Moraxella bovoculi is frequently cultured from the ocular secretions and conjunctiva of cattle with Infectious Bovine Keratoconjunctivitis (IBK). Previous work has shown that single nucleotide polymorphism (SNP) diversity in this species is quite high with 81,284 SNPs identified in eight genomes representing two distinct genotypes isolated from IBK affected eyes (genotype 1) and the nasopharynx of cattle without clinical IBK signs (genotype 2), respectively. The goals of this study were to identify SNPs from a collection of geographically diverse and epidemiologically unlinked M. bovoculi strains from the eyes of IBK positive cattle (n = 183) and another from the eyes of cattle (most from a single population at a single time-point) without signs of IBK (n = 63) and to characterize the genetic diversity. Strains of both genotypes were identified from the eyes of cattle without IBK signs. Only genotype 1 strains were identified from IBK affected eyes, however, these strains were isolated before the discovery of genotype 2, and the protocol for their isolation would have preferentially selected genotype 1 M. bovoculi. The core genome comprised ~74% of the whole and contained >127,000 filtered SNPs. More than 80% of these characterize diversity within genotype 1 while 23,611 SNPs (~18%) delimit the two major genotypes. Genotype 2 strains lacked a repeats-in-toxin (RTX) putative pathogenesis factor and any of ten putative antibiotic resistance genes carried within a genomic island. Within genotype 1, prevalence of these elements was 0.85 and 0.12 respectively in strains from eyes that were IBK positive. Recombination appears to be an important source of genetic diversity for genotype 1 and undermines the utility of ribosomal-locus-based species identification. The extremely high genetic diversity in genotype 1 presents a challenge to the development of an efficacious vaccine directed against them, however, several low-diversity pilin-like genes were identified. Finally, the genotype-defining SNPs described in this study are a resource that can facilitate the development of more accurate M. bovoculi diagnostic tests. Infectious Bovine Keratoconjunctivitis (IBK) is an important disease that affects 2–10% of beef cattle in the United States  and is aMoraxella bovis [Mycoplasma spp. [Moraxella bovoculi [M. bovis is transmitted by direct contact [The only experimentally reproducible etiologic agent for IBK is la bovis ,9–10, thsma spp.  and Morabovoculi . M. bovi contact , ocular  contact , and mec contact ,15–17. T contact . Eye pat contact . IBK can contact . The eff contact ,6,20–24.M. bovoculi, has been widely associated with IBK in the absence of M. bovis since its initial description in 2007 [M. bovoculi is the most frequently isolated bacteria from clinical cases [M. bovoculi may exacerbate M. bovis infections or play other roles in the IBK disease state.The gram-negative coccobacillus,  in 2007 ,12,25–30al cases . While tal cases –32 and nal cases –35. WhilM. bovoculi genomes indicative of very high genetic diversity in this species [M. bovoculi genetic diversity found in IBK and apparently healthy cattle eyes.Previously, >81K SNPs were identified from 8 complete  species . FurtherMoraxella bovoculi strains were used in this study. Of these, 183 were epidemiologically unlinked clinical strains originating from IBK cattle in 22 US States with heavy bias toward the state of Nebraska (60.1% of strains) and bordering states (10.9%)  . These snotype 2 –36. The bovoculi . Use of 2. Single colonies were then inoculated in brain heart infusion broth and grown overnight without shaking. DNA was extracted from the cultures using an UltraClean -htp 96 Well Microbial DNA Isolation kit  and libraries were developed with a Nextera XT kit  and AMpure beads . Whole genome sequencing was then conducted on a MiSeq machine (Illumina) to generate 300 bp paired reads.To construct DNA libraries for sequencing, cultures were streaked from frozen stocks on chocolate agar plates and passaged twice overnight at 37C° and 5% COM. bovoculi reference genome [M. bovoculi [tuf and surrounding regions of the genome, were also removed from the core genome SNP dataset. PARSNP utilized FastTree 2 [Reads from each library were mapped to a complete e genome   genes located within a genomic island, or a repeats-in-toxin (RTX) putative pathogenesis factor [M. bovoculi genomes containing the RTX operon, so that RTX SNPs could be extracted for detailed analysis.Since it was previously reported that not all IBK associated s factor , strainsThe SNP alignment  for 220 M. bovoculi draft genome, AOMT00000000 contig 37 [M. bovoculi genome. Because of the importance of pilin serotype in M. bovis and the new GenBank pilin annotations not previously discussed in the literature, a hidden Markov model (HMM) of the type IV pre-pilin domain (TIGR02532) was built to search for any additional pilin-like genes in the reference genome.A single pilin gene had been previously identified in the ontig 37  and annoontig 37 . These aM. bovoculi, PARSNP did not recover any SNPs at this locus. Second, 13 unique TIGR02532 amino acid haplotypes were identified from the two pilin-like genes located at coordinates 503488–505002 in CP011374. Third, iterative PSI-BLAST [M. ovis and M. bovis and progressively expanded to Moraxella sp. and members of the family Moraxellaceae using the consensus from the previous progression in each successive search. Multiple PSI-BLAST iterations were conducted at each taxonomic level until no additional haplotypes were identified at the default P-value threshold. Only 2 iterations were conducted for the search against members of the Moraxellaceae since the webserver returned out-of-memory errors after 702 sequences were found. One hundred of the most diverse TIGR02532 members and 14 of the top listed sequences as identified by NCBI were included in the HMM for a total of 896 haplotypes. The HMM was built and all annotated protein coding sequences of CP011374 were queried using HMMER 3.1b2 [To conduct the HMM search, SNPs were first identified in the PilA gene, using the mapping, SNP calling, and alignment portion of the custom bioinformatic pipeline described above and in reference 37. Though this gene is in the core genome of ER 3.1b2 .Mean coverage for the 246 sequenced genomes ranged from 15.0 to 148.3 . The GC content ranged from 45.5–46.3%. The core genome comprised ~74% of reference genome GenBank CP011374. A total of 142992 SNPs within the core genome were identified by PARSNP and, of those, 127918 met our filter criteria.An aML phylogeny was constructed with 4014 SNPs removed that were identified by PARSNP as recombinant . The trew = 0.38, expected Φw = 0.57, p = 0.00). Thus, the relationships among genotype 1 were also examined by a neighbor-net tree, which could account for recombination  conferring resistance to macrolide, lincosamide and streptograminB, and mph(E) conferring macrolide resistance, which were found in 9 of the 22 strains. Regarding the subtypes of genotype 1, the RTX frequency was 100%, 91%, 92%, and 59% in subtypes a-d respectively and AR frequency was 9%, 20%, 6%, and 5% in subtypes a-d respectively.Neither RTX or any of the 10 previously reported AR genes were found in genotype 2 strains . The fre animals . Twenty- animals . The mosM. bovis, or a species closely related to M. bovis, as the hypothesized recombinants placed phylogenetically intermediate between the two species [M. bovis mosaics at RTX operon genes mbvB and mbvD, three are mosaics at mbvD only, and six strains are interspecies mosaics at rRNA genes. A single strain, Mb68506, is recombinant at both loci.Apparent recombination was detected at both the rRNA and RTX operon loci. The rRNA genes and surrounding non-coding repetitive regions contained 686 SNPs that did not meet our filter criteria for SNP identification. Visualizing the SNP profiles in Geneious showed dense SNP clusters in some strains , which m species . The RTXM. bovoculi genome CP011374, a fourth gene that is likely either a type II pilin or pseudopilin was identified in CP011374 , (GspH) conserved domain (M. bovoculi genotype 1 and genotype 2 with >28% of its reference length comprised of diagnostic SNPs. This is the same gene that is orthologous to the PilA gene in AOMT00000000. The remaining genes showed low divergence (~1%) between the two genotypes , . This ged domain . Of the enotypes .M. bovoculi from 183 IBK cattle case submissions covering a large portion of the United States and 63 non-IBK eye strains. This system differentiates two major genotypes of M. bovoculi. Strains belonging to genotype 1 have incredibly high SNP diversity, some of which we have attributed to interspecies recombination at both the 16S and RTX loci. Lastly, the frequencies of pathogenic and AR genes in Genotype 1 were higher in strains from IBK versus non-IBK eyes.This study developed a SNP-based classification system for M. bovoculi in IBK eyes is unknown [While genotype 2 was originally characterized based on strains recovered from the deep nasopharynx of cattle , genotyp unknown –36. Whil unknown , so it m unknown .M. bovoculi PilA gene, while in the present study no SNPs were identified by PARSNP even though the PilA gene is present in all genomes. While this change is predicted to reduce the number of SNPs found, the large increase in genome count should increase the SNP count. Indeed, there is a >2-fold increase in the number of SNPs variable within genotype 1. Due to these two factors, the type-defining SNPs identified in this study can be viewed as a high-quality conservative subset of the total, which come from more conserved portions of the M. bovoculi core genome.Of the 127918 SNPs identified with PARSNP, ~18% are diagnostic for separating the two major genotypes. This is a significant decrease from  in both M. bovoculi and the oft-studied homolog of M. bovis [M. bovis PilA gene undergoes inversion mediated phase variation that renders I and Q versions of the gene. We looked for evidence of an I/Q phase shift in our strains of M. bovoculi and found none. Additionally, the PilA genes of M. bovis and M. bovoculi are ≥60% divergent at the amino acid level [M. bovis as it has for M. bovoculi, and these may be critical to further understanding host-pathogen interaction.While no SNPs from the PilA gene were recovered by PARSNP, 164 SNPs from this locus were obtained using a custom in-house pipeline of which 130 diagnostically separated genotypes 1 and 2. This represents a >23-fold higher divergence among genotypes than seen in the other three pilin-like genes. The orthology of the PilA gene in M. bovis –60 is noid level . Extremeid level –23, seveid level , 57–60. M. bovoculi strains were collected at the US Meat Animal Research Center (USMARC) in the fall of 2015 versus the epidemiologically unlinked collection of IBK positive eye strains, there were elemental frequency differences among genotype 1 strains from IBK and non-IBK eyes. The RTX operon was found at a reduced frequency in non-IBK eyes, though this frequency still exceeded 50% in non-IBK eyes within USMARC and UNL animal populations. This might be a useful metric to track in other cattle populations, especially those with frequent IBK outbreaks. In contrast, AR genes within a previously characterized genomic island were not detected at all in strains from non-IBK eyes. This island is associated with an increased AR phenotype [M. bovoculi in the US. While the strains utilized in this paper from [95% 1–6)  .Due to high recombination in genotype 1, subtypes a-d are separated by very short branches and contain no tagging SNPs though their clade support was high. This was despite the removal of ~4K SNPs with the strongest recombination signal. As such, though there may be differences in pathogenicity potential among the subtypes, rampant recombination has obscured these differences at the SNP level. This highlights the difficulty of leveraging genomic data to immediate control solutions such as vaccines in the face of organisms with high recombination rates.M. bovoculi strains, the M. bovis 16S mosaic import corresponds to the latter 80% of variable region 6 (V6) nucleotides [Moraxella bovoculi since we not only found evidence of it in 3 of 220 (1.4%) genotype 1 strains, but also found 3 rRNA sequences  featuring the same mosaic pattern in the GenBank nr database. These GenBank samples have been identified, we believe incorrectly, as M. bovis. Such sequence-based misidentifications are a foreseeable consequence of 16S V6 mosaicism in M. bovoculi. Alternative identifications methods, such as MALDI-TOF MS [M. bovoculi and M. bovis, including isolates with mosaicism, [In three leotides  Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(CSV)Click here for additional data file.


XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between  Recently, genome-wide association study (GWAS) provided the first opportunity to have a comprehensive screen for susceptibility genes and up to 50 loci had been reported2. The discovery of novel susceptibility genes by GWAS included genes coding for key cytokines involved in Th17 activation  and NF-κB pathway also contributed to the aetiology (TNFAIP3 and TNIP1)3. In addition, macrophage and dendritic cells are also involved. HLA-C locus had been a known susceptibility gene before the GWAS era4. Exaggerated expression of keratinocyte antigens (like LCE3D) was also identified as susceptibility genes5.Psoriasis is a common polygenic chronic inflammatory skin disease affecting up to 3% of population6. While heritability of psoriasis had been estimated to be as high as 60–90%, the genetic risk of all variants added up could only account for one-fourth of susceptibility due to genetics. This phenomenon is called missing heritability7. One possible source of unaccounted risk is interactions (including gene-gene and gene-environment interaction), which is the risk of certain genotype may be altered to a large extent in the presence of another risk factor, which is also known as non-additive effects9. Various algorithms have been proposed to detect the interactions between SNPs or genes in GWAS data11. They all faced the difficulties of large search space, exponential increase in SNP combination with increasing level of interaction, and limited statistical basis of the proposed methods. On the other hand, examples of epistasis were found in model organisms and human diseases13. We had proposed a biological framework of gene-gene interaction and suggested that typical samples size (thousands of cases and controls) should have sufficient power to detect such gene-gene interaction with simulation data14. There were also some suggestions of epistasis in psoriasis but replication in subsequent studies were lacking8.Although many predisposition genes for psoriasis have been identified, individually they accounted for very small effect size, for example their odds ratios were typically less than 1.215. These DNA sequences can be arranged into a matrix A where each column (except the last column) corresponds to a SNP and each row corresponds to a sample as shown in Table Ai,j corresponds to the genotype of ith sample at jth SNP under the encoding scheme shown in Table A.Biological pathways are regulated by the interactions among bio-molecules constructed according to the genetic instructions stored in deoxyribonucleic acid (DNA). A single nucleotide polymorphism (SNP) is a variation at a specific DNA position among a population of organisms which may affect the structure of these bio-molecules. In a genome-wise association study (GWAS), DNA sequences of a large population of patient samples (cases) of a particular genetic disease and healthy samples (controls) are collected and researchers can discover disease-associated SNPs through comparing the DNA sequences between cases and controls17. Although many SNPs are weakly associated to the genetic diseases when they are analysed independently, some of them may show a stronger association only when they are analysed in combination with other SNPs. A typical example of this phenomenon is shown in Figs p-value less significant than 1 × 10−6 and an odds ratio smaller than 1.7 if they are analysed independently. Meanwhile in Fig. p-value of 2.52 × 10−14 and an odds ratio of 2.2719. Therefore, rs3132486 and rs3130048 may have a potential non-linear interaction associated to psoriasis. Similar but much weaker phenomena have also been observed between SNPs among two known psoriasis associated gene-gene interactions 8 and 3 and are shown in Supplementary Figs As previously discussed, the cause of many genetic diseases can be better explained through certain combinations of SNPs (i.e. SNP-SNP interactions) rather than a number of independent SNPs alone19. Any researcher who performs his/her analysis with a search space confined to statistically significant single SNPs only will prematurely filtered out some disease-associated SNPs which are statistically insignificant on their own similar to the ones shown in Figs k different SNPs (i.e. kth order SNP-SNP interaction) would take a time complexity of O(mkn) where m is the number of SNPs and n is the number of samples. Given the fact that there are around 38 million SNPs with one million tag SNPs across the whole human genome20 and a GWAS dataset often contains thousands of samples and hundreds of thousands of SNPs, this problem is difficult to be solved without any advanced computational technique. Driven by the continuous growth of computational power, it is now becoming possible to perform exhaustive search for 2nd order SNP-SNP interactions in a GWAS dataset within a reasonable amount of time28. However, existing exhaustive search algorithms are not driven by any biological knowledge and they evaluate the disease association of each SNP combination solely based on heuristic or statistical parameters. Therefore, they may found statistical significant SNP-SNP interactions which may not be biologically interpretable.In previous GWAS researches, filtering out a large proportion of statistically insignificant SNPs or performing search with greedy and stochastic search algorithms are two popular techniques for shrinking the search space of SNP-SNP interactionsp-value and improvement metric value. Most of these novel potential psoriasis-associated SNPs are reported to be Expression quantitative trait loci (eQTLs) of known psoriasis-associated genes like HLA-B and HLA-C in GTEx Portal29. After mapping the nearest gene to each SNP involved in top 10000 SNP-SNP interactions in terms of improvement metric value from both dataset, we have constructed a disease-associated gene network. In our network, almost half of the gene-gene interactions inferred are consistent with existing literature. Meanwhile, some of the remaining gene-gene interactions are potentially due to the long-distance haplotype interactions presence in the HLA region of Chromosome 6. Furthermore, we have discovered an interaction between 2 SNPs located in gene XXbac-BPG154L12.4 and RNU6-283P of the HLA loci, which doesn’t correspond to any short-distance or even long-distance haplotype interactions and hasn’t been reported in existing literature as well. Therefore, the interaction between XXbac-BPG154L12.4 and RNU6-283P is a potential new discovery. In addition, we have shown that counting contingency table through a pre-computed look-up table is effective in speeding up the process of exhaustive search.We have developed an exhaustive search algorithm driven by eight biological plausible SNP-SNP interaction and applied it on two psoriasis GWAS datasets (phs000019.v1.p1 and phs000982.v1.p1). We have first discovered a number of statistically significant SNP-SNP interactions which may have a stronger association to psoriasis then their component SNPs measured independently similar to the ones shown in Fig. 30. The parameters of data cleansing can be found as Supplementary Tables 31.We have downloaded two psoriasis GWAS datasets namely phs000019.v1.p1 and phs000982.v1.p1 from the database of Genotypes and Phenotypes (dbGaP). Data pre-processing has been performed on these two datasets to remove low quality SNPs and samples with PlinkAfter data cleansing, there are 352945 SNPs and 1593 samples  in dataset phs000019.v1.p1 and there are 790527 SNPs and 2689 samples  in dataset phs000982.v1.p1. The genotypes of every SNP in these two datasets are encoded as 0, 1, 2, 3 according to the encoding scheme shown in Table p-value of a 2nd order SNP combination under a particular genotype interaction pattern is referring to the 1 d.f. chi-square p-value of its 2 × 2 contingency table founded from a real psoriasis dataset with the procedure illustrated in the Methods section. Additionally, we have defined another measurement metric called improvement metric value to prioritise statistically significant SNP-SNP interactions where its two components SNPs are far less associated to psoriasis when they are analysed independently. It compares the pairwise p-value of a 2nd order SNP combination under a particular genotype interaction pattern against the standalone 1 d.f. chi-square p-value of both of its component SNPs and can be calculated by equation nd order SNP combination has a higher improvement metric value, its component SNPs are far more statistically significant when they are considered as a SNP combination rather than separately considered as two independent SNPs.In this paper, SNP-SNP interactions are prioritised with the following two measurement metrics. The pairwise nd order SNP-SNP interactions. There are 62284910040 and 312466073601 unique 2nd order SNP combinations in datasets phs000019.v1.p1 and phs000982.v1.p1 respectively. Each SNP combination is subjected to eight 1 d.f. chi-square statistic tests corresponding to eight genotype interaction patterns and eight pairwise p-value is thus calculated. Therefore, there are 498279280320 and 2499728588808 statistical tests performed on phs000019.v1.p1 and phs000982.v1.p1 datasets respectively. Among these tests, there are 3058119 and 59810682 statistically significant pairwise SNP-SNP interactions found in datasets phs000019.v1.p1 and phs000982.v1.p1 which have a pairwise p-value smaller than 1 × 10−13 and 1 × 10−14 respectively. We have sorted these statistically significant SNP-SNP interactions by their pairwise p-value and improvement metric value separately for further analysis.We have performed exhaustive search on the two cleansed psoriasis GWAS datasets to discover biologically plausible and statistically significant 2p-value, we have selected the top 20 statistical significant SNP-SNP interactions in terms of pairwise p-value which are listed in Supplementary Tables After sorting the interactions found in datasets phs000019.v1.p1 and phs000982.v1.p1 by their pairwise 35, rs126507836, rs289420732, rs313046732, rs313051732 and rs313057333. According to GTEx Portal29, there are nine SNPs acting as eQTLs of the following four known psoriasis associated genes, HLA-C38, HCP540, PSORS1C142 and MICB43. First, rs2244027 and rs2894176 are found to be eQTLs of HLA-C. After that, rs2516417, rs2516510, rs2523708 and rs2844502 are found to be eQTLs of HCP5. Then, rs9262492 and rs9262498 are found to be eQTLs of PSORS1C1. Finally, rs2534666 is found to be eQTLs of MICB. Therefore, these nine SNPs have a high potential to be associated to psoriasis and may be new discoveries.Among the interactions shown in Supplementary Table 44, rs1048455447 and rs1772833850. Meanwhile, rs4349859 