B. napus genomic breeding and research.Rapeseed is a globally cultivated commercial crop, primarily grown for its oil. High-density single nucleotide polymorphism (SNP) arrays are widely used as a standard genotyping tool for rapeseed research, including for gene mapping, genome-wide association studies, germplasm resource analysis, and cluster analysis. Although considerable rapeseed genome sequencing data have been released, DNA arrays are still an attractive choice for providing additional genetic data in an era of high-throughput whole-genome sequencing. Here, we integrated re-sequencing DNA array data  from 505 inbred rapeseed lines, allowing us to develop a sensitive and efficient genotyping DNA array, Bnapus50K, with a more consistent genetic and physical distribution of probes. A total of 42,090 high-quality probes were filtered and synthesized, with an average distance between adjacent SNPs of 8 kb. To improve the practical application potential of this array in rapeseed breeding, we also added 1,618 functional probes related to important agronomic traits such as oil content, disease resistance, male sterility, and flowering time. The additional probes also included those specifically for detecting genetically modified material. These probes show a good detection efficiency and are therefore useful for gene mapping, along with crop variety improvement and identification. The novel Bnapus50K DNA array developed in this study could prove to be a quick and versatile genotyping tool for  Brassica napus L; AACC; 2n = 38) is the third widely grown oil crop in the world and is a rich source of healthy, edible oil. It is also grown to produce edible vegetables, succulents, and ornamental flowers. In addition, rapeseed can be used as a protein source for animal feedstuff and as a source of bioenergy. United States Department of Agriculture (USDA) data shows that the total global production of rapeseed oil and meal in 2018 was 71.94 and 38.79 million metric tons, respectively; this was second only to soybean production. The production of refined rapeseed oil was 27.46 million metric tons, with only palm and soybean oil exceeding this production value . The breeding requirements of rapeseed are important for optimizing its production; therefore, there is a need to diversify these requirements given the versatility and worldwide use of this crop.Rapeseed  and B. oleracea  , was first sequenced in 2014 by Chalhoub and his colleagues, providing rich genomic background knowledge for the species. Thereafter, other researchers annotated two winter-type cultivars, “Darmor-bzh” and “Tapidor,” using the same method and identified differences in gene numbers , a well-known genotyping method, has the advantages of high throughput, flexibility, and efficiency. However, this method has some shortcomings; in particular, it is difficult to directly compare single nucleotide polymorphism (SNP) base genotyping data between different individuals in low-coverage sequencing. In addition, the processing of sequencing data requires expert knowledge of bioinformatics, which has limited applications . These sDNA arrays (commonly known as DNA chips) consist of a collection of microscopic DNA spots attached to a solid surface. They are used to measure the expression levels of large numbers of genes simultaneously, or to genotype multiple regions of a genome . Fixed SBreeding chips have been successfully applied to multiple crops. For example, in rice, the Illumina Infinium Rice6K , following the manufacturer’s protocol. Array assays were performed according to the Infinium assay standard protocol , using the HiScan scanner .All All probes were divided into three categories: Illumina Infinium Brassica 60 K high-quality probes (B. napus cultivars and 18 B. rapa cultivars), high-quality probes were screened according to the following principles: (1) call frequency ) > 0.8; (2) cluster separation score (Cluster Sep) ≥ 0.15; (3) frequency of the minor allele (minor frequency) > 0.05; (4) a unique match on the Darmor-bzh genome ; and (5) probes not located within 200 bp of each other. After screening, 17,758 probes remained.First, based on the chip detection results of the 523 varieties natural population  (https://sourceforge.net/projects/bio-bwa/) to align them with the Darmor-bzh genome (v4.1). Subsequently, 32,216,304 SNPs were extracted from the re-sequencing data using GATK version 3.6-0-g89b7209  was split into 8,495 bins, with 100 kb per bin. Thereafter, the r2 value of linkage disequilibrium (LD) for any two SNPs located on the same bin was calculated and SNPs were grouped using a greedy algorithm, based on whether r2 ≥ 0.65 , mitotype-specific sequences, and mitochondrial specific sequences  following the protocols provided by Illumina (www.illumina.com). Illumina manufacturing processes were employed to successfully synthesize 42,090 markers (Supplementary Table S2), which were analyzed with respect to genotype clustering using GenomeStudio 2.0.4  .The B. napus and the fact that when both homologous loci are polymorphic, some SNPs will reflect homologous site blending by displaying cluster-like patterns. The third type was generated when the homologous loci were monomorphic, that is, five genotypes could be distinguished by their cluster . Finally, in the fourth type, the software could not genotype correctly . Our markers  were further characterized into 9,474 polymorphic, 32,184 monomorphic, and 28,606 intergenomic markers. The default cluster file mistakenly recognized the P1 genotyping results as the hybrid F1 genotyping results; therefore, we needed to correct the cluster file based on the test materials to obtain the correct genotyping results .Detection results were imported into the GenomeStudio software and the default diploid pattern was used for genotyping. Statistical analysis showed that SNP loci consisted of the following types: SNP loci with no detectable signal , SNP loci without polymorphism , and SNP loci with polymorphism . Polymorphic SNP loci genotyping results could be classified as one of four types. The first type correctly classified the same SNP into the three genotypes, AA, AB, and BB . In the second type, the software recognized a high number of heterozygotes ; this was due to the strong sequence similarity between the A and C genomes of For most data, we used R/ggplot2 software for visualization, and TBtools to show the location of functional probes  were screened. A total of 26,184 high-quality probes were obtained for synthesis, with characteristics such as a unique matching position on the Darmor-bzh genome, high call frequency, good cluster typing in the population, and physical distance between the probes of more than 200 bp. Furthermore, probes with MAFs of less than 0.05 were filtered out to ensure that our chosen probes are suitable for used in a wide range of research and breeding programs.To develop an effective SNP array for bzh genome , and the average distance between adjacent SNPs on the Bnapus50K array was 8 kb, with only 0.7% of the SNP gaps being greater than 100 kb .Usually, SNPs within genic regions have greater potential to affect gene function than do intergenic regions. In this study, 30% of the SNPs were located in coding regions(exon regions and intron regions), 16% of the SNPs were located in no coding regions(1 kb promoter region and 3’UTR) . All these probes were distributed evenly along the whole Darmor-www.illumina.com). Therefore, by developing robust cluster files, the most efficient high-throughput applications can be achieved for Illumina arrays. However, the software’s default cluster file is based on diploid humans; thus, adjustment is needed to obtain a suitable cluster file for rapeseed.The data generated by the Illumina Infinium platform were analyzed using GenomeStudio software, whose principle is to score each SNP genotype as AA, AB, or BB if there are three separated signal clusters . Subsequently, we constructed a cluster file containing 28,509 high-quality probes. These were obtained by removing SNP sites that could not detect signals  or which had poor genotyping ability . There were 22,355 high-quality monomorphic markers, 19,752 of which were located on chromosomes  were different. These findings confirmed the high reproducibility of this efficient genotyping array. To validate the genotyping accuracy, the reference rapeseed cultivar “Westar” was used to compare the SNP sites that could be detected by the high-quality probes on array with the re-sequencing results; the consistency between the two was 98.4%.A reliable array should have characteristics suitable for a variety of detection needs; therefore, we investigated the polymorphism of the screened high-quality probes in 144 cultivar and 27 parent lines. After excluding functional probes, we identified 28,156 high-quality probes (GenTrain score > 0.5) for analysis. We then examined whether the polymorphisms of these high-quality probes in the 171 lines were similar to the results obtained in previous simulations. We, thus, calculated the number of marker polymorphisms between the cultivar and parent lines  and obtaRfp restorer  were confined to a 215.96 kb interval on the Darmor-bzh genome, suggesting that the infiltration of targeted genome fragments can aid in improving resistance.An effective method for improving biotic or abiotic stress resistance is to breed corresponding varieties for each stress type. Here, two sets of genotype data were obtained from the clubroot-resistant line “B409R” and its recipient parent “B409.” Our results showed that the polymorphism probes were clustered around the disease resistance gene CRb . AdditioB. napus varieties.To conduct breed heterogeneity testing, the inbred lines “ZS9” (ZY821/84004 x ZS4) and “ZS11” (ZS9/2F10 x 26102) were genotyped. Our results showed that there were 5,268 (16.1%) polymorphism loci distributed in 2,176 genome bins , confirm2F1 population constructed from “B409” (restorer) and “1318” (sterile) lines was used to map the fertility-restorer gene Rfp (previously mapped on chrA09 of Darmor-bzh) of “Polima” (pol) CMS  CMS . NotablyFourteen transgenic elements involved in eight independent transformation events were tested for probe quality (Supplementary Table S7) using nontransgenic varieties as negative controls. Furthermore, to explore the detection limits of the transgenic probes, we mixed the negative and positive individual DNA in proportions of 5:1, 10:1, 30:1, 50:1, and 100:1. B. napus, the efficient and rapid identification of these types are becoming increasingly important. As such, we designed cytoplasmic detection probes for nap, cam, ole, pol, hau, and ogu cytoplasm, based on MSS. Our results showed that these probes can clearly and specifically distinguish between different cytoplasm types, with four types of cytoplasmic probe being verified . Previous studies have confirmed that such consistency of physical and genetic distribution significantly improves the detection effect (https://doi.org/10.25387/g3.14798436. The cluster file of Bnapus50K array is available at https://github.com/Sherry-DD/Bnapus50K-array.All data generated or analyzed during this study are included in this published article and its supplementary information files. Supplementary files are available at figshare:


