Malus×domestica) breeding germplasm. For genome-wide SNP discovery, 27 apple cultivars were chosen to represent worldwide breeding germplasm and re-sequenced at low coverage with the Illumina Genome Analyzer II. Following alignment of these sequences to the whole genome sequence of ‘Golden Delicious’, SNPs were identified using SoapSNP. A total of 2,113,120 SNPs were detected, corresponding to one SNP to every 288 bp of the genome. The Illumina GoldenGate® assay was then used to validate a subset of 144 SNPs with a range of characteristics, using a set of 160 apple accessions. This validation assay enabled fine-tuning of the final subset of SNPs for the Illumina Infinium® II system. The set of stringent filtering criteria developed allowed choice of a set of SNPs that not only exhibited an even distribution across the apple genome and a range of minor allele frequencies to ensure utility across germplasm, but also were located in putative exonic regions to maximize genotyping success rate. A total of 7867 apple SNPs was established for the IRSC apple 8K SNP array v1, of which 5554 were polymorphic after evaluation in segregating families and a germplasm collection. This publicly available genomics resource will provide an unprecedented resolution of SNP haplotypes, which will enable marker-locus-trait association discovery, description of the genetic architecture of quantitative traits, investigation of genetic variation , and genomic selection in apple.As high-throughput genetic marker screening systems are essential for a range of genetics studies and plant breeding applications, the International RosBREED SNP Consortium (IRSC) has utilized the Illumina Infinium® II system to develop a medium- to high-throughput SNP screening tool for genome-wide evaluation of allelic variation in apple ( Understanding the links between phenotypic variations and their underlying DNA variation is the major challenge facing plant geneticists today. Recent advances in genomics technologies, including highly parallel sequencing and genetic marker methods for genome-wide assays of allelic variation, have now made high-resolution genetic characterization of crop germplasm feasible. The use of genomics tools has tremendous potential to assist Rosaceae crop breeders to produce significant genetic gains for consumer and grower traits more precisely and efficiently, as well as to improve understanding of the genetic architecture of agronomic characters SNP marker development for a genome screening tool is based on three steps: detection, validation, and final selection. The objectives and throughput required are different for each step. The detection step requires identification of a large pool of SNPs in the crop and typically involves non-targeted techniques such as re-sequencing, or High Resolution Melting (HRM) Once SNP sets have been developed, screening can be carried out with a range of highly parallel techniques on segregating germplasm sets according to the research need or breeding application. Multiplexing and automation have enhanced the efficiency of SNP genotyping enormously and several high-throughput platforms are now available for the genotyping of a variable number of samples for one to up to one million SNPs in parallel. They include, but are not limited to, TaqMan® from Applied Biosystems and array-based technologies from Illumina (GoldenGate® and Infinium®) or Affymetrix www.rosbreed.org; We used next-generation sequencing (NGS) to detect SNPs covering the apple genome. This effort involved re-sequencing a small set of cultivars, ancestors, and founders, chosen to represent the pedigrees of worldwide apple breeding programs by RosBREED, a consortium established to enable marker-assisted breeding for Rosaceae crops , ‘Dolgo’, ‘Duchess of Oldenburg’, F226829-2-2, ‘Frostbite’, ‘Fuji’, ‘Geneva’, ‘Golden Delicious’, ‘Granny Smith’, ‘Haralson’, ‘Honeycrisp’, ‘Idared’, ‘James Grieve’, ‘Jonathan’, ‘McIntosh’, ‘Ralls Janet’, ‘Red Dougherty’, ‘Rome Beauty’, ‘Splendour’, ‘Zestar’, and Malus sieversii PI613981. Each accession was sequenced using one lane of Illumina GA II (except for ‘Cox's Orange Pippin’ that was sequenced twice) with 75 to 80 cycles per read and small insert paired-end or single-end sequencing as specified in Soap2A SNP detection panel of 27 apple germplasm accessions was chosen for whole-genome, low coverage re-sequencing. The accessions were founders, intermediate ancestors, or important breeding parents used extensively in apple breeding programs worldwide. The complete set maximized coverage of the genetic background of the world's cultivated apple. Individuals in the set were SoapSNP (http://soap.genomics.org.cn/soapsnp.html) essentially as described by SoapSNP calls were discarded if (1) the SNP call was within the first or last 60 bases of a linkage group (LG) sequence, (2) the reference genome base was ambiguous at the SNP position or within 60 bases of it, (3) another SNP was detected within 50 bases, (4) the average copy number of the SNP flanking region was more than two, (5) the Illumina quality score of either allele was less than 20, (6) the number of reads supporting either allele was less than two reads per accession for at least half the accessions, (7) the number of reads for either allele across all accessions was greater than the average read depth of all SNPs plus three standard deviations, and (8) if the call was an A/T or C/G transversion. This filtration yielded “Stage 1 SNPs”.SNPs were detected using Malus Sequence Database libraries gmapStage 1 SNPs were then subjected to a Stage 2 filter, whereby SNPs not located in a predicted exonic region were discarded. For each LG, the exon space was defined by mapping all gene models Ma locus; Ma locus SNPs were spaced an average of 75 Kb apart (ranging from 5 Kb to 186 Kb). It was planned that approximately 20% of the 144 SNPs would be accession-specific, i.e., their minor allele would be detected in only one re-sequenced accession of the detection panel. As only four of the evenly spread SNPs met this criterion, 24 additional SNPs were chosen as accession-specific. These latter SNPs were spread over the genome at 1–2 per LG but otherwise randomly distributed on each LG. Approximately 20% of the 144 SNPs were allocated to meet the criterion of being within exons of candidate genes for fruit quality and plant architectural traits. These included 24 of the evenly spread SNPs, four of the Ma locus SNPs, and one of the accession-specific SNPs, totaling 27 exonic candidate gene SNPs. The 144 SNPs also deliberately included a wide range of minor allele frequencies (MAFs) and a wide range of Illumina Assay Design Tool (ADT) scores. Approximately 5% of SNPs were chosen from those previously validated in ‘Golden Delicious’ (GDsnp) using the SNPlex technique Ma locus SNP, to serve as positive controls.A subset of 144 SNPs was chosen to validate the efficiency of SNP detection and fine-tune the filtering parameters . The inihttp://rtsf.msu.edu/illumina-beadxpress-reader-system), where the GoldenGate® assay was performed following the manufacturer's protocol . Following amplification, the PCR products were hybridized to VeraCode microbeads via the address sequence for detection on a VeraCode BeadXpress Reader. SNP genotypes were scored with the Genotyping Module of the GenomeStudio Data Analysis software (Illumina Inc.).To test the above SNP parameters for their effects on genotyping efficiency, a validation panel of 160 apple accessions  was screhttp://genomics.research.iasma.it/cgi-bin/cmap/viewer), followed by Rosaceae Conserved Orthologous Set , created a list of homologous apple sequences that were then located