Prunus persica (L.) Batsch] and related Prunus species, only a limited number of genetic markers, including simple sequence repeats (SSRs), have been available to date. To address this need, an international consortium  has pursued a coordinated effort to perform genome-scale SNP discovery in peach using next generation sequencing platforms to develop and characterize a high-throughput Illumina Infinium® SNP genotyping array platform. We performed whole genome re-sequencing of 56 peach breeding accessions using the Illumina and Roche/454 sequencing technologies. Polymorphism detection algorithms identified a total of 1,022,354 SNPs. Validation with the Illumina GoldenGate® assay was performed on a subset of the predicted SNPs, verifying ∼75% of genic (exonic and intronic) SNPs, whereas only about a third of intergenic SNPs were verified. Conservative filtering was applied to arrive at a set of 8,144 SNPs that were included on the IPSC peach SNP array v1, distributed over all eight peach chromosomes with an average spacing of 26.7 kb between SNPs. Use of this platform to screen a total of 709 accessions of peach in two separate evaluation panels identified a total of 6,869 (84.3%) polymorphic SNPs.Although a large number of single nucleotide polymorphism (SNP) markers covering the entire genome are needed to enable molecular breeding efforts such as genome wide association studies, fine mapping, genomic selection and marker-assisted selection in peach [The almost 7,000 SNPs verified as polymorphic through extensive empirical evaluation represent an excellent source of markers for future studies in genetic relatedness, genetic mapping, and dissecting the genetic architecture of complex agricultural traits. The IPSC peach SNP array v1 is commercially available and we expect that it will be used worldwide for genetic studies in peach and related stone fruit and nut species. Dissection of the genetic components underlying complex agricultural traits in plants has so far used mainly experimental bi-parental crosses and a limited number of genetic markers. The growing number of draft genome sequences of many species coupled with Next Generation Sequencing (NGS) technologies has quickly changed the paradigm of plant genome analysis. Alignment of the short NGS reads obtained from diverse and unrelated varieties to a reference genome allows the identification of large numbers of Single Nucleotide Polymorphisms (SNPs) and small Deletion/Insertion Polymorphisms (DIPs) and the contextual estimation of their minor allele frequencies (MAF).Prunus. High-density SNP genotyping arrays have been designed for several domestic animals including cattle http://www.illumina.com/agriculture).Simultaneous genotyping of hundreds of thousands of SNPs in a single assay has become feasible due to innovative combinations of assay and array platform multiplexing Prunus persica L. (Batsch)] and related Prunus species, QTL studies have been conducted using experimental bi-parental crosses and a limited number of genetic markers In peach [The availability of the peach reference genome sequence recently released by the International Peach Genome Initiative Alu1 and size-selected for 400–500 bp fragments. At IGA the CASAVA 1.7.0 version of the Illumina pipeline was used and at the OSU CGRB the CASAVA 1.6.0 version of the Illumina pipeline was used. Raw sequences were retrieved and kept separate for each accession and then aligned to the Peach v1.0 reference genome Soap2A SNP detection panel of 56 peach breeding-relevant accessions assembled with the goal of achieving an efficient coverage of the genetic background of cultivated peach  was usedSoapSNP (http://soap.genomics.org.cn/soapsnp.html) essentially as recommended by Li SNP detection and filtering followed a multi-step procedure . SNPs frWe compared the SNP calls in three of four pairs of accessions independently sequenced in different labs . SNP calFreestone-Melting flesh [F-M] locus) Y locus on LG1 , the Cs locus on LG3 (two SNPs 38 kb apart), and the G locus on LG5 (two SNPs 272 kb apart) according to the genomic positioning reported by Dirlewanger A set of 96 SNPs was chosen from the Stage 1 SNPs from pools 6–11 to validate the efficiency of SNP detection and adjust subsequent filtering parameters . The iniPrunus accessions . DNA was quantitated with the Quant-iT™ PicoGreen® Assay , using the Victor multiplate reader . Concentrations were adjusted to a minimum of 50 ng/µl, in 5 µl aliquots and were submitted to the Research Technology Support Facility at Michigan State University , where the GoldenGate® assay was performed following the manufacturer's protocol (Illumina Inc.). After amplification, 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 GenomeStudio Data Analysis software (Illumina Inc.).To test the variables of genomic location, genic location, and MAF for their effects on genotyping efficiency, a validation panel of 160 Stage 1 SNPs from pools 1–5 and pools 6–12 were independently converted to Illumina Assay Design Tool (ADT) format with custom scripts and scored by Illumina. “Infinium® I"-type SNPs (A/T or C/G transversions) were removed, as well as SNPs with any failure code or ADT score <0.9. Then the two datasets were merged, and duplicates were removed with custom scripts. The remaining SNPs were filtered by removing those: (1) where the last 25 bp of the 50 bp probe were duplicated; (2) supported by less than five accessions; (3) not in predicted coding regions; and (4) not located on one of the first eight pseudomolecules representing the eight peach chromosomes. This process yielded “Stage 2 SNPs" .Pre-validated SNPs were obtained from several sources: (1) polymorphic SNPs from the GoldenGate® validation activity; (2) peach RosCOS SNPs For the final choice of 9,000 SNPs for the IPSC peach 9 K SNP array v1, filtered pre-validated SNPs were automatically included. For the remainder, SNPs were chosen to provide an even spacing across the genome, corresponding to one SNP selected for every 4.74 Stage 2 SNPs .Prunus species or their hybrids with peach. The US panel comprised 479 accessions that included pedigree-linked cultivars, breeding lines, and seedlings (Prunus species or were known interspecific hybrids: 5% had peach-related (P. davidiana and P. mira) ancestry, 10% had almond (P. dulcis), and 3% had almond-related (P. argentea and P. scoparia) ancestry. Genomic DNA extraction and quantitation were conducted as described above for the SNP validation panel for the U.S. accessions. For the EU panel, genomic DNA was extracted using the DNeasy Plant Mini Kit (Qiagen) and quantitated using a Fluoroskan Ascent  microplate reader. The IPSC array, employing exclusively Illumina Infinium® II design probes and dual color channel assays , was used for genotyping, following the manufacturer's recommendations. SNP genotypes were scored with the Genotyping Module of the GenomeStudio Data Analysis software . A GenTrain score of >0.4 and a GenCall 10% of >0.2 were applied to remove most SNPs that did not cluster (homozygous), or had ambiguous clustering. SNPs that did not cluster for more than 50% of samples were also eliminated from further consideration. The threshold of allowed No Calls (failed genotyping) was ‘relaxed’ in anticipation of the presence of null alleles for some SNPs contributed by non-peach species.The IPSC peach 9 K SNP Infinium® II array v1 was evaluated using 709 accessions divided in two independent evaluation panels, one panel from the European Union (EU) and the other from the USA (US). The EU panel comprised 232 accessions, of which 229 were peach cultivars and three were wild related eedlings . OverallA total of 25.4 Gb of DNA sequence (111.7× coverage of the peach genome) was obtained from 279.7 million reads (Illumina and 454) generated for the 56 peach accessions multiplexed among 12 pools . ExcludiWhen the SNP calls were compared among the three pairs of independently sequenced accessions, the majority of the positions were not covered by at least one of the two datasets and corresponded to 297,078, 301,617 and 309,027 positions in ‘Elberta’, ‘Big Top’ and ‘O'Henry’, respectively. Thus, the SNP positions that could be compared among these accessions ranged from 3.75% and 4% of the SNP calls in ‘O'Henry’ and ‘Big Top’ (sequenced by Illumina and 454) to 7.48% in Illumina-resequenced ‘Elberta’. In these positions, most of the SNPs had the same genotype  while a negligible number of positions did not share any allele (0.05% of positions in ‘Big Top’ to 0.6% in ‘Elberta’ and ‘O'Henry’).F-M locus also provided a high proportion of low-MAF SNPs, but had a high failure and monomorphism rate as did the SNPs targeted to other trait loci  resulted in polymorphism (MAF 23%) with a joint genotype that unexpectedly placed it on LG2 (bin 2:13).Of the 32 validation SNPs that were polymorphic in the interspecific T×E bin-mapping set, 31 had joint