Transgenic crops have been utilized for decades to enhance agriculture and more recently have been applied as bioreactors for manufacturing pharmaceuticals. Recently, we investigated the gene expression profiles of several in-house transgenic soybean events, finding one transformant group to be consistently different from our controls. In the present study, we examined polymorphisms and sequence variations in the exomes of the same transgenic soybean events. We found that the previously dissimilar soybean line also exhibited markedly increased levels of polymorphisms within mRNA transcripts from seed tissue, many of which are classified as gene expression modifiers. The results from this work will direct future investigations to examine novel SNPs controlling traits of great interest for breeding and improving transgenic soybean crops. Further, this study marks the first work to investigate SNP rates in transgenic soybean seed tissues and demonstrates that while transgenesis may induce abundant unanticipated changes in gene expression and nucleotide variation, phenotypes and overall health of the plants examined remained unaltered. Glycine max as a model system for the generation and storage of recombinant proteins that are currently expensive to manufacture and difficult to procure or synthesize in bacterial and cell-based cultures and as oral vaccine candidates by targeting gut-associated lymphoid tissues (GALT) for immune stimulation or suppression [Over the past three decades, transgenic plant biotechnology has become integrated into the agriculture of nearly all societies worldwide. Genetically modified food crops have revolutionized conventional farming methods by increasing yields , bolsterpression , 10–13. pression .Selective farming of agricultural food sources has been conducted to propagate advantageous phenotypes such as improved germplasm size and quantity, particularly in crop plants of maize, soybean, rice, cereals, and potato . BackcroGlycine max, or the modern cultivated soybean, is a legume branched from the wild species Glycine soja and is classified as a diploidized tetraploid (2n = 40). Soybean has a relatively lengthy growth cycle (~6 months) and is primarily cultivated for its seed, which is high in both protein and oil content [ content . It is a content . This ma content –9. This  content , removin Glycine soja, all but eliminating rare allelic combinations in domesticated varieties [ Glycine max shares only 97.65% genomic sequence similarity with Glycine soja and contains 425 novel genes that are not present in the wild cultivar that directly control seed development, oil content, and protein concentration [Single nucleotide polymorphisms (SNPs) and insertions/deletions (INDELS) are major driving forces behind species evolution and diversity, either changing a single nucleotide base in the former or shifting the transcriptional reading frame in the latter. The end results, depending on the position of the base changes, may alter protein products controlling certain traits. SNPs may result in benign variations in humans such as eye color, skin color , and finarieties . Due to ntration .Recently, we investigated the pleiotropic effects on the transcriptome of soybean seed tissues expressing and accumulating high levels of recombinant protein . ResultsIn order to elucidate possible transcript modifications as a result of transformation, we utilized the publicly available RNA-seq datasets generated by our previous transcriptome sequencing study  to asses Agrobacterium transformation and taken to homozygosity over multiple generations prior to sequence acquisition. The transgenes and recombinant proteins expressed and accumulated in the seed tissues have been described previously by our lab [In this study, we utilized datasets obtained from whole-transcriptome sequencing of transgenic soybean seed tissues expressing three different transgenes to detect exomic polymorphisms. Each experimental group consisted of nine seeds from each of the three transgenic events  as well as the wild type controls. All transgenic plants were generated using our lab . Qualityfaidx command to allow alignment of nonreference transgene transcripts. Alignment files previously generated by TopHat [ -mpileup command with -g and -f parameters to specify the output format and to use the indexed reference fasta file. The bcftools call command was subsequently used on the indexed  .bcf files with the -c parameter to invoke the original consensus calling method enabling SNP and INDEL identification. The stat and plot-vcfstats commands were used to generate statistical summaries for each sample. Total SNP counts for each sample were averaged to calculate the standard deviation and standard error for each experimental group of wild type and transgenic seeds, and unpaired two-tailed t-tests were used to compare each transgenic group with wild type. Individual nucleotide base changes, transition and transversion rates, INDELS, and single and multiallele SNPs were also recorded and compared between groups. To identify SNPs present in each experimental group, the intersections of each variant file for all nine individual replicates in the ST77, ST111, 764, and WT groups were taken. SNPs in these combined files overlapping between all three transgenic groups, as well as those unique to each event when compared to WT, were pulled from the  .vcf files using the -vcf-isec command in vcftools [Soybean cultivation, RNA extraction, and differential gene expression analyses were carried out using RNA-seq as previously described in Lambirth et al. . In ordey TopHat  for all y TopHat  in  .bamvcftools . SNPs sh -bcftools view-o command; then, using the cut -f 1,2,6 command, base calls, quality score metrics, and chromosomal locations were extracted. Formatted tab delimited files were converted to the input text file for Circos using an in-house script generated in Python (version 2.7.11), which filtered the calls to exclude all polymorphisms with quality scores below 15. The script is included in the Additional Files of this manuscript . Plots were then generated using the -circos -conf circus.conf command to display SNP distributions of the transgenic and wild type samples for all 20 soybean genomic chromosomes. SNPs were spaced in the plots every 20 bases and layered vertically.Visualization of SNPs and INDELS was conducted using Circos version 0.69-2 . Variancbuild command consisting of the soybean genome FASTA reference file described previously containing our T-DNA sequences, as well as the gene model  .gff3 file from the Cufflinks output from our previous RNA-seq data [ Glycine max utilizes standard codon triplets allowing snpEff to run with the default parameters, employing the SNP/INDEL call  .vcf file from bcftools as input. Multithreaded processing using the -t option was not used, as this removes statistical calculations and the resulting reports from the output file. Statistical comparisons of SNP rates and effects between each group were conducted using two-tailed unpaired t-tests in Microsoft Excel. Functional annotation of genes containing variants was accomplished by loading the gene output list from snpEff into the agricultural gene ontology (GO) enrichment tool AgriGO [ Glycine max Wm82.a2.v1 background reference. Significant terms were calculated using Fisher's exact test with a corrected false discovery rate p value threshold of 0.05. All computations were conducted on an Apple Macbook Pro with a 2.7 GHz quad-core Intel i7 processor and 16 GB of DDR3 RAM.To predict any possible translational effects resulting from detected SNPs and INDELS, snpEff version 4.1i  was utilseq data  obtainedl AgriGO  using thTotal SNPs detected by samtools and bcftools across the wild type control group reported a mean of 20,707 SNPs per sample , with a The ST111 experimental group reported an average of 20,208 SNPs per individual sample , with a The ST77 experimental group was also comparable to wild type with 21,666 average SNPs per sample  at a ratLastly, the 764 experimental group contained more substantial nucleotide alterations when compared to wild type and the other two experimental groups (ST111 and ST77) revealing an average SNP count of 38,188 per individual , nearly Polymorphic sites unique to each transgenic event were identified by comparing each transgenic variant list to both WT and the remaining two transgenic groups. The ST77 transgenic group shared 2196 common gene variants with WT and exhibited 981 unique polymorphisms. The ST111 transgenic line revealed similar values sharing 1983 gene variants with the controls, while the 764 group shared 1912 variants. The ST111 and 764 lines each reported 927 and 7717 unique gene variants, respectively. Transgenic lines also revealed 127 gene variants shared between the three transgenic groups.Overall, SNP calls clearly illustrated that the 764 experimental group contained significantly increased levels of polymorphisms over the wild type controls and other transgenic experimental groups, while concurrently demonstrating an increased quantity of more acute transversion type mutations. Average total SNPs, INDELS, SNP rates, and transition/transversion ratios for each experimental group are shown in Following SNP and INDEL detection with samtools, snpEff version 4.1i  was usedAll four experimental groups showed a high prevalence of SNPs of common types, namely, resulting in downstream gene variants (~24%), intron variants (~25%), missense variants (~11%), synonymous variants (~7%), and upstream gene variants (~16%). The most common locations for these variants are intron regions (~25%), the downstream transcription start site (~23%), exons (~23%), introns (~23%), and the upstream transcription start site (~16%). The 764 group distribution of polymorphism types and affected regions seem to mirror those of the wild type, ST111, and ST77 groups, with a slight reduction of SNPs in exonic regions. All individuals also reported variants within 5′ and 3′ untranslated regions , as well as minimal detection of frameshifts, stop, and start variants (<1%). Regions and predicted effects of detected polymorphisms are shown in Figures Transition base changes of A↔G and C↔T were most prevalent in all four experimental groups, while the transversion A↔T was ~50% more common than A↔C, C↔G, or G↔T transversions. Codon changes varied most