and rs4418214 have already been found to be strongly associated to HLA-b27 allele51 and HIV infection52 respectively in other studies. According to GTEx Portal29, rs45533135 is an eQTLs of MICA. Since HIV53, HLA-B37 and MICA56 are strongly associated to psoriasis, these three SNPs have a high potential to be associated to psoriasis and may be new discoveries.On the other hand among the interactions we have selected in dataset phs000982.v1.p1, there are 3 psoriasis associated SNPs which are already reported in existing literature: rs13203895p-value much smaller than the standalone p-value of their component SNPs. Therefore, these interactions may be non-linear and further verification through wet-lab experiments should be performed in the future.After sorting the statistically significant SNP-SNP interactions found in both datasets by their improvement metric value, we have selected top 20 statistically significant SNP-SNP interactions in terms of improvement metric value and are listed in Supplementary Tables 32, rs938023732 and rs285395057 are literature reported psoriasis associated SNPs. According to GTEx Portal29, there are three SNPs which are eQTLs of the following two known psoriasis associated genes, PSORS1C142 and HLA-DQB159. First, rs7756521 and rs2844645 are found to be eQTLs of PSORS1C1. After that, rs8365 is found to be an eQTL of HLA-DQB1. The association between these three SNPs and psoriasis may be new discoveries.In Supplementary Table 61. Meanwhile, SNPs rs1265112 and rs746647 are reported to be in complete linkage disequilibrium (r2 = 1.00) with the SNP rs1576 in an existing literature62. Therefore, SNPs rs1265112 and rs746647 can both be considered as a proxy SNP of a literature-reported psoriasis associated SNP and they are not new discoveries. According to GTEx Portal29, there are ten SNPs which are eQTLs of the following two known psoriasis associated genes, HLA-C38 and MICA56. Four of them are found to be reported as eQTLs of HLA-C namely rs2517985, rs1265079, rs1265114 and rs1265067. Meanwhile, six of them are found to be reported as eQTLs of MICA namely rs4358666, rs2395491, rs4624908, rs7754026, rs13194571 and rs7775117. The association between these ten eQTL SNPs and psoriasis may be new discoveries.Meanwhile there are no common SNPs between Supplementary Tables 63. The genomic position of these SNPs and their nearest genes (if available) are shown in Supplementary Table XXbac-BPG248L24.13 . Since LOC105375015 is reported to be associated to HIV and AIDS progression64 and HIV is associated to psoriasis, rs13191519 may be associated to psoriasis as well. Meanwhile, rs3094205 is located at the upstream region of CDSN and CDSN is reported to be associated to psoriasis in an existing literature66. Therefore these two SNPs are likely to be a associated to psoriasis and may be a new discovery.By referring to the genome assembly GRCh37 published by Genome Reference Consortium, the genomic position of every SNP can be retrieved. After knowing the genomic position of every SNP, we have annotated every component SNP of the top 20 SNP-SNP interactions in terms of improvement metric value through CADD version 1.3SNPi and SNPj have an SNP-SNP interaction, Genei and Genej will have a gene-gene interaction where Genei and Genej are the closest genes to SNPi and SNPj respectively, we can predict gene-gene interactions based on the SNP-SNP interactions we have found after annotated with CADD.By making an assumption that if XXbac-BPG248L24.13 and CCHCR1, there are 60 interactions left and are listed in Supplementary Table We have selected top 10000 statistically significant SNP-SNP interactions in terms of improvement metric value from both datasets. Then we have predicted a number of gene-gene interactions based on these SNP-SNP interactions. As shown in Supplementary Fig. HLA-B37, HLA-C38, PSORS1C1(which was previous named as SEEK1)42, CCHCR168, HCP540, CDSN66, USP8P144, MICA69, PSORS1C370, HCG2771, POU5F172, WASF5P44, MICB43, C273. On the other hand, these 60 common gene-gene interactions can be arranged into a network with a circular layout as shown in Fig. 74, RAID75, lncRNA2target76, LncReg77, InBio_Map78, HPRD79 and BioGRID80, a new edge with a colour corresponding to the reporting database will be added to the network. Furthermore, the gene network can also be arranged with a linear layout  along Chromosome 6 from position 30734602 to 32233615 are shown in Supplementary Fig. 30.Among these 60 common gene-gene interactions, there are 29 unique genes. Through referencing Ensembl release 75, the positions of these 29 genes under GRCh37 can be found . Similar to XXbac-BPG248L24.13, XXbac-BPG299F13.15 may be a discovery and its effect can only be verified through wet-lab experiments. In Supplementary Fig. HCP5 and MUC22. Since there is a long-distance haplotype between HCP5 and MUC22, the gene-gene interaction predicted has a high potential to be representing the effect of a long-distance haplotype. Similarly, LINC00243 and PSORS1C1 have a predicted gene-gene interaction which corresponds to a long-distance haplotype. Clearly, long-distance disease-associated haplotype can be found through our exhaustive search algorithm as well. Finally, the gene-gene interaction predicted between XXbac-BPG154L12.4 and RNU6-283P may be a novel discovery. Although RNU6-283P and a known psoriasis-associated gene HLA-B are both located at the same LD region, XXbac-BPG154L12.4 is not in any LD region which contains any known psoriasis associated gene. Therefore, XXbac-BPG154L12.4 may be interacting with a proxy gene of a known psoriasis-associated gene HLA-B. Furthermore, there is a strong recombination site in between XXbac-BPG154L12.4 and any other known psoriasis associated gene. Therefore, the interaction between XXbac-BPG154L12.4 and RNU6-283P cannot be simply explained as a long-distance haplotype and it may be a new discovery.As seen in Fig. We have performed simulations to compare the time of counting contingency table of each SNP-SNP interaction under every pattern with pre-computed look-up table instead of naively counting the number of cases and controls under black and white genotype under every pattern. In this simulation, we have executed our program under the synthesis datasets generated by us. The average run-time of our program with or without pre-computed look-up table under datasets with different numbers of SNPs and heritabilities are shown in Supplementary Table 1. A strong role of genetic predisposition in its etiology has been confirmed by recent family-based linkage studies and twin studies. Recent GWAS also confirmed a number of predisposition SNPs, particularly in Chromosome 6 HLA loci region2. In this paper, we aim at demonstrating SNP-SNP interactions also play an important role in causing psoriasis. Our results have shown that interactions between SNPs are present in psoriasis patients. Although these interacting SNPs are mostly found in the extended region of HLA in Chromosome 6, there are SNP-SNP interactions spanned across recombination hotspots which excluded the possibility that the epistasis is due to long-distance haplotype effect. Existing gene/protein interaction database also confirmed that the bio-molecule products of these genes in the extended region of HLA in Chromosome 6 are indeed interacting with each other.This project studies the genetic risk factors of psoriasis. Psoriasis is a chronic inflammatory dermatitis characterised by hyperproliferation of the epidermis. It is a common dermatitis affecting up to 3% of the general population81. Its heritability is as high as 90% which is one of the highest among common diseases and it can equally affect both male and female82. On the other hand, the best predisposition SNPs found in HLA-C could only increase disease risk by around 4 folds83. This level of odds ratio is not sufficient to account for the high heritability of psoriasis. Other mechanisms must be involved. Gene-gene interactions or gene-environmental interactions are among the most likely explanations for this phenomenon of missing heritability.Performing GWAS through analysing the independent effect of every SNP did not provide an adequate explanation for the hereditability of psoriasis. Previous studies have shown that monozygotic twins were more likely to be affected together than dizygotic twins9 possible genotype interaction patterns available. Such exhaustive enumeration of interaction patterns is unnecessary as most of these patterns are not biologically interpretable under our assumption. Based on our previous analysis on the distribution of bio-molecule complexes between two different bio-molecules, we have shown that the search space can be restricted to only 8 genotype interaction patterns as shown in Fig. HLA region in Chromosome 6.Unlike other existing GWAS researches, we mainly focus on searching SNP-SNP interactions which can be explained as interactions between two bio-molecules. Although there are many potential mechanisms which can explain the interaction between two loci, interaction between 2 bio-molecules is the most valid and feasible hypothesis. With this idea in mind, we can introduce new constraints to the genotype interaction patterns to shrink the search space. Given any 3 × 3 genotype table of any 2 SNPs, there are total 2HLA-C and HLA-B, these 2 genes are probably expressed together and their protein products can interact with antigens on cell surface. It is possible some alleles of the HLA lead to a more intense inflammatory response. For example, HLA-b27 has been known to be a genetic factor of psoriasis. Similar HLA alleles have already been found in other diseases like HIV infection and Diabetes.Among the gene-gene interactions we have found, some are obviously associated to psoriasis. For example, HLA loci is still less understood. However, genome-wide protein-protein interactions were evident in numerous wet experiment. In fact, it is not uncommon to have interacting gene partners (like ligands and receptors) located in nearby genomic region, with the benefit that they could be regulated simultaneous during organism development. This genomic arrangement leads to various example of gene clusters, like those of cytokines, chemokines and their receptor etc.Knowledge of interaction between other bio-molecules in the extended nd order SNP combination  can be arranged into a 3 × 3 genotype table similar to the table shown in Fig. SNPi, SNPj). A genotype in a 3 × 3 table can be labelled as a high-risk or low-risk genotype through applying statistical or heuristic algorithms like multi-factor dimensionality reduction (MDR) algorithm and its derivatives84. However, the patterns of the genotype label generated