The common approach to SNP genotyping is to use (model-based) clustering per individual SNP, on a set of arrays. Genotyping all SNPs on a single array is much more attractive, in terms of flexibility, stability and applicability, when developing new chips. A new semi-parametric method, named SCALA, is proposed. It is based on a mixture model using semi-parametric log-concave densities. Instead of using the raw data, the mixture is fitted on a two-dimensional histogram, thereby making computation time almost independent of the number of SNPs. Furthermore, the algorithm is effective in low-MAF situations.www.math.leidenuniv.nl/~rrippe.Comparisons between SCALA and CRLMM on HapMap genotypes show very reliable calling of single arrays. Some heterozygous genotypes from HapMap are called homozygous by SCALA and to lesser extent by CRLMM too. Furthermore, HapMap's NoCalls (NN) could be genotyped by SCALA, mostly with high probability. The software is available as R scripts from the website  Genotyping algorithms for SNP chips can be partitioned roughly into two classes: 1) those that call genotypes for individual SNPs for a set of arrays and 2) those that call all SNPs for a single array. The first approach is the common one: for each SNP it collects pairs of fluorescence intensities for all arrays and applies a clustering algorithm. This is known as multi-array genotyping. In principle it has the advantage of being able to account for SNP-to-SNP variation. However, the number of available data points is limited to the number of samples: fewer data generally yield less reliable results. The latter problem is especially troubling if the SNP has a very low minor allele frequency (MAF), the minor allele being the one that has the lowest frequency in a given population. Low MAFs are known to have a detrimental effect on downstream analyses. Tabangin et al. In case of low MAF, there are very few or even no observations in some clusters. It is clear that based on these 90 samples from the Central European (CEU) population, genotype calls for some SNPs can hardly be made effectively without the use of external information. For these reasons, common calling algorithms like BirdSeed de novo calling for small sets of samples. For each allele it introduces one-dimensional mixtures of normal distributions, one component for noise  and the other for the signal . Wright et al. A second approach is to cluster all SNPs on single arrays, using a mixture model. ALCHEMY Along similar lines, MAMS We find it convenient to transform the allele channel signals to tion see , three hIn this section we compare genotype calls from SCALA, GenoSNP and CRLMM with the consensus genotypes from HapMap. We explore call differences and evaluate SNPs that are not called by CRLMM and HapMap in terms of SCALA calls. We exclude the copy number probes. Our model has three clusters and so implicitly assumes normal DNA without copy number changes and null alleles.We use probe set averages of the Affymetrix SNP6.0 CEL-files from the CEU population, CUPID set. To start the EM algorithm the data are split on the basis of Here we compare genotype calls from SCALA to those from HapMap. Our algorithm fits a mixture of three densities and computes for each SNP its probability of belonging to each of them. The largest of the three probabilities determines the assigned genotype. HapMap is the best we have to judge genotype calling algorithms, but it is not a gold standard. To put this in perspective, we study a small example, summarized in The message of To understand this better, we have plotted in Multi-array plots per SNP are useful, but unfortunately but their number is too large to handle. We present a selection from the two extremes of the spectrum from high to low quality. The average of To provide a more general indication, we have calculated cross-tables as in We have also compared SCALA performance to the GenoSNP algorithm for Illumina arrays. The results on previously mentioned arrays from the Erasmus Medical Center, provided in In summary we found that overall agreement between SCALA and HapMap is comparable to that of CRLMM. However, for the AB calls from HapMap we see differences in the direction of both AA and BB labels, for both SCALA and CRLMM, where the differences for SCALA were about twice as large, up to 4.99% of all HapMap ABs. However, after visual inspection of their location in their single array genotype clustering, for a large number of these differences it seems almost strange that they were called AB by HapMap: they lie in or close to the AA or BB cluster in the single array. In addition we found that for many genotypes that were not called in HapMap, probably due to problems with minor allele frequencies or low call probabilities we could call those SNPs with a probability larger than 0.95 in most cases. Further visual inspection revealed that those SNPs lie close to the center of one of the three clusters in a single array setting.We presented a fast novel approach to call SNP genotypes in individual arrays using semi-parametric log-concave mixtures.To assess performance we compared genotype calls from a multi-array method (CRLMM) and from our single-array method (SCALA) to a set of consensus genotypes from HapMap. The number of agreements and differences in terms of homo- and heterozygous calls showed that SCALA can be used to call genotypes efficiently and effectively. Even SNPs that were not genotyped in HapMap can be genotyped with reasonable certainty using a single chip. We also evaluated performance against the single-array algorithm GenoSNP, dedicated to Illumina chips. Strong agreement was found. We conclude that our approach can handle a variety of platforms and cluster shapes.The semi-parametric densities in the model can adapt there shape easily and automatically to a variety of cluster shapes. We did not observe cases where our model did not work well. So we did have no need for a “catch-all” component like in GenoSNP, a uniform density to handle observations that fall out of the three main clusters. It would be little work to ad such a component to SCALA.The logistic advantages of singe-chip genotyping are large. Each array can be processed as soon as it becomes available; there is no need to wait until a large enough number has been collected. This is especially relevant when developing (new) chips for new or existing organisms. Quality control is also improved. It is easy to judge the estimated mixtures visually, using plots as in In this section we describe how we fit a mixture of three two-dimensional semi-parametric log-concave densities to transformed fluorescence signals, as illustrated in We describe in some detail how to fit a mixture of log-concave densities in one dimension, borrowing from To compute a smooth density for a one-dimensional data set, we first construct a histogram with many bins, say Assuming a Poisson distribution for the counts, we maximize the penalized log-likelihoodSmoothness is tuned with the parameter Setting the derivative of To estimate a mixture with three smooth components, we use the familiar EM (expectation-maximization) algorithm. Two steps are repeated until convergence: 1) split the counts In two dimensions we use the same idea as described above, but now a two-dimensional histogram is formed, and the log of a density is formed by a sum of tensor products of B-splines. We sketch the adaptations that have to be made. Let One could vectorize Our model is flexible enough to adapt to the quite different cluster shapes of different microarray platforms. The mixture components give three expected values for bin The semi-parametric mixture model has a number of parameters that can be chosen by the user. For the histogram we advise a 100 by 100 grid. The domain of the histogram is covered by bases of 10+3 cubic B-splines . For the smoothing parameter we choose To start the EM algorithm, we split the data in three groups by a very simple procedure. In the plot of On a Core2 duo, level 2 cache 512 mb, 4 GB memory on a 64 bit OS, it takes around 20 seconds to call genotypes for a single Affymetrix SNP6.0 CEL file. This computation time includes building the histogram. Approximately the same time is needed for other arrays, almost independent of the number of SNPs, because the data are first summarized by a two-dimensional histogram. The latter is not a costly affair. We compared four strategies on one million pairs of uniformly distributed numbers (in parentheses times in seconds):a loop over all observations (5.4);a compiled version of the loop, using cmpfun (1.2);using the function table (5.6);using a feature of the sparse matrix package package spam (0.31).To put these numbers in perspective: the singular value decomposition of a 1000 by 1000 matrix takes 7.1 seconds on this computer. The memory footprint is small: it is far less than what is needed the one million pairs of fluorescence intensities for one chip.Our genotyping algorithm has been implemented in R Appendix S1We describe the translation step to match HapMap genotype calls to the SCALA {AA, AB, BB} format. We compare genotype calls to those of Phase III. We only compare calls to SNPs that have matching ‘RSid's. almost half of the total. We disregard the four allelotypes  and refer to homozygous genotypes as AA or BB and the heterozygous as AB. Since genotype calls AA from either method are highly unlikely to be mistaken for BB, we can apply the above forced classification from the HapMap homozygous genotype calls into homozygous calls from SCALA.(PDF)Click here for additional data file.


