the biggest challenges in identification of such interaction relationship is computational efficiency since in the worst case an exponentially large number of SNP combinations need to be evaluated. As discussed by a number of authors ,4,15, efThrough analyzing the ReliefF-based algorithms, we discovered a previously unknown anomaly in both ReliefF and TuRF. We show these two popular filtering algorithms are sensitive to sample ordering, therefore, giving unstable and suboptimal SNP ranking in different runs when sample order is permuted. However, we found that such an unstable behavior can be effectively utilized in an ensemble learning framework. Using a simple aggregation procedure based on the general theory of ensemble learning, we can vastly improve the stability and reliability of the SNP ranking generated by these algorithms.kNN procedure is still noteworthy in the development of new algorithms.ReliefF based algorithms are also used to perform feature selection tasks for a range of machine learning problems including gene selection in microarray analysis. This implies our findings are not limited to the field of gene-gene interaction identification in GWA studies, and may have relevance to the broader machine learning community. Although we recognize that the sample order sensitivity problem is of less relevant to continuous datasets since tie-breaking is less likely to occur, the potential problem caused by tie-breaking in a Our work indicates that new algorithms should be validated against a range of criteria. Many bioinformatics algorithms have been developed to perform such filtering task. These algorithms are mostly assessed and compared based on its objective, in our situation, how well can a filtering algorithm retain functional SNP pairs. However, much less focus has been placed on analyzing whether the results generated by a SNP filtering algorithm satisfy a set of desirable properties. The sample order dependency property in this paper is one such example as it is not natural to expect the SNP ranking to change due to reordering the samples in a dataset. In fact, the importance of validating a bioinformatics algorithm and its software implementation is increasingly being recognized , and we We proposed an ensemble approach for gene-gene interaction filtering of GWA study dataset. Our approach aggregates the ranking scores of each SNP generated from multiple runs of RelieF or TuRF with sample-order permuted datasets. Such an ensemble method is robust to sample order dependency observed in commonly used ReliefF and TuRF algorithm. Based on the analysis using a number of real and simulated datasets, we demonstrated that the proposed approach can produce much more stable SNP ranking. In addition, the ensemble of TuRF performed the best in retaining interaction SNP pairs, superseding the performance of other traditional methods as well as state-of-the-art ReliefF-based algorithms.The software of ReliefF-E and TuRF-E are available from:http://www.cs.usyd.edu.au/~yangpy/software/EnsembleFilter.htmlThe authors declare that they have no competing interests.


Eucalyptus. Although SNP assay success was high, the proportion of polymorphic SNPs declined as phylogenetic distance between species increased, down to <20% when contrasting E. grandis and E. globulus, the two main worldwide commercially planted species were considered [Eucalyptus.The availability of next generation sequencing (NGS) technologies has opened the door to new strategies of SNP discovery and genotyping. Rapid genome-wide SNP detection via deep resequencing of reduced representation libraries of restriction digested pools of genomic DNA combined with a reference genome has been successfully used for SNP discovery in microorganisms , plants nsidered . In thisE. grandis and 18 of E. globulus. For each species three bulks of six individuals were prepared with equimolar amounts of picogreen quantified DNA. DNA samples were delivered to Floragenex who carried out the RAD reduced representation library preparation using PstI and Illumina 75 bp single-end sequencing on a GAIIx. Raw sequence data was filtered for quality and mapped onto the 11 chromosomes of the E. grandis reference genome available in Phytozome. SNPs in the short sequence tags were called for nucleotides with quality Q> 30 at the position and a minimum of 6X coverage.DNA was extracted separately from 18 unrelated individual trees of The average sequencing depth exceeded 28X for all bulked samples, providing a minimum estimated ~5X coverage for each individual present in each bulk providing a 93.75% probability of detecting a heterozygous SNP position. With 18 individuals per species (36 chromosomes), the probability of detecting a SNP allele with a Minimum Allele Frequency (MAF) > 0.1 is > 95% [Out of a total of 200,712 SNPs declared with high confidence, 42,300 were simultaneously polymorphic in the two species while the remaining were fixed in one or the other. These 42,300 SNPs provide an average density of one SNP every 14 kbp in the Eucalyptus genome. These SNPs could be immediately used to select an evenly spaced set of SNPs for the development of a high density SNP genotyping platform.Eucalyptus . All these NGS based genotyping methods will cause a paradigm shift in our ability to carry out high density, high throughput and low-cost genotyping of large numbers of samples, unlocking incredible opportunities in forest tree genetics and breeding in the years to come.Taken together, the RAD tags plus the SNPs into them provide excellent marker density for applications such as Genomic Selection . Besides


The analysis of human Y-chromosome variation in the context of population genetics and forensics requires the genotyping of dozens to hundreds of selected single-nucleotide polymorphisms (SNPs). In the present study, we developed a 121-plex (121 SNPs in a single array) TaqMan array capable of distinguishing most haplogroups and subhaplogroups on the Y-chromosome human phylogeny in Europe.We present data from 264 samples from several European areas and ethnic groups. The array developed in this study shows >99% accuracy of assignation to the Y human phylogeny .We have created and evaluated a robust and accurate Y-chromosome multiplex which minimises the possible errors due to mixup when typing the same sample in several independent reactions. The development of high-throughput technologies to genotype hundreds of thousands of markers has yielded an increase in the understanding of the genetic diversity of our species . This geThe human Y chromosome has been extensively analysed in forensic and evolutionary studies because of its unique properties. Despite being a complex chromosome with highly repetitive sequences , its excIt is preferable to genotype, in a reproducible manner, around 100 SNPs at a time. We therefore aimed to design a single multiplex reaction of this size using TaqMan probes . TaqMan probes are robust and reproducible assays that have been used extensively to type SNPs in single reactions, that is, one SNP per TaqMan reaction. Each assay is based on TaqMan probes that are hydrolysis probes that anneal within a specific DNA region amplified by polymerase chain reactions (PCRs). The probes present a fluorophore  attached to the 5′-end and a quencher at the 3′-end that prevents fluorescence of the fluorophore. As the AmpliTaq Gold DNA Polymerase (Applied Biosystems) extends the primer, its exonuclease activity degrades the probe that has annealed to the template and releases the fluorophore, relieving the quenching effect and allowing fluorescence of the fluorophore. In a TaqMan assay, two probes labelled with different fluorophores are used, each one complementary to one of the two alleles  of a SNP. In this way, in a real-time PCR, the alleles of one SNP of a DNA sample can be interrogated using two TaqMan probes labelled with different fluorophores. In the present study, our goal was to perform more than 100 TaqMan assays in a single array to define detailed haplogroups and subhaplogroups of the human Y chromosome in European populations.Of the 128 SNPs designed in the TaqMan OpenArray plate , 121 were successfully typed. The internal controls gave completely concordant results, as M145 genotypes were always concordant with M203, and the two different assays for the marker M9 gave identical results in all the individuals. In addition, the genotypes obtained for the control samples  were identical to those previously obtained with the same TaqMan assays used one-by-one, giving a concordance rate of 100%  was high (average 96.4%), with the lowest value being for L23 (84.8%) Table , 40 of w10831.1 , but was ancestral for all the rest of the SNPs in the array , it was also derived for M89 , M168 , M42  and SRYThe aim of the present study was to create a SNP multiplex for the human Y chromosome in a single array that could be used to define Y-chromosome haplogroups in European populations. Previously, genotyping of Y-chromosome SNPs has been performed in single-plex reactions or using limited multiplexes -16. The The amount of DNA used in the present sample set as recommended by the manufacturer is high (300 ng); however, high pass rates and calling rates (similar to the ones provided in the present sample set) have been obtained with as little as 90 ng , provided that the A260/A280 DNA absorbance ratio is approximately 1.8 (data not shown), which stresses the relevance of the DNA purity to perform the analyses. This DNA amount is within the range described in previous works multiplexing the Y chromosome, mitochondrial DNA and autosomal SNPs ,12,16, aAlthough the present multiplex was designed to screen populations of European origin, it