by these algorithms may not be biologically interpretable. In this paper, we have applied eight 2nd order biological plausible SNP-SNP interaction patterns14 for labelling genotypes into high-risk and low risk genotypes. The principles and assumptions in deriving these eight SNP-SNP interaction patterns are shown in Fig. SNP1 and SNP2 are located within two different functional sites Site1 and Site2 respectively. Major alleles are represented by upper-case letters  and minor alleles are represented by lower-case letters .SNP1 and SNP2 can affect their respective functional sites and cause each site to produce at most two different subtypes of bio-molecules. For example, bio-molecules pA is generated from Site1 with SNP1 having an major allele.Site1 and Site2 can randomly dock with each other to form at most four different bio-molecule complexes For example, complex pApB is composed by bio-molecules pA and pB generated from Site1 and Site2 respectively.The bio-molecules generated from A bio-molecule complex is associated to a genetic disease if (1) Only its solo presence (i.e. no other bio-molecules are present) or (2) Its presence can either promote or inhibit a disease.The number of cases and controls under each genotype of any 2pApB is the only disease-associated bio-molecule complex and its presence can either promote or inhibit a disease (i.e. condition 4b), samples carrying genotype {“AA”, “BB”}, {“AA”, “Bb”}, {“Aa”, “BB”} and {“Aa”, “Bb”} obviously would have a different level of disease risk comparing to samples carrying other genotypes. After labelling these two groups of genotypes with two different colours, pattern 1 can hence be defined. On the other hand considering pattern 5 in Fig. pApB is the only disease-associated bio-molecule complex and only its solo presence can either promote or inhibit a disease (i.e. condition 4a), samples carrying genotype AA, BB would have a different level of disease risk comparing to samples with other genotypes. Similarly, other patterns shown in Fig. The eight biologically plausible SNP-SNP interaction patterns are shown in Fig. 9 to the eight patterns shown in Fig. Since other genotype interaction patterns are not biologically plausible, we can reduce our search space on genotype interaction patterns from 2nd order SNP combination  into a 2 × 2 contingency table is being demonstrated. Considering a 2nd order SNP combination  after its genotypes being labelled according to genotype interaction pattern 1 in Fig. ND,B and NH,B respectively. Meanwhile the number of cases and controls of white genotypes are summed up as ND,W and NH,W respectively. This summation process is shown in Supplementary Fig. ND,B, NH,B and ND,W and NH,W can then be arranged into a 2 × 2 contingency table as shown in Supplementary Fig. https://imputationserver.sph.umich.edu) which is based on Minimac3 imputation algorithm85.In Supplementary Fig. SNPi, it corresponds to a vector ith column of matrix A in Table SNPi from cases and control vector p genotypes in a vector can be combined into a 2p bit integer g. Given any possible pair of integer g, the distribution of black and white genotypes in cases and controls under every genotype interaction pattern can be pre-computed and stored in a look-up table located at the main memory. Therefore, the 2 × 2 contingency table of any pairs of SNP can be found without direct counting. Instead, the distribution of black and white genotypes in cases under any pair of SNP SNPi and SNPj can be found by retrieving and summing the distribution of black and white genotypes of every corresponding pair of integer g between case vectors We propose to accelerate the exhaustive search process through a pre-computed look-up table. For each mentclass2pt{minimSupplementary Material


Fasciola hepatica) and bovine lungworms (Dictyocaulus viviparus) are the most important parasitic agents in pastured dairy cattle. Endoparasite infections are associated with reduced milk production and detrimental impacts on female fertility, contributing to economic losses in affected farms. In quantitative-genetic studies, the heritabilities for GIN and F. hepatica were moderate, encouraging studies on genomic scales. Genome-wide association studies (GWAS) based on dense single nucleotide polymorphism (SNP) marker panels allow exploration of the underlying genomic architecture of complex disease traits. The current GWAS combined the identification of potential candidate genes with pathway analyses to obtain deeper insights into bovine immune response and the mechanisms of resistance against endoparasite infections.Gastrointestinal nematodes (GIN), liver flukes  in GWAS based on 423,654 SNPs from 148 DSN cows. We identified 44 SNPs above the genome-wide significance threshold (pBonf = 4.47 × 10− 7), and 145 associations surpassed the chromosome-wide significance threshold (range: 7.47 × 10− 6 on BTA 1 to 2.18 × 10− 5 on BTA 28). The associated SNPs identified were annotated to 23 candidate genes. The DAVID analysis inferred four pathways as being related to immune response mechanisms or involved in host-parasite interactions. SNP effect correlations considering specific chromosome segments indicate that breeding for resistance to GIN or F. hepatica as measured by fecal egg counts is genetically associated with a higher risk for udder infections.A 2-step approach was applied to infer genome-wide associations in an endangered dual-purpose cattle subpopulation [Deutsches Schwarzbuntes Niederungsrind (DSN)] with a limited number of phenotypic records. First, endoparasite traits from a population of 1166 Black and White dairy cows [including Holstein Friesian (HF) and DSN] naturally infected with GIN, We detected a large number of loci with small to moderate effects for endoparasite resistance. The potential candidate genes regulating resistance identified were pathogen-specific. Genetic antagonistic associations between disease resistance and productivity were specific for specific chromosome segments. The 2-step approach was a valid methodological approach to infer genetic mechanisms in an endangered breed with a limited number of phenotypic records.The online version of this article (10.1186/s12864-019-5659-4) contains supplementary material, which is available to authorized users. Dictyocaulus viviparus) and the liver fluke (Fasciola hepatica) are the most important parasitic helminths of pastured dairy cows , indicating a genetic component for pathogen-specific susceptibility  were detected in Scottish Blackface lambs [A. hebraeum and R. evertsi evertsi [GWAS results for GIN infections in previous studies , 29 reflce lambs . Further evertsi .pBonf were not related to potential candidate genes. Efforts to characterize genes or genomic regions for liver fluke traits in ruminants were reported for Fasciola gigantica in sheep [. Thus, the current findings present a novelty for enhancing disease resistance to F. hepatica in cattle breeds.Regarding rFEC-FH, the three significant markers based on in sheep . Thus, tEGFR gene for rFEC-GIN. In addition, this pathway was identified in a GWAS for Angus cattle [EGFR for rFEC-GIN. Experiments in mice have indicated that parasites can exploit the hormonal host microenvironment to favor their establishment, growth and reproduction rate [Taenia crassiceps, an increase in the physiological concentrations of the (host) hormone 17-ß-estradiol was associated with an increase in the reproductive capacity of T. crassiceps cysticerci [Schistosoma mansoni and increased the length of Ascaris suum larvae in its host [Neospora caninum, an intracellular parasite that causes high economic losses in the cattle industry [Our study identified the cytokine-cytokine receptor interaction pathway for the s cattle . The mosion rate , 75. In sticerci . Moreoveits host , 78. Howits host , 80. Furindustry .ALCAM) gene on BTA 1 detected for rFEC-FH is related to the cell adhesion molecules (CAMs) pathway, and it plays a crucial role in immune response mechanisms, e.g., cell adhesion interactions of T cells. Interestingly, the same pathway was also detected for D. viviparus as being related to the CDH2  gene on BTA 24. Our GWAS revealed several genes and three pathways as being involved in T lymphocyte interactions for rFEC-GIN, rFEC-FH and rFLC-DV. The cellular mechanisms mediated by T lymphocyte recruitment are typical features of immune response to endoparasite  infections in its host [F. hepatica induce Th2-associated reactions, with simultaneous inhibition of Th1 cell activity [D. viviparus in cattle [Cooperia punctata and Haemonchus placei was probably mediated by Th2 cytokines in the resistant cattle group and induced by Th1 cytokines in the susceptible ones [CDH2 and PCDH15 genes on BTA 24 and 26 for rFLC-DV coded for adhesion molecules, and they were expressed in the cattle selected for either resistance or susceptibility to nematode parasites [The activated leukocyte cell adhesion molecule  differs from those for GIN and D. viviparus [, where the immune response is predominantly mediated by IgA, IgE, IgG and IgM [Differing correlations for SNP effects between rFEC-GIN, rFEC-FH and rFLC-DV indicate the complexity of resistance against different infectious agents. In most cases, negative SNP effect correlations were observed between rFEC-FH and rFEC-GIN, implying that genomic selection on improved resistance to initions , 20. Oneiviparus , where t and IgM , 91.The correlations based on the SNP effects between the two nematode traits rFEC-GIN and rFLC-DV were positive for most of the identified ROI, confirming the estimates from a quantitative-genetic study . High poOstertagia ostertagi, the most common GIN species in cattle. In our study, mostly negative SNP correlations were inferred between rMY and rFLC-DV. Thus, on a genomic scale, breeding for higher milk production reduces larvae shedding of the bovine lungworm. Highly positive SNP effect correlations between rFLC-DV and rMY were detected for ROI on BTA 5 and 21, indicating a coregulation of both traits in these regions. Another possibility is that the genes affecting FLC for D. viviparus and MY were in a low linkage disequilibrium.The SNP effect correlations between rFEC-GIN and rMY were negative for three of the five identified ROI, corresponding to the estimates from pedigree-based random regression models . Twomey ALCAM gene) reflects the pedigree-based estimates, i.e., the negative genetic correlations in the course of lactation [F. hepatica induces an increase in SCS. Vice versa, breeding on low somatic cells implies increasing FEC for GIN and F. hepatica. Such findings have practical relevance when developing breeding programs with a focus on both disease resistance and tolerance [F. hepatica