BMS483 and MNB-209 via genome scanning followed by fine mapping.We previously localized a quantitative trait locus (QTL) on bovine chromosome 6 affecting milk production traits to a 1.5-Mb region between UGDH) was suggested as a potential positional candidate gene for milk production traits based on its corresponding physiological and biochemical functions and genetic effects. By sequencing all the coding exons and the untranslated regions in UGDH with pooled DNA of 8 sires represented the separated families detected in our previous studies, a total of ten SNPs were identified and genotyped in 1417 Holstein cows of 8 separation families. Individual SNP-based association analysis revealed 4 significant associations of SNP Ex1-1, SNP Int3-1, SNP Int5-1, and SNP Ex12-3 with milk yield (P < 0.05), and 2 significant associations of SNP Ex1-1 and SNP Ex12-3 with protein yield (P < 0.05). Furthermore, our haplotype-based association analyses indicated that haplotypes G-C-C, formed by SNP Ex12-2-SNP Int11-1-SNP Ex11-1, T-G, formed by SNP Int9-3-SNP Int9-2, and C-C, formed by SNP Int5-1-SNP Int3-1, are significantly associated with protein percentage  and fat percentage . Finally, by using an in vitro expression assay, we demonstrated that the A allele of SNP Ex1-1 and T allele of SNP Ex11-1of UGDH significantly decreases the expression of UGDH by 68.0% at the RNA, and 50.1% at the protein level, suggesting that SNP Ex1-1 and Ex11-1 represent two functional polymorphisms affecting expression of UGDH and may partly contributed to the observed association of the gene with milk production traits in our samples.Totally 15 genes were mapped within such linkage region through bioinformatic analysis of the cattle-human comparative map and bovine genome assembly. Of them, the UDP-glucose dehydrogenase (UGDH gene could be involved in genetic variation underlying the QTL for milk production traits.Taken together, our findings strongly indicate that  With the completion of genome sequencing of cattle and development of comparative map of human/bovine, it is feasible to identify the genes or other functional elements, which are responsible for a quantitative trait with comparative candidate positional cloning strategy[DGAT1 on BTA 14[GHR on BTA20[OPN on BTA6[ABCG2 on BTA6[Both theoretical and simulation studies agree that application of gene-assisted selection has the potential to increase the rate of genetic gain by pre-selecting young candidate bulls prior to progeny testing in dairy cattle. This kistrategy. In receon BTA20 for fat  on BTA6 for milkBM470 with a confidence interval of 4 cM on BTA6 by using both linear regression and variance component approaches[BMS690 and BM4528 in 918 daughters of 8 segregating families identified by Chen et al.[BMS483 and MNB209, which spans a narrow genetic distance of 0.6 cM and a physical distance of 1.5 Mb[http://www.ncbi.nlm.nih.gov), a total of 15 known genes have been mapped within such narrow linkage region. Out of them, we herein focused on UGDH as the most plausible comparative functional candidate gene affecting milk production traits. The enzyme UGDH can convert UDP-glucose to UDP-glucuronic acid, a critical component of the glycosaminoglycans, hyaluronan, chondroitin sulfate, and heparan sulfate, thus promotes normal cellular growth, embryogenesis and adult organism physiology[UGDH gene on milk yield and component traits in dairy cows and regulation by functional polymorphisms.Even though QTLs for milk production traits have been found in almost 29 chromosomes, BTA6 is one of the most concerned chromosomes in QTL mapping for milk yield and content. Of the 1651 reported QTLs for milk production traits, 193 are on BTA6. In our previous study in a daughter-design Chinese Holstein population consisting of 26 sire families, a QTL for milk yield, fat yield, protein yield and fat percentage was detected around the microsatellite marker proaches,9. This proaches-13. Thusf 1.5 Mb. In the f 1.5 Mb-17 and tysiology. The purThe blood samples and frozen semen were collected along with the regular quarantine inspection of the farms and breeding station, so no ethical approval was required for this study.A total of 1417 Chinese Holstein cows were selected from 8 sire families with 67–540 daughters in each family. Such 8 sires represent all the segregating families detected in our previous QTL mapping studies,14. EstiGenomic DNA was extracted from whole blood samples of cows and frozen semen of the 8 bulls by a standard phenol-chloroform method and stored at −20°C.BMS483 and MNB-209 on BTA 6 where harbored a QTL for milk production traits[http://www.ncbi.nlm.nih.gov). Then, based on Btau 4.0, full-length cDNA sequences of candidate genes were obtained and totally 88 pairs of primers were designed with Primer3.0 program to amplify the entire coding region of each candidate gene to identify potential polymorphisms (primers were not shown with the exception of UGDH).Within the 1.5-Mb region between microsatellites n traits, position traits-17 and tUGDH were presented in TableTwelve pairs of primers for amplification of all the 12 exons and partial introns of y = μ + G + a + e, where y is the EBV of cows, μ is the general mean, G is the SNP genotype effect, a is the random polygenic effect with distribution of N , and e is a random residual with distribution of N .Associations between individual SNP and the five milk production traits were estimated using the MIXED procedure of the SAS 8.02 software based on the model yij = μ + si + bhij + eij, where si is the fixed effect of sire i, b is the regression coefficient, hij is an indicator variable with a value 0, 1 or 2 to indicate the copy number of the haplotype carried by the individual.Pair-wise linkage disequilibrium (LD) between all SNP pairs and haplotype blocks were estimated using the program Haploview4.0. Then, hBoth analyses  were corrected for multiple testing using false discroverage rate (FDR) methods. We declared a significant SNP or haplotype if the corresponding FDR value < 0.05.′ and 3′end of the synthesized gene, respectively. The synthesized DNA fragment was double-digested with restriction enzymes BamH I and Xho I and then cloned into the digested pcDNA3.1/myc-HisA expression vector  with the same enzymes; this yielded the first plasmid construct, named as pcDNA3-GAC (g-a-c). The other seven plasmid constructs  were generated from the g-a-c plasmid using a site-directed mutagenesis kit purchased from Stratagene Inc. . All eight plasmid constructs were verified by automated DNA sequencing in both directions.On the basis of association analysis results and the nucleotide location of each SNP within the gene, we investigated three SNPs with two located in exon 1 (SNPs exon1-1 and exon1-2), one in exon 11 (SNP exon11-1). The complete coding region of UGDH was synthesized based on the sequence of cattle full-length cDNA at NCBI (GenBank Accession No. NM: 174211). To make cloning step easier, we added a restriction endonuclease enzyme digestion site to the 52. The culture medium was changed every other day. The cells were transfected by Lipofectamine 2000  according to the manufacturer’s protocol. Briefly, a day prior to transfection, the cells were trypsinized and plated at 4 × 104 cells per well on a 48-well plate. Subsequently, 0.4 μg of plasmid DNA containing the appropriate structure was diluted in 20 μl of Opti-MEM I  Reduced Serum Medium and mixed with an equal volume of Opti-MEM I Reduced Serum Medium containing 1 μl of Lipofectamine 2000. The mixture was incubated for 20 min at room temperature and added directly to the cells in a 48-well plate. The cells were cultured for an additional 48 hours prior to harvesting for extraction of RNA and protein for real-time RT-PCR and Western blotting, respectively.COS-7 cells (an African Green Monkey SV40-transf'd kidney fibroblast cell line) were purchased from the American Tissue Cell Culture Inc. , and cultured in RPMI-1640 supplemented with 10% fetal bovine serum  at 37°C in 5% COTo evaluate the regulatory effect of SNPs exon1-1 (G/A), exon1-2 (A/C) and exon11-1 (C/T) on the expression of UGDH, eight plasmids were constructed corresponding to different allele combinations of exon1-1 (G/A), exon1-2 (A/C) or exon11-1 (C/T). Total RNA from cell transfected by eight plasmids respectively and control cells was extracted using TRIzol reagent . One microgram of total RNA was reverse transcribed in a final volume of 20 ul containing 4 ul of 5 first-strand buffer , 10 mM DTT, 0.5 mM each dNTP, 40 U RNaseOUT™, 1 ul of 50 nM random hexamers, and 200 U of Superscript II RNase H- reverse transcriptase (Invitrogen). The TaqMan® specific probes and primers used for real-time PCR were designed according to the above synthesized UGDH fragment and myc sequences in pcDNA3.1/myc-HisA expression vector