in the apple genome and became new focal points. Stage 2 SNPs identified in these genes were subjected to Stage 3 filtering criteria, except that the “2 Kb minimum distance” rule was relaxed so that multiple SNPs could be chosen within a gene to form a cluster. Instead, up to four SNPs within each candidate gene were manually chosen to lie as far from each other as possible. Finally, SNPs with an ADT score below 0.7 were discarded.A clustering strategy was devised that would evenly span the genetic map of apple with clusters of exonic SNPs, in order to provide a final SNP genome scan with the capability of determining SNP haplotypes at distinct loci. The design featured focal points at approximately 1 cM intervals, with 4–10 SNPs clustered at each focal point. Choice of focal points began with GDsnps  RosCOS;  loci to 1 population of 186 seedlings 1 crosses that are used as a training population for genomic selection at Plant & Food Research and comprise 1313 individuals p50GC<0.54), as well as seedlings putatively resulting from an unintended pollination, were removed from the analysis. SNPs with a GenTrain score >0.6 were retained and those with scores ranging between 0.3 and 0.6 were visually checked for accuracy of the SNP calling. Clusters were manually edited when the parent-offspring segregation was not correct, or when the number of missing genotypes was greater than 20.A set of populations from various crosses, as well as accessions from the apple germplasm, were used to evaluate the Apple 8K Infinium® II array. This set included a ‘Royal Gala’×‘Granny Smith’ Fwww.flexqtl.nl) for each cluster of SNPs.Two trios with both parents and one seedling were used to test the usefulness of SNP clusters for identifying haplotypes: ‘Royal Gala’×‘Braeburn’−>‘Scifresh’ and ‘ Gala’×‘Splendour’−>‘Sciros’. SNPs were coded using A and B alleles and haplotypes were inferred using FlexQTL™  passed the filtering criteria for Stage 1 detection . SNPs located within candidate genes were generally more successful than the overall set (63% polymorphic). Of the 28 SNPs chosen on the basis of their accession specificity, 14 (50%) had a MAF <0.05, of which 11 were not monomorphic. The Illumina ADT score calculated for each SNP did not significantly influence the success of the SNPs  and 12 accessions failed because of poor DNA quality. Seventy-three (50.7%) of SNPs were polymorphic, 46 (31.9%) had failed reactions, and 25 (17.3%) were monomorphic (MAF <0.05 or A/B frequency <0.1) . All eigthe SNPs .The third stage of filtering, which involved designating focal points at approximate 1 cM intervals over the apple genome and choosing SNPs in 100 Kb windows around them , was iniThe 7867 SNPs were uniformly represented on the 17 apple LGs . The numGenTrain scores for the polymorphic SNPs ranged from 0.35 to 0.92 and the call frequency ranged from 0.73 to 1. In total, 1061 polymorphic SNPs had a very low MAF of between 0 and 0.05, while 3371 polymorphic SNPs had MAF>0.05 with a call frequency >0.95 and reliability score (50% GC) >0.4. Of the previously validated GDsnps and GoldenGate®-validated SNPs, 629 (90.7%) and 55 (74.3%), respectively, were polymorphic. In the ‘Royal Gala’×‘Granny Smith’ segregating population, 2810 SNPs in 994 clusters were polymorphic. Of these, 2223 SNPs (79.2%) were heterozygous for one parent and homozygous for the other (pseudo-testcross configuration) and the remaining 587 (20.8%) were heterozygous for both parents. Polymorphic pseudo-testcross SNPs in this population were successfully used as independent markers for linkage map construction  of which 5554 (72.2%) were polymorphic . The remtruction . The aveThe small region of LG1 presented in An international community of apple genomics, genetics, and breeding researchers recently united with the goal of developing high-resolution genome scanning capability for germplasm of cultivated apple. The resulting medium to high-throughput multiplexed SNP assay presented and evaluated here is the culmination of a collective technical effort and shared resources that would have been beyond the means of any single entity. This publicly available genomics resource will provide unprecedented resolution for discovery of marker-locus-trait association discovery, for description of the genetic architecture of quantitative traits, for investigation of genetic variation , and will enable genomic selection in apple. To ensure maximum utility, the apple SNP array involved several key design features.To ensure that genome scans using the SNP array relate directly to the draft apple genome sequence of p50GC >0.4, call rate >0.95) polymorphic markers provided by the array developed here is greater than that was used for the genome assembly (3371 vs. 1643), the new set of SNPs from the array will be useful for improvement of the apple genome assembly.Genetic and physical distribution of SNPs across the apple genome was also a strong consideration in array design. The whole genome sequence of apple www.flexqtl.nl), which supports the Pedigree-Based Analysis approach A haplotype-targeting strategy was adopted to maximize information gained using the SNP array and to decrease computation time for mapping software. Clusters of multiple SNPs were spaced at 1 cM intervals , improving detection of heterozygosity for any assayed germplasm individual, while lower-MAF SNPs (MAF <0.2), tend to come from specific founders and improve identification of rare haplotypes.Based on information derived from a consensus apple genetic map of 1343 cM , the IRSC apple 8K SNP array v1 corresponds to an average density of one focal point every 1 cM. Overall, the IRSC apple 8K SNP array v1 was successful, as 5554 markers were polymorphic in the examined germplasm set, with 4368 SNPs having a MAF>0.05, and 3371 having a high call rate, reliability score, and MAF. While this result indicates that at least 3371 markers are likely to provide a genotype when the array is used for screening any germplasm set, any given genetic mapping experiment can expect to obtain ∼4000 polymorphic bi-allelic markers or ∼1000 fully informative haplotypes. The number of SNPs in the array that can successfully provide a genotype might be improved by enhancing DNA quality of the template used for the experiment. In our array evaluation, the set of accessions tested was from a breeding program where the number of individuals was maximized at the expense of the DNA quality.We have demonstrated that the array is effective for use in genetic mapping, providing 994 focal points in a ‘Royal Gala’×‘Granny Smith’ segregating population, a sufficient number for the construction of a highly saturated genetic map. This resolution will be also sufficient to implement genomic selection in apple The International RosBREED SNP Consortium apple 8K SNP array v1 has been developed for public use by apple geneticists worldwide. The design and evaluation of the array has indicated that it will be effective for a wide range of germplasm and applications such as high-resolution genetic mapping, QTL detection and characterization, marker-assisted introgression, and genomic selection.www.rosaceae.org). SNPs are available in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/) under accessions ss475875741 to ss475892397.All SNPs detected, the SNPs chosen for the IRSC apple Infinium® II 8K array, and the GenomeStudio cluster file developed are deposited in the Genome Database for Rosaceae Click here for additional data file.Table S2List of 7867 apple SNPs on the Apple Infinium® II array v1. The NCBI dbSNP accession, location on the ‘Golden Delicious’ genome assembly (XLS)Click here for additional data file.