infections and SCS were close to zero , reflecting the results from previous studies in HF dairy cow populations [Regarding the associations between endoparasite traits and SCS, the SNP effect correlations between rFEC-GIN, rFEC-FH and rFLC-DV with rSCS for the ROI partly reflect quantitative-genetic estimates. May et al.  estimateactation . Hence, olerance . In partolerance , are a folerance . Phenotyulations , 96. Assulations , 97. NevNAV3 gene) was only identified for GIN and D. viviparus on BTA 5. Five of the identified possible candidate genes were directly involved in immune response mechanisms. The inferred estrogen signaling pathway is involved in host-parasite interactions, and it appears to be specific for rFEC-GIN. Functional gene annotations identified a common immunological pathway  for different endoparasite traits. The SNP effect correlations between rFEC-GIN and rFLC-DV were quite large for most of the ROI, indicating a partly joint genetic basis for traits representing the same biological order. The negative SNP effect correlation between rSCS and rFEC-FH is in agreement with pedigree-based genetic correlations, and it indicates an antagonistic association between disease resistance for udder and endoparasite infections. Generally, we demonstrated that resistance to the nematodes GIN and D. viviparus and to the trematode F. hepatica is under polygenic control through a large number of loci with moderate to small effects. The SNP effect correlations for specific endoparasite ROI provided deeper insight into trait associations and contributed to physiological explanations of possible genetic antagonisms between disease resistance and productivity. Predominantly negative SNP effect correlations between GIN or F. hepatica with SCS indicate the complexity of immune response mechanisms but also raise critical questions regarding breeding strategies on low somatic cell scores.The 2-step approach using precorrected phenotype data based on a larger dataset of related genetic lines was a valid approach to estimating SNP marker effects and to inferring possible candidate genes and biological pathways for endoparasite resistance in a small sample of genotyped dual-purpose DSN cows. Such a methodological approach might be suitable for genomic studies with a focus on novel traits in small populations. In total, 23 potential candidate genes were annotated to SNP marker associations for rFEC-GIN, rFEC-FH and rFLC-DV. A shared ROI  and the effective number of independent SNP markers based on linkage disequilibrium (LD). (DOCX 16 kb)Additional file 2:Figure S1. Principal component analysis of the 148 DSN cattle for (A) the mean values of FEC-GIN, (B) the mean values of FEC-FH, (C) the mean values of FLC-DV and (D) the three different farms. Plot of the first two principal components (PC1 and PC2) of each individual cow based on SNP information to evaluate the extent of the population structure. (PDF 31 kb)Additional file 3:Table S2. List of all SNP markers associated with the residuals of gastrointestinal nematodes (rFEC-GIN) identified in Black Pied dairy cattle by genome-wide analysis. (DOCX 19 kb)Additional file 4:Table S3. List of all SNP markers associated with the residuals of Fasciola hepatica (rFEC-FH) identified in Black Pied dairy cattle by genome-wide analysis. (DOCX 15 kb)Additional file 5:Table S4. List of all SNP markers associated with the residuals of Dictyocaulus viviparus (rFLC-DV) identified in Black Pied dairy cattle by genome-wide analysis. (DOCX 38 kb)


Gossypium hirsutum L.) is the most important natural fiber worldwide, and it is extensively planted and plentifully used in the textile industry. Major cotton planting regions are frequently affected by abiotic stress, especially drought stress. Drought resistance is a complex, quantitative trait. A genome-wide association study (GWAS) constitutes an efficient method for dissecting the genetic architecture of complex traits. In this study, the drought resistance of a population of 316 upland cotton accessions was studied via GWAS. (2) Methods: GWAS methodology was employed to identify relationships between molecular markers or candidate genes and phenotypes of interest. (3) Results: A total of 8, 3, and 6 SNPs were associated with the euphylla wilting score (EWS), cotyledon wilting score (CWS), and leaf temperature (LT), respectively, based on a general linear model and a factored spectrally transformed linear mixed model. For these traits, 7 QTLs were found, of which 2 each were located on chromosomes A05, A11, and D03, and of which 1 was located on chromosome A01. Importantly, in the candidate regions WRKY70, GhCIPK6, SnRK2.6, and NET1A, which are involved in the response to abscisic acid (ABA), the mitogen-activated protein kinase (MAPK) signaling pathway and the calcium transduction pathway were identified in upland cotton at the seedling stage under drought stress according to annotation information and linkage disequilibrium (LD) block analysis. Moreover, RNA sequencing analysis showed that WRKY70, GhCIPK6, SnRK2.6, and NET1A were induced by drought stress, and the expression of these genes was significantly different between normal and drought stress conditions. (4) Conclusions: The present study should provide some genomic resources for drought resistance in upland cotton. Moreover, the germplasm of the different phenotypes, the detected SNPs and, the potential candidate genes will be helpful for molecular marker-assisted breeding studies about increased drought resistance in upland cotton.(1) Background: Upland cotton ( Gossypium hirsutum L.) productivity and fiber quality, drought resistance is a complex, quantitative trait controlled by more than two genes [Water shortages are among the most serious limitations to global agricultural production due to the complexity of water-deficient environments and climatic variation ,2. Drougwo genes ,4. It isGossypium hirsutum L.; AADD, 2n = 4× = 52) is one of the most important economic crop species. It produces the most significant natural fiber worldwide, is a source of seed oil, and it accounts for approximately 95% of cotton production worldwide [Allotetraploid upland cotton  methodologies are widely employed to identify relationships between molecular markers or candidate genes and phenotypes of interest. This method takes advantage of high-density SNP markers, associates molecular polymorphisms with a phenotype, and offers advantages over traditional linkage analyses such as more accurate positioning and mapping, simultaneous assessments of multiple alleles at a locus, and no requirements for linkage group construction ,16. Compbidopsis , rice [2bidopsis , maize [bidopsis , and whebidopsis . It has bidopsis ,24, yielbidopsis , verticibidopsis , and fusbidopsis ; howeverbidopsis ,29. Usinbidopsis . The facbidopsis .The use of genetic and genomic analyses to identify DNA markers associated with drought resistance can facilitate breeding strategies for crop improvement . It is aIn order to systematically explore the genetic basis and mechanism of drought resistance traits of upland cotton, a set of 316 accessions was selected from a natural population of 355 cultivars or lines a,b, and To explore drought resistance of upland cotton, we collected a total of 316 upland cotton cultivars, including 286 cultivated accessions from the major cotton cultivation regions such as the Yangtze River basin region (YZR), the Yellow River basin region (YR), the northern specific early maturation region (NEM), and the northwestern inland region (NW) and foreign region (FR), including others from America, Tajikistan, and Uzbekistan. Based on the pair-wise genetic distance determined using whole genome SNP markers among the 316 accessions, we explored the phylogenetic relationships of these cotton germplasms, which could be divided into three divergent groups a. To furr2 value and decreased with physical distance between SNPs. By using all selected SNPs, the extent of LD for every group and all accessions was measured when the LD decreased to half of the maximum value. The observed extent of LD decay in the population was estimated to be 488 kb, with an r2 = 0.42 at half of the maximum value. According to the results of the LD analysis in conjunction with ecological region, the LD decay occurred at 351 kb (r2 = 0.42) in the YR region and at 408 kb (r2 = 0.42) in the NW region, but it increased to 584 kb (r2 = 0.43) in the YZR region (Linkage disequilibrium (LD) was indicated by the R region .There was abundant variation in the phenotype of cotton at the seedling stage under drought stress. To obtain insight into the genetic variants associated with drought tolerance at the seedling stage in upland cotton, based on the 316 cotton accessions, we investigated the phenotypic data for 4 traits and conducted a genome-wide association study to analyze and identify the genetic loci of those traits. For the analysis, multifarious distinct models, including the GLM and the FASTLMM, were used for different traits.p) ≥ 6.53)  first. Seventeen SNP loci associated with three traits  related to drought tolerance and two SNP loci surpassed the threshold of significance of 0.01 (−log10( ≥ 6.53) . With re ≥ 6.53) a, three  ≥ 6.53) b, and si ≥ 6.53) c. In the ≥ 6.53) d. Howeve ≥ 6.53) .p) values as high as 7.14  were located within the same Gh_A05G3452 gene that encodes nuclear RNA polymerase C2, their flanking sequences were in the position of the intronic region, making it highly difficult for us to further study them. We further performed an LD block analysis for six significant SNPs  associated with the EWS trait. One LD block (89.70–90.60 Mb) was ultimately found on chromosome A05  were found on the basis of these SNP alleles . The haplotype (TT-CC-GG-AA-GG-AA) included 109 accessions, accounted for 71.7% of all the haplotypes, and was used as the favorable haplotype (FH) because this haplotype had positive phenotypic effects on the EWS and showed less leaf wilting than the other haplotypes. The mean value (2.0) of the favorable haplotype (FH) for the EWS trait was obviously lower than the average (3.7) of the corresponding haplotype , which included 19 accessions. The third haplotype was considered a heterozygous haplotype  and included 24 accessions, with the mean value of 2.9 for EWS trait e. AccordGh_A05G0418, was found to be located approximately 0.14 Mb away from the peak SNP A05:4580354 and encoded CBL-interacting protein kinase 6 (CIPK6), which is homologous to the gene AT4G30960 in Arabidopsis. Based on the results of RNA-seq, with increasing drought stress, the expression of this gene gradually increased, after which it ultimately peaked and reached an extremely significant expression