were: forward primer 5′-CTCCTTCTGGTGAAATTCCAAAGT-3′, reverse primer 5′-TCTAGACTCGAGTACTCTGGGTTTCTT-3′, and the probe 5′-AGTCTTCAGGATATGTGCCCAA-3′. Amplification of 2 ul of cDNA was carried out in a total volume of 20 ul according to the manual of TaqMan® Gene Expression Assays (Applied Biosystems). The mRNA level of each gene was determined using a calibration method[Total protein was extracted from individual transfected cell from an independent experiment by homogenization in RIPA buffer , and the protein concentration was determined by the Bio-Rad Protein Assay . Ten micrograms of total protein was separated by 10% SDS-PAGE followed by transfer to nitrocellulose membranes (0.45 um) at 25V overnight at 4°C. The membrane was first incubated in a blocking buffer (5% nonfat milk and 0.2% Tween 20) for 1.5h at room temperature and then 1.5h at room temperature in the blocking buffer containing cattle anti-His antibody . After three washes in TBST  for 10 min each, the membranes were exposed to horseradish peroxidase-conjugated secondary antibody at room temperature for 1.5h, and then exposed to X-ray film. After hybridization with the antibody of interest, membranes were stripped and re-probed with antibody to β-Actin , which was used for normalization of the protein content of each sample. Then, the films were scanned for quantitative analysis with ImageQuant 5.2 .TBC1D1, KLF3, TLR10, TLR1, TLR6, LOC511583, TMEM156, LOC528668, WDR19, LOC514842, LOC514925, RPL9, LIAS and UGDH. Of them, five genes including KLF3, KLHL5, RFC1, WDR19 and UGDH, were suggested as candidates for milk production traits based on their corresponding physiological and biochemical functions in human, so that the entire coding region of each gene was screened to identify any potential polymorphisms by direct sequencing of pooled DNA from 8 segregating sire families. As a result, a total of 64 SNPs were revealed and their associations with five milk yield and milk composition traits were determined . We herein considered UGDH as the most plausible comparative functional candidate gene affecting milk production traits.With bioinformatic analysis of whole-genome cattle-human comparative map and the bovine genome assembly Btau 4.0, totally 13 annotated genes and 2 putative genes were mapped within the previously identified 1.5Mb linkage region on BTA6 , which were compose of UGDH gene, a total of 22 SNPs, including 12 in exons and 10 in introns, were found from the pooled DNA of the 8 unrelated bulls. 10 SNPs out of them were selected for the association analysis according to their heterozygosities among the 8 bulls and locations . The second haplotype, T-G, formed by SNP Int9-3 and SNP Int9-2, was significantly associated with fat percentage . The last one, C-C, formed by SNP Int5-1 and SNP Int3-1, exhibited a significant association with fat percentage also  which remained significant after multiple testing correction of FDR (adjusted significance level was 0.0083 for four major haplotypes).FigureUGDH and its mutant Ex1-1 (G/A), Ex1-2 (A/C) and Ex11-1 (C/T).Analysis of UGDH, we created plasmid constructs for the eight possible combinations  by site-directed mutagenesis from a complete UGDH cDNA clone (g-a-c) and then transfected them into COS 7 cells. Following transit transfection, we measured the mRNA and protein expressions from each construct using real-time RT-PCR and Western blotting , Ex1-2 (A/C) and Ex11-1 (C/T) on the expression of g Figure.UGDH mRNA was down-regulated by 63%, 62%, and 68%, respectively . Moreover, three mutant constructs  were observed to be highly significant (P < 0.01), where UGDH and that all differences observed among the eight constructs were attributable primarily to SNPs Ex1-1 and Ex11-1, for which the A allele of SNP Ex1-1 and T allele of SNP Ex11-1 caused lower expression than the G and C allele respectively. But at protein level, the two alleles of SNP Ex1-1 play more important role in the expression of UGDH than those of SNPs Ex1-2 and Ex11-1, where the G allele of SNP Ex1-1 corresponds to higher protein concentration.These results indicate that at mRNA level, the mutation of Ex1-2 has little impact on the expression of UGDH, with 2 milk production traits in a Chinese Holstein cattle population. Moreover, we demonstrated that the SNPs Ex1-1 and Ex11-1 in exonic regions of UGDH are two functional polymorphisms in which A allele of SNP Ex1-1 and T allele of SNP Ex11-1 cause lower expression level of UGDH compared with the G and C allele respectively. Together, our findings not only confirmed our early finding that a QTL located in the 1.5-Mb region between BMS483 and MNB-209 for milk yield and protein yield[UGDH might be QTN responsible for this QTL.In this study, we provide a strong evidence for the significant associations of in yield, but alsBMS690 to BMS4528 near BMS470 and genotyping them for 918 daughters from 8 sires families which had been proved to contain segregating QTLs affecting milk production traits in the previous studies[MNB-209 by employing linkage disequilibrium (LD) mapping approaches[UGDH gene within this interval was selected as a potential positional candidate gene for further study by genetic association and functional analysis as reported in this study. Association analysis of individual SNPs indicated that the SNP Ex1-1 located in the 5` UTR (c.exon1-1 G>A) was significantly associated, even after multiple testing correction of FDR, with milk yield (P = 0.003). Moreover, haplotype analysis revealed two significant associations of two percentage traits with haplotypes formed by SNP Ex12-2- SNP Int11-1- SNP Ex11-1 and SNP Int5-1- SNP Int3-1, respectively. Given the aforementioned association results of SNP Ex1-1 and SNP Ex11-1 with milk production traits and their locations within the gene, we hypothesized that these two SNPs and the other SNP Ex1-2 (located in the 5` UTR of UGDH and near to the SNP Ex1-1) contribute to the observed association of UGDH with milk production traits. By expressing plasmid constructs containing different alleles for above three SNPs in COS 7 cells, we found that the plasmid construct containing the A allele of SNP Ex1-1 and T allele of SNP Ex11-1 produced significantly lower expression of mRNA than the G and C allele respectively. At protein level, the two alleles of SNP Ex1-1 play more important role on the expression of UGDH than those of SNPs Ex1-2 and SNP Ex11-1, where the G allele of SNP Ex1-1 corresponds to higher protein concentration. Thus, we concluded that SNP Ex1-1 is involved in the regulation of the expression both at the transcription and protein level and SNP Ex11-1 is involved in the regulation of the expression of UGDH at the transcription level Although further study is required to determine how these two DNA polymorphisms modulates the expression of UGDH, they are most likely responsible, at least part, for the observed effect of above QTL affecting milk yield and composition in dairy cattle used in current study. As for SNP Ex1-2, in agreement with the results of the individual association analysis on this SNP, we did find no significant difference between the two alleles at either the RNA or the protein level. Although a non-synonymous SNP in a gene can change its function(s), causing the gene to be associated with a disease[Previously, we mapped a QTL near BMS470 with effects on milk production traits with daughter design using 14 microsatellites on bovine chromosome 6 in a Chinese Holstein population involving 26 paternal half-sib families with 2356 daughters,9. By in disease or affecBMS483 and MNB-209 on BTA6 including a QTL for milk production traits, the UGDH gene was identified and shown to be associated milk yield and milk composition. Further in vitro expression assay demonstrated that the SNPs in exon1 and exon11 of UGDH represent two functional polymorphisms affecting expression of UGDH and might partly contributed to the observed association with milk production traits.Based on the previously identified 1.5-Mb region between markers ATP-binding cassette; BTA: Bos Taurus Automosome; DAC: Dairy association of China; DGAT1: AcylCoA: diacylglycerol acyltransferase; GHR: Growth hormone receptor; LD: Linkage disequilibrium; SNP: Single nucleotide polymorphism; OPN: Osteopontin; QTL: Quantitative trait locus; UGDH: UDP-glucose dehydrogenase.ABCG2: The authors declare that they have no competing interests.QX performed the cell related experiments and prepared the manuscript. GM contributed to the SNP discovery and genotyping and association analysis. DS conceived and designed the experiments and prepared the manuscript. QZ and YZ participated in the experiment design and result interpretation. CY and HC performed the QTL mapping. XD and JL participated in the data analysis. All authors read and approved the final manuscript.