genotypes corresponding to their expected genomic region. The presence of null alleles was evident for 14 of these polymorphic SNPs. One intergenic SNP targeted to the Of the 55 SNPs polymorphic in the entire validation panel, 52 (95%) were polymorphic in at least one of the five six-seedling breeding progenies. One SNP was polymorphic in all five progenies, 10 in four progenies, 17 in three progenies, 10 in two, and 14 in just one progeny. Proportions of the 55 polymorphic SNPs that were polymorphic within individual progenies were 58%, 40%, 53%, 44%, and 42% from Arkansas , California , California , South Carolina , and Texas (Texas A&M University-College Station Research farm), respectively. These proportions were lower bounds, given that the presence of segregating null alleles was not considered for these progenies.Stage 2 filtering reduced the one million available SNPs by 96%. Restricting the SNPs to Infinium® II types eliminated 18% of Stage 1 SNPs from consideration, the thresholds of ADT≥0.9 and supporting evidence from at least five accessions eliminated 50% of Stage 1 SNPs, and restriction to genic regions cut an additional 23%. Considering only exonic SNPs more than halved the remaining 86,360 to 41,800. Finally, 1006 SNPs located on peach genome scaffolds as yet unassigned to one of the eight chromosomes of this crop were discarded, resulting in 40,794 Stage 2 SNPs , Table 3A total of 649 pre-validated SNPs considered for inclusion in the final array were reduced to 387 by the filtering parameters used for these SNPs. Of the 55 polymorphic SNPs from the GoldenGate® validation assay, 45 were included. Of the 453 peach RosCOS SNPs considered, 225 were included in the final array. SNPs requested for inclusion from the international community were 108 from the DRUPOMICS project  and 33 from the University of Chile , of which 87 and 30 were included, respectively. Nineteen of the 387 pre-validated SNPs were located on unanchored scaffolds of the Peach v1.0 assembly, four of which were RosCOS SNPs. Inclusion of the 387 pre-validated SNPs left 8,613 positions available in the final array for SNPs distributed over the eight peach chromosomes.The IPSC peach 9 K SNP array v1 achieved an average spacing of 26.7 kb between SNPs . DistribOf the 9,000 candidate SNPs, 8,144 remained on the array after Illumina technical dropout (loss during array manufacturing). Of these, 8,125 were located on the first eight pseudomolecules of the peach genome. The evaluation of IPSC peach 9 K SNP array v1, performed in Europe and U.S., revealed no significant difference in the number of polymorphic, monomorphic and failed SNPs between peach-only samples and samples with interspecific backgrounds (data not shown). The only difference was observed for SNPs located in RosCOS, with only 2% of them being polymorphic in peach-only samples versus 6% in all samples, including those with interspecific ancestry. Moreover, independent evaluation of the IPSC peach 9 K SNP array v1 using EU and US accession panels revealed almost identical results for the numbers of polymorphic, monomorphic and failed SNPs. Genome scans performed with the IPSC peach 9 K SNP array v1 on the EU and US evaluation panels resulted in a SNP polymorphism rate of 70% for each panel and a failure rate of only 5% for each . Of the The IPSC peach 9 K SNP array v1 achieved a total of 6,869 polymorphic SNPs across the 709 accessions scanned in the two panels combined , represeThe large difference in the number of SNPs identified between pools 1–5 and pools 6–11 is not surprising given the much higher sequencing coverage of pools 1–5 (84.8×) than 6–11 (25.1×)  and the Prunus species as they can be searched for additional sequence variation in regions underlying traits of economic importance.The low overall percentage of SNPs with matching genotypes in the three independently sequenced pairs of accessions is caused by low sequence coverage targeted here. The sequences generated in this project that were not included in the IPSC peach 9 K SNP array v1 are a valuable resource for peach and other SNP validation was a critical step for empirically determining appropriate parameters for prioritizing detected SNPs, improving the SNP detection for pools 1–5 that was conducted after SNP detection for pools 6–11, and to choose from among approximately one million SNPs just 9,000 used in the final array design. The validity of the GoldenGate® genotyping results for extrapolation of parameter thresholds to all detected SNPs was supported by the correct genomic location assignment of polymorphic SNPs by bin-mapping. Our observation that conversion of NGS-detected SNPs to true SNPs was more reliable for polymorphisms located in predicted exons and introns was consistent with observations in apple The reason for differential performance of SNPs among some LGs is not clear. In the development of a genome-wide array, it is not advisable to avoid certain LGs or to saturate others just to increase the overall proportion of successful SNPs. These differences were not maintained in the final array  and henc1 populations included in the validation assay, together these four populations captured most (95%) of the polymorphisms. This observation highlights the importance of utilizing diverse target germplasm rather than a narrow subset of the target genepool, such as a single population, for SNP evaluation and assessment of array informativeness.Although only about half of the successful SNPs segregated in any of the four individual FThe SNPs identified within this internationally coordinated effort were subjected to stringent filtering criteria to maximize the efficacy of the IPSC peach 9 K SNP array v1. Given that most candidate SNPs were essentially anonymous, Infinium® I SNPs were avoided because these require two bead types and thus use two SNP slots of the assay, unlike the single bead type for Infinium® II SNPs. Infinium® II SNPs have also been reported to perform better (92% vs. 85%) Prunus species An important aspect we took into consideration in choosing the SNPs for the array was their genomic context. The large number of SNPs discovered in this study allowed us to select 9,000 exonic markers to be included in the array. The high level of transferability of transcriptomic markers across Prunus reference map Finally, another important criterion for a whole genome genotyping assay is to have a uniform distribution of the SNPs across the genome, as this greatly facilitates finding associations between markers and phenotypes. The SNPs on our array cover most of the peach genome with markers well distributed over all chromosomes. The average gap size across the genome achieved was 26.7 kb and increases to 31.5 kb when only polymorphic SNPs are considered. The average ratio of genetic to physical distance in peach is about 440 kb/cM, which was obtained by comparing the Peach v1.0 assembly with an updated version of the In summary, the total of 6,869 SNPs on the IPSC peach 9 K SNP array v1 verified as polymorphic through extensive empirical evaluation represent an excellent source of markers for studies in genetic relatedness, genetic mapping, and dissecting the genetic architecture of complex agricultural traits. The SNPs included in the array were successfully used for genotyping 709 accessions in two independent evaluation panels. The majority (86.9%) of these markers were polymorphic in both experiments  indicatiThe SNP array described here will foster genetic studies in the stone fruits and will help bridge the gap between genomics and breeding activities because breeding germplasm was the basis of detected SNPs and SNP choices of the final array. The IPSC peach 9 K SNP array v1 is commercially available and we expect that it will be used worldwide for genetic studies in peach and related stone fruit and nut species.Figure S1Characteristics of 96 SNPs used in a validation assay to test various parameters.(TIF)Click here for additional data file.Figure S2Performance of a 96-SNP subset according to genic and genomic location, using the GoldenGate® assay on 160 accessions of the validation panel.(TIF)Click here for additional data file.Table S1Accessions of the validation panel used for a GoldenGate® assay of 96 SNPs. (a) 124 accessions . (b) Six full sib progenies of 6 seedlings each. “-" = unknown parent.(DOCX)Click here for additional data file.Table S2Accessions in two independent evaluation panels used for evaluation of the OPSC peach 9 K SNP array v1. (a) 232 cultivars and selections from the European Union (EU panel). (b) 115 cultivars and selections from the USA  (c) 362 seedlings of breeding progenies from the USA (remainder of US panel). “-" = unknown parent.(DOCX)Click here for additional data file.Table S3Performance of the IPSC peach 9 K SNP array v1 on (a) EU (n = 232) and (b) US (n = 477) evaluation panels.(DOCX)Click here for additional data file.