commonly at the wobble base position, typically resulting from a transition in both wild type and transgenic samples. Following SNP patterns previously described, the prevailing modified codons appeared commonly shared between all individuals measured. Base changes in the first and second positions of the codon were much less frequent across all groups. Heat maps of the codon base changes representative for each experimental group are shown in Supplemental Figure 1. Predicted amino acid changes resulting from codon alterations predominantly consist of synonymous substitutions; however, other frequently altered residues include valine to alanine, alanine to valine, leucine to phenylalanine and proline, glutamate to glycine and lysine, serine to proline, and phenylalanine to leucine. Supplemental Figure 2 shows the distribution heat maps of detected amino acid substitution averages representative for all events.Minor changes were also detected within the additional genomic scaffolds containing the transgene sequences supplemented to the alignment reference file. Specifically, snpEff reported SNPs in the transgenes of samples 764B3, 764H3, and 764K1 at position 80, ST77D2 at position 1400, ST77F1 at positions 500 and 700, ST77J3 at position 600, ST111B3 at position 800, ST111I3 at position 190, and ST111K1, ST111B1, and ST111K2 at position 1010. Read alignments were unable to verify all called variants in the transgenes, likely due to areas of low coverage; however, one area was corroborated as a consistent variant in the filler DNA of all constructs between the right border and promoter region , which was included as a control. Supplemental Table 1 summarizes all SNP calls and between group statistical tests. Summaries of all SNP/INDEL calls from bcftools and their respective effects from snpEff, along with chromosomal distribution plots, additional statistics, and lists of SNPs shared between groups and within each group, are available from the iPlant collaborative Discovery Environment . Links t Glycine maxV2.1 background set of 29,501 GO terms, wild type matched 11,245, ST77 matched 11,660, ST111 matched 10,326, and 764 matched 13,321. GO categories were similar between the wild type, ST77, and ST111 groups with minor variations, with the majority of terms putatively annotated to translational or RNA processes , ST77 , ST111 , and 764  lines were loaded into the AgriGO online gene ontology analysis tool and subjected to a single enrichment analysis with multiple corrections to deduce the functional roles of polymorphic genes. Out of the total Figures . Intrace Figures . Three c Figures . GTP binAnnotated GO terms for SNPs exclusive to each transgenic event displayed similar functions, with ST77 reporting 295 gene variants involved with intracellular organelles, photosystem II, cytoskeletal components, and protein complexes. ST111 displayed similar numbers of terms totaling 273 annotations, including photosynthesis and photosystem II, intracellular organelles, ribonucleoprotein complexes, thylakoids, protein complexes, and oxidoreductase activity. The 764 line exhibited the highest number of unique annotated variants with 2366 involving translation, gene expression, biosynthetic processes, ribosomal structures, and protein complexes. Of the 127 polymorphic genes shared by all transgenic specimens, 40 annotations returned 10 GO terms pertaining to intracellular organelles, macromolecular complexes, and protein complexes. Specific GO terms for each group are shown in  Glycine soja and occur at an even lower frequency than the highly characterized Arabidopsis thaliana self-crossing model [ Glycine max also contributes evolutionarily to the constriction of genomic variances in domesticated cultivars. Thus, with such selective pressures bottlenecking natural variations in cultivated soybean varieties, it is quite intriguing to see such consistently increased numbers of SNPs in only one transgenic experimental group with no definitive initiating event.SNP rates have been previously evaluated in soybean using multiple fragment analysis on several genotypes of cultivated varieties, including Lincoln, Mandarin, Peking, and Richland , 35. Frong model . More reng model , 37. Furng model . In addiSNPs detected in our datasets were divulged from seed transcriptome sequences, which represent ~6.5% of the total soybean genome and 65% of total genomic protein coding sequences. The SNPs described here are not considered to be representative of the frequencies that may be detected in other soybean tissues, or in different developmental stages of these specific transgenic plants; rather, they demonstrate increased transcript polymorphism rates witnessed in a single soybean transformant out of several different experimental groups. Although RNA-seq is focused on the functional segments of the genome and does not always capture regulatory regions such as promoters or nontranscribed regions , one aspect of the resulting effects of polymorphisms in these regions can be directly witnessed through examination of gene expression levels. Because the soybean expression system described previously by our lab , 14, 39 Base changes for wild type, ST77, and ST111 events were all comparable, while the 764 event consistently reported SNP rates nearly double that of the other groups , which is still well below the previously reported SNP rate of 1 SNP per ~1,400 nucleotides in soybean seeds .Interestingly, the 764 line had the lowest overall missense to silent polymorphism ratio, demonstrating that while the 764 line contained the highest overall number of SNPs, a higher percentage of the total polymorphisms were silent mutations (~45%) compared to the other three experimental groups . This reWhile many SNP studies have excluded synonymous mutations and those present in noncoding regions, recent works have examined these sequences in light of their possible epigenetic effects on transcription and translation; however, more advanced tools for the prediction of possible effects are still in development and require further testing for reliable forecasts . Nonsyno Agrobacterium transformation. Epigenetic alterations and nucleotide transpositions have been detected up to five generations forward from transformation in Arabidopsis [ Agrobacterium transformation or planting event, and have been stably integrated and carried forward to the generations examined. Considering the consistency of the SNP patterns, these polymorphisms have likely originated at the genomic level and are not likely to appear in such large quantities due to transcriptional proofreading errors.Spontaneous mutations may also occur as a result of a previously occurring stressful event, such as parental linebidopsis , 45; howbidopsis . SegregaDetected alterations occurred in a “bowtie”-shaped distribution across the 20 chromosomes, with lower instances of SNPs near the centromeres and the highest incidence of SNPs near the euchromatic telomeric ends of the chromosomal arms. This is where gene density is the highest, which has been illustrated previously by several works conducted on soybean polymorphism rates , 38, 47.Gene ontology terms from SNP gene lists of all experimental groups were very similar, returning many enriched terms regarding protein transport and localization as well as RNA and transcriptional processes even in the wild type group ( Glycine max with a nontransgenic wild type line, all derived from the Williams 82 soybean cultivar. Previously, we examined gene expression changes in these transgenic events, finding only the 764 line to be significantly altered when compared to the other transgenic lines. In the same manner, we have expanded our previous work and reported transcriptome single nucleotide polymorphism rates for the ST77, ST111, and 764 transgenic events compared to wild type. Mirroring the results from our differential expression study, the samples of the 764 transformation event exhibited transcript SNP rates nearly double that of the wild type, ST77, and ST111 events, while also demonstrating higher amounts of transversion mutations. While the exact cause for both the large number of differentially expressed genes and polymorphisms present in the 764 event is not known, it is highly likely that the two phenomena are correlated. Our results show that, in addition to differential expression, transgenesis may result in unpredicted base changes within gene transcripts and regulatory regions without impacting plant phenotype.This study describes the first direct comparison of polymorphism rates present in the seed transcriptome of three independent transgenic lines ofhttp://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64620 through NCBI's Gene Expression Omnibus under accession number GSE64620. Summary files for bcftools and snpEff outputs encompassing variant calls and predicted variant effects are available from the iPlant Collaborative Discovery Environment directory available at http://de.iplantcollaborative.org/dl/d/44044BEE-69B8-4129-A6B5-4C9291B6B4A1/snpeffsummaries.zip and http://de.iplantcollaborative.org/dl/d/AD464040-9169-4E52-87D5-DEF7466A2C1E/variantsummaries.zip. Lists of genes containing detected variants from all samples are available from iPlant in a compressed  .zip archive available at http://de.iplantcollaborative.org/dl/d/533570A3-1EFB-4864-B9A9-9D82F17E09A8/snpeffgenes.zip.The RNA sequencing data described herein may be accessed at Supplemental Figure 1. Heatmap of detected codon changes across wild type and all three transgenic groups. Green color denotes less frequent occurrence, and red denotes a higher occurrence. Supplemental Figure 2. Amino acid change heatmap for wild type and the three transgenic groups. Darker colors represent a higher detected instance of the respective amino acid alteration. Supplemental Figure 3. Base change controls in transgenes. Variants located at bases 232 and 233 were located in the padded sequence after the right border, and were detected consistently in all events with transcripts across this region, demonstrating the repeatability and consistency of the SNP calls. Supplemental Figure 4. AgriGO single enrichment analysis results of the genes containing effectual SNPs in transgenic events. Darker colors indicate a higher level of significance for each node. Supplemental Table 1. Raw SNP, base changes and deviation values for each sample.