Prunus species include many important perennial fruit crops, such as peach, plum, apricot, and related wild species. Here, we report de novo genome assemblies for five species, including the cultivated species peach (Prunus persica), plum , and apricot (Prunus armeniaca), and the wild peach species Tibetan peach (Prunus mira) and Chinese wild peach (Prunus davidiana). The genomes ranged from 240 to 276 Mb in size, with contig N50 values of 2.27−8.30 Mb and 25,333−27,826 protein-coding gene models. As the phylogenetic tree shows, plum diverged from its common ancestor with peach, wild peach species, and apricot ~7 million years ago (MYA). We analyzed whole-genome resequencing data of 417 peach accessions, called 3,749,618 high-quality SNPs, 577,154 small indels, 31,800 deletions, duplications, and inversions, and 32,338 insertions, and performed a structural variant-based genome-wide association study (GWAS) of key agricultural traits. From our GWAS data, we identified a locus associated with a fruit shape corresponding to the OVATE transcription factor, where a large inversion event correlates with higher OVATE expression in flat-shaped accessions. Furthermore, a GWAS revealed a NAC transcription factor associated with fruit developmental timing that is linked to a tandem repeat variant and elevated NAC expression in early-ripening accessions. We also identified a locus encoding microRNA172d, where insertion of a transposable element into its promoter was found in double-flower accessions. Thus, our efforts have suggested roles for OVATE, a NAC transcription factor, and microRNA172d in fruit shape, fruit development period, and floral morphology, respectively, that can be connected to traits in other crops, thereby demonstrating the importance of parallel evolution in the diversification of several commercially important domesticated species. In general, these genomic resources will facilitate functional genomics, evolutionary research, and agronomic improvement of these five and other Prunus species. We believe that structural variant-based GWASs can also be used in other plants, animal species, and humans and be combined with deep sequencing GWASs to precisely identify candidate genes and genetic architecture components. Prunus genus contains several fruit tree species with important economic value, such as peach (Prunus persica), plum , and apricot (Prunus armeniaca), as well as wild peach species such as Tibetan peach (Prunus mira) and Chinese wild peach (Prunus davidiana). The fleshy fruit of Prunus crops offers an abundance of nutrients, such as carbohydrates, organic acids, vitamins, and minerals. Extensive phenotypic variation contributing to these fruit characteristics exists within or between species. Although these species have considerable economic value, the genetic mechanisms underlying favorable traits are poorly understood. This lack of understanding is, to some extent, attributed to the scarcity of genomic resources, which severely limits efforts to improve Prunus crops, particularly for plum, Prunus mira, and Prunus davidiana. Owing to their small genome sizes (~250 Mb) and relatively short juvenile periods (2−3 years), most of the aforementioned Prunus species are promising candidates for functional and evolutionary studies of the Rosaceae family, particularly peach1, which originated in China, was domesticated 4,000 years ago and was subsequently dispersed to Europe through the Silk Road2. Extensive genetic efforts have identified various quantitative trait loci (QTLs) that influence many important traits in peach3, but only a few genes have been identified as candidate genes for traits such as flesh texture and adhesion4, flesh color5, fruit hairlessness6, dormancy7, and tree architecture8.The Rosaceae family includes many genera with different types of fruit, of which the Arabidopsis9, rice10, maize11, tomato12, and upland cotton13. The detection power of GWASs is majorly affected by two factors14. The first is population structure, and the second is linkage disequilibrium (LD), which is species-specific and genomic interval-specific and determines the resolution of the GWAS. The single nucleotide polymorphism (SNP)-based GWAS approach uses LD to relate the top associated SNPs to flanking genes and directly or indirectly identifies candidate genes and genetic architecture components15. However, in some cases, the SNPs with the strongest associations may be very far from the candidate gene because of the high LD in the candidate interval10. When LD is low in some species, such as peach, candidate genes may not be identified due to nonlinkage of the strongest associated SNPs with these genes17. Furthermore, SNP-based GWASs easily identify the biological effects of most associated SNPs on a phenotype when the SNP is located within regions of the gene body or the flanking interval11; however, it cannot directly do so when the SNP is located within the intergenic interval. Unfortunately, this so-called “junk DNA” interval often comprises the majority of the genome, such as ~97% in the human genome18. Very few intergenic SNPs have a significant effect on phenotype; however, structural variants (SVs) located at intergenic regions have been found to have major effects on traits21. Furthermore, the candidate genetic architecture components for the phenotype will have a stronger associated signal than that of its flanking SNP in GWASs, as crossover and recombination reduce the linkage of these noncausal SNPs with phenotypes. Other factors, such as genetic drift and mutation, may also affect the linkage of noncausal SNPs with phenotypes22. Genetic variants rather than SNPs may be directly responsible for phenotypes11, considering that the genetic architecture for traits is complex and diversified.Genome-wide association studies (GWASs) have identified many candidate genes for key traits in 23. These SVs mainly include deletions (DELs), inversions (INVs), duplications (DUPs), insertions (INSs or LIs), and translocations (BNDs). Most traditional SNP-based GWASs are performed on samples with a relatively shallow depth of sequencing (~5x); however, this can increase the uncertainty of genotyping and the missing rate. Occasionally, the candidate bases for a trait will be lost after filtering. Thus, deep sequencing of samples  is recommended for SV-based GWASs. Although many tools for discovering SVs have been developed26, very few software tools can be used to call and genotype SVs at a large population scale. Thus, unlike classic SNP-based GWASs, GWASs based on SVs at a large population scale are rarely reported.Large structural variations (SVs), defined as at least 50 bp in size, were recently recognized as important variant types for traits and diseases in plants, animal species, and humansde novo genome assemblies for peach, plum, apricot, and the wild peach species Prunus mira and Prunus davidiana. We additionally analyzed whole-genome resequencing data of 417 peach accessions and used an SV-based GWAS approach to explore the candidate genes and genetic architecture for key agronomic traits in peach. Using this approach, we successfully identified a number of new candidate genes and genetic architectures influencing key agronomic traits. These genomic resources represent a possible foundation for functional and evolutionary studies and will aid in marker-assisted breeding in Prunus species in the future. We believe that this approach can also be used in other plants, animal species, and humans combined with deep-sequencing GWASs to precisely identify candidate genes and genetic architecture components.In this study, we report five de novo assembled the plum, Prunus mira, and Prunus davidiana genomes for the first time and improved the peach and apricot genomes by integrating single-molecule real-time (SMRT) long-read sequencing (PacBio), short high-quality Illumina paired-end sequencing, and Hi-C technology. First, we used SMRT reads , which consisted of 2,525 contigs with an N50 of ~250 kb (https://www.rosaceae.org), our final peach assembly consisted of 315 contigs with an N50 of ~4,640 kb and resulted in a reduction in the gap number from ~2,300 to ~140 in the final assembly. Therefore, our peach assembly showed an ~18-fold increase in the length of the contig N50 and ~16-fold fewer gaps than the Lovell assembly. Compared with the recently released apricot assembly27 (~220 Mb of genome size), which consisted of 444 contigs with an N50 of 1.02 Mb, our apricot assembly consisted of 122 contigs with an N50 of 3.31 Mb and resulted in a reduction in the gap number from 241 to 163. Our apricot assembly, therefore, showed an ~3.25-fold increase in the length of the contig N50 and ~1.48-fold fewer gaps compared with the recently published apricot assembly. Our five assembled Prunus genomes depicted high congruence because the strongest signals from the Hi-C data clustered at the expected diagonal , woodland strawberry (Fragaria vesca), almond (Prunus dulcis), peach (Prunus persica), mei (Prunus mume), and sweet cherry (Prunus avium). From the results of gene family clustering, 248 single-copy orthologous genes were used for tree construction and species divergence time estimation. As the phylogenetic tree shows , the improved accessions had the highest value , and the LD values for the whole population were ~25k at r2 = 0.39. These values were comparable to those estimated in a previous study29 and in cultivated maize30 (22−30 kb) but far smaller than those estimated in rice10 (~167 kb) and cotton31 (~145.5 kb). The characterization of this GWAS population suggested that this population was suitable for performing GWASs according to its small LD decay value and lack of population structure. Using this population, we identified a number of candidate gene loci for key agronomic traits in peach , and the remaining 258 accessions were newly sequenced in this study. Furthermore, 182 of these 258 accessions were sequenced twice and merged at ~20x (each is ~10x), and the other 78 accessions were sequenced at ~10x. We called a total of 3,749,618 high-quality SNPs and 577,154 small indels from the 417 accessions. Additionally, we identified 31,800 DELs, DUPs, and INVs and 32,338 LIs from 326 deeply sequenced cultivars. We used 99,265 pruned SNPs for the phylogenetic tree, population structure, PCA, and LD decay analyses of the 417 accessions. From the phylogenetic tree Fig. , we grou34. Previous GWASs have discovered that some SNPs in chromosome 6 are closely associated with this trait17. In this study, we first performed a SNP-based GWAS and identified a SNP with the strongest association   that showed the strongest association with this trait  with Hap.1 (reference genome) had round-type fruit, while all accessions (n = 34) with Hap.2 had flat-type fruit. To validate whether this inversion event was responsible for the flat peach trait, we first analyzed the expression patterns of flanking genes during fruit development based on public RNA-seq data37. We found that Prupe.6G290900 expression was significantly higher in flat-type fruit than in round-type fruit at each time point  was higher than that in the round-type population (n = 25) , the expression of Prupe.6G290900 was always higher than that in any of the round-type fruit accessions (n = 25) , while it was significantly different (28% v 72%) in flat-type accession at 80 DAFB based on RNA-seq , all fruits were flatter than wild-type fruits  showed an ~300 bp DNA product with the primer pair upstream of this inversion; however, none of the round-type accessions (n = 32) showed this ~300 bp DNA product  Fig. . We lateait Fig. . The ups900 Fig. , which eant Fig. . All accint Fig. , especia25) Fig. . At the 25) Fig. . The ratseq Fig. , suggestato Fig. , and we its Fig. , m and wits Fig. . The fruits Fig. , and the900 Fig. , m. Baseuct Fig. . Moreoveuct Fig. . This st39. Although the SNP/indel and SV datasets used for GWAS were all analyzed  