Our method provides a quick, simple way to clone genes in maize.Forward genetics remains a powerful method for revealing the genes underpinning organismal form and function, and for revealing how these genes are tied together in gene networks. In maize, forward genetics has been tremendously successful, but the size and complexity of the maize genome made identifying mutant genes an often arduous process with traditional methods. The next generation sequencing revolution has allowed for the gene cloning process to be significantly accelerated in many organisms, even when genomes are large and complex. Here, we describe a bulked-segregant analysis sequencing (BSA-Seq) protocol for cloning mutant genes in maize. Our simple strategy can be used to quickly identify a mapping interval and candidate single nucleotide polymorphisms (SNPs) from whole genome sequencing of pooled F2 individuals. We employed this strategy to identify  Forward genetics remains a powerful way to ‘ask the plant’ which genes matter for a particular trait or phenotype . BecauseLinkage mapping, using a combination of bulked-segregant analysis (BSA) and fine mapping, has been very successful for cloning maize genes . The proIn bulked-segregant analysis, pools of wild-type and mutant individuals are genotyped at markers spread across the genome. In chromosomal regions not linked to the mutant lesion, markers will be segregating according to typical 1:2:1 (or 1:1 in backcross) segregation ratios. In a pooled BSA sample, these unlinked markers will all be genotypically heterozygous. Linked markers will be homozygous for the mutant parent genotype, unless F1 recombination has happened between the mutant lesion and a particular marker. A chromosomal region enriched for these linked markers represents a likely location for the mutant gene under study, and the coordinates of these linked markers define an initial mapping interval. Once this initial mapping interval is identified, F1 recombination is again used to place recombination breakpoints in individual mutants, and thus narrow the mapping interval using fine mapping . The finvs. non mutant BSA pools at many thousands of markers spread across the genome. These BSA-Seq data can reveal the genomic interval that contains the mutant gene of interest, and with enough coverage, the lesion itself  means that BSA can be used to very quickly identify a very small mapping interval, and even the causative lesion, without fine mapping. In BSA-Seq, whole genome shotgun sequencing using NGS can be used to quickly genotype mutant narrow odd dwarf (nod) as an enhancer of teosinte branched1 (tb1) using conventional BSA-Seq, and identified a seedling lethal allele of defective kernel1 (dek1) using MutMap (tb1 enhancer data to design insertion-deletion (indel) markers for downstream fine mapping. This fine mapping to reduce the recombination interval is critical in cases where only a single allele for a particular mutant exists, and in cases where no clear candidate lesion is identified. Our method provides a quick, easy method for cloning mutant genes in maize.Here, we report a user-friendly protocol for cloning mutant maize genes using BSA-Seq. We used this protocol to clone two genes recovered from EMS mutagenesis screens. We used conventional BSA-Seq in the case of one gene, and MutMap in the other. We identified tb1 (narrow sheath (ns) , and ns in an unknown background obtained from Pioneer Hi-Bred Intl. , using established protocols (tb1 enhancer (ten) and very narrow sheath (vns). ten, which arose in the A619 genetic background, was crossed to the B73 genetic background and then selfed to generate an F2 mapping population , and the progeny selfed to generate a mapping population . Because the vns genetic background is unknown, we also collected tissue samples for 9 wild-type siblings in the vns mapping population and for the parents of the EMS mutagenesis screen. To characterize the genetic interaction between tb1 and ten, we counted tillers in families segregating tb1-sh and ten.For BSA-seq, tissue samples were collected from 101 ten mutant pool on an Illumina Hi-Seq 2500 at Brigham Young University. Sequenced reads were 125bp long with paired-ends. We sequenced vns mutants, vns wild-type siblings, and unmutagenized parents on an Illumina Hi-Seq 2500 at Cornell University. Sequenced reads from vns were 150bp long with paired ends. Different sequencing platforms were used because of differing availability at our respective institutions.Tissue samples were pooled by hole punching each leaf twice to ensure equal representation of individuals. We extracted DNA from the pools of tissue using a CTAB method . LibrariWe used the genomic tools hosted on Galaxy web portal to process our Next-Gen sequencing data . We usedSAMtools mpileup (v. 2.1.3) was used to output variants from the indexed bam file into pileup or variant call format (vcf) files  to identify SNPs common to all 5 datasets (File S1).We also used our mapping data to generate an A619 variant file. We used Varscan (v. 0.1) to call SNPs in Each filtered pileup file was split into 10 chromosomes. At each nucleotide position in the pileup file, we calculated variant allele frequency by dividing the number of reads that differed from the B73 reference sequence by the quality adjusted total number of reads at that position. This variant allele frequency was added as a new column in the pileup file. Nucleotide position, reference allele, alternate allele, coverage, and variant allele frequency were extracted from the pileup file to create a variant file for downstream analysis in R .vns, we went on to remove SNPs that were in the wild-type sibling and unmutagenized parent datasets, and plot only those homozygous SNPs that were unique to the mutant pool. For both ten and vns, we also plotted the number of homozygous canonical EMS variants (G to A and C to T transitions) per 1Mbp bin. We defined the limits of the ten and vns mapping intervals as those chromosomal coordinates where variant frequency returned to background levels.We plotted our data against the B73 reference genome, to identify chromosomal regions enriched for homozygous SNPs, presumably in linkage with the causative lesions. Before plotting, we filtered positions with coverage >100 to remove highly enriched positions in our data. High coverage SNPs are likely in repeat rich regions and will not be informative for mapping region identification . For theten and vns, we searched for potentially causative lesions in each of these intervals. Here, we returned to the vcf file generated using Varscan. Although we could have investigated the filtered EMS SNPs from our pileup file used to determine the mapping region directly, we chose to use Varscan because it outputs a vcf file, needed for downstream candidate SNP analyses using SnpEff . Once we had a set of positions enriched for SNPs unique to each of our particular mutant pools, we filtered out SNPs that were not homozygous and were not canonical EMS changes (G to A or C to T) . SnpEff identifies nonsense SNPs and splice site mutations as likely to have highly deleterious effects on gene function, and all missense SNPs as likely to have moderate effects on gene function  to identify insertion and deletion polymorphisms (indels) in our mapping interval that differentiated our mutant pool from the B73 reference genome. We chose indels that were 15 base pairs in length or longer, and designed primers to flank these indels by 100-150bp (Table S1). DNA was extracted from each of the 101 F2 mutants originally pooled into one NGS library and used for Indel PCR (To refine our ndel PCR . Size dihttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA476333). A protocol for NGS data analysis and plotting in R is available at protocols.io (https://www.protocols.io/view/bsa-seq-in-maize-qyedxte). Supplemental material available at Figshare: https://doi.org/10.25387/g3.7014851.Raw sequencing data available at NCBI SRA  (very narrow sheath (vns) was uncovered in an independent EMS mutagenesis screen. vns single mutants are seedling lethal and often fail to develop more than 3 leaves  . very na3 leaves . Althougten and tb1, we counted tillers in a population segregating both tb1-sh and ten. Tiller number (per plant) was counted in 15 tb1-sh single mutants, 15 ten single mutants and 15 tb1-sh; ten double mutants. tb1-sh and ten single mutants had an average of 4 and 2 tillers respectively. ten tb1-sh double mutants had an average of 9 tillers, showing a synergistic interaction between ten and tb1-sh in tiller development  mapped to version 4 of the maize genome, and 91% were properly paired . For vns, 90% of our reads  mapped, and 77.5% were properly paired . The average coverage of all ten and vns mapped reads was 26-fold and 17-fold respectively , per 1 Mbp bin. This plotting quickly identified a tall peak on the short arm of chromosome 1, between 0 and 20 Mbp. Plotting only homozygous canonical EMS SNPs (G to A and C to T transitions) identified a peak at the same position, although this peak was shorter   included in each vns pool, or the increased recombination at the end of chr.1 where ten is located. As with ten, plotting only EMS SNPs still revealed a (shorter) peak. The presence of many non-EMS SNPs in the vns mapping interval is likely a result of heterozygosity in the parental ns1 ns2 stock. The vns mapping interval included 651 genes.For g., B73) . Thus, pd 70 Mbp . This vnten and vns pools were sequenced fairly deeply  .Next, we wanted to identify candidate SNPs in our BSA-seq mapping regions. We used a variant calling program, Varscan, to call genotypes at variant positions based on allele frequency (th of 8) . We remoten mapping interval. The vns mapping interval included 1 splice site mutation, and 2 missense mutations  (Zm00001d027725); an SH2 domain protein (Zm00001d027752); Histone H2A 8 (Zm00001d027775); and a known maize gene - narrow odd dwarf (nod) (Zm00001d027722). nod encodes a maize MID-COMPLEMENTING ACTIVITY protein  (Zm00001d028818)  . Dek1 endek1 was responsible for the vns phenotype, we crossed heterozygous vns plants with heterozygous dek1 plants. If vns were an allele of dek1, we expected about one quarter of the F1 progeny to show the dek1 phenotype, and three quarters of the F1 progeny to show a wild-type phenotype. Otherwise, if vns were not an allele of dek1, we expected all F1 progeny to show a wild-type phenotype. In the F1 progeny, 9 of 36 plants were very small, and died shortly after producing only a small number of leaves . Thus, vns failed to complement the dek1 mutant phenotype, indicating that vns is an allele of dek1. Henceforth we will refer to vns as dek1-vns.To determine if the SNP in nod and tb1 . This has the potential to be useful for anyone working in A619 - including for making a synthetic A619 reference genome . In maize, hormone metabolism and cell division are overrepresented GO categories in nod-1 mutant transcriptomes, relative to wild-type (tb1 is downregulated (2.4-fold) in nod-1 shoot apices, relative to wild-type , and a lower recombination rate where dek1-vns is located  more genes than the nod-ten mapping interval. Mean coverage in both datasets was fairly high (26-fold for ten and 17-fold for vns), allowing for both causative lesions to be captured. To identify just the genomic interval that contains a particular gene, sequencing depth could be reduced, but the chances of catching the causative lesion will be similarly reduced. In a case where no clear candidate lesion is recovered, and/or there are many genes in a BSA-Seq mapping interval, fine mapping would become essential. Thus, our results indicate that sequencing to the deepest level affordable is more important than a big mutant pool for a successful BSA-Seq experiment.In any BSA-Seq experiment, the key goals are to define the smallest mapping interval possible, and to identify a small number of potentially causative lesions. To achieve these goals, both the size of the mutant pool and sequencing depth must be considered. In our experiments, the 51 genes . Thus, dvs. wild-type pools , which makes SNP filtering challenging. However, high homozygosity that is polymorphic with a reference genome will still reveal the chromosomal location of the causative lesion, and streamline subsequent fine mapping.Another advantage of BSA-Seq over MutMap is that even if the lesion is not captured, or if there are many candidate lesions in a mapping interval, a researcher is immediately poised to design indel markers and commence fine-mapping in an identified genomic interval. Numerous candidate lesions are more likely when the mutant under study is not from an EMS mutagenesis experiment, which is fairly common in maize (Technical and conceptual advances will eliminate some, but not all, sources of uncertainty when it comes to capturing the candidate lesions underlying mutant phenotypes. With shallow read depths, causative lesions may not be captured; but deep coverage is likely to become ever more attainable as NGS costs drop, thus eliminating this source of uncertainty. There may be a number of potentially causative lesions in a candidate region; but as the maize pan-genome is resolved (