in leaves and roots of the accession compared to control treatments under severe drought conditions  trait were also detected. Three SNPs were significantly associated with the CWS trait; these SNP loci were positioned at 9,221,3571, 4,580,354, and 45,127,798 on chromosomes A01 (1), A05 (1), and A11 (1), respectively b. We ananditions c,d. MoreWe further analyzed the types of haplotypes for the CWS trait to find favorable haplotype for significantly associated SNPs. With respect to the wilting score mean of the three types of haplotypes, the favorable haplotype (AA), heterozygous haplotype (AT), and unfavorable haplotype (TT) scores were 3.21, 3.95, and 4.07, respectively. In the natural population, the favorable haplotype (FH) accounted for 50%, the heterozygous haplotype (HH) accounted for 34%, and the unfavorable haplotype (UFH) accounted for only 16% e. Based Gh_A11G1858, was homologous to the AT4G33950 gene in Arabidopsis thaliana. The gene AT4G33950 is named SnRK2.6/OST1 (sucrose non-fermenting 1-related protein kinase 2.6/OPEN STOMATA 1), which encodes a protein kinase superfamily protein. According to the RNA-seq analysis, the expression of this gene was gradually upregulated under mild (T1) and moderate drought conditions (T2) but decreased under the severe drought conditions in the leaves and roots of the accession  was found on chromosome A11 a,b. Therccession c,d. MoreBased on the analysis of the haplotypes, the average values of the wilting score of the three haplotypes, favorable haplotype (CC), heterozygous haplotype (TC), and unfavorable haplotype (TT) were 1.71, 4.34, and 3.87, respectively e. Based Gh_D03G0728 is named NET1A, which is homologous to AT4G30960 (CIP1) in Arabidopsis and encodes COP1-interactive protein 1. This gene was induced in the leaves and roots in response to drought stress  were found c,d. Whent stress c,d.Additionally, we analyzed the types of haplotypes for the LT trait to further identify favorable haplotype for five significantly associated SNPs. Similarly, there were three types of haplotype  based on the SNP alleles . Because osmotic adjustment substances such as abscisic acid (ABA) accumulate under drought stress, osmotic pressure in guard cells gradually increases and induces stomatal pore apertures to become narrow or to completely close. As a result, plant transpiration decreases, and the temperature of leaves consequently increases. This mechanism is more important for drought-resistant plants in order to protect their growth and development process than for drought-sensitive materials. Therefore, the haplotype AA-TT-AA-AA-AA was regarded as the favorable haplotype (FH) because the mean value (34.6 ℃) of the haplotype for the LT trait was higher than the average (33.7 ℃) of the corresponding haplotype ). The mean (34.0 ℃) of the haplotype AT-TC-AC-GA-AG was in between that of the favorable haplotype (FH) and the unfavorable haplotype (UFH); this haplotype was regarded as a heterozygous haplotype (HH) e. AlthouDrought stress significantly affects the growth and development, yield, and fiber quality of cotton by restricting and affecting plant height, leaf weight, stem weight, node number, transpiration rate, stomatal conductance, and so on. . The seeAccording to the results of the GWAS of the 316 upland cotton accessions, 17 SNPs were found on chromosomes A01, A05, A11, and D03 via other traits . Plant cells are subjected to a series of physiological reactions under drought or osmosis stress; water extrusion, reduction in osmotic potential (OP), and even plasmolysis can occur in the mesophyll cells of plants, which results in leaf rolling and ultimately gives rise to wilting of the plant leaves with increasing drought stress ,34. LeafGh_A05G3499 is homologous to AT3G56400 in Arabidopsis, which encodes WRKY DNA-binding protein 70 (WRKY70). WRKY70, with one WRKY domain and a CCHC zinc finger motif, is a member of the WRKY family [Arabidopsis [Gh_A05G3499 (WRKY70) was induced in the leaves and roots by drought stress. According to the results of RNA sequencing, we inferred that this gene not only may be involved in drought stress but also may regulate developmental processes. In the candidate region of SNP A05:4580354 for the CWS trait, we found 46 genes according to the LD block, six of which had an unknown function, and other annotated genes. One QTL that contained 61 and 14 genes was found on chromosomes A05 and A11, respectively, for the EWS trait . AccordiY family , and manY family ,39. In aY family . Furtherbidopsis . In thisGh_A05G0418 is homologous to AT4G30960, which encodes CBL-interacting protein kinase 6 (CIPK6) in Arabidopsis thaliana. Previous studies revealed that the transcripts of the AtCIPK6 gene largely accumulated in abscisic acid-treated seedlings compared to basal level expression, and overexpression of the AtCIPK6 gene enhanced plant resistance to salt stress but increased sensitivity to ABA [Brassica napus, researchers found that activation of BnCIPK6 confers ABA hypersensitivity to Arabidopsis plants, and overexpression of BnCIPK6 in Arabidopsis cipk6 mutants completely rescued the ABA-insensitive phenotypes of this mutant, which further indicates that BnCIPK6 is involved in the plant response to ABA [GhCIPK6 gene was induced by salt, drought, and ABA treatments in Gossypium hirsutum. Moreover, overexpression of GhCIPK6 significantly increases tolerance to salt, drought, and ABA stresses in transgenic Arabidopsis thaliana, which suggests that GhCIPK6 plays a significant role and can serve as a positive regulator in response to salt and drought stresses [Gh_A11G1858, on chromosome A11 is named SnRK2.6/OST1 and encodes a protein kinase superfamily protein. The sucrose non-fermenting-1-related protein kinase 2 (SnRK2) family consists of ten members in both the Arabidopsis (SnRK2.1–10) and rice (known as osmotic stress/ABA-activated protein kinases (SAPK1–10)) genomes, and the members are serine/threonine protein kinases that function as central and positive regulators of the ABA signaling pathway [The gene y to ABA . In Brase to ABA . Similarstresses . Another pathway . Half of pathway . These p pathway ,48. SnRK2.6 gene plays a role in the mitogen-activated protein kinase (MAPK) signaling pathway and is induced by salt, drought, and osmotic stresses. MAPK signaling genes activate various drought-related pathways to induce tolerance in plants [Gh_D03G0728 (NET1A) was induced by drought stress in this study. With respect to the homologous gene (CIP1), a previous study indicated that the promoter activity of CIP1 can be induced by osmotic stress and ABA, and that cip1 loss-of-function mutants have ABA-insensitive phenotypes [hirsutum × G. barbadense [Moreover, according to KEGG pathway and mapping analyses, the n plants . Leaf ten plants . Previoun plants  and on cn plants . Similarenotypes . We infeenotypes , and therbadense . HoweverGhMYB5 and GhMAP3K40 [GhCIPK6, GhCDPK2, and GhCDPK38 [RD2, HAT22, and PIP2 genes were associated with drought tolerance and were identified via water culture experiments of cotton plants treated with PEG 6000 [In addition, in recent years, genes involved in drought resistance have been gradually found in upland cotton; some of these genes encode transcription factors, such as hMAP3K40 ,55, and GhCDPK38 . AccordiPEG 6000 . Most ofPEG 6000 .Gossypium hirsutum L.) accessions was provided by the cotton early maturing breeding laboratory at the Cotton Research Institute of Chinese Academy of Agricultural Sciences (CRI-CAAS). The natural population comprising 316 upland cotton cultivars or lines  with 40 g of carbendazim (50%) per cubic meter of mixture, was uniformly mixed and covered with plastic paper to sterilize the germplasm, which were then exposed to air after 48 h. The seeds of the germplasm were soaked in water for 24 h with carbendazim (500 times 50%) before planting. All pots were filled with 450 g of the soil mixture and were watered to field capacity; excess water was allowed to drain for 48 h before planting. Each genotype was planted with six seeds that were visually selected for having similar size and good quality in each pot. Each pot was watered with 100 mL with a graduated cylinder every four days after cotton emergence and then watered with 200 mL with a graduated cylinder at one-leaf stage every four days. All seedlings were thinned to three plants per pot at one-leaf stage approximately 15 d post-planting, and watering was stopped when the plants were at the three-leaf stage. The leaf chlorophyll content (LC) of individual plants was measured by using a leaf chlorophyll meter  Section, Minolta Camera Co., Osaka, Japan), and the leaf temperature of the second leaf from the top of each plant was measured. In addition, the volumetric water content (VWC) of the soil in each pot was measured daily for 35 d after planting, and individual plants were scored for wilting. The wilting was scored every day for 7 consecutive days; the wilting was scored on a scale of 0 to 6, with 0 being no sign of wilting and 6 being completely wilted [All 316 upland cotton cultivars and lines were planted during the winter and spring of 2017 and 2018  in the greenhouse at the Cotton Research Institute of the Chinese Academy of Agricultural Sciences (CRI-CAAS), Anyang, Henan, China . Summer seasons were avoided to limit confounding factors from extremely high heat and the abundance of mixed gas and airflow produced by the fan drafts and cooling systems in the greenhouse. The greenhouse experimental design consisted of randomized complete blocks with three replications; each replication per material consisted of two 2000 cmy wilted .http://www.ncbi.nlm.nih.gov/bioproject/PRJNA314284/SRP071133) [The 355 cotton accessions or varieties were genotyped using SLAF-seq with an Illumina HiSeq 2500 system  at Biomarker Technologies Corporation in Beijing. A sequencing dataset consisting of 355 cotton accessions or varieties was generated and consisted of 96.10 Gb, which included 874.44 million paired-end reads with a length of ~80 bp. According to the minor allele frequency (MAF), 81,675 SNPs with a MAF ≥ 0.05 were ultimately used for analyses, and the 81,675 SNP markers covered all 26 chromosomes (P071133) . Based oP071133) . AccordiP071133)  softwarer2) of alleles by using GAPIT [The population structure of all 316 accessions was estimated via Admixture software for individual ancestry and allele frequency correlations. Ten thousand iterations were performed with the maximum likelihood method, and the number of populations (k) was tested from 2 to 20. The relative kinship between individuals was calculated by SPAGeDi  softwareng GAPIT  softwarep values of ≤0.1  = 5.53, red line in the Manhattan plots) and 0.01  = 