Vulpes vulpes) offers a novel model for studying the genetics of social behavior and animal domestication. Selection of foxes, separately, for tame and for aggressive behavior has yielded two strains with markedly different, genetically determined, behavioral phenotypes. Tame strain foxes are eager to establish human contact while foxes from the aggressive strain are aggressive and difficult to handle. These strains have been maintained as separate outbred lines for over 40 generations but their genetic structure has not been previously investigated. We applied a genotyping-by-sequencing (GBS) approach to provide insights into the genetic composition of these fox populations. Sequence analysis of EcoT22I genomic libraries of tame and aggressive foxes identified 48,294 high quality SNPs. Population structure analysis revealed genetic divergence between the two strains and more diversity in the aggressive strain than in the tame one. Significant differences in allele frequency between the strains were identified for 68 SNPs. Three of these SNPs were located on fox chromosome 14 within an interval of a previously identified behavioral QTL, further supporting the importance of this region for behavior. The GBS SNP data confirmed that significant genetic diversity has been preserved in both fox populations despite many years of selective breeding. Analysis of SNP allele frequencies in the two populations identified several regions of genetic divergence between the tame and aggressive foxes, some of which may represent targets of selection for behavior. The GBS protocol used in this study significantly expanded genomic resources for the fox, and can be adapted for SNP discovery and genotyping in other canid species.The silver fox ( Vulpes vulpes) and the gray wolf (Canis lupus), both members of the Canidae family, diverged from a common ancestor approximately 10 million years ago [Canis lupus familiaris) is a recent descendant of the modern gray wolf ancestor and the only historically domesticated canid. In 1959, work began at the Institute of Cytology and Genetics  to domesticate the silver fox, a coat color variant of the red fox [The red fox . TheSince commercial farms usually eliminate animals with an excessively aggressive response to humans, a parallel breeding program was begun in 1970 to preserve this behavior for research , 10. SelThe genetic basis of tame and aggressive behavioral phenotypes has been clearly demonstrated in multiple experiments , 8, 12. Genotyping-by-sequencing (GBS), like some other current technologies –21, usesIn a previous study, quantitative trait loci (QTL) analysis of fox experimental pedigrees identified several regions in the fox genome implicated in behavior   in Novosibirsk, Russia. All animal procedures at the ICG complied with standards for humane care and use of laboratory animals by foreign institutions. The study was approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Illinois at Urbana-Champaign. DNA was extracted from blood using Qiagen Maxi Blood kits, as per the manufacturer’s instructions .Genotyping-by-sequencing (GBS) set. We used samples from 40 individuals, 20 each from the tame and aggressive populations, for the genotyping-by-sequencing (GBS) experiment. Each population sample included equal numbers of males and females, and individuals were not closely related .Validation set. The second sample set was selected to validate the differences in allele frequency of SNPs from the distal part of fox chromosome 3 (VVU3) which were observed between the tame and aggressive populations using GBS data. The validation set included 45 aggressive, 46 tame, and 92 conventional farm-bred foxes (foxes which were not deliberately selected for behavior). Again, animals were, generally, not closely related and animals from both genders were equally represented.Genetic mapping set. Fox F2 pedigrees including parents, grandparents, and 536 offspring were used for construction of a meiotic linkage map of VVU3 and quantitative trait loci (QTL) analysis. The F2 pedigrees were developed in a previous study [us study , 25 by cus study , 12, 25.Three sets of DNA samples we used in this study:ApeKI, EcoT22I, PstI, and EcoT22I/PstI double digest) according to the enzyme manufacturer’s protocol . Samples were digested at 37°C for 2 hours and then, incubated at 80°C for 20 minutes to inactivate REs. Digested DNAs were ligated to adapters as previously described [Taq master mix  and 25 pmol of each of two primers containing complementary sequences to ligated adapters and Illumina solid-phase oligonucleotides bound to the flowcell lane surface. PCR was performed following the previously described protocol [ApeKI and EcoT22I enzymes showed enrichment for fragments of desirable sizes (To determine the best restriction enzyme (RE) system for reducing the complexity of the fox genome, DNA (100 ng) from a single individual was digested, separately, with several REs  are listed in ApeKI GBS library construction were published previously [Taq master mix  and 12.5 pmol of each PCR primer  in the same sequencing flowcell lane (single-end sequencing with 100 bp reads), with approximately half of the sequencing coming from each.The first run of sequencing was done with separate libraries made with ApeKI library was 1,482,234, with high variation in read numbers among samples . Nine animals received so little sequence that they were judged as failed. This was likely due to technical errors during the library preparation. Due to uneven results we did not use sequencing data generated for the ApeKI library in the following analysis.The average number of reads for the EcoT22I library was 1,181,370  and no samples failed. Because the EcoT22I library produced good results in the first run we sequenced already created EcoT22I library on an additional flowcell lane to obtain deeper sequencing data for this library. This strategy produced coverage depths equivalent to 32-plex sequencing.The average number of reads per sample for the SNPs were called from raw DNA sequences using the GBS pipeline as implemented in TASSEL 3.0.166 . Raw seq“GQ: The Genotype Quality, or Phred-scaled confidence that the true genotype is the one provided in GT. In the diploid case, if GT is 0/1, then GQ is really L(0/1) / (L(0/0) + L(0/1) + L(1/1)), where L is the likelihood that the sample is 0/0, 0/1/, or 1/1 under the model built for the NGS dataset. The GQ is simply the second most likely PL—the most likely PL. Because the most likely PL is always 0, GQ = second highest PL—0. If the second most likely PL is greater than 99, we still assign a GQ of 99, so the highest value of GQ is 99.”http://gatkforums.broadinstitute.org/discussion/1268/how-should-i-interpret-vcf-files-produced-by-the-gatk accessed on 21 November 2014)(Duplicate SNPs were merged with the MergeDuplicateSNP_vcf_Plugin (options:-ak 3). For VCF files, likelihood scores were calculated according to Etter et al., 2011, formula 3.8 . The mosTASSEL 4.3.7 was used to merge 7 replicated samples using the plugin MergeIdenticalTaxaPlugin  .Two sets of SNPs, filtered and stringently filtered sets, were produced using VCFtools . FilteriConsistency of SNP genotype calling was evaluated by comparing the two replicates from each of the seven duplicated DNA samples. Both filtered and stringently filtered SNP data sets were created for the unmerged data (where replicates of the duplicated samples were treated as separate samples), in the same manner as described above for the merged data set. In the filtered set, two types of discordance between the replicates were observed: 1) when one sample in a pair did not receive a call but the other did (missing data) and 2) SNPs for which both samples in the pair received a genotype call but the genotypes differ between the replicates. In the stringently filtered set, only second parameter was present because all SNPs in this data set had genotyping calls for all samples. The percent of concordant genotypes was calculated for each of the duplicated individuals in both the filtered and stringently filtered sets. The percent of the discordant genotypes due to missing data was also calculated for each duplicated individual but only in the filtered set.The depth of the read coverage for the concordant and discordant SNPs was determined using the unmerged, filtered set of SNPs. The discordant genotypes due to missing data were not included in this analysis, only those SNPs that have genotypes for both replicates of the duplicated individuals were used. The mean and standard deviation of the depth was calculated for both concordant and discordant SNPs in this data set. The average heterozygosity of SNPs located on the X chromosome outside of PAR (0–6.65Mb)  was calcThe location of GBS SNPs on the fox chromosomes was estimated by alignment of the fox meiotic linkage map against the dog genome as previously described , 13. Brihttps://www.cog-genomics.org/plink2) [The stringently filtered SNP set  was used for population structure analysis using Principal Component (PC) analysis and the Bayesian inference program STRUCTURE . PC anal/plink2) . Cluster/plink2) , 33 at 12) were done in PLINK2 [2 between adjacent SNPs was computed by grouping SNPs by pairwise estimated physical distances into 14 bins ranging from 1–1000 bp to 100–210 Mb (size of the largest approximated fox chromosome is 210 Mb) .A subset of stringently filtered SNPs, whose positions were extrapolated on fox chromosomes , was used to estimate linkage disequilibrium (LD) decay in both tame and aggressive populations. Calculations of the squared correlation of the alleles at two loci (rn PLINK2 . Distanc 210 Mb) . LD was The set of stringently filtered mapped SNPs was also used to calculate effective population size (Ne) using the LD method of Waples and Do  as impleThe filtered SNP set  was used to calculate population parameters. Observed heterozygosity was computed as the percent of heterozygous individuals  among the total number of individuals that received genotype calls for that SNP. The average observed heterozygosity of the population was calculated as a sum of the SNP heterozygosity in that population divided by the total number of SNPs identified in the population. The mean, median and standard deviation of the minor allele frequency (MAF) were calculated separately for the tame and aggressive populations. Expected heterozygosity was calculated for each SNP by formula 2.16b from Hedrick, 4th Edition, page 93 . The obtTo identify regions in the fox genome where the two populations differed in allele frequency, 48,042 SNPs from the filtered set that mapped to the fox genome were compared between the tame and aggressive samples using the (--assoc--adjust) option in PLINK2  with BonST) using VCFtools [ST” is reported. The FST