Furthermore, the associations of the 13 polymorphic genetic variants with milk production and composition were investigated. Thirty-five SNPs did not show polymorphism in the studied populations. Polymorphic SNPs were detected in the following genes: CAST, CLEC4E, DES, GHRHR, HSP90AA1, IL15RA, IL1RN, IL8, MITF, PPRC1, SOCS3, TNF and TNFSF13. The studied Alpine population was in Hardy-Weinberg disequilibrium at the g.62894878A>G locus (rs671391101) (P < 0.05). The results showed that four SNPs rs671391101 (GHRHR), rs640582069 (IL1RN) rs635583012 (SOCS3) and rs635969404 (IL15RA) out of the 13 polymorphic markers were significantly associated with milk production, protein, fat and lactose content in the Alpine breed. However, no significant effect was recorded in the Saanen population regarding milk yield or milk chemical composition. The current results provide new insights for the development of SNP marker-assisted selection technology in the goat industry and confirm the potential of using SNPs for the GHRHR, IL1RN, SOCS3, and IL15RA genes as candidate genes for selection, highlighting the direct implications of such genes for farm production outputs. The results from this study are relevant for future goat genomic studies and the inclusion of the associated traits into up-to-date selection schemes.Using a novel and fast genotyping method called Kompetitive Allele Specific PCR (KASP™), we carried out a pilot study on 48 single nucleotide polymorphisms (SNPs) belonging to 40 genes in French Alpine ( The goat sector within the European Union accounts for 12.3 million head , concentConsequently, farmers started to import Saanen and French Alpine goats, the main specialized dairy goat breeds reared worldwide. The milk yield estimated for the two breeds ranges between 800 to 1100 kg/lactation when they are reared in intensive production systems . Both ths1 casein (CSN1S1)—on milk composition (mainly protein content) has received significant attention from both geneticists and animal scientists in the last decade. Moreover, the state of knowledge on goat genetics has been significantly improved from the year 2010 onward, subsequent to the sequencing of the goat genome and the release of the goat SNP50, which allowed several genome-wide association studies (GWAS) (The ongoing growth and increasing accessibility of genetic and genome sequencing data has given new perspectives for research in domestic ruminant species . Polymors (GWAS) . The aimForty-nine genetically unrelated multiparous goats (3rd lactations) reared under intensive commercial conditions (Northern Romania: 47°34′15″N 23° 25′41″E), including twenty-five French Saanen and 24 French Alpine individuals, were included in this study. The commercial farm manages a total of 1035 breeding goats in a mixed-breed system, with both Saanen and Alpine being reared under identical management and feeding conditions.At the farm level, both the Saanen and Alpine herds have been selected predominantly to increase milk yield, with the main selection aim being set at a production of 5,000 kg of milk per productive life. The animals selected for the current study were in the top 20% for milk yield, and were designated to produce the replacement does and bucks. All does were inseminated with frozen semen imported from France and Austria in order to avoid inbreeding within the populations. The does taken into the study were descendants of purebred animals introduced into Romania in 2009, following an import of Saanen and Alpine goats from France, representing a diverse sampling of genetic lines (12 lines/breed unit). Reproductive failure and udder-related problems were the criteria normally used to define which does would be culled. The culling rate at the farm level was 25%, with a conception rate of 83% and an average prolificacy of 186%. Pregnancy scanning was practised starting on day 45 after artificial insemination.All animals were included in the Official Performance and Recording Scheme, being registered as nucleus reference breeding herds, which produce and disseminate tested bucks. Does were milked twice per day in a ‘herringbone’ milking parlour (2 sides × 24 units). The average lactation length on the studied farm was 280 ± 15 days. The following production data were recorded for the studied animals: milk yield (kg), fat (%), protein (%) and lactose (%).2 per head in the resting area and free access to water and outdoor paddocks of 4.5 m2/goat. They received a daily feed ration made of high-quality alfalfa hay ad libitum and pelleted feed (16% CP). Does were housed in groups of 40 to 50 animals, according to lactation stage and productivity, regardless of their breed.Does were kept on a straw bedding, with a space allowance of 1.7 mHair follicles were taken from each animal and kept at 4 °C until further processing. The research activities were performed in accordance with the European Union’s Directive for animal experimentation (Directive 2010/63/EU).A total of 48 single nucleotide polymorphisms (SNPs) of 40 genes have been chosen based on the results of prior genome-wide association study (GWAS) and marker assisted selection study across the goat genome  Table 1Table 1. Genomic DNA was isolated using a method described by the The raw allele calls received from LGC Genomics were analysed with KlusterCaller software from LGC Genomics. Further, a genetic association study was conducted on polymorphic SNPs.yijk is the milk yield, fat percentage, protein percentage or lactose percentage; µ is the overall mean; SNPi is the effect of the selected SNP polymorphism ; BREEDj is the breed effect (Saanen or Alpine); SNPxBREEDij is the interaction between SNP polymorphism and breed; and ϵijk is the random error.To determine genotype frequencies for all polymorphic SNPs and estimate the effect of SNP polymorphism and breed on milk yield and composition, we performed factorial ANOVA using the statistical software Statistica  with the following model: chi-squared test (χ2) was used to determine whether the populations were in Hardy-Weinberg equilibrium (HWE). HWE analysis was performed for setting the genetic association studies.The The significance of each main effect (SNP and breed) as well as that of the interaction between main factors was verified using Shapiro–Wilk’s test . The sigFor each SNP, only the animals found to have been genotyped for that SNP were taken into consideration. This is why the number of individuals varied across the SNPs.CAST, CLEC4E, DES, GHRHR, HSP90AA1, IL15RA, IL1RN, IL8, MITF, PPRC1, SOCS3, TNF and TNFSF13. Although the set of SNPs was selected from dbSNP, a large number of SNPs  were not polymorphic and were therefore excluded from the association study. Thirty-five SNPs were not polymorphic  among the selected 48 SNPs were polymorphic across the two populations and further used for the association study . The polymorphic .rs660778229-HSP90AA1 and rs635583012-SOCS3), two in the 3′ UTRs (rs671256096-DES and rs664283580-MITF), and one was an upstream variant (rs669986850-CLEC4E). In addition, eight SNPs were missense substitutions .Among the polymorphic SNPs, two were located in introns (rs671391101) (P < 0.05).The different genotype frequencies are shown in rs671391101 (GHRHR), rs640582069 (IL1RN) rs635583012 (SOCS3) and rs635969404 (IL15RA)—had significant effects on milk production, protein, fat and lactose in the Alpine breed and in both populations collectively ; the g.46353777T>G SNP in the IL1RN gene with protein percentage (P < 0.05); and the g.52626440T>G SNP in the SOCS3 gene with lactose percentage. Out of these associations, the g.52626440T>G (rs635583012) SNP located in the intron of the SOCS3 gene and the g.46353777T>G (rs640582069) SNP located in the IL1RN gene were associated with lactose percentage; the g.62894878A>G SNP in the GHRHR gene and the g.46353777T>G SNP located in the IL1RN gene were associated with protein percentage in the Alpine population (P < 0.05). The current findings also underline the significant differences found between the Saanen and Alpine goat breeds based on all 13 markers for milk yield (P ≤ 0.05).The studied individuals’ genotypes were analysed for association with milk yield and composition in the Saanen and Alpine breeds and in both populations together. The four SNPs—ectively . No signThe Kompetitive Allele Specific PCR genotyping assay has been successfully used in order to genotype different organisms . In our CC of rs669986850, TT of rs671256096, AG of rs671391101, TT of rs664283580, AA of rs671509714 and GG of rs669561078 were not observed in the Saanen or the Alpine. The missing genotypes may be attributable to the relatively small number of animals studied.The results of this study showed that only 13 of the studied SNPs were polymorphic. The genetic variants rs671391101) (P < 0.05) and therefore this locus raises question for further study on large number of samples. The Hardy-Weinberg disequilibrium for this SNP in French Alpine may be attributable to either selection or the genotyping error of KASP assays that produced 18 identified allele calls and 6 unidentified allele calls. Among the polymorphic SNPs, two were located in introns , and those variants have recently been demonstrated to be significantly associated with quantitative traits in different species, despite the fact that they do not change amino acids  family of proteins that has a negative effect on cytokine signalling  was found to have wide range effect on a variety of physiological parameters such as lactation, reproduction, growth traits and metabolism  genotypes significantly affect milk yield in the breed. In contrast, significant differences in milk traits included milk yield, fat, protein, casein and lactose percentage were detected among Sarda sheep genotypes at polymorphic loci only for the GH2-Z gene  was not polymorph. Members of the IL-1 family are pleiotropic cytokines that have important rules in inflammation, immunoregulation and other homeostatic functions in the body . In human interleukin-15 receptor alpha is a high affinity IL-15 binding protein that is crucial for mediating IL-15 functions such as memory CD8 T, cell proliferation and NK, NK/T cell, and intestinal intraepithelial lymphocyte development (In the current study, two less-studied interleukins in goats were found to have a significant association with milk related traits. The the body . Marcos-elopment . MoreoveSOCS3, GHRHR, IL1RN and IL15RA variants on relevant traits in dairy production.In summary, this study validated a KASP genotyping assays in goat that can replace the different genotyping assays developed for marker-assisted selection. The validated KASP genotyping assay used for the 48 SNPs will be helpful for diverse applications in goat breeding programs. Furthermore, preliminary results of the current study shows evidence of significant associations between four SNPs located on four genes, which were poorly studied before within the genetic variants and milk production traits in Alpine goats, indicating the potential role of 10.7717/peerj.4416/supp-1Data S1Click here for additional data file.