Several methods have recently been developed to identify regions of the genome that have been exposed to strong selection. However, recent theoretical and empirical work suggests that polygenic models are required to identify the genomic regions that are more moderately responding to ongoing selection on complex traits. We examine the effects of multi-trait selection on the genome of a population of US registered Angus beef cattle born over a 50-year period representing approximately 10 generations of selection. We present results from the application of a quantitative genetic model, called Birth Date Selection Mapping, to identify signatures of recent ongoing selection.We show that US Angus cattle have been systematically selected to alter their mean additive genetic merit for most of the 16 production traits routinely recorded by breeders. Using Birth Date Selection Mapping, we estimate the time-dependency of allele frequency for 44,817 SNP loci using genomic best linear unbiased prediction, generalized least squares, and BayesCπ analyses. Finally, we reconstruct the primary phenotypes that have historically been exposed to selection from a genome-wide analysis of the 16 production traits and gene ontology enrichment analysis.We demonstrate that Birth Date Selection Mapping utilizing mixed models corrects for time-dependent pedigree sampling effects that lead to spurious SNP associations and reveals genomic signatures of ongoing selection on complex traits. Because multiple traits have historically been selected in concert and most quantitative trait loci have small effects, selection has incrementally altered allele frequencies throughout the genome. Two quantitative trait loci of large effect were not the most strongly selected of the loci due to their antagonistic pleiotropic effects on strongly selected phenotypes. Birth Date Selection Mapping may readily be extended to temporally-stratified human or model organism populations. Several statistical tests have been developed to identify the genomic regions that have been subjected to strong recurrent selection. Most have been based on extreme population differentiation-3, the eConcurrent with the development of new approaches for the detection of selective sweeps, the statistical models employed for genome-wide association studies have been improved. Some of the refinements deal with the effects of population structure and kinship between sampled individuals, since not accounting for these effects can significantly increase the number of false positive associations  could be regressed on birth date using a Poisson model to identify loci that have rapidly changed in frequency over time. However, in the presence of any sampling bias , this approach suffers from a very high false-positive rate of detection of loci subject to selection  is large. For these variants, the probability of a specific genotype will be approximately constant in time and knowledge of an individual’s genotype will not be strongly predictive of that individual’s birth date. On the other hand, if a variant is under strong directional selection, changes in allele frequency will be consistent in direction and may become large over several generations. For these variants, genotype will be predictive of birth date. For example, if the A allele is increasing in frequency in time, AA individuals are much more likely to be born recently than in the distant past. We utilize mixed model methods to account for the pedigree-based structure in our sample through the use of the genomic relationship matrix[In our approach, rather than regressing allele frequencies (dependent variable) on birth date (explanatory variable), we invert the relationship and fit birth date as the dependent variable and identify SNPs that are strongly associated or predictive of birth date. If a neutral DNA variant is drifting through a population, changes in allele frequency will be stochastic and small provided the effective population size  and for the incidence of unassisted births (CED and CEM) have increased annually. Weaning weight has increased, on average, by 2.81 pounds per year and the rate of unassisted births (CED) has increased by 0.56% per year—remarkable achievements by Angus breeders considering the 50-year span of these data.Deregressed estimated breeding values (EBVs) for 16 pp-values for tests of significance of individual SNPs. Rather, ASEs were converted to estimates of additive genetic variance associated with each SNP and plotted  for birth date for 45,073 SNPs using genomic best linear unbiased prediction (GBLUP),24,25 apq-values representing the expected proportion of false positives among all SNP effects as extreme as observed for the current SNP[We next used EMMAX to indivrent SNP. CompareFinally, we used GenSel to fit aThe SNP ASEs estimated with GBLUP and EMMAX had a Spearman correlation of 0.92 and a Pearson correlation of 0.78. The difference in ASE magnitude identified by the Pearson correlation essentially reflects the difference in estimates achieved due to fitting SNPs as uncorrelated random or fixed effects. The SNP ASEs estimated by GBLUP and BayesCπ had a Spearman correlation of 0.85, but had a Pearson correlation of only 0.56 due to the strong shrinkage of large effect SNPs in GBLUP  and the strong shrinkage of small effect SNPs in BayesCπ. The EMMAX and BayesCπ ASEs had a Spearman correlation of 0.83 and Pearson correlation of 0.46. In the GBLUP and EMMAX analyses, the multilocus genotypes explained 0.534 and 0.531 of the variance in birth date , respectively, as estimated using restricted maximum likelihood (REML) estimation of variance components. In the BayesCπ analysis, genotypes explained 0.717 of the variance in birth date.F) for all animals from which we estimated effective population size from the regression of F on generation number. From a principal component analysis of the SNP genotypes, we identified two distinct subgroups within our sample. In Additional fileTo ascertain whether drift is a significant force influencing allele frequency changes within the artificially selected US Angus breed, we estimated the inbreeding effective population size, under the neutral model, and modelled the effects of drift on neutral loci. Using a pedigree of up to 63 generations and which comprised 91,001 Angus animals including the 3,570 genotyped animals and all known ancestors, we estimated the generation interval for US Angus cattle to be 4.99 years, which was the average age of animals born between 1941 and 1990 at the birth of their male and female registered progeny. From this pedigree, we also estimated inbreeding coefficients  than do the pedigree F (s2 = 0.0014), and the two measures of F had a Pearson correlation of 0.65 , we also estimated Ne using the genomic F coefficients for North American Angus animals born after 1980. This resulted in an Ne of 94.18 ± 0.10 . The second contained 60 animals, consisting of 20 animals randomly sampled from each of pedigree generations 58, 59, and 60 . The third consisted of 1,237 animals randomly sampled from the entire data set . The final sample included 60 animals randomly sampled from the entire data set . Additional filers110059753, was among the most strongly associated of all SNPs with CED, BW, WW, YW, HP, CEM, MILK, MW, MH, CW, MARB, RE, and FAT . The most likely location of the pleiotropic QTL on chromosome 20 was estimated to be at the position of SNP rs43711332 at 4.62 Mbp .We analysed deregressed EBVs in a weighted analysis for 16 p Figures by GBLUP Figures, and a sR2 of 0.6625, which is only slightly (0.0457) larger than the R2 for the weaning weight model. Individual terms from the multiple regression are not reported due to multicollinearity between several traits.To assess the identity of the trait or combination of traits that have historically been under selection in Angus cattle and that produced the molecular signals of selection, we individually regressed the SNP ASEs for birth date on standardized SNP ASEs (see Methods) for all 16 production traits for which the AAA routinely produces EPDs  constrains both the phenotypic and genotypic response to selection.F coefficients is likely to be due to the assumption that base animals are not inbred, errors in the pedigrees, and missing pedigree information likely due to the large-scale importation of Canadian Angus cattle in the 1940s and 1950s that were not carriers of dwarfism alleles, which had been driven to high frequency due to selection at the time[F coefficients for the Wye herd that was largely derived from British stock with more complete pedigree records than the remaining US registered Angus cattle , it is logical that loci that influence calving ease, growth to weaning, and milking ability should have been found to be under selection. Angus breeders have successfully selected to increase calving ease and body weight by selecting for body shapes that allow a calf’s easy passage through the dam’s pelvis. This is supported by the finding of an enrichment of gene ontology terms related to limb morphogenesis and anatomical structure development within regions of the genome detected as responding to selection. It has previously