was calculated in windows of 1 Mb with the overlap of 500 Kb using the Weir and Cockerham estimator [In addition to the above analysis, we also computed fixation index  and to extend the existing linkage map for this chromosome, four new fox markers (three SSRs and one indel) were developed. The indel was identified through sequencing fox amplicons produced using dog derived primers; two of the SSR markers corresponded to microsatellites identified in the dog genome, and one SSR was selected from the dog meiotic linkage map . Primershttp://graphpad.com/quickcalcs/contingency2/) was used to evaluate the significance of the differences between each pair of populations.The indel marker (26749b) was also genotyped in 183 additional animals from three populations: tame (46), aggressive (45), and conventionally farm-bred foxes (92). The Fisher exact test  20.8 Gbp of sequence was retained for analysis. On average, this dataset contained 5,202,895 reads of 100 bp length per sample .The The GBS data were analyzed using the TASSEL-GBS pipeline to produce SNP calls . A totalThe SNP call reproducibility was evaluated using duplicated samples. In the stringently filtered set, the genotypes of replicates were in perfect concordance  for 97.9–99.2% of SNPs among the seven duplicated samples.In the filtered data set, the genotypes were in concordance 66.5–73.8% of the time. The disagreement was due to one sample not receiving a genotype call 55.6–71.7% of the time (i.e. missing data). The average depth for the discordant SNPs was 14.0 ± 25.4 reads, while for concordant SNPs the average depth was 34.0 ± 41.6 reads, thus the average depth for the concordant SNPs was 2.4 times greater than for the discordant ones . We hypoThe dog is the fox's closest relative with a sequenced genome. We used the comparative dog/fox map  and the The stringently filtered set of 8,437 SNPs with no missing data for any of the 40 individuals was subjected to Principal Component (PC) analysis. The PC analysis revealed genetic differences between the two strains . PC1 sepThe genetic structure analysis at K = 2 clearly separated the tame and aggressive individuals into two clusters . At K = 2≥ 0.5 was observed for SNP pairs with inter-marker distance less than 1 Kb in both tame and aggressive populations. An average r2≥ 0.2 was observed for SNPs located within 100–500 Kb in tame and 50–100 Kb in the aggressive population  in tame and 0.058 (±0.079) in aggressive populations.To test that our data do not show significant spurious associations between unlinked SNPs the r2 ≥ 0.2 was reported for SNPs located at the distance 500 Kb for several dog breeds [2 = 0.15 and 0.2 were reported in two pig breeds for SNPs located 1 Mb apart [2 = 0.3 was reported for SNPs located less than 100 kb in Holstein cattle and r2 = 0.25 for SNPs less than 20 kb in Fleckvieh breed [2 = 0.5 was reported for SNPs located 2 Mb apart [Although estimation of the LD in fox strains could be influenced by the small sample size used in this study, the small number of SNPs, and imperfect calculation of intermarker distances based on fox/dog synteny, it still provides a useful indication of the extent of LD in these populations. The length of LD observed in the tame fox population is comparable with average LD length in dog breeds (rg breeds ) and pigMb apart ); and iteh breed –45). TheMb apart ). The LDMb apart . The difEffective population sizes of the aggressive and tame populations, respectively, were 38.0 (95% CI: 37.8–38.2) and 81.5 (95% CI: 80.5–82.5). Although the aggressive population has a smaller number of breeding animals and lower effective population size, more genetic variation has been preserved in this population. These findings may reflect the fact the aggressive population had an inclusion of outside individuals in the recent past in comparison to the tame population, which experienced a longer period of closed breeding.-7). These included 19 “isolated” SNPs (> 2Mb from another significant SNP), and 49 SNPs located within 11 clusters on seven fox chromosomes. Clusters comprised at least two SNPs located within a 2 Mb interval  of fox populations identified 28 windows with FST ≥ 0.5  14 windows overlap with the genomic regions identified in the analysis of allele frequency differences . To distinguish between these processes, we hypothesized that regions representing true selection targets would be identified by both selective sweep mapping in fox populations and QTL mapping of behavioral traits in fox experimental pedigrees. Regions influenced by random fixation, on the other hand, would be identified only by homozygosity mapping, but would not co-segregate with behavioral phenotypes in informative pedigrees. To test this hypothesis, three genomic regions on fox chromosomes 3 (VVU3), 8 (VVU8) and 14 (VVU14), which were among the regions which showed the greatest allele frequency differences between the two populations ; S3 Fig,The distal part of VVU3 contains a genomic region that appears to be approaching fixation for different alleles in the tame and aggressive populations. Three SNPs located within a 117 bp interval showed significant differences in allele frequencies including SNP-S1_1977727423, which showed the most significant differences in the Bonferroni corrected association test among all fox SNPs. The SNP-S1_1977727423 was monomorphic in the aggressive population while the frequency of the only allele observed in the aggressive population was 0.03 in the tame population. The allele frequencies for two other SNPs (SNP-S1_1977727319 and SNP-S1_1977727436) were the same for both SNPs: the minor allele frequency for these SNPs was 0.85 in the aggressive and 0.03 in the tame population. The distal region of VVU3 had been excluded from previous QTL mapping studies because this region has not been represented on the fox meiotic linkage map.Allele frequency differences on distal VVU3 were validated in an additional sample set comprising tame (46), aggressive (45), and conventionally farm-bred foxes (92) using an indel marker, 26749b, located about 122 Kb from SNP-S1_1977727423. Details of this analysis are presented in To evaluate the effect of distal region of VVU3 on fox behavior we extended the meiotic linkage map for VVU3  using neGRIN2B (NMDA) with GO terms including (http://amigo.geneontology.org/) “startle response” and “behavioral fear response” and GABARAPL1 (GEC1), which enhances expression of the kappa opioid receptor [VVU8 contains 29 out of the 68 SNPs which showed significant differences in allele frequency between the tame and aggressive strains. The region of genomic divergence on VVU8 includes three of the 11 SNP clusters. It is estimated to be about 52 Mb in the fox genome and spans two different dog chromosomes, CFA27 and CFA17. In this extended region there are also many SNPs that do not reach our threshold, but show a high level of allele frequency differences between the tame and aggressive populations ; S3 Fig.receptor , both stCOUP-TFII (NR2F2). COUP-TFII is an orphan nuclear receptor belonging to the superfamily of steroid/thyroid hormone receptors [in vitro [COUP-TFII is an interesting candidate gene and its role in regulation of behavior in foxes will be further evaluated.Comparison of allele frequencies between tame and aggressive individuals identified 11 SNPs on fox chromosome 14 (VVU14) which met our significance threshold, 10 of which are grouped tightly within a 1.3 Mb region , Table 2eceptors . COUP-TFeceptors –50. It hin vitro . COUP-TFIdentification of loci under selection in artificially selected populations and natural bottlenecked populations remains to be a challenge , 53. ResThe pilot analysis of the genetic structure of tame and aggressive fox strains using GBS clearly shows the potential for using these strains for high-resolution genetic mapping of behavioral phenotypes. The genetic structure and the effective population size of tame and aggressive strains indicate that a significant amount of genetic diversity has been preserved in both populations. Simultaneously, intensive selection for behavior in these populations must favor the accumulation of specific alleles in the targeted regions of selection. Understanding genetic composition of these populations will facilitate the use of this unique animal model for studying the genetics of social interactive behavior. The current analysis confirmed loci previously determined to be under selection   revealed the genetic structure of the tame and aggressive strains and identified several genomic regions with significantly different allele frequencies between the two populations. The population genomic parameters of the fox strains clearly indicated the deep potential of these strains for high resolution mapping of behavior. The growth of sequencing technologies will allow us to search for signals of selection in these fox strains by whole genome sequencing. The VVU14 region with high allele frequency differences between the two strains overlaps with a previously identified QTL for behavior in fox experimental pedigrees emphasizing the importance of this region for behavior. Comparative analysis of selective sweep positions and QTL intervals can be used as a promising approach for differentiation between selective sweeps associated with selection and regions of random fixation , 60. IdeS1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