might be used to detect the presence of individuals of non-European ancestry in European samples and as a first approach for other admixed populations with subsequent typing for those haplogroups not common in Europe, since some branches of the Y-chromosome phylogeny are not defined in the present assay . Nonetheless, the present results show that the TaqMan assay multiplex technology exhibits successful and robust results and that other combination of SNPs could be designed to genotype all branches of the Y-chromosome phylogeny or to focus on specific regions of the phylogeny. The OpenArray technology has the potential to combine up to 256 TaqMan assays in the same plate, which is enough to genotype the main diagnostic SNPs of most branches and subbranches of the human Y-chromosome phylogeny. In addition, the present methodology is flexible enough to include those markers on the Y chromosome that are required for a specific analysis which contrast with the rigidity of commercial high-throughput arrays that also include several SNPs in the Y chromosome. Although several genome-wide human SNP arrays have incorporated hundreds of Y-chromosome SNPs, just a few of the SNPs in our array share commonality with some commercially available SNP arrays . Thus, to our knowledge, this study is the first to describe typing with flexibility to such a high accuracy the human Y chromosome in a single array.We have created and evaluated a robust and accurate Y-chromosome array to genotype 121 SNPs at a time using TaqMan probes that classifies individuals into the main haplogroups and the main European subhaplogroups in the human Y-chromosome phylogeny, substantially decreasing time of laboratory work and minimising the possible errors due to mixup when typing the same sample in several independent reactions.DNA samples were obtained from 22 healthy unrelated males from each of the following 12 populations: Bulgarians, Bulgarian Roma, Spanish, Spanish Roma, Italians, Germans, British, Faroe Islanders, Danish, Greeks, Hungarians and Ukrainians. DNA samples from another 18 individuals of diverse origins obtained from the Coriell Institute for Medical Research  were used as internal controls of the accuracy of the multiplex, since their haplogroup affiliations had previously been determined by two different laboratories using the same SNPs in single reactions incorporated into the present multiplex assay. Informed consent was obtained from all participants, and the project was approved by the Clinical Research Ethics Committee at the Institut Municipal d'Assistència Sanitària (IMAS reference 2006/2600/I). DNA extraction was carried out using a standard phenol/chloroform method.et al. [http://isogg.org/tree/ISOGG_YDNA_SNP_Index09.html) were used for SNP selection. The flanking sequences for each of the SNPs were then investigated for the design of the SNP TaqMan probes and submitted to the TaqMan assay design pipeline . See Additional file A total of 128 Y-chromosome SNPs defining the main haplogroups and subhaplogroups were chosen, with special attention to SNPs defining haplogroups and subhaplogroups reported as variable in European samples -19. DataIn the present analysis, a multiplex of 128 TaqMan assays was performed in a single array for each DNA sample. Each TaqMan OpenArray plate was designed to contain the 128 assays for a total of 24 samples (22 DNA samples and 2 NTCs). Technical exigencies of the autoloader machine require double the amount of DNA to be present in the loading tips. A total of 300 ng of genomic DNA was used, and a final amount of 150 ng was incorporated into the array with the autoloader and genotyped according to the manufacturer's recommendations. The multiplex TaqMan assay reactions were carried out in a dual 384-well GeneAmp 9700 Thermal Cycler  with the following PCR cycle: an initial step at 93°C for 10 minutes followed by 55 cycles of 45 seconds at 95°C, 13 seconds at 94°C and 2 minutes, 14 seconds at 53°C.et al. [http://isogg.org/tree/ISOGG_YDNA_SNP_Index09.html).The fluorescence results were read using Autocaller software . The genotypes were compiled and used to assign each sample to its Y-chromosome haplogroup according to the Y-chromosome phylogeny published by Karafet et al.  and the The authors declare that they have no competing interests.BMC and DC planned the study and wrote the manuscript. BMC analysed the data. JZ designed the assays. PS, GS, RA and SP performed the laboratory work. All authors read and approved the final manuscript.Figure S1. Phylogenetic tree showing the haplogroup tested with the OpenArray following the nomenclature published by Karafet et al. [http://isogg.org/tree/ISOGG_YDNA_SNP_Index09.html). Recurrent mutations of three markers  are shown in different branches of the tree.t et al.  and on tClick here for fileTable S2. List of the primers used for the amplification of every assay implemented in the OpenArray.Click here for file


Single nucleotide polymorphisms (SNPs), the most abundant variations in agenome, have been widely used in various studies. Detection andcharacterization of citrus haplotype-based expressed sequence tag (EST) SNPswill greatly facilitate further utilization of these gene-basedresources.in silico validation of these SNPs, in additionto and consistent with the rate (81%) validated by sequencing and SNaPshotassay.In this paper, haplotype-based SNPs were mined out of publicly availablecitrus expressed sequence tags (ESTs) from different citrus cultivars(genotypes) individually and collectively for comparison. There were a totalof 567,297 ESTs belonging to 27 cultivars in varying numbers andconsequentially yielding different numbers of haplotype-based quality SNPs.Sweet orange (SO) had the most  ESTs, generating 11,182 qualitySNPs in 3,327 out of 4,228 usable contigs. Summed from all the individuallymining results, a total of 25,417 quality SNPs were discovered –15,010 (59.1%) were transitions (AG and CT), 9,114 (35.9%) weretransversions , and 1,293 (5.0%) wereinsertion/deletions (indels). A vast majority of SNP-containing contigsconsisted of only 2 haplotypes, as expected, but the percentages of 2haplotype contigs varied widely in these citrus cultivars. BLAST of the25,417 25-mer SNP oligos to the Clementine reference genome scaffoldsrevealed 2,947 SNPs had “no hits found”, 19,943 had 1 unique hit/ alignment, 1,571 had one hit and 2+ alignments per hit, and 956 had 2+hits and 1+ alignment per hit. Of the total 24,293 scaffold hits, 23,955(98.6%) were on the main scaffolds 1 to 9, and only 338 were on 87 minorscaffolds. Most alignments had 100% (25/25) or 96% (24/25) nucleotideidentities, accounting for 93% of all the alignments. Considering almost allthe nucleotide discrepancies in the 24/25 alignments were at the SNP sites,it served well as in silico validation, inaddition to SNaPshot and sequencing validation of selected SNPs.High-quality EST-SNPs from different citrus genotypes were detected, andcompared to estimate the heterozygosity of each genome. All the SNP oligosequences were aligned with the Clementine citrus genome to determine theirdistribution and uniqueness and for  Single nucleotide polymorphism (SNP) refers to an allelic single-base variationbetween two haplotype sequences in an individual or between any paired homologouschromosomes across homogenous members. SNPs are most abundant among genomic DNAvariations and ubiquitous in both functional genes and non-coding regions . BecauseSNP discovery usually is accomplished through computational alignment of redundantDNA sequences with each other or with a high-quality reference genome wherediscrepant nucleotides can be detected and evaluated. For the redundancy-basedcomputational approach, in addition to sequencing errors as a source of false SNPs ,7,10, itCitrus and those in the sexually compatibleFortunella (kumquat) and Poncirus (trifoliate orange) genera.Citrus fruit types are diverse, and include sweet orange (Citrus sinensis),mandarin (C. reticulata), grapefruit (C. paradisi), lemon (C.limon), lime (C. aurantifolia), pummelo (C. maxima), andcitron (C. medica). Each type consists of many cultivars primarily selectedfrom spontaneous bud sports, chance seedlings, induced mutants, or conventionalhybrids. It is widely believed that only C. maxima, C. reticulata,and C. medica are true species, although the binomial names for the otherancestral hybrid and introgression cultivars are widely accepted and used [Most citrus species are diploid (2n = 2× = 18), withhighly heterozygous and relatively small genomes and over 30,000 predicted genes . In geneand used ,18. Thesand used -21. A haand used , along mand used ,21,23.silico validation. Selected SNPs were also validated by SNaPshot andsequencing.In this paper, SNPs in public ESTs from 27 different citrus genotypes were detectedby the QualitySNP pipeline and compared to estimate the heterozygosity of eachgenome. All of the short SNP oligo sequences were also aligned with the Clementinecitrus genome to determine their distribution and uniqueness in the genome and forin Citrus sinensis) was selected for SNP validation because themost ESTs and SNPs are from sweet orange and it is a parent to several widelyused mapping populations.All citrus ESTs were retrieved from the National Center of BiotechnologyInformation (NCBI) EST database or ftp repository if available. There were 27citrus cultivars or biotypes with ESTs Table . In addi2O at 10 μM. The format facilitated easyprimer positioning and channel pipetting during the genotyping and sequencingpreparation.The QualitySNP pipeline was installed and used for SNP discovery, following theprogram manual and recommended parameters . QualityAll 25-nucleotide oligo sequences (SNP in the middle nucleotide) generated fromevery citrus genotype by QualitySNP were combined together and used to align tothe haploid Clementine reference genome  using BLASTN  and a cuBigDye Terminator V3.1 Cycle Sequencing Kit and SNaPshot Multiplex Kit  were used to validate SNPs, following themanufacturer’s protocols with some modifications in reaction volumesand/or quantity of proprietary reagents. 