located upstream of the candidate Prupe.5G005400 gene that showed the strongest association with this trait, which is a different locus from the candidate reported by a previous study17 and encodes a switch subunit three protein. After careful analysis of the candidate interval, we identified a gene involved in fruit sugar content variance, as a sugar QTL and sugar/acidity QTL were previously reported in the same interval42. We identified a single SNP  in the third exon of Prupe.5G006300  , only individuals with the GG genotype were selected , individuals with the GT genotype were preferably selected  where no suitable candidate genes were identified for this trait  had significantly longer fruit development periods than those with Hap. 2  and Hap. 3   also had significantly longer fruit development periods than those with Hap. 3   was significantly lower than those in the population with Hap. 2  and Hap. 3   was significantly lower than that in the population with Hap. 3  , we found a clear allele-specific expression pattern of Prupe.4G187100 at the mature fruit stage  Fig. . Accessi16) Fig. . We latevel Fig. . The exp−4) Fig. . The exp−8) Fig. . At the ons Fig. ; most acest Fig. . The expage Fig. ; 65 DAFBait Fig. .Fig. 4A 53 and was mapped to chromosome 254. The second locus, identified as a single dominant gene (Dl2/dl2), which was first described by Beckman et al.55, was assigned to chromosome 6 and identified as a TOE-type AP2 gene. Deletion of the miR172 target site in this gene is responsible for the dominant double-flower trait in Rosaceae57. However, to date, the Dl gene is still controversial. In this study, we first performed SNP-based GWAS and identified the top associated SNP  dataset (https://blast.ncbi.nlm.nih.gov/Blast.cgi), we discovered that this gene is actually a noncoding RNA that transcribes the miR172d precursor , we identified two independent insertion events within the promoter of Prupe.2G237700 and a total of three main haplotypes based on a large insertion SV  for double flowers was described by Lammerts SV Fig. . Hap. 3  SV Fig. . Hap. 2  SV Fig. . These tPrupe.5G196100 in the middle of scaffold Pp056. A large insertion variant was discovered in the third exon of this gene and was shown to cause loss of function and the hairlessness phenotype6. To determine if this event was the only event responsible for all hairlessness phenotypes, we first performed a SNP-based GWAS and found that the top associated SNP  was located ~700 kb downstream of this gene locus  . We further analyzed this insertion event in all nectarine accessions and found that all nectarine accessions from Gansu Province did not have this large insertion variant at this site. To determine other variants responsible for hairlessness traits in accessions from Gansu Province, we first performed a SNP-based GWAS on this trait, but we defined other nectarine accessions with known LIs  as hairy peaches. We identified one SNP with the strongest association signal  located in the third exon of the same gene, Prupe.5G196100  was located in a conserved position  was the cause of the hairlessness trait: all accessions with only one copy of the insertion (Hap. 2) were normal peach, but an accession with one copy of the insertion (Hap. 2) and SNP749 (Hap. 3) was a nectarine. Hap. 3 was only present in nectarines originating from Gansu Province. These observations suggested that the hairlessness trait originated from at least three independent mutation events.Nectarine in peach is a key agricultural character affecting both appearance and ecological adaptation. The nectarine trait is recessive to normal peach, and the major gene controlling this trait is the MYB gene cus Fig. , where n15) Fig.  was loca100 Fig. , c. Hapl100 Fig. . Hap. 1 ion Fig. . AnotherPrupe.1G255500 was considered responsible for the trait, and three independent mutation events were considered responsible for the origin of yellow flesh61. In the present study, we first performed a SNP-based GWAS to try to relate the top associated SNPs to this gene. However, we discovered that the top SNP  was ~3 Mb away from this gene  in the intron of Prupe.1G255500  on linkage group 161; only the eighth strongest signal in this study was in this interval and thus discovered.Flesh color (white or yellow) in peach affects fruit nutritional value and consumer preference. White color is dominant to yellow color. The candidate gene ene Fig. . We late500 Fig. , which w62. In this study, we de novo assembled five species in the genus Prunus, demonstrated the utility of these genomes for the identification of structural variants, and provided a basis for functional genomics and comparative genomics in Prunus species.A chromosome-grade genome assembly is valuable for identifying genetic variants and performing GWAS, providing new insights into the genetic architecture of key agronomic traits and genomic evolution13. However, this classic SNP-based approach was not very successful in peach, as few candidate genes and genetic architecture components were identified for traits in previous studies17 and in this study. To identify candidate genes, we had to adapt the traditional SNP-based GWAS approach for the fruit nonacidity and nectarine traits. After adjusting the GWAS approach, we identified a gene involved in fruit sugar content variance linked with nonacidity to some degree and a new allele for the nectarine trait from Gansu Province. Although selecting a sugar content variance-related gene in a GWAS of a nonacidity trait seemed improbable, this gene is located in a linkage interval harboring fruit sugar, acidity, and taste QTLs42. Additionally, Prupe.5G006300 may be the candidate gene for this sugar QTL and may interact with genes associated with acidity to determine fruit taste. The allele frequency in the West and East populations suggested that this locus showed differential selection due to preferences for different tastes. For the nectarine trait from Gansu Province, a new allele of the candidate gene Prupe.5G196100 was identified. This finding suggested that the nectarine trait had multiple origins, similar to the fruit flesh color61 and double flower traits in this study. This mechanism may be the universal mechanism for the adaptation of plants and natural or artificial selection.GWAS is a versatile tool for identifying candidate genes and genetic architecture components for diseases and key agronomic and economic traits. Indeed, many studies have confirmed that this SNP-based approach was successful in discovering candidate genes5. The second possibility is that there was no casual indel for these traits or that the indel was not linked to any candidate genes or genetic architecture components. When traits were directly regulated by SVs identified by our SV-based GWAS (as described below), we found that it was difficult to relate the top associated SNPs with these SVs. One possible explanation is that the crossover and recombination rate could be extremely high in these flanking intervals if the SNP was not generated later than the SV or the flanking SNP was generated much later than the SV. In these two cases, some accessions with SVs will have no SNPs. The third possibility may be that very low LD in the peach genome prevents the linkage of the SNPs with the SVs; this case may have occurred in maize, which has a similar LD value30. Furthermore, in some cases, the SNP is not linked with the candidate SV owing to a variety of mechanisms, such as mutation, genetic drift, and selection20. In these cases, the candidate SV rather than the SNP was directly selected and enriched in a population, and the classic SNP-based GWAS will lose substantial detection power for key traits. SV-based GWASs will provide a new approach to identify candidate genes and genetic architecture components for key traits.We also used small indels as genetic markers to conduct GWASs, but this strategy was not successful in our study. The GWAS results were similar to those generated by SNPs. Additionally, no causal indel was identified for any trait. One possibility is that allelic heterogeneity is poor in this analysis, such as in the case of fruit flesh color17, and we identified the top associated signals on these genes or very near them. These findings further indicated that INSs, especially transposable elements, are key genetic variants for many key agronomic traits, considering that transposable element events were responsible for three traits in this study and that transposable elements occur in the majority of the genome sequence.The genetic basis for agronomic traits in plant species varies; thus, only one approach to identifying candidate genes for key traits is limiting. A candidate SNP can be directly detected by traditional SNP-based GWASs, and as SVs are actually genetic marks similar to SNPs, candidate SVs may also be directly identified by SV-based GWASs. Thus, we used a combined SV-based GWAS approach to increase the success of identifying the underlying genetic architecture for traits. We provide evidence that SV-based GWASs can be used to identify candidate genes and genetic architecture components. Genotyping of DELs, DUPs, and INVs in a large population is feasible. Using the SV-based approach , we successfully identified a large inversion event responsible for flat shape in fruits and the premature abortion of fruits, as well as complex genomic rearrangements and tandem repeat events related to fruit development period and fruit maturity date traits. As the genotyping of INSs at a large population scale cannot be performed to date to our knowledge, we adapted a simplified genotyping approach, although it decreased the power of GWAS detection. Using this approach, we identified transposable element insertion events as the underlying causal factors for double flower, nectarine, and flesh color traits. All of these genes and candidate bases were lost in previous GWAS studies17 identified a SNP completely linked with this trait, which is ~80 kb upstream of this INV. We inferred that this SNP was generated in conjunction with this INV as the same event and was thus linked with this trait as an INV, so these two variants were located in the same LD block. Our preliminary SNP-based GWAS did not identify this SNP as the top associated signal . After we decreased this parameter to 0.04, this SNP became the top associated signal . Second, the transgenic phenotype of the two alleles  was not associated with fruit ripening (data not provided). Third, the phylogenetic tree suggested that this gene may be related to the stress response46. However, these three lines of evidence  all supported Prupe.4G187100 as the candidate gene for development period traits. The association of Prupe.4G187100 with fruit ripening was also validated by a previous study48. Although a few genes and genetic architecture components were identified for some traits, the genetic architecture of many traits in this study was still not identified by the SV-based GWAS approach. Other types of variants, such as epigenetic footprints, may be the underlying genetic basis for some traits. Allelic heterogeneity may also hinder the discovery of candidate genes and genetic architecture components. The environmental effect on phenotypes was not uniform. Further effort is still needed to identify and validate more candidate genes for these key traits. For INS-based GWAS, major efforts are also needed to develop tools and software for genotyping this SV at a large population scale.We, fortunately, identified two independent transposable element events for double flowers, as only seven