Brassica napus  comprises of two highly homoeologous sub-genomes derived from its progenitor species B. rapa  and B. oleracea , and is an ideal species to exploit methods for reducing the interference of extensive inter-homoeologue polymorphisms (mHemi-SNPs and Pseudo-simple SNPs) between closely related sub-genomes.Single nucleotide polymorphism (SNP) markers have a wide range of applications in crop genetics and genomics. Due to their polyploidy nature, many important crops, such as wheat, cotton and rapeseed contain a large amount of repeat and homoeologous sequences in their genomes, which imposes a huge challenge in high-throughput genotyping with sequencing and/or array technologies. Allotetraploid B. napus 6K SNP array, we developed a bi-filtering procedure to identify unauthentic lines in a DH population, and mHemi-SNPs and Pseudo-simple SNPs in an array data matrix. The procedure utilized both monomorphic and polymorphic SNPs in the DH population and could effectively distinguish the mHemi-SNPs and Pseudo-simple SNPs that resulted from superposition of the signals from multiple SNPs. Compared with conventional procedure for array data processing, the bi-filtering method could minimize the pseudo linkage relationship caused by the mHemi-SNPs and Pseudo-simple SNPs, thus improving the quality of SNP genetic map. Furthermore, the improved genetic map could increase the accuracies of mapping of QTLs as demonstrated by the ability to eliminate non-real QTLs in the mapping population.Based on a recent B. napus and may find wide applications to SNP analyses in polyploid crops.The bi-filtering analysis of the SNP array data represents a novel approach to effectively assigning the multi-loci SNP genotypes in polyploid The online version of this article (doi:10.1186/s12864-015-1559-4) contains supplementary material, which is available to authorized users. Brassica napus L., AACC, 2n = 38) is one of the most important oil crops in the world, which provides not only edible oil but also raw materials for bio-energy applications. B. napus is an allotetraploid that was generated from the natural hybridization of its two progenitor diploids of Brassica rapa  and Brassica oleracea  about 7,500 years ago [B. rapa and B. oleracea were produced by extensive triploidization of their ancestral species at the genomic level [B. napus two subgenomes An and Cn are largely collinear (93%) to the corresponding diploid Ar (B. rapa) and Co (B. oleracea) genomes [Arabidopsis thaliana [B. napus genome [B. rapa and B. oleracea remain as homoeologous pairs in B. napus An and Cn subgenomes (An ortholog gene in the An genome in most cases has a highly homologous copy of the sequence in the Cn genome) [Oilseed rape  sequencing method [et al. [B. napus inbred lines and their 91 doubled haploid (DH) progenies [B. napus with SNP markers, including mini-sequencing [B. napus based on the Illumina Infinium HD Assay have been developed, and used for QTL mapping [Single nucleotide polymorphism (SNP) markers have a wide range of applications in the construction of genetic maps, mapping and cloning of quantitative trait locus (QTL), linkage analysis, molecular marker-assisted selection (MAS), and molecular breeding of crops -12. Edwaion SNPs ,14. Westectively ,16. Recequencing -19. Bus  [et al. , and a urogenies . Severalquencing , Illuminquencing , Invaderquencing  assays a mapping ,27, geno mapping , and gen mapping ,30.B. napus faces more challenges due to the species’ complex genome structure [B. napus genome [Compared with diploid species, such as rice ,31, maiztructure ,37,38. Ftructure , resultitructure ,38. Becatructure ,27. The s genome ,5, whichDifferent parameters have been proposed for quality evaluation of SNP arrays in diploid species . For instance, the cluster separation score (CSS) is often used to select high-quality SNP probes (CSS > 0.3). However, the CSS is not always suitable for the determination of SNP loci ,40, as iB. napus, it is not suitable to simply apply the parameters and criterions developed in the diploid species to evaluating the quality of SNP array probes in polyploid species. Therefore, there is a need for development of effective procedures to assess the quality of the SNP array probes and to make full use of SNP genotyping data in polyploid species.Due to the high frequency of multi-loci SNPs (hemi-SNP) in polyploid species, such as B. napus was applied to genotyping a DH population and its parents [B. napus.In this study, a 6K SNP array (Illumina Infinium HD Assay)  for B. n parents . A proceB. napus variety, and J7005, a winter-type B. napus pure line. The two parents were purified by microspore culture before hybridization. Detailed information about this population was described in Wu et al. [et al. [The HJ DH population was produced from microspore culture of F1 buds of the cross between Huashuang 5 (Hua5), a semi-winter type u et al.  and Cai  [et al. .The DH lines, together with their parental lines, F1 and RF1 hybrids were grown in a semi-winter rapeseed crop area, Wuhan in 2009–2010, 2010–2011, Huanggang in 2010–2011, and a spring rapeseed crop area Gansu in 2011, respectively. The field experiment followed a randomized complete block design with three replications. Each line was planted in two rows and 10–11 plants were maintained in each row, with a distance of 17 cm between plants within each row and 30 cm between rows. The parental line Hua5 was grown in every 20 lines as a control. The field management followed essentially regular breeding practice.et al. [at al. [Primer sequences for the SSR markers used for genetic mapping were described by Fan et al.  and the  [at al. .B. napus  developed by the University of Queensland. The SNP genotyping was conducted following the instructions from Infinium HD Assay Ultra Protocol Guide (http://www.illumina.com/). All the SNP array data were clustered and visualized for further analysis using the Illumina GenomeStudio software . Each SNP was re-checked manually to determine if any error was observed during the clustering analysis. Detailed information about SNP array genotyping and data processing was described in Cai et al. [The genotyping of SNPs was performed using a 6K Illumina Infinium HD Assay SNP array of i et al. .et al. [et al. [The method for genetic linkage map construction was described by Cai et al. . QTLs weet al. . A signi [et al. .B. napus [Previously, the HJ-DH population and its parental lines were genotyped with SSR markers and the 6K SNP arrays for B. napus ,30 and tAmong the 1,850 polymorphic loci out of the 4728 probes (39.1%) , 1,149  can be calculated with the following formula:For the DH lines listed in the vertical direction, the PNPG of DH lines  in all SNP loci of the jth line, and Mj represents the number of detected SNP loci of the jth line.Where PNPGFor a true DH line, the PNPG value should theoretically be zero. However, many factors such as genetic mutations, the stability of the SNP detection system, and the mechanical or biological contamination of the samples, can affect genotyping results. However, the probability that all of these factors will have a significant impact on the genotyping results is small. Based on the above considerations, in the subsequent analysis, PNPG =0.05 was set as a threshold value to determine the authenticity of a given DH line.The PNPG values were calculated for each DH line based on 3456 monomorphic SNP loci, including the SNPs that had more than 20% of missing data  can be calculated with the following formula:Due to the existence of a large number of inter-homoeologues in the A and C subgenomes of B. napus ,49, it its Table , althouge et al. , in whice et al. . In suchcapacity -53. For capacity -53. Such_SNP represents the percentage of NPGs in the ith SNP loci of all DH lines, PGi represents the number of PGs in the ith SNP loci of all DH lines, and Mi represents the number of detected DH lines in the ith SNP loci. After excluding the unauthentic DH lines, if the genotypes of both parents are AA at a SNP locus, the genotype of all DH lines is theoretically AA at this SNP locus. If a different genotype (such as AB or BB) is detected, it is very likely to be the result of a detection error. In this case, a PNPG value of 0.05 was still considered as the threshold to determine the reliability of call for a SNP locus.Where PNPGB. napus SNP array, it is most likely caused by inter-homoeologue polymorphism or signal superposition of multiple SNP loci. The generation of these heterozygous signals (AB) is due to the complexity of the genome of allotetraploid B. napus.Next, the PNPGs for 3,456 monomorphic SNPs in the horizontal direction were analyzed. A PNPG ≥0.05 was found at 108 (3.13%) SNP loci, indicating that the detection of most of the SNP loci was reliable. When excluding these 108 SNP loci, the average PNPG for the remaining SNP loci was 1.60E-03 Table . The anaThe above analysis showed that, using a two-dimensional matrix constructed with the genotypes of the monomorphic SNPs in the DH population and PNPG analysis, unauthentic DH lines could be excluded (using columns in the matrix), and the error and stability of the system could be estimated (using rows). These analyses can improve the quality and utilization efficiency of SNP array data.B. napus, a considerable number of the SNP probes cannot meet this requirement. To reduce the impact of multi-loci SNPs on the subsequent genetic linkage analysis, we further divided Hemi-SNPs into two sub-groups, sHemi-SNPs and mHemi-SNPs, according to whether the NPG can be identified in the DH population. A so-called sHemi-SNP refers to the probe call that generates heterozygous signal (AB) in one of the two parental lines and the parental genotypes can be detected but no NPG will be produce detected in their offspring DH lines. As illustrated in Figure Previously, the assignment of polymorphic SNP loci was conducted using two methods. The first method is to simply remove the loci that exhibit heterozygous signals in one of the parents, and the other method is to mark signals in the segregating population that have the same P1 value as genotype A, signals that have the same value of P2 as B, and non-parental signal values with missing values (“-”) ,27,30. TTo identify the difference between mHemi-SNP and Pseudo-simple SNP and the other two types of SNPs (Simple SNP and sHemi-SNP), a method similar to the one above used for the analysis of monomorphic SNPs was applied to analyze the PNPG values of polymorphic SNP loci.If the genotype of the parents was AB/BB of the two types of SNP: sHemi-SNP and mHemi-SNP, the PNPG values in the DH population were different Figure .2 test) as the probability of NPGs appearance (expected frequency versus observed frequency) in the DH population. We used PNPG =0.05 as the threshold to judge the presence of NPG or not.Once we have the expected frequencies for all possible four genotypes in the DH population, we can easily distinguish different types of SNPs listed in Figure There was an exception in the type of Pseudo-simple SNP for above analysis, where the two polymorphic genotypes of AA/NC and BB/NC  and 42 Pseudo-simple SNPs (P = 0.0012) by χ2 test.Based on the PNPG values of the SNP loci, the genotype data for several other polymorphic genotypes were identified from the single-locus sHemi-SNPs (PNPG <0.05). There were 175 (9.5%) SNP loci for the two polymorphic genotypes of AA/NC and BB/NC, including 53 loci with PNPG values >0.05; the remaining 122 loci  can be separated into 80 Simple SNPs (2 test to extract single-locus SNPs (Simple SNPs and sHemi-SNPs) from the SNP array data can maximize the utilization of the total SNP loci and remove the multi-loci SNPs (mHemi-SNPs and Pseudo-simple SNPs), which were difficult to identify in previous studies.Based on the above analysis, the SNP loci with PNPG values >0.05 can be considered as multi-loci SNPs , whereas the SNP loci with PNPG values <0.05 were considered as single-locus SNPs (Simple SNPs and sHemi-SNPs). Based on this standard, 1,573 SNP loci (85.0%) were screened from 1,850 polymorphic SNP loci for the subsequent analysis. Using the PNPG value and χB. napus genome. In this case, the