Understanding of genetic diversity and linkage disequilibrium (LD) decay in diverse maize germplasm is fundamentally important for maize improvement. A total of 287 tropical and 160 temperate inbred lines were genotyped with 1943 single nucleotide polymorphism (SNP) markers of high quality and compared for genetic diversity and LD decay using the SNPs and their haplotypes developed from genic and intergenic regions. Intronic SNPs revealed a substantial higher variation than exonic SNPs. The big window size haplotypes (3-SNP slide-window covering 2160 kb on average) revealed much higher genetic diversity than the 10 kb-window and gene-window haplotypes. The polymorphic information content values revealed by the haplotypes (0.436–0.566) were generally much higher than individual SNPs (0.247–0.259). Cluster analysis classified the 447 maize lines into two major groups, corresponding to temperate and tropical types. The level of genetic diversity and subpopulation structure were associated with the germplasm origin and post-domestication selection. Compared to temperate lines, the tropical lines had a much higher level of genetic diversity with no significant subpopulation structure identified. Significant variation in LD decay distance (2–100 kb) was found across the genome, chromosomal regions and germplasm groups. The average of LD decay distance (10–100 kb) in the temperate germplasm was two to ten times larger than that in the tropical germplasm (5–10 kb). In conclusion, tropical maize not only host high genetic diversity that can be exploited for future plant breeding, but also show rapid LD decay that provides more opportunity for selection. Maize has adapted to different environments with its spread from the center of origin in Mexico to various parts of the world and its evolution into a cultivated and productive plant for food, feed, and industrial products. Depending on the latitude and the environment in which it is grown, maize can be mainly classified into two distinct types, tropical and temperate maize, plus some subtropical lines The genome-wide genetic diversity and population structure analysis has been largely based on random markers that are developed with limited information on their positions P-values With high throughput genotyping and dense SNP maps, the sliding window approach becomes appealing for analyzing haplotypes, with several groups explored this approach from statistical and applied aspects Genetic diversity has certain relationship with linkage disequilibrium (LD) decay, which in turn affects the diversity and LD-based association mapping. Factors affecting the rate of LD decline with distance include founder effect, mutation, population, selection, mating patterns, genetic drift and migration Selection in breeding programs has significant contribution to various levels of genetic diversity and population structure. On one hand, breeding for heterotic hybrids in temperate maize, mainly in industrial countries, has contributed to the increased genetic difference between heterotic groups, although within-group genetic diversity is relatively low In our previous study, a total of 447 inbred lines were selected from diverse maize breeding programs to represent tropical and temperate maize diversity With SNP markers and their haplotypes developed from different genic and intergenic regions and between genes, this study was designed to: (1) compare the genetic diversity between temperate and tropical maize collections and their relationship with geographic origin and breeding activities; (2) understand the population and subpopulation structure in all tested lines and their relationship with post-domestication selection; (3) understand distribution of LD across the genome and how its breakdown relates to chromosomes and subgroups of germplasm of geographic origins. The information generated in this study will help characterize the genetic diversity and LD in the two major types of maize germplasm that are important to maize genetics and breeding.in silico onto the maize genome with unique positions and high quality were selected and used for analysis of 447 maize inbreds. These informative SNPs were well distributed across 10 chromosomes, and the coverage ranged from 124 SNPs on chromosome 10 to 316 SNPs on chromosome 1. Based on the sequence annotation analysis performed on the maize B73 filtered gene set, 273 SNPs are from intergenic regions, while the remaining 1670 SNPs are from intragenic regions which involve 1180 genes. Among the 1670 genic SNPs, 512 were designed from intronic sequence, and 1158 from exonic sequence  >0.05 was investigated among the entire (447 lines), temperate and tropical sets. In the 447 inbred lines, LD was significant at the 0.01 level between 34.0% of the pairwise SNPs, in which 9.9% of the pairwise SNPs had the than 0.1 . In the R2 values exceeding 0.1 , while Chromosome 1, 2 and 5 had more SNP loci (> 200 loci each) (The extent of LD across 10 chromosomes had distinct differences. The percentage of pairwise SNPs in significant LD on chromosome 10 in the temperate set (46.9%) was higher than others, and 64.1% of the significant pairwise SNPs exhibited the ding 0.1 . The perci each) .2≥0.9 in tropical maize , LD decay distance for R2 values pooled over all 10 chromosomes for the entire, temperate and tropical maize germplasm sets were calculated and shown in R2 gradually declined with increased physical distance, similar with previous studies R2 to decrease to 0.1 in temperate set (10–100 kb) was much larger than in tropical set (5–10 kb), the former being 10 times larger than the latter. In conclusion, the percentage of pairwise SNPs in significant LD demonstrated a much lower extent of LD in tropical maize than in temperate maize, which was consistent with the result that LD broke down significantly slower in temperate maize than it did in tropical maize.The mean SNPs are usually biallelic, each providing less PIC, so that marker density should be increased. Thus, haplotype-based analysis becomes more attractive and important Arabidopsis genotypes revealed that SNPs occurred at a frequency of 1.4 times greater and indel polymorphisms at a frequency of 3 times greater in introns than in exons. Promoter and intron gene regions have significantly greater length polymorphism within species than exon regions Comparison of large segments of DNA sequence from two We used different types of SNPs and SNP haplotypes to compare genetic diversity between the tropical and temperate maize germplasm sets. Tropical germplasm showed much higher genetic diversity than temperate germplasm as revealed by each type of SNP markers and haplotypes. Haplotype-based diversity analysis not only confirmed previous studies using SSR markers and more recently using SNP markers In this study, 1715 SNPs with MAF>0.05 were used to determine the structure of LD in the maize genome with a large, diverse collection of 447 lines. A total of 34.0% and 34.1% of the SNP pairs across the genome exhibited significant LD in the entire and temperate germplasm sets, respectively. However, only 5.6% of SNP pairs showed significant LD in the tropical germplasm set. The average decline of LD distance was 10–100 kb in the temperate germplasm set, which was 2∼10 times larger than that in tropical germplasm set (5–10 kb). This significant difference can be explained by the fact that tropical maize has experienced much more intensive recombination and contain more rare alleles than temperate lines 2 or recombinant inbred populations. Our results provide a rough picture of the structure of LD in maize at the whole genome level and implication for GWAS. The average distance of LD decay revealed in this study (5–10 kb) with a genome size of 2.3 Gb suggests that 230,000 to 460,000 markers will be needed for whole genome scan. Considering that approximately 80% of the maize genome consists of repetitive sequence and also SNPs can be specifically developed from genic regions based on research targets, the actual number of SNPs required for GWAS can be considerably reduced.LD observed in a population is the result of interplay of many factors including linkage, population structure, relatedness, selection and genetic drift In a previous study, 770 maize inbred lines representing temperate and tropical maize germplasm were chosen from breeding programs and germplasm collections worldwide based on their phenotypes and origins for molecular characterization http://www.panzea.org/.Illumina BeadStation 500 G  was employed for SNP genotyping at the Cornell University Life Sciences Core Laboratories Center following the protocols http://www.maizesequence.org) 15 were considered. In 447 inbred lines, the two chips provided 1943 informative SNP markers of high quality with unique physical positions. Detailed information of these informative SNPs can be found in http://www.maizesequence.org), we categorized SNPs into those located in genic regions (intragenic) and those that fell between genic regions (intergenic) based on their physical positions. Moreover, intragenic SNPs were divided into exonic and intronic SNPs, which were compared with the intergenic SNP for genetic diversity in different maize germplasm sets.To determine the physical positions of the SNP markers used in this study, the original sequences used to develop these SNP markers were used to carry out a BlastN search against the B73 genome sequence  To reveal genetic difference using different marker sets, comparative analysis of genetic diversity was performed for inter and intra-genic SNPs, intronic and exonic SNPs, and three types of haplotypes. Diversity statistics for the SNPs and haplotypes were calculated for all the lines and compared with two geographically distinct sets of maize germplasm: tropical and temperate, respectively.The genetic distance between genotypes was computed using the Rogers genetic distances (RD) R2 was used to estimate LD between SNPs with MAF>0.05 on each chromosome for the entire germplasm set R2 was also calculated separately for the tropical and temperate germplasm sets. TASSEL 3.0 software R2) between SNP pairs at P = 0.01 LD measurement parameter Figure S1Cluster dendrogram constructed for 447 maize inbred lines genotyped with 1943 SNP markers. Two major groups were identified as “Chinese maize lines” and “CIMMYT maize lines”. Six groups were identified within “Chinese maize lines” by different colors as Lancaster, Landrace from Southwest China, SPT, BSSS, PA, and PB(TIF)Click here for additional data file.Table S1Detailed information of SNPs used in the study. The SNP name, chromosome, physical position, SNP type, gene name and ID harboring SNPs, minor allele frequency, polymorphic information content (PIC), and heterozygosity were provided.(XLSX)Click here for additional data file.Table S2List of 447 maize lines used in the study. Information provided in this supplemental table includes brief name, sample name, origin and adaptation.(XLSX)Click here for additional data file.