Helianthus annuus L.). This work involved the development of a reference transcriptome assembly for sunflower, the discovery of thousands of high quality SNPs based on the generation and analysis of ca. 6 Gb of transcriptome re-sequencing data derived from multiple genotypes, the selection of 10,640 SNPs for inclusion in the genotyping array, and the use of the resulting array to screen a diverse panel of sunflower accessions as well as related wild species. The results of this work revealed a high frequency of polymorphic SNPs and relatively high level of cross-species transferability. Indeed, greater than 95% of successful SNP assays revealed polymorphism, and more than 90% of these assays could be successfully transferred to related wild species. Analysis of the polymorphism data revealed patterns of genetic differentiation that were largely congruent with the evolutionary history of sunflower, though the large number of markers allowed for finer resolution than has previously been possible.Recent advances in next-generation DNA sequencing technologies have made possible the development of high-throughput SNP genotyping platforms that allow for the simultaneous interrogation of thousands of single-nucleotide polymorphisms (SNPs). Such resources have the potential to facilitate the rapid development of high-density genetic maps, and to enable genome-wide association studies as well as molecular breeding approaches in a variety of taxa. Herein, we describe the development of a SNP genotyping resource for use in sunflower ( Until recently, a major limitation in the genetic dissection of complex traits in both plants and animals has been the lack of availability of large numbers of genetic markers that can be assayed in an efficient manner. When combined with rapid advances in next-generation sequencing technologies, however, the high levels of single nucleotide polymorphism (SNP) diversity that are present in most plant and animal gene pools e.g.,  have madHelianthus annuus L.).The development of high-throughput SNP genotyping assays requires large-scale SNP discovery. Moreover, for such assays to be generally useful, the SNPs should be selected to represent the diversity present across the target gene pool, as opposed to being population specific. This, in turn, requires large amounts of sequence data from a diverse panel of individuals. While the necessary genomic resources already exist for a number of species e.g., , many otH. annuus has emerged as a model species for genetic and genomic studies in the Compositae, which is one of the largest and most diverse families of flowering plants. While great strides have been made in the development of sunflower genomic resources Sunflower is a globally-important oilseed crop that is grown on ca. 25 million hectares per year. Due to the economic and ecological importance of cultivated and wild sunflower, In this study, we describe the development of a sunflower reference transcriptome assembly based primarily on long-read ESTs, as well as the generation and analysis of short-read transcriptome data from numerous accessions chosen to represent the diversity within the sunflower gene pool. This work resulted in the production of ca. 6 Gb of next-generation sequence data which was then compared against the reference transcriptome assembly for the purposes of SNP discovery. We identified thousands of high-quality SNPs using a customized bioinformatics pipeline, and selected the best set of 10,640 SNPs for the development of a high-throughput SNP genotyping array using Illumina's iSelect Infinium platform. We then used this SNP array to screen a diverse panel of sunflower accessions as well as a pair of related species. The results of this work revealed a high frequency of polymorphic SNPs and relatively high levels of transferability of assays across species, making this SNP array an ideal tool for the genetic analysis of sunflower and related species.de novo transcriptome assembly using MIRA http://cgpdb.ucdavis.edu/) via Sanger sequencing and subsequently deposited in GenBank (http://www.ncbi.nlm.nih.gov/dbEST/). In contrast, we produced the 454 data from HA89 by extracting RNA from developing seeds, roots, disc florets, and leaves using TRIZOL Reagent  according to the manufacturer's guidelines, preparing cDNA using the SuperScript III Reverse Transcriptase kit (Invitrogen), pooling and normalizing the cDNA using the TRIMMER-DIRECT kit , and sequencing the resulting normalized pool using a Roche 454 GS FLX instrument with XLR (Titanium) sequencing chemistry. This sequencing was performed at the Georgia Genomics Facility at UGA.In order to build a reference transcriptome assembly, we started by combining publicly-available long-read ESTs from elite, inbred sunflower lines . Finally, long-read ESTs generated from wild sunflower (ANN1238) by the CGP were separately assembled into contigs and singletons with TGICL Transcriptome re-sequencing data was obtained from multiple elite, inbred sunflower oilseed lines representing the two major heterotic groups, including: RHA373, RHA415, RHA455, RHA468, HA89, HA383, HA412-HO, and HA434 . For RHAThese short-read ESTs, along with the long-read ESTs derived from RHA801 and HA89, were then mapped onto the reference sequence scaffold using MOSAIK  for SNP discovery. Pairwise alignment of reads with the reference sequence was performed with the MosaikAligner module using the alignment algorithm “all” to store all hash positions per seed. The alignment mode “unique,” which places only uniquely aligned reads onto the reference, was used for the Illumina, Sanger, and GS FLX reads, while the alignment mode “all,” which finds all possible alignments (as suggested for resolving paired-end reads) was used for the Illumina paired-end data. We used a hash size of 14 for Sanger and GS FLX Titanium reads and hash size of 15 for Illumina reads. We allowed 4 and 12 mismatches for Illumina and Illumina paired-end reads, respectively. For the Sanger and GS FLX reads, we allowed up to 5% mismatches and a maximum of 100 hash positions per seed. Multiple sequence alignment was conducted with the MosaikSort module using only uniquely aligned Illumina, Sanger, and GS FLX reads, while for Illumina paired-end reads we allowed sorting of orphaned unique reads when one of the two paired-end mates could not be uniquely aligned, and we ignored all reads when both paired-end mates were not-uniquely aligned. The MosaikAssembler module was used to produce a final multiple sequence alignment in an assembly format.http://int-citrusgenomics.org/usa/ucr/Files.php) with updated Arabidopsis genome sequence information available from TAIR (http://www.arabidopsis.org/) to discard SNPs in regions spanning putative introns in sunflower unigenes.We mined SNPs from the Illumina-Sanger-GS FLX contigs with a minimum of 6 reads and a maximum of 5000 reads using the following criteria: (i) a minimum of two reads (either short- or long-read ESTs) per genotype at the SNP position, (ii) a minimum of two distinct genotypes at the SNP position that satisfy criterion (i), and (iii) a minimum allele frequency of 0.9 at the SNP position of interest within each given genotype. We also used a tblastx-based intron finding Perl script (http://sourceforge.net/projects/ngmagic/). This database allows for filtering of reference unigenes according to their name, length, and depth (number of assembled sequences), displays the aligned contig corresponding to each unigene, and highlights sequence variants  for each genotype. Sequence variants can also be filtered using multiple criteria, such as unigene name, genotype identity, class (SNP or INDEL), number of reads at the variant position, and major allele frequency. Moreover, a blast tool permits blastn and tblastn to the reference unigenes for the identification of unigenes with sequence similarity to candidate genes and the development of SNP assays that will allow genetic mapping of these genes. During the SNP discovery analysis, custom scripts were also used to improve the quality of the reference assembly using short-read sequence information from contigs with sufficient depth to correct ambiguous nucleotides in the reference scaffold.We deposited the results of our SNP discovery analysis in a modified database built on the MAGIC interface Arabidopsis thaliana (L.) Heynh, Ricinus communis L. (castor), Lotus japonicus L., Medicago truncatula Gaertn., Carica papaya L. (papaya), Populus trichocarpa Torr. & Gray (poplar), Sorghum bicolor (L.) Moench, Solanum lycopersicum L. (tomato) and Vitis vinifera L. (grape vine). All unigenes with hits  to proteins from at least one of the nine plant genomes were considered putative “genes” if their predicted open reading frame (ORF) was longer than 50 amino acids, and “pseudogenes or gene fragments” if their predicted ORF was shorter than that value. Unigenes that had no similarity with putative proteins from any of the nine genomes were classified as “possible novel genes” if their predicted ORF was longer than 75 amino acids, and “not genes” (presumably UTR sequences) if their predicted ORF was shorter than that value. These length thresholds  were empirically determined based on the size distributions of the longest predicted ORFs that either matched or did not match putative genes from the nine sequenced species.Nucleotide sequences from all unigenes used for SNP discovery were translated in all six reading frames and the length of the longest open reading frame was recorded. These sequences were also blasted against all predicted proteins from nine fully-sequenced genomes, including: http://www.blast2go.de/) http://www.goat.no/) http://www.genome.jp/kegg/pathway.html) Sequences of the 10,640 unigenes selected for inclusion on the Infinium Beadchip (see below for details) were next imported into Blast2GO  were included twice as controls to assess the repeatability of allele calls. For each accession, DNA was extracted from fresh or lyophilized leaf tissue using a modified CTAB method Infinium Beadchips were manufactured by Illumina in a 24×1 format and used to genotype a panel of 36 accessions including oilseed and confectionery  cultivars, landraces, wild ephrodes . The oilK population genetic clusters based on their multi-locus genotypes. Clusters were assembled to minimize intra-cluster Hardy-Weinberg and linkage disequilibrium and, for each