Listeria monocytogenes and Salmonella enterica including: subculture of isolates, DNA extraction, sequencing and bioinformatics analysis. This end-to-end WGS workflow was evaluated according to the following performance criteria: stability, repeatability, reproducibility, discriminatory power, and epidemiological concordance. The current study showed that few single nucleotide polymorphism (SNPs) were observed for L. monocytogenes and S. enterica when comparing genome sequences from five independent colonies from the first subculture and five independent colonies after the tenth subculture. Consequently, the stability of the WGS workflow for L. monocytogenes and S. enterica was demonstrated despite the few genomic variations that can occur during subculturing steps. Repeatability and reproducibility were also demonstrated. The WGS workflow was shown to have a high discriminatory power and has the ability to show genetic relatedness. Additionally, the WGS workflow was able to reproduce published outbreak investigation results, illustrating its capability of showing epidemiological concordance. The current study proposes a validation approach comprising all steps of a WGS workflow and demonstrates that the workflow can be applied to L. monocytogenes or S. enterica.Whole genome sequencing (WGS), using high throughput sequencing technology, reveals the complete sequence of the bacterial genome in a few days. WGS is increasingly being used for source tracking, pathogen surveillance and outbreak investigation due to its high discriminatory power. In the food industry, WGS used for source tracking is beneficial to support contamination investigations. Despite its increased use, no standards or guidelines are available today for the use of WGS in outbreak and/or trace-back investigations. Here we present a validation of our complete (end-to-end) WGS workflow for  The use of Whole genome sequencing (WGS) for outbreak investigation and pathogen source tracking has been described in several publications since a few years , it is important to emphasize the validation should cover the end-to-end workflow including all steps. The end-to-end WGS workflow in the current study consisted of four steps: subculturing isolates to obtain pure colonies, extracting DNA, performing short read sequencing with MiSeq Illumina and carrying out bioinformatics analysis based on read mapping allowing identification of hqSNPs with the CFSAN SNP Pipeline. Even though the CFSAN SNP Pipeline was previously evaluated on its robustness and accuracy . Twenty microliter of lysozyme from chicken egg solution  was added and incubated at 37°C with agitation at 900 rpm for 2 h (Eppendorf ThermoMixer C). Proteinase K  was added and the solution was incubated 1 h at 56°C. The QIAamp DNA Mini kit was used according to the supplier's instructions. DNA was eluted in 100 μL Elution buffer (Qiagen) and stored at −20°C until further analysis.The bacterial pellet was suspended in 180 μL Buffer ATL (Qiagen). Proteinase K  was added and the solution was incubated 1 h at 56°C. The QIAamp DNA Mini kit was used according to the supplier's instructions. DNA was eluted in 100 μL Elution buffer (Qiagen) and stored at −20°C until further analysis.The Qubit™ dsDNA HS Assay kit (Invitrogen) was used to measure the DNA concentration with the Qubit® 2.0 Fluorometer (Invitrogen) according to the supplier's instructions. DNA was standardized to 10 ng/μL with Elution buffer (Qiagen) and stored at −20°C until used for sequencing.The Gentra Puregene Yeast/Bact. kit (Qiagen) was used for the DNA purification of bacterial cultures.One colony (see section Bacteria and Subculture Conditions) was used to inoculate BHI broth and grown at 37°C overnight. Five mL of broth were centrifuged  and supernatant was discarded. The pellet was washed three times using 1 mL of Phosphate-Buffered Saline (PBS). DNA extraction was carried out following supplier's instructions indicated in the following protocol: DNA purification from Gram-Positive bacteria.One colony (see section Bacteria and Subculture Conditions) was used to inoculate BHI broth and grown at 37°C until mid-exponential phase (4–5 h incubation). Five milliliter of broth were centrifuged  and supernatant was discarded. The pellet was washed three times using 1 mL of PBS. DNA extraction was carried out following supplier's instructions indicated in the following protocol: DNA purification from Gram-Negative bacteria.DNA was stored at −20°C until further analysis. The Qubit™ dsDNA BR Assay kit (Invitrogen) was used to measure the DNA concentration with the Qubit® 2.0 Fluorometer (Invitrogen) according to the supplier's instructions.DNA was normalized at 0.2 ng/μL in order to start with 1 ng to perform a sequencing library preparation using Nextera XT kit (Illumina) following the supplier's instructions. A final AMPure beads purification at ratio 0.6 was performed on a Sciclone robotic platform from Perkin Elmer. The quality and quantity of each library were evaluated using a capillary electrophoresis method (LabChip GX Touch from Perkin Elmer). Libraries were pooled based on molarity calculated by the LabChip GX Touch. The equimolar pool was assembled using a Hamilton robotic platform.The sequencing was performed on a MiSeq platform (Illumina) using v2 chemistry, for a 2 × 250 cycles run. The pool was spiked with 2% PhiX, loaded at 12 pM and 16 to 19 samples were loaded per MiSeq flow cell (corresponding to ~2 million reads pass filter per sample).To ensure each library was present in the pool before sequencing, the equimolar pool was controlled by a MiSeq run v2 chemistry for 2 × 20 cycles. Then the pool was sequenced on a HiSeq 2500 platform (Illumina) using Rapid v2 chemistry. The pool was spiked with 2% PhiX, loaded between 9 or 12 pM and respectively 190 or 84 samples were loaded per HiSeq flow cell (corresponding to ~3 or ~9 million reads pass filter per sample).6.All sequences have been submitted to the National Center for Biotechnology Information (NCBI) in BioProject: PRJNA420913 and all sequence read archive numbers (SRR) are available in Tables High molecular weight DNA was sheared using g-TUBE (Covaris) to obtain around 20 kb DNA fragments. After shearing, the DNA size distribution was checked using the TapeStation2500 system (Agilent) or Fragment Analyzer .Procedure and Checklist −20 kb Template Preparation Using BluePippin™ Size-Selection System. (Pacific Biosciences). The library was size selected using a BluePippin system (Sage Science). Due to low DNA quantity after shearing for isolates PIR00544 and PIR00547, the 10–20 kb Template Preparation and Sequencing with Low-Input DNA protocol (Pacific Biosciences) was applied without size selection.DNA was quantified using the Qubit system (Qubit 2.0 Fluorometer and dsDNA Assay HS) and around 5 μg of the sheared DNA was used to prepare a SMRTbell library following the protocol The library was sequenced using RSII platform (Pacific Biosciences) on one or two SMRT cells with P6-C4 chemistry and MagBeads loading (Pacific Biosciences). Sequencing time was between 240 and 380 min.PRJNA420913).All sequences accession numbers are available in Table http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). To choose the best isolate for genome assembly that will serve as reference during hqSNP analysis, the reads should pass the “per base sequence quality” FASTQC threshold.In order to evaluate the sequencing run quality, fastq files were assessed with the FastQC software (v0.11.5) (de novo assembled the trimmed reads and generated a multi contigs genome.Illumina reads were first trimmed with Trimmomatic software (version 0.36)  version 3 (Pacific Biosciences), closure of the genome by Circlator  was used for SNP calling on http://tree.bio.ed.ac.uk/software/figtree/). All trees shown in this paper are midpoint rooted.Maximum-likelihood phylogenetic trees were built with GARLI (Version 2.01.1067) on the SNP analysis result and visualized with Figtree (version 1.4.3) (Listeria monocytogenes and S. enterica strains from −80°C were streaked on TSA and Columbia agar (BioMérieux) respectively, for 24 ± 2 h at 37°C to obtain single colonies. One colony was dissolved in 0.5 mL tryptone  salt  solution streaked on Columbia agar (L. monocytogenes) or TSA (S. enterica), and incubated for 24 ± 2 h at 37°C to ensure colonies were pure. This procedure was repeated three times. Afterwards, five colonies  were picked from the same agar plate and each colony was grown in 4 mL BHI broth as indicated in section Bacteria and Subculture Conditions. DNA was extracted as described in section DNA Extraction and sequenced as described in section Sequencing. In addition, colony A was subcultured 10 times by emulsifying one colony in tryptone salt solution, streaking on TSA/Columbia agar and incubating for 24 ± 2 h at 37°C. This procedure was repeated 10 times. After 10 subcultures, five colonies  were selected and each colony was grown in 4 mL BHI broth as indicated in section Bacteria and Subculture Conditions. DNA was extracted as described in section DNA Extraction and sequenced as described in section Sequencing. The dataset used to evaluate stability is presented in Tables L. monocytogenes and eight S. enterica from the stability experiment . The dataset used to evaluate reproducibility is presented in Tables Since the WGS method has a high resolution capability, not only isolates known to be unrelated , but also isolates having clonal relatedness were included in the analysis in order to show that the end-to-end workflow was able to discriminate