locus cannot be assigned as a NPG count. Second, if such a SNP locus is located right at the breakpoint of the homoeologous recombination fragments, the locus will not be identified, thus resulting in a false NPG count. It is now known that the homoeologous recombination between the A and C genomes occurred in a relatively low frequency at the level of large fragments [It is worth to point out that homoeologous recombination between the A and C genomes might result in non-parental phenotype. There are two consequences if such recombination events happen. First, if a given SNP locus is located within the homoeologous recombination fragment, its genotype will be identified as a regular locus, no matter where the fragment is distributed in the ragments ,30. The The signal values of the 1,573 valid SNP loci were converted to genotype values. Genotypes that were the same as that of parent P1 were recorded as “A”, genotypes that were the same as that of parent P2 were recorded as “B”, and non-parental genotypes were treated as missing (“-”). We named this method of filtering out unauthentic lines and mHemi-SNP loci in SNP array data using the PNPG values as bi-filtering analysis. In brief, the bi-filtering method could be summarized as follows. First, we used the monomorphic SNPs to calculate the PNPG value of a given DH line  to filter out the unauthentic lines  with 190 DH lines and 2,323 polymorphic markers   by meansCompared with the Map C, the Map B now had an increased marker density after bi-filtering analysis of both unauthentic DH lines (11 lines) in mapping population, and mHemi-SNP and Pseudo-simple SNP markers in SNP arrays  2.68-21.56% (on average 9.96%), with logarithm of odds (LOD) score 3.66-21.1 (on average 7.07). However, these QTLs could not be identified in the Map B, because the pseudo fragments have been filtered and the pseudo-QTLs resulted from the mHemi-SNPs and Pseudo-simple SNPs eliminated. Moreover, the mHemi-SNPs and Pseudo-simple SNPs dispersed along the different LGs of the Map C also affected the QTL identification. There were 18 QTLs that could be identified in the Map B but not in the Map C. Above data thus illustrated that the mapping accuracy of QTLs can be affected to a significant extent by mHemi-SNPs and Pseudo-simple SNPs.Next, we analyzed if the mHemi-SNP and Pseudo-simple SNP loci could have any adverse effects on QTL mapping. Results of QTL mapping of 20 agronomic traits in four environments were compared between the two maps. On the whole, 346 and 364 QTLs of 20 traits were identified by the Map B and Map C, respectively. There were 36 QTLs located on the excess fragments of LGs A03, A07, C01, C06 and C08 in the Map C, which can explain the contribution of phenotype (In order to more clearly illustrate this effect, we focused on the QTLs on the LGs A07 and C01 Figure . In Map We have developed a novel bi-filtering method to effectively identify unauthentic DH lines as well as mHemi-SNP and Pseudo-simple SNP loci resulted from the superposition of the multiple SNP loci signals in SNP arrays. Such a bi-filtering analysis of the SNP array data can maximize the use of the SNP array data more accurately in polyploid species, to which many important crops belong. The power of the method would be more obvious in higher density arrays where manual filtering analysis will become difficult.


Saccharum spp.) is an important sugar and biofuel crop with high polyploid and complex genomes. The Saccharum complex, comprised of Saccharum genus and a few related genera, are important genetic resources for sugarcane breeding. A large amount of natural variation exists within the Saccharum complex. Though understanding their allelic variation has been challenging, it is critical to dissect allelic structure and to identify the alleles controlling important traits in sugarcane. To characterize natural variations in Saccharum complex, a target enrichment sequencing approach was used to assay 12 representative germplasm accessions. In total, 55,946 highly efficient probes were designed based on the sorghum genome and sugarcane unigene set targeting a total of 6 Mb of the sugarcane genome. A pipeline specifically tailored for polyploid sequence variants and genotype calling was established. BWA-mem and sorghum genome approved to be an acceptable aligner and reference for sugarcane target enrichment sequence analysis, respectively. Genetic variations including 1,166,066 non-redundant SNPs, 150,421 InDels, 919 gene copy number variations, and 1,257 gene presence/absence variations were detected. SNPs from three different callers  were compared and the validation rates were nearly 90%. Based on the SNP loci of each accession and their ploidy levels, 999,258 single dosage SNPs were identified and most loci were estimated as largely homozygotes. An average of 34,397 haplotype blocks for each accession was inferred. The highest divergence time among the Saccharum spp. was estimated as 1.2 million years ago (MYA). Saccharum spp. diverged from Erianthus and Sorghum approximately 5 and 6 MYA, respectively. The target enrichment sequencing approach provided an effective way to discover and catalog natural allelic variation in highly polyploid or heterozygous genomes.Sugarcane ( Saccharum spp.) is grown in over 100 countries, mainly in tropical and sub-tropical areas of the world. Recently, sugarcane was considered as a sustainable feedstock for next generation cellulosic biofuel production  is the primary sugar producing species, which has been domesticated from wild species S. robustum (2n = 6x–8x = 60–80) (S. spontaneum is autopolypoid (2n = 4x–16x = 32–128) with excellent stress tolerance  with 80% of the chromosomes from S. officinarum species, 10% from S. spontaneum and 10% recombination of these two species  in the Saccharum complex were used as breeding materials to broaden the genetic base and increase the stress tolerance of modern sugarcane cultivars (Sugarcane (oduction . The genposition . Sacchar= 60–80) . S. sponolerance . S. barbectively . Modern  species . The Sacultivars .Saccharum complex , presence-absence variation (PAV), copy number variation (CNV) and insertions and deletions (InDels) are abundant in plant genomes and many are functionally relevant . These vSaccharum species to capture multiple alleles from a large number of diverse samples. It requires a careful downstream sequence analysis pipeline to exclude non-specific target sequences. Target enrichment combined with NGS has been effectively used for detecting SNPs in several other plant species with complex genomes, including maize , in which genomic regions of interest are captured for subsequent sequencing, is an effective approach to investigate allelic variation across a large number of samples, specifically for species with complex genomes. The hybridization based enrichment relies on the probe sequence homology or similarity to fish out the target DNA fragments for enrichment. Typically, if the sequence similarity of the target region is more than 80% with the probe oligo sequences, the target regions will be captured, thus all possible allele variants, even paralogous allele variants will be included in the enriched samples, achieving high sensitivity in retrieving target fragments. Therefore, the hybridization based enrichment is suitable for polyploids and heterozygous ng maize , wheat , two accessions of S. spontaneum , one accession of S. barberi (Pathri), one accession of S. robustum (NG57-054), one accession of S. sinense (TekchaOk), one accession of E. procerum , one accession of E. rufipilus (US57-060), one accession of unknown species (US61-037), and two accessions of S. spp. hybrids   using BLAST version 2.2.31 , probe sequences, and a unigene set assembled from in-house RNA-seq data were also used as the reference for alignment and comparison. For the RNAseq unigene set assembling, Trinity v2.1.16 with default settings was used. Assembled contigs with more than 200 bases were used as the reference for alignment , hairpin, and dimer scores were calculated using Python scripts according to To assess the probe capture efficiency and the captured sequence depth on the target region, we aligned all the probe sequences against the sorghum genome with BLAT v20140318  . A bed fS. officinarum (NG96-024). Only the probes with >26 (10% of the average uniquely mapped reads/probe) and <974 uniquely mapped reads (99th percentile of the distribution of the uniquely mapped reads/probe) were considered as successful probes to reduce potential bias due to sequence difference between designed probe sequences and target regions. The correlation coefficients between thermodynamic parameters were estimated using R 3.0.2 . SNPs and InDels were called with three different variant callers, namely, Samtools (v 0.1), UnifiedGenotyper implemented in Genome Analysis ToolKit (GATK) v3.30, and Freebayes v0.9.21 respectively  representing four different species and unknown species were used as template for amplification of the target SNPs. The amplified products were purified and sequenced by ABI 3730 sequencer and the sequencing results were analyzed using DNAbaser 4.2010.The SNPs identified above were selected for validation by Sanger sequencing. Primers , the number of uniquely mapped reads to sorghum gene model was recorded for each pair of accessions. The depth of coverage was normalized using the following formula: read depth of gene model = [109× (no. of reads in gene)]/[ × (length of gene)] to eliminate the effect of read depth of each sample. For each gene, the log2 ratio of read depth of query accession to subject accession was used to assess CNV. To reduce technical variation between two accessions, we used 99th percentile cut-off. Up-CNVs were identified as above 99th percentile cut-off in query accession, and Down-CNVs were identified as below 99th percentile cut-off in subject accession. The gene PAVs were identified between every two accessions which met the criteria including; (1) no reads aligned to query accession; and, (2) at least 10 reads aligned to subject accession at the same gene region with normalized read depth of no less than 0.1.Structural variations including gene CNV and gene PAV were analyzed following the approach described by 11. The GOslim annotation of genes that were involved in the structural variation including CNVs and PAVs was performed using agriGO12  with an average of one probe every 13 kb of the sorghum genome. Sequencing of fragments captured by the probes from the 12 accessions generated a total of 488.1 million 100-base single end reads ranging from 22.8 to 56.7 million reads with an average of 40.7 million reads per accession. The raw data was deposited in NCBI SRA with accession number PRJNA300644. There were 411.7 (84.3%) million