individual, the proportion of membership in each cluster was estimated. This analysis did not rely on prior population information . For each analysis, K = 1-12 population genetic clusters were evaluated with 5 runs per K value. After checking to ensure that the results of each run were in general agreement, the probability values were averaged across runs for each cluster. For each run, the initial burn-in period was set to 50,000 with 100,000 MCMC iterations. The most likely number of clusters was determined using the DeltaK method of Evanno et al.In order to assess the utility of the SNP array for genetic analyses in diverse germplasm, the resulting data were analyzed in a population genetic framework and used to investigate genetic differentiation amongst the genotyped accessions. Gene diversity  for each SNP was estimated using GenAlEx v. 6.1 As noted above, the reference transcriptome assembly was based on a large collection of Sanger and 454 sequences from cultivated sunflower as well as Sanger sequences from wild sunflower (ANN1238) . After eAfter discarding sequences that did not meet the criteria outlined in the After blasting the 37,545 sunflower unigenes used for SNP discovery against all predicted proteins of the nine sequenced plant genomes, 10,314 unigenes (24.5%) were found to have no significant hits, and were thus classified as “not genes” or “possible novel genes,” depending on the length of their predicted ORFs. The remaining 27,231 unigenes (72.5%) shared identity with proteins from at least one of the nine genomes, and 86.0% of these had hits with proteins in seven or more of the nine genomes. Of these, 1,237 were classified as putative “pseudogenes or gene fragments” due to their short predicted ORFs, resulting in 25,994 unigenes being classified as putative genes.The subset of 10,640 unigenes that was ultimately selected for inclusion on the Infinium Beadchip, each of which came from a different unigene, included 8,229 unigenes with blast-based annotations augmented by InterProScan and Annex, 867 unigenes that had no blast hits to the NCBI nr database, 468 unigenes that had several blast hits, but which lacked GO-terms, and 1,076 unigenes that had blast hits and GO-terms, but no blast-based annotation for the selected parameters . An average of 3.2 GO-terms (ranging from 1 to 27) were available for 9,305 of these unigenes. The 20 most abundant molecular functions, biological processes, and cellular components of the Gene Ontology vocabulary for the 10,640 unigenes are presented in H. annuus, there were 7,970 SNPs that gave usable data for at least 80% of the individuals tested. Of these, 7,723 gave successful allele call data in both H. annuus and H. argophyllus, and 7,490 in both H. annuus and H. niveus ssp. tephrodes. Taken together, 7,381 of the SNPs gave usable data in all three species. Note that differences in the number of loci that worked in the H. argophyllus and H. niveus ssp. tephrodes were consistent with known phylogenetic relationships. That is, H. argophyllus is the sister species to H. annuus while H. niveus ssp. tephrodes resides in a different (sister) clade H. annuus DNA samples that were included twice as controls, the genotype calls differed for less than 2% of the SNPs. In all cases, these differences could be attributed to missing data in one or the other replicate, such that there were no instances in which different alleles were called between replicates.Of the 10,640 targeted SNPs, 9,480 SNPs were included on the Infinium Beadchips due to a manufacturing loss of 10.9% of attempted SNPs, which was within the expected limits. For H. annuus were polymorphic. As expected, the modern breeding lines  had significantly fewer loci scored as heterozygous when compared to the OPVs and landraces . Of the polymorphic loci, 6,692 of these had a minor allele frequency ≥10%. All subsequent genetic diversity analyses were performed on this reduced set of 6,692 SNPs. Gene diversity, or expected heterozygosity, calculated across the 32 H. annuus genotypes was 0.426±0.001 (mean ± standard error). After excluding the two wild H. annuus individuals, gene diversity dropped slightly to 0.424±0.001. This overall level of SNP diversity at polymorphic sites is comparable to previous estimates derived from the re-sequencing of PCR amplicons from cultivated sunflower accessions H. argophyllus and H. niveus ssp. tephrodes, these species were excluded from genetic diversity analyses.In terms of polymorphism, 7,640 of the 7,970 SNPs that produced usable data in H. annuus, the DeltaK method of Evanno et al.K = 2; K are reported in K = 3 revealed additional differentiation, with the wild, OPV/landrace, and non-oil lines showing high membership in the same group, and the RHA and HA oil lines emerging as relatively distinct groups. Inspection of the PCO plot  accessions from the improved  accessions. These results are generally consistent with previous population genetic analyses of the cultivated sunflower gene pool, where oilseed RHA lines have appeared to be relatively distinct from the balance of the gene pool With regard to genetic differentiation in PCO plot  reveals th century, however, it was taken to Europe by Spanish explorers where it was initially grown as an ornamental, but later became an important source of vegetable oil; breeding efforts thus focused increasingly on improving oil yield. Eventually, the germplasm that gave rise to the modern oilseed sunflower gene pool was brought back to North America and commercial production in the United States commenced in the 1960s, primarily using open-pollinated oilseed cultivars These overall patterns of genetic differentiation are largely congruent with the evolutionary history of sunflower as a crop plant. Following its domestication from wild sunflower in what is now the central United States, cultivated sunflower was first used as a source of edible seeds and for a variety of non-food applications  Given our results, the high-density SNP array described herein appears to be an excellent resource for agricultural applications as well as evolutionary genetic studies in cultivated sunflower and its wild relatives. Indeed, the high level of polymorphism revealed by this array makes it an ideal tool for the development of a high-density genetic map of the sunflower genome, and the relatively high level of cross-species transferability of these assays suggest that it will also be a powerful tool for comparative genetic mapping studies aimed at understanding patterns of genome rearrangement between sunflower and related species. In addition, this array should prove useful for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in sunflower. Finally, because our design criteria included GoldenGate probe design scores, the full set of SNPs and associated polymorphism data will provide researchers with a rich source of information for developing smaller, more targeted SNP arrays for a variety of applications.Figure S1Frequency of SNP types based on the full set of 85,063 sunflower SNPs.(TIFF)Click here for additional data file.Figure S2The 20 most common GO terms in each of three categories for the 10,640 SNP-containing unigenes. A) Biological Process. B) Molecular Function. C) Cellular Component.(TIF)Click here for additional data file.Figure S3Log-likelihood and DeltaK plots for the STRUCTURE analyses. A) Log-likelihood plot. B) DeltaK plot.(TIF)Click here for additional data file.Supporting Information S1Supporting Information for “SNP Discovery and Development of a High-Density SNP Genotyping Array for Sunflower.”(PDF)Click here for additional data file.Dataset S1(RAR)Click here for additional data file.Dataset S2(XLS)Click here for additional data file.Dataset S3(XLS)Click here for additional data file.


For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for  Complex diseases, such as cancers, arise from alterations in these networks and thus contribution of any single gene to disease risk or progression should be viewed in the context of molecular networks focal genes, i.e., genes that code for proteins which interact or belong to the same molecular network with a protein coded by a candidate gene. Additionally, CANGES is able to identify central SNPs, i.e., genetic variations that are located in the focal genes' coding or regulatory regions, such as splice and 3′ UTR sites (We introduce a data integration workflow CANGES (Candidate Genes and SNPs) to rapidly identify TR sites . Thus, cTo identify focal genes, CANGES uses KEGG and Reactome pathway databases CANGES integrates data from SNP, protein and linkage disequilibrium databases and thus belongs to the meta-server class of services p53 and the second one focuses on assessing the context for three genes reported in a recent glioblastoma GWA study.CANGES workflow diagram, including modules, resources, bioinformatics components and interfaces, is shown in p53 is one of the most studied genes in cancer biology. However, the function and interplay of p53 with the other proteins is still unclear. We used CANGES to provide the most comprehensive set of proteins that function within the same pathways with p53 or have direct PPI with p53  , CCDC26 (Swiss-Prot: Q8TAB7), CDKN2A , CDKN2B , RTEL1 (Swiss-Prot: Q9BW37 Q9NZ71) and PHLDB1  with elevated risk of glioma TERT, CDKN2B and CDKN2A were found in KEGG, Reactome or PINA. These three genes resulted in a total of 1,346 focal genes (Glioblastoma multiforme (GBM) is grade IV glial originating tumor type that responds poorly to all available therapeutic regimens and has a median survival of one year al genes , which wanalysis .We used The Cancer Genome Atlas (TCGA) GBM data set of 209 samples subjected to 550k SNP-array experiments to identify SNPs associated with poor survival using Kaplan-Meier analysis with log-rank test. We initially considered all 35,622 central and tag-SNPs for the survival analysis. However, only 1,888 SNPs were found from the TCGA genome-wide SNP-array for GBM. This small number is due to the low number of central SNPs in the SNP-arrays in general; here, 0.02% of the central SNPs were on the array. We were able to estimate the survival effect for 995 central SNPs directly or through their tag-SNPs. Survival analysis of the set of 1,888 SNPs resulted in 18 SNPs with a significant survival effect. From these, eight were central SNPs, and 10 SNPs further tag 18 central SNPs totaling