correctly while not missing similarities.L. monocytogenes strains to be included in the discriminatory power experiment was based on serotype selection . For serotype 4b, two strains known to be unrelated were selected .S. enterica, three serovars  were selected to perform three distinct discriminatory experiments. For each serovar, strains were selected based on the epidemiological relationships information, in order to create three to four groups to be discriminated while not missing similarities within groups. Table For S. Enteritidis, group 3 was composed of three related isolates from an European egg related outbreak  published in 2016 by CFSAN FDA  and five from independent colonies after the tenth subculture . The number of SNPs was determined between the 10 generated genome sequences per strain.The stability of L. monocytogenes. For L. monocytogenes PIR00540, PIR00542, PIR00543, PIR00545, PIR00546, and PIR00547, no SNP was found between colonies of the first subculture  and after the tenth subculture . For two strains, PIR00541 and PIR00544, few SNPs were identified. For PIR00541, colony B had 1 SNP compared to colonies A, C, D, and E. Colonies A, C, D, and E had 0 SNPs between them. After the tenth subculture, colonies A1, A2, A3, A4, and A5 acquired 1 SNP compared to colony A. However, this acquired SNP was different from the one from colony B. For PIR00544, no SNPs was found between colonies A, B, C, D, and E, but after the tenth subculture a maximum of 3 SNPs were identified for two colonies out of the five.Table S. enterica per serovar. For S. Hadar, no SNP was found between all colonies for the two strains PIR00533 and PIR00534. No SNP was found between all colonies for S. Typhimurium strain PIR00536. The hqSNP analysis of sequences obtained from strain PIR00538 revealed that colony A2 had 1 SNP compared to all the others colonies . For S. Enteritidis, no SNP was found between all colonies for strain PIR00532. Colonies A1, A2, A3, A4, and A5 of strain PIR00558 had 1 SNP compared to the colonies from the first subculture . Finally, for S. Tennessee, no SNP was found between all colonies for strain PIR00537, while PIR00535 colonies A1, A2, A3, A4, and A5, after the tenth subculture, had 1 SNP compared to the colonies from the first subculture .Table L. monocytogenes strains and eight S. enterica strains were analyzed.The sequence data used were selected from the stability experiment  for seven strains out of eight. For PIR00541, 1 SNP was observed in colony B compared to colonies A, C, D, and E from the first subculture, and after the tenth subculture another SNP was acquired. Knowing from the stability experiment that this strain acquired a random SNP, we assume that this acquisition was not due to the repeatability testing.Table S. enterica. For the eight tested S. enterica strains, no SNP was found between the five independent colonies .Table L. monocytogenes genome sequences and 18 S. enterica genome sequences were analyzed. For L. monocytogenes and S. Typhimurium and S. Hadar, no SNP was found between genome sequences obtained from different laboratories, operators and instruments.A total of 18 L. monocytogenes and Salmonella enterica, were in agreement with the design  using the CFSAN SNP pipeline. This reference allowed to group together the Ohio patient isolate (SRR1263956) with the Canadian lettuce isolate (SRR3026472) and six additional patient isolates . All the other isolates had ≥100 SNPs and were not considered genetically linked to this outbreak. Since a distantly related reference genome can result in an underestimation of the genetic relatedness of the isolates being investigated, the group of eight isolates was analyzed separately. To analyze in more details the group composed of eight isolates with the CFSAN SNP Pipeline, the reads from the Canadian lettuce isolate were assembled to obtain a reference genome. With our analysis, 1 SNP was found between the sequence from the Ohio patient and the prepackaged romaine lettuce. SRR1451258 and SRR972392 had 28 and 51 SNPs and all the other isolates had ≥80 SNPs with the lettuce isolate, respectively  declared an outbreak. The outbreak control team was in charge of finding the source of infection. A total of 136 cases were identified from a large outbreak over 20 months implicating UK patients, Spanish patients. After epidemiological investigation, eggs consumption was suspected to be at the origin of the outbreak. Additional selected clinical sequences from GenomeTrakr were also included in the analysis.In England and Wales, the national reference laboratory uses WGS with hqSNP analysis routinely, since April 2015, on all cultures of S. enterica Enteritidis AM933172 genome as reference. At the exception of 14 isolates which had more than 45 SNPs compared to the rest of the isolates, all the isolates had < 20 SNPs differences between them and could be grouped together  using the same r Figure .With our methodology the food isolate would be the preferred reference to conclude if any patient isolate was related to the food. Therefore, in the second approach, analysis was performed on 177 isolates and the same results were obtained Figure .de novo assembled Illumina contigs and a genome obtained with PacBio contigs for L. monocytogenes and S. enterica strains.HqSNP analysis is based on the mapping of Illumina reads on a reference genome. As a good practice, not only the reference genome needs to be genetically related to the genomes analyzed, but also needs to be the most complete as possible, since absent regions will not be analyzed and potential SNPs in these areas would not be detected. Even if there is an advantage of PacBio read length over Illumina, it is less costly and faster to use Illumina data to obtain a reference genome for hqSNP analysis. For that reason, in this study, the impact of the sequencing technology to obtain the reference genome was assessed by using as reference a genome obtained with de novo assembled Illumina contigs. For all L. monocytogenes and S. enterica strains, the exact same SNPs were identified when using the PacBio genome or using the Illumina assembled contigs.The evaluation was done by the SNP calling on sequences obtained from the stability, repeatability and reproducibility experiments. A total of 22 results obtained with PacBio reference genomes were compared to 22 results obtained with L. monocytogenes and of S. enterica. Previously, molecular subtyping tools such as PFGE had been used for source tracking to differentiate related and nonrelated microbial strains to support epidemiological investigations of foodborne outbreaks   and the serotype commonly related to outbreaks (4b) were evaluated (Nelson et al., S. enterica, three different serovars were evaluated including a previously described clonal serovar such as S. Enteritidis (Taylor et al., L. monocytogenes and S. enterica, the complete WGS workflow allowed to differentiate unrelated strains without losing the ability to identify closely related strains.We assessed the discriminatory power to ensure that the typing system assigns a different type to two unrelated strains. Unlike conventional typing methods, the ability of WGS to differentiate strains is less of a concern as whole genome data offer the highest possible resolution to elucidate phylogenetic relations (Ronholm et al., L. monocytogenes and one S. enterica outbreak investigation in which other bioinformatics pipelines were used. For both studies, agreement between the results obtained with the CFSAN SNP Pipeline and the author's pipelines was observed.The epidemiological concordance evaluated the capability of the CFSAN SNP Pipeline to reproduce results from previously published outbreak investigations using other bioinformatics pipelines. In this exercise, only the bioinformatics analysis was considered as the sequences were obtained from the public database at NCBI or directly from the authors of a published outbreak investigation. We selected one The ideal read mapping analysis would require the usage of a closed genome as reference for SNP calling. The impact of using a high quality Illumina assembly or PacBio assembly genome as reference was evaluated. To the best of our knowledge, no data are reported in the literature to show equivalence. In the present study, we report no impact in terms of SNP calling with these two sequencing technologies, and therefore we conclude that both alternatives to obtain the reference sequence are equivalent in the setup evaluated.L. monocytogenes and S. enterica, using the stability, repeatability, and reproducibility evaluations. Additionally, the complete WGS workflow was shown to have a high discriminatory power while enabling the ability to show genetic relatedness. For other bacteria, a validation would be required where the same performance criteria, as proposed in the current study, can be used. We believe that this work contributes as a first step to harmonize methods and thus obtain reliable results.In conclusion, the current study is the first study proposing a validation approach including all steps of the end-to-end WGS workflow. We validated this WGS workflow, for the application of source tracking of JG and LB performed the lab experiment steps including the DNA extraction step. CF performed the sequencing of all DNA used in this publication. CN-B, CB, LB, and A-CP designed the experiments described in this publication. A-CP, CN-B, and CB run the bioinformatics analyses. All authors contributed to the writting 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.