accessions with this phenotype were found in the 326 accessions in our GWAS panel. We concluded that the success was because no accessions were derived from crosses of edible peach and ornamental peach. Notably, the favorable traits of fruits and flowers were independently improved by breeders in the past. For the flat fruit shape trait, a previous studynal Fig. . This SNnal Fig. . Althougde novo assembled five species in the genus Prunus and generated a useful sequence dataset, which will help promote Prunus functional genomics and comparative genomics in fruit species in the future. The identified candidate genes and genetic architecture components by GWASs may provide targets for molecular marker selection and the improvement of key traits. Additionally, this comprehensive GWAS approach could be used in future deep sequencing studies to more precisely identify candidate genes and genetic architecture components for diseases and key traits in plants, animal species, and humans.In summary, we Prunus species were grown in our fruit field . Fresh leaflets were collected and stored in liquid nitrogen until DNA extraction and sequencing. The long-read, short-read, and Hi-C libraries were all prepared and sequenced at Annoroad Genomics  (http://en.annoroad.com/) following the manufacturer’s standard protocols. The 20 kb PacBio library was prepared and sequenced on a PacBio Sequel II using P6-C4 chemistry. A short-insert library with 400 bp inserts and fragment sizes of 300−500 bp was constructed for the Hi-C library and sequenced with an Illumina HiSeq X-Ten platform.Heterozygous diploid trees of five 63 with a genome size estimation of 250 Mb as an input. One copy of the contigs from heterozygous regions was retained by using redundant sequences (v0.14a) with the following parameters: Prunus salicina: --identity 0.8 --overlap 0.8; Prunus persica: --identity 0.9 --overlap 0.9; Prunus armeniaca: --identity 0.8 --overlap 0.8; Prunus mira: --identity 0.9 --overlap 0.9; and Prunus davidiana: --identity 0.85 --overlap 0.8. The resulting assembly was polished by aligning PacBio reads with Quiver64 followed by running Pilon (v1.20)65 with the Illumina short-read sequences. The reads from the Hi-C library were aligned to the primary assembly using Bowtie266. The resulting bam files together with the contigs were used as input for Lachesis67 with the cluster number set to 9 and the remaining parameters set as default. The pseudoscaffolds constructed by Lachesis67 were split into bins of 100 kb and used to construct an interaction heatmap for validation and manual correction. The serial numbers of the chromosomes were manually adjusted in descending order of chromosome length . Assembly completeness was assessed with BUSCO  (v3.0.1)28 using 1,375 plant ortholog groups (embryophyta v10) with default parameters.Filtered PacBio subreads were first assembled with Falcon (v0.4)de novo prediction strategies. We generated RNA-Seq libraries from a mixture of leaves, phloem, fruit, and seeds and conducted full-length sequencing on the PacBio-Iso platform. The full-length transcripts were directly used to predict gene models with PASA68. Homology-based evidence was derived from protein sequences blasted against UniProt assemblies. De novo prediction of gene models was performed using Augustus69. Complete and nonredundant gene models were combined using EvidenceModeler70. Repeated annotation of the assemblies was based on homology and de novo prediction strategies. The homology-based prediction was performed with RepeatMasker71 using the RepBase database72. De novo assembly of repeat sequences was conducted with RepeatModeler (http://www.repeatmasker.org/RepeatModler/), followed by the repeatMasker tool.Genome annotation was based on full-length RNA-iso sequencing and homology-based and 73 to perform protein alignments for each single-copy gene family with the default sets. The maximum likelihood tree was constructed using PhyML software74 with the default parameters. The divergence time of each node in the phylogenetic tree was estimated based on the BRMC model in the MCMCTree program from the PAML package75. The species divergence time calibration was based on TimeTree (http://timetree.org/).Phylogenetic tree construction was performed based on 248 single-copy genes extracted from the gene family cluster analysis. We utilized MUSCLE softwarePrunus accessions were collected as described previously29, representing most ecotypes worldwide. The 258 peach accessions newly sequenced in this study were collected from various regions in China and planted in the Peach Germplasm Repository, Shandong Agricultural University, China. These 417 accessions included 5 accessions of P. davidiana, 11 accessions of P. ferganensis, 3 accessions of P. kansuensis, 8 accessions of P. mira, 1 accession of P. dulcis, 1 accession of P. tangutica, and 388 accessions of P. persica peach varieties. For the 258 newly sequenced accessions, the fresh leaf of a single individual was used for DNA extraction using the CTAB method, the insert size of the libraries was 350 bp, and the paired-end read length was 150 bp. The 258 samples were sequenced on the Illumina HiSeq 2500 platform.A total of 417 accessions (159 + 258) were characterized by whole-genome resequencing. Among them, 159 76. The traits for the 159 previously analyzed accessions were collected from previous papers29 and books77.Nineteen agronomic traits were phenotyped in this study, including 11 qualitative and eight quantitative traits. The 11 qualitative traits included fruit shape (flat/round), flesh color (yellow/white), fruit hairiness (peach/nectarine), fruit nonacidity (high/low acidity), flesh adhesion (clingstone/freestone), flesh texture (hard/soft), flesh color around the stone (red/white), flower double (double/single), flower morphology (showy/nonshowy), male sterility (fertility/sterility), and leaf gland (globular/reniform). The eight quantitative traits included fruit development period, chilling requirement, leaf width, top leaf anthocyanin content, middle leaf anthocyanin content, bottom leaf carotenoid content, bark Chl a/b, and bark carotenoid content. The fruit traits were evaluated using fully matured fruits. All traits were analyzed in at least five fruits, flowers, leaves, and bark sections, which were collected from the tree that was sequenced for each accession. All agronomic traits considered here were characterized based on previously published plant genetic resource evaluation criteriahttps://www.ncbi.nlm.nih.gov/sra). Then, 182 of 258 peach accessions were sequenced at a coverage of ~20x , and 76 of 258 peach accessions were sequenced at a coverage of ~10x. The 159 cultivated and wild peach accessions were sequenced unevenly from 3x to 100x. The 159 public accessions were downloaded and mapped to the peach reference genome v2.03 with bwa-mem78, and the 258 accessions were mapped to the peach reference genome v2.03 with minimap279. The mapped results were sorted and filtered to remove PCR duplicates with samtools80 and Picard tools (https://broadinstitute.github.io/picard/), respectively. The Genome Analysis Toolkit81  was used to realign mapped sequences within the interval with INDEL. The Genome Analysis Toolkit81  was used for jointly calling SNPs and small indels throughout the entire collection of 417 peach accessions with default parameters and recommended hard filtering. The SNP set was further filtered , and only the biallelic SNPs were retained for downstream analysis.In this study, we analyzed a total of 417 whole-genome sequences of 417 peach accessions. A total of 258 peach accessions were sequenced in this study. An additional 159 cultivated and wild peach accessions were downloaded from a public dataset (https://github.com/brentp/smoove) using input bam files generated as above. This pipeline was first used to call SVs for each accession to obtain a union of sites across all samples. These variant sites were used to genotype each accession, and the resulting single samples were merged to generate a raw variant dataset. This pipeline was used to call and genotype DELs, DUPs, and INVs in the 326 accessions. The Pindel tool (https://github.com/genome/pindel) was used to call large insertions  in the 326 accessions, and the resulting variants were subsequently converted to vcf files. The pipeline for calling large insertions  was first used for each accession and later merged with bcftools (https://samtools.github.io/bcftools/bcftools.html).The 18 wild relative accessions and 73 accessions with a low depth of sequencing were excluded from downstream analyses, and 326 accessions were kept for SV calling and genotyping at a population scale. The 326 accessions were analyzed following the guidance of Smoove (82 (--indep 50 5 2). The genetic distance between two given accessions was calculated with dnadist in Phylip83 (v3.96), and a neighbor-joining tree was constructed and visualized in itol (https://itol.embl.de/). The population genetic structure was examined via an expectation-maximization algorithm, as implemented in the program Admixture84. The number of assumed genetic clusters K ranged from 2 to 9, with 10,000 iterations for each run. PCA was performed using EIGENSTRAT tools85 and plotted with R software. LD decay was measured using PopLDdecay software86, which directly uses the Variant Call Format (VCF) file with many variants as input to produce the LD decay statistics and plot the LD decay graphs in a pipeline manner.The phylogenetic tree, PCA, population structure, and LD analyses were performed on 99,526 high-quality SNPs pruned with plink87. Population structure was corrected using a kinship (K) matrix  estimated by the emmax-kin-intel package of EMMAX based on 999,567 SNPs. The first ten principal components of the PCA were included as the variance-covariance matrix for adjusting for population stratification based on 99,456 pruned SNPs. The genome-wide significance thresholds of all the traits were determined using the Bonferroni test. According to a nominal level of 0.05, the threshold was determined by a threshold P . The GWAS based on SNPs and indels was performed on 417 samples, and these samples were used to calculate the K matrix and variance-covariance matrix. The GWAS based on SVs was conducted on 326 samples, which were used to estimate the K matrix and variance-covariance matrix. To identify the variant associated with hairlessness in nectarines without the previously reported LI, we considered all nectarine accessions with the LI as normal peaches and the remaining nectarines without LI events as the other class of phenotypes. The Manhattan plots and local plots were generated with the Sushi88 and qqman89 packages.A total of 999,567 high-quality SNPs in 417 samples, along with 152,210 indels, 21,416 DELs, DUPs, and INVs (further filtered with --MAF 0.05 and --max-missing 0.75), and 32,338 LIs in 326 samples (further filtered with --MAF 0.01 and --max-missing 0.75) were used to perform genome-wide association analysis on key traits. All GWASs were conducted with efficient mixed-model association expedited (EMMAX)90 with the default parameter. The resulting bam files were sorted, and PCR duplicates were removed with a Picard tool (https://broadinstitute.github.io/picard/). To analyze the allele-specific expression pattern of Prupe.6G290900 and Prupe.4G187100, we used accessions with heterozygous SNPs located at this gene locus to perform RNA-seq. We generated a total of ~288 Gb of raw sequencing data for 48 peach accessions and ~80 Gb for an additional 8 peach accessions. The allele-specific expression pattern of