Salmonids are popular sport fishes, and as such have been subjected to widespread stocking throughout western North America. Historically, stocking was done with little regard for genetic variation among populations and has resulted in genetic mixing among species and subspecies in many areas, thus putting the genetic integrity of native salmonid populations at risk and creating a need to assess the genetic constitution of native salmonid populations. Cutthroat trout is a salmonid species with pronounced geographic structure (there are 10 extant subspecies) and a recent history of hybridization with introduced rainbow trout in many populations. Genetic admixture has also occurred among cutthroat trout subspecies in areas where introductions have brought two or more subspecies into contact. Consequently, management agencies have increased their efforts to evaluate the genetic composition of cutthroat trout populations to identify populations that remain uncompromised and manage them accordingly, but additional genetic markers are needed to do so effectively. Here we used genome reduction, MID-barcoding, and 454-pyrosequencing to discover single nucleotide polymorphisms that differentiate cutthroat trout subspecies and can be used as a rapid, cost-effective method to characterize the genetic composition of cutthroat trout populations.Thirty cutthroat and six rainbow trout individuals were subjected to genome reduction and next-generation sequencing. A total of 1,499,670 reads averaging 379 base pairs in length were generated by 454-pyrosequencing, resulting in 569,060,077 total base pairs sequenced. A total of 43,558 putative SNPs were identified, and of those, 125 SNP primers were developed that successfully amplified 96 cutthroat trout and rainbow trout individuals. These SNP loci were able to differentiate most cutthroat trout subspecies using distance methods and Structure analyses.Genomic and bioinformatic protocols were successfully implemented to identify 125 nuclear SNPs that are capable of differentiating most subspecies of cutthroat trout from one another. The ability to use this suite of SNPs to identify individuals of unknown genetic background to subspecies can be a valuable tool for management agencies in their efforts to evaluate the genetic structure of cutthroat trout populations prior to constructing and implementing conservation plans. Single nucleotide polymorphisms (SNPs) are powerful genetic markers that are increasingly being used in phylogenetic and population genetic studies ,2. AdvanOncorhynchus clarkii, a native western North American salmonid, has ten extant subspecies (and two extinct subspecies). The species appears to be monophyletic . We acknowledge that mtDNA SNPs are useful in many cases, but the purpose of this study was to discover unlinked nuclear markers for additional statistical power in future population genetic studies. 7) SNPs were limited to one per contig in an attempt to minimize the number of linked alleles included. Limiting the SNPs to one per contig also served as a screen to eliminate paralogous sequence variants (PSVs). A position weighted window filter was also used to determine if SNPs were in a poor alignment region. Every polymorphism that occurred within the window was assigned a score based on its relative position to the SNP, the total score was added up and if a threshold score ≥6 was reached the SNP was considered erroneous and skipped. This window filter was designed to avoid calling SNPs in misaligned regions, as well as to avoid areas that aligned paralogous loci, thus serving as an additional screen for PSVs. Primers for 288 SNP loci that met the above criteria (enough to fill three Fluidigm chips) were designed using the default settings in the PrimerPicker software program [Putative autapomorphic SNPs were identified by comparing sequences derived from a single cutthroat trout subspecies to the sequences of the other subspecies combined. Comparisons were completed using SNP_Finder_Plus, a custom perl script described by Maughan et al.. A similet al. against  program . Primer Prior to genotyping the SNPs, specific target amplification (STA) was used to pre-amplify each SNP locus. STA primers are non-allele specific and do not carry a polymorphic base on their 3’ end  using the Fluidigm  nanofluidic 96.96 dynamic arrayTM[et al. [The SNPs were genotyped using KASParc arrayTM, followiM[et al. . End-poiM[et al. .To visualize the ability of the SNPs to discern among cutthroat trout lineages, three analyses were performed. First, a NeighborNet phylogenetic network was created using the software program SplitsTree v.4.12.6 . Second,The mitochondrial ND2 gene was re-sequenced for some individual samples that did not group with their pre-defined subspecies as expected (see Results). Amplification of ND2 was achieved using PCR primers Gln56F (5’-ACT ACA CCA CTT TCT AGT AAG GTC AGC-3’) and Ala13R (5’-GCA TTC AGA AGA TGT GGG ATA AAG TC-3’). Reaction cocktails were 12.5 μl in volume, and contained ~100 ng genomic DNA, 2.25 μl nuclease free water, 0.5 μl each primer, and 6.25 μl Promega GoTaq® Hot Start Green Master Mix. The thermal profile contained an initial denature of 95°C for two minutes to activate the enzyme, 35 cycles of 95°C for 30 seconds, 48°C for 30 seconds, and 72°C for 90 seconds, followed by a rapid cool down to 12°C. The light and heavy strands were each sequenced in 10.5 μl reactions using the same primers that amplified the ND2 gene and Big Dye chemistry. Excess dye terminator was removed using Sephadex columns. Sequencing was performed on an ABI 3730xl automated sequencer located in the DNA Sequencing Center at Brigham Young University.454-pyrosequencing produced 1,499,670 reads, with an average read length of 379 bp, for a total of 569,060,777 bp from a single run. The reads have been made publicly available via the NCBI Sequence Read Archive (Study #SRA062178). Reads were not equal for each subspecies. This inequality was likely influenced by the differences in sample size for each subspecies since N ranged from 2 to 4 for each subspecies of cutthroat that was included on the 454 plate  had BLAST hits to the GenBank refseq_protein database, and 37,175 (85%) did not. Our selection process revealed that 28,887 of those putative SNPs met the first five criteria outlined above for designing SNP primers . Pairwise comparisons showing the number of putative SNPs between each subspecies are shown in Table A total of 125 of the subset of 288 SNP loci for which we developed primers 43%) produced clean amplification signal, yielding genotypic clusters that were visibly separated from each other and could be scored following PCR amplification and SNP genotyping. Sequence information for all 125 validated SNPs has been deposited in the GenBank database [GenBank accession #s are listed in Additional file % produceThe diversity panel was comprised of 95 individuals representing nine lineages of cutthroat trout (n=10 per lineage), rainbow trout (n=5), and a negative control. The total number of polymorphic SNPs ranged from 19 to 75 for these cutthroat trout subspecies and rainbow trout Table . Minor-aa priori subspecies designation. One Lahontan Basin cutthroat individual (LAH95362) grouped with rainbow trout rather than with the other Lahontan Basin individuals. Three Rio Grande cutthroat individuals  clustered with the group containing Yellowstone and Bear River cutthroat rather than with the other seven Rio Grande cutthroat individuals. One Bear River cutthroat individual (BR239318) grouped with Bonneville cutthroat, and one Colorado River cutthroat individual (CR134518) connected to the base of a branch leading to a group containing seven Rio Grande cutthroat individuals. Re-sequencing of the mitochondrial gene ND2 showed that the “problematic” Lahontan Basin cutthroat individual carried rainbow trout mtDNA, and that the Colorado River cutthroat individual carried Greenback cutthroat trout mtDNA. The Rio Grande and Bear River cutthroat individuals carried mtDNA sequences of their respective subspecies.The SplitsTree analysis produced a NeighborNet phylogenetic network that separates the cutthroat trout subspecies Figure . CoastalPrincipal coordinates analysis results showed that Principal Coordinate 1 explained 35.8% of the total variance, and Principal Coordinate 2 explained 15.7% Figure . Thus, ta priori  are warranted, particularly when it comes to searching for fixed alleles between Bear River and Yellowstone cutthroat, and between Colorado River and greenback cutthroat trout, if they exist.The SNP markers reported here have added to a rapidly growing body of markers that can be used in cutthroat trout population genetic studies, and should be a valuable resource in future attempts to evaluate the genetic composition of cutthroat trout populations in western North America, including the detection of hybrids. These results reiterate that cutthroat trout subspecies are geographically and evolutionarily distinct, and ought to continue to be managed as such by state and federal agencies.Amaranthus[The method used to discover these SNP loci in cutthroat trout was developed for SNP discovery in the Eudicot genus maranthus. BecauseThe authors declare that they have no competing interests.DDH, DKS, JSKK, PJM and RPE designed the study. DDH, PJM, SMS and RBS generated the data. DBE, DDH, PJM and SMS analyzed the data. DDH, DKS, PJM, JSKK and RPE contributed to the writing of the manuscript. All authors have read and approved the final manuscript.SNP primer table, SNP marker names, GenBank accession numbers, the type of polymorphism for each SNP, allele specific primers, common reverse primers and specific target amplification primers are listed herein.Click here for fileCharacterization of each SNP locus, Major and minor alleles for all 125 SNP loci, along with the proportions of individuals within each a priori designated subspecies that carry the minor allele, as well as minor allele frequencies for each SNP locus are listed herein.Click here for file