96-well plates were used for PCR,enzymatic incubation, and denaturation on iCycler  and/orGeneAmp PCR System 9700 , and forgenotyping and sequencing on 3130xl Genetic Analyzer . Unless otherwise stated, brief centrifugation up to 1000 rpm inJuan MR 23i was applied after addition of a solution or before implementation ofnew steps, and all the PCR and enzymatic incubation programs were set to hold at4°C indefinitely at the end until a next procedure.2O, 1.0 μl 10x dNTPs (2 mM),2.0 μl 5x colorless GoTaq Flexi buffer, 0.8 μl 25 mMMgCl2, 0.4 μl F and R primers each,0.1 μl GoTaq Flexi , and2 μl genomic DNA (10 ng/μl). The touch-down PCR programstarted from an initial denaturation at 94°C for 3 min, followed by10 cycles of 93°C for 30 sec, 56°C for 45 sec, 72°C for 45 sec, and 30continuing cycles with 51°C at the annealing step, plus a final elongationat 72°C for 15 min. Removal of primers and unused dNTPs was performedby addition of 1 μl of ExoISAP-IT  intoeach well of the plate, and incubation at 37°C for 60 min and75°C for 15 min.For both dye terminator sequencing and SNaPshot assays to validate SNPs, templatepreparation was carried out in 10 μl in each well consisting of3.3 μl ddH2O and6 μl Hi-Di formamide in each well, denatured, and loaded to thegenetic analyzer to sequence. The sequence files generated were analyzed bySequencing Analysis software  to generatesequences and electropherograms, in which a validated SNP was confirmed bycorrect alignment of SBE primer sequence into the corresponding sequences andvisualization of two different overlapped nucleotide peaks at the nucleotidesite in the electropherograms.Sequencing reactions for SNP validation were prepared in 10 μl in eachwell of a new plate including 2 μl 5x sequencing buffer,2 μl ready reaction premix in the sequencing kit, 1 μl10 μM SNP F primer, and 5 μl ExoSAP-IT treated PCR product,started at 95°C for 1 min, followed by 25 thermal cycles of 95°Cfor 10 sec, 50°C for 5 sec, and 60°C for 4 min.Following the manufacturer’s instructions, ethanol/EDTA/sodium acetateprecipitation was used to purify the sequencing product in the plate, which wassubsequently air dried, then mixed with 2 μl ddH2O, 1.0 μl 10x SAP buffer, and0.5 μl 1u/μl SAP) into the SBE reaction mix, and incubation at37°C for 60 min and 75°C for 15 min. Genotyping wasperformed using 8 μl mix in each well of a new plate consisting of1 μl SAP treated SBE product, 0.25 μl GeneScan 120 LIZ sizestandard, and 6.75 μl Hi-Di formamide, which was denatured at95°C 3 min then immediately moved on ice for at least 2 min. TheSNaPshot files were used to score SNPs by GeneMarker  in which a validated SNP consisted of two differentnucleotides.The SBE reaction for SNaPshot assays was prepared in 5 μl in each wellin a new plate including 0.5 μl ready reaction premix in the SNaPshotkit, 1 μl SBE 10 μM primer, and 3.5 μl ExoSAP-ITtreated PCR product, and repeated in 25 thermal cycles of 95°C for10 sec, 50°C for 5 sec, and 60°C for 30 sec. Removal ofunincorporated dye-labeled ddNTPs was completed by addition of 5 μlSAP mix  using the QualitySNP pipeline and summarized in detail  and no quality SNP identified inthe additional 1,001 contigs  with potential SNPs. There were3.4 quality SNPs per contig and one quality SNP per 723 bp in the contigson average. Of these 11,182 qSNPs, 6,822 (61.0%) were transitions (AG and CTtype), 3,879 (34.7%) transversions , and 481 (4.3%)insertion/deletion (Indels); and 2,619 (23.4%) were nsSNPs and 4,038 (36.1%)were sSNPs. The absolute numbers of quality SNPs were not comparable due tovarying numbers of ESTs among citrus cultivars, but the number of potential andquality SNPs from each cultivar were strongly correlated with its number ofESTs; more ESTs yielded more usable contigs (>=4 ESTs) available for SNPmining, as well as more quality SNPs  due to introduction of more haplotypes from different types of citruscultivars, compared to their counterpart averages of each group . As a consequence, more qSNPs in higher qSNPs/pSNPs andqSNPs/ESTs ratios were found in the three grouped EST datasets , compared to their counterparts  summed from theindividually mined cultivar EST results, but the ratio of contigs with qSNPs andcontigs used was the opposite are considered as potential SNPs for further quality and confidenceinterrogation. Only those potential SNPs passing confidence scores areidentified as quality SNPs. In Additional file A total of 25,417 25-mer sequences , accounting for 98.6% ofthe total. The remaining 338 were on 87 small scaffolds. Figure Taking these multiple scaffold hits and alignments into account, the total numberof scaffold hits was 24,293 with a total of 43,668 alignments on the scaffolds.Most had 100% (25/25) or 96% (24/25) nucleotide identities to those on thereference genome, accounting for 93% of all the alignments. Almost all thenucleotide discrepancies in the 24/25 alignments were at the SNP sites, which isan encouraging Of the 96 randomly selected sweet orange SNPs, 68 were validated by sequencingand 74 by SNaPshot in sweet orange ; L7, 7limes/lemons (14–20); C27, all 27 citrus varieties (1–27);and three summed/averaged results, SM12, SL7 and SC27, respectively fromthe 12 individually mined mandarins, 7 limes/lemons, and all 27varieties, which were used for comparison to M12, L7, and C27.Click here for file25417 25-mer sequences of SNPs and forward, reverse, single baseextension (SBE) primer, and SBE 5'‒tail sequences for 96 SNPsselected from sweet orange.Click here for fileSNP distribution on the Clementine reference genome Scaffold_2. Eachinterval of the x-axis represented 500 kb of the scaffold, and they-axis represented the number of SNPs in each 500 kb on thescaffold. SO – sweet orange (A); TO – trifoliate orange (B);CM – Clementine mandarin (C); “_a” – counts ofall alignments generated by all SNPs; “_1” – counts ofSNPs of only 1 unique hit/alignment in the genome. Differences betweenthe “_a” and “_1” numbers were observed inseveral regions of each cultivar.Click here for fileSNapShot chromatograph of a SNP validated by the assay, generated byGeneMarker . The y-axis represents theintensity of, and x-axis the approximate length of, thefluorescently-labeled SBE products ending with A and G.Click here for file


The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs  in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%. Genome-wide association study (GWAS) meta-analyses routinely use genotype imputation The recently released 1000 Genomes haplotypes It is therefore useful to quantify the likely benefit of adding local reference data to 1000 Genomes data, particularly for less common variants, and especially if the population is genetically distant from the 1000 Genomes populations.We used data from the CROATIA-Korcula and Orkney Complex Disease studies (ORCADES) Genotypes obtained from the whole exome sequencing of 91/89 CROATIA-Korcula/ORCADES quality controlled samples were used to supplement the 1000 Genomes reference panel. We focused on less common (MAF 1–10%) exonic variants already in 1000 Genomes which, unlike low frequency, and rare (MAF<1%) or private variants, can be meta-analyzed in typically sized consortia.In this paper, we therefore seek to determine if imputation accuracy can be improved by the addition of local sequences to a global reference panel.The ORCADES and CROATIA-Korcula studies both had ethical approval for genetic research into the basis of complex traits, approved by the appropriate committees in each country. For ORCADES the committees were the Orkney Local Research Committee and the North of Scotland Research Ethics Committee . For CROATIA-Korcula the committees were the Ethics Committee of the Medical School, University of Split  and the NHS Lothian . All participants provided written informed consent.Array genotypes were obtained from Illumina Hap370CNV array, at 319,552 SNPs for CROATIA-Korcula subjects and Illumina Omni1 array at 1,140,419 SNPs or the Illumina Human Hap300 array at 293,687 SNPs for ORCADES subjects. For ORCADES a common panel of intersecting Hap300 and Omni1 SNPs was first created. The panel for CROATIA-Korcula was then restricted to these SNPs, to ensure similar panel sizes.Subjects to be sequenced were selected from the wider study populations that were genotyped on the Illumina Hap (370CNV/300) arrays to minimize relatedness, and thus to maximize representation of study population haplotypes. The selection was carried out by tracking the identity-by-descent sharing structure, as determined by the array genotypes using the program ANCHAP Quality control (QC) of genotyping array data, that were subsequently used for imputation, was in accordance with best practice for association studies −4, were also removed, leaving 99/95 CROATIA-Korcula/ORCADES subjects genotyped for 102,192/97,052 variants. The HWE test was a more stringent test than for the array data reflecting lower sample numbers and the desire to particularly ensure integrity for reference data. We restricted our analysis to individuals with exome sequences and merged the exomes with the array data for these subjects. Subjects/variants with more than 50/30 mismatching calls, between the array and sequence data were excluded, although no variants failed this test. This resulted in exomes for 93/90 subjects genotyped at 102,192/97,052 exonic SNPs being merged with array data at 170,134/171,749 SNPs for these individuals. The resulting panels had 265,929/262,513 variants which were 99.91%/99.92% concordant, based on the genotypes called on both panels for 6,397/6,285 overlapping variants. As the overall genotypic concordance could mask discrepancies for minor alleles, particularly the less common variants of interest, concordance rates for minor allele calls were calculated in the MAF 1–3% range separately. Only 1/1 (CROATIA-Korcula/ORCADES) call was discrepant on each overlapping panel, giving minor allele concordance of 99.7% in both studies for these variants.Exome sequence data were also subjected to rigorous QC to ensure they were of high quality so that that the local reference panel we created did not have a significant number of incorrect haplotypes. Variants were called by first aligning the raw sequence data to the human hg19 reference genome using the Stampy short read aligner 8,150/10,964 Korčulan/Orcadian variants other than single base substitutions, for example insertions or deletions, were excluded. 