These collectively indicate that complex SNP-SNP interactions of the SP genes may contribute to the pulmonary disease in CF patients. We speculate that SPs may serve as modifiers for the varied progression of pulmonary disease in CF and/or its severity.Surfactant proteins (SP) are involved in surfactant function and innate immunity in the human lung. Both lung function and innate immunity are altered in CF, and altered SP levels and genetic association are observed in Cystic Fibrosis (CF). We hypothesized that single nucleotide polymorphisms (SNPs) within the SP genes associate with CF or severity subgroups, either through single SNP or via SNP-SNP interactions between two SNPs of a given gene (intragenic) and/or between two genes (intergenic). We genotyped a total of 17 SP SNPs from 72 case-trio pedigree (SFTPA1 (5), SFTPA2 (4), SFTPB (4), SFTPC (2), and SFTPD (2)), and identified SP SNP associations by applying quantitative genetic principles. The results showed (a) Two SNPs, SFTPB rs7316 ( Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) protein are key in CF pathogenesis . CFTR islt in CF , 4. DeplPulmonary function deterioration is one of the primary complications of CF and pulmonary surfactant is essential for normal lung function. Pulmonary surfactant, a surface-active lipoprotein complex, is composed of 90% lipids and 10% surfactant proteins. The latter includes plasma proteins and surfactant proteins SP-A1, SP-A2, SP-B, and SP-C. The surfactant proteins comprise a hydrophobic group of proteins (SP-B and SP-C) and a hydrophilic group of proteins (SP-A1 and SP-A2). The SP-D, although co-isolates with surfactant, is not an integral part of the surfactant complex, but it is grouped with SP-A1/A2 based on its structural similarity and function , 6. PulmUnlike in rodents that have a single gene encoding surfactant protein A (SP-A), in humans SP-A consists of SP-A1 and SP-A2 encoded by SFTPA1 and SFTPA2, respectively; each gene has been identified with several genetic variants , and theLung surfactant proteins may contribute to the outcome in CF . BronchoMoreover, in CF patients with well-conserved lung function , SP-C waBecause individuals with identical CFTR mutations may differ in their pulmonary disease, it has been postulated that other genetic factors (i.e. gene modifiers) as well as the microenvironment may contribute to the variable outcome of pulmonary disease –41. SurfThe patient samples were collected with informed and written consent from patients and/or parents under an approved protocol by the institutional review board from the Human Subject Protection Office of the Pennsylvania State University College of Medicine. The clinical data of the study samples are given in Supplementary Table 1). FEV1/FVC: mild = 70–89%; moderate = 40–69%; severe ≤ 40% (Cystic Fibrosis Foundation). The number of patients in the present study under this classification is as follows: severe in 4 cases, moderate in 11 cases, and mild in 64 cases.Seventy-two pedigrees (family trees) were studied, of which, 43 pedigrees had one case with two parents, 21 pedigrees had one case with a single parent, five pedigrees had 2 cases with 1 or 2 parents, and 3 pedigrees had 1 or 2 cases in 3 generations. There were a total of 205 study samples in the 72 pedigrees and of these 79 were CF cases. Their ethnicity was as follows: 198-White, 7-Hispanic; 196-American, 6-Mexican, and 3- Unknown .The target surfactant protein genes, SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD, were selected based on gene function and association with lung diseases  from our findings and other published data as described above. A total of 17 genetic SNPs were selected. These SNPs were previously shown to associate with various lung diseases, be important in function or structure, or other: 5 SNPs from SFTPA1, rs1059047, rs1136450, rs1136451, rs1059057, and rs4253527; 4 SNPs from SFTPA2, rs1059046, rs17886395, rs1965707, and 1965708; 4 SNPs from SFTPB, rs7316, rs2077079, rs3024798, and rs1130866; 2 SNPs from SFTPC, rs4715 and rs1124; and 2 SNPs from SFTPD, rs721917 and rs2243639. The SNP ID, other used name, nucleotide change, and association with human disease as well as related references are given in Table The PCR-based RFLP or cRFLP  method wp-values for each genetic component. A Bonferroni correction was used to adjust for multiple comparisons.We used the Wang et al.'s  approachP = 0.0053). This means that the heterozygote is beyond the mean of two homozygotes in the degree of severity at this SNP. (2) SFTPA1 rs1059047 × SFTPC rs1124 has a significant additive x dominant epistatic effect (a1d2) (P = 0.0014). This means that the combination of the homozygote at the first SNP and the heterozygote at the second SNP is significantly different from other combinations. (3) SFTPA1 rs1059047 × SFTPC rs1124 has a significant dominant x dominant epistatic effect (d1d2) (P = 0.0021). This means that the combination of the heterozygote at the first SNP and the heterozygote at the second SNP is significantly different from other combinations. In general, it looks like SFTPB and SFTPC have a dominant effect in most of the interactions, whereas the genes encoding the hydrophilic proteins exhibit primarily an additive effect ; (2) another two SFTPB SNPs rs2077079 and rs3024798 are associated with CF through an intragenic interaction . (3) SFTPC SNP rs1124 is associated with CF ; (4). Although no single SFTPA1 SNP by itself is associated with CF, the SFTPA1 rs1136451 SNP in an intragenic interaction with either SNP rs1059057 or rs4253527 is shown to associate with CF  ; 18 interactions for SFTPC ; 13 interactions for SFTPA1 , 9 interactions for SFTPA2 , and 10 interactions for SFTPD . Below, we present significant interactions where each interaction contains a SNP from a given gene.A total of 34 intergenic interactions of different combinations between SNPs of the studied genes were observed to associate with CF after Bonferroni correction. Significant interactions that include each of the studied genes are as follows: 19 interactions for SFTPB   Table  and seve3) Table .n = 3), one SNP in SFTPA2, one SNP in SFTPC, and one SNP in SFTPD. SFTPB SNP rs2077079 is associated with CF by intragenic interaction with rs3024798, and by intergenic interaction with SNPs in SFTPA2 (n = 2), SFTPC (n = 1), and SFTPD (n = 2). SFTPB SNP rs3024798 is associated with CF, in addition to the intragenic interaction with rs2077079 (noted above), by intergenic interactions with SFTPD (n = 2). SFTPB SNP rs1130866 is associated with CF by intergenic interaction with SFTPA2 , and SFTPD   Table , and thr7) Table .Of the 18 intergenic SFTPC SNP-SNP interactions, only 4 are with SFTPB the gene that encodes the other hydrophobic surfactant protein, whereas 14 interactions are with genes encoding the hydrophilic surfactant proteins . SNP rs1124 as noted above Table , and SFTn = 2), SFTPB (n = 3), and SFTPC (n = 8)  (Table All of the 5 studied SFTPA1 SNPs are associated with CF through 13 intergenic SNP-SNP interactions with SNPs in SFTPA2 (7) Table . Of inteOf the 13 intergenic SFTPA1 SNP-SNP interactions, 11 are with SFTPB and SFTPC encoding the hydrophobic surfactant proteins, and only two interactions are with SFTPA2 that encodes the hydrophilic surfactant protein A2 Figure , whereasn = 2), SFTPB (n = 4), and SFTPC (n = 3)   and SFTPC (n = 2); rs2243639 is associated with CF also through 5 intergenic interactions but four of these are with SFTPB and only one with SFTPC  Figure . SFTPD SC Figure .P = 0.0014), the combination of the homozygote at the first SNP and the heterozygote at the second SNP is significantly different from other combinations. Thus, we can make a prediction of patients' severity based on their genotypes at these two SNPs.In summary, when we studied the entire CF cohort, we observed a) Two SNPs (one from SFTPB and the other from SFTPC) to be individually associated with CF; b) Three intragenic interactions (2 of SFTPA1 and one of SFTPB) to associate with CF; c) A total of 34 intergenic interactions of different combinations between the various genes studied, except between SFTPD and SFTPA1 or SFTPA2, to associate with CF; A summary of all the significant intragenic and intergenic interactions is shown in Supplementary Table n = 64) and moderate/severe (n = 15). The data showed that after Bonferroni correction a single SFTPB SNP (rs7316) to be associated with mild CF and no other SNPs were found to associate with either CF subgroup. However, there are a number of intergenic SNP interactions (p < 0.01 prior to Bonferroni correction) that associated with each CF subgroups  observed in each subgroup that were also significant after Bonferroni correction in the entire CF group. These are shown in Table n = 4) as they did with SFTPA1+SFTPA2 (n = 4). More interactions were observed between SFTPB and SFTPA1 (n = 3) than SFTPA2 (n = 1) in the mild subgroup. No significant associations with SFTPC were observed with either disease severity group. In addition similar to the entire CF group, no associations were found between SFTPD and SFTPA1 or SFTPA2 in either subgroup.Given the smaller number of subjects in each CF subgroup, and as we wished to gain further insight into the interactions observed, we focused our attention on significant associations (p < 0.01) to associate with mild or moderate/severe CF subgroups  For the entire CF group, we observed that all 5 surfactant protein genes are associated with CF by single SNP association, intragenic Table , and/or Human diseases are complex and determined by environmental factors and genes. Single genetic mutations or multiple mutations in a single gene, constitute a part or a small part of disease mechanisms. To study the full spectrum of gene(s) contributing to a disease either by being the primary cause or by modifying disease expression, an integrated genetic approach is needed to understand genetic control and clinical therapy. By integrating quantitative genetic principles  a statisSurfactant proteins SP-B and SP-C are hydrophobic membrane proteins that increase the rate