been shown that calving ease is negatively correlated with several body measures, such as head circumference, head width, hip width, hip height, chest girth, and cannon bone circumference-40. LikeNOD2, C3, and DBH, have strongly responded to selection  were estimated as a by-product of using the rapid algorithm for producing the inverse of a numerator relationship matrix[F were estimated by subtracting 1 from the diagonals of the genomic relationship matrix, which was estimated according to[Pedigree inbreeding coefficients  ≈ 2NeSE(ΔF), in which SE(ΔF) is the standard error of the estimated slope of the regression. Because the depth of available pedigree information varied substantially for the 3,570 sampled Angus animals  we considered the estimates of pedigree generation to be unreliable from the perspective of estimating Ne. Accordingly, we estimated generation number for each of the 3,570 genotyped animals by subtracting 1950 from their birth year and dividing by the generation interval of 5 years. Because of the closed nature of the Wye herd and complete pedigree information back to foundation animals, we fit separate models for the Wye and remaining North American Angus animals. For the North American Angus subset, we fit two models using generation number estimated from birth year for animals born after 1930 and for animals born after 1980 where there appeared to be an inflection in the rate of increase in inbreeding. This corresponds to the point in time when the increased use of artificial insemination became significant within the breed.The inbreeding effective population size erations and is e10 = 8) confidence interval for the change in allele frequency due to drift /2Ne, for pi the frequency of the Ai allele in generation 58 and Ne = 116.15[ith SNP was pi(1-pi)/1.5Ne. Loci for which the allele frequency change exceeded the boundaries of the confidence interval were concluded to be changing in frequency due to selection rather than drift.For each of the 44,817 SNPs, we directly estimated the change in allele frequency that occurred between the 460 individuals assigned to pedigree generation 58 and the 450 individuals assigned to pedigree generation 59 using PLINK[piqi, in which pi and qi= 1 - pi are the base generation allele frequencies at the ith SNP[In a weighted analysis using deregressed EBVs as previously described, GBLUP was used ith SNP. Base gep-values for each SNP produced by EMMAX were adjusted to q-values using the method of Benjamini and Hochberg[q-values or variances to be plotted on the Y-axis.SNPs with the greatest changes in allele frequency over time will have the largest ASEs for birth date. The ASE reflects the amount of response to selection realized by the genomic region tagged by a SNP. Genome-wide associations with birth date were analysed by GBLUP using custom developed software described in, by EMMAHochberg as impleHochberg. The addHochberg, with R Hochberg which waq = −ipqa/σp, where i is the selection intensity, a is one half the phenotypic difference between homozygote mean phenotypes, σp is the trait variance, and p and q are allele frequencies. Assuming the dominance deviation is zero, the ASE α is equal to the genotypic value a. Thus, we use the ASE as a proxy for a which we then scaled as pqASE/σASE to form the independent variables for each of the 16 production traits which were individually regressed on the birth date ASEs to provide estimates of the relative selection intensity i for each trait . For each trait, σASE represents the ASE standard deviation in the equation above. Regressions were performed using generalized least squares, with e ~  where V was the matrix of correlations between alleles at pairs of SNPs estimated using PLINK. When multiple, contiguous SNPs had r = ±1 , only the first SNP was retained, resulting in the removal of 13 SNPs. The model was fit to the 935 SNPs with the largest birth date ASEs.From Falconer and MacKay, the chaBos taurus, Homo sapiens, Mus musculus, Rattus norvegicus, Canis lupus, Pan troglodytes, Macaca mulatta, Equus caballus, Pongo abelii, Sus scrofa, Ovis aries, and Oryctolagus cuniculus for GO enrichment analysis.Due to the extent of LD within the bovine genome,65, we iThe authors declare that they have no competing interests.JFT and JED created the methodology and designed the experiment. DAV, JED and JFT analyzed data. SDM, MCM, MMR, JWK, and RDS extracted DNA. MMR and SDM prepared samples for genotyping, SDM ran the Illumina assay, and RDS genotyped samples and managed the genotype database. SLN provided pedigree and estimated genetic merit data and SB and BWW provided genotypes on about 900 Angus animals. JED and JFT wrote the manuscript and other authors provided feedback. All authors read and approved the final manuscript.Genotypes are available to scientists interested in non-commercial research upon signing a Materials Transfer Agreement (MTA).Supplementary Material. File includes supplementary information, supplementary figures 1 through 35, and supplementary TablesClick here for fileChart of enriched GO terms in Excel xlsx format. We included charts for DAVID’s GOTERM_BP_FAT , GOTERM_BP_ALL , GOTERM_BP_2 , GOTERM_BP_3 , GOTERM_BP_4 , GOTERM_BP_5 , GOTERM_CC_FAT (lower levels of cellular component ontology), GOTERM_MF_FAT (lower levels of molecular function ontology), and KEGG_PATHWAY with each as an individual tab in the file. We supplied the DAVID resources with a list of 2,074 genes annotated in the UMD 3.1 assembly.Click here for fileChart of enriched GO terms in Excel xlsx format. We included charts for DAVID’s GOTERM_BP_FAT, GOTERM_BP_ALL, GOTERM_BP_1, GOTERM_BP_2, GOTERM_BP_3, GOTERM_BP_4, GOTERM_BP_5, GOTERM_CC_FAT, GOTERM_MF_FAT, and KEGG_PATHWAY with each as an individual tab in the file. We supplied the DAVID resources with a list of 1,996 genes annotated in the UMD 3.1 assembly.Click here for fileChart of enriched GO terms in Excel xlsx format. We included charts for DAVID’s GOTERM_BP_FAT, GOTERM_BP_ALL, GOTERM_BP_1, GOTERM_BP_2, GOTERM_BP_3, GOTERM_BP_4, GOTERM_BP_5, GOTERM_CC_FAT, GOTERM_MF_FAT, and KEGG_PATHWAY with each as an individual tab in the file. We supplied the DAVID resources with a list of 1,059 genes annotated in the UMD 3.1 assembly.Click here for file


Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets. Principal component analysis (PCA) is a widely-used tool in genomics and statistical genetics, employed to infer cryptic population structure from genome-wide data such as single nucleotide polymorphisms (SNPs) However, traditional approaches to computing the PCA, such as those employed by the popular EIGENSOFT suite In recent years, research into randomized matrix algorithms has yielded alternative approaches for performing PCA and producing these top PCs, while being far more computationally tractable and maintaining high accuracy relative to the traditional “exact” algorithms Here we present flashpca, an efficient tool for performing PCA on large genome-wide data, based on randomized algorithms. Our approach is highly efficient, allowing the user to perform PCA on large datasets , extracting the top principal components while achieving identical results to traditional methods.http://www.hsph.harvard.edu/alkes-price/software/) and shellfish (http://www.stats.ox.ac.uk/~davison/software/shellfish/shellfish.php). In addition, we included the R 3.0.2 First we used an LD-pruned HapMap3 genotype data Next, we analyzed an LD-pruned celiac disease Immunochip dataset consisting of 16,002 individuals and 43,049 SNPs after thinning by LD Due to the substantial time required by shellfish and smartpca to complete the largest runs, we only ran flashpca on the larger datasets , thus essentially removing the effects of population from data prior to further downstream association analysis More generally, the approach behind flashpca could prove useful for accelerating other methods that depend on performing a large number of eigen-decompositions across many samples, such as varLD All subjects included in the celiac disease dataset provided written and informed consent. For details, see the original publication The HapMap phase 3 dataset consists of 1184 human individuals across 11 populations  assayed for 1,440,616 SNPs The celiac disease Immunochip dataset We represent the genotypes as an PCA relies on finding the eigenvectors of the The SVD of In the eigen-decomposition approach, the covariance matrix The principal components (PCs) Note that some tools, such as smartpca and shellfish, output the eigenvectors In traditional PCA, such as that implemented in R’s prcomp, prior to running the SVD/eigen-decomposition itself, the matrix Performing PCA on large matrices  which is substantially faster than PLINK v1.07. Reducing a dataset to ∼10,000–50,000 SNPs is usually sufficient to achieve an accurate PCA, and can be done using –indep-pairwise.Prior to PCA, thinning of the SNPs by LD and removal of regions with known high LD or other artefacts such as inversions have been recommended File S1Concordance in principal components 1–10 between smartpca, flashpca, shellfish, and R’s prcomp on the HapMap3 dataset.(PDF)Click here for additional data file.