Prupe.4G187100 was visualized by IGV91. The transcripts of each sample and expression levels of the genes were built and estimated by using Stringtie92 with the default parameters. To estimate the FPKM value, we downloaded the data from NCBI . The reads were mapped against the peach reference (V2.0) with hisat290, and the FPKM value was estimated for the gene with StringTie92.To detect the expression level of candidate genes among different peach accessions, we sampled the fruit flesh of 48 peach cultivars at ~40 days after flowering. To analyze the allele-specific expression pattern of candidate genes, additional ripening fruit flesh tissue of eight peach cultivars at 80 days after full blooming was also collected. These fruit flesh tissues were used to extract RNA. A library with an insert size of ~350 bp was constructed, and 150 bp paired-end reads were sequenced using HiSeq 4000. Reads were mapped onto the peach reference genome (V2.0) using hisat2Prupe.6G290900, Prupe.6G323700, and Prupe.4G187100 in varieties at 40 DAFB were detected using qRT-PCR. Total RNA was extracted from whole fruit tissue with an EASYspin Plus Plant RNA Kit (Aidlab Biotech) and reverse transcribed with a PrimeScript RT Reagent Kit with gDNA Eraser (TaKaRa). Real-time PCR was performed in triplicate twice with SYBR Premix DimerEraser  (TaKaRa). Prupe.6G163400 was used as an internal control to normalize gene expression. The results were analyzed according to the ΔCt method93.The expression patterns of Prupe.6G290900 was cloned into the PbI121 vector under the control of the 35S promoter and transformed into tomato plants using the Agrobacterium-mediated transformation approach with kanamycin as a selectable marker. Transgenic plants were validated by both PCR with genomic DNA and quantitative real-time RT-PCR. The transgenic lines (T0) of Prupe.6G290900 were photographed at various stages of fruit development.The CDS of P = 0.05) was performed using R software and SPSS 22.One-way ANOVA (PRJNA655343.The genome assemblies, annotation files, and raw data of DNA and RNA sequences have been deposited in the NCBI Sequence Read Archive under accession number Supplementary Material


Prunus persica is an economically important characteristic and comprises the classification criteria. The mapping and identification of a complete linkage marker to the fruit skin trichome trait locus of peach fruit are critical for the molecular marker-assisted selection for peach/nectarine. In this study, the BC1 population was constructed from the parents “Zhongyou No. 4”, the recurrent parent, and “Baihuashanbitao”, the non-recurrent parent. Based on the 38 BC1 individuals’ phenotypes and their genotyping using next-generation sequencing, the G (Glabrous skin) locus of the gene was first identified between 14.099 and 16.721 Mb on chromosome 5. Using other individuals of this population, the gene was fine-mapped in the range of 481 kb with SNP markers. Based on the resequencing data of other cultivars (lines), the candidate SNP in the gene Prupe.5G196400 was obtained. Subsequently, the SNP marker was designed and applied to natural and hybrid peach populations. Via genotyping analysis, we confirmed co-segregation between the peach/nectarine phenotype, which was used in the identification of peach or nectarine with 100% accuracy.The fruit skin pubescence of  Prunus persica L. Batsch) is one of the most important deciduous fruit trees in China, accounting for 826,325 ha of cultivation area in 2018. It is in high demand because of its flavor and high amount of nutrients [Peach  locus was first mapped at the proximal end (bottom) of linkage group 5 [PpeMYB25, controlling the peaches’ skin hair, has been identified. The insertion of the LTR transcription factor in the third exon of PpeMYB25 in the MYB gene family led to the appearance of a recessive hairless phenotype [To locate and identify the genes controlling the fruit skin hairiness of screened , and theal peach . The G  and to find molecular markers linked to the trait, with the aim of establishing a reliable SNP maker for peach/nectarine identification. Our results provide technical support for parental selection and early identification of hybrid progenies in peach/nectarine breeding.The cultivation of peaches, as a perennial fruit tree, using traditional breeding methods is impeded by the long juvenile stage, significantly restricting the development of the peach industry ,17. In t1 population were distinguished in the field based on fruit skin appearance. For further observation, we used a stereomicroscope to observe the differences in the fruit skin between peaches and nectarines. At 30× magnification, the peach cultivars showed white filiform growth, which was absent in the nectarine cultivars. In the progenies, the phenotype was segregated into hairy and non-hairy  was used to select the reference genome for genotyping 38 BC1 individuals, with scanning of the whole peach genome (chromosomes 1–8). We found a region with the same genotypes and phenotypes on chromosome 5  within the Prupe.5G196400 CDS region was identified based on resequencing data for two parents  was further verified (1 population was 100% (The reference peach genome database (version 2.0) contained 85 known transcripts within 481 kb of the fine-localization region. To acquire the candidate gene, we analyzed the correlation between the genotypes and phenotypes of the parents and 38 BC parents . To valiverified . The reswas 100% . In the was 100%  and we dG locus and can be used in molecular marker-assisted selection of target varieties.To confirm the feasibility of the identification of peach and nectarine varieties with the SNP maker in the coding region of Prupe.5G196400, 122 cultivars or lines were tested. Based on the Sanger sequencing results, there were three genotypes of SNPs  in 122 cultivars (lines) . Among tIn addition, three populations were used to identify the phenotype of the offspring, using the SNP marker. The result indicates that the SNP  markers can distinguish the phenotypes of the individuals with 100% accuracy .In China, peaches have been cultivated for more than 3000 years . BecauseTTG1 and GL1, two genes that play a key role in the growth of Arabidopsis thaliana hairs, affect the growth of hairs [PpeMYB25; the authors observed that the insertion of the 7 kb LTR reversal loci in exon 3 of PpeMYB25 gene was related to the recessive characteristic of nectarines [PpeMYB25 locus was observed. An SNP in ORF of Prupe.5G196400 was found, which is tightly linked to the trait and could be effectively used to distinguish peach and nectarine varieties in germplasm resources.Recently, several genes related to the development of fruit pubescence have been identified and cloned. For example, Payne et al. found that mutations of of hairs . Li et aof hairs  identifiPrupe.5G196400. Although our gene has not yet been functionally verified, through our 122 peach varieties (lines) and four populations, this marker can distinguish the peach/nectarine characteristics of peaches with 100% accuracy. For this reason, we consider it as a potential candidate gene. Our results provide a foundation for the selection of early follow-up parents, variety identification, early phenotype prediction of progeny representative type, and variety selection in peach breeding.In this study, we used next-generation sequencing and SNP markers to perform the fine mapping of the gene controlling peach/nectarine traits and obtained the candidate gene 1 lines generated from “Zhongyou No. 4”, the recurrent parent, and “Baihuashanbitao”, the non-recurrent parent, were generated in 2012. “Zhongyou No. 4” is a yellow-flesh and early-ripening nectarine cultivar bred by the Zhengzhou Fruit Research Institute, CAAS, whereas “Baihuashanbitao” is an ornamental peach from P. davidiana with female abortion, bred by the Beijing Forestry University.The BC1 individuals were phenotyped as hairy and non-hairy and subsequently genotyped using NGS for mapping. Within the mapped region, SNP markers were designed based on parental sequencing data and tested in the other 129 BC1 individuals for fine mapping. To validate the candidate gene, resequencing data for 16 individuals were used, and 122 cultivars or lines were tested to confirm the linkage between genotypes and phenotypes.The parents and 38 BC1 individuals, genomic DNA was extracted using the CTAB method, with slight modifications [1 individuals and 122 cultivars (lines), genomic DNA was isolated with the simplified CTAB method, with one step of chloroform: isoamyl alcohol  to remove potential contaminants.For the recurrent (Zhongyou No. 4) and non-recurrent (Baihuashanbitao) parents and for the 38 BCications . In the 1 individuals and the 122 cultivars for mapping and fine mapping were evaluated in the breeding pool  by visualization. In addition, the skin was scanned at 30×, using a digital microscope VHX-7000 .The fruit skin traits (hairiness/non-hairiness) of BCWe used 1.5 μg DNA per sample for DNA resequencing. Sequencing libraries were constructed using the Truseq Nano DNA HT sample preparation kit  following the manufacturer’s instruction; index codes were added to each sample. Briefly, the DNA sample was sheared by sonication into 350 bp fragments and purified. Each sample was then sequenced using HiSeq 2500 , and 150 bp paired-end reads were generated. The peach genome (version 2.0) was used as the reference genome .We applied the BWA (Burrows–Wheeler Aligner)  to align1 individuals were divided into two phenotypic classes and then their graphical genotypes were compared in Excel file [The homozygous SNPs, genotype AA and BB, in two parents were extracted, respectively, from the vcf files for SNPs. Based on the NGS data in the Excel file, a bin map was drawn to identify the introgression fragments from “Baihuashanbitao”. For the mapping analysis, the BCcel file . We idenhttp://bioinfo.ut.ee/primer3-0.4.0/ accessed on 18 June 2020) was used to design the primers [Based on the resequencing data of parents, the SNPs in the candidate region were selected. We used the Integrative Genomics Viewer 2.3 software  to read  primers . For SNPFor the fine-mapped region, the genes were listed referencing the annotation in the GFF file of the peach genome (version 2.0) . Variati1 individuals from “Zhongyou No. 4” as the recurrent parent and “Baihuashanbitao” as the non-recurrent parent, the G locus was mapped between 14.099 and 16.721 Mb on chromosome 5 and fine-mapped in the range of 481 kb with SNP markers, using the other 129 BC1 individuals of this population. A candidate SNP in the gene Prupe. 5G196400 was obtained based on the resequencing data of other cultivars (lines). The SNP marker was able to identify the phenotypes in 122 cultivars (lines). By genotyping analysis, the SNP was co-segregated with the fruit skin fuzzy phenotype and used for identification of peaches or nectarines in three segregation populations with 100% accuracy.Using 35 BC