that surfactant spreads over the alveolar surface, and are required for proper biophysical function of surfactant and lung function. SP-B is also important for SP-C processing, as indicated in SP-B deficient states where an aberrant SP-C was observed , 143.All four of the SFTPB SNPs studied showed significant interactions with SNPs of one or more SP genes. The rs7316 is located in the 3′-UTR and could affect regulation of polyadenylation  and has SP-C is a hydrophobic surfactant protein and plays an important role in surfactant function. Mutations in SP-C have also been shown to associate with a number of pulmonary diseases , such asp = 0.0083) and SFTPC rs1124 (p = 0.0154) , whereas SNPs of SFTPA1 are primarily found in interactions with SNPs of SFTPC (n = 8). The latter is of interest because SP-A1 has been shown to affect more efficiently (compared to SP-A2) the structural organization of surfactant , we found after Bonferroni correction a single SFTPB SNP rs7316) to associate with mild CF. This SNP has also been shown to associate with acute lung injury . Lack of316 to asp < 0.03) (Supplementary Table The SFTPB SNP (rs7316) that is significant by itself in mild CF after Bonferroni correction was the only SFTPB SNP that interacted with SNPs of SFTPA1 or SFTPA2, whereas the SNP-SNP intergenic interactions between SFTPB and SFTPD include three of the four SFTPB SNPs studied. These indicate the importance of SFTPB in mild CF which may provide protection in the sense of enabling a milder form of pulmonary CF through its surfactant-associated function or perhaps other currently unknown function. Based on the differences of SNP-SNP interactions; it is likely that the mechanisms via which the hydrophilic proteins contribute to mild CF may differ. The SFTPA1 SNP rs4253527) was also significant by itself  of SFTPA1 and SNP rs1059046 (aa 9 Thr/Asn) of SFTPA2. This interaction may be unique to moderate/severe disease group because it was not identified in the mild group even in interactions with In summary, a single SNP of the SFTPB is a marker for mild pulmonary disease in CF. A number of intergenic interactions that all include SNPs of SFTPB as well as a single intragenic SFTPA1 and SFTPA2 interaction are likely to be markers for mild and moderate/severe pulmonary disease in CF, respectively.1 and FVC; (b) The limited clinical information; the samples were collected at an earlier time using only FEV1 as a discriminator for the severity subgroups. However, this is still the main biomarker to assess disease severity. (c) The lack of associations with bacterial strain and correction for age. (d) The CFTR mutation is not known although most of the subjects are expected to carry the ΔF508, which is approximately found in 70% of the CF patients.Limitations of the study include: (a) The small number of subjects especially after the CF group was divided in the mild and moderate/severe subgroups. This also precluded analysis of the individual components of FEVHowever, in spite of these limitations the present findings indicate that both groups of surfactant proteins, those involved in innate immunity, and those affecting surfactant functions, associate with CF via complex interactions. These may contribute to pulmonary disease progression in CF, by affecting surfactant structure and function and/or by affecting innate immunity functions. Altered surfactant leads to a compromised lung function, and lung function deterioration is a major setback in CF pulmonary disease. Similarly host defense and inflammatory processes are partially affected in CF and SPs may play a role in these. Thus, based on the collective information with regards to their function and the observations made here, the SP genes are likely to be significant gene modifiers of CF and must be studied further. As gene modifiers, SPs may explain the varied progression of the pulmonary disease in CF in terms of lung function and host defense.All the data are presented as supplementary files.All protocols used in this study were evaluated and approved by the institutional review board from the Human Subject Protection Office of the Pennsylvania State University College of Medicine.ZL analyzed and synthesized the data, contributed to the manuscript writing. NT analyzed and synthesized data for CF subgroups and contributed to manuscript writing. RW performed statistical analysis and contributed to manuscript writing. SD performed all the genotyping. MY assisted with statistical analysis. NJT attended to human subjects issues, sample collection and contributed to manuscript writing. XL checked genotype data sheets after multiple transfers. TL reviewed literature and made Table The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AT and handling Editor declared their shared affiliation.


Echinacea is a well-known medicinal plant and products derived from it are sold globally for its cold- and flu-preventative and general health-promoting properties. Despite the well-documented phytochemical profile of Echinacea plants and products, little research has looked into the possible role of ITFs in these products. This review aims to highlight the occurrence of ITFs in Echinacea derived formulations and the potential role they play in immunomodulation.Throughout history, medicinal purposes of plants have been studied, documented, and acknowledged as an integral part of human healthcare systems. The development of modern medicine still relies largely on this historical knowledge of the use and preparation of plants and their extracts. Further research into the human microbiome highlights the interaction between immunomodulatory responses and plant-derived, prebiotic compounds. One such group of compounds includes the inulin-type fructans (ITFs), which may also act as signaling molecules and antioxidants. These multifunctional compounds occur in a small proportion of plants, many of which have recognized medicinal properties.  The use of herbal medicine is an ancient practice that relies on the use of a broad range of plant and plant-derived products  for medicinal purposes. These herbal products are prepared and used in various forms, including capsules (dry powder), tinctures (ethanol extracts), infusions , macerations , and decoctions . The largest proportion 80%) of communities in developing countries (particularly Asia and Africa) relies on herbal medicine and associated herbal preparations as primary healthcare. Compared to other therapeutics used in modern medicine (MM), herbal medicines are perceived as being more affordable, accessible, and acceptable to the communities using it ,2. The u0% of comEchinacea preparations are among the most popular herbal products with immunomodulatory properties [Echinacea preparations have been highlighted before [Echinacea derived inulin-type fructans (ITFs) and highlight their presence in commercially available Echinacea products. The potential contribution of ITFs to immunomodulatory effects in the human body is discussed.Immunomodulators could act (stimulate or suppress) on both the innate- and adaptive immune systems to (i) exert positive effects on the host defense mechanisms, (ii) have an anti-proliferative effect on tumor cells and, (iii) enhance the host’s ability to tolerate damages caused by toxic compounds (such as chemotherapeutics). operties ,12. Whild before ,15,16,17Echinacea is a plant genus within the family Asteraceae (previously termed the Compositae) and is comprised of 11 taxa of herbaceous and flowering plants [Echinacea preparations  are commonly used for preventing and alleviating the symptoms of bacterial and viral infections [Echinacea preparations are known to exert antioxidant and anti-inflammatory activity espousing to its potential immunomodulatory activities [g plants ,19. Echifections ,18. Furttivities ,19,20.in vitro and in vivo studies demonstrated that different extracts from Echinacea, either from roots, above-ground parts, or a mixture of both, could stimulate and signal immune responses [Echinacea  could induce transcriptional changes that activate immunomodulation pathways [Echinacea are well-described, relatively little information is available about the polysaccharide fraction. In general, immunomodulatory and prebiotic polysaccharides have been implicated in the overall health and well-being of humans by (i) enhancing physiological parameters , (ii) increasing tolerance against pathogens and, (iii) modulating immune responses [Both esponses . Both aqesponses . Immunolesponses ,21,22. Apathways ,27,28,29esponses ,31. Of iEchinacea) families. Based on structural differences, three types of fructans can be distinguished: inulin (β2→1 linkage), levan (β2→6 linkage), and graminan (β2→1 and β2→6 linkages). The chain-length of plant fructans (defined by the degree of polymerization: DP) is variable, with a few species  standing out, producing higher DPs by specific fructosyltransferases [Fructans are water-soluble, sucrose-derived compounds that occur in about 15% of flowering plants , includisferases . Short-csferases , well-knsferases ,37,38,39sferases ,41,42. Tsferases ,43.The immunomodulatory function of ITFs was shown to be dependent on Toll-Like Receptors (TLRs), in particular, TLR2 and to a lesser extent, TLR4, 5, 7, 8 . FructanEchinacea extracts show profound antiviral activity against several viruses , and reversed virus-induced pro-inflammatory responses [Respiratory infections, such as the common cold and the seasonal flu, are often infectious diseases caused by different viruses . Althougesponses ,49,50. Iesponses ,52.3) isolated from traditional Japanese herbal medicine (Chikuyo–Sekko–To) was shown to be effective against herpes simplex virus type 2 (HSV-2) in a murine model [Cichorium intybus) are known to increase NO production in INF-γ-primed RAW 264.7 cells in an NF-κB-dependent manner [Allium fistulosum L.) demonstrated an inhibitory effect on influenza A virus replication in mice [Arctium lappa L.) strongly stimulate NO synthesis and defense signaling in plants [Allium sativum) have the capacity to activate macrophages and subsequently phagocytosis, again in combination with a release of NO [Inula racemosa and Bombax ceiba. The immunomodulating properties of these three plants were attributed to their high fructan content [Allium cepa), yacon , Curcuma (Curcuma kwangsiensis), blue agave (Agave tequilana), and mugwort (Artemisia vulgaris) [Fructans have been demonstrated to exert immunomodulatory and antiviral effects directly. An ITF  ,63,64,65Echinacea and several other plants encompassed in herbal medicine  have been shown to alleviate the effects of inflammation. It is interesting to note that these plants display enhanced antioxidant capacity, which could, in turn, be used to manage inflammation-related disorders induced by oxidative stress [Fructans and other plant-derived sugars are known to exert antioxidant properties by directly and indirectly affecting ROS balances and countering the damage caused by oxidative stress ,66,67,68e stress .in vitro and in vivo studies, suggesting that the consumption of foods rich in fructans could exert beneficial immunomodulatory properties by acting as antioxidants [Fructan-related antioxidant capacity (and ability to act as ROS scavengers) has also been demonstrated in humans in both oxidants ,70,71. Toxidants ,72. In loxidants . TLR2 sioxidants ,75. Inteoxidants . ITFs maoxidants .Echinacea preparations are well-known and have been ascribed to the myriad of compounds that display antioxidant activity [Echinacea angustifolia and Echinacea purpurea were shown to improve symptoms of colitis in rats by decreasing the number of inflammatory