The field of genetics has come to rely heavily on commercial genotyping arrays and accompanying annotations for insights into genotype-phenotype associations. However, in order to avoid errors and false leads, it is imperative that the annotation of SNP chromosomal positions is accurate and unambiguous. We report on genomic positional discrepancies of various SNP chips for human, cattle and mouse species, and discuss their causes and consequences. Array based genotyping provide a powerful tool to interrogate genetic variation. It enables a broad variety of applications such as genome-wide association studies, evaluation of genetic merit in breeding applications, linkage disequilibrium studies, comparative genetic studies, as well as for characterizing biodiversity To detect genomic position discrepancies of SNPs in genotyping arrays, we used BLAST http://www.illumina.com/products/bovine_snp50_whole-genome_genotyping_kits.ilmn) with the ones made by Illumina™. From the 54,001 SNPs present on the chip, we found that only 41,496 (77%) had a unique and perfect match in both our and Illumina™ mappings  came into the market which includes 54,609 SNPs in comparison to 54,001 SNPs from the previous version. Using the same procedure we mapped the SNP postions for this beadchip using only the SNPs that have a unique perfect hit in the genome assembly (UMD3.1 in this case). From 48,284 SNPs, we detected 449 SNP genomic position discrepancies, of which 248 (55%) were in different genes. These 449 discrepancies lead to a change in the genomic order of 13,133 SNPs, with 90% being less or equal than 2 index positions away (updated mapping file provided as http://www.illumina.com/products/bovinehd_whole-genome_genotyping_kits.ilmn), and found that only 14 SNPs (2 in different chromosomes) retrieved mapping to different genomic positions, of which 2 (14%) were observed in different genes. These 14 discrepancies lead to a change in genomic order of 182 SNPs, with 98% being less or equal than 1 index away . We detected that 620 SNPs (5 in different chromosomes) retrieved different genomic mapping positions, of which 66 (11%) are in different genes. These 620 differences lead to a change in genomic order of 271,325 SNPs, with 98% being less or equal than 2 indexes away  and the Affymetrix's™ Genome-Wide Human SNP Array 6.0 . Concerning the Affymetrix™ human genotyping array, we detected 25 differences (5 in different chromosomes), of which 10 (40%) were in different genes. These 25 discrepancies lead to 61,916 SNPs being in a different genomic order, with 89% being less or equal than 2 indexes away  should be used in order to achieve an accurate chromosomal alignment instead of retrieving a partial best alignment with extra SNPs, indels or less SNP flanking sequence aligned .Since wrongly mapped SNPs can change in which genic and regulatory regions they overlap, it can trigger erroneous variant effect conclusions. Large SNP positional discrepancies can also affect studies of genotype imputation and linkage disequilibrium, leading to false coverage and power of genome-wide association analysis and erroneous evaluation of the choice of SNP platform to use We would recommend the providers of commercial SNP chips to always provide (for each chip) a technical report on how they exactly did the mapping. Specifically, refer to which mapping algorithm and its parameters used, genome assembly version, and the location of SNP flanking sequences in their websites. It was our experience that trying to retrieve this information revealed to be a cumbersome task, with little or no information provided regarding the SNP mapping procedure.In Supplementary data we provide our mappings for the genotyping platforms tested here, and we hope that investigators using different genotyping platforms are encouraged to map them using an accurate and sensitive procedure . The SNPftp://hgdownload.cse.ucsc.edu/goldenPath/mm9/bigZips/chromFa.tar.gz (mouse assembly mm9), ftp://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/chromFa.zip (human assembly hg18), ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus/Bos_taurus_UMD_3.1/bos_taurus.fa.gz (cow assembly UMD3.1) and ftp://hgdownload.cse.ucsc.edu/goldenPath/bosTau4/bigZips/bosTau4.fa.gz (cow assembly BosTau4.0).All SNP discrepancies reported here are relatively to the genome build to which the chip was initially mapped to. The fasta files for the genome assemblies of each species queried were retrieved from The cow genome has currently two genome versions available, one (Btau4.0) from the public consortium that sequenced the bovine genome ftp://ftp.illumina.com/Whole%20Genome%20Genotyping%20Files/. This site is only accessible through password that can be provided by Illumina™ customer services. Inside this folder there are subfolders containing the four type of arrays tested here . The files used were BovineHD_B.csv, BovineHD_777962_Name_Chr_Coord.csv, BovineSNP50_B.csv, BovineSNP50_Final_SNPs_54001.csv, BovineSNP50v2_AlleleReport_revB, BovineSNP50v2_FinalSNPList_54609_09Apr10.csv, Human1M-Duov3_B_csv, Human1M-Duov3_FinalMarkerList_1199187.txt.The genomic coordinates of each SNP and the fasta files for the oligomer sequences flanking the SNPs in each chip were taken from different sources.For the Illumina™ arrays, these data were fetched from http://www.affymetrix.com/browse/products.jsp?productId=131533&navMode=34000&navAction=jump&aId=productsNav#1_3 and from http://www.affymetrix.com/estore/browse/products.jspjsessionid=07BF945B0A18133EA55E7EE9D965B154?productId=prod100002&categoryId=cat30008#1_3. The files used were GenomeWideSNP_6.bed, GenomeWideSNP_6_flanking_sequences_fasta and MOUSEDIVm520650.na31.annot.csv. Contrary to the human array, the Affymetrix™ website for the mouse array did not contain the flanking sequences for the respective SNPs. After contacting Affymetrix™ support, they told us that The Mouse Diversity Genotyping array was not designed by Affymetrix, but by The Jackson Laboratory and the University of North Carolina. As such, they suggested us to take a look at the website of the Jackson Laboratory (http://cgd.jax.org/tools/diversityarray.shtml) to see whether we could find the flanking sequences files. Unfortunately we were not able to get it and therefore we retrieved this information from dbSNP mouse build 129 (http://www.ncbi.nlm.nih.gov/projects/SNP/), where the SNP flanking information was stored. Consequently, for this SNP chip we did not get all the data from the primary source but from a secondary source which might add to the reasons for this array having the biggest number of diverging SNP positions.For the Affymetrix™ arrays, the data was fetched from http://genome.ucsc.edu/). For human and mouse, the gene track ‘UCSC Genes’ table ‘knownGene’ was used, while for cow the gene track ‘Ensembl Genes’ table ‘ensGene’ was used for assembly Btau 4.0 and the file ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus/Bos_taurus_UMD_3.1/annotation/UMD3.1.gff.gz was used for assembly UMD3.1.”The NHGRI catalog of GWAS studies (http://www.genome.gov/gwastudies/) was used to select papers published in 2010 from which significant SNPs were detected to have mapping discrepancies.“The gene annotations were retrieved from UCSC genome browser  T (FULL SW ALIGN) OR F[GAPPED ALIGNMENT] T[OPEN PENALTY] -2[EXTEND PENALTY] -1It should be noted that the performance speed depends on the WORD SIZE and QUERY INCREMENT, with lower word size and lower query increment increasing the sensitivity of the alignment.The aligned output files were formatted with Unix commands. After, the genomic coordinates of each perfectly unique mapped SNP were compared with the original genomic coordinates annotated by Affymetrix™ and Illumina™. This was done with custom R scripts. Both Unix and R commands are provided in Data S1BovineSNP50 v1 beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S2BovineSNP50 v2 beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S3BovineHD beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S4Affymetrix mouse diversity genotyping array uniquely mapped SNPs .(RAR)Click here for additional data file.Data S5Affymetrix Genome-Wide Human SNP Array 6.0 uniquely mapped SNPs .(RAR)Click here for additional data file.Data S6Illumina Human1M-Duo DNA Analysis beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S7Unix and R commands used to post-process the aligned sequences.(TXT)Click here for additional data file.


Malus × domestica Borkh.) seedlings genotyped for an 8K SNP array, we report the first systematic evaluation of the relative contributions of different genomic regions to various traits related to eating quality and susceptibility to some physiological disorders. Single-SNP analyses models that accounted for population structure, or not, were compared with models fitting all markers simultaneously. The patterns of linkage disequilibrium (LD) were also investigated.Understanding the genetic architecture of quantitative traits is important for developing genome-based crop improvement methods. Genome-wide association study (GWAS) is a powerful technique for mining novel functional variants. Using a family-based design involving 1,200 apple  level estimated genetic correlations.The accuracy of artificial selection in plants species can be increased by using more precise marker-derived estimates of realized coefficients of relationships. All-marker analyses that indirectly account for population- and pedigree structure will be a credible alternative to single-SNP analyses in GWAS. This study revealed large differences in the genetic architecture of apple fruit traits, and the marker-trait associations identified here will help develop genome-based breeding methods for apple cultivar development. During the last 10 years, the genome sequences of about 20 plant species, including some from the Rosaceae family, were made publicly available [Until the end of the 20vailable . The undvailable . Both GSvailable . GS is uvailable ,6. The oArabidopsis[GWAS studies in humans have used two fundamentally different designs : family-abidopsis. Other fabidopsis,11,13.G) calculated using molecular markers is more efficient than the pedigree-based kinship matrix (A) as it can account for Mendelian sampling and segregation distortion [Population stratification and cryptic relatedness among studied individuals, when not taken into account, could lead to spurious genotype-phenotype associations in GWAS. Population stratification refers to the inclusion of individuals from different populations, while cryptic  relatedness refers to the presence of varying degree of genetic relationships between individuals within the study sample. GWAS methods based on the unified mixed linear model (MLM) were developed by  to accoustortion ,15. As fstortion . Furtherstortion . Developstortion .Malus × domestica Borkh.) genome sequence using DNA from a popular apple variety ‘Golden Delicious’ [In 2010, an international consortium published the first draft of the apple  and SNP-based estimates of pair-wise coefficient of relationships. The average pair-wise coefficient of relationships among all study individuals, obtained from the A and G matrices, were 0.36 and 0.50 respectively, reflecting that there are many more relationships not captured by the known pedigree records. The proportion of phenotypic variation explained G matrix (in Equation 1) was higher than that using the A matrix for all traits , suggested that the magnitude of differences in G would better account for population stratification than A, so only GWAS results (Equation 3) using G are presented here.A plot of the first two principal components of the SNP genotypes data matrix grouped seedlings largely according to their familial relationships Figure . Some inr2) decay in our GWAS population of 1120 individuals  was compared with that observed in the successive generation . Results showed a high degree of LD even at longer distances between markers; for example, in the second generation the average r2 for SNPs separated by 0.1 Mbp, 0.5 Mbp (approximately 1 cM in apple), and 1.0 Mbp was 0.28, 0.21, and 0.16, respectively  and ‘K’  models were compared to understand whether population structure could bias results. Different numbers of PCs of the SNP genotypes matrix constituted the p-values (in terms of –log10(p)) for all tested SNPs for each trait are illustrated in Figure p-values of p < 5 × 10-7, which roughly equates to a genome-wide p-value of 0.00125 (= 2500 × 5 × 10-7) as we tested a total of 2,500 SNPs, was used as a significance threshold for individual SNP testing. The numbers of genome-wide significant SNPs detected for fruit firmness (FF), weighted cortical intensity (WCI), internal browning (IB), titratable acidity (TA), fruit cracking (CR) and bitter pit (BP) were 3, 36, 31, 18, 9 and 13 respectively. Most of the significant SNPs for any trait were clustered within a small genomic region, suggesting the presence of large-effect QTL at those positions. SNP-trait association signals for FF were identified on linkage groups (LG) LG3 and LG10; for WCI and IB on LG9 and LG16; for TA on LG8; for CR and BP on LG16  gene (MDP0000232611), 20.833 kb from the top of LG10 (Table MdMYB10 gene (MDP0000259616), 32.840 kb from the bottom of LG9. A cluster of SNPs with large effects on CR and BP, and moderate effects on WCI and IB, resides within the Leucoanthocyanidin Reductase (LAR1) gene (MDP0000376284) that is located between 1.496 kb and 1.669 kb on the top of LG16. The most significant SNPs described here are probably not the causative ones for our study traits due to extensive LD. For all traits except FF, the Q-Q plots  values over most of the range, implying that the significant SNPs (highlighted in green colour) identified by the unified MMA are unlikely to be biased by population stratification.The SNP with the largest effect on FF was located on LG10, and this SNP is a T/G variant located within the first exon of the 9 Figure . This SN MDP000029616, 32.via SNP-derived G) captured nearly all genetic variance (i.e. heritability) for most of the traits , while the largest-effect SNP explained 2, 17, 7, 6, 9 and 11% of the phenotypic variation for FF, WCI, IB, TA, CR and BP respectively (data not shown). The joint contribution of genome-wide significantly associated SNPs was also investigated. Because of LD, there were many significant SNPs within a small genomic region, so only the SNPs with the largest test statistics within 5 Mb regions were chosen. The joint rg) for these two pairs of traits. Results suggested that rg values for the LGs harboring common significant regions were relatively higher than those for other LGs. Some of these LG-level correlations were quite different in magnitude as well as direction from the whole-genome correlation  were further investigated by comparing LG-level estimated genetic correlations  gene, which depolymerizes cell wall pectin, and the involvement of this gene in the fruit softening process has been previously demonstrated in apple [With only a little loss of power, family-based designs in association studies offer various advantages compared with population-based designs ,8. The aand MdLAR. Peak asin apple .MdMYB10 gene on anthocyanin biosynthesis in red-fleshed apple was demonstrated using various approaches [MdMYB10, which is physically close to the motif in the MdMYB10 promoter that causally regulates transcription of MdMYB10 itself and thereby anthocyanin synthesis in apple flesh [MdLAR1 candidate gene. LAR1 is a key enzyme in the flavonoid biosynthetic pathway, reducing leucoanthocyanidin into the flavanol compound catechin, a monomer of condensed tannins . Perhaps condensed tannins (CTs) act as co-pigments of cyanidin to create more intense red coloration in the fruit and hence the effect on WCI. Common genomic regions  found associated with WCI and IB are supported by results showing high genetic correlation (≈ 0.60) between these two traits [MdMYB10 transgenic lines of cultivar ‘Royal Gala’ showed varying degrees of symptoms of IB [MdMYB10 on WCI and IB. In our study, the estimated genetic correlation differed in degree as well as direction across different LGs, suggesting some possibility of breaking this undesirable correlation by means of carefully designed breeding and selection strategies, but further work on elucidating the genetic architecture of WCI and IB is required first.Red color in apple flesh results from a high concentration of anthocyanins. The role of the proaches , and thiproaches . The SNPle flesh . A clusto traits . Also, cms of IB , suggestThe distribution of SNP effects for TA Figure  suggestsh2 from a previous study [MdLAR1 candidate gene on LG16, which was previously reported to influence some other fruit quality traits such as astringency [CR, which is a pre-harvest physiological disorder of apple, can be a serious economic problem for some cultivars . Howeverus study  indicateus study . To our ringency .Bp-1 and Bp-2[BP is also a serious physiological disorder whose expression is generally observed in fruit after storage, but symptoms can also be observed on the fruit surface at harvest , and in and Bp-2. DiffereMLM that concurrently fits all available SNPs has been adapted in recent GWAS in animals ,25 follor2 = 0.25) largely because of relatedness  among seedlings and small effective population size. It is not uncommon to find different LD structures in different types of plant populations within a species. For example, in maize, LD decays within 1 kb in land races, within 2 kb in diverse inbred lines, and can extend up to 500 kb in commercial elite inbred lines (reviewed by [r2 = 0.08) of that in this study. One practical implication of these results is that marker-trait associations identified in advanced-generation crosses may not be repeated in relatively less improved breeding material (e.g. diverse germplasm).In our study population, the average LD between SNPs separated by 500 kb was high (iewed by ). PrelimOne of the key goals of GWAS is to identify large-effect marker-trait associations that can be deployed through marker-assisted selection (MAS) in subsequent generations of cultivar development populations. However, in order to conduct MAS using these SNPs in successive generations, strong LD between marker and QTL must persist across generations. Generally, only a selected set of individuals from the current generation are used as parents for the subsequent generation. Selection will lead to changes in allele frequencies at marker and trait loci, potentially reducing the LD between two loci, similar to that observed in the second generation of our study material  mating design. One of the crosses was unsuccessful, leaving seven full-sib families. Seedling numbers varied between families, ranging from 40 to 350, with a total population size of 1,200. Seedlings were planted into the orchard  in July 2008 using a randomized block design. Further details of this experiment such as, parents involved, orchard management, harvesting and fruit storage protocols, were reported earlier . Six tra® GenomeStudio software (Illumina Inc.). The reliability of each genotype call was measured using the GenCall score, and SNPs were subsequently discarded using a sequence of criteria in the following order: GenCall score at the 50% rank (50% GC) < 0.40; cluster separation (ClusterSep) < 0.25; more than 5% missing calls; segregation discrepancy. Finally a high quality set of 2,500 SNPs was retained, and BEAGLE 3.1 software [Details of genotyping protocols for our study population were reported earlier . Brieflyr2[Before looking at marker–trait associations, we calculated pairwise LD between SNPs, as a surrogate to LD between markers and QTLs, to evaluate the extent of LD in the study population  described above. These LD patterns were compared with those in an another population (second generation) comprised of 1600 seedlings derived from an incomplete factorial mating between six paternal parents (identified from the first generation) and four maternal parents (identified from previous selections). The degree of LD was quantified with the parameter r2 estimater2.A matrix) based on pedigree records were compared with their realized counterparts (G) obtained using all available SNPs following [A and G matrices. We also compared the goodness-of-fit of a mixed model using realized or expected relationships:The expected coefficients of relationship  of each trait obtained using the A or G matrices. Equation 1 was fitted using ASReml software [where logLsoftware .The unified mixed linear model (MLM) approach  that accβ is an unknown vector containing fixed effects, including a genetic marker, population structure (Q), and the intercept; X is the known design matrices relating to β. All other effects are same as in Equation 1. Equation 3 was fitted using GAPIT software [n × m) as covariates (i.e. Q matrix). The optimal number of PCs was determined by forward model selection using the Bayesian information criterion as implemented in GAPIT. In Equation 3, each SNP was tested in turn using a t-test (H0: No additive association between the SNP and trait), and p-values were obtained. Uncorrected comparison-wise p-value of p < 5 × 10-7, which is generally accepted to represent very strong proof of genome-wide association [p-values from Equation 3 were plotted against their expected value under the null hypothesis of no association with the trait. To compare the relative contributions of each SNP, we used where software , which usoftware  and CMLMsoftware . To avoiociation ,48, was G) matrix in Equation 1, i.e. a ~ N following [Allele substitution effects (ASE) at each SNP obtained from a single-SNP analysis (Equation 3) were compared with those obtained from a model (e.g. RR-BLUP) that concurrently fits all available SNPs. In our case, RR-BLUP is theoretically similar to using a SNP-derived relationship  ,49. So tollowing :pi is the frequency of the A allele at the ith SNP ; qi = 1 - pi; elements of the ith column of M are 2qi, qi - pi and - 2pi for AA, AB and BB genotypes at a SNP locus. Product–moment correlations between ASE of SNPs obtained from Equations 3 and 4 are reported in this study.where rg). For this purpose, correlation between the estimated BLUP-BV (from Equation 1) for two traits was termed as genome-level genetic correlation. BLUP-BVs of each seedling for each trait was then decomposed into LG-level BVs by using SNP ASEs (from Equation 4) and seedling’s SNP genotypes at each LG. These LG-level BVs were then used to estimate LG-level between-trait genetic correlations.Common genomic regions showing a significant effect on a pair of traits were further investigated by comparing LG-level estimates of between-traits genetic correlations  is a random variable with non-central Student’s t-distribution with v (= n-2) degrees of freedom and non-centrality parameter δt. The expression of δt presented in [t as:where ented in  is that R (≈ 1 - r2h2(1 - h2)) is the ratio of the approximate non-centrality parameter for genetically related individuals versus unrelated individuals, assuming that resemblance between individuals is due to additive genetic effects [r is the coefficient of relationship and h2 is the narrow-sense heritability; Γ(.) is a gamma function; b and σb are the regression coefficient and its standard deviation respectively. For derivations of b and σb, refer to [where  effects ; r is threfer to .The authors declare that they have no competing interests.SK, DC, RV and CW conceived and designed the experiment; SK, DC, CW and RV performed the experiment; SK, DG and MB did analysis and interpretation of results; SK, DG and MB drafted the manuscript. All authors read and approved the final manuscript.