to 26 central SNPs putatively having survival impact.CAMK2D, CCNB1, CD82, CEP192, CLASP2, FLT3, KIF2B, LAMA1, MTR, PML, PSMF1, SEC13, SGOL2 and SMAD5. The lowest p-value . Kaplan-Meier estimates for rs2275565 and rs100192 are illustrated in We then mapped the set of 26 central SNPs with putative survival effect to genes. The SNPs are located in 14 genes: A number of individual cancer associated genetic variants have been found recently, but the signaling-level context for these variants has been challenging to establish. Finding genetic variants causing functional effects to a protein network, which also contains a risk or cancer-driving gene's protein product, are of particular interest as these are candidates for SNP-SNP or gene-gene interactions. In order to obtain a comprehensive network for a set of genes, there is a need to integrate several databases, such as PPI, genomic and pathway repositories. CANGES is hypothesis generating tool that provides a significant step forward in obtaining a comprehensive view for a gene set. Furthermore, CANGES is implemented to run on Anduril workflow environment Many genome-wide SNP arrays, such as Illumina HumanHap arrays, are designed so that the probes query for tag-SNPs instead of central SNPs p53 gene is a prime example of a well-studied gene that produces a very large result set. On the other hand, only three out of six GBM risk genes were found from the pathway- or PPI databases.CANGES provides a means to integrate information from several databases that are accessed through their programming interfaces when such interface is provided. This allows the use of the latest releases of the databases. CANGES may produce very large result sets for queries including genes having a lot of interactions or activity in signaling pathways. In such cases CANGES analysis may take several hours to complete when executed through the CANGES web interface. Therefore, processing of large queries is more efficient through local installations of CANGES than with the website. Furthermore, it is also challenging to estimate the size of the result file because the amount of data in the databases varies for different genes and pathways. The p53 case study, only PolyPhen-2 was able to predict that rs1042522 is damaging. Even F-SNP, which uses 16 methods and datasets to predict functional effects of SNP p53Our case studies demonstrate that SNP-protein function prediction tools resulted in widely dissimilar results. For example, in the MTR) that encodes the enzyme 5-methyltetrahydrofolate-homocysteine methyltransferase, which catalyzes methione biosynthesis. MTR was recently identified as a cancer susceptibility gene regardless of environmental factors MTR.In our second case study we identified genes belonging to the same pathway or directly interacting with protein products of three glioma risk genes and further calculated survival estimates for 1,888 SNPs in these genes. This analysis resulted in a SNP (rs2275565) in the gene Methionine synthase (CCNB1), may have an impact on GBM survival. We note that rs164390 itself was not found from the GBM dataset and its effect on the survival could not be estimated directly. This is a common situation in studies integrating data from different cohorts with varying SNP-array designs; and the CANGES results provide a means to rapidly integrate such data. Interestingly, CCNB1 belongs to the same pathways with CDKN2A and CDKN2B and is regulated by p53. An increase in the copy number in the chromosomal region of CCNB1 has been shown to be associated with an increase of the cell growth rate in glioma cell lines CCNB1 along with other cell-cycle genes indicates poor survival in various cancers CCNB1 alone is suggested to be a marker for a poor prognosis in breast cancer CCNB1 an interesting target candidate also in GBM. While it is not surprising to identify to find 18 SNPs with a survival effect from a set of 1,888 SNPs, the two most interesting genes identified here were not be detected in genome-wide context.Our results further indicate that also rs100192, which is a tag-SNP for rs164390 in 5′ UTR region of a gene G2/mitotic-specific cyclin-B1  the SNP module and 3) the evaluation module as illustrated in The CANGES data integration pipeline is divided into three modules, 1) The protein module searches for focal genes from the pathway databases KEGG The SNP module annotates central SNPs for the queried genes from the ENSEMBL genome database benign, possibly damaging, probably damaging) based on the rules regarding the SNPs effect on protein functional sites, the protein structure and the presence of the same peptide substitution in homologous sequences. PolyPhen may produce several predictions for one SNP and all of the predictions are included into the results. SNPs3D calculates scores for SNPs using a protein structure stability model and a feature profile that captures sequence conservation with a support vector machine algorithm. Negative scores predict deleterious effects. The CANGES evaluation module uses the lowest score from the available SNPs3D scores for each SNP. SIFT's predictions are based on the probability of SNP's peptide in homologous sequences, and it considers peptide probability lower than 0.05 damaging for the protein function. SIFT gives a prediction for both SNP's optional peptide variants and the evaluation module selects the lower value indicating more deleterious effect for the prediction. For the predictions, we use local copies of pre-calculated datasets: PolyPhen  and PolyPhen-2  are from dbSNP build 126 and 130, respectively, whereas SNPs3D uses dbSNP build 128 with 77,999 SNPs and and it uses dbSNP build 129 . The input of the evaluation module is a list of SNP rs-identifiers and the output is the predictions.The evaluation module produces predictions for functional effects of SNP to protein function for non-synonymous coding SNPs, using four prediction tools: PolyPhen The effects of GBM focal genes on patient survival were calculated using 209 GBM patient blood samples subjected to 550k SNP-array from the TCGA data set http://csbi.ltdk.helsinki.fi/CANGES/. The Anduril components are also available at this site. Related databases and the Anduril workflow for the survival analysis are available upon request. Future work focuses on integrating SNP-protein function predictors when such become available. We will also include more variation types from the ENSEMBL database when their consequences on the gene products can be estimated. One future direction of CANGES is to select SNPs for customized high-throughput genotyping experiments.The CANGES workflow is available as a web service in Figure S1The Kaplan–Meier curves from the 16 SNPs showing survival effect from the glioblastoma multiforme case study.(PDF)Click here for additional data file.Table S1The complete description of annotations from both ENSEMBL Perl and BioMart interfaces.(XLS)Click here for additional data file.Table S2The list of proteins and their SNP annotations (ENSEMBL Perl interface) of focal genes from the p53 case study.(XLS)Click here for additional data file.Table S3The list p53 focal genes' coding SNPs that all prediction methods in CANGES estimate of being functional.(XLS)Click here for additional data file.Table S4The list of the central SNPs and their annotations (ENSEMBL Perl interface) from the glioblastoma multiforme case study.(XLS)Click here for additional data file.Table S5The central SNPs from the glioblastoma multiforme case study that show a survival effect either itself or through tagSNPs. Columns from survival analysis are the following: SurvivalMarker  =  the SNP for which survival is estimated, Hetero OR  =  the odds ratio between heterozygous and homozygous genotypes, Homo OR  =  the odds ratio between homozygous genotypes, pvalue  =  the p-value.(XLS)Click here for additional data file.


Juglans regia L.).A genome-wide set of single nucleotide polymorphisms (SNPs) is a valuable resource in genetic research and breeding and is usually developed by re-sequencing a genome. If a genome sequence is not available, an alternative strategy must be used. We previously reported the development of a pipeline (AGSNP) for genome-wide SNP discovery in coding sequences and other single-copy DNA without a complete genome sequence in self-pollinating (autogamous) plants. Here we updated this pipeline for SNP discovery in outcrossing  species and demonstrated its efficacy in SNP discovery in walnut  end sequences (BESs) produced by Sanger sequencing during the construction of a walnut physical map. A total of 22,799 putative SNPs were initially identified. A total of 6,000 Infinium II type SNPs evenly distributed along the walnut physical map were selected for the construction of an Infinium BeadChip, which was used to genotype a walnut mapping population having ‘Chandler’ as one of the parents. Genotyping results were used to adjust the filtering parameters of the updated AGSNP pipeline. With the adjusted filtering criteria, 69.6% of SNPs discovered with the updated pipeline were real and could be mapped on the walnut genetic map. A total of 13,439 SNPs were discovered by BES re-sequencing. BESs harboring SNPs were in 677 FPC contigs covering 98% of the physical map of the walnut genome.The updated AGSNP pipeline is a versatile SNP discovery tool for a high-throughput, genome-wide SNP discovery in both autogamous and allogamous species. With this pipeline, a large set of SNPs were identified in a single walnut cultivar. Juglans regia L., 2n = 32, ~606 Mb per 1C genome, Horjales et al. 2003 inhttp://data.kew.org/cvalues/) is an economically important tree widely cultivated for its nuts and timber. A long reproductive cycle[Walnut  is the most abundant type of DNA variation in most species. The advent of massively parallel next generation sequencing (NGS), coupled with high throughput genotyping technology, makes it relatively easy to identify and use SNPs-15. An eGenome-wide SNP discovery utilizing NGS is predicated on bioinformatic tools facilitating mapping NGS reads to reference sequences-22 and vOne such strategy is to substitute shallow genome coverage of long sequence reads, or their assemblies, generated with the Roche 454 or Sanger sequencing technology for a complete genome sequence. This strategy was implemented in the AGSNP pipeline for genome-wide SNP discovery in self-pollinating (autogamous) plants