mediators (IL-1β and TNF-α) [Echinacea preparations were also reported to reduce inflammatory conditions caused by insulin resistance and induced by pathogens, respectively [It is well-known that ITFs indirectly influence AMP-activated protein kinase (AMPK) signaling through modulation of the microbiota, for instance, by stimulating the growth of Lactobacilli and Bifidobacteria, producing short-chain fatty acids (SCFAs) that activate AMPK . It is aactivity . Both Ecd TNF-α) . Echinacectively ,87,88,89Echinacea preparations, but its absorption from the gastrointestinal tract into the human body is not clear. Nevertheless, similar to what has been proposed for fructans, immunomodulatory, and anti-inflammatory effects, linked to AMPK signaling and downregulation of NF-κB, have also been suggested [in vitro, suggesting that the interaction between phenolic compounds and ITFs is complex, particularly in the human system, and remain to be fully elucidated [Echinacea preparations have such powerful immunomodulatory properties because of their complex composition [Chicoric acid is a well-known antioxidant , and oneuggested ,91. Moreuggested  suggesteuggested ,93,94. Cucidated . The speposition .Echinacea preparations are among the best-selling herbal medicines in the Western world for the treatment and possible prevention of upper respiratory infections, including the common cold and seasonal flu [Echinacea preparations have usually been explained by a combination of antiviral, immunomodulating, anti-inflammatory, and antioxidant activities [Echinacea preparations were reported to stimulate increases in both the number of white and red blood cells [Echinacea preparations [Echinacea extracts are potent activators of NK cell cytotoxicity. Echinacea extracts increase the frequency of NK cell target conjugates and speed up their lytic activities [Echinacea preparations is based on differential multi-level modulation of the responses of different types of leukocytes and T cells, regulating the production of chemokines and cytokines such as IL1-β, IL-6, IL-8, IL-10, IL-12, IFN-γ, and TNF-α [Echinacea was shown to inhibit inflammatory responses by inhibiting the expression of the NF-κB p65 protein [® has been shown to prevent virus-induced bacterial adhesion by suppressing the expression of NF-κB [Echinacea preparations.onal flu ,12,26. Ttivities . Echinacod cells . In linearations , it has tivities . It has nd TNF-α ,102,103.nd TNF-α ,22,104.  protein . Furtherof NF-κB . There iEchinacea preparations. The connection between the Echinacea polysaccharides and their immunomodulating properties has been suggested multiple times [Echinacea purpurea was already reported decades ago [Further research is necessary to decipher whether, and to what extent, ITFs contribute to the observed immunomodulatory effects of le times ,109,110.ades ago , but unfEchinacea [Echinacea preparations. A range of low and high DP ITFs was found [Echinacea preparations was already reported by Giger et al. (1989), who found that the fructan content differed between Echinacea angustifolia and Echinacea purpurea, depending on the harvesting period [Echinacea preparations and found that the amount of ethanol used for extraction determines the fructan content of the extract [The occurrence of ITFs in chinacea  promptedas found . The aveg period . They al extract ,115. As in vitro. Interestingly, we found that the polysaccharide fraction in two out the five products displayed significant differential TAC profiles, although not displaying clear DNA protection ability. In contrast, the polysaccharide fractions of the remaining three products displayed protective DNA abilities. We speculate that the occurrence of high DP fructans within polysaccharide fractions of these products could play some role in the efficacy thereof. Since ITFs only represent a small part of these preparations, it seems unlikely that prebiotic effects on their own would explain the observed immunomodulatory and health-promoting effects. More likely, ITFs present in Echinacea preparations stimulate the activity of immune cells directly by activating signaling pathways in vivo, as described [Echinacea preparation [Echinacea products contain these fructans, strongly suggests that they could be among the contributors for the overall effect of these products.We further demonstrated the total antioxidant capacity (TAC) and consequent DNA protection ability of the commercial products and the polysaccharide fractions, derived thereof, respectively. Not surprisingly, the products displayed significant TAC, as well as the ability to protect human DNA escribed ,117,118.paration ,120,121.Numerous herb- and medicinal plant extracts have been successfully employed as immunomodulatory substances for maintaining human health. The resources of these extracts have historically been part of cultural traditions  and have been carefully developed through anecdotal or pragmatic experience. Currently, a holistic approach based on a combination of modern therapeutics and herbal medicines is targeted for disease prevention. Various plant extracts are known to contain an array of active compounds with the ability to activate or regulate the immune system through direct or indirect mechanisms . Immunomodulatory compounds from plant extracts include (i) flavonoids, (ii) glycosides, (iii) polysaccharides, (iv) terpenoids, (v) essential oils, and (vi) alkaloids. Many efforts have been undertaken to demonstrate the multidimensional effects of these compounds on overall health and well-being by influencing the immune system. The polysaccharide fraction, with emphasis on ITFs, of plant extracts used in herbal medicine has been largely overlooked in recent years. We speculate that these fractions may play an important role in the modulation of the immune system, either independently or synergistically. Further research efforts are required to identify and determine the functionality of these polysaccharides, especially ITFs, among various herbal medicines.


Pig scrotal hernia is one of the most common congenital defects triggered by both genetic and environmental factors, leading to severe economic loss as well as poor animal welfare in the pig industry. Identification and implementation of genomic regions controlling scrotal hernia in breeding is of great appeal to reduce incidences of hernia in pig production. The aim of this study was to identify such regions or molecular markers affecting scrotal hernia in pigs. First of all, we summarized and analyzed the results of some international teams on scrotal hernia and designed a specially population which contains 246 male individuals. We then performed genome-wide association study (GWAS) in this specially designed population using two scenarios, i.e., the target panel data before and after imputation, which contain 42,365 SNPs and 18,756,672 SNPs, respectively. In addition, a series of methods including genetic differentiation analysis, linkage disequilibrium and linkage analysis (LDLA), and haplotype sharing analysis were appropriate to provide for further analysis to identify the potential gene underlying the QTL. The GWAS in this report detected a highly significant region affecting scrotal hernia within a 24.8Mb region (114.1–138.9Mb) on SSC8. And the result of genetic differentiation analysis also showed a strong genetic differentiation signal between 116.1 and 132.7Mb on SSC8. In addition, the QTL interval was refined to 2.99Mb by combining LDLA and genetic differentiation analysis. Finally, two susceptibility haplotypes were identified through haplotype sharing analysis, with one potential causal gene in it. Our study provided deeper insights into the genetic architecture of pig scrotal hernia and contributed to further fine-mapping and characterize haplotype and gene that influence scrotal hernia in pigs. Pig hernias are of the most common congenital defects which cause severe economic losses as well as poor animal welfare in the pig industry. The most common types of hernias in pig are scrotal and umbilical hernia. Scrotal hernia is the phenomenon of abdominal contents falling into scrotum from the unilateral or bilateral inguinal rupture, causing local expansion bulge . As a co2 intercross using nonparametric genome-wide linkage (NPL) analysis and transmission disequilibrium test (TDT)  . Du et aanalysis . Sevillaanalysis . However2 population using 194 microsatellites and identified one chromosomal region distributed on SSC8 for the scrotal hernia. Generally speaking, nonparametric linkage analysis (NPL) evaluates allele sharing among affected individuals and comes to a result without particular model assumptions, and the TDT was proposed as a family-based association test for the presence of genetic linkage between a genetic marker and a trait; more computational details with this 2 statistical methods were showed by 3 population which was mated with full-sibs or half-sib of the affected individuals and imputed the chip SNPs to whole-genome sequences  in the FAll procedures including experimental animals established and tissue collection were performed in accordance with the guidelines approved by the Ministry of Agriculture of China. This study was approved by the ethics committee of Jiangxi Agricultural University.1 boars, and 59 F1 sows were randomly selected to produce a total of 1,912 F2 pigs in 6 batches avoiding full-sib mating  and 500 F3 were genotyped. For this study, 246 male F3 pigs were chosen for GWAS analysis, which contain 18 available DNA samples for affected individuals. Furthermore, 19 F0, 68 F1, and 516 F2 male pigs, and 246 F3 male pigs were used in haplotype sharing analysis.A four-generation resource population was developed from the intercross of 2 White Duroc boars (PIC 1075) and 17 Chinese Erhualian sows between 2,000 to 2,006. In briefly, two White Duroc boars were crossed to 17 Erhualian sows, then 9 Fb mating . Last, 62 and 500 F3 were genotyped with the Illumina PorcineSNP60 BeadChip and GeneSeek GGP Porcine 50K BeadChip on an iScan System  following the manufacturer’s protocol, respectively (http://asia.ensembl.org/Sus_scrofa/Info/Index). Quality control procedures were implemented by PLINK (version 1.07). Briefly, SNPs were removed if their positions on the genome build 11.1 were unspecific, call rate <90%, and minor allele frequency (MAF) <1%. Animals more than 10% missing genotypes were removed. To keep the alleles consistency with the sequencing data, we firstly aligned the primer sequences of each SNP to the reference porcine genome assembly Sus scrofa 11.1 by BLAST. Then, the genotypes of reversed SNP strands in target panel were flipped using PLINK (v1.9) software , and GATK IndelRealigner option was carried out for local realignments. Then, variants were filtered with GATK VariantFiltration option. VCFtools was used to remove the structural variants. Subsequently, the haplotypes of 109 individuals with cleaned SNP data were constructed by Beagle (v4.1)  . SpecifiWhole-genome sequence imputation between target and reference panel was conducted by Beagle (v4.1) using the default parameter settings . Specifi0 who were the progenitor of the 500 F3 populations were also regarded as reference panel in order to obtain more accurate phase information. Then, the genotypic concordance rate and the squared correlation (R2) between best-guess imputed and the original variants as imputation accuracy. The genotypic concordance rate used a cross-validation strategy described in previous studies , where τ−1 is the variance of the residual errors, and λ is the ratio between τ−1 and the variance of the residual errors; K is