clean reads left after quality control and adaptor trimming. These clean reads comprise of 97 Gb of high quality sequences with an average read length of 87 bases . The uniquely mapped reads covered approximately 27 Mb of intron region and 26 Mb of exon region of the sorghum reference genome.A total of 55,946 120-mer probes were designed from sugarcane and sorghum sequences to capture the corresponding target regions of the 12 accessions in Saccharum species was slightly higher than that in Erianthus species, since Saccharum species is genetically closer to sorghum than Erianthus species and sorghum genome was used as a reference for the alignment of the sequence reads. For all Saccharum species, 60% target regions had about 50 × MSD, while less than 50% target region had 50 × MSD in Erianthus species , hybridization folding energy (PHFE), hairpin score, and dimer score  were used to construct a model for explaining the probe efficiency. For the simplicity of this model, we tentatively assume that the probe sequences have equal copy numbers in the genomes. The GC content of probe sequence was highly correlated with Tm and PHFE. Therefore, only GC content was chosen from this correlation group in combination with other three parameters, hairpin, dimer scores and PMFE used for the model construction. The ANOVA results indicated that these four thermodynamic parameters significantly (p < 0.001) contributed to uniquely mapped reads of probes, from which a linear regression model was constructed as: Uniquely mapped reads = 5.08∗GC content + 13.3∗PMFE -2.11∗Dimer – 10.67∗Hairpin + 164.46. Based on the established model, higher GC content and PMFE could lead to more uniquely mapped reads, while Dimer and Hairpin had negative effects on the uniquely mapped reads or capture efficiency. The importance of the four parameters, PMFE, GC, Hairpin, and Dimer, showed a decreasing order  for Table 1). Therefore, the sorghum genome was used as a reference for alignment of the reads from different species in Saccharum complex.To select the best aligner for efficiently calling sequence variants, three most popular aligners for NGS read alignment, Bowtie2, BWA-mem, and MOSAIK were compared by aligning the cleaned reads from a single library to its assembly and sorghum genome respectively. The portion of mapped reads using Bowtie2, BWA-mem, and MOSAIK with less than 5 mismatches were 92.8, 92.3, and 87.4%, respectively. Both Bowtie2 and MOSAIK have less uniquely mapped reads (64.1 and 77%) to contig assembly from NG96-024 than BWA-mem (88.6%) indicating that BWA-mem was more sensitive for the uniquely mapped reads alignment. A follow-up visual examination on randomly selected alignment regions further confirmed that BWA-mem could accurately and sensitively call the uniquely aligned reads. Therefore, BWA-mem was used for all the alignments in this study. To determine the best reference sequence for the alignment (since the sugarcane genome is not available currently) the clean reads of the 12 accessions were aligned to sorghum genome v3.1, probe sequences, assembled sugarcane transcript sequences, and assembly of the total reads (contig length >200 bp) respectively using BWA-mem. The alignment rate using sorghum genome as the reference was 83%, ranging from 80–86% for each accession, the highest in the four reference sources  with a total of 1,166,066 non-redundant SNPs and 411,585 commonly called SNPs by three callers. In terms of the types of SNP changes, transition (Ts) and transversion (Tv), the Ts/Tv mutation ratios of SNPs produced from SAMtools, GATK, and Freebayes were 1.64, 1.61 and 1.76, respectively. In order to validate the accuracy of SNP calling of these three callers, 13 SNP-containing regions were selected for validation by Sanger sequencing including 69, 31, and 57 SNPs called by Samtools, Freebayes, and GATK respectively. Among those SNPs, 22 were called by all three callers, 47 SNPs specifically called by Samtools, 35 by GATK, and 9 by Freebayes .Single nucleotide polymorphisms and InDels were called based on unique alignments of cleaned reads to sorghum genome using BWA-mem. To create a set of high-confidence SNPs and InDels, three different callers, namely, SAMtools, GATK, and Freebayes were used. The chromosome number and ploidy level of each accession were estimated  and 24,718 (2.2%) of missing SNPs due to lack of reads alignment . Nevertheless, the ploidy level of these 12 accessions, varied from 6 to 12, and the frequency of multi-allelic SNP loci for each single accession was very low (less than 1%) . The number of accession-specific bi-allelic SNPs varied from 5,154 (0.47%) for S. officinarum accession (NG96-024) to 72,266 (6.74%) for E. procerum accession  . The SNPs were further estimated according to the types of nucleotide change, transitions or transversions and the Tv/Ts ratio was observed to be lower in the two Erianthus species than that in the other Saccharum species.To further evaluate the SNP number variation and identify the common SNPs among the 12 accessions, the SNPs called by GATK  were sorted out for each accession. GATK with consideration of ploidy level called more SNPs than FreeBayes and had better accuracy in SNP calling than SAMtools. The average read depth for SNP called by GATK was 1,653. With the read depth increasing, the number of SNPs called decreased and reached to a flat line when the read depth was between 1,500 and 2,900, which might be a reliable depth range for SNP calling. Each of the 12 accessions possessed more than 95% of the SNPs called by GATK. In total, 1,091,239 (99.2%) SNPs were present in all 10 Saccharum species and between the two Erianthus species. Since several factors can affect the read depth including ploidy level, capture efficiency, read number of each sample, and alignment rate; the read depth was normalized against the ploidy level and number of reads per sample. Accessions within the same genus were grouped together for gene CNV calling to minimize various homology levels between different species which can cause alignment bias. In total, 919 gene CNVs were identified including 118 gene CNVs between two Erianthus species and 821 gene CNVs among 10 Saccharum species. The largest number of gene CNVs (172) were identified between S. spontaneum accession (IND81-14) and S. hybrid accession (R570) including 117 up-CNVs and 55 down-CNVs in S. spontaneum accession (IND81-14), followed by 137 gene CNVs between S. officinarum accession (NG96-024) and S. spontaneum accession (IND81-14) including 56 up-CNVs and 81 down-CNVs in S. officianrum accession (NG96-024). The smallest number of CNVs (22) was identified between S. officinarum accession (P-MAG-84) and S. hybrid accession (Q050) including 16 up-CNVs and 6 down-CNVs in S. hybrid accession (Q050). Compared to E. procerum accession , 41 up-CNVs and 77 down-CNVs were identified in E. rufipilu accession (US57-060) (Table 2). PAVs represent the extreme form of down-CNV where copy number became zero in case of absence. A total of 1,257 gene PAVs were identified including 51 gene PAVs between two Erianthus species and 1,215 gene PAVs among 10 Saccharum species. The largest number of PAVs (222) was identified between S. hybrid accession (R570) and S. spontaneum accession (IND81-14) including 207 gene deletions in S. hybrid accession (R570) and 15 gene deletions in S. spontaneum accession (IND81-14), followed by 137 PAVs between S. hybrid accession (R570) and S. barberi accession (Pathri) including 130 deletions in S. hybrid accession (R570) and 7 gene deletions in S. barberi accession (Pathri). The smallest number of 14 PAVs was observed between unknown accession (US61-037) and S. officinarum accession (P-MAG-84) including 6 presence and 8 absence in unknown accession US61-037 (Table 2).Read depth was used to evaluate gene CNV among the 10 Figure 1). The largest number of SNPs was distributed on sorghums chromosome 1 , followed by chromosome 3 . The smallest number of SNPs was found on chromosome 5  followed by chromosome 8 . To demonstrate the distribution of SNPs relative to the coding regions and their potential impacts on the sequence function, the locations of SNPs called by GATK were projected onto sorghum gene models. In total, 987,237 out of 1,100,040 SNPs called from GATK, might have effects on gene functions due to various locations on the gene models. Out of 811,201 SNPs, 322,533 (29.3%) were located in the exon region and 488,668 (44.4%) were located in the intron region of the gene models. SNP density was lower in exon regions (1 SNP/82 bp) as compared to that in the intron regions (1 SNP/55 bp). The SNPs located on the exon regions included 170,544 non-synonymous and 156,816 synonymous SNPs. SNP sites in the exon regions included 3,519 SNPs introducing premature stop codons, 92 of the SNP sites causing start codon mutation, and 1,558 splice sites. Among the remaining SNPs, 112,803 (10.3%) were located on intergenic regions, and 147,958 (13.4%) were located in the UTRs . In total, 14,035 sorghum gene models contained SNPs showing an average of 71.6 SNPs per gene model ranging from 1 to 1,157 SNPs  per gene model, which resulted in an average SNP density of 1 SNP/15 bp in the genic region . In consistence with SNP distribution, InDels density on chromosome 1 (24 kb/InDel) and 3 (25 kb/InDel) was four times higher than that on the chromosome 5 (97 kb/InDel) indicating the genome of certain chromosomes were more active in evolving than others. Furthermore, the abundance of different InDel length was investigated. As InDel size increased, the total number of InDels reduced  might have effects on gene functions. Approximately 6%  of those InDels were located in exon regions, 56.4%  in intron regions, 20.7%  in UTR region, and the rest in exon–intron junctions  annotation in sorghum genome and 521 genes did not have any GO annotation. Gene family enrichment analysis indicated that genes with CNVs were significantly (P < 0.001) enriched in 14 GO terms related to photosynthesis and nucleotide binding , terpene synthase (Sobic.007G133900), and PR5-like receptor kinase (Sobic.003G097200), had a significantly low copy number in the two S. spontaneum accessions but much high copy number in other Saccharum spp. , E. procerum accession , unknown accession (US61-037), S. hybrid accessions (R570), S. robustum accession (NG57-054), S. officinarum accession (P-MAG-84) and S. officinarum accession (NG96-024), but present in S. sinense accession (TekchaOk), S. spontaneum accessions (SES196), S. spontaneum accessions (IND81-14) and S. hybrid accessions (Q050), which may be associated with different capabilities to handle biotic stress in wild and cultivated sugarcanes.The functional effects of structural variations including CNVs and PAVs were also annotated according to