However, a goose’s genetic diversity is inaccurately explored, mainly because the advantages of the most commonly used tools are strongly limited in non-model organisms. One of the most accurate used markers for population genetics is single nucleotide polymorphisms (SNP). A highly efficient strategy for genome-wide SNP detection is genotyping-by-sequencing (GBS), which has been already widely applied in many organisms. This study attempts to use GBS in 12 conservative goose breeds and the White Kołuda® breed maintained in Poland. The GBS method allowed for the detection of 3833 common raw SNPs. Nevertheless, after filtering for read depth and alleles characters, we obtained the final markers panel used for a differentiation analysis that comprised 791 SNPs. These variants were located within 11 different genes, and one of the most diversified variants was associated with the EDAR gene, which is especially interesting as it participates in the plumage development, which plays a crucial role in goose breeding.Poland is the largest European producer of goose, while goose breeding has become an essential and still increasing branch of the poultry industry. The most frequently bred goose is the White Kołuda The Polish goose constitutes economically significant livestock that is distributed worldwide and, above all, it is well-known for its excellent properties of meat and plumage ,2,3. In n Poland . It was n Poland ,3. MoreoCommonly used markers for population genetics are single nucleotide polymorphisms (SNPs). SNPs are single base changes in a DNA sequence and are widely applied for genetic studies because of their biallelic character, abundance and dense distribution across the genome, and low mutation rate . SNPs caCurrently, genotyping-by-sequencing is an often-used method for small projects or initial research worldwide. However, the GBS method has some potential drawbacks, such as uneven genome coverage and a high percentage of the intergenic variants. Frequently, low coverage sequencing applied in some experiments leads to a high amount of missing data or genotyping errors, resulting in false genotypes. Furthermore, it is extremely important to choose the appropriate restriction enzyme for a given species. Additionally, the reduction in genome complexity using restriction enzymes means that, in case of any mutation at the restriction site, the genomic DNA of this region is not available to be PCR amplified and, consequently, the SNPs of this region will become undiscovered ,10.In the beginning, GBS was mainly applied to plants, but it has also found application in SNP detection and genotyping in animal genomics. The GBS has been successfully used in chicken, pig, cattle, sheep and horse ,18,19,20The present work tests a cost-effective genotyping procedure based on low coverage NGS on goose breeds maintained in Poland for single nucleotide polymorphism discovery and genetic differentiation analysis. Our research is the first attempt in the application of genotyping-by-sequencing to geese species.® geese, and additionally, we sourced 96 blood samples from White Kołuda® geese. In total, 340 samples were taken for analysis. The samples were selected randomly from different cages and flocks. Moreover, the samples were taken from male and female individuals. The examined blood samples were collected during the routine slaughter of geese in Kołuda Wielka, so the ethical approval was not needed for this research.The experimental material originates from the National Research Institute of Animal Production, Department of Water Fowl Breeding in Kołuda Wielka and Dworzyska. We collected 240 plumage samples from 12 geese breeds: Kielecka (Ki), Podkarpacka (Pd), Garbonosa (Ga), Pomorska (Po), Rypinska (Ry), Landes (La), Lubelska (Lu), Suwalska (Su), Kartuska (Ka), Romanska (Ro), Slowacka (Sl) and Kubanska (Ku)—20 plumage samples from each breed. We collected 4 plumage samples from White KołudaDNA was extracted using the Sherlock AX Isolation KIT  according to the provided protocol for both kinds of biological material. The calamus was taken as a source of DNA from plumage.DNA was pooled in groups according to the breed, with ten individuals per pool. As a result, we obtained 34 pools with a final DNA concentration of 100 ng/µL .Prepared DNA pools were digested overnight in 37 °C with the restriction enzyme PstI-HF . Afterwards, products of digestion were ligated with 4.8 ng of adaptors using T4 DNA ligase  in 60 min incubation in 22 °C. In our analysis, we used 48 indexed adapters designed with GBS Barcode Generator software as described in the previous study [The obtained libraries were sequenced in a single 50 bp run on the HiScanSQ system  with the use of TruSeq v3 chemistry and v3HiSeqflowcell.Anser cygnoides (Swan goose) genome assembly (GooseV1.0) as a reference. TASSEL filtering steps were first applied to the reads, which did not allow base quality lower than Q20 and kept reads from having a barcode and a cut site, and no N’s in the useful part of the sequence. The procedure also trimmed off the barcodes and truncated sequences that (1) had a second cut site or (2) read into the common adapter. Default TASSEL pipeline parameters were used for SNPs detection, except kmer Length which was set to 40 and had a minimum count of reads for a tag with an output set to 5. Tags were mapped to the reference genome using a Bowtie2 aligner [Raw reads obtained after sequencing were checked for quality using FastQC software. Then, the TASSEL 5 GBS v2 Discovery Pipeline  was perf aligner .Polymorphisms resulting from the default TASSEL Production Pipeline were initially filtered to remove indels and multiallelic markers with an average coverage lower than 170 reads (5 per pool on average). Additionally, individual pool ‘genotypes’ were removed if the coverage was lower than 5 reads. Then, SNPs located on X and Y chromosomes were excluded because of the uneven distribution of sex within pools.The read counts obtained for separate alleles were used to calculate allele frequencies for individual SNPs in separate pools. Then, overall allele frequencies were calculated for breeds as an average for all pools sequenced per breed. In rare cases of no coverage for some SNPs in single pools, allele frequencies were derived from the remaining pools.Frequency data were used to perform breeds differentiation analysis by principal components analysis (PCA) and hierarchical clustering in ClustVis software . Then, NTo identify variants that were mostly associated with the breeds differentiation, the variance of allele frequency was calculated for each SNP. The 10% of SNPs with the highest variance among breeds were further screened for colocalization with genes. The associated genes were analyzed for their functions using the Panther Classification System  and humaAnser cygnoides . The expected fragment was amplified using HotStarTaq DNA Polymerase (1000 U)  according to the attached protocol, then the one-step enzymatic purification of the PCR product was performed using ExoBap  and sequenced using BigDye® Terminator v3.1 Cycle Sequencing Kit . DNA sequencing products were separated using the 3500xl Genetic Analyzer  and analyzed in BioEdit software.GBS accuracy was established by EDAR gene sequencing in 96 selected individuals from each goose breed examined in this analysis. Primers were designed using the Primer 3 software, based on the DNA sequence for EDAR of PstI cut site detected) were used for tags detection and SNPs discovery  removed 3042 SNPs. The final markers panel used for differentiation analysis comprised of 791 high-quality SNPs. The average coverage of the filtered SNPs was 840.7 and ranged from 171 to 25,962.The analysis of the SNP parameters showed that their expected heterozygosity ranged from 0.02 to 0.499 and was 0.215 on average. The evaluation of missing genotypes showed that individual SNPs were characterized by 0% to 53% of missing genotypes, with an average of 8.1%. The list of variants used in this study is presented in The expected heterozygosity calculated per breed ranged from 0.16 in Ku to 0.23 in BK . The dis® breed. In contrary, the results obtained with hierarchical clustering were rather more similar to those obtained with PCA . A relatively high number of variants were also found in the gene introns (14.6%), as well as in the upstream gene regions, including promotes (13.3%). Among SNPs located in coding sequences, 58% were synonymous .CEL), salivary gland morphogenesis (EDAR), pigmentation (KIF13A), skin epidermis development and hair cycle (EDAR)  .TSPAN18), Kinesin-like protein KIF13A (KIF13A), RING finger protein 223 (RNF223) and Ectodysplasin A receptor (EDAR), and were located within the gene introns. The most polymorphic gene was KIF13A, where 10 potential SNPs were found. The genes are connected with various biological processes, including regulation, cellular processes, metabolism, response to stimulus and signaling.Based on variance distribution, 79 SNPs with the largest differences in allele frequencies between the breeds were selected for functional analysis . The mosEDAR gene  . This maunlikely . Moreove® geese maintained in Poland allowed us to characterize their genetic diversity and mutual phylogenetic relationships. The dendrograms of examined geese showed a clear division into three distinct groups (Ansercy gnoides) than all the other populations (Anser anser). Using microsatellite markers for phylogenetic analysis of the same material did not allow for such clear identification of descendants of separate species [® geese were placed in the same cluster despite Podkarpacka and Kielecka come from the southern regions of Poland, while Kartuska and White Kołuda® geese come from the northern regions. In conclusion, we may say that our GBS analysis agrees with the phylogenetic origin of geese, but it does not reflect their geographic origins provided by historical data [The GBS analysis of the conserved flocks and the White Kołudat groups  Garbonos species , which pcal data .Current GBS analysis and our previous studies based on microsatellite markers suggest that geese maintained in Poland are closely related to each other and genetic variation between breeds is low, making it difficult to distinguish them from each other. The present analysis shows no clear division depending on phenotypic traits such as geese body weight or plumage color. Among breeds analyzed by us, some are generally considered as heavy weight  and light weight geese . Slowacka goose body weight is considered as medium when compared to the others. Another morphological trait that could shape genetic patterns of variation in Polish geese may be plumage color and color patterns. Pomorska, Slowacka and Romanska are characterized with white plumage while Kartuska, Podkarpacka and Rypinska are white and speckled grey or brown . ContrarEDAR which had SNP variants located within the intron. This seems to be especially interesting, as EDAR encodes an ectodysplasin A receptor that plays a crucial role in developing ectodermal tissues, such as skin or plumage [EDAR) plays a critical role in this. The EDA signal co-operates with spreading a wave of cellular density to spark the formation of regularly spaced cell aggregation, each of which serves as the precursor to a feather [EDAR gene contains several single nucleotide polymorphisms which would be an interesting object to examine deeper on a larger number of animals.One of the genes associated with the most diversified genome region detected in this study was  plumage . In some plumage . Plumage feather . We coulOur findings proved that the GBS method is a useful tool for population genetics, SNP discovery and genetic differentiation analysis in non-model species (without reference genome sequence) such as goose. Moreover, our results showed that GBS-derived SNPs have more power than microsatellite markers to detect the Polish geese’s genetic differentiation. Some patterns of variation have been captured that may be related to the breeds phylogenetic origin and also some of their geographic origin, body weight and plumage color. Additionally, analysis in variation across the genome allowed us to point to the gene that is potentially responsible for the feather’s development.