without a reference genome sequence. In AGSNP, deep genome coverage of NGS reads from one homozygous individual was mapped to shallow 454 reads of another homozygous individual. SNPs were discovered between the two sets of reads. The assumption of homozygosity limits the universal utility of the pipeline because many plants and most animals are allogamous and hence heterozygous. We report here an update of AGSNP for applications in allogamous species.BAC end sequences are one of several possible sources of shallow coverage, genome-wide, long DNA reads. BESs are often developed from BAC libraries and are used for marker development and genome sequence composition surveys before whole genome sequencing. BESs ca1 mapping population. The genotyping results were analysed to validate the SNPs and to improve the SNP discovery rate with the pipeline by adjusting the SNP filtering criteria.To develop markers for the construction of walnut genetic map and anchoring walnut FPC contigs on it, a total of 54,912 BESs from walnut cv ‘Chandler’, a major walnut scion cultivar grown in California, have been generated by Sanger sequencing. SNPs wehttp://avena.pw.usda.gov/wheatD/agsnp.shtml.Walnut is an outcrossing species, and walnut cultivars, such as ‘Chandler’, are highly heterozygous clones propagated by grafting. NGS reads generated from a single walnut cultivar can consequently be derived either from a single haplotype if a locus is homozygous or from two different haplotypes if the locus is heterozygous. Hence, observing two different nucleotides at a nucleotide position in a stack of mapped reads should be used as evidence for an SNP Figure . BecauseVF), defined as the number of SOLiD reads in a stack having a nucleotide at a specific nucleotide position that was different from the reference sequence divided by the total number of reads in the stack and ‘folded variant frequency’ (FVF), derived from VF were used to discriminate between these two possibilities. FVF is 1-VF if VF > 0.5 and equal to VF if VF ≤ 0.5. The domain of VF was 0 to 1 and that of FVF was 0 to 0.5. If FVF was 0, the locus was homozygous. If FVF was minor, read variation was likely a sequencing or mapping error and if it was near 0.5, read variation was likely caused by SNP.Updating the AGSNP pipeline was based on the following rationale. Reads forming a stack may vary either due to heterozygosity or sequencing and mapping errors. If heterozygosity was the cause of variation, two variants were expected at a nucleotide position in a stack of reads, and the expected frequency of each variant was 0.5. If a sequencing or mapping error was the cause of variation, two or more variants were expected at a nucleotide position in a stack of reads, and the frequency of one of the variants was expected to be minor. Variables ‘variant frequency’ . If the test indicated that FVF at a nucleotide position was not significantly different from 0.5, the variant nucleotide was assumed to be a true SNP. A special case was when FVF > 0 but the stack of reads varied for two nucleotides, both differing from the reference sequence  were located in 804 of the 916 FPC contigs assembled from fingerprinted BAC clones (not shown), which indicated that the BESs were distributed across the entire walnut genome. To annotate these BESs, homology was searched between the 48,661 BESs and walnut cDNA sequence contigs at 1E-10. A total of 29,223 BESs showed homology to cDNA sequences. Those BES were called genic BESs whereas the remaining 19,438 were called non-genic BESs.A total of 395,528,231 high-quality SOLiD reads 50 bp long were retained after removing low quality reads with an average read quality score < 20. The total length of the reads was 19,776 Mbp, which translated to ~32.6X walnut genome equivalents. The filtered SOLiD reads were mapped to the 48,661 reference BESs using the BWA program, and SNPThe SNP filtering criteria initially used are summarized in Table SNPs are divided into two categories in the Infinium HD assay, Infinium I type  and Infinium II type , according to probe or bead type design. The Infinium II probe design employs one probe per SNP  whereas Infinium I probe design employs two probes . As the pricing and ordering of the custom BeadChip product are determined by the number of bead types, rather than the number of SNPs, using only Infinium II SNPs increases the number of SNP loci genotyped per constant number of probes and is therefore more economical. In this study, 88%  of SNPs were of Infinium II type Table . SNPs ofAll of the 22,799 putative SNPs identified in genic and non-genic BESs were evaluated using Illumina’s ADT software. In the 20,092 SNPs of Infinium II type, 17,019 SNPs (85.2%) had a score ≥0.9, and 19,891 (99%) of SNPs had a score ≥ 0.7 Table . The des2   (Table RMDs (R2 = 0.0942) . It was therefore concluded that unscorable SNPs were most likely due to random effects in the Infinium genotyping assay itself while ADT design score (≥ 0.7) and other SNP discovery factors did not impact the success rate of the conversion of SNPs to markers.To examine the reasons why 580 SNP markers failed to generate scorable genotyping data, ADT design score and the following five SNP discovery criteria or variables were compared between the 5,420 genotyped SNPs and 580 unscorable SNPs. The criteria were: average reference quality score , reference SNP base quality score, SNP mapping quality score ; SNPs from non-genic single-copy BESs had a higher chance of being true-positive (58.3%) than those from genic BESs (47.0%).Out of the 2,655 false-positive SNPs, 1,850 SNPs were from genic BESs and 805 SNPs were from non-genic BESs, while of the 2,765 true-positive SNPs, 1,638 SNPs were from genic BESs and 1,127 SNPs were from non-genic BESs. There was a highly significant relationship between SNP source and true/false-positive SNP outcome  was significantly positively correlated to FVF and MQS, and negatively to RMD , RMD (X2) and FVF(X3) was fitted (Table p/(1-p)) = 10.3976899 - 0.3077982X1 + 0.07896269X2 - 5.2585503X3, where p is the probability of an SNP to be declared false-positive. This model was statistically significant  and can therefore be used to predict whether an SNP is true-positive or not. A total of 1,777 of 2,655 false-positive SNPs were correctly predicted with this model with an accuracy rate of 66.9% and 2,203 of 2,765 true-positive SNPs were correctly predicted with this model with an accuracy rate of 79.7%, showing that the model had higher prediction accuracy for true-positive SNPs than for false-positive SNPs. The overall prediction accuracy was 73.4% . The predicted rate of true-positive genic SNPs was 53.3%  and 63.9%  of true positive non-genic SNPs. The overall rate of true-positive SNPs was 57.0%, higher than the actual rate in 6,000 SNPs (51.0%).Correlation analyses revealed that the rate of true-positive SNPs .The above analyses showed that the rate of true-positive SNPs was strongly affected by  Figure , which iRMD ≤ 25, MQS ≥ 30, and a non-significant difference of FVF from 0.5 were used to filter 5,420 SNPs used for genotyping, 69.6%  of true-positive SNPs were obtained; a much higher rate than 51.0% when initial cutoff values were used. When these criteria were used to filter all SNPs identified  to 61.7% . But when cutoff was set on the basis of the binomial test, the false-negative SNP rate significantly decreased from 23.1%  to 9.4% .SNP filtering is a critical step for removing false-positive SNPs from the pool of putative SNPs during SNP discovery. Ae. tauschii[s to identify single copy reference sequences or to set a maximum read mapping depth. In contrast, in walnut,s turned out to be an optimal cutoff value. Similarly, more stringent SNP filtering cutoff values were required for the SNP mapping quality score, average reference quality score, and reference SNP quality score. After adjusting the cutoff values, a 69.6% true-positive SNP rate was obtained in walnut, which was much higher than 51.0% with the initial cutoff values. This validation rate was lower than that obtained in self-pollinating species[The SNP discovery in  tauschii used a c species,30-33 bu species-36.FVF will decrease false-positive SNP rate. Using paired NGS reads would probably also help since it will increase the likelihood of mapping reads to their correct locations. In addition, more stringent mapping parameters in the mapping software, e.g., the number of mismatched bases and the number of gaps, should be applied if reads from heterozygous genomes are used for SNP discovery with the AGSNP pipeline.The analysis of validated SNPs showed that factors such as read mapping depth, SNP mapping quality score, and folded variant frequency were closely related to the rate of true-positive SNPs in the updated AGSNP pipeline. All those factors were directly or indirectly associated with a fundamental issue: mismapping of NGS reads to a reference sequence. Because reference sequences and NGS reads are derived from heterozygous loci, mismapping can easily result in a large proportion of false-positive SNPs. Focusing SNP discovery on genic regions and single-copy non-genic sequences, increasing the stringency of mapping depth, increasing the SNP mapping quality score, and increasing A total of 6,000 SNPs scattered along most of the FPC contigs were selected to generate a 6 K iSelect Infinium BeadChip. Of them, 90.3% produced genotyping data and 86.1% were converted to potential SNP markers for genetic mapping. This conversion rate of SNP sequence to SNP markers is higher than rates in an outcrossing tree species, maritime pine, (63.6%–74.8%) using thr = 0.66, significant at p = 0.05) between heterozygosity and coefficient of parentage (COP)  between pairwise dissimilarity among the 32 walnut accessions and COP  than in allogamous species (69.6%). The greater fidelity of SNP discovery in autogamous species is undoubtedly related to greater efficiency with which the pipeline is able to separate SNPs from sequencing and mapping errors in autogamous species. The updated pipeline can be downloaded atA fragment library was constructed from genomic DNA isolated from the walnut cultivar ‘Chandler’ using the Applied Biosystems Fragment Library Construction Kit . Templated beads were prepared from the fragment library using the ePCR kit v.2 and the Bead Enrichment Kit from Applied Biosystems for SOLiD4+. Workflow