Single nucleotide polymorphisms (SNPs) are an important class of genetic marker for target gene mapping. As of yet, there is no rapid and effective method to identify SNPs linked with agronomic traits in rapeseed and other crop species.10p value over 16 were selected to be further analyzed. The distribution of these SNPs appeared a tight cluster, which consisted of 14 associated SNPs within a 396 kb region on chromosome A09. Our evidence indicates that this region contains a major quantitative trait locus (QTL). Finally, two associated SNPs from this region were mapped on a major QTL region.We demonstrate a novel method for identifying SNP markers in rapeseed by deep sequencing a representative library and performing bulk segregant analysis. With this method, SNPs associated with rapeseed pod shatter-resistance were discovered. Firstly, a reduced representation of the rapeseed genome was used. Genomic fragments ranging from 450–550 bp were prepared from the susceptible bulk  and the resistance bulk (ten F2 plants with SSRI >0.90), and also Solexa sequencing-produced 90 bp reads. Approximately 50 million of these sequence reads were assembled into contigs to a depth of 20-fold coverage. Secondly, 60,396 ‘simple SNPs’ were identified, and the statistical significance was evaluated using Fisher's exact test. There were 70 associated SNPs whose –log70 associated SNPs were discovered and a major QTL for rapeseed pod shatter-resistance was found on chromosome A09 using our novel method. The associated SNP markers were used for mapping of the QTL, and may be useful for improving pod shatter-resistance in rapeseed through marker-assisted selection and map-based cloning. This approach will accelerate the discovery of major QTLs and the cloning of functional genes for important agronomic traits in rapeseed and other crop species. Single nucleotide polymorphisms (SNPs) are the most abundant variations and currently an important class of genetic marker distributed throughout the genome In the past decade, SNPs have been used as genetic markers in many cultivated plants such as rice Bulk segregant analysis (BSA) is efficient for selective genotyping wherein DNA samples of extreme individuals from each tail of a phenotypic distribution are pooled and the two resultant bulks need to be genotyped Reduced representation libraries (RRLs) and pyrosequencing technologies have facilitated the high throughput discovery of SNPs Brassica napus; genome AACC, 2n = 38) is an allopolyploid species that originated in a limited geographic region via spontaneous hybridizations between turnip rape  and cabbage  genotypes Rapeseed  and their F2 population progenies  were identified with a random impact test designed for screening pod shatter-resistance (The silique shattering resistance index (SSRI) of two rapeseed lines  is a recent allopolyploid species that originated in a limited geographic region through spontaneous hybridizations between turnip rape  and cabbage  B. rapaB. oleracea (unpublished), were used as reference genomes. Firstly, the genome sequences of B. rapa and B. oleracea were combined and the combined sequences were then simulated to be digested with all of our blunt end enzymes. In the end, four blunt end enzymes, AluI, DpnI, RsaI and RseI, were selected with the goal of minimizing repetitive content in the target size range of 450–550 bp and interpreted to be suitable for RRL construction. By digesting with these enzymes, we were assured that the size of the DNA fragments would range from 450 to 550 and account for about 10% of the size of the whole sequences  of B. rapa and B. oleracea, were used as reference genomes. Subsequently, the sequences were mapped to the reference genomes using SOAP software. We discarded reads that matched exactly with more than one position in the reference genomes, and the unique sequences were chosen for SNP discovery. The most frequently occurring sequence  was a perfect match to more than 100 locations in the reference genomes and represented a contaminating repetitive element. After removing 1,102,886 sequences with low-quality scores, 47,786,006 reads (4.3 billion bp) corresponding to 25,048,721  unique sequences remained for SNP discovery.We independently sequenced the two RRLs (SK and RK), and a total of 48,888,892 reads (90 bp) were obtained . BecauseThe sequences obtained from SK and RK libraries covered 14%  and 11%  of the reference genomes, respectively. The overlap of the two libraries covered 8%  of the reference genomes and achieved a 20-fold average coverage depth, and that coverage depth was sufficient for SNP identification.10pwas over 2, 5, 10 and 16, respectively. In order to reduce the number of associated SNPs in our study, only those putative associated SNPs with the value of –log10pover 16 were selected for further analysis. Thus, these 70 SNPs associated with rapeseed pod shatter-resistance were obtained and listed in The unique sequences produced ‘simple SNPs’ between two bulks by custom perl scripts B. rapa and B. oleracea, 70 associated SNPs were mapped to the reference genomes using SOAP software  and B. napus cv. R1 (very susceptible germplasm to pod shattering) were selected in this study. An F2 population that included 276 individuals derived from the cross of zy72360 and R1 was used. All plant materials were planted in a field located in Wuhan, China. All lines were planted in fields and each condition duplicated.Two rapeseed lines, i is the number of broken and damaged silique at each replicate, i. Each accession was tested twice and an average value was calculated.Random impact test was conducted according to the method in Genomic DNA was isolated from fresh leaves using cetyl trimethyl ammonium bromide. The concentration of DNA was adjusted to 50 ng/µL for PCR amplification. Megabase-size genomic DNA was isolated from bulk leaves of F2 individuals following the procedure described by Zhang et al. AluI, DpnI, RsaI and RseI), producing fragments with blunt ends, were tested for library construction. Digestion of DNA was performed for each enzyme as suggested by the manufacturer (New England Bio Labs) and was digested overnight to ensure complete digestion. The digested DNA was then run on a 2% agarose gel and the digestion products were excised from the agarose gel at the 450 to 550 bp region. The QIAquick Gel Extraction Kit  was used following the manufacturer's protocol to obtain a total of 5,000 ng of size-selected DNA. The blunt-end DNA fragments were given to BGI  for sequencing on the HiSeq2000  after adding a 10 bp index. The sequence data obtained from BGI contained 90 bp sequence tags in which every base was given a quality score that is similar to a Phred score. We screened the data for overall quality by looking for an average base quality score of at least 25 across the entire tag for all reads according to a previously described method Four different restriction enzymes (B. rapa and B. oleracea genotypes B. rapaB. oleracea (unpublished) were used as reference genomes. The sequences from the two bulks were mapped to the references using SOAP software Rapeseed is a recent allopolyploid species that originated in a limited geographic region through spontaneous hybridizations between 2, 0.25 mmol/L of each dNTP, ×reaction buffer and 0.75 U Taq polymerase. Thermocycling was performed using a DNA Engine Peltier Thermal Cycler, with 30 cycles of 94°C for 30 s, 55°C for 30 s, 72°C for 1 min, and a final extension at 72°C for 5 min before holding at 10°C. The PCR products were separated on an 8% non-denaturing polyacrylamide gel, and were stained with AgNO3The F2 population, consisting of 276 individuals, was utilized to construct a genetic map and validate the associated SNPs. The primers sequences which were used for the genetic map are listed in Linkage analysis and genetic map construction was done using JoinMap 3.0 Figure S1The frequency distribution of the silique shattering resistance index. The three dashed frames show three peaks.(TIF)Click here for additional data file.Figure S2Distribution of associated SNPs detected among rapeseed SK and RK bulks. The star indicates the tightly clustered SNPs region.(TIF)Click here for additional data file.Figure S3Plot of the major QTL for pod shatter-resistance on linkage group A9 in B. napus.(TIF)Click here for additional data file.Table S1The silique shattering resistance index of two rapeseed lines (B. napus cv. zy72360 and B. napus cv. R1) and their F2 population progenies .(XLS)Click here for additional data file.Table S2The details of 70 SNPs associated with rapeseed pod shatter resistance from B. napus.(XLSX)Click here for additional data file.Table S3Primers of 14 SNPs of the tightly clustered SNPs region on A09 chromosome.(XLSX)Click here for additional data file.Table S4Primers sequences of the genetic map on A09 chromosome.(XLSX)Click here for additional data file.