The advances and decreasing economical cost of whole genome sequencing (WGS), will soon make this technology available for routine infectious disease epidemiology. In epidemiological studies, outbreak isolates have very little diversity and require extensive genomic analysis to differentiate and classify isolates. One of the successfully and broadly used methods is analysis of single nucletide polymorphisms (SNPs). Currently, there are different tools and methods to identify SNPs including various options and cut-off values. Furthermore, all current methods require bioinformatic skills. Thus, we lack a standard and simple automatic tool to determine SNPs and construct phylogenetic tree from WGS data.Here we introduce snpTree, a server for online-automatic SNPs analysis. This tool is composed of different SNPs analysis suites, perl and python scripts. snpTree can identify SNPs and construct phylogenetic trees from WGS as well as from assembled genomes or contigs. WGS data in fastq format are aligned to reference genomes by BWA while contigs in fasta format are processed by Nucmer. SNPs are concatenated based on position on reference genome and a tree is constructed from concatenated SNPs using FastTree and a perl script. The online server was implemented by HTML, Java and python script.V. cholerae, S. aureus CC398, S. Typhimurium and M. tuberculosis). The evalution results for the first three cases was consistent and concordant for both raw reads and assembled genomes. In the latter case the original publication involved extensive filtering of SNPs, which could not be repeated using snpTree.The server was evaluated using four published bacterial WGS data sets (http://www.cbs.dtu.dk/services/snpTree-1.0/.The snpTree server is an easy to use option for rapid standardised and automatic SNP analysis in epidemiological studies also for users with limited bioinformatic experience. The web server is freely accessible at  The dramatic decrease in cost for whole-genome sequencing (WGS) has made this technology economically feasible as a routine tool for scientific research, including infectious disease epidemiology. In addition, WGS has major applications for health service providers working with infectious diseases  as such The ideal microbial typing technique should enable differentiation of epidemiological unrelated strains and group epidemiological related (outbreak) strains,  and giveEspecially outbreak isolates normally have very little diversity and require extensive genomic methods to differentiate and catagorize the isolates . Single Currently, There are a number of available non-commercial NGS genotype analysis software such as SOAP2 , GATK 88 and SAMVibrio cholerae [Staphylococcus aureus CC398 [Salmonella Typhimurium [Mycobacterium tuberculosis [Here we introduce snpTree. A server for online-automatic SNP analysis and SNP tree construction from sequencing reads as well as from assembled genomes or contigs. The server is a pipeline which intregrates avaliable SNPs analysis softwares such as SAMtools  and MUMmcholerae , Staphylus CC398 , Salmonehimurium  and Mycorculosis .https://github.com/srcbs/GenoBox. For contigs or assembled genomes .Prior to mapping raw reads to a proper reference genome, the sequence data in fastq format are filtered and trimmed according to the following criteria : (i) reaTrimmed raw reads are aligned against a reference genome using BWA  with minSNPs calling and filtering are accomplished by SAMtools that is a software package for parsing and manipulating alignments in the generic alignment format (SAM/BAM format) . The snpde novo assemblies to a reference genome chosen by the user (default settings). SNPs are then called from the resulting alignments with another MUMmer application named "show-snps" (with options "-CIlrT"). A pruning is then applied, if chosen by the user, and the SNPs are written into a VCF formatted file for each of the analyzed genomes.A pipeline has been developed around the software package MUMmer version 3.23  Figure . An applOne VCF formatted file is needed for each Operational Taxonomic Unit (OTU). The SNPs are then concatenated into a single alignment by ignoring indels. Including indels would disturb the position of SNPs in the sigle alignment. To include indels in any trees, it requires some sensible way to represent them numerically as distances in an evolutionary space, and there is no any ways to achieve this. Indels could theoretically be included in a multiple sequence alignment, since such alignments can handle gaps but it's difficult to score them. "Blast-like" gap penalties certainly would not work, since they are optimized for much larger gaps, e.g. recombination events.It is important to note that SNPs not found in a VCF file is interpreted as not being a variation and the corresponding base in the reference is expected. This might not always be the right choice, because a SNP not found in a VCF file could be a result of an INDEL. It is expected to be a rare case and probably won't disturb the phylogenetic signal.The alignment is passed on to Fastree , which csnpTree server provides an output to users with SNPs tree figure in SVG format, number of SNPs and other relevant output files such as (i) SNPs files, which contains identified SNPs including indels for each input genome in VCF format , (ii) caV. cholerae [S. aureus CC398 [S. Typhimurium [M. tuberculosis [The snpTree was evaluated using raw reads and assembled genomes from four published bacterial WGS data sets . The evde novo assembly using Velvet 1.1.04 [WGS from published data set were subjected to snpTree server in order to generate SNP trees. The tree topology evaluation was based on percentage of concordance. If the strain in the tree from snpTree server matches exactly with the tree from published data, it was considered as an exact match. If the strains were grouped into the same cluster with published data, it was considered as a cluster match. In addition, the snpTree server was evaluated with assembled genomes or contigs. The raw reads were assembled prior by t 1.1.04 . The assV. cholerae data set, the performance of snpTree from raw reads  with matching genotype suggesting that the outbreak was clonal [Another data set that consists of 32 s clonal . The perIn order to evaluate the sensitivity and specificity of SNP calling method, the artificial sequence was created from a genome of 4,878,012 bp with 1,000 randomly SNP artificial inserted. The simulated sequence was aligned to a reference genome and identified SNPs using SNP idenfication pipeline for assemble genome. SNPs calling was performed with varied two cut-off values which are minimum number of bp between SNPs (prune) and minimum number of bp from a sequence end (e). The sensitivity and specificity for SNP identification were summarized in Table The sensitivity for prune cut-off Table  was sligUsing minimum number of bp from a sequence end as a varied cut-off, the sensitivity was very high and stable for all varied values. It is quite rare to have SNPs occurred in the tails of sequence so this cut-off less affects to the SNP calling process. The specificity for both cut-off were very high. It is because the number of SNP inserted is extreamly low  compared to the whole genome .The rapid technological advantages in WGS and rapidly decreasing cost has made the technology available for large groups of scientists as well as clinical microbiologists. It is expected that WGS will very soon find widespread use in clinical and public health microbiology, as has already been shown . The impThe advance of WGS and the use of epidemiological genomics underline the potential of practical application of WGS for clinical microbiology and emphazies the importance of biology and evolution in developing reliable and accurate genomics tools for clinical use. In addition, SNP-typing phylogenetic methods can distinguish very closely related isolates to a degree not achievable by widely employed sub-genomic typing tools. snpTree server might be not a perfect tool but it is an option for easy and rapid standardised and automatic SNP analysis tool in epidemiological studies. It is also useful for users with limited bioinformatic experience.The authors declare that they have no competing interests.PL planned the study, carried out web-server construction and drafted the manuscript. RKM constructed SNPs analysis pipeline for assembled genomes and automatic SNP tree construction pipeline. MCFT participated in web-server construction. CF constructed automatic SNPs tree construction pipeline. SR constructed SNPs analysis pipeline for raw reads and developed Genobox toolbox. FMA supervised, planned the study and drafted the manuscript. All authors have read and approved the final manuscript.Example of SNP annotation output.Click here for fileV. cholerae data set SNP trees from contigs of .Click here for fileS. aureus CC398 data set SNP trees from raw reads of .Click here for fileS. aureus CC398 data set SNP trees from contigs of .Click here for fileS. Typhimurium data set SNP trees from raw reads of .Click here for fileS. Typhimurium data set SNP trees from contigs of .Click here for fileM. tuberculosis data set SNP trees from raw reads of .Click here for fileM. tuberculosis data set SNP trees from contigs of .Click here for file