a known kinship matrix, ∈ is an vector of errors following the multivariate distribution nMVN, and In is an n × n identity matrix. Normally, significance threshold of multiple test in chip array-based GWAS was adjusted by naïve Bonferroni corrections, which is 0.05 divided number of examined SNPs. However, this approach would lead to over correction and decreasing the detection power in GWAS as these tests are non-independent for the linkage disequilibrium between markers. We herein used 5E−08 as a genome-wide suggestive significance threshold following Pe’er et al. and Johnson et al. (2) between SNPs were estimated by plink 1.07 , α is a c-vector of fixed effects, n et al. . The popn et al. . To checink 1.07 , the defTo elucidate whether there is genetic differentiation exist in scrotal hernia pigs and health pigs, we divided the affected pigs and the unaffected pigs into two groups, as the method did by cn is the average sample size across samples, which incorporates and corrects for the variance in the sample size over populationwhere MSG represents the observed mean square errors for loci within populations, MSP denotes the observed mean square errors for loci between populations, and in and Aip denote the sample size and the frequency of SNP allele A in the ith population, respectively, and Ap across populations. The negative Fst didn’t have any biological interpretation and were set to 0 to fit the definition of Fst ranging from between 0 and 1  locates at 124.1Mb within a 10.6Mb region (116.1–126.7Mb) on SSC8  locates at 122.2Mb within a 21Mb region (115.5–136.5Mb) on SSC8 ; the most likely confidence interval of the QTL was approximately 3Mb (121–123.99Mb), based on the LOD drop off 2  for scrotal hernia on SSC8. The LDLA results showed the strongest association SNP was rs330263452 . This result is indicative of that the hypothesized Q haplotype was involved in the occurrence of scrotal hernia in pigs. Next, we tried to identified whether there is the same sharing susceptibility haplotype existed in F2 affected individuals and trace this susceptibility haplotype back to the F1 and F0 generations. The result showed that 13 of the 19 affected pigs in the F2 population also carried Q1 or Q2 haplotype flanked by markers rs81275702 and rs81404172  and three Chinese Erhualian sows  when we traced those two haplotypes to the F0 generation, respectively. Therefore, it is concluded that two susceptibility haplotypes underlying the SSC8 were identified for pig scrotal hernia, and there should be some important pathogenic mutations. In addition, it was worth mentioning that the significantly associated SNP rs81404013 , rs318390967 , and rs333147082  that contained in this confidence interval have strong linkage disequilibrium extents (r2 > 0.9) to each other  has a low linkage disequilibrium extents (r2 < 0.5) with those three loci. We take a region flanked by markers rs341392224 and rs326688253, which contain rs320409365, as well as it’s left and right two loci. Then, we count the types of haplotype in this interval and take a chi-square test with them  was significantly associated with scrotal hernia.The result of haplotype sharing analysis on FADH6, ADH4, ADH5, METAP1, EIF4E, TSPAN5, RAP1GDS1, STPG2), which indicated that few genes are the most likely candidate genes that caused scrotal hernia in pigs  and rs81404013  located in the intron of EIF4E gene when we condition the strongest significantly associated SNP rs333147082; no additional association signals appeared in this loci (QTL) was detected  encompasses eight annotated genes  within a 16.6Mb region (116.1–132.7Mb) on SSC 8 was detected. This result indicated that the affected pigs and the unaffected pigs had a greater genetic differentiation in this confidence interval. Moreover, there is a high coincidence of the top SNPs detected through genetic differentiation analyses and GWAS.According to our original intention, we identified 13 SNP loci significantly associated with scrotal hernia on chromosome 8 through GWAS analysis with the specially designed FP-value = 1.58 × 10−17), and the most likely confidence intervals around the 121–123.99Mb region on SSC8. Furthermore, we found out that the confidence intervals mapped by LDLA contained within the region mapped by genetic differentiation analysis. We narrow the confidence interval to 2.99Mb by picking up the intersection of those two intervals for further analysis.Additionally, in consideration of single-marker GWAS, it was hard to properly estimate the confidence interval of the detected QTL, as LD varied severely among nearby SNPs while haplotypes have stable LD than SNPs. Thus, we conducted haplotype-based LDLA analysis, by simultaneously taking advantage of recent and ancestral recombination events to increase the efficiency and detect confidence interval. The LDLA results showed that the SNP with the strongest association at the locus was rs330263452 (0-73) and three Chinese Erhualian sows , respectively. It is incomprehensible that the White Duroc boar (F0-73) carried the susceptibility haplotype, but it was unaffected. Actually, whether a certain mutation is not directly related to scrotal hernia as we explained earlier. Similarly, 163 of 497 unaffected pigs in the F2 population also carried the susceptibility haplotypes, echoing the result that there was no significant signal when we performed GWAS in the F2 population. When we merge the F2 and F3 populations and then conducted chi-square test with them , this result is also indicative of that the hypothesized Q haplotype was involved in the occurrence of scrotal hernia in pigs. In addition to discovering two susceptibility haplotypes, we further found that there are nine annotated genes in this 2.95Mb interval in total, and the EIF4E was selected as potential candidate gene based on its biochemical and physiological functions.Lastly, we identified two susceptibility haplotypes underlying the SSC8 associated with scrotal hernia after performed a haplotype sharing analysis, and those two haplotypes were from one White Duroc boar . Therefore, the significant signals obtained in our GWA study were not accidental but were caused by differences in the genomes of affected and unaffected individuals, which were reliable.3 specially designed population using GWAS. There is one more point: two susceptibility haplotypes (Q1 and Q2) flanked by markers rs81275702 and rs81404172 and one potential causal gene underlying the SSC8 were identified through a series of methods including genetic differentiation analysis, LDLA, and haplotype sharing analysis. Last but not the least, we explain why many international research teams do not have a high repeatability of the results of scrotal hernia research, and some research studies haven’t even found any associated locus with scrotal hernia. Further studies will be devoted to confirming the detected haplotype and gene in outbred populations.In summary, in the first place, we discovered a major quantitative trait loci (QTL) for pig scrotal hernia on chromosome 8 in an Fhttps://figshare.com/s/d661962aa6c0740caeab), and the genotypic data of 516 F2 individuals analyzed for this study can be found in the Dryad Digital Repository (https://doi.org/10.5061/dryad.7kn7r)  . The rawd.7kn7r) .This study was approved by the ethics committee of Jiangxi Agricultural University. All procedures including experimental animals established and tissue collection were performed in accordance with the guidelines approved by the Ministry of Agriculture of China.LH and ZZ conceived and designed the experiments. WX, GY, and TH analyzed the data. DC and SX contributed materials and analysis tools. WX, ZZ, and LH wrote the manuscript. All authors read and approved the final manuscript.This work was supported by National Natural Science Foundation of China (31760656) and Guangdong Sail Plan Introduction of Innovative and Entrepreneurship Research Team Program (No. 2016YT03H062).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


One, one, three, eleven, three, one, three, three and one SNPs were distributed on chromosome 1, 4, 6, 8, 9, 11, 17, 18, and 20, respectively. Favorable SNPs and six haplotypes were identified based on ten functional SNPs; among those Hap2 and Hap3 were considered as optimal haplotypes. In addition, based on GWAS results, the candidate gene Glyma09g06290 was identified. Quantitative real-time PCR (qRT-PCR) results and polymorphism analysis suggested that Glyma09g06290 might be involved in pod dehiscence. Furthermore, a derived cleaved amplified polymorphic sequences (dCAPS) marker for Glyma09g06290 was developed. Overall, the loci and genes identified in this study will be helpful in breeding soybean accessions resistant to pod dehiscence.Pod dehiscence (shattering) is the main cause of serious yield loss during the soybean mechanical harvesting process. A better understanding of the genetic architecture and molecular mechanisms of pod dehiscence is of great significance for soybean breeding. In this study, genome-wide association analysis (GWAS) with NJAU 355K SoySNP array was performed to detect single nucleotide polymorphisms (SNPs) associated with pod dehiscence in an association panel containing 211 accessions across five environments. A total of 163 SNPs were identified as significantly associated with pod dehiscence. Among these markers, 136 SNPs identified on chromosome 16 were located in the known QTL  Glycine max (L.) Merr.] is one of the major oil crops and provides edible oil and abundant protein for human beings. Soybean pod dehiscence (shattering) is when mature pods open along their dorsal or ventral sutures to release seeds , detected by fine mapping (located at the qPDH1 QTL) and encoding a dirigent-like protein, promotes pod dehiscence by increasing the torsion of dried pod walls  by analyzing the changes due to domestication between cultivated soybean and wild soybean. This gene activates secondary-cell-wall biosynthesis and promotes the thickening of fiber-cap cells in pod sutures  gene related to isoflavone biosynthesis in soybean via GWAS . This population was collected from different geographic origins that exhibit variant phenotypes for pod dehiscence, and provided by the National Center for Soybean Improvement of China. Experiments to assess association panel were sowed in two places from 2013 to 2017: Jiangpu Experimental Station (32°12 N 118°37 48 E) in 2013 and 2014 ; and the Experimental Farm of Jiangsu Yanjiang Institute of Agricultural Sciences (31°58 48 N 120°53 24 E), Nantong, China, in 2015–2017 . A complete randomized block design with three replications was used for all trials. Each line of the populations was planted in three rows, and each row was 200 cm long with 50 cm row spacing. At the seedling stage (about 2 weeks after germination), the number of plants in per row was limited to 15–20. Nutrition, water supply, weeding and insect control were maintained throughout the experiment.Three plants per line were harvested randomly, and twenty pods per plant were examined. The maturity pods were stored in a kraft paper bag after harvested and then put in the heating ovens as soon as possible. The degree of pod dehiscence was identified by monitoring the percentage of dehiscent pods after heat treatment at 60°C for 3 h . As showh2. Variable components  were evaluated in R with the “lme4” package using the BLUP model. The BLUP model was based on the following formula  with the R software , including descriptive statistics, ANOVA, and  formula :In this formula, In which The 211 soybean accessions were genotyped with the NJAU 355K SoySNP array containing 282,469 SNPs . After