Rht-B1, which is known to have a strong effect on plant height and is a strong candidate for the observed phenotype. We performed simulation experiments to determine the impacts of sequencing depth and bulk size and discuss the importance of considering each factor when designing mapping-by-sequencing experiments in wheat. This approach can accelerate the identification of candidate causal point mutations or linked deletions underlying important phenotypes.Forward genetic screens of induced mutant plant populations are powerful tools to identify genes underlying phenotypes of interest. Using traditional techniques, mapping causative mutations from forward screens is a lengthy, multi-step process, requiring the identification of a broad genetic region followed by candidate gene sequencing to characterize the causal variant. Mapping by whole genome sequencing accelerates the identification of causal mutations by simultaneously defining a mapping region and providing information on the induced genetic variants. In wheat, although the availability of a high-quality draft genome assembly facilitates mapping and mutation calling, whole genome resequencing remains prohibitively expensive due to its large genome. In the current study, we used exome sequencing as a complexity reduction strategy to detect mutations associated with a target phenotype. In a segregating wheat EMS population, we identified a clear peak region on chromosome arm 4BS associated with increased plant height. Although none of the significant SNPs seemed causative for the mutant phenotype, they were sufficient to identify a linked ~ 1.9 Mb deletion encompassing nine genes. These genes included The online version of this article (10.1007/s00438-017-1401-6) contains supplementary material, which is available to authorized users. Forward genetic screens of induced mutant populations have been instrumental in identifying genes responsible for phenotypic variation in plants -induced mutant populations  to characterize the full complement of genetic variants within a mutant or a mapping population  arising from a heterogeneous genetic background and the number of segregating loci. This approach was originally described in rice (‘MutMap’), whereby WGS was conducted on a bulked DNA sample of individuals from an isogenic F2 population which exhibited a mutant phenotype . This last result suggests that EMS treatment increases the frequency of large deletions in wheat  populations in wheat  exhibiting an increased height phenotype when compared to non-mutagenized control lines . Within this population, individual plant heights ranged from 70 to 129 cm and exhibited a bimodal pattern of segregation  homozygous wild-type, 25.0% (19 lines) heterozygous, and 37.5% (28 lines) homozygous mutant. Based on these expected frequencies, we selected the 24 shortest (average height 82 cm) and 24 tallest M4 plants (average height 119 cm) for genotyping  showed near-perfect co-segregation with the mutant phenotype (Online Resource Table S2). However, this peak SNP could not be the causal mutation because the tall plants (mutant phenotype) were homozygous for the wild-type C allele (SNP in repulsion phase). Among the 48 M4 mapping individuals, the 17 plants carrying the wild-type C SNP were on average 49% taller (120.9 cm) than the 23 plants carrying the mutant T SNP (81.4 cm), while eight plants heterozygous for this SNP exhibited intermediate height  were located on chromosome arm 4BS (12.5–95.3 Mb). The remaining 11 significant SNPs were distributed across four different chromosomes (Table P = 1.1 × 10−14) encoded an intergenic variant (Table P = 1.6 × 10−11) encoded a missense mutation in the coding region of a homolog of LIPOXYGENASE1  at this SNP exhibited a clear increased height phenotype , a gene known to control plant height , while 23 short lines homozygous for the T allele and eight heterozygotes produced expected amplicons . This result demonstrates that the increased height phenotype in this population is associated with the complete deletion of a chromosome region including Rht-B1.Although our initial mutation analysis revealed no obvious candidate point mutations, the highly significant association between the SNPs in the peak region on chromosome arm 4BS and the increased height phenotype suggested that the causal variant was in close proximity. Therefore, we searched for nearby annotated genes and found that Rht-B1 0.9 Mb, aRht-B1 0.9 Mb, aRht-B1 by re-mapping the exome sequencing reads from the 48 M4 mapping individuals to the IWGSC RefSeq v1.0 wheat genome assembly. To eliminate complications from heterozygotes, coverage analysis of the eight M4 lines heterozygous for the peak SNP (C3986T on IWGSC_CSS_4BS_scaffold_4881784) was conducted separately from the 23 short and 17 tall M4 sister lines homozygous for this SNP. This analysis revealed a region on chromosome 4B (28.95–30.86 Mb) encompassing nine annotated genes which all showed high coverage for the 23 homozygous short sister lines, but almost zero coverage in the 17 homozygous tall sister lines . Normalized read depth from the eight heterozygous lines in this region was approximately half that of the 23 homozygous short sister lines, indicating hemizygosity  and the 24 tallest lines as a mutant bulk  for each segregating SNP to identify chromosomal regions exhibiting the highest |Δ(SNP-index)| values Fig. a. ConsisWe next studied the effect of bulk size on mapping accuracy by performing a series of simulations using bulk sizes ranging from 1 to 24 individuals . The three most significant SNPs  were consistently ranked in the same order until bulk size decreased to 11, indicating that in our study, the same peak region could be mapped using smaller bulk sizes . Furthermore, the two most significant SNPs were consistently ranked even in bulk sizes of 5, although in smaller bulk sizes, high |Δ(SNP-index)| values of several unlinked SNPs meant that this ranking was lost . The |Δ(SNP-index)| values of the top three SNPs increased as bulk size decreased from 24 to 13 . This was likely due to enhanced phenotype accuracy obtained through selecting mapping individuals with more extreme phenotypes, which removed heterozygous lines that had been included in the larger bulks (Online Resource Table S2).n = 24 and n = 12) , will go undetected. An analysis of open chromatin regions of the maize genome revealed regulatory regions totaling < 1% of all sequences that were responsible for approximately 40% of observed phenotypic variation   , whereby a capture assay including only genes that encode nucleotide binding and leucine-rich repeat (NLRs) proteins was used to clone two stem rust resistance genes, In addition to complexity reduction, a number of other factors must be considered when designing a mapping-by-sequencing experiment. We briefly discuss each of these factors in the sections below.4 population derived from an EMS-mutagenized tetraploid M2 wheat line that had previously been exome-sequenced , selecting the 24 shortest and 24 tallest lines for genotyping based on expected allele frequencies. The optimal population size will depend on the heritability of the trait (phenotyping accuracy) and will likely be larger for traits with lower heritability. Larger segregating populations facilitate the selection of larger and more homogenous pools, improving mapping resolution.In the current study, we mapped the causative mutation using a relatively small population size , a C3HC4-Type RING Finger (TraesCS4B01G042900) and Rht-B1 . Plants homozygous for this mutation are significantly taller than wild-type plants, while individuals heterozygous for the mutation are intermediate in height . This suggests that the deletion of Rht-B1 is the most likely explanation for the results observed in the mutant reported in this study. The wheat variety Kronos carries the gain-of-function Rht-B1b allele that encodes a constitutively active repressor of gibberellin (GA) signaling which confers a semi-dwarf phenotype  was grown in one-meter head rows, with 30 seeds per line, in the field at the University of California, Davis, CA  in the 2013–14 growing season. Through a visual screen, we identified a mutant line (T4-3822) which exhibited an increased height phenotype segregating within the M3 row. M4 seeds were bulk-harvested from the M3 row and sown in the same field location in the 2014–15 growing season. Seventy-five M4 seeds were sown in 15 one-meter rows to maintain five individual plants per row. We measured final height at maturity and selected the 24 shortest and 24 tallest plants for genotyping by exome sequencing.A tetraploid wheat TILLING population (variety ‘Kronos’) was previously developed using EMS  method , amplified by eight cycles of PCR following the KAPA HTP Library Preparation Kit protocol , and purified with the Agencourt AMpure XP system . Eight amplified libraries were pooled (150 ng each) per capture reaction and six captures were prepared using the 84 Mb SeqCap EZ Design , which targets 286,799 exons  from 82,511 non-redundant wheat transcripts  and ‘Scythe’  to remove low quality and contaminating adapter sequences, respectively. Using ‘BWA’  , 1.9 kb of 5′ UTR sequence and 0.5 kb of 3′ UTR sequence. PCRs were performed using template DNA from non-mutagenized Kronos, the three shortest and three tallest individuals from the M4 mapping