119/110 conflicting map positions and individuals called at fewer than 80% of the combined SNP panel were then excluded, leaving 91/89 subjects typed across 257,633/251,439 SNPs. Our focus was on the potential to improve power in meta-analyses, so polymorphisms that were unique to each cohort were excluded. This was done by comparison to the 1000 Genomes project map and those variants not present in the 1000 Genomes reference data or with mismatches in allele codes were excluded.e of 11,418 and the default settings The merged sequence and array data consisting of 233,195/232,096 variants for 91/89 subjects were then phased by SHAPEIT, using the recommended NHaving created suitable post-QC array data and secondary reference panels, imputations were performed using genome-wide array data plus (i) 1000 Genomes haplotypes e was set to 20,000; all other settings were left at their default values. For the one panel imputation, the 1000 Genomes Phase 1 worldwide integrated variant set (March 2012 release) 2) of the allelic dosages imputed was measured across samples against the known exome sequence-called genotypes.Imputation of the 91/89 subjects with and without the benefit of local reference data was carried out using IMPUTE2, using the phased reference panel option, the phased array data haplotype option, and with the software splitting the genome into chunks, which had been predetermined to be less than 5 Mb in size and avoiding crossing the centromeres. NAs evidenced by the genome-wide SNP array concordance data, noted above, there was close agreement between the exome sequence and independent genotyping data, indicating that the sequences were a suitable gold standard. Furthermore exome array data were also available for the CROATIA-Korcula study  and concordance between exome array and exome sequence genotypes was 99.5% and was similar across all MAF bands.The dual use of exome sequences both as a secondary reference panel and as the gold standard to obtain imputation accuracy was considered appropriate since a subject’s imputation panel did not include their own sequence, avoiding circularity at the imputation stage.2 of imputed against known allele dosages across samples for a given SNP) from use of a local reference panel, which was often substantial for less common variants  for CROATIA-Korcula/ORCADES and 0.112/0.089 (15%/11% improvement) for variants with MAF between 0.032 and 0.100. The high accuracy of the 1000 Genomes imputation for more common variants (MAF >0.1) provided more limited scope for improvement in this category, although even for the most common variants (MAF>0.32) the accuracy of imputation increased by 0.039/0.031 (4%/3% improvement) for CROATIA-Korcula/ORCADES after adding the second  reference panel.2 close to zero with the 1000 Genomes-only imputation and which were imputed more accurately with the addition of the local panel (2<0.2) using 1000 Genomes data alone. About one-fifth (17.1%/19.9%) of these poorly imputed SNPs imputed well (r2>0.8) after the addition of the local reference panel.Much of the improvements arise from SNPs that have an ral panel . For CROSNPs that were less frequent in 1000 Genomes than in our sequences generally improved more, as illustrated in Counts of the SNPs in each cell of 2 increase as a function of European 1000 Genomes MAF. As stated above, for SNPs with a MAF of 1–3.2% in our local sequences, the mean increase in r2 was 0.193/0.167. For these SNPs, the increase in r2 was 0.297/0.264 for those in the European 1000 Genomes MAF band <1%, 0.137/0.112 for MAF band 1–3.2% and 0.086/0.072 for MAF >3.2%.We also looked at rOur results show that use of a secondary local reference panel in addition to the 1000 Genomes reference haplotype data can significantly increase the quality of imputations, particularly for less common alleles and the improvement is greater when the study population is genetically further from the populations in the reference data.We estimated imputation accuracy using a leave-one-out cross-validation approach, in which we compared known genotypes to imputed ones using either the 1000 Genomes reference panel alone or accompanied by a panel obtained from sequence data of individuals from our study populations. Although we took care in our cross-validations to avoid circularity by using the leave-one-out approach in the imputations, for practical reasons, especially computing time, the phasing stage was done only once including all subjects (and therefore included the subject being blinded at the imputation stage). We acknowledge that this could potentially slightly inflate the reported increase in accuracy when using the second reference panel.Imputation accuracy is not only affected by the quality and composition of the reference data used, but also by the design of the genotyping array, in particular array density and whether the array captures population specific variants Consistent with this hypothesis, the accuracy of base imputations using only the 1000 Genomes reference panel was greater for ORCADES than CROATIA-Korcula, presumably due to the greater proximity of Orkney to subjects in the 1000 Genomes reference panel. Twenty three Orcadians, 77 mainland British and 100 of northern European ancestry individuals are present in the 1000 Genomes data, and principal component analysis shows that Balkan populations (such as Korčula) are more distant from the nearest subjects in 1000 Genomes , than the variation observed within the British Isles Similarly the much greater improvements in accuracy for SNPs where the MAF is greater in our sequences than 1000 genomes, perhaps not surprisingly, shows that local sequences will add value to imputations in regions of the genome where drift, or other forces, have created a distinct genetic structure.2 of 0.70–0.75 from 90 reference panel subjects in addition to 1000 Genomes seem consistent with those of Liu et al 2 of around 80% with ∼2,000 subjects on their (array data) reference panel with unfiltered results, whilst Auer et al obtained an r2 of 82% with 761 exome reference panel subjects, albeit filtering out lower quality results, using an Rsq threshold of 0.8, where Rsq is equivalent to the squared correlation between nearby imputed and genotyped SNPs Comparing these results with those of other researchers who have examined the benefits of study specific reference panels, often using 1000 Genomes like us or HapMap 2 of 90% for their global panel only imputation rising to 94% after the addition of their local panel. In our study, we report mean r2, but our median r2 was 0.77/0.83 rising to 0.88/0.92 after adding the local reference panel for CROATIA-Korcula/ORCADES for a MAF bucket 3–5%. The choice of a 3–5% MAF is intended to correspond to typical array SNPs with MAF<5%. Our results therefore appear consistent with the results of Surakka et al. despite the differences in study design. The study by Uricchio et al 2 (99%), and the technique used for imputation, identifying runs of identity-by-descent (IBD), should be particularly accurate, but its application is restricted to populations which share long haplotypes to a much greater extent than is common in most genetic studies, and we therefore feel our strategy of using 1000 Genomes reference data and adding sequence data from a subset of one’s own study subjects is a good practical way forward for many studies.Many aspects of our study were similar to a study by Surakka et al 2 has the same effect on power as a corresponding increase in study size A proportionate increase in rOur study focused on the exome, but the results should extend to any other genomic region of interest. Moreover, the similar results obtained in our study for two independent populations suggest that corresponding benefits will be found in other studies.The meta-analysis of multiple populations imputed using local exome sequence data will likely identify new SNP associations. However the amount of variance explained by less common variants individually is likely to be small and will make their detection challenging. This will put increasing emphasis on the use of analytical methods that consider jointly groups of variants, be it gene Given the cost and significant practical difficulties in subject recruitment, sequencing a subset of cohort members, for either part or all of the genome, and using these results for imputation will provide significant added value to association studies.Methods S1Quality Control of Array Data.(DOCX)Click here for additional data file.Table S1Counts of SNPs in each cell underpinning .(DOCX)Click here for additional data file.