Analysis was done after the first round of templated bead preparation for each library using the Workflow Analysis kit from Applied Biosystems to check library quality and the amount of templated beads generated per ePCR. Additional Templated beads were deposited on slides using the Bead Deposition kit from Applied Biosystems. One full slide of the fragment library was sequenced. Greater details of SOLiD library preparation and sequencing were published earlier.A total of 20 tissue-specific mRNA-Seq libraries were constructed and sequenced on the Illumina GAII platform to characterize the walnut transcriptome. Over 1 billion RNA-Seq reads were generated and trimmed for quality with a custom script. The trimmed reads derived from each sample were assembled using velvet v1.12/oases v1.15 and tgicHindIII and MboI BAC clones of walnut cultivar Chandler[http://probes.pw.usda.gov:8080/walnut/Database) but details of contig assembly and the construction of walnut physical map will be published elsewhere.FPC BAC contigs were constructed from 113,063 fingerprinted Chandler. A totalMboI and HindIII BAC clones has been described in detail previously[The development of BESs for eviously. A totaleviously.The AGSNP pipeline was updaAegilops tauschii, SNPs located in repeated sequences and those due to mapping errors were removed using the average read mapping depth (RMD) (s) estimated from the fitted extreme value distribution of mapping depths of all mapped sequences used in SNP discovery. A cutoff value ofRMD less than this value were assumed to be single copy and those greater than this value were assumed to be repeated. The same strategy was used in this study. The filtered SOLiD reads were mapped to the 29,223 genic BESs using the updated AGSNP pipeline. The RMD (s) were estimated to be 15.9 reads and 19.1 reads, respectively  is one of the criteria to filter out potentially false-positive SNPs among reads generated for an outcrossing individual. FVF represents the frequency of minor read variants in a stack of reads, which follows a binomial distribution and is expected to be 0.5 in a random mating population. A t-test was used to test whether or not the FVF of an SNP statistically deviated from 0.5 (H0). If the FVF is significantly different from 0.5 at the 0.05 probability level, the SNP was inferred to be false-positive. The test statistics is as follows:Folded variant frequency . A total of 6,000 SNPs were selected for iSelectInfinium genotyping. To obtain a dense, genome-wide genetic map, SNP markers should be distributed evenly across the entire genome or be present in all FPC contigs. To maximize the likelihood of that, SNP selection was based on the following criteria: (1) only one SNP was chosen per BES, (2) at least one SNP marker was chosen per FPC contig, (3) the number of selected SNPs per FPC contig was proportional to contig size, and SNPs were evenly distributed along the contig, (4) if the same gene was in multiple BESs only one BES was chosen, and (5) only SNPs of Infinium II type were used.1 progeny produced from across between cultivars ‘Chandler’ and ‘Idaho’ was used for genotyping. The F1 individuals, along with their parents, were grafted on to ‘Paradox’ rootstock or grown on their roots in the field. The mapping population was segregating for a number of phenological and metric traits. A set of 20 microsatellite loci were used to confirm that the individuals in the mapping population were true hybrids.A mapping population consisting of 428 F1 progeny from the cross with cv ‘Idaho’.The standard PCR-based approach of SNP validation by designing primers flanking an SNP, sequencing amplicons, and comparing them with expected genotype is not strictly applicable for a heterozygous genome due to possible amplification artifacts. SNPs were therefore validated indirectly through Infinium genotyping. SNPs were declared to be false positive if no polymorphism was observed at that nucleotide in cv ‘Chandler’ and its FPedigrees of walnut cultivars including ‘Chandler’ released by the UC Davis walnut breeding program were collected from annual breeding progress reports and published papers,9. The pIn order to assess the utility of SNPs discovered in the single cultivar ‘Chandler’, a total of 32 walnut cultivars including ‘Chandler’ and ‘Idaho’ were genotyped with the 6 K Infinium genotyping assay. The heterozygosity percentage for each was calculated as the number of heterozygous loci divided by the total number of SNP markers. The pairwise dissimilarity coefficients were computed based on heterozygosity data using the improved coefficient definition and calculation methods for diploids and codominant markers.All statistical analyses, including significance test, correlation analysis and logistic regression modeling, were performed using JMP 7.0 (SAS Institute Inc.) and Microsoft Excel (Microsoft).RMD: Read mapping depth; VF: Variantfrequency; FVF: Folded variant frequency; ADT: Assay design tool; MQS: Mapping quality score; RTP: Rate of true-positive SNPs.SNP: Single nucleotide polymorphism; BAC: Bacterial artificial chromosome; BES: BAC end sequence; RAPD: Random amplified polymorphic DNA; SCAR: Sequence characterized amplified region; AFLP: Amplified fragment length polymorphism; SSR: Simple sequence repeats; NGS: Next generation sequencing; PCR: Polymerase chain reaction; FPC: Fingerprinted contig; The authors declare that they have no competing interests.FMY, AMD, CAL, MA, JNL, DL, JD and MCL planned the work. KRD performed SOLiD sequencing. MTB performed cDNA sequence assembly. FMY performed SNP pipeline improvement, data analysis, SNP discovery, and SNP selection and design for the Infinium assay. MCL and JW conducted Infinium genotyping and data analysis. FMY drafted the manuscript and JD revised it. All authors read and approved the final draft of the manuscript.List of 6,000 SNPs used in the Infinium assay and their features.Click here for fileList of the 13,439 discovered SNPs.Click here for fileSequences of 6,000 SNPs used in the Infinium assay.Click here for fileSequences of discovered 13,439 SNPs.Click here for file


Next-generation sequencing technology is an important tool for the rapid, genome-wide identification of genetic variations. However, it is difficult to resolve the ‘signal’ of variations of interest and the ‘noise’ of stochastic sequencing and bioinformatic errors in the large datasets that are generated. We report a simple approach to identify regional linkage to a trait that requires only two pools of DNA to be sequenced from progeny of a defined genetic cross  at low coverage (<10×) and without parentage assignment of individual SNPs. The analysis relies on regional averaging of pooled SNP frequencies to rapidly scan polymorphisms across the genome for differential regional homozygosity, which is then displayed graphically.Tribolium castaneum (F4 and F19) segregating for the phosphine resistance trait were exposed to phosphine to select for the resistance trait while the remainders were left unexposed. Next generation sequencing was then carried out on the genomic DNA from each pool of selected and unselected insects from each generation. The reads were mapped against the annotated T. castaneum genome from NCBI (v3.0) and analysed for SNP variations. Since it is difficult to accurately call individual SNP frequencies when the depth of sequence coverage is low, variant frequencies were averaged across larger regions. Results from regional SNP frequency averaging identified two loci, tc_rph1 on chromosome 8 and tc_rph2 on chromosome 9, which together are responsible for high level resistance. Identification of the two loci was possible with only 5-7× average coverage of the genome per dataset. These loci were subsequently confirmed by direct SNP marker analysis and fine-scale mapping. Individually, homozygosity of tc_rph1 or tc_rph2 results in only weak resistance to phosphine (estimated at up to 1.5-2.5× and 3-5× respectively), whereas in combination they interact synergistically to provide a high-level resistance >200×. The tc_rph2 resistance allele resulted in a significant fitness cost relative to the wild type allele in unselected beetles over eighteen generations.Progeny from defined genetic crosses of T. castaneum. This is a relatively simple and rapid approach to identifying genomic regions associated with traits in defined genetic crosses that does not require any specialised statistical analysis.We have validated the technique of linkage mapping by low-coverage sequencing of progeny from a simple genetic cross. The approach relied on regional averaging of SNP frequencies and was used to successfully identify candidate gene loci for phosphine resistance in  High-throughput “next-generation” sequencing is now a cost effective tool for most genetics laboratories, making new approaches for genetic analysis a possibility. One of the promising aspects of next-generation sequencing data is that it can be used to rapidly define genomic regions of interest in linkage and quantitative genetics experiments. There are myriad ways that this can be accomplished but no straightforward statistical and data processing approach has been developed to distinguish the ‘signal’ from the ‘noise’. Some researchers have simplified the analysis by establishing specially prepared genetic strains, as in linkage by introgression . In othe3) in a cosmopolitan grain insect pest, Tribolium castaneum. Phosphine is an effective fumigant, used worldwide to protect grain harvest from insect pests ; tc_rph1: Gene1 conferring resistance to phosphine in The authors declare that they have no competing interests.RJ, DS and PE designed the study. Bioinformatic analyses were performed by DS with input from RJ. RJ performed the genetic crosses, genome sequencing and linkage mapping experiments and AF contributed to the fine scale mapping. RJ and DS drafted the manuscript; and PE revised the manuscript and provided critical comments. All authors read and approved the final manuscript.4 generation Selected vs Unselected datasets in an Excel™ spreadsheet.An example table for averaging variant (SNP) frequency for chromosome 8 for the FClick here for file4 and F19 datasets (Selected vs Unselected).Illustrating the average SNP frequency across all chromosomes from FClick here for fileListing markers used for mapping resistance loci on Chr8 and Chr9 (Uns-7).Click here for file