Mangifera odorata (Huani), Mangifera altissima (Paho), and Mangifera indica “Carabao” variety using Illumina HiSeq 2500, to identify and analyze their genome-wide variants (SNPs and InDels).The Philippines is among the top 10 major exporters of mango worldwide. However, genomic studies of Philippine mangoes remain largely unexplored and lacking. Here, we sequenced the whole genome of the three Philippine mango species, namely, M. odorata , followed by M. altissima , and lastly, M. indica Carabao . Approximately 50, 46, and 38% of the variants were unique in the three Philippine mango genomes. The analysis of variant effects and functional annotation across the three mango species revealed 56,982 variants with high-impact effects mapped onto 37,746 genes, of which 25% were found to be novel. The affected mango genes include those with potential economic importance such as 6945 genes for defense/resistance/immune response, 323 genes for fruit development, and 338 genes for anthocyanin production.The high confidence variants were identified by successfully mapping 93–95% of the quality-filtered reads to the Alphonso and Tommy Atkins mango reference genomes. Using these two currently available mango genomes, most variants were observed in To date, this is the first sequencing effort to comprehensively analyze genome-wide variants essential for the development of genome-wide markers specific to these mango species native to the Philippines. This study provides an important genomic resource that can be used for the genetic improvement of mangoes.The online version contains supplementary material available at 10.1186/s43141-022-00326-3. Mangifera indica) [The Philippines is among the top 10 major exporters of fresh and dried mangoes in the world. The country’s mango export is valued at USD 91 million and contributes a 4% share of the global market . The manMangifera. Mangifera indica, the common mango, is a juicy drupe that is usually found in tropical countries. It has varying sweetness and texture across cultivars and has a high incidence of hybridization with other members of its genus. This results in new varieties or species such as Mangifera odorata (M. indica x M. foetida) which is commonly known as Huani in the Philippines [Mangifera altissima which is locally known as Paho. Its unripe fruits are small and oftentimes used in salads in the Philippines.Mango belongs to the kingdom Plantae, order Sapindales, family Anacardiaceae (cashew family), subfamily Anacardioideae, and genus lippines . Huani in=20 chromosomes), and its haploid genome size is relatively small (approximately 400 Mb) but complex due to its innate heterozygosity [Mango has a diploid chromosome  and are important varieties in the mango international trade. With the availability of WGS data, in-depth genome analysis can be performed to unravel gene networks, reveal intron-exon boundaries, detect transposable elements, discover novel biological processes, develop molecular markers tagging economically important traits for breeding , and identify genome-wide variants such as single-nucleotide polymorphisms (SNPs) and insertions-deletions (InDels) [Recently, the chromosome-level whole-genome sequencing (WGS) of Alphonso  and Tomm(InDels) –14. SNPsMangifera odorata (Huani), Mangifera altissima (Paho), and Mangifera indica ‘Carabao’ using Illumina HiSeq, to identify and characterize their genome-wide variants (SNPs and InDels). The high confidence variants were identified by successfully mapping the quality-filtered reads to the Alphonso and Tommy Atkins mango reference genomes. This study provides valuable information and resources for mango breeding and genetic studies.In this study, we sequenced the whole genomes of three Philippine mango species, namely, Mangifera indica Carabao, Mangifera odorata (Huani), and Mangifera altissima (Paho). A high-quality DNA was extracted from three mango trees of the same species using the method of Inglis et al. [Three mango species native to the Philippines were used in this study, namely, s et al.  with modThe quality of DNA was checked via gel electrophoresis using 1.5% agarose with SYBR Safe nucleic acid stain  and viewed using a gel documentation system . DNA samples showing bands were further checked using Epoch Microplate Spectrophotometer and fluorometer (DeNovix QFX Fluorometer), to ensure high-quality DNA that is amenable for the next-generation sequencing.The extracted high-quality DNA from three mango species were submitted for sequencing using the Illumina HiSeq 2500 platform  with a sequencing coverage of 1X per sample. Three DNA samples were sequenced per mango species. The raw reads of all samples were deposited in the NCBI under the BioProject number PRJNA740276.The low-quality base score sequences and adapter sequences from raw reads produced by Illumina HiSeq 2500 sequencing (short reads) were removed using Trimmomatic v0.36  followinThe pre-processed paired sequences of three samples per mango species were concatenated and then mapped to the recently published mango reference genomes of Alphonso   file was generated using the Picard tools  followinThe generated VCFs of the three mango species were analyzed for variant effects on the gene regions using the SnpEff toolbox . The Snpe value of 1e–3). The BLAST results were then mapped and annotated to produce the GO annotations from the three domains of molecular function (MF), biological processes (BP), and cellular component (CC) assigned to each protein sequence. GO enrichment analysis of biological processes was performed using agriGO [P < 0.05. KEGG analysis [The protein sequences of gene IDs identified as high impact were retrieved and Gene Ontology (GO) analysis was performed using the BLAST2GO package . The homg agriGO , 30. Theanalysis  was alsoM. odorata (Huani), 20.7 million reads for M. altissima (Paho), and 18.9 million reads for M. indica Carabao  are presented in Figs. M. odorata and lowest in M. indica Carabao  were detected in the genes with high-impact variants , followed by M. altissima (1.3 million), and lastly M. indica Carabao (1.1 million) using the two reference genomes. All allele data  are presented in Supplemental Table M. odorata and M. indica Carabao while clade II includes M. altissima only  and least in M. indica Carabao  . Phylogenetic and kinship analyses also revealed that M. indica Carabao is more related to M. odorata than with M. altissima, as shown in the clustering in the dendrogram and kinship (IBS) values  were observed in Mangifera species. On the other hand, approximately 50, 46, and 38% of the variants were unique to M. odorata, M. altissima, and M. indica Carabao, respectively, upon comparison to the two currently available mango reference genomes  and diseases  –44 limitArabidopsis thaliana; RPP proteins  which provide resistance against downy mildew caused by Peronospora parasitica [Phytophthora infestans [Pseudomonas syringae [Puccinia triticina [Analysis of variant effects and functional annotation across the three mango species revealed that 25% of genes containing high-impact variants were found to be novel, or their biological functions have not yet been investigated in mangoes , M. altissima (Paho), and M. indica Carabao was successfully sequenced and compared to two currently available mango reference genomes. This revealed the genome-wide variants (SNPs and InDels) including those putative genes with high-impact effects on economically important traits. To date, this is the first sequencing effort to comprehensively analyze genome-wide variants essential for the development of genome-wide markers specific to the Philippine mango species. The availability of this information provides novel genomic resources positioned to revolutionize the mango breeding programs in the Philippines.The whole genome of three Philippine mango species Additional file 1: Supplemental Figure 1. Phylogenetic analysis of mango species.Additional file 2: Supplemental Figure 2. KEGG pathway (flavonoid biosynthesis).Additional file 3: Supplemental File 1. A_Alphonso SnpEff. B_Tommy Atkins SnpEff.Additional file 4: Supplemental File 2. GO enrichment analysis results.Additional file 5: Supplemental File 3. A_Alphonso Blast2GO results. B_Tommy Atkins Blast2GO results.Additional file 6: Supplemental File 4. Commonly shared genes with high impact variants.Additional file 7: Supplemental Table 1. Breakdown of genes with high impact variants.Additional file 8: Supplemental Table 2. Alleles observed in the mango species.Additional file 9: Supplemental Table 3. Kinship analysis of mango species.