Gossypium hirsutum, and create a functional SNP index for efficiently mapping sequence reads to the D-genome sequence of G. raimondii. PolyCat is appropriate for all allopolyploids and all types of next-generation genome analysis, including differential expression (RNA sequencing), differential methylation (bisulfite sequencing), differential DNA-protein binding (chromatin immunoprecipitation sequencing), and population diversity.Read mapping is a fundamental part of next-generation genomic research but is complicated by genome duplication in many plants. Categorizing DNA sequence reads into their respective genomes enables current methods to analyze polyploid genomes as if they were diploid. We present PolyCat—a pipeline for mapping and categorizing all types of next-generation sequence data produced from allopolyploid organisms. PolyCat uses GSNAP’s single-nucleotide polymorphism (SNP)-tolerant mapping to minimize the mapping efficiency bias caused by SNPs between genomes. PolyCat then uses SNPs between genomes to categorize reads according to their respective genomes. Bisulfite-treated reads have a significant reduction in nucleotide complexity because nucleotide conversion events are confounded with transition substitutions. PolyCat includes special provisions to properly handle bisulfite-treated data. We demonstrate the functionality of PolyCat on allotetraploid cotton,  Arabidopsis thaliana  identification within and between extant diploid relatives. Most of these SNPs are vertically inherited from diploid ancestors to allopolyploid derivatives, so they are present both between diploid relatives and between coresident homeologous genomes of the allopolyploid. These “homoeo-SNPs” can be used to reduce mapping efficiency bias through the use of SNP-tolerant mapping, as with heterozygous genes in humans  conversion events. Because transition SNPs comprise a majority of all SNPs, including homoeo-SNPs, treatment with BS causes a majority of homoeo-SNPs to be potentially uninformative for categorizing BS sequencing (BS-seq) reads.Gossypium). The most common form of domesticated cotton (Gossypium hirsutum) is an allopolyploid composed of homeologous AT- and DT-genomes, where the ‘T’ subscript indicates genomes within the tetraploid nucleus , along with a web portal in which evaluation sequence data sets may be submitted for mapping and categorizing. PolyCat produces genome-specific BAM files as output, which may be immediately used by most current bioinformatics tools for downstream analyses, such as differential expression (RNA-sequencing [RNA-seq]), differential methylation (BS-seq), differential DNA-protein binding (chromatin immunoprecipitation sequencing), and population diversity.The PolyCat source code and the current cotton SNP-index is publically available for other studies (https://github.com/najoshi/sickle) was used to trim all sequence reads with a quality cutoff of 20. We used the Genomic Short-read Nucleotide Alignment Program (GSNAP) (G. raimondii (D5), and 4,070,680,434 reads from G. arboreum (A2) to the G. raimondii reference genome   to map 1e genome . SAMtool Archive .G. hirsutum  and Gossypium tomentosum, an endemic polyploid cotton species of Hawaii. Because the cotton AT and DT genomes are more similar to their extant diploid relatives than they are to each other  of matches for one genome were categorized as AT or DT, accordingly. Reads with matches to both were categorized as chimeric (X). Reads without SNP positions or matches were categorized as unknown (N).We illustrate read categorization with RNA-seq reads from cotton petals in two allopolyploid cotton species: T-genome , dependiBisulfite treatment deaminates unmethylated cytosines to uracils. During subsequent polymerase chain reaction, the uracil is interpreted as a thymine for complementary strand synthesis. After sequencing, cytosine-to-thymine mismatches (C→T) between the read and the reference sequence indicate unmethylated cytosines on the sequenced ‘+’ strand. Guanine to arginine mismatches (G→A) indicate unmethylated cytosines on the sequenced ‘−’ strand. This conversion looks like a transition SNP and requires tracking by PolyCat to avoid data loss.For BS-treated data, PolyCat first inferred the origin strand of each read by counting C→T and G→A conversions. More C→T conversions indicated ‘+’ strand, whereas more G→A conversions indicated ‘−’ strand. Ambiguous strands were counted as half reads for both strands. For ‘+’ strand reads, PolyCat accepted a thymine as a match for a cytosine allele; for ‘−’ strand reads, PolyCat accepted an adenosine as a match for a guanine. Knowing the strand origin allowed PolyCat to maximize information from each SNP. Because transition SNPs comprised the majority of the SNP index , most re5-genome reference .After categorizing each read, PolyCat reported the number of cytosines and thymines, or guanines and adenosines, at each cytosine or guanine reference position, along with the methylation context—CG, CHG, or CHH—according to the Deference . Separat2 and D5, respectively) to the D5-genome reference. We refer to these single-nucleotide differences between homeologous loci as homoeo-SNPs. Our SNP index consisted of 2,633,689 SNPs .A SNP index largely composed of differences between homeologous loci was created by comparing the alignments of reads from A- and D-genome diploid species . Chromosomes 6, 7, and 9 had slightly more than 4 SNPs/kbp, whereas Chromosomes 5, 10, and 13 had slightly less than 3 SNPs/kbp. The remaining chromosomes had between 3 and 4 SNPs/kbp.SNPs were distributed evenly across the genome, reflecting the gene density of the A total of 1,123,129 SNPs were in annotated genes, including 579,259 in exonic sequence (9.4 SNPs/kbp). This increased SNP density in genes was likely due to increased sequence conservation between the A- and D-genomes. . The nume.g., Brassica napus and Tuber aestivum) have not used SNP-tolerant mapping. To demonstrate the effectiveness of SNP-tolerant mapping, GSNAP . Erroneous categorization occurred most frequently in BS-treated reads (2.1%). A small number of reads from diploids (<1%) were categorized as chimeric, indicating nucleotide matches at separate loci (within a read) to both the A- and D-genomes. Chimeric reads were slightly more common in A2 than D5. The low level of erroneous or chimeric categorization shows that PolyCat successfully categorized the vast majority of sequence reads.After mapping, PolyCat categorized each read based on matches to the SNP index . To test2 and D5, so it includes false homoeo-SNPs that are really allelic SNPs specific to A2 or D5. (2) After polyploidization, gene (or intergene) conversion events between the allopolyploid genomes could have replaced the nucleotides of one genome with the nucleotides of the other. At homoeo-SNP positions, conversion events can be detected in reads from an allopolyploid . Two factors may explain the increase in chimeric categorization in reads from allopolyploids: (1) The SNP index was based on AApproximately one-half of the polyploid reads could not be categorized because they did not overlap a homoeo-SNP. The uncategorized fraction of reads varied by length and by quality of reads. In the reference genome, only 163 Mb of 749 Mbp were within 100 bp (the length of Illumina HiSeq reads in our dataset) of a homoeo-SNP, resulting in a 21.78% theoretical probability of any whole genome shotgun read being categorized. Genic regions (120 Mbp) had a greater density of putative homoeo-SNPs than intergenic regions because of our large collection of diploid RNA-seq data. In these regions, the theoretical probability of categorization was higher (60.7%) than the remainder of the genome . These de.g., < single homoeo-SNP/read) caused fewer reads to be categorized in some regions than would have been otherwise categorized had only one of the individual causes been a factor.The BS-treated reads had a decreased level of uncategorized reads because of the information loss caused by BS conversion. Each transition homoeo-SNP was only informative for half of the reads (C-T SNPs for ‘+’ strand reads and A-G SNPs for ‘−’ strand reads). Although the same portion of the genome (120 Mbp) could have been theoretically be categorized after BS treatment, the combination of transitions confounded with BS treatment and of uneven distribution of homoeo-SNP density (T and DT). These allele-SNPs represented loci that were heterozygous within the subgenomes of G. hirsutum and G. tomentosum . The DT-genomes had more allele-SNPs, representing more genes, than their coresident AT-genomes. Approximately 75% of allele-SNPs were novel . A small number of indexed homoeo-SNPs also appeared as allele-SNPs within the genome-specific assemblies. These SNPs may reflect homeologous gene conversion events, or they may be false homoeo-SNPs.After read categorization, SAMtools , which suggests that most existing homoeo-SNPs between the AT- and DT- genomes were identified using the diploid genomes as surrogates. Therefore, increased sequencing of tetraploid transcriptomes will only minimally augment the number of “new” homoeo-SNPs; however, it would likely decrease the number of false-positive homoeo-SNPs resulting from diploid specific nucleotides.By comparing the ARead mapping in polyploid genomes is a natural application of DNA sequencing, although the practical challenges of mapping to the duplicated loci of polyploid genomes have not received much attention. These challenges include (1) mapping duplicated reads to a single reference genome, (2) the difference in similarity between the subgenomes of an allopolyploid and the diploid reference sequence, (3) gene conversion, (4) allopolyploid autapomorphies, and (5) diploid autapomorphies. Carefully classified SNPs can be used to address some of these challenges, despite the lack of a read-mapping program capable of mapping to a duplicated reference genome. For evolutionary and plant improvement studies, reads are best classified within a phylogenetic context using SNP positions and their corresponding nucleotides.1) and Parent 2 (P2) are combined into a common nucleus and form an F1  was created by a recent hybridization. Categorization of reads from F1 had fewer chimeric (X) reads than reads from the natural allopolyploids  and (2) they originated in only one genome and in only one of two germline chromosomes. After a single base substitution, drift, selection, or both will move the allele frequency of the derived base toward fixation or elimination. Thus, allele-SNPs can be found within individual genomes where a particular accession is heterozygous or by the comparison of two different homozygous accessions. These allele-SNPs would independently assort during meiosis after nucleotide substitution, regardless if they were identified in homozygous or heterozygous individuals. SNP identification efforts in other species have used confusing, alternative notation  if the allele-SNPs were initially identified in a heterozygote as opposed to a homozygote  impact mapping of sequence reads from allopolyploids . Allele-T- and DT-genomes in cotton). Mapping all sequence reads to a single genome reference allowed for an aligned, comparative analysis between the two genomes within a given accession, as well as for more accurate analyses between accessions. Although these tools have been developed for cotton, they can be readily applied to any allopolyploid by providing an appropriate genome reference FASTA file, SNP index, and sequencing reads.The SNP index and read categorization process facilitated the analysis of allopolyploid cotton by reducing the bias in mapping efficiency between the two genomes and by providing a means to separate data generated for each allopolyploid genome (APolyCat is ultimately limited by the density of homoeo-SNPs across the genome. Reads belonging to a particular region of the genome can only be categorized if it has one or more homoeo-SNPs because every categorized read must overlap at least one SNP. The use of longer reads could improve the rate of categorization.https://github.com/pezmaster31/bamtools) and Bioperl (http://bioinfo3.pgml.uga.edu/polyCat/upload.html). In the online version, 1 GB of sequence reads (non BS-seq) in FASTQ format can be categorized by PolyCat in approximately 15 min. Additional sequencing and development of software algorithms and tools will provide continued insights into polyploid genomes, their interactions, and their resultant phenotypes.PolyCat is written in C++ and Perl, using BamTools (