Panicum virgatum L.) is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS) and genomic selection (GS). All of the 840 individuals were then genotyped using genotyping by sequencing (GBS), generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid) species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP) discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK). Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet  P. Beauv.), two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics of switchgrass.Switchgrass  technology, we developed a new single nucleotide polymorphism (SNP) discovery protocol, the Universal Network-Enabled Analysis Kit (UNEAK), which can be widely used in any species, regardless of genome complexity or the availability of a reference genome. Here we test this protocol on switchgrass, currently the prime energy crop species in the United States of America. In addition to the discovery of over a million SNPs and construction of high-density linkage maps, we provide novel insights into the genome complexity, ploidy, phylogeny, and evolution of switchgrass. This is only the beginning: we believe UNEAK offers the key to the exploration and exploitation of the genetic diversity of thousands of non-model species. Panicum virgatum L.) has been targeted as a prime candidate energy crop. As a C4 grass, switchgrass has high biomass production with minimal field-based inputs. Its adaptability allows it to be grown productively in large areas of the USA, including marginal lands. In addition, propagation by seed and the perennial growth habit of switchgrass enable relatively effortless establishment, field management and harvest. Although switchgrass shows great promise as a bioenergy feedstock, it would never be considered a model species for genetic or genomic research. Most of the fundamental characteristics of its biology render switchgrass a difficult taxon for the genetic dissection of even the simplest of its useful biofuel-related traits. Switchgrass is a largely self-incompatible and highly heterozygous species In the past decade, switchgrass . This protocol is a multiplexed, high-throughput, and low-cost method to explore the genetic diversity in populations Currently, the RRL strategy has been used for diversity evaluation in various species, resulting in the discovery of hundreds of thousands of SNPs. In most of these cases, the libraries were sequenced on the Illumina platform, and the SNP calling relied on having a reference. The reference could be a high-quality genome sequence To enable genome-wide association studies (GWAS) and genomic selection (GS) in switchgrass, we developed both linkage and association populations. Phenotypic data from these populations were collected over three field seasons. All 840 individuals in the linkage and association populations were genotyped with GBS. To overcome the inherent difficulties of the lack of a reference genome, multiple ploidy levels and high heterozygosity, a bioinformatics pipeline for SNP discovery based on a species-wide network approach called the Universal Network-Enabled Analysis Kit (UNEAK) was developed. This pipeline was validated in maize and then successfully applied to switchgrass GBS data. High density SNPs were generated to enable future GWAS and GS. Further analysis of the SNP data sets provided unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, and evolutionary dynamics of switchgrass.When a reference genome is available, SNP discovery can be easily performed by aligning reads to the physical map. However, when there is no reference genome, as is the case for the majority of species, significant challenges arise. The UNEAK pipeline overcomes many of these challenges. The general design of UNEAK is as follows : Reads aTo account for sequencing errors, we introduced a parameter called the error tolerance rate (ETR) to improve our initial network filter (see Methods). Without this feature (ETR = 0), sequencing errors can have a substantial negative impact upon the number of retained SNPs, especially when the depth of coverage is high. When sequencing errors occur and error detection is not employed, affected tag pairs are no longer reciprocal and therefore are removed from the data set . By emplTo validate the UNEAK pipeline, we tested it with GBS data from a single RIL family (B73×B97) from the maize nested association mapping (NAM) population ApeKI is a partially methylation sensitive enzyme in silico ApeKI digest of the maize reference genome and identified 8,420,424 potential B73 GBS tag loci. All of the 6,994,161 tags from the B73×B97 family were then aligned to the reference genome; 2,966,692 of these matched perfectly, and thus were B73 tags. These B73 tags accounted for only 35.2%  of the potential B73 tags from the in silico digest. Hence, there is a strong possibility that a large proportion of the 21.4% of SNPs from reciprocal tag pairs that align to multiple positions in fact derived from a single genomic position. For example, those SNPs that aligned to two sites  had only a 35.2% chance of originating from two genomic positions. Therefore, we estimated that the SNPs essentially aligned to unique positions should be greater than 85%.To evaluate the effectiveness of the network filter, we ignored the existence of the maize B73 reference genome and called SNPs at two stages in the pipeline, before and after application of the network filter. The first data set had 336,020 SNPs, which were composed of all tag pairs with a 1 bp mismatch. The second data set was comprised of the 92,951 SNPs that passed the network filter. Only 23.3% of the SNPs in the first data set aligned to a unique site in the maize reference genome. In contrast, after application of the network filter, 78.6% of the SNPs aligned to unique positions. Here, for a uniquely mapped SNP, one tag had a single perfect match to the reference; the other had a single best match at the same site. For the other 21.4%, either or both tags of a SNP aligned equally well to more than one site. Among them, 48.6% SNPs aligned to two sites. Considering that The marked difference between the allele frequency distributions before and after application of the network filter demonstrates that this filter substantially improves the quality of the data. In contrast to the pre-network filter distribution, in which only low and high frequency error peaks were discernible, the post-network filter distribution was dominated by a central peak around the expected allele frequency of 0.5. At the same time, the two error peaks located at the two ends of the distribution were significantly reduced .r2) between these SNPs and the 1106 Illumina Golden Gate SNP markers developed in NAM r2 with the four adjacent markers were calculated. The results showed that 92.8% of the GBS SNPs were in LD with a flanking NAM SNP with an r2 greater than 0.2  analysis and sequence alignment. First, we calculated LD , a half-sib linkage population (n = 168) and an association panel   . Using Gn = 540) . The SNPThe parents of the full-sib population are upland tetraploids. In general, a stretch of DNA sequence should have four orthologous copies in tetraploids. Therefore, when considering the allele frequency distribution of an F1 population, we expected to see seven peaks, representing all possible allele frequency ratios of two parents . However, only three peaks were observed  after the network filter was implemented , the sig2 test (α = 0.05) based on the null hypothesis that, in diploid species, the counts of the two paired tags of a SNP are equal in all heterozygous individuals. A substantial number of incorrect SNP calls were removed from the data set of the F1 full-sib population  method 2 to order the markers within each of the linkage groups. This is an example of the travelling salesman problem (TSP), with the additional complication of missing data and that a proportion of the heterozygotes were miscalled as homozygote as a result of the low depth of coverage. We tried several combinatorial optimization methods  is the closest relative of switchgrass with an available reference genome (490 Mb) n = 9) of the two species. Thus we expected that each of the chromosomes of foxtail millet should align with two linkage groups of switchgrass.Foxtail millet  as the seed and then attempted to fit as many SNPs as possible into these groups. However, the large proportion of missing data may have a major impact on the clustering. Therefore, markers with call rates of 0.2, 0.5 and 0.9 were used to check the clustering quality . Via ali2 within their assigned chromosomes. A paternal linkage map  and a maternal map  were constructed. Both framework maps and the high density linkage maps can be found at http://www.maizegenetics.net/snp-discovery-in-switchgrass.To strike a balance between the