6.53, blue line in the Manhattan plots) were used as a threshold of significance and a suggestive threshold to estimate whether significant associations existed for the GWAS. Based on the GWAS results, SNP loci significantly associated with drought resistance traits were further analyzed.For all the SNPs from the 316 accessions and phenotypic data, we used the GLM with Q and K matrices to perform an association analysis between the wilting, LC, and genetic data. Kinship was derived from all the SNPs. The GLM was applied by using TASSEL version 5.2.34 software, and FASTLMM was applied by using FASTLMM software for the analysis of genome-wide associations [Each haplotype was selected by the types of SNPs associated with the target traits. The corresponding phenotypic value was calculated based on the average of the phenotypic value among all accessions in the haplotype. According to the research objective of each target trait, the favorable haplotype (FH) and other haplotypes were analyzed by using R software. On the basis of the corresponding phenotypic value of the different haplotypes, box plots of each haplotype were generated by using R software, and then percent stacking bar charts of several types of haplotypes were analyzed by using statistical software.TM 2000 UV-vis spectrophotometer . An Agilent 2100 Bio-analyzer  and RNase-free agarose gel electrophoresis were also used to qualify and quantify the 48 samples. The mRNA was subsequently enriched using oligo (dT) magnetic beads (Qiagen) and then broken into short fragments. The Illumina HiSeq™ 2000 platform using paired-end (PE) technology with raw reads of approximately 100 bp in length of Gene Denovo Co  was used to generate transcriptome sequences. Clean reads were selected by removing low-quality reads, adaptor-containing reads, and reads containing >5% N bases. A false discovery rate (FDR) was applied to determine the threshold of p-values in multiple tests and analyses. Moreover, the DEGs were obtained according to FDR ≤ 0.005 and |log2FC| ≥ 1.5.For the transcriptome analysis, we selected upland cotton accession G8, which was screened and identified in a growth chamber at CRI-CAAS. Eight upland cotton leaf samples and root samples, including three biological replicates of a single plant per replicate, were used for RNA sequencing. Leaves and roots at four treatment times—the CK (RWC 75% ± 5%), T1 (RWC 60% ± 5%), T2 (RWC 45% ± 5%), and T3 (RWC 30% ± 5%)—were harvested, immediately frozen in liquid nitrogen, and then stored at −80 ℃, after which they were prepared for RNA extraction. For each sample, total RNA was isolated using Column Plant RNAout . The quality and concentration of RNA were then verified using a NanoDrop


Capsicum spp.) is one of the most diverse vegetables; however, little research has focused on characterizing the genetic diversity and relatedness of commercial varieties grown in China. In this study, a panel of 92 perfect single-nucleotide polymorphisms (SNPs) was identified using re-sequencing data from 35 different C. annuum lines. Based on this panel, a Target SNP-seq genotyping method was designed, which combined multiplex amplification of perfect SNPs with Illumina sequencing, to detect polymorphisms across 271 commercial pepper varieties.The widely cultivated pepper .The perfect SNPs panel had a high discriminating capacity due to the average value of polymorphism information content, observed heterozygosity, expected heterozygosity, and minor allele frequency, which were 0.31, 0.28, 0.4, and 0.31, respectively. Notably, the studied pepper varieties were morphologically categorized based on fruit shape as blocky-, long horn-, short horn-, and linear-fruited. The long horn-fruited population exhibited the most genetic diversity followed by the short horn-, linear-, and blocky-fruited populations. A set of 35 core SNPs were then used as kompetitive allele-specific PCR (KASPar) markers, another robust genotyping technique for variety identification. Analysis of genetic relatedness using principal component analysis and phylogenetic tree construction indicated that the four fruit shape populations clustered separately with limited overlaps. Based on STRUCTURE clustering, it was possible to divide the varieties into five subpopulations, which correlated with fruit shape. Further, the subpopulations were statistically different according to a randomization test and Target SNP-seq developed in this study appears as an efficient power tool to detect the genetic diversity, population relatedness and molecular breeding in pepper. Moreover, this study demonstrates that the genetic structure of Chinese pepper varieties is significantly influenced by breeding programs focused on fruit shape. Capsicum, which originated in South America and represents one of the most economically important vegetable crops worldwide [Capsicum have been reported . Of these, C. annuum, C. frutescens, C. chinense, C. baccatum, and C. pubescens are thought to have been domesticated [C. annuum, which has numerous commercial varieties varying greatly in size, shape, pungency, and color.Pepper are members of the genus orldwide –3. To dasticated . GloballAs the seed trade has developed and globalized, the commercial quality of seeds, which is based on authenticity and purity, has become increasingly important . TraditiC. annuum cultivars, Zunla-1 and CM334, were sequenced [Recently, the genomes of two equenced , which pequenced –20. Althwww.kbioscience.co.uk). In recent years, high-throughput transcriptome sequencing and genotyping-by-sequencing (GBS) have been successfully used in pepper, generating highly informative genome-wide SNP data [One approach for meeting such high standards is the use of single-nucleotide polymorphisms (SNPs), which are good markers for genotyping because of their whole genome coverage and primarily biallelic nature. Accordingly, multiple high-throughput SNP genotyping platforms have been developed, including the GoldenGate  and InfiSNP data –30. HoweGenotyping by target sequencing (GBTS) is a targeted sequence-capture strategy that can genotype more than thousands of SSRs or SNPs using high-throughput sequencing technology. The two main types of GBTS are multiplex PCR and probe-in-solution-based target sequencing; the technology has been commercialized as AmpliSeq , NimbleGPeppers from China constitute one-third of the world’s pepper production . Until nC. annuum) in this study generated a total of 872 Gb of paired-end sequence data, at an average depth of ~ 8.4. After mapping to the Zunla-1 genome [C. annuum genome contains variable SNP sites. A total of 21,237,194 SNPs, with minor allele frequency (MAF) > 5% and missing data < 10%, were considered high-quality SNPs for downstream analyses. Using C. annuum’s progenitor cultivar, Chiltepin, as an outgroup, the phylogenetic tree showed that pepper lines could generally be classified according to fruit shapes, except for three long horn-fruited lines that grouped with the linear-fruited lines. Based on the genetic distance, the transitions in fruit shapes were from Chiltepin-like peppers followed by the linear-fruited, short horn-fruited, long horn-fruited, finally to blocky-fruited peppers, which were the furthest from the Chiltepin-like peppers  and Subgroup 2 (long horn-fruited), whereas Group 2 was composed of Subgroup 3 , Subgroup 4 (linear-fruited), and Subgroup 5 .Re-sequencing of the 31 pepper lines  to 0.59 (CaSNP009) with an average of 0.28, and 11 SNPs exhibited higher Ho (> 0.4)  to 0.5 (CaSNP043 and CaSNP094) , Ho (0.16), and PIC (0.15) (Table He (0.39), Ho (0.36), and PIC (0.31).The average values of the genetic parameters across the four fruit shape populations were also compared for genetic diversity, and the results showed that the blocky-fruited population had the lowest average values for He 0.8, Ho  within certain fruit populations, of which 16, 1, 3, and 5 loci were for the blocky-, long horn-, short horn-, and linear-fruited population, respectively  was performed using the 92 perfect SNPs to investigate population clusters across the 271 varieties Fig. a. AccordK). Evanno’s correction [K at K = 2, which suggests the presence of two main populations, denoted as Pop1 and Pop2. Pop1 comprised 160 varieties (59.0%), containing all blocky-fruited varieties, 60.2% of long horn-fruited varieties, and only two linear-fruited varieties  revealed that 33.04% of the total genetic variation was partitioned among Pops, 8.47% within Pops, and the remaining 58.49% within varieties (Table Fst showed that population differentiation between Pop1 and Pop2 was high (Fst = 0.35). The pairwise Fst between the five Subpops ranged from 0.13 between Subpop2 and Subpop3 (both consist largely of long horn-fruited varieties) to 0.48 between Subpop1 (mostly blocky-fruited varieties) and Subpop4 (short horn-fruited varieties) (Table Fst = 0.43) was observed between Subpop1 and Subpop5 (mostly consisting of linear-fruited varieties), whereas lower genetic differentiation was observed between Subpop4 and Subpop5 (Fst = 0.14).To test for significant variations between Pops and among Subpops, a randomization test was performed , but no differences were detected between long horn and short horn-fruited populations. FSI values of more than 9.5 are typical of linear fruits.A wide range of variation was observed for fruit shape index (FSI) in the 271 pepper varieties , a total of nine loci  were identified as significantly associated with FSI under a threshold p-value of 0.0001  [fs2.1) on chromosome 2 [Ovate gene (CA02g22830) at approximately 158 Mb of the CM334 genome [Ovate gene (CA02g22830). The Ovate gene was initially discovered in the tomato, where it controlled the fruit shape transformation from round to pear-shaped fruit [fs4.2) was detected at the end of chromosome 4, which explained the 26.1% phenotypic variation [Ovate genes, CA06g21580 and CA12g07370, could be considered candidate genes, as they had significant effects on fruit shape.Fruit shape is an important trait in pepper breeding programs. A number of QTLs controlling FSI have been identified in intraspecific and interspecific populations from a cross between the bell pepper and a small-fruited hot pepper –53. The  group 3 . This QT group 3 , 47, 51. group 3 . In the 3g16080) . Zygier mosome 2 . This QT4 genome , 53. In ed fruit , 55. A sariation . We founTobamovirus resistance gene L3 and L4 [Phytophthora capsici resistance genes CaDMR1 and Phyto5NBS1 [Bs3 [potato virus Y resistance gene pvr1 [Of note, foreground selecting markers that are suitable for specific primer design could also be added to the perfect SNP panel used in our Target SNP-seq. For example, based on the functional site of the 3 and L4 , the