Many plants have large and complex genomes with an abundance of repeated sequences. Many plants are also polyploid. Both of these attributes typify the genome architecture in the tribe Triticeae, whose members include economically important wheat, rye and barley. Large genome sizes, an abundance of repeated sequences, and polyploidy present challenges to genome-wide SNP discovery using next-generation sequencing (NGS) of total genomic DNA by making alignment and clustering of short reads generated by the NGS platforms difficult, particularly in the absence of a reference genome sequence.Aegilops tauschii-the diploid source of the wheat D genome, and with a genome size of 4.02 Gb, of which 90% is repetitive sequences. Genomic DNA of Ae. tauschii accession AL8/78 was sequenced with the Roche 454 NGS platform. Genomic DNA and cDNA of Ae. tauschii accession AS75 was sequenced primarily with SOLiD, although some Solexa and Roche 454 genomic sequences were also generated. A total of 195,631 putative SNPs were discovered in gene sequences, 155,580 putative SNPs were discovered in uncharacterized single-copy regions, and another 145,907 putative SNPs were discovered in repeat junctions. These SNPs were dispersed across the entire Ae. tauschii genome. To assess the false positive SNP discovery rate, DNA containing putative SNPs was amplified by PCR from AL8/78 and AS75 and resequenced with the ABI 3730 xl. In a sample of 302 randomly selected putative SNPs, 84.0% in gene regions, 88.0% in repeat junctions, and 81.3% in uncharacterized regions were validated.An annotation-based, genome-wide SNP discovery pipeline is reported using NGS data for large and complex genomes without a reference genome sequence. Roche 454 shotgun reads with low genome coverage of one genotype are annotated in order to distinguish single-copy sequences and repeat junctions from repetitive sequences and sequences shared by paralogous genes. Multiple genome equivalents of shotgun reads of another genotype generated with SOLiD or Solexa are then mapped to the annotated Roche 454 reads to identify putative SNPs. A pipeline program package, AGSNP, was developed and used for genome-wide SNP discovery in Ae. tauschii SNPs can be accessed at (http://avena.pw.usda.gov/wheatD/agsnp.shtml).An annotation-based genome-wide SNP discovery pipeline for NGS platforms was developed. The pipeline is suitable for SNP discovery in genomic libraries of complex genomes and does not require a reference genome sequence. The pipeline is applicable to all current NGS platforms, provided that at least one such platform generates relatively long reads. The pipeline package, AGSNP, and the discovered 497,118  Single nucleotide polymorphisms (SNPs) are valuable markers for the construction of genetic and physical maps, genome sequencing, marker-assisted selection, and for other genetic and genomic applications. Resequencing is the most efficient approach for a large scale, genome-wide SNP discovery. For example, resequencing with the Sanger sequencing technology using an annotated genome sequence as a reference has been an effective strategy for genome-wide SNP discovery in low or moderately complex genomes . CompareMany plants have large and complex genomes with a great abundance of repeated sequences. Polyploidy, a frequent evolutionary strategy in the plant kingdom, further increases genome size and complexity. These attributes are common in the tribe Triticeae, which includes such economically important plants as wheat, barley and rye. These features of the Triticeae genomes present a formidable challenge to genome-wide SNP discovery with NGS platforms, primarily because the abundance of highly repetitive sequences makes alignment and clustering of the short reads generated by some of the NGS platforms difficult.Brassica napus [Strategies are available to ameliorate these difficulties. Reduced representation libraries (RRLs) include only a subset of sequences present in a complex genome. The RRL subset is then used for resequencing, sequence alignment, assembly, and SNP discovery ,2. In plca napus . While bAn undesirable feature of transcriptome resequencing for SNP discovery is unavailability of potentially useful transposable element (TE)-derived polymorphisms. In contrast to SNPs embedded within repeated sequences (which are notoriously difficult as markers), the junctions of neighbouring repeated sequences are potentially unique -11, can de novo identification of alleles) was recently released for the identification of SNPs between two closely related genotypes without the help of a reference genome sequence [de novo assembler to identify variants. This tool can be used for the clustering of reads of genome and transcriptome sequences from Roche 454 and Solexa even with a shallow depth of genome coverage. However, the tool has been tested only on relatively low- or moderately-complex genomes. It does not allow SNP discovery in repeat junctions and is applicable only to base-space reads, such as with Roche 454 and Illumina Solexa, but is not applicable to the two-base encoded, color space SOLiD reads.A genomic sequence serving as a reference has been the basis of genome-wide SNP discovery utilizing NGS technologies. Tremendous progress has been achieved in the development of algorithms and software tools for mapping short reads from different NGS platforms to a reference genome and then identifying variants between individual sequences and the reference genome sequences -16. Howesequence . This toAe. tauschii (AL8/78 and AS75), the parents of the F2 mapping population used for the construction of an Ae. tauschii genetic map [Aegilops tauschii contains the core genome of the Triticum-Aegilops alliance [We report here an annotation-based genome-wide SNP discovery pipeline using NGS data for complex genomes without a reference genome sequence. In this pipeline, Roche 454 shotgun reads with low genome coverage of one genotype are annotated to distinguish single-copy reads covering genes, repeat junctions and other sequences from repetitive sequences and paralogous gene sequences. The annotation dramatically reduces the complexity of the genomic sequences by removing undesirable sequences. Resulting reads are assembled into sequence contigs if possible. The assembled Roche 454 contig and singleton sequences mimic a reference sequence. Shotgun short reads of another genotype with high genome coverage generated with the SOLiD or Solexa NGS platforms are then mapped to the annotated single-copy Roche 454 reads/contigs to identify SNPs in single copy DNA across the entire genome including repeat junctions. Based on this strategy, the pipeline program package, AGSNP, was developed and used for SNP discovery between two accessions of etic map . Aegilopalliance  and is talliance ,21. Its alliance  and 90% alliance . It is aAe. tauschii genotypes (AL8/78 and AS75)  were used to sequence two 5) Table . GenomicAe. tauschii (AL8/78) BAC clones were separately fragmented and shotgun sequenced with an ABI 3730 xl (henceforth Sanger sequence). These Sanger sequences were used as a reference in the estimation of sequencing error rates. Pooled DNAs of the 13 Ae. tauschii BAC clones were sequenced on three platforms to depths ranging from 12.9X for Roche 454 to 326.9X for SOLiD . In brief, ten micrograms of Ae. tauschii genomic DNA or pooled DNA of 13 Ae. tauschii BACs were sheared by nebulization and fractionated on agarose gel to isolate 400-750 base fragments. These were used to construct a single-stranded shotgun library that was used as a template for single-molecule PCR. The amplified template beads were recovered after emulsion breaking and selective enrichment. The Genome Sequencer FLX Titanium flows 200 cycles of four solutions containing either dTTP, αSdATP, dCTP and dGTP reagents, in that order, over the cell.Preparation and sequencing of the 454 sequencing library was performed according to the manufacturer's instructions , using the instrument software to select a region comprising the main library peak. Based on the calculated value, the library was applied to an Illumina single read flow cell at 5 pM concentration and clusters were generated according to manufacturer's instructions. Sequencing was carried out on an Illumina Genome Analyzer GAIIX for 85 cycles. Two, version 3, 36 cycle, kits were used. Data was generated following completion of the run using the Illumina Pipeline 1.4 from the sequencing images. A phix control lane was used to generate phasing and matrix values that were then applied to the experimental samples for basecalling analyses.Ae. tauschii accession AS75 as described in Dvorak et al. 1988 [DNA was isolated from nuclei of al. 1988 . A fragmAe. tauschii line AS75 was grown in a solution culture containing 0.5X Hoagland solution and total RNA was isolated from both roots and shoots according to manufacturer's instructions using the Ambion RNAqueous kit and the Ambion Plant RNA Isolation Aid . mRNA was isolated from total RNA according to manufacturer's instructions using the Applied Biosystems Poly(A) Purist Kit . The transcriptome library was constructed according to manufacturers' instructions using the Whole Trascriptome Anaysis Kit from Applied Biosystems . In brief, mRNA was fragmented using RNase III and size-selected. The size-selected RNA was reverse transcribed, and the cDNA size selected. The size-selected cDNA was amplified using 15 cycles to create the transcriptome library. This library was quantified using the Agilent DNA high-sensitivity kit on an Agilent 2100 Bioanalyzer . The root and shoot cDNAs were combined.To construct a cDNA library, Templated beads were prepared from both the fragment library and the transcriptome library according to manufacturer's instructions using the ePCR kit v.2 and the Bead Enrichment Kit from Applied Biosystems  for SOLiD3. Workflow Analysis was done after the first round of templated bead preparation for each library according to manufacturer's instructions using the Workflow Analysis kit from Applied Biosystems  to check