Chronic headache pain is one of the most commonly reported comorbid pain conditions with post-traumatic stress disorder (PTSD) patients and resistant to effective treatment, yet no combined preclinical model of the two disorders has been reported. Here, we used a modified chronic headache pain model to investigate the contribution of single prolonged stress (SPS) model of PTSD with sodium nitroprusside (SNP)-induced hyperalgesia. Injection of SNP  occurred every other day from day 7 to day 15 after initiation of SPS in rats. Paw withdrawal threshold (PWT) to von Frey stimuli and tail flick latencies (TFL) dramatically decreased as early as 7 days after SPS and lasted until at least day 21. Basal PWT and TFL also significantly decreased during the SNP treatment period. The lower nociceptive thresholds recovered in 6 days following the final SNP injection in SNP group, but not in SPS + SNP group. Elevated nociceptin/OFQ (N/OFQ) levels observed in cerebrospinal fluid of SPS rats were even higher in SPS + SNP group. Glial fibrillary acidic protein (GFAP) and N/OFQ peptide (NOP) receptor mRNA expression increased in dorsal root ganglia (DRG) 21 days after SPS exposure; mRNA increases in the SPS/SNP group was more pronounced than SPS or SNP alone. GFAP protein expression was upregulated in trigeminal ganglia by SPS. Our results indicate that traumatic stress exaggerated chronic SNP-induced nociceptive hypersensitivity, and that N/OFQ and activated satellite glia cells may play an important role in the interaction between both conditions. Estimates of past-year and lifetime prevalence of PTSD are 4.7 and 6.1%, respectively, in the USA , an established animal model for PTSD, simulates many of the PTSD symptoms reported in humans such as exaggerated negative feedback of the HPA axis, hypocortisolism . After arrival, rats were acclimated to the animal facility for at least 7 days before experiments were initiated. They were housed in the animal facility under a 12-h light:12-h dark cycle (lights on at 0600 h) with free access to food and water. Experimental protocol was approved by the Institutional Animal Care and Use Committee of the University of Oklahoma Health Sciences Center. All experiments conformed to the guidelines of the International Association for the Study of Pain. Every effort was made to minimize animal discomfort and reduce the number of animals used. A total of 30 rats were used in this study.n = 7), SNP (n = 8), SPS (n = 7), and SPS + SNP (n = 8) groups; the experimental paradigm is illustrated in Fig. g at 4 °C for 5 min and the serum was collected and stored at − 80 °C. CSF from each rat was withdrawn by inserting a 26-gauge needle into the cysterna magna and immediately stored at − 80 °C. Trigeminal and L4–6 dorsal root ganglia were excised and frozen for biochemical analyses.Rats were randomly divided into control  was applied to the midplantar aspect of the left paw. Paw withdrawal threshold (PWT) was determined by averaging 3 assessments, spaced 3 min apart. Following PWT assessment, a tail flick unit with a tail temperature sensor  was used to assess the nociceptive sensitivity by radiant heat tail flick latency (TFL) assay, with the lamp set at 25% active intensity  according to the manufacturer’s protocol, and data are presented as N/OFQ-IR. The sensitivity of the assay was < 10 pg/mL; non-specific binding was 2.9%. There was no cross-reactivity with Dynorphin A (1–17), enkephalin, or β-endorphin. Corticosterone levels in serum also were determined by RIA kit  according to the manufacturer’s manual. The sensitivity of the assay was 25 ng/mL and non-specific binding was 2.6%. Total amount of corticosterone was calculated and expressed as ng/mL. Samples and standards were assayed in duplicate. RIA curves and data were analyzed using GraphPad Prism 8.2 software.Ct method, and compared with control values  was immediately added to TG and DRG tissue for mRNA extraction. cDNA was synthesized using Super-Script III Reverse Transcriptase . Real-time PCR was performed using SYBR Green Master Mix  and 125 nM forward and reverse primers  in an ABI 7000 Sequence Detection System . The GAPDH gene was used as an internal standard to which expression of other genes was normalized. Data were analyzed using the comparative 2deoxycholate, 0.1% SDS, 5 mM EDTA, 10 mM NaF, PBS) containing freshly added protease and phosphatase inhibitors and incubated for 30 min on ice, with subsequent centrifugation at 14,000 × g for 10 min. After protein concentration determination with Pierce BCA protein assay kit (ThermoFisher), homogenates were solubilized with 4 × Laemmli buffer and stored at − 80 °C. Samples were resolved using SDS-PAGE on 8–15% Tris–glycine gels , transferred to polyvinylidene fluoride membranes, blocked with 5% milk in TBS-Tween buffer, and incubated overnight at 4 °C with goat anti-GFAP antibody . Secondary antibody conjugated to horseradish peroxidase was incubated for 1 h at room temperature in 5% milk in TBS-T. Immunoreactive bands were visualized by chemiluminescence, captured with the Ultralum Omega Imaging System and densitometry was performed using Ultra Quant 6.0. Membranes were rinsed, stripped, and re-probed with anti-actin  as an internal loading control.DRG and TG tissue was homogenized with RIPA buffer  = 87.49, P < 0.0001], time , and the interaction of SNP × Time  on PWT with control and SNP rats  = 6.448, P = 0.0247], and only at 4 h post SNP injection (P < 0.05). These results suggest that the single injection of SNP introduced acute mechanical allodynia and mild thermal hyperalgesia that were still present after 4 h.Since nitric oxide donors nitroglycerin and sodium nitroprusside (SNP) induce allodynia and hyperalgesia to thermal stimuli, the ability of SNP  = 40.33, P < 0.001], time: , and treatment × Time:  on PWT were revealed by two-way ANOVA mixed-effects model (REML). Tukey’s multiple comparisons tests confirmed that SPS produced mechanical allodynia through day 21 as previously reported compared to control (*). SNP alone did not reduce PWT until day 13; and returned to baseline levels by day 21. PWT was significantly reduced in SPS + SNP rats compared to control (*), SNP alone (Δ), or SPS alone (#), that was maintained through day 21. Similar to PWT but less pronounced, the mixed-effects model found significant effects of treatment , Time , and significant interaction between treatment and time  on TFL. Post hoc tests confirmed that SNP + SPS exposure produced significantly greater nociceptive sensitivity on day 21 than either SNP or SPS alone. However, while SNP treatment reduced TFL by day 13, this hypersensitivity was gone by day 21. Unlike responses to mechanical stimuli, SPS exposure did not reduce TFL significantly until day 19 and through day 21. Enhanced sensitivity to mechanical and thermal stimuli was found in the combined SPS + SNP group 4 days after the final SNP injection. Overall, these results suggest that SPS exacerbated SNP-induced hyperalgesia and prevented its recovery by day 21.Once acute headache allodynia and hyperalgesia results were obtained with SNP, SNP was substituted for NTG  = 4.82, P = 0.0372], as well as significant effects of SPS  and SNP  on N/OFQ levels in CSF  = 11.33, P = 0.0024, Fig. We previously reported that SPS elevated N/OFQ levels in CSF and serum  = 4.351, P = 0.048]  = 12.17, P = 0.0017] and preproN/OFQ  mRNA expression in DRG. NOP receptor mRNA expression in SPS + SNP rats was significantly higher than that in controls or SNP alone, as determined by Tukey’s multiple comparisons post hoc test. In contrast to its effects on the NOP receptor, the effect of SPS on preproN/OFQ mRNA was to decrease expression. There was significant interaction between SPS and SNP, and a significant effect of SPS on GFAP mRNA expression in DRG  = 6.764; P = 0.015; SPS: F = 5.796, P = 0.024) and TG  = 6.036; P = 0.021; SPS: F = 13.36, P = 0.001) as determined by two-way ANOVA. Moreover, post hoc analysis indicated significantly higher GFAP mRNA expression in SPS + SNP rats than in control, SPS, or SNP alone  = 7.849, P = 0.0095]  is an established model that mimics many physiological and behavioral alterations described in PTSD patients including enhanced negative feedback to the HPA axis, anxiety behavior, and cognitive impairments  axis; cortisol levels are increased in some PTSD patients and decreased in others (Handwerger Growing evidence indicates that satellite glia cells (SGCs) in sensory ganglia play important roles in pain modulation. SGCs are activated after nerve injury (Liu et al. The novelty of this study is combining two preclinical models of PTSD and chronic migraine to explore how the combination alters responses to each condition. Our major findings are that SPS exacerbated severity and duration of chronic pain by SNP, accompanied by elevated N/OFQ levels in serum and CSF, higher expression of NOP mRNA and lower expression of ppN/OFQ mRNA in DRG, and enhanced expression of GFAP mRNA in TG and DRG. Taken together, our results suggest that the N/OFQ-NOP system and activated SGCs may play important roles in the interaction between PTSD-like and chronic pain conditions, presenting two potential targets for therapeutic approach of comorbid PTSD and pain.