sorghum gene models. Out of 34,213 gene models, 919 had CNVs among the 10 E. rufipilus accession (US57-060) to 0.96% in unknown spp. (US61-037). Among Saccharum spp., S. barberi (0.52%), S. sinense (0.6%) and hybrid R570 have relatively higher heterozygosity rate in target region than the others  to 390,676  .Genotype calling based on SNPs in polyploids is critical in determining the allele dosage and also is very challenging due to high ploidy level, limited number of nucleotides , and sometimes insufficient sequence depth or low quality. Since the ploidy levels included 6, 8, and 12 in the 12 accessions, the genotypes were called at different ploidy levels using bi-allelic SNPs for each of the 12 accessions  to 1,493 in unknown accession (US57-060). In general, the number of haplotype blocks and the SNP density of single candidate gene increased with increasing number of uniquely mapped reads . The unknown accession (US61-037) was closer with Saccharum species than with the two Erianthus species, most likely a hybrid clone derived from a cross between the Saccharum and Erianthus. To estimate divergence among the 12 accessions in Saccharum complex, the substitution rates of synonymous (Ks) for 25 homologs  compared to Ks value between two different species . Based on the common synonymous substitution rate for grass lineage  (with S. spontaneum) to 6.5 MYA (with Erianthus species). The divergence times between Erianthus and all Saccharum species were estimated as 4.6–5 MYA. Within the Saccharum species, the divergence time between each different species was estimated to be 0.7 (between S. Sinense and S. Barberi) to 1.2 MYA .Phylogenetic analysis of the 12 accessions revealed that the two  lineage , the divSaccharum complex and enriched widely distributed target genomic regions of 12 diverse accessions representing a big scope of species in the Saccharum complex for sequencing. Sequencing by methylation filtration (MF) approach can be used to enrich the genic region sequences and it has been successfully utilized to assemble gene-enriched regions of a sugarcane cultivar  from all reads was used as reference, the alignment rate was relatively low as well (Table 1), due to the removal of singletons or contigs less than 200 bp in length. When assembly with a lower length cut-off (60 bp) was used, the alignment reads increased to 98%. However, to facilitate the downstream variants distribution and annotation analysis, the sorghum genome was selected as the reference since the alignment rate (83%) was highly acceptable. The high level of nucleotide similarity (97%) between Saccharum and sorghum coding region demonstrated the premise that the sorghum genome can serve as a good reference.Bioinformatics analysis on NGS data is critical for reliable genomic variants calling. We established a tailored pipeline for SNP calling for the polyploid otations . The sugotations  thus cauBrassica napus and peanut  the sequence identity of 95% between the sorghum genome and sugarcane sequences in the genic region  Saccharum complex.The choice of sequence variant caller might lead to variability in the variants called. SAMtools and GATK are widely used callers, and often used in combination for variant calling . The comErianthus species and a S. spontanum species however have had similar percentage of SDM as other accessions with higher ploidy levels . We further identified 524 common SDMs between our dataset and another SNP dataset called for a mapping population with 173 individuals (unpublished data). Sixty of the 524 SNPs were confirmed as SDMs in the bi-parental F1 population. The rest of the 524 SDMs were mostly from Erianthus species or other different genetic background. The large number of SNPs identified in this study included SDM, which is a magnificent genetic resource to the sugarcane community. In addition, we have identified several other structural variances such as InDels, CNVs, and PAVs providing the first systematic study across the Saccharum complex. Identification of InDels proved to be more challenging than SNPs  to 29% (TekchaOk) SNP loci among ilibrium . The outilibrium .Haplotype phasing has been a powerful approach for gene mapping. Many methods have been developed for haplotype phasing and assembly , althougSaccharum complex has been an interesting topic for decades. It was reported that the divergence between sorghum and Saccharum spp was 5, 7.7, or 8–9 MYA in different studies based on different small sets of sequences from selected gene or region (Saccharum species (Saccharum complex was estimated based on genome-wide targeted genes. The Saccharum species and sorghum was predicted to have diverged 5.7-6.1 MYA, quite similar to previous reports (Saccharum and Erianthus was first estimated as approximately 5 MYA in our study. These relative divergence times further support the phylogenetic relationship among the 12 accessions.The divergence and speciation within the r region . The Ks  species  are simi reports . The divSaccharum complex. A pipeline for variants calling within polyploid genome was established and demonstrated to be effective. A large number of genetic variations including SNPs, InDels, gene CNVs and gene PAVs were identified in Saccharum species and Erianthus species indicating the existence of a high level of genetic variations within the Saccharum complex. The hybridization-based target enrichment sequencing approach proved to be an efficient way to discover natural allelic variation in highly polyploid and heterozygous species, which would be costly with whole genome shotgun sequencing. The results can be applied to identify different gene alleles and allele variants recombination that is responsible for adaptation to different environmental conditions and other important traits of interests.In this study, a hybridization-based target enrichment sequencing approach was used to investigate the allelic variation of 12 accessions representing variety of different species in JW conceived and designed the experiments. JS, MR, LN, and JT performed the experiments. JS, XY performed data analysis. JCC, JZ provided materials and reference. JS, XY, and JW drafted the manuscript. JW, MR, LN, JT, JZ revised the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genome Wide Association Studies (GWAS) have been recently used to dissect complex quantitative traits and identify candidate genes affecting phenotype variation of polygenic traits. In order to map loci controlling variation in tomato marketable and nutritional fruit traits, we used a collection of 96 cultivated genotypes, including Italian, Latin American, and other worldwide-spread landraces and varieties. Phenotyping was carried out by measuring ten quality traits and metabolites in red ripe fruits. In parallel, genotyping was carried out by using the Illumina Infinium SolCAP array, which allows data to be collected from 7,720 single nucleotide polymorphism (SNP) markers.trans-lycopene, six with titratable acidity, and only one with pH and fresh weight. Co-localization of a group of associated loci with candidate genes/QTLs previously reported in other studies validated the approach. Moreover, 19 putative genes in linkage disequilibrium with markers were found. These genes might be involved in the biosynthetic pathways of the traits analyzed or might be implied in their transcriptional regulation. Finally, favourable allelic combinations between associated loci were identified that could be pyramided to obtain new improved genotypes.The Mixed Linear Model used to detect associations between markers and traits allowed population structure and relatedness to be evidenced within our collection, which have been taken into consideration for association analysis. GWAS identified 20 SNPs that were significantly associated with seven out of ten traits considered. In particular, our analysis revealed two markers associated with phenolic compounds, three with ascorbic acid, β-carotene and Our results led to the identification of promising candidate loci controlling fruit quality that, in the future, might be transferred into tomato genotypes by Marker Assisted Selection or genetic engineering, and highlighted that intraspecific variability might be still exploited for enhancing tomato fruit quality.The online version of this article (doi:10.1186/s12870-014-0337-9) contains supplementary material, which is available to authorized users. The genetic architecture of nutritional and quality traits in tomato has been extensively investigated due to the economic importance of this species worldwide. However, the genetic dissection of such traits is a challenging task due to their quantitative inheritance. To assist in this effort, an increasing number of genomic and genetic resources are today exploitable, including genome and transcriptome sequences, dense SNP maps, germplasm collections and public databases of genomic information -6. The aet al. [et al. [et al. [et al. [Many association studies have been published to date for studying morpho-physical and fruit quality traits in tomato. Mazzucato et al.  studied  [et al.  and Xu e [et al.  investig [et al.  studied ov) loci [S. lycopersicum [Association mapping requires high-density oligonucleotide arrays to efficiently identify SNPs distributed across the genome at a density that accurately reflects genome-wide LD structure and haplotype diversity. For tomato, a high-density single nucleotide polymorphism (SNP) array was recently built, which resulted suitable for genome-wide association analysis. The SolCAP array, with 7,720 SNPs based on polymorphic transcriptome sequences from six tomato accessions , to map ov) loci , to revepersicum , to invepersicum  and to ipersicum ,15,20,21cis-lycopene (c-LYC), trans-lycopene (t-LYC) and phenolics (PHE), whereas the latter consisted of dry matter (DMW) and fresh fruit weight (FW), pH, soluble solids content (SSC) and titratable acidity (TA). Detailed information on phenotyping performed for each trait and genotype is reported in Additional file The tomato collection was phenotyped for five nutritional and five fruit quality traits. The former group included metabolites with antioxidant activity, such as ascorbic acid (AsA), β-carotene (β-C) cis-lycopene  among traits  and a negative value between pH and TA (r = −0.70). In addition, AsA, PHE and TA were positively correlated with SSC and negatively correlated with FW. PHE content was also negatively correlated with t-LYC and c-LYC , whereas it was positively correlated with AsA (r = 0.55).The Pearson correlation coefficients  a 0.2 baseline was used to predict the highest reliable decay, following also previous results reported in tomato [2 = 7.3%), c-Lyc (R2 = 10.5%), DMW (R2 = 10.8%), SSC (R2 = 11.5%), AsA (R2 = 17%) and PHE (R2 = 17.5%). A greater