f1, and soybean microarray expression data was downloaded from Plant Expression Database2 (GEO Accession: GSE26443). The heat maps were generated by using R software packages “heatmap.”Two databases were used to identify the tissue expression patterns of candidate genes. RNA-Seq data from 14 tissues, including three vegetative tissues  and the whole seeds from 11 stages of reproductive tissue development , were downloaded from SoyBase3). Meanwhile, BLASTP analysis was also performed against Arabidopsis proteins using the amino acid sequences of these putative genes.Based on the LD decay distance of significant SNPs in the GWAS results, we predicted the putative genes related to pod dehiscence according to the soybean genome annotation  with SYBR Green Real-time Master Mix (Toyobo). Three biological and three technical replicates were used. The constitutively expressed soybean tubulin gene (GenBank accession number: AY907703) was used as a control. The normalized expression, reported as fold change, was calculated for each sample as The soybean accession Williams 82 was selected to analyze the tissue expression patterns of the putative genes. First, seeds were grown in growth chambers under the conditions of 16/8 h (day/night), 28/25°C (day/night), and 60% relative humidity. Then, roots, stems, leaves, and flowers were sampled during the full-bloom period; pods were sampled on the 7th, 15th, 25th, and 45th days after flowering (DAF), and mature seeds were also collected. The expression levels of the putative genes were detected in 15-day pods in a subset of 18 soybean accessions, representing varieties with high and low PPD. Total RNA was isolated using a Plant RNA Extract Kit , and cDNA was synthesized using a TaKaRa Prime Scriptator . The pri4 was used to analyze the gene structure. The protein sequences of homologous genes from different plants were obtained from NCBI database5 . These sequences were aligned using the ClustalX software version 1.8, and the SNPs with MAFs ≥ 0.05 were identified among these accessions. The LD level was calculated by the HapView 4.0 software program . In addition, the haplotype block was defined by the “Solid Spine of LD” algorithm. LD analysis of 112 significant SNPs located on chromosome 16 was performed using the R software package “LDheatmap” (A pair of gene-specific primers was designed (Primer 5.0) to amplify  program . The corheatmap” .Glyma09g06290 was developed based on the S_-500 (C/G). The gene-specific primers were designed by dCAPS Finder 2.06 . The PCR products were digested with restriction endonuclease NcoI . Digestion products were assayed by 8% polyacrylamide gel electrophoresis to distinguish the polymorphic fragments.The dCAPS marker of h2 of the PPD were identified for the association panel in five environments  in association panel.Descriptive statistics, ANOVA and ronments . The PPD in RILs . The fre in RILs , not a n in RILs . In addiQTL mapping suffers from fundamental limitations; one of these is the limited allelic diversity between recombinant inbred population and parents . In contPod dehiscence 2-1 and Pod dehiscence 3-4) and partially overlapped with Pod dehiscence 1-7 and Pod dehiscence 4-2 ; the R2 ranged from 11.9 to 38.5% (n = 83); and Hap1 (CCTTT) was the second largest group (n = 72). The other haplotypes, Hap3 (CCCAA), Hap4 (CGTTT), Hap5 (CCT0T), and Hap6 (CC0TT), comprised seven, fourteen, eight and five accessions, respectively.One SNP was mapped in both five environments and BLUP on chromosome 16; we also found that nine SNPs were mapped in four environments and BLUP on chromosome 16 . Therefore, Hap1 and Hap2 were used to analyze the geographical distribution of soybean landraces with different pod dehiscence traits. After filtering out accessions with indeterminate ecoregions, 66 and 76 accessions remained in Hap1 and Hap2, respectively. Hap1 was observed in the three main growing ecoregions in China  and was In GWAS results, under the threshold of FDR < 0.05, we detected three significant SNPs on chromosome 9. Moreover, two of them were close to each other and repeatedly identified in different environments. Interestingly, in other regions, there was no significant SNP that was repeatedly detected except on chromosome 16. Notably, one of these three SNPs, AX-93762848, was also detected by using MLMM model . It demoArabidopsis genome against these 26 genes. The results showed that two genes were without any explicit biological function annotations. Among the other 24 genes with functional annotations, Glyma09g06320 and Glyma09g06390 were predicted to be involved in cellular differentiation and cell wall formation. Additionally, the gene Glyma09g06290 is homologous to At2g18969 which belongs to bHLH (basic helix-loop-helix) gene family. In Arabidopsis, two bHLH transcriptional factors (IND and ALC) were reported to be related to silique dehiscence  may be related to pod dehiscence.There were 26 genes detected in the 130 kb flanking regions (LD decay distance of cultivated soybeans) of the two SNPs . We thenhiscence . The othectively . Moreoveectively . The RNA7, we listed the candidate genes’ coding product and metabolic processes , Medicago truncatula Medtr2g036670 (identity of 61.8%), Populus trichocarpa Potri.018g090200 (identity of 61.8%), M. truncatula Medtr4g127700 (identity of 50.7%) and A. thaliana columbia At2g18969  were identified, including Site_-500 , Indel_-230, S_-128 in the 5′-UTR, Indel_766 and S_767 in the 3′-UTR (n = 24) is the largest group, and Hap2 (n = 13) is the second largest group. Statistically, Hap2 had significantly higher PPD than Hap1   . TherefoGlyma09g06290 were identified and associated with pod dehiscence  prior to harvest is a major cause of yield loss in soybean production. Thus, it is extremely important to identify QTLs or genes related to pod dehiscence and apply them to MAS. In this study, by using the association panel which possessed abundant natural variations, GWAS was performed to genotype 211 cultivated soybean accessions with high-quality SNP markers. We identified 163 SNPs. Of which, 136 significant SNPs on chromosome 16 ranged from 29135922 to 29865027 (∼729 kb). These results were consistent with those of a previous study, which showed that the major QTL was located on chromosome 16 . SimilarPod dehiscence 4-1; On chromosome 9, 2 SNPs were co-identified in Env3 and Env5; there are one, one, three, one, three, three and one significant SNPs identified on chromosome 1, 4, 6, 11, 17, 18, and 20, respectively. These SNPs or regions may be the novel loci related to pod dehiscence. Overall, the SNPs identified in this study are helpful to further understanding the genetic basis of soybean pod dehiscence.In addition, we also detected 25 significant SNPs except on chromosome 16 . For exapdh1 allele  were observed under the threshold of FDR < 0.05 in two environments (Env3 and Env5) . Moreove low PPD . Then wehiscence . However–03 , and were close to Pod Dehiscence 4-1 (Glyma08g42110 (one of them was detected within the exon). In addition, the Glyma08g42110 is homologous to ATGSL07, which encodes callose synthase 7 , a phloem-specific callose synthase responsible for callose deposition in developing sieve elements during phloem formation. ATGSL07 mutant plants exhibited moderate reduction in seedling height and produced aberrant pollen grains and short siliques with aborted embryos in A. thaliana (In addition, the GWAS results revealed that 25 significant SNPs were identified in only one environment except for the SNPs on chromosome 16 . Howeverence 4-1  (Supplemence 4-1 . Interesthaliana . Our furGlyma09g06290. Expression patterns and allelic variation showed that Glyma09g06290 was associated with the pod dehiscence. Additionally, we also developed a functional marker in Glyma09g06290 for pod dehiscence in soybean, which may be useful for the breeders. Overall, these results would provide insights for molecular-assisted breeding strategies for resistance to pod dehiscence in soybean.In this study, 163 SNPs were identified using GWAS across multiple environments. In addition, favorable SNPs and six haplotypes relevant to pod dehiscence were identified. Furthermore, based on GWAS results, we identified a candidate gene http://www.soybase.org/.Publicly available datasets were analyzed in this study. This data can be found here: DY and FH designed the study. DHu, XL, and GK conducted the GWAS. DHu, DHao, WH, ZY, and XH carried out the field experiments. XL, HY, and YL carried out the qRT-PCR analysis. DHu wrote the manuscript. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Lolium perenne) and red clover (Trifolium pratense). We simulated different levels of intraspecific functional diversity by sowing single cultivars or by combining cultivars with contrasting growth habits, in monospecific or bispecific settings . Replicate field plots were established for seven seed compositions. We determined yield parameters and monitored differences in genetic diversity in the ryegrass component among seed compositions, and temporal changes in the genetic composition and genetic diversity at the within plot level. The composition of cultivars of both species affected the yield and species abundance. In general, the presence of clover had a positive effect on the yield. The cultivar composition of the ryegrass component had a significant effect on the yield, both in monoculture, and in combination with clover. For the genetic analyses, we validated empirically that genotyping-by-sequencing of pooled samples (pool-GBS) is a suitable method for accurate measurement of population allele frequencies, and obtained a dataset of 22,324 SNPs with complete data. We present a method to investigate the temporal dynamics of cultivars in seed mixtures grown under field conditions, and show how cultivar abundances vary during subsequent years. We screened the SNP panel for outlier loci, putatively under selection during the cultivation period, but none were detected.Agricultural grasslands are often cultivated as mixtures of grasses and legumes, and an extensive body of literature is available regarding interspecific interactions, and how these relate to yield and agronomic performance. However, knowledge of the impact of intraspecific diversity on grassland functioning is scarce. We investigated these effects during a 4-year field trial established with perennial ryegrass ( Such diversity effects result from functional complementarity among members of the same or different species, resulting in structural, trophic or phenological niche differentiation. A large part of the Earth’s terrestrial ecosystem is covered with grasslands [Whereas it is widely accepted that plant species richness and plant trait diversity have a positive effect on the functioning of ecosystems, recent progress in community ecology has also emphasized the importance of intraspecific diversity –4. Positasslands , which sasslands . These sasslands . IdentifLolium (accounting for about 23% of the grasslands), with L. perenne  as the most prevalent species [Phleum pratense) or tall fescue (Festuca arundinacea), or with legumes such as white clover (Trifolium repens) or red clover (Trifolium pratense). Although perennial ryegrass is still frequently cultivated as a monoculture, sustainable agricultural practice is progressing towards combinations of ryegrass with legumes, which reduces the need for nitrogen fertilizer application [Grasslands can develop progressively upon grazing or mowing activity, but in highly productive livestock systems they are sown for the production of high quality forage. These agronomic grasslands support the production