quality and number of SNPs, the 88,217 SNPs with MAFs between 0.12 and 0.38 and call rate >0.5 were chosen to add to the 36 linkage groups to construct high density linkage maps. Out of these 88,217 SNPs, 9,437 could be aligned to unique positions in the foxtail millet genome; physical and genetic chromosomal assignments agreed for 7,245 of these 9,437. Based on the strong synteny between switchgrass and foxtail millet, we were able to order the 7,245 uniquely aligned SNPs, resulting in paternal and maternal framework maps consisting of 3,244 and 4,001 ordered markers, respectively. To check the quality of the synteny based order in the framework maps, we calculated the pairwise Spearman's rank correlation coefficients for the markers. High coefficient values were distributed along the diagonal in the heat map . This in2 test, 2.4% and 6.6% of the SNPs had a significantly larger number of genotype calls in 4× and 8× switchgrass, respectively (p<0.05), which is similar to the expected type I error rate of 5%. Moreover, the sequencing depth for the 4× and 8× plants was similar, specifically 1.60× and 1.55× coverage for the 4× and 8× switchgrass, respectively. This analysis indicated that the SNPs were suitable for phylogenetic analysis across different ploidy levels.In addition to the genomic analysis of the bi-parental populations, phylogenetic analysis was performed using the SNPs discovered in the diverse association panel. We selected all of the markers with call rates greater than 0.5 in 540 individuals, which included both tetraploid (4×) and octoploid (8×) plants. Because the size of the 8× genome is approximately twice the size of the 4× genome, the SNPs may be biased towards 8× specific SNPs. Furthermore, the octoploid plants may have half the sequencing depth of the tetraploids. Both of these factors have the potential to affect the phylogeny reconstruction. Hence, this data set was evaluated for ploidy specific SNPs as well as for coverage of 4× and 8× switchgrass. Based upon a χUsing 29,221 markers with call rate greater than 0.5, a neighbor- joining (NJ) tree was constructed based on the pairwise genetic distance among the 540 individuals . To avoiP-value<0.001). A direct comparison of the groups indicated by the phylogeny with their geographic origins  or octoploid (8×). We estimated ploidy level in at least one clone per population in the diversity set using flow cytometry . This pl origins  further Clearly, the phylogeny of switchgrass concurs well with ecotypes, ploidy level and geographic distribution. However, what does the phylogeny tell us about the evolutionary origin of the upland octoploid? Is it an allopolyploid, formed by a wide hybridization between an upland ecotype 4× and a lowland ecotype 4×? Or is it the product of a combination of two upland 4×, resembling more of an autopolyploid origin? The first scenario is not likely, because the upland 8× is not intermediate between the ecotypes, but more closely related to the upland 4× . To addrFoxtail millet, which proved to be highly informative for linkage mapping in switchgrass, was an ideal outgroup for this study. As demonstrated, it is possible to uniquely align approximately 10% of switchgrass SNPs to foxtail millet. Of the 29,221 markers used for the phylogeny analysis, 3,144 aligned to the foxtail millet genome. Comparing these SNPs to the foxtail millet genome, we identified the ancestral alleles of switchgrass and assigned 3.1 kb of foxtail millet sequence as the outgroup. Next, a NJ tree was constructed with 500 bootstrap replicates . Upland Taking the lowland ecotype as the outgroup, we bootstrapped the tree based on 29,221 markers . The resIn fact, our analysis suggests the opposite: upland 4× arose from upland 8×. The upland south clade is the outgroup of three other clades of upland switchgrass with a bootstrap value of 100% . Further590 times more likely than the alternate, constrained topology, strongly supporting the scenario where upland 4× arose from upland 8×.To formally test the two competing scenarios, namely of (1) upland 4× arising from upland 8×, versus (2) upland 4× arising from lowland 4×, we constructed an alternate, constrained topology consistent with scenario two . Using tGenomic selection and GWAS have the potential to substantially improve the efficiency of breeding programs SNP discovery from next-generation sequencing data is particularly challenging in the absence of a reference genome. Our SNP calling pipeline, UNEAK, was developed specifically in response to this challenge. Unlike most non-reference SNP calling protocols, UNEAK does not require a partially sequenced genome, contigs from additional sequencing platforms SbfI, that can be successfully used for phylogeographic study Continuing efforts have been made to call SNPs in species lacking a reference genome. For example, the restriction-site associated DNA (RAD) Even though UNEAK was designed for SNP discovery in species without reference genomes, it can also be used for species with a reference genome. In fact, most reference genomes generally do not cover the whole genome of a species, for two reasons. The first is technical: a reference genome derived from a single individual is usually incomplete because of technical difficulties. In other words, some genomic regions are “technically missing”. The second is biological: one individual's genome does not completely represent the whole genome of that species, because of presence and absence (PAV) variation. Regions containing PAVs can be “biologically missing.” In either case, the genetic variation in the missing genome is basically inaccessible when using SNP discovery methods that rely on the reference genome. UNEAK makes it possible to gain access to those missing regions.In this study, maize was used to validate the UNEAK pipeline. Maize is a large and complex genome, which experienced multiple genome duplication events The UNEAK pipeline was designed to perform SNP discovery in a broad range of species, therefore only the network filter, which is the key to UNEAK, was implemented and evaluated in the maize test. Essentially, based on the tag count file output from UNEAK, end users can design filters specific to the biology of their study population. For example, repulsion of alleles in inbred lines, or equal tag count of alleles at heterozygous sites in diploid species can be tested to filter for higher quality SNPs.ApeKI.Although UNEAK generates reliable SNPs, it cannot guarantee high quality genotype calls when sequencing coverage is low. Coverage has a major impact on genotype quality . Call raA high quality linkage map is essential for QTL mapping and the assembly of whole genome sequence. The average coverage of the SNPs in the switchgrass full-sib population is only 0.95×. Even so, we were able to identify linkage groups. Using the 3,000 SNPs with the highest call rate (>0.9), 18 linkage groups were successfully identified and confirmed by alignment to the foxtail millet genome, for both paternal and maternal markers. However, without relying on synteny with foxtail millet, we were unable to order the markers within the linkage groups, even after trying many different algorithms. Our inability to order the markers stemmed from the low sequencing coverage; this led to high amounts of missing data, and heterozygotes often being miscalled as homozygotes, even for SNPs with high call rates . For theApeKI is used, but higher coverage can be obtained by choosing enzymes with longer recognition sites PstI in GBS, which is expected to result in at least 8 times higher coverage than ApeKI and thus should provide a better balance between coverage and total number of SNPs.The GBS protocol generally provides low coverage if the enzyme Ploidy level variation significantly complicates genetic research in switchgrass. The F1 full-sib population of switchgrass in this study was made by crossing two upland tetraploids. Although seven peaks were expected in the allele frequency distribution in the F1s, the three peaks clearly indicated that the tetraploid switchgrass behaves like a diploid . We alsoBased on 29,221 markers, the phylogenetic analysis in this study provides high resolution to cluster the 540 individuals from 66 populations. As reported by previous studies using either sequence from chloroplast genomes To investigate if non-random patterns of shared missing genotypes between individuals affected the tree topology, we evaluated the ploidy specific SNPs and coverage of the 4× and 8× switchgrass. Only 6% of the SNPs (slightly higher than the type I error rate) were octoploid specific, and sequence coverage was quite similar in the two ploidy groups. These observations indicate that octoploid switchgrass behaves like an autotetraploid . Hence, −4 to 10−2, from the 2n = 4x = 36 to the 2n = 2x = 18 ploidy level Our results suggest that the upland 4× arose from upland 8×. We used a stepwise method to designate the lowland ecotype as the outgroup of the upland ecotype. Phylogenetic analysis indicated the upland 8× is closer to this external, lowland