population using PCR conditions previously reported , 40 cycles of 94 °C (20 s), 60 °C (30 s), and 72 °C (1 min), followed by the final extension at 72 °C (7 min). PCR products were visualized on a 1.5% agarose gel using ethidium bromide.To confirm the deletion of the complete Rht-B1b, GA insensitive) and Gredho , and a T4-3822 M5 line homozygous for the deletion on chromosome arm 4BS. Seeds were sown 2.5 cm below the top edge of germination paper (26 × 13 cm2) moistened with distilled water, kept at 4 °C for 48 h, and moved to room temperature in solutions containing different concentrations  of GA3 . Coleoptile and shoot length were measured after 10 days. The experiment was conducted as a randomized complete block design with four blocks, one replication (eight subsamples) per block/treatment combination.GA sensitivity assays were conducted using wild-type seedlings of the varieties Kronos  v1.0 assembly (https://wheat-urgi.versailles.inra.fr/Seq-Repository/Assemblies) using ‘BWA’, and BAM files were generated as described above. To minimize confounding effects from reads with multiple mapping positions, we extracted only reads with Phred-scaled mapping quality score > 20 using ‘SAMtools’ (command “view” with -q 20), which excludes reads mapping to multiple locations. ‘BEDGRAPH’ files for chromosome 4B were created using ‘Bedtools’   through n = 1  using a custom Python script . Sequencing depth simulation was performed by randomly subsampling different proportions of mapped reads  using ‘SAMtools’ . Subsampled BAM files were analyzed using the same informatic tools and parameters described above to generate genotype data of the 48 mapping individuals. BSA simulation was conducted by calculating |Δ(SNP-index)| as described above, with two different bulk sizes (n = 24 and n = 12). The number of false positives was estimated at each sequencing depth under the two bulk sizes by counting the number of SNPs with |Δ(SNP-index)| ≥0.8 outside the peak candidate region . Further details on the calculations used to estimate the proportion of false positives are provided in Online Resource Text S1.We also conducted simulations to test the impact of bulk size and sequencing depth on BSA results. Bulk size simulation was performed by decreasing bulk size from Below is the link to the electronic supplementary material.Supplementary material 1 (PDF 1724 KB)


Gallus gallus). Although some studies have identified genes affecting egg quality, little is known about the genes responsible for oviduct development. To address this issue, here we used a genome-wide association (GWA) study to detect genes or genomic regions that are related to oviduct development in a chicken F2 resource population by employing high-density 600 K single-nucleotide polymorphism (SNP) arrays. For oviduct length and weight, which exhibited moderate heritability estimates of 0.35 and 0.39, respectively, chromosome 1 (GGA1) explained 9.45% of the genetic variance, while GGA4 to GGA8 and GGA11 explained over 1% of the variance. Independent univariate genome-wide screens for oviduct length and weight detected 69 significant SNPs on GGA1 and 49 suggestive SNPs on GGA1, GGA4, and GGA8. One hundred and fourteen suggestive SNPs were associated with oviduct length, while 73 SNPs were associated with oviduct weight. The significant genomic regions affecting oviduct weight ranged from 167.79–174.29 Mb on GGA1, 73.16–75.70 Mb on GGA4, and 4.88–4.92 Mb on GGA8. The genes CKAP2, CCKAR, NCAPG, IGFBP3, and GORAB were shown to have potential roles in oviduct development. These genes are involved in cell survival, appetite, and growth control. Our results represent the first GWA analysis of genes controlling oviduct weight and length. The identification of genomic loci and potential candidate genes affecting oviduct development greatly increase our understanding of the genetic basis underlying oviduct development, which could have an impact on the selection of egg quality.Molecular genetic tools provide a method for improving the breeding selection of chickens ( Egg production and egg quality, as economic traits, play important roles in the poultry industry. Increasing the level of egg production to 500 by 100 weeks of age is an ultimate goal; however, as hens grows old, the albumen becomes thin and eggshell quality decreases sharply. Additionally, the frequency of abnormal eggs increases, which reduces the number of high-quality eggs. The oviduct is defined as having five segments: the infundibulum, magnum, isthmus, uterus, and vagina. After oviposition, the F1 follicle is captured by the infundibulum, and the albumen is formed in the oviduct magnum, while the eggshell is produced in the uterus , mainly Gallus gallus), compared with viviparous animals, must contain all the nutrients required to sustain embryonic development. Thus, the development of the oviduct will directly affect embryonic and chick development [The eggs laid by oviparous chickens . Animal experiments were permitted by the Institutional Animal Care and Use Committees at the Poultry Institute, Chinese Academy of Agricultural Science, Yangzhou, China (permit number: JPIAE 2011–0005), and the Animal Ethics Committee of China Agricultural University, Beijing, China (permit number: SYXK 2007–0023).ad libitum at the Jiangsu Institute of Poultry Science, Yangzhou, China. Birds belonging to the F2 resource population were decapitated at 72 weeks of age after they were rendered unconscious by 60%–70% carbon dioxide. Then, their oviducts were isolated, weighed, and their length was measured.The F2 resource population in our experiment was described in previous reports –17. It oPhenotypic data were computed by the MEANS procedure of SAS software using all available records. Only values that varied by +3 or −3 standard deviations from the mean were removed from the subsequent analysis. The RANK procedure in SAS was used for rank-based inverse normal transformations to convert trait deviations to normality.p, was < 1×10−6, the minor allele frequency was < 5%, and the imputation quality score, R2, was ≤ 0.5, as determined using the software Affymetrix Power Tools v1.16.0 (APT) (http://affymetrix.com/), the PLINK v1.90 program [Blood samples were drawn from the wing vein, and DNA was extracted by the standard phenol/chloroform method and genotyped with the 600 K SNP Array . Detailed information regarding genotyping and quality control procedures is available in previous studies , 16, 17. program , and theP-values were 8.43 × 10−7  and 1.69 × 10−5 , respectively.Initially, a principal component analysis was used to adjust for the influence of spurious associations resulting from a hidden population stratification or the presence of cryptic relatedness in the analyzed dataset using a GWA matrix based on marker information. Furthermore, a test of thresholds for genome-wide significant/suggestive associations was performed using the simpleM method . After aP-value was derived by a Wald test using the equation:The effect of genotype on the oviduct was first estimated from a single-traits analysis, which was conducted with the valid samples and SNPs using the GEMMA v0.94 package . To detey is a vector of phenotypic values for n individuals; W is a matrix of covariates (fixed effects with a column of 1s and the top five principle components (PCs)), αis a vector of the correspondence between the coefficients, including the intercept; x is a vector of the genotypes of the SNP marker; β is the effect size of the marker; μ is a vector of random individual effects; and ε is a vector of random errors.Where Manhattan and quantile–quantile (Q–Q) plots were constructed by the “gap” and “qqman” packages  in the RA linkage disequilibrium (LD) analysis was performed in Haploview v4.2 . In thish2snp) and genetic correlations using the software GCTA v1.24 [G. gallus genome supported by the National Center for Biotechnology Information and Ensembl, located in or near leading loci were identified as potential candidate genes [First, we estimated the heritability from the eligible SNPs  heritability, and the genetic correlation for the oviduct are shown in Table 1. Oviduct length (OL) displayed a lower coefficient of variation (13.91%) than OW (23.37%). The confidence intervals of the CVs for OL and OW were 13.40%–14.43% and 22.50%–24.49%, respectively, which means that the data are sufficiently reliable for the GWA analysis. The phenotypic and genetic correlations were high between OL and OW. The estimated heritability for OL and OW were 0.354 and 0.392, respectively, which suggests that there is a considerable genetic contribution to these oviduct traits.Means, standard errors, the SNP-based (Table 2). For OL, five and 17 SNPs located on G. gallus chromosome 2 (GGA2) and GGA4, respectively, reached the suggestive level. The significant SNPs located on GGA1 spanned from 169.19 to 175.05 Mb. For OW, eight and three SNPs located on GGA4 and GGA8, respectively, showed a significant association with OW. The available information for genome-wide significant SNPs is illustrated in  Manhattan and Q–Q plots are shown in Fig 1. Moreover, we constructed a Venn diagram for the significant loci on GGA1 because the genetic correlation was high (0.799) . We obtained 21 SNPs that were related to both OW and OL.The GWAS resulted in the detection of 82 and 52 SNPs that showed a significant