of meat and dairy products, and the total value of grass production in the EU is estimated at more than 23 billion Euro . Such gr species . Cultivalication , 11. Thelication . While mlication , 14. Esplication , increasThe importance of intraspecific diversity for the functioning of cultivated grasslands has not been investigated in depth so far. However, the intraspecific diversity of perennial ryegrass might be an important factor for sward productivity and resilience, as cultivars of this species are typically genetically very diverse –18. Ryegpool) with AAF calculated from the constituent individual samples (AAFind). (3) Characterizing the temporal changes in genetic diversity of the ryegrass component during the cultivation period, based on AAFpool. (4) Screening for the presence of outlier SNP loci, which may be indicative for selection of specific alleles during cultivation. (5) Investigating how temporal changes in genetic diversity of the perennial ryegrass component relate to the composition of the initial seed mixture, forage production and abundance of the ryegrass and clover components.Here, we monitored changes in the genetic diversity and forage yield of cultivated ryegrass populations over the course of four years. Experimental field plots were established with populations of perennial ryegrass, with or without red clover. We simulated different levels of potential niche differentiation by mixing phenotypically contrasting cultivars in different combinations, i.e. low vs. high tillering cultivars for the ryegrass component , and ere2 for the ryegrass monoculture plots and 1190 seeds/m2 for the mixed species plots, with a ratio of 70 perennial ryegrass seeds to 30 red clover seeds, as is common agricultural practice.A field trial was established in April 2011 on a sandy loam soil in Merelbeke, Belgium (50.9867 N 3.7912 E). Seven different seed compositions  were sowThe trial was fertilized and weeded according to common agricultural practice , and mowLeaf samples for genetic analysis were collected in 2011 after establishment of the 14 plots, and immediately before the spring cut during three subsequent years (2012–2014). At each sampling moment, 40 perennial ryegrass leaves were collected in each plot, rendering a total of 56 sets of 40 leaves . Leaves The pool-GBS procedure was validated on a set of 40 plants derived from seed composition 1 . The 40 individual leaf samples were genotyped individually and allele frequencies were calculated  as descrPstI , and barcoded adapters were ligated with T4 ligase  in a final volume of 50 μL. Ligation products were purified with AM-pure magnetic beads [DNA of the 40 individuals and three pools of the validation set was isolated with the Bio-Nobile Quickpick Plant DNA extraction kit. DNA of the 112 tissue pooled samples was isolated using the CTAB procedure of Doyle . DNA intic beads  and elutThe protocol for the 112 tissue pool samples was slightly adjusted, based on the results of the validation step. In this case, the 50 μL bead-purified ligate was split into three aliquots, and 16.7 μL was used for three replicate PCR reactions to account for PCR amplification bias. Amplified libraries were quantified as described above and pooled in equal amounts into three ‘super’ libraries (one for each set of replicated PCR reactions). Super libraries were again bead-purified and 100 bp single-end sequenced on two parallel lanes per super library on a Hiseq2500 instrument  . In thisReads were demultiplexed with GBSX 1.3  allowingFor each genomic position, we counted the number of missing data across samples. For the individual plants, we filtered on maximum of 1, 5 or 10 missing genotype calls per locus. For the pool samples, we filtered the loci on > 0 missing data. The three datasets, individual plants, pool replicates of the validation and 56 pools of the field experiment were also filtered on excessive RD . The thresholds for this filtering step were determined for each dataset separately; maximum 6 k total RD for the dataset of 40 individual samples , 3.5 k for the three replicate pools of the validation experiment, 7 k for the pairwise merged pools and 150 k for the 56 pooled samples of the field experiment.RD saturation curves were constructed by first merging all read data of the validation experiment, followed by computational subsampling of reads, read mapping, and calculating the number of genomic reference positions with a minimal RD of 10, 30, 100, or 300 reads at increasing numbers of reads mapped.ind at each SNP position over the set of 40 individual plants was calculated as the number of genotypes with the alternative allele, counting homozygous reference calls as 0, heterozygous calls as 1, homozygous alternative calls as 2. This value was divided by the number of sampled chromosomes. To identify SNPs and estimate AAFpool of the pooled samples, we used SNAPE-pooled. This is a Bayesian method to differentiate genotyping errors from real alleles on a per-sample basis, using the Watterson’s estimator theta as a diversity prior determined by NPstat [pool values based on allelic RDs, and assigns probabilities to an allele being fixed reference if 1− p(0) < 0.9 or fixed alternative if p(1) > 0.9. A custom python script was used to merge SNAPE-pooled output files to a single AAFpool matrix and set frequencies to zero if 1− p(0) < 0.9 and to one if p(1) > 0.9, where p represents the posterior probability as described in Raineri [For individual plants, SNPs were called with GATK HaplotypeCaller 3.3.2 . Indels y NPstat , 43. The Raineri . After aind and AAFpool for SNPs that were identified by both approaches.For the validation experiment, we calculated the Pearson’s correlation and the median deviation between AAFlmer function of the lme4 package (version 1.1–13) [anova function. The following three models were tested:The herbage DMW harvested each year  was anal 1.1–13) . To testlmjk represents the total DMW (both species) of field plot replicate j of the seed composition with ryegrass cultivar l (Merks or Meloni) and clover component m  in year k (1 to 4). (Ll x Tm) is the interaction between Ll and Tm, which are both fixed effects. Rj (replicate) and Yk (year) are random effects. The term μ represents the mean total DMW, and ϵij is the error term. Model 2 is similar to model 1, but the clover component (and the interaction term) have been excluded. In this case, the ryegrass component l is either Merks, Meloni or a mixture of both cultivars. Model 3 is similar to model 2, but in this case Ll has been replaced by Tm. In this case, the clover component m is either Lemmon, Crossway or a mixture of both cultivars. Additionally, H2 and H3 were tested considering the DMW of grass and clover separately.In model 1, Ypool per locus across the 56 samples (see prcomp function (3.0.2) on the centered, non-scaled AAFpool data (samples were oriented as rows and SNPs as columns). Hence, the scores on the principal components represent the 56 samples, i.e. 14 field plots at 4 time points, and a lineplot of year against score for one of the principal components represents shows changes in the genetic composition of each field plot over time.The SNPs were filtered on minimum 10% and maximum 90% mean AAFpool values following eH = 2 × poolAAF × (1 − poolAAF). Subsequently, the genetic diversity contained in each sample was calculated by averaging He over all SNPs (i.e. mean He). As each plot was sampled at four discrete time points, the data were organized in 14 temporal series (corresponding to the 14 field plots) for further inspection. He values are independent of the alternative allele being the minor or major allele, and allow comparing genetic diversity in samples corresponding to different field plots, or in samples corresponding to different time points of the same field plot. Mean He is maximal (0.50) when all alleles occur in equal proportions and decreases as the frequency of one of the allele  increases. A low mean He value might thus be indicative of an overall lower level of diversity when samples of different plots are compared. Correspondingly, changes in mean He might be indicative of selection when samples were compared that were taken at different time points. Mean He data were analyzed with a linear model with the aov function in R (model 4).The expected heterozygosity was calculated per locus and sample, based on the AAFe ijk representing the genetic diversity of field plot replicate j (A or B) of seed composition i  in year k (1 to 4). The factor Si represents seed composition, and the interaction of seed composition and replicate (Si x Rj) represents differences of AAFpool between replicate field plots (irrespective of time). The interaction of seed composition and year (Si x Yk) captures temporal changes in mean He, which are consistent across replicate field plots of a given seed composition. Plot-specific changes of mean He are captured by the residual term ϵijk. The degrees of freedom of this model are 55 for the dependent variable mean He, 6 for Si, 7 for (Si x Rj), 21 for (Si x Yk) and 21 for the residuals.With mean Hpool per sample was calculated for both private SNP sets and represented graphically for inspection.Next, we investigated possible temporal changes in the relative abundance of the two ryegrass cultivars in the plots in which both were sown together (seed compositions 5 to 7), using cultivar private SNPs. We considered ‘Merks private SNPs’ those that are not polymorphic in plots in which only Meloni was sown, but polymorphic in plots where Merks was sown (either as a single ryegrass cultivar or in mixture with Meloni). Conversely, Meloni private SNPs were not polymorphic in plots in which only Merks was sown, but polymorphic in plots where Meloni was sown. The mean AAFpool frequencies were analyzed with a similar approach as for mean He (described above). He reflects the balance in allele frequency of two alleles at a single locus. This on its own does not inform us about the identity of the allele that is more or less abundant. Linear regression of AAFpool values allows investigating these aspects. The AAFpool data was analyzed with the aov function using the following model:The time series of AAFe (see pool was partitioned to estimate the relative importance of the interaction between seed composition and year (Si x Yk). This component captures the variance attributed to temporal changes in AAFpool that are consistent for field plots of the same seed composition.Model 5 is similar as model 4, except for an additional covariate PC1 that represents the scores of the first principal component of the PCA. In this model, PC1 captures the differentiation between Merks and Meloni, and changes in the relative abundance of these two cultivars in the plots in which they were sown together (seed compositions 5 to 7). This was considered necessary after inspection of the PCA results and the temporal changes of mean Hi x Yk) and the residual sum of squares (RSS) (model 5) was used as test statistic. This value represents the ratio of variance explained by changes in allele frequency that are consistent for replicated field plots of the same seed composition to changes that are not consistent. This model assumes that the ryegrass component of replicate field plots experiences similar selection pressures and responds similarly. We compared several probability density functions to fit the null distribution of the test statistic, including the chi-square, F, gamma and log-normal distribution (see fitdistr function of the MASS package of R (version 7.3–47) was used for maximum likelihood-fitting of the distributions .Finally, we screened