Phyyto5NBS1 , 68, bacBS1 [Bs3 , and potene pvr1 , specifiThe Target SNP-seq developed in this study is a high-throughput and reliable tool for the investigation of genetic diversity, variety identification, and characterization of population structure in peppers. The use of PCA, phylogenetic tree generation, and STRUCTURE revealed that the genetic structure of commercially available pepper varieties in China had been significantly influenced by fruit shape selection through breeding. Finally, association analysis of a limited number of markers allowed for the identification of previously reported and novel genomic regions that control fruit shape.A total of 271 pepper varieties, which were kindly supplied by 60 different breeding companies in China, were analyzed in this study. Information on these hybrid seeds, including variety name and source, is available in Additional file Varieties were planted under greenhouse conditions at the Vegetable Varieties Exhibition Center in the Tongzhou District of Beijing. The greenhouse temperature ranged from 25 to 30 °C 08:00–20:00) and 20–25 °C (20:00–08:00), with natural light. Each variety consisted of at least four plants. The fruits were categorized into one of four fruit shapes: blocky-, long horn-, short horn-, and linear-fruited types , including 30 inbred lines from our ongoing breeding programs in BVRC and PI640446 provided by the U.S. National Plant Germplasm System, were selected for re-sequencing on the Illumina X Ten platform at Shanghai Majorbio Biopharm Technology Co. Ltd. . The 31 pepper lines had diverse genetic backgrounds and horticultural traits, including eight blocky-fruited lines, 13 long horn-fruited lines, five short horn-fruited lines and five linear-fruited lines  and its wild progenitor Chiltepin (C. annuum var. glabriusculum) [C. annuum cv. Perennial and C. annuum cv. Dempsey [K) was determined following the standard procedure [K varying from 1 to 15. The optimum K was defined according to Evanno’s delta K method [The raw reads of the 31 re-sequenced lines and four previously sequenced cultivars; Zunla-1  MAF > 0.4 to filter out uninformative SNPs; (ii) miss rate < 0.2; (iii) heterozygosity < 0.2; (iv) no sequence variation in the 100 bp flanking sequence of the SNP locus; and (v) 2 alleles per locus for the SNPs.Taq enzyme , 8 μl nuclease-free water, and 1 μl of primers with the following sequences: forward 5′- AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGA -CGCTCTTCCG-3′ and reverse 5′-CAAGCAGAAGACGGCATACGAGAT -XXXXXXXXGTGACTGGAGTTCCTTGGCACCCGAGA-3′ (barcodes are indicated by underlined sequences). The PCR procedure was 95 °C for 3 min; 7 cycles of 95 °C for 15 s, 58 °C for 15 s, and 72 °C for 30 s with a final 4 min extension at 72 °C. The PCR products were then purified with 100 μl of 80% alcohol and 23 μl Tris-HCl buffer . After that, a Target SNP-seq library was sequenced using an Illumina X Ten platform at Molbreeding Biotechnology Company .The Target SNP-seq procedure was completed as previously described using the SNPs identified above . In brieThe raw data from the Target SNP-seq was de-multiplexed to determine the exact genotypes for each variety based on the sample-specific barcodes using the Illumina bcl2fastq pipeline . Clean data were filtered out using Trimmomatic, and the reads of each variety were mapped to the pepper reference genome of Zunla-1  using BWUniformity inferred to the proportion of the SNPs with a depth of > 10% of the average depth.where S indicates the total base generated from Target SNP-seq; M indicates the total number of varieties; N indicates the total number of SNPs; L indicates the average read length.SNP genotypes were called using GATK. Based on the high-throughput sequencing results, the SNP alleles with the maximum numbers of reads and the second maximum numbers of reads were treated as the major and minor allele for each target SNP locus. When the read frequency of the major allele was higher than 0.7, the locus was described as homozygous. If the read frequencies of the major and minor allele were both more than 0.35, the locus was described as heterozygous.Sequence depth for each variety = Total base generated from variety / total base length of the 92 targeted genome region.Ho), expected heterozygosity (He), and polymorphism information content (PIC) [l is the allele locus, and Pi and Pj represent the population frequency of the ith and jth allele. The chromosomal distribution of the perfect SNPs was mapped using Circos software (http://circos.ca/) with the SNP region magnified to 2 Mb.Genetic parameter statistics of the perfect SNPs, including the observed heterozygosity (nt (PIC)  were calK) was determined as described above, and the unrooted phylogenetic tree was constructed using the Ape and Poppr packages in R based on the neighbor-joining method with the tree viewed using MEGA v5.1 [Genetic relationships among varieties were investigated using three different methods: PCA, STRUCTURE, and a phylogenetic tree. PCA was carried out using the FactoMineR package in R . The BayEGA v5.1 , 80.Ho, He, PIC, and MAF analyses, were calculated using the methods mentioned above. To measure genetic differences of populations and subpopulations, AMOVA and pairwise Fst were performed using poppr R package and the function pairwise.neifst in the Hierfstat R package, respectively [ade4 package [https://grunwaldlab.github.io/Population_Genetics_in_R/AMOVA.html and https://rdrr.io/cran/poppr/man/poppr.amova.html.The different fruit shape populations, as well as the subpopulations inferred from STRUCTURE, ectively , 82. Ran package . DetaileTo determine a minimal number of SNPs for distinguishing the maximum number of pepper varieties, a Perl script was developed to determine the best discrimination power for 1 to 92 perfect SNPs according to the following algorithm.ij = 1 if genotype difference existed for jth pairwise comparison of ith SNP . Xij = 0 if genotype difference did not exist for jth pairwise comparison of ith SNP ; c) The SNP with a maximum value of mj = 1 if genotype difference existed for jth pairwise comparison of mth SNP combination . Xmj = 0 if genotype difference did not exist for jth pairwise comparison of mth SNP combination ; d) the SNP combination with a maximum value of 1) Selection of the first SNP: a) pairwise comparison between varieties were conducted for each SNP, which included 36,585 comparisons for each SNP; b) Xwww.kbioscience.co.uk. The Perl script, as mentioned above, was used to select a core-SNP set from the successfully verified SNP markers. Finally, the SNP markers associated with the maximum variety discrimination  were identified as a core-SNP set. The primer sequences of the core-SNP markers are shown in Additional file To develop a set of core SNPs that discriminates between varieties using the KASPar platform, two allele-specific forward primers and one common reverse primer were designed for each perfect SNP marker. The 23 to 95 commercial varieties were then used to assess the potential utility of the SNP markers through the KASPar platform; fluorescence was detected as previously described . DetaileK matrix), and a general linear model (GLM) using fruit shape populations (Q matrix) as a fixed factor were used for association identification of loci conferring fruit shape. Significance of marker-trait association was indicated when the p-value was less than 10− 4. Because it has been popularly proved that the MLM + Q + K model is more effective than other models in detecting loci [R2-value obtained from the MLM. Candidate genes between the nearest up- and down-stream SNP loci to the significantly associated loci were identified from the protein annotation published using the CM334 genome [FSI for each variety was calculated as the ratio of maximum height to maximum width. Ninety-two SNP loci  blocky-fruited: blocky shape, 5.0–12.5 cm wide at the shoulder, 7.0–18 cm long, 3–4 lobes, including Fang Jiao, Chang Fang Jiao, and Ma La Jiao, as named in China; (B) long horn-fruited: long horn shape, 3.0–8.0 cm wide at the shoulder, 10.0–35.0 cm long, without lobe, including Niu Jiao Jiao, Yang Jiao Jiao, and Luo Si Jiao, as named in China; (C) short horn-fruited: cone-shaped, medium-hot, 1.0–3.0 cm in diameter at the base, 3.5–10.0 cm in length, and with very thin pericarp, including Gan Jiao and Chao Tian Jiao, as named in China; (D) linear-fruited: cayenne type, 1.0–3.0 cm wide by 10.0–35.0 cm long, without shoulder and lobe, including Xian Jiao, Tiao Jiao and Mei Ren Jiao, as named in China.Additional file 2: Figure S2. Target SNP-seq genotyping analysis results. Distribution of the average read depths (A), reads alignment rate to the pepper reference genome (B), target region alignment rate (C), and uniformity for 271 pepper varieties (D).Additional file 3: Figure S3. Genetic diversity analysis for the 92 perfect SNPs across 271 pepper varieties. Minor allele frequency , observed heterozygosity , expected heterozygosity , and polymorphism information content .Additional file 4: Figure S4. Kompetitive allele-specific PCR (KASPar) results of the 35 core SNP markers genotyped across 23 to 95 pepper varieties.Additional file 5: Figure S5. Significance testing of differentiation between Pops and among Subpops. The graphs show significant population differentiation at all levels given that the observed line (black) does not fall within the distribution expected of the permutation.Additional file 6: Figure S6. Chromosomal map of a subset of markers used in association analysis with fruit shape index (FSI). The physical position of each marker on 12 chromosomes of C. annuum Zunla-1 [ Zunla-1  are showAdditional file 7: Figure S7. Manhattan plots (A) and quantile-quantile plots (B) of fruit shape index (FSI) in the 271 pepper varieties. Red dashed line indicates high probability of associated loci with FSI.Additional file 8: Table S1. Classification of and information on the pepper varieties used in this study.Additional file 9: Table S2. Multiplexed primers panel of the 92 perfect SNPs used for Target SNP-seq.Additional file 10: Table S3. Characteristics of the 92 perfect SNPs and the diversity detected in the 271 pepper varieties and four fruit shape populations.Additional file 11: Table S4. Primer sequences of the 35 core-SNP markers developed in this study.Additional file 12: Table S5. Range, mean, and standard deviations (SD) collected for fruit shape index (FSI) in the pepper varieties.Additional file 13: Table S6. Association regions with fruit shape index (FSI) in the reference genomes of CM334 and Zunla-1.Additional file 14: Table S7. Specific primers used to detect functional resistance loci against four pepper diseases through Target SNP-seq.