effect was observed for FW, since more than 40% of phenotypic variance was explained by the population structure. The relative kinship was also estimated and the matrix of genetic relatedness is presented as a heat map in Additional file Genotyping was performed using the Illumina array consisting of 7,720 bi-allelic SNPs. On average, there were 638 SNPs per chromosome with a minimum number for chromosome 12 (391 SNPs) and a maximum for chromosome 11 . Eighty-one SNPs with missing data >10% were removed from the dataset. Of the remaining 7,639 SNPs, 2,072  were monomorphic, 2,626 34%) were polymorphic with MAF < 5% and finally 2,941 (38.4%) were polymorphic with MAF >5%. On removing SNPs with MAF <5% the average number per chromosome decreased to 241. The minimum value was detected for chromosome 10 and the maximum for chromosome 11. The distribution of total SNPs and of SNPs with MAF > 5% across chromosomes is summarized in Additional file % were poc-LYC , and mapped 300 bp downstream to Solyc08g006170.1.1. The last marker was 1081 on chromosome 11, and it was the only confirmed using MAF >10%. Moreover, since the multiple regression analysis evidenced a great impact of the genetic structure only on FW, for this trait the association analysis was carried out also on the three separate Q sub-populations. Results confirmed that association of marker 1081 is maintained within the sub-populations , suggesting that the association of this marker could be considered adequately robust. TA was associated with marker 955 on chromosome 1, which explained about 25% of the phenotypic variation (R2). The other five markers explaining the remaining part of phenotypic variation were marker 2032 mapping on chromosome 2, markers 443 and 3999 co-localized on chromosome 3, and markers 1010 and 1210 on chromosome 4. Finally, only one significant SNP was associated with pH, and explained 16.8% of phenotypic variation. Genotypes exhibiting minor allele for all markers associated with TA and pH had significantly higher value than genotypes with the major allele.FW was associated with six markers when the MLM was applied using MAF > 5%: the first was 2992 on chromosome 2, which explained about 17% of the phenotypic variation. Three markers co-segregated on chromosome 8, and mapped in the same gene (Solyc08g006170.1.1). The fifth marker 2275 explained the largest phenotypic variation  and group 4 the lowest . For β-C, 46 genotypes in group 1 and six in group 2 had allele combinations associated with a low content and 30 genotypes with a high content. t-LYC showed three allele combinations. Four genotypes with yellow fruits belong to group 1 associated with the lowest lycopene content , whereas 67 genotypes showed an allele combination associated with high lycopene content . For PHE, four groups were observed. The largest was group 1, including 49 genotypes and showing the minimum amount of phenolics , while the group associated to the maximum amount  included eight genotypes. Eleven allele combinations were identified for titratable acidity and the one associated with the highest value (0.745 g citric ac. 100 mL−1 of juice) was detected in two genotypes, whereas that associated with the lowest value (0.418 g citric ac. 100 mL−1 of juice) was detected for a group of 48 genotypes. Intermediate values were detected for the other nine groups.Finally, we evaluated the effect of different allele combinations at t Figure . For eacResults of association mapping studies depend on different factors, including type and size of mapping population, trait investigated, number of environments and years used for phenotyping, and type and genome coverage of molecular markers. The present study took into account a collection of cultivated tomato genotypes, including mainly Italian landraces but also Latin American and other worldwide-spread landraces and varieties. Genotypes were selected for the high variability of fruit morphological traits, such as size, shape, skin and flesh colour (data not shown), whereas little or no information was available regarding their nutritional and quality traits. Population structure and familial relationships, likely due to local adaptation, selection and breeding history, were found in the collection. Large populations are desirable for association mapping studies in order to obtain a high power to detect genetic effects of moderate size ,23; howeUsing the MLM and the MAF threshold >5%, 24 SNPs associated with seven out of ten traits were identified, even though the GLM detected a higher number (170 SNPs) of markers associated with nine traits. Since previous works highlighted the greater efficiency of the K + Q model in correcting spurious associations in tomato populations  and othet-LYC and PHE were searched for, since these are bioactive compounds exhibiting beneficial effects on human health [Fas-associated factor 1-like and Genomic DNA chromosome 5 P1, respectively. The other gene (Solyc05g052410.1.1) was located on chromosome 5 and annotated as Ethylene-responsive transcription factor 1 (ERF1). The Fas-associated factor 1-like protein is involved in an apoplastic mechanism and no direct evidence was reported to correlate its function with AsA accumulation. Since no specific functions were also assigned to Solyc03g112670.2.1, it was thought that the polymorphisms identified in this region of chromosome 3 could be in LD with other candidate genes. In order to verify this hypothesis, a scan was performed of the surrounding genomic area in LD with markers 2383 and 7588. A cluster of pectinesterases (120 kbp from marker 7588), one pectate lyase (240 kbp from marker 7588) and one polygalacturonase (350 kbp from marker 7588) were detected in LD block 23 on chromosome 3. These findings suggest that the alternative D-galacturonic biosynthetic pathway could contributes to regulate AsA variation in the tomato population under study, as previously reported in tomato [ERF1 gene, Di Matteo and colleagues [S. pennellii introgression line a different expression of genes associated with ethylene biosynthesis might trigger pectin degradation resulting in AsA accumulation. Taken together, these results suggest a possible regulation of genes associated with markers 2383 and 7588 (related to pectin degradation) via Ethylene Responsive Factor 1 associated with marker 1241.Concerning antioxidants traits, markers associated with AsA, β-C, n health . In partn health  using a n tomato  and othen tomato ,32. In alleagues  showed tCis and trans isomers of lycopene derive from a cascade of enzymatic reactions taking place in plastids [cis-configured. A pro-lycopene isomerase (CrtISO) then produces all-trans-lycopenes from tetra-cis-lycopene. Subsequent reactions convert trans-lycopene into β-carotene by the action of a lycopene β-cyclase (β-Lcy). Although no associations were detected for c-LYC, three significant associations with t-LYC were identified. Markers 3525 and 3526, co-localized on chromosome 3, matched a putative metallocarboxypeptidase inhibitor (Solyc03g031480.2.1) and tyrosyl-DNA phosphodiesterase (Solyc03g031820.2.1) whereas marker 3104 on chromosome 10 did not match annotated genes. Interestingly, even if they are not directly linked to the trans-lycopene content, analysis of the genomic area highlighted the presence of a phytoene synthase 1 (Solyc03g031860.2.1) close to markers 3525 and 3526  and a lycopene β-cyclase 2 (Solyc10g079480.1.1) at 9 kbp from marker 3104. This showed that the association mapping approach used was able to validate two candidate genes already known to be involved in the carotenoid pathway. In fact, the identified phytoene synthase 1, which catalyzes a rate-limiting step in the carotenoids pathway, corresponds to the locus “r” [r” and all have yellow flesh fruit as a consequence of a low trans-lycopene content. In addition, on chromosome 10, besides the lycopene β-cyclase, also a carotene isomerase , which converts pro-lycopene to trans-lycopene [24-C-sterol-methyltransferase was found that is involved in steroid biosynthesis. Moreover, a cluster of three carotenoid cleavage dioxygenase 1 (CCD1) genes was also identified at 300 kbp from marker 2022. CCD1 genes cleave the carotenoid substrate at different double bonds to produce terpenoid flavour volatiles (apocarotenoids) that contribute to the overall aroma and taste of tomato fruit [plastids . Intermeocus “r”  that carlycopene , and Solto fruit . It is het al. [et al. [Finally, two polymorphic markers significantly associated with PHE were identified: marker 354 (Solyc08g082350.2.1), which encodes for a protein of unknown function, and marker 4365 (Solyc11g010170.1.1), encoding for a LanC-like protein2, which is involved in the modification and transport of peptides in bacteria . Howeveret al.  and Di M [et al. , suggestMajor fruit quality traits of interest for both the fresh market and processing tomatoes include fruit size, shape, total solids, colour, firmness, ripening, pH, titratable acidity, soluble solids content and dry matter. In this study, a large number of associations with FW and TA were found, only one association with pH and no association with SSC and DWM.fw1.1, fw1.2, fw2.1, fw2.2, fw3.2 and fw11.3) localized on chromosomes 1, 2, 3 and 11 [fw11.3).FW is a quantitatively inherited trait controlled by up to 28 QTLs, even though QTL analyses in previous studies revealed that most (67%) phenotypic variation in fruit size could be attributed to six major loci (3 and 11 -47. The fw11.3 locus (starting 20 kbp downstream of marker 1081) and the fas-YABBY locus (24 kbp upstream of the marker), previously hypothesized to determine fruit size [fw11.3 in FW variation but also restricted the region of putative candidate genes with respect to the previously identified region of 149 kbp, which included 22 predicted genes [fw11.3 of around 70 kbp , including eight predicted genes. This region must be enriched in SNPs to locate precisely one or more responsible polymorphisms associated to the trait and further investigation should be carried out to fine map the putative gene responsible for the trait variation.Indeed, on chromosome 11 marker 1081 matched Solyc11g071840.1.1, annotated as a calmodulin binding protein, and was located in the LD block 40 that spans an interval of 167 kbp. This region contains both a portion of the uit size . Therefoed genes . Indeed,ATPase and a Sucrose_H+ symporter co-localized with this marker; they are proton pumps responsible for acidifying cellular compartment and might correlate with this trait. Moreover, previous studies identified a pH QTL in this region [Among important quality traits in tomato, TA influences shelf-life of processed tomato and low pH values reduce the risk of pathogen growth in tomato products . One locs region ,50 suppoaquaporin (Solyc03g093230.2.1) was identified as a potential candidate. The involvement of the aquaporin gene family in the modulation of fruit acidity was hypothesized in tomato antisense experiments, indicating a strong effect of this protein on the sugar/organic acid ratio in fruit [purple acid phosphatase (Solyc04g005450), a metalloenzyme that hydrolyses phosphate esters and anhydrides under acidic conditions [The most significant association with TA was with marker 955, which explains around 25% of the variation. The marker matched the predicted gene Solyc01g107550.2.1, which encodes for a methylthioribose kinase, an enzyme involved in recycling of methionine through the methylthioadenosine (MTA) cycle. The marker did not fall in any significant LD block on chromosome 1 and for this reason a narrow area around the marker was investigated to look for candidate genes. Four annotated genes not related to the trait under study and various unknown genes were identified, leaving the mechanism responsible for TA variation of this locus still obscure. Other significant associations were found on chromosome 3 for markers 443 and 3999 matching Solyc03g083440.2.1 (Glutamate synthase) and Solyc03g093310.2.1 (F-box family protein), respectively, in a region where previous studies detected QTLs for TA . Markersin fruit . An addinditions . No relet-LYC, β-C content and FW. Consequently, it can be argued that all the SNPs identified might be exploited in the future as markers targeting the specific desirable phenotype for assisted selection. This is noteworthy if considering the different allelic combinations we identified for some traits, whose pyramiding would further enhance tomato fruit quality of the improved genotypes. A further validation in independent accessions panels or bi-parental populations would be in any case desirable.The association mapping approach undertaken allowed detection of 20 SNPs associated with seven traits that are essential for breeding work aimed at improving nutritional and quality traits in both fresh market and processing tomatoes. The findings suggest that the use of a high marker density array and a highly efficient statistical model (K + Q) were suitable for detecting associations with the traits considered. Indeed, the co-localization of a group of associated loci with formerly identified candidate genes/QTLs validated the approach chosen, as evidenced for markers related to In addition, a number of new putative candidate genes were detected in the genomic area in linkage disequilibrium with markers that were not functionally congruent with the trait to which they were associated with. These promising genes might be involved in the pathways controlling the biosynthesis and accumulation of the metabolite/trait analysed, as in the case of a group of genes related to cell wall metabolism that might be hypothesized to contribute to a higher AsA content in tomato fruit or a group of vacuolar transporters that might regulate the accumulation of phenolic compounds. In order to detect the functional SNPs determining the different phenotypes, the identified candidate genes are being investigated by a target resequencing approach in a group of varieties belonging to the same collection. Finally the role of these new candidate genes will be validated in the future by functional genomics approaches.per replicate), in field plots at the Agricultural Experiment Station of CRA-ORT in Battipaglia . Field trials were conducted in accordance with the Italian legislation. All genotypes were subject to phenotypic and genotypic analyses. Out of 96 genotypes, six were excluded from phenotyping, since they produced few fruits or segregated for some morphological traits. Five additional genotypes were filtered out since were considered unreliable due to the large number of low quality SNP scores. Therefore, the number of samples finally used for association mapping was reduced to 85.Plant material consisted of 96 tomato genotypes, including Italian and Latin American landraces, and vintage and modern varieties collected from seed banks in Italy and worldwide. In detail, accessions were derived from Italian breeders’ collections, Regional and National Italian Institutions , and International Institutions . All genotypes were grown during the seasons 2011 and 2012, according to a randomized complete block design with three replicates : ascorbic acid (AsA), β-carotene, o et al.  with minet al. [−1 and at 0.005 AUFS  by using a reversed phase, analytical polymeric C30 column . Results were expressed as μg g−1 of FW. Solvents used for sample preparation and extractions were of analytical grade, while those for HPLC analysis  were of HPLC grade; all were obtained from Merck . Trans-carotenoid standards (lycopene and β-carotene) used in HPLC analyses were purchased from Sigma Chemical Co .Extraction and analysis of carotenoids were carried out on 5 g of fruit pericarp, according to Ishida et al. . Reverseg for 5 min; afterwards, 62.5 μL of the supernatant, 62.5 μL of Folin–Ciocalteu’s reagent (Sigma) and 250 μl of deionized water were mixed and incubated for 6 min; 625 μL of 7.5% sodium carbonate and 500 μL of deionized water were added to the samples and incubated for 90 min at room temperature in the dark. Absorbance was measured at 760 nm. The concentration of total phenolics was expressed in terms of mg of gallic acid equivalents (GAE) per 100 g−1 of FW.Total phenolics content was assayed using a modified procedure of the Folin–Ciocalteu test . In brie10 transformed before performing the association mapping analysis. In order to test the consistency of phenotypic characterization over years, heritability values were calculated as reported in [For each year, the normal distribution of data was verified using a Shapiro-Wilk test. Six  out of 10 traits were not normally distributed and were logorted in . Moreoveorted in .et al. [et al. [http://solgenomics.net/).Samples were genotyped using a tomato array built in the framework of the Solanaceae Coordinated Agricultural Project (SolCAP) from NIFA/USDA and based on the ILLUMINA Infinium Technology. The SolCAP tomato panel includes 7,720 markers constructed on eSNPs deriving from six tomato genome sequences. Details of the SolCAP SNP discovery pipeline are described in Hamilton  [et al. . Informahttp://www.genomix4life.com) using 250 ng of DNA per accession following the manufacturer’s protocol for the Illumina Infinium assay. Intensity data and SNP calls were performed by GenomeStudio version 1.7.4 . SNPs were called using the Infinium chip cluster file based on the SolCAP tomato collection and a manual classification was implemented when the default clustering was not clearly defined. In addition, quality and reproducibility were tested using duplicated DNA samples.For each accession, genomic DNA was extracted from fresh, young leaf tissue using the DNeasy Plant Mini kit  according to the manufacturer’s recommendations. DNA quality and concentration were evaluated on agarose gel and spectrophotometrically using the Nanodrop instrument . Genotyping was conducted at the Genomix4Life S.r.l (http://solgenomics.net).The set of SNPs was filtered in order to perform molecular analyses. Markers with more than 10% missing genotypes and with minor allele frequency (MAF) <5% were removed. The physical position of all SNPs on the 12 tomato chromosomes was obtained from the SGN database  using R software and testing smoothing parameters fixed to 0.2. In order to determine the distance of LD decay, for each chromosome different r2 baseline values  were tested [et al. [P value (Bonferroni correction) and the threshold for the association was set to <5.88×10−4 (0.05/85). In addition, in order to estimate LD blocks according to the definition of Gabriel et al. [Linkage disequilibrium (LD) values were calculated on SNP set with MAF greater than 5%. Pairwise EL v.4.0 . Values e tested . A Bayese tested . Since p [et al.  transfor [et al.  and two l et al. , the HAPl et al.  was usedhttp://dx.doi.org/10.6070/H4TT4NXN.The list of genotypes including details on source and distribution, and the SNP genotype dataset are deposited on the LabArchives repository hosted


IL2RA) and 15 (IL15RA). Both interleukins were previously found to lead to protective T helper 1 cell (Th1) response against Leishmania spp. in humans and mice. The associated marker on chromosome 1 was located between two predicted transcription factor binding sites regulating the expression of the transducin-like enhancer of split 1 gene (TLE1), an important player in Notch signaling. This pathway is critical for macrophage activity and CD4+ T cell differentiation into Th1 and T helper 2. Together, these findings suggest that the human and mouse model for protective response against Leishmania spp., which involves Th1 and macrophage modulation by interleukins 2, 15, gamma interferon and Notch signaling, may also hold for the canine model.We conducted a genome-wide scan for visceral leishmaniasis in mixed-breed dogs from a highly endemic area in Brazil using 149,648 single nucleotide polymorphism (SNP) markers genotyped in 20 cases and 28 controls. Using a mixed model approach, we found two candidate loci on canine autosomes 1 and 2. The positional association on chromosome 2 mapped to a predicted DNAse sensitive site in CD14+ monocytes that serve as a cis-regulatory element for the expression of interleukin alpha receptors 2 ( Canis lupus familiaris) is the primary urban reservoir of visceral leishmaniasis (VL), a neglected tropical disease that affects about 500,000 people every year . A balanced subset of the identified cases and controls was randomly chosen and genotyped for 173,661 SNP markers using the Illumina CanineHD BeadChip, according to the manufacturer's protocol.http://goldenhelix.com/SNP_Variation/) software to test for phenotype-genotype associations via a linear mixed model controlled for random polygenic effects [−5, and sample call rate greater than 90%. Markers were prioritized for investigation based on a significance threshold of p < 1 x 10−5. Log-odds ratios for these markers were estimated based on the slopes from a generalized linear model assuming a binomial distribution and a logit link function using glm in R v.3.1.0 (available at: http://www.r-project.org/).We used the SNP & Variation Suite 8  and the Ensemble Genes 61 reference set. Prediction of regulatory elements was performed by lifting over CanFam2.0 coordinates to the Human GRCh37/hg19 assembly by orthology, and annotating ENCODE tracks in the UCSC genome browser (available at: http://genome.ucsc.edu/).Genomic regions harboring significant signals were visually inspected for gene annotation using the GoldenHelix Genome Browse software (available at: S1 File(ZIP)Click here for additional data file.