the SNPs for outliers, i.e. loci that were putatively under selection during the cultivation period. The allele frequencies of loci under selection (or linked with unobserved loci under selection), are expected to change more dramatically over time than those of neutral loci. The quotient of the mean sum of squares (MSS) of . The interaction of these effects was not significant (pLRT = 0.145). The total DMW was higher when perennial ryegrass was sown in combination with red clover (Hypothesis 1 was tested using seed compositions 1 to 4. Both the ryegrass cultivar (either Merks or Meloni) and the presence of red clover significantly affected the herbage yield . However, the effect on the DMW of both species separately was reversed. The highest yield of clover was obtained with Lemmon, and the lowest yield of ryegrass was obtained with Crossway (For field plots in which a mixture of both ryegrass cultivars was sown in combination with red clover (seed compositions 5 to 7), plots containing only Lemmon had the highest yield, followed by mixed red clover cultivars, and finally Crossway (seed composition 6 > 5 > 7). The effect of red clover composition was significant for the total DMW, and for the grass DMW and clover DMW separately  displayed a better competitive ability than Crossway (creeping growing habit), resulting in a larger total DMW although the yield of the grass component was significantly lower when combined with Lemmon.First, the pool-GBS procedure was validated using a subset of 40 leaf tissue samples. GBS sequencing resulted in 2.4 ± 0.4 M reads for the individual plants (n = 40) and 14.1 ± 1.1 M reads for the pools used for validation (n = 3). To determine the optimum number of reads per sample, we constructed RD saturation curves at varying levels of minimum RD threshold. The saturation curves suggest that the majority of potentially available GBS loci are covered if at least ~20 M reads are obtained per sample and that the coverage increases when data of replicate pools are merged .ind) to the allele frequencies estimated in three replicate pools (AAFpool). Because the 40 individual plants were not sequenced to saturation  shows that individual genotyping was highly reproducible. On average 93.6% of the genotype calls were identical in the three pairwise comparisons and 92.4% of the genotype calls was identical across all three replicate SNP sets . Next, wturation , we consturation , columnsturation , rows. Increased . Therefoncreased , and shoparisons . The hig calling . TherefoMore than half of the SNPs were called uniquely by SNAPE-pooled in a given pool sample but were not identified by GATK in the set of 40 individuals even though sufficient RD was available on the respective loci . This waWe ran the NPstat/SNAPE-pooled pipeline with a range of parameter values to test if the number of non-reproducible SNPs could be reduced (results not shown). The minor allele read count (MAC) threshold of NPstat was increased from minimum 2 (default) up to minimum 6, which resulted in (reproducible) estimates for theta from ca. 0.014 to ca 0.007. However, varying the theta diversity prior for SNAPE-pooled in this range did not reduce the number of non-reproducible SNPs in the output of SNAPE-pooled.pool values  SNPs versus SNPs that were consistently called (reproducible) by SNAPE-pooled in replicate pools. Pairwise comparisons of replicate pools revealed that non-reproducible SNPs were strongly skewed towards low AAFl values . For inspool < 90% for at least one sample out of 56 as criterion to retain SNPs for further data analysis.In conclusion, SNP filtering based on p(0) and p(1) values as recommended by SNAPE-pooled needs to be complemented with additional filtering based on the allele frequency spectrum. We chose a cutoff of minimum 10% < mean AAFpool profiles of the 56 samples of the field experiment  , which cover ca. 1.1 Mb of the reference genome sequence. We removed approximately 15 kb of the base positions were covered with excessive total RD , and represent genomic repeat sequences. The remaining read data covered ca. 1.085 kb, which represents ca. 0.1% of the 1.13 Gbp reference genome sequence [Sequencing resulted in 29.3 ± 5.2 M reads per pooled sample, which cover on average 2.3 ± 0.2 MB of the reference genome with sufficient RD to estimate AAF, we only retained SNPs with a mean AAFpool per SNP of minimum 10% and maximum 90%, resulting in a dataset of 22,324 SNPs. Usually, heteroscedastic allele frequencies are normalized [pool values close to either zero or one and/or present in only a few samples, which are abundant in our dataset.SNAPE-pooled identified 238 k SNPs. We removed 9 k SNPs with more than two alleles across all samples. As expected from the validation experiment, many SNPs which were polymorphic in just one sample had a low AAFrmalized . Howeverpool of 22,324 SNPs. One meaningful principal component was obtained, explaining 77% of the variance , which captures changes in overall genetic diversity that are consistent across replicates   . Regardie values , to differences between replications (Si x Rj), or due to temporal changes in AAFpool (Si x Yk), were relatively small compared to the residual variance  = 0.0033; median MSS of (Si x Yk) = 0.0035; median RSS = 0.0032). The quotient of MSS of (Si x Yk) to the RSS follows a log-normal distribution , the variance captured by each predictor component (MSS) and the residual variance (RSS) are shown in ribution . The p-vribution . TherefoThe herbage yield differed among seed compositions throughout the four years of cultivation, including the first year of establishment. This suggests that plant-plant interactions affected biomass production from the establishment of the field and onwards. Fluctuations in yield and species abundance were consistent between field plot replicates. The total DMW peaked during the second year, and competition might have been the most intense during this period.Red clover often lacks persistence, but in this field trial, the proportion of clover to grass did not decrease, confirming the viability of red clover as a companion for perennial ryegrass , 48. As It is expected that these functional interactions are affected by the intraspecific diversity of the component species . In thisSingle nucleotide polymorphisms (SNPs) have proven to be effective to characterize the genetic diversity among plant populations. Using whole genome shotgun sequencing (pool-seq), allele frequencies can be estimated directly from pools of plants –54. Yet e strongly depends on the set of genomic loci under consideration. For comparison of samples, mean He is preferentially calculated for a common set of loci. High proportion of missing data have been reported for GBS data [A central goal of this study was to compare quantitative estimates of genetic diversity across populations. Estimation of the diversity index mean HGBS data , 63. Thepool is directly estimated from the read counts at a certain locus, genotyping errors can be confounded with low frequency alleles [pool estimated in three replicate pools correlated very well with the AAFind estimated from individual GBS genotyping. However, pool-GBS was sensitive to genotyping errors, and stringent filtering of the SNP loci was required to identify a reliable set of 22,324 high quality SNPs across the 56 population samples.As AAF alleles , 53. Mor alleles , 64, 65. alleles  and Ashr alleles  sampled pool of 22,324 SNPs, similarly as in Byrne [pool dataset. perennial ryegrass cultivars are genetically very diverse [The perennial ryegrass cultivars could be differentiated with PCA based on the AAFin Byrne . The gen diverse –18, and  diverse . Similar diverse . They obpool data. An alternative explanation for not detecting outlier loci is related to linkage disequilibrium (LD) patterns in these populations. With the current spacing of GBS tags across the genome (approx. 1 stack of 100 bp per 100-200kbp), the genetic markers observed may not be in genetic linkage with loci under selection, if LD extends only for short distances [We developed a statistical test to identify loci that significantly change in time. However, we did not identify convincing signatures of selection in the perennial ryegrass populations investigated. Possibly, selection pressures were too low to have any significant detectable effect on AAFistances . In thisistances .The interactions between neighboring plants plays an important role in the functioning of cultivated grasslands, and are determined by the functional diversity of the sown species and cultivars. This study showed that herbage yield and species abundance of field plots is affected by the mixture of perennial ryegrass and red clover cultivars used. Genetic diversity among ryegrass populations, investigated by GBS genotyping of pooled samples, showed that the abundance of perennial ryegrass cultivars is highly dynamic in mixtures. Taken together, these results illustrate that phenotypic traits of both perennial ryegrass and red clover affect their behavior in seed mixtures of both species and that the dominance of cultivars in mixtures can shift throughout the cultivation period.S1 Table(XLSX)Click here for additional data file.S2 Table(TSV)Click here for additional data file.S1 Figet al., 2015) that are covered (y-axis) at various minimum RD threshold; RD 10 (blue), RD 30 (red), RD 100 (orange) and RD 300 (green). A shows the data of individual plants (between 0 and 4 M reads), data of three replicate pools (between 12 and 14 M reads) and pairwise merged data of pools (between 26 and 30 M reads) of the validation experiment. The line curves were constructed by resampling reads of the validation experiment. These curves suggest that the larger part of potentially available GBS loci are covered if at least ~20 M reads are obtained per sample. B shows the data for the 56 population samples of the field experiment. Data of the technical replicates (between 0 and 14 M reads), were merged for each population sample (between 20 and 54 M reads) (see pooling and replication scheme Saturation curves show the relationship between the number of reads per sample (x-axis) and the number of base positions of the perennial ryegrass reference genome (Byrne (PDF)Click here for additional data file.S2 Figind) and three pool replicates of the same set (AAFpool), showing the number of SNPs (n), the median deviation (d), Pearson’s correlation coefficient (r) and the least squares regression (red). A The effect of SNP filtering on the correlation of AAFind and AAFpool. The SNPs of the individuals were filtered on maximum missing data (MD) 1, 5 or 10 out of 40 samples, and the SNPs of the pool were filtered on minimum RD of 30, 100 or 300. For subsequent comparisons we consistently the thresholds RD 30 and MD 5 B: Correlation of AAFpool of the three pool replicates to AAFind. C: Pairwise correlations of AAFpool of the three pool replicates. Only SNPs that were also detected in the individuals were considered for this comparison. D: Correlation of AAFpool obtained by pairwise merging of pool replicates to AAFind.Allele frequency correlations of SNPs that were identified in a set of 40 plants (AAF(PDF)Click here for additional data file.S3 FigA—D follows the same order as the allele frequency correlations Click here for additional data file.S4 Figpool distributions of replicate pools. Each distribution shows the AAFpool distribution of a pooled sample, colors indicate whether the SNP was detected in the corresponding replicate . Non-reproducible SNPs are strongly skewed towards low AAFpool values.Pairwise comparisons of AAF(PDF)Click here for additional data file.