library quality and the amount of templated beads generated per ePCR. An additional Workflow Analysis was done for both libraries after it was estimated that a sufficient number of templated beads were produced. Templated beads were deposited on slides according to manufacturers' instructions using the Bead Deposition kit from Applied Biosystems . One full slide was run for the transcriptome library, while 5 full slides (2.5 full runs) were run for the fragment genomic library.The single read sequencing error rate of the Roche 454 GS-FLX Titanium platform was estimated by comparing the single read alignment of AL8/78 reads in a pool of 13 BAC clones previously sequenced with the Sanger method. Because Sanger BAC sequences were based on the shotgun sequencing method they had a negligible error rate. Alignments were obtained using BLASTN of Roche 454 reads against 13 AL8/78 BAC sequences. Insertion and deletion (INDEL), and single base substitutions were counted. The sequencing error rate was calculated as total erroneous bases divided by the total length in bp of Roche 454 reads.AL8/78 reads from Roche 454, SOLiD or Solexa were mapped to the 13 BAC sequences generated by the Sanger method using the bwa package ,16 at deAL8/78 reads from SOLiD, Solexa or Roche 454 were mapped to Roche 454 genomic contigs or singletons using the bwa package ,16 at deGenome-wide SNP discovery involves two basic steps: (1) the alignment of sequences of two or more genotypes and (2) variant calling in the aligned sequences. Alignment of NGS on a reference genome sequence is called read mapping to the reference sequence. When a reference genome sequence is available, even short reads can be relatively easily mapped and aligned for the purpose of variant calling. In the absence of a genome sequence, long reads (such as those produced by Sanger or Roche 454 sequencing) from different genotypes can be clustered and aligned via multiple alignment algorithms . DifficuThe following rationale is used to identify (annotate) Roche 454 single-copy sequences. It is assumed that most genes are in a single-copy dose in a genome and sequences of duplicated genes are usually diverged to such an extent that most of their reads do not cluster together. Therefore, the read depth (number of reads of the same nucleotide position) mapped to coding sequences of known genes estimates the expected read depth of all single-copy sequences in a genome. Sequences showing greater read depth are assumed to be from duplicated or repeated sequences. To implement this rationale, shallow genome coverage by long Roche 454 sequences is used to identify genic sequences by homology search against gene databases. Multiple genome coverages of short SOLiD or Solexa sequences are then used to estimate the read depth of genic sequences in a population of SOLiD or Solexa reads. The estimate is in turn used to identify (annotate) the remaining single-copy Roche 454 reads. This combination of Roche 454 and SOLiD or Solexa platforms combines the long length of Roche 454 reads with the high coverage of the SOLiD/Solexa sequencing platforms, thus reducing costs associated with the development of reference sequence, as already pointed out by Hyten et al. (2010) . Short SThe aim of read annotation is to classify the Roche 454 reads on the basis of their homology and copy number. For annotation of a large volume of Roche 454 shotgun reads from multiple runs, a substantial amount of time and computer resources is required. Annotation was therefore divided into several steps, and a corresponding program was developed for each step. Figure The purpose of pre-processing of reads is to remove the chloroplast and mitochondrial sequences. BLASTN against complete wheat chloroplast and mitochondrial genome sequences (AB042240 and AP008982) was performed at an E value of 1E-10. A total of 14,087,315 Roche 454 reads of the AL8/78 genomic library were processed. After the removal of chloroplast and mitochondrial reads, artificial replicates of reads were filtered out using the cd-hit-454 program  at 98% aThe first annotation step is to identify reads of known (characterized) repeats. To extract all characterized repetitive reads, all possible plant repeat databases used in RJPrimers , includiAe. tauschii, including the following: complete genome gene databases of Brachypodium (v1.0), rice (RAP-DB) (build 5) [Arabidopsis (build #79); the UniProt protein database , and the Brachypodium protein database (v1.0). An E value of 1E-10 was used for both BLASTN and BLASTX searches. Reads related to transposable elements existing in protein or unigene databases were also removed. Some unknown gene reads were further identified using SOLiD cDNA reads of AS75 (~22X gene coverage) mapping to the Roche 454 reads of AL8/78 with the pipeline program  -33, sorgypodium v.0, rice ypodium v.0, rice Ae. tauschii accession AS75 were mapped to known gene reads obtained in the gene annotation routine using the mapping pipeline  of mapped read depth are calculated from the fitted extreme value distribution. The s of read depths is used as a cut-off value for single-copy sequences. If a read depth is < the cut-off value, the reads are considered to be single-copy sequences; if it is > the cut-off value, they are assumed to be repeated. For instance, the mapping of AS75 SOLiD genomic reads  to Roche 454 characterized gene reads generated a depth distribution with an s of 21.3  in Roche 454 sequences. The 3' end bases of a read have an increasingly higher error rate. Thus, SNPs with ≤30 bp on the 3' end of single reads or contigs are ignored.Sequence quality deteriorates with the length of a Roche 454 read. Hence, there is less confidence in SNPs on the 3' end of a reference sequence than on the 5' end (beginning). Therefore, the quality of bases is related to their relative location in a read  of sequence on either side of each SNP and a minimum of 60 bp between two contiguous SNPs. These requirements for Illumina genotyping are optionally applied in the pipeline program.More stringent SNP filtering criteria are imposed for uncharacterized reads because most of them should be unknown low-copy repetitive sequences. We set the maximum mapped read depth cut-off to 5 reads instead of 8 for both Roche 454 and Solexa reads, and 25 instead of 53 for SOLiD with the aim to eliminate SNPs in potentially low-copy repetitive sequences. In addition, reads with only one SNP are retained.Ae. tauschii accession AS75  [A total of 192 gene-related sequences, 96 repeat junction sequences and 95 uncharacterized sequences with at least one SNP were randomly selected among the identified SNPs for PCR validation. Primers flanking SNPs were designed with BatchPrimer3 . DNA tar. (2007) .Sequencing errors may potentially be an important source of SNP errors, particularly since only 1.35X genome equivalents of Roche 454 sequences were used to construct the reference sequence. The single read error rate of Roche 454 was estimated to be 0.74%. Single read error rates could not be estimated for the other two platforms because of short reads. Consensus sequences generated from multiple read alignments can efficiently correct single read errors. Consensus error rates of Solexa and SOLiD appeared to be similar (0.043%-0.044%) and lower than the Roche 454 consensus error rate, which was 0.13% , 13,856,244 reads were retained. A total of 1,570,944 reads (11.33%) was classified as artificial replicates detected with the cd-hit-454 software [A total of 14,087,315 Roche 454 reads was generated by shotgun sequencing of genomic DNA of software . After tsoftware  charactesoftware   of all putative gene-related SNPs  or had no alignments in the SNP locations (5 out of 20) reflecting diversity between AL8/78 and AS75 in repeated sequences. The remaining 52 RJ targets could be amplified in both DNAs and were expected to contain 67 putative SNPs in the aligned regions. Of these 67 putative SNPs, 59 (88%) were present  and 34 sequences amplified only in one of two accessions or their target sequences had no alignments in the SNP locations. The high failure rate of PCR amplification is likely due to diversity between AL8/78 and AS75 in uncharacterized regions because most of SNPs in uncharacterized sequences should be located in non-coding regions. The remaining 43 sequences amplified in both DNAs were expected to have 48 putative SNPs in the aligned regions. Of these 48 putative SNPs, 39 (81%) were validated  of Roche 454 reads of a single genotype to three categories: (1) characterized gene reads, (2) characterized repeats, and (3) uncharacterized reads. The second objective is predicting single-copy reads and identifying the putative SNPs by mapping multiple genome equivalents of SOLiD, Solexa or Roche 454 reads to the annotated, single-copy Roche 454 reads. The use of single-copy reads in the reference sequence dramatically reduces data processing and computation time to a manageable amount during the SNP discovery phase.We report here the development of a pipeline for large-scale, genome-wide SNP discovery in large and complex genomes with NGS platforms. This pipeline does not require a reference genome sequence, and its utility is illustrated using the 4.02 Gb genome of An asset of the pipeline is that it employs computational tools for read mapping and SNP calling that are applicable to any NGS platform. The pipeline is consequently of a universal utility with the existing and future NGS platforms. The Roche 454 platform used here to generate long reads for the construction of the reference sequence can be replaced by any platform that produces reads of a comparable or greater length, particularly if it would have higher throughput than the Roche 454. Flexible and stringent SNP filtering criteria implemented in the pipeline result in the discovery of large numbers of SNPs and low false-positive SNP rates. A total of 497,118 SNPs was identified, of which 195,631 were in genes. SNPs in genes had an 84% validation rate, those in RJs had an 88% validation rate, and those in