association with OL and OW, respectively . The SNPs on GGA1 accounted for more than 4% of the phenotypic variance of OW and OL, while the rs80668034 locus that showed a suggestive association with OW explained 2.48% and 1.97% of the phenotypic variance of OL and OW. The locus rs80715313 on GGA8 accounted for the lowest phenotypic variance for OL. Moreover, the suggestive locus on GGA2 accounted for 2.05% of the phenotypic variance of OW.The significant loci were searched against the Ensembl database using the Basic Local Alignment Search Tool, and the results showed that they mapped to regions that were distant from the nearest known genes. The leading SNPs on GGA1, GGA4, GGA8, and GGA2 were mapped to the genes rs318027552, rs80668034, and rs80715313) were analyzed to compare the actual phenotypes of the oviduct as well as egg quality differences among the three genotypes , while the CC genotype of rs80715313 had a lower OW and albumen height .The three leading significant SNPs . Additionally, GGA1, GGA4, and GGA8 explained 9.49% 5.40%, and 1.49%, respectively, of the phenotypic variance. After the three leading SNPs were fitted as covariates, the proportion of variance explained by GGA1, GGA4, and GGA8 decreased to 2.91%, 2.97%, and 0.78%, respectively .We conducted a partitioning of genetic variation for OW because only OW showed the significant effects of individual chromosomes. We observed a strong, linear relationship between the estimate of the variance explained and chromosome length for OW  was lower than that of OW (23.37%). This may be because the length of the oviduct is relatively stable during the reproductive season, while the weight in relation to the laying hen is not. Additionally, the oviduct capacity remains unchanged during the laying season, while it increases when laying .rs318027552 for egg quality showed a consistent trend with oviduct traits, which means that the selection of these oviduct traits will greatly improve egg quality. Moreover, we found that the heritability of OW displayed a positive, linear correlation between the variance explained by each chromosome and the relative chromosome length, which is consistent with previous findings [rs318027552 that is located in this region explained the greatest amount of the phenotypic variation, which suggests that a significant SNP on GGA1 or a quantitate trait locus plays a key role in determining OW.The heritabilities of OL and OW were moderate , and the genotypic effect of findings  and othefindings , 16. In In our study, the characteristics of OL and OW were analyzed for the first time using a GWAS. Previously, no association had been found between SNPs and oviduct traits in chickens. We found 81 SNPs on GGA1 that were associated with OL, while 41, 8, and 3 SNPs on GGA1, GGA4, and GGA8, respectively, were associated with OW.rs318027552, is located 88.12 kb from CKAP2. CKAP2 encodes cytoskeleton associated protein 2 that is involved in cell proliferation and cell survival [CKAP2 may participate in the hyperplasia and hypertrophy of the oviduct before the laying of the first egg.The significant SNPs on GGA1 are near the 170 Mb region that is also associated with egg weight and eggshell quality , 31, as survival , and whisurvival . A previsurvival ; thus, Crs80668034) is located downstream of CCKAR, which encodes cholecystokinin type-a receptor that is associated with appetite control [CCKAR in chickens is responsible for up to a 19% difference in body weight at 12 weeks of age [CCKAR is associated with residual feed intake. During the laying period, egg formation is related to food consumption [rs80668034 had a higher albumen weight and eggshell weight; therefore, we hypothesize that CCKAR plays a role in the use of energy in the oviduct, as well as in egg formation.The locus associated with OW is located from 74.03 to 76.70 Mb on GGA4. Yi and Sun , 31 repo control . Previous of age , and thas of age , 41. Mors of age  showed tsumption , and thers312570847, is located near the gene NCAPG , which has pleiotropic effects on multiple traits, such as body weight [NCAPG affected egg formation or eggshell weight in chickens [NCAPG may be related to OW, which accounts for a larger proportion of the body weight during the reproductive season. Moreover, SNPs close to CCKAR and NCAPG also showed suggestive associations with OL . In summary, CCKAR and NCAPG may affect OW by promoting albumen synthesis and eggshell formation during the reproductive season.Another SNP, y weight  and resiy weight  and withy weight . In our chickens , 17, whirs80715313 on GGA8 is GORAB that encodes SCY1-like 1-binding protein 1, which localizes predominantly to the trans Golgi network [rs80715313 on albumen height, suggests that GORAB may have effects on the secretion of egg white proteins during egg formation. Moreover, findings obtained from human studies showed that autosomal recessive mutations of GORAB cause gerodermia osteodysplastica, which is characterized by wrinkly skin and osteoporosis [GORAB may participate in calcium deposition during eggshell formation in the uterus.The nearest gene to  network , where i network . The abu network , and ova network , which coporosis . Thus, Grs312614123, on GGA2 having a suggestive association with OL explained 2.05% of the CPV, and it contained a SNP located in the gene IGFBP3, which encodes insulin like growth factor (IGF) binding protein 3. IGFBP3 is one of the proteins that binds IGF, a potent mitotic agent that is involved in many biological functions such as protein synthesis, cell differentiation, and ovary development [IGFPB3 was shown to be associated with early sexual maturation in chickens [The most meaningful locus, elopment . Cell dielopment , which celopment . Moreovechickens , which sCKAP2, CCKAR, NCAPG, GORAB, and IGFBP3, which may play a role in determining oviduct characteristics. These findings increase our understanding of the molecular controls involved in oviduct development.Our study is the first to show a novel association between potential genes and oviduct traits. The results showed that OL and OW are moderately heritable. Using a GWA analysis, we found 82 and 42 genome-wide, significant SNPs for OL and OW. The significant regions located from 167.7–169.9 Mb on GGA1 were associated with both OL and OW. Using a statistical analysis, we detected the candidate genes S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.


Capsicum annuum L.), but the genetic mechanisms that control FFN are still poorly understood. In the present study, we developed 249 F2 plants derived from an intraspecific cross between the inbred pepper lines Z4 and Z5. Thirty plants with the highest FFN and 30 plants with the lowest FFN were chosen and their DNAs were pooled according to phenotype to construct two bulked DNA pools. Specific-locus amplified fragment sequencing (SLAF-seq) was combined with bulked segregant analysis (BSA) to identify candidate regions related to FFN. According to our genetic analysis, the FFN trait is quantitatively inherited. A total of 106,848 high-quality single nucleotide polymorphism (SNP) markers were obtained, and 393 high-quality SNP markers associated with FFN were detected. Ten candidate regions within an interval of 3.98 Mb on chromosome 12 harboring 23 candidate genes were identified as closely correlated with FFN. Five genes  are predicted based on their annotations to be associated with expression of the FFN trait. The present study demonstrates an efficient genetic mapping strategy and lays a good foundation for molecular marker-assisted breeding using SNP markers linked to FFN and for cloning and functional analysis of the key genes controlling FFN.First flower node (FFN) is an important trait for evaluating fruit earliness in pepper ( Capsicum annuum L.), Solanaceae, has a sympodial shoot structure with a solitary flower known as the first flower. During vegetative growth, the pepper shoot apical meristem (SAM) produces stems and leaves that are arranged in an alternate spiral pattern. The SAM can later undergo a transition to an inflorescence meristem that subsequently develops into the first flower at start of the transition from vegetative to reproductive growth [FASCICULATE (FA) and CaJOINTLESS (CaJ) genes control sympodial shoot development in pepper. FA also stimulates late flowering [CaJ promotes first flower development while suppressing inflorescence development in pepper [Ca-ANANTH, Ca-LEAFY, and Capsicum annuum S promote flower formation [CaBLIND and CaHAM regulate axillary branching [CaJ and CaBLIND are both epistatic to FA for controlling flowering time and suppressing vegetative growth during the reproductive phase of pepper [CaRNA-binding protein has a repressive effect on the flowering time [Ca-APETALA2, which maps to pepper chromosome 02, represses flowering in pepper [Pepper  in pepper. The position of this node, or its node number, on the primary axis from the cotyledonary node to the first flower node defines the FFN trait. Pepper species exhibit extensive natural variation in FFN , 10. ThuBrassica napus [Phytophthora root rot to a 2.57-Mb region [Cucumber mosaic virus on pepper chromosome 02 [Bulked segregant analysis (BSA)  is a simca napus , and melca napus , among ob region . This coosome 02 . These s2 population derived from a cross between inbred parental lines Z4 and Z5 in pepper. Our objectives