branch. Additionally, upland 4× showed less diversity than upland 8×. Both lines of evidence support the hypothesis that upland 8× gave rise to upland 4×. This conclusion is contradictory to the accepted evolutionary trajectory of higher ploidy level being derived from either auto- or allo-polyploid events involving lower ploidy taxa. A reversion to the lower ploidy could occur via apomixis, whereby an unfertilized haploid (4×) gamete becomes a viable embryo. This is a well-documented phenomenon in perennial grasses According to the phylogeny  and the The switchgrass germplasm used for this phylogenetic study derived mainly from northern-adapted populations, with very restricted sampling of populations from southern and central regions. Based on the geographic distribution and deep divergence of the upland and lowland ecotypes The association panel consisted of 66 diverse switchgrass populations grown from seed in the greenhouse of the USDA-ARS Dairy Forage Research Center in Madison WI in 2007. This panel was mainly composed of northern adapted upland populations . In addiApeKI, which has a 4.5 bp cut site . The resulting libraries were sequenced on the Illumina HiSeq 2000. Ninety five samples (plus a blank negative control) were sequenced per lane.The reduced representation libraries were constructed and sequenced according to the published GBS protocol http://www.maizegenetics.net/gbs-bioinformatics) was developed for SNP discovery and genotyping in species like switchgrass , are assumed to result from sequencing error. For two tags (t1 and t2) with a 1 bp mismatch and read counts c1 and c2, if c1/(c1+c2)<ETR, then t1 is assumed to be a sequencing error of t2. The edges connecting real tags and error tags are then sheared, dividing the network complexes into parts. Remaining reciprocal, real tag pairs are then identified as SNPs .According to the frequency distribution of tag pairs in structured populations , we founTo calculate the coverage in the SNP data sets, we calculated the sum of the read counts of all of the sites across all the individuals. This sum is then divided by the number of sites and number of individuals. So the measurement of coverage in this study is reads/site/individual.The maternal and paternal parents of the full-sib linkage population, U518 and U418, were sequenced via GBS at about 6×, i.e., at six times higher coverage than the F1 individuals in the full-sib population. SNP markers that were homozygous in one parent and heterozygous in the other and which had a minor allele frequency (MAF) in the progeny between 0.2 and 0.3 were chosen for linkage analysis via the pseudo-testcross mapping approach Initial linkage groups were constructed based upon the 6,000 markers with the highest call rate (>0.9). The MMC method Markers representing the cross of AA×Aa segregate from either a paternal or maternal linkage group. Using the 36 linkage groups produced by MMC from these initial 6,000 markers as seeds, 88,217 markers  were then assigned to linkage groups based upon their Spearman's rank correlation coefficient to each seed linkage group.http://www.phytozome.net/foxtailmillet.php) via Basic Local Alignment Search Tool (BLAST) P-value cutoff of 1e-5. The 7,245 markers that mapped to a single site of the foxtail millet genome, and clustered with one of the syntenic linkage groups of switchgrass, were used to construct the synteny based framework linkage maps. To construct the high density linkage maps, the rest of the 80,972 markers were mapped to the framework marker with highest value of Spearman's rank correlation coefficient within their assigned linkage groups.To order the markers within each linkage group, we relied upon extensive synteny between the switchgrass and foxtail millet genomes. Markers were mapped to the foxtail millet genome . An MDS plot was also generated based upon the kinship matrix of individuals calculated from the 29,221 markers in TASSEL Figure S1Details of the network filter. The dots represent tags. The size of dots increases with tag count. Blue dots are putative sequencing errors (rare tags). Red dots are real, more common tags. Arrows from the blue dots to the red dots indicate where the errors come from. (A) A network of tags. (B) The sequencing errors are identified if their counts are much fewer than the counts of adjacent tags. (C) The edges connecting the real tags and errors are sheared. (D) The network is divided into sub-networks. The reciprocal tag pair is kept as a potential SNP. The network with multiple tags is discarded. (E) Possible tag topologies of potential SNPs after passing through the network filter.(TIF)Click here for additional data file.Figure S2Effect of the network filter on actual allele frequency distributions in biparental populations. SNPs were called in a single family (B73×B97) from the maize NAM population (TIF)Click here for additional data file.Figure S3r2) with 4 adjacent, external SNPs was calculated.LD distribution of SNPs generated from UNEAK versus 1106 external SNP markers in the maize NAM family B73×B97 (TIF)Click here for additional data file.Figure S4The relationship between coverage and SNP call rate in the switchgrass data sets. The call rate represents the proportion of individuals that was covered by at least one read. A total of 3,000 SNPs are plotted in each subfigure. (A) is from the full-sib population . (B) is from the half-sib population . (C) is from the association populations .(TIF)Click here for additional data file.Figure S5Relative depth of coverage (read frequency) of the two SNP alleles at heterozygous loci in the tetraploid U518 (A), the tetraploid U418 (B), and the octoploid K101 (C).(TIF)Click here for additional data file.Figure S6The relationship between sequencing coverage and the proportion of miscalled heterozygous genotypes. A total of 3,000 SNPs are plotted. Due to the limited sequencing depth, heterozygotes are often miscalled as homozygotes.(TIF)Click here for additional data file.Figure S72 indicates tight linkage.Marker selection and linkage detection via a pseudo-testcross strategy. The red and blue blocks represent bi-allelic SNPs in each site. (A) Only SNPs with allele frequencies falling within 0.2<MAF<0.3 in the F1 were selected. These SNPs must be homozygous in one parent and heterozygous in the other parent. (B) A subset of SNPs which are all homozygous in one parent and all heterozygous in the other parent, but with unknown linkage phase, were selected. (C) Close linkage of SNPs was detected based upon Spearman's rank correlation. When two SNPs are in coupling phase, the correlation is positive; when they are in repulsion, the correlation is negative. High r(TIF)Click here for additional data file.Figure S8Maternal linkage groups in a biparental switchgrass family and their alignment to foxtail millet genomes. (A) 3,000 markers were clustered into 19 groups. (B) The 19 groups were aligned to the foxtail millet genome. The majority of the SNPs on each linkage group aligned to the same chromosome of the foxtail millet genome (red dots); there were some exceptions, however (blue dots). In most cases, each foxtail millet chromosome matches two linkage groups of switchgrass. The sole exception, chromosome 1, has three matching linkage groups . Based on later analyses, we merged linkage groups 1 and 3.(TIF)Click here for additional data file.Figure S9Pairwise Spearman's rank correlation coefficient (r) of ordered markers on paternal linkage group 12. A total of 228 markers were ordered based on their alignment to the foxtail millet genome. Pairwise r was calculated for these markers. The high r values were distributed along the diagonal.(TIF)Click here for additional data file.Figure S10Neighbor-Joining tree of 540 individuals from 66 diverse populations of switchgrass. In the tree on the left, the red branches indicate the upland ecotype of switchgrass. The blue branches are the lowland ecotype. Details of a portion of the tree are shown on the right. Individuals within a box are from the same population.(TIF)Click here for additional data file.Figure S11Switchgrass ecotypes with different ploidy levels resolve into distinct phylogenetic clades.(TIF)Click here for additional data file.Figure S12Switchgrass Neighbor-Joining phylogeny constructed with 3,144 markers. Foxtail millet was used as an outgroup.(TIF)Click here for additional data file.Figure S13Two competing evolutionary models of upland 4× switchgrass. (A) Upland 4× arose from upland 8× switchgrass. (B) Upland 4× arose from lowland 4× switchgrass.(TIF)Click here for additional data file.Figure S14Migration patterns of switchgrass.(TIF)Click here for additional data file.Table S1Background information on the germplasm used in study.(PDF)Click here for additional data file.Table S2Individual clones sequenced in the association panel.(PDF)Click here for additional data file.Table S3Quality of linkage group clustering at different call rates.(PDF)Click here for additional data file.Text S1Technical details of UNEAK pipeline.(DOCX)Click here for additional data file.