uncharacterized sequences had 81% validation.Ae. tauschii or related wheat.In the pipeline, a reference sequence of relatively shallow genome coverage of one genotype is compared with reads of another genotype with deep coverage. The latter reads can be short, and although any of the current NGS platforms can in theory be used, the overriding requirement is that the platform has a very high throughput to minimize sequencing costs. This requirement is particularly critical for large and complex genomes, such as that of Ae. tauschii, only 2.5 runs with the SOLiD v3 were needed to generate a sufficient number of genomic reads to control error during SNP discovery. A single run of SOLiD v4 would be needed to achieve the same coverage. An additional disadvantage of using data of cDNA alone, in addition to the labour associated with the construction of a cDNA library, is that it does not facilitate the annotation of uncharacterized single-copy sequences in the reference sequence; only genes can be used for SNP discovery. Therefore, the use of cDNA for SNP discovery limits the total amount of DNA used for SNP discovery and hence the total number of SNPs discovered.The pipeline can be used to discover SNPs in both genomic DNA and cDNA. Because genomic DNA is more complex than most cDNA resources, more than twice as many SNPs were identified in genomic DNA than cDNA here. Genomic DNA is therefore preferable for SNP discovery over cDNA. Even for a genome as large as that of Another advantage of using genomic DNA for SNP discovery is access to RJs, which are an important source of polymorphisms. SNPs were 1.7 times more frequent per kb in RJ than in genic regions, while having equally high validation rate. Higher polymorphism in RJ makes them particularly valuable for plants with generally low levels of SNP. The RJPrimers program used in one of the sub-pipelines in the AGSNP pipeline, facilitates the identification of single-copy repeat junctions. SNPs in repeat junction regions can by genotyped in a high-throughput mode, e.g., with Illumina's GoldenGate assay , which mErrors in SNP discovery have two major sources: (1) sequencing errors and (2) errors in mapping of short reads to Roche 454 reference sequence. The sequencing errors for NGS platforms are less than 1%. The vast number of sequencing errors in all three NGS platforms is INDELs . FilteriThe validation rate of RJ SNPs (88%) was as high as that of gene SNPs (84%). All RJ SNPs used for validation were randomly selected from a set of the predicted single-copy RJ sequences with a SNP within 50 bp of RJ locations, which are Illumina genotyping-ready. Previous study indicated that in the Illumina GoldenGate genotyping assays the success rate was higher when a repeat junction was in the vicinity of the target SNP as compared with RJ SNPs without a repeat junction . RJ SNPsEucalyptus grandis cDNA sequenced with Roche 454 [Brassica napus cDNA sequenced with Solexa [Ae. tauschii, which underscores the general utility of the AGSNP pipeline, its high SNP discovery rate, and its particular utility for SNP discovery in large and complex genomes, such as those of many plants.The 84%, 88% and 81% SNP validation rate for genes, RJs and uncharacterized regions achieved here with genomic DNA is comparable to that reported by others with NGS of cDNA libraries; an 83% SNP validation rate was reported for oche 454 , an 85% oche 454 , and an h Solexa . Our valh Solexa . HoweverAe. tauschii with one SNP per 202 bp [Ae. tauschii, nucleotide polymorphism was estimated to be 2.44 × 10-3 [A total of 195,631 SNPs in genes and 145,907 SNPs in repeat junctions were discovered in this study. The SNP frequencies were one SNP per 876 bp for genes, and one SNP per 612 bp for repeat junctions, respectively. Repeat junctions have a higher SNP frequency than genes, which is consistent with the results from Paux et al. (2010) . But thir 202 bp . In the 4 × 10-3 , which i4 × 10-3  - the nuAe. tauschii genome using next generation sequencing and account for the fact that only about one half of genic SNPs expected were discovered. (1) Low genome coverage (~1.35X) of Roche 454 sequences of one genotype AL8/78 was used as reference sequences. According to simulation results from 13 Ae. tauschii BACs , cd-hit-454License: GNU PGLAny restrictions to use by non-academics: NoneFMY, YQG, MCL, DL, PEM, JD, and ODA planed the work. NH and YQG performed Roche 454 sequencing and KRD, MCL, and JD performed SOLiD sequencing. Solexa sequencing was performed in UC Davis Genome Center. NH, YQG, and KRD performed BAC sequencing with the Sanger method. FMY performed pipeline development, data analysis and SNP discovery. NH and YQG performed SNP validation. FMY and JD drafted the manuscript. All authors read and approved the final draft of the manuscript.Supplementary tables and figures. The file contains Table S1, Figure S1, S2 and S3. Table S1 lists all pipeline scripts for annotation-based SNP discovery. Figure S1 shows the relationship of percentage of the same SNPs identified by two NGS platforms with overlapping percentage of reads generated between two NGS platforms. Figure S2 presents gene coverage of NGS reads with different genome coverage of NGS reads and with different sequencing platforms. Figure S3 depicts the relationship of the number of detected SNPs with the number of genotypes used for SNP discovery.Click here for file


We investigated using lipidomic techniques, the effects of diets high in n-3 PUFA before mating, during gestation and lactation, and until weaning. Male offspring were continued on their mothers’ diets for 16 weeks. Hepatic and plasma lipids were extracted in the presence of non-naturally occurring internal standards, and tandem electrospray ionization mass spectrometry methods were used to measure the fatty acyl compositions. There was no significant difference in total concentrations of phospholipids in both groups. However, there was a significantly higher concentration of eicosapentaenoic acid containing phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and cholesteryl esters (CE) (p < 0.01) in the high n-3 PUFA group compared to the low n-3 PUFA group in both liver and plasma. Plasma and liver from the high n-3 PUFA group also had a higher concentration of free n-3 PUFA (p < 0.05). There were no significant differences in plasma concentrations of different fatty acyl species of phosphatidylethanolamine, triglycerides, sphingomyelin and ceramides.Female C57BL/6 mice were fed semi-purified diets (20% w/w fat) containing varying amounts of n-3 PUFA caused enrichment of n-3 PUFA in PC, LPC, CE and free fatty acids in the plasma and liver of C57BL/6 mice. PC, LPC, and unesterified free n-3 PUFA are important bioactive lipids, thus altering their fatty acyl composition will have important metabolic and physiological roles. Our findings reveal for the first time that a diet high in  N-3 PUFA such as docosahexaenoic acid  and eicosapentaenoic acid  have attracted a lot of attention in the past years as a result of their potential health benefits  - between m/z 200 and 900. Sodiated species of TG were identified in positive ion mode by survey scan for [M+Na]+ between m/z 800 and 1000. All data analyzed were corrected for 13C isotope effects as described by Han et al. [Samples were analysed using direct-infusion ESI-MS in positive or negative ion mode using a Thermo Fisher TSQ Quantum Ultra system with XCalibur data acquisition software . The tunn et al. .P value was < 0.05. Data were analysed using GraphPad Prism software (version 5.0). Statistical significance for differences between groups was determined by unpaired t-test. Results are expressed as mean ± standard deviation (SD). Differences were considered to be statistically significant if the associated n-3 PUFA fed mice  increased significantly in the plasma of the low n-3 PUFA group compared to the high n-3 PUFA group  [n-3 PUFA has been shown to regulate lipogenesis by inhibiting LXR and SREPB-1c [n-3 PUFA has been shown to possess potent anti-inflammatory properties [N-3 PUFA alleviates inflammation by directly regulating transcription factors involved in inflammation [n-3 PUFA have also been shown to improve symptoms of dyslipidaemia [Plasma PC is rapidly metabolised by phospholipase An plasma . We founplements ,54. We o 30 days . In addi (NAFLD) . MechaniSREPB-1c ,57. Chrooperties . N-3 PUFammation -61 and iammation ,63. In apidaemia -11. n-3 PUFA considerably increased plasma circulating levels of 20:5 LPC, and drastically reduced plasma concentration of 20:4 LPC. Similar to our plasma data, we found a higher concentration of hepatic 20:5 LPC and a low concentration of hepatic 20:4 LPC in the high n-3 group. Interestingly, there was no significant difference in hepatic 22:6 LPC, although there was a trend towards an increase in the high n-3 PUFA group. Ottestad et al. recently reported a similar finding where fish oil supplementation to healthy humans significantly increased plasma 20:5 LPC and 22:6 LPC [Another important bioactive product of enzyme hydrolysis of PC is LPC. We found that feeding a diet high in 22:6 LPC . Another22:6 LPC . However22:6 LPC ; tracer 22:6 LPC . It wouln-3 PUFA have been shown to possess beneficial properties. N-3 PUFA species of LPC were found to reduce inflammation [sn-1 position exhibits significantly higher anti-inflammatory properties compared to LPC with either linoleic or arachidonic acid [n-3 PUFA exhibit their anti-inflammatory properties through hydrolytic cleavage of the n-3 PUFA moiety, or oxygenation by 15-lipooxygenase (15-LOX) to produce inflammation resolving lipids such as 1-(15-hydroperoxyeicosatetraenoyl)-LPC and 1-(17- hydroperoxydocosahexaenoyl)-LPC [n-3 PUFA rich LPC were shown using LPC containing n-3 PUFA at sn-1 position. We have not investigated the position of the acyl group of our LPC, however, it is known that there is rapid isomerization of acyl group from sn-2 to a more stable sn-1 position in LPC [sn-1 position in LPC [sn-1 position. Studies have controversially linked LPC with the development of atherosclerosis -27. Thisammation ,72, and nic acid ,73,74. In in LPC . It has n in LPC . We can n-3 PUFA involve remodelling and improving the quality of bioactive lipid mediators without affecting their concentrations.Also noteworthy is the fact that, despite the changes in fatty acyl species of PC and LPC in response to diet, there was no difference in the total concentrations of PC and LPC between the two experimental groups. A similar observation was found by Ottestad et al., where they found no difference in total concentrations of PC and LPC despite the apparent changes in individual fatty acyl species in response to fish oil supplementation . This inn-3 PUFA had no effect on different fatty acyl species of plasma TG, confirming preferential incorporation of n-3 PUFA into PC. However, the high n-3 PUFA diet led to an increase in plasma and liver concentrations of n-3 PUFA CE, and a decrease in 20:4 CE. CE is less polar than free cholesterol and it functions as an inert storage molecule. The high incorporation of n-3 PUFA in CE could be explained as a response to the high n-3 PUFA in the diet. This would simply indicate that n-3 PUFA are stored and will be later released for other physiological functions. A cross sectional study has reported a positive correlation between dietary PUFA and CE [A diet rich in A and CE .n-3 PUFA in the liver and plasma PC and LPC of mice fed high n-3 diet, it was also imperative to ascertain the effect of high n-3 PUFA diet on PE, which is also abundant in the membranes. There was no difference in plasma PE between the two dietary groups; the only difference detected in the liver was an increase in 18:0-22:6 PE in the high n-3 group. Our findings confirm that fish oil supplementation leads to a preferential incorporation of n-3 PUFA into PC and LPC compared to other phospholipids [Although we have shown that there is high incorporation of holipids . n-3 PUFA group. The functional roles of this CER species are currently unknown and needs to be explored. There was no difference in plasma and liver SM concentrations between the two groups. The physiological functions of SM have not been extensively studied. However, it is known that SM are involved in the formation of specialized membrane microdomains known as lipid rafts involved in signalling. Sphingolipids, such as CER and SM, are important signalling molecules. CER is a sphingolipid linked with inflammation and the pathogenesis of cardiovascular diseases (CVD) ,78. Thern-3 PUFA alter plasma and liver lipidomic profile of the offspring. We found that n-3 PUFA is preferentially incorporated into PC and LPC, and despite the changes in lipidomic profile, the total concentrations of these lipids were not altered. Additionally, we found that dietary n-3 PUFA is capable of remodelling the fatty acyl moieties of PC, LPC, and CE, which may have important physiological implications, and needs to be further investigated. Future studies will be undertaken to investigate the mechanism(s) by which n-3 PUFA remodelled bioactive lipids regulate metabolic pathways. In conclusion, our findings have shown that diets high in File S1Table S1, Body weight at sacrifice, and average weekly food intake of mice fed high and low n-3 PUFA diets; Table S2, Total concentrations of plasma and liver phospholipids of mice fed high and low n-3 PUFA diets.(DOCX)Click here for additional data file.


Phaseolus vulgaris L.) which do not have a whole genome sequence available, the use of next generation sequencing for SNP discovery is much more difficult and costly. To this end we developed a method which couples sequences obtained from the Roche 454-FLX system (454) with the Illumina Genome Analyzer (GA) for high-throughput SNP discovery.Next generation sequencing has significantly increased the speed at which single nucleotide polymorphisms (SNPs) can be discovered and subsequently used as molecular markers for research. Unfortunately, for species such as common bean .Through combining two next generation sequencing techniques we have developed a method that allows high-throughput SNP discovery in any diploid organism without the need of a whole genome sequence or the creation of normalized cDNA libraries. The need to only perform one 454 run and one GA sequencer run allows high-throughput SNP discovery with sufficient sequence for assay development to be performed in organisms, such as common bean, which have limited genomic resources. DNA markers are invaluable tools across many species for use in QTL mapping, marker assisted selection, association analysis, and fine mapping for cloning of genes of interest. By far the most abundant source of DNA variation for marker development is the single nucleotide polymorphism (SNP). The SNP marker has become the marker of choice for many research applications because of the abundance of SNPs and the several technologies available for the high-multiplex assay of SNPs . The exiin silico analysis of existing sequence data or the resequencing of a small number of genotypes for the identification of sequence variants in existing sequence data .The same restriction digestion and size selection was also performed on BAT 93 DNA to obtain a digestion product ranging from 300 to 350 bp. The 300 to 350 bp RLL of both Jalo EEP 558 and BAT 93 DNA were put through a series of restriction digestions as shown in Figure de novo assembler, Newbler from Roche Applied Science to generate the Jalo EEP 558 reference sequence. The default overlap detection parameters with the assembler were used for assembly. Each base from the contigs and the singletons was screened for its Phred quality. If the quality was less than 20, the corresponding base was changed to "N". The contigs and singletons less than 61 bases or with an "N" at more than 75% of their total length were eliminated. The sequences were then Blasted to the sequences of Phaseolus vulgaris chloroplast [GenBank:NC_009259] using standalone megablast with W = 30. Any sequences that aligned to the P. vulgaris chloroplast DNA were eliminated.The sequences of Jalo EEP 558 from the Roche 454-FLX system were assembled using the software 454 GS The short reads of BAT 93 and Jalo EEP 558 generated by the Illumina Genome Analyzer II were analyzed and called by the Sequence Analysis Software of Pipeline version 1.4 of the Genome Analyzer . ELAND of the Pipeline version 1.4 was used for the alignment of the BAT 93 short reads to the Jalo EEP 558 reference sequences. A SNP allele was called using the software package of the Consensus Assessment of Sequence and Variation (CASAVA) . The constraints of minimum allele call score of 10 at the SNP position and minimum read depth of 3 were imposed to filter SNPs. To reduce the SNPs associated with the homologs or paralogs, the Jalo EEP 558 reference sequences were further mapped and assembled with the short reads of Jalo EEP 558 sequences by CASAVA, the SNPs which were identical to the SNPs kept in the previous procedure were eliminated. In addition, the Jalo EEP 558 reference sequence at the positions where SNPs were called was replaced with the BAT 93 alleles and the Jalo EEP 558 GA short reads were aligned to the BAT 93 allele consensus sequence. SNPs were called and used for the verification of the SNPs called from BAT 93 short reads vs. Jalo EEP 558 reference sequences.http://phaseolus.genomics.purdue.edu/data/pv_gba_recon_repeats.fasta or with bacterial sequences deposited in GenBank, as identified with the Megablast software with W = 30, were also eliminated. The screening against bacterial sequences was performed at the end of the SNP discovery pipeline rather than at the beginning due to the size of the data set at the beginning of the SNP pipeline which would have required significantly more computational time.The SNPs residing in the fragments which were significantly aligned with common bean repetitive sequences Based upon the underlying Jalo EEP 558 454-FLX reference sequence, polymerase chain reaction (PCR) primers were designed to the flanking sequence of a random set of 192 putative SNPs using Primer3 . The tarThe 4,341 predicted SNPs were screened to insure that all GA sequence reads that predicted a particular SNP contained the same allele. After this screen the remaining 3,487 SNPs were submitted to Illumina Inc. to obtain a SNP score by the Illumina assay design tool for the GoldenGate assay. A SNP score > 0.6 is used by Illumina to predict those SNPs which will have high conversion rates into successful GoldenGate assays  and has Phaseolus vulgaris oligo pool all -1). The GoldenGate assay was performed on BAT 93 and Jalo EEP 558 and 96 diverse common bean germplasm accessions (Additional File The 1,536 GoldenGate assay contained a total of 1,050 candidate SNPs from the mtRRL method described here, all of which had a SNP score ≥ 0.8. The average SNP score of the 1,050 SNPs was 0.93. The remaining 486 SNPs were discovered through other methods and their results are not reported here. The 1,536 GoldenGate assay was named PvOPA-1 (DLH, designed and oversaw the study, designed the GoldenGate OPA, performed genotyping analysis of the GoldenGate assay, and drafted the manuscript. QS, designed and performed the SNP discovery analysis and assisted in preparing the manuscript. EWF, designed and created the multi-tier reduced representation library and performed sequencing of the validation sets and assisted in preparing the manuscript. CVQ, performed GA sequencing and assisted in preparing the manuscript. J-SL and I-YC, performed 454 sequencing. E-YH, isolated DNA and assisted with the analysis of the diverse germplasm material. MPC, provided plant material. PBC, designed and oversaw the study and assisted in preparing the manuscript. All authors read and approved the final manuscript.SNPs used for GoldenGate design. The 3,487 SNPs along with the flanking sequence submitted for GoldenGate assay design.Click here for fileDiverse Germplasm. List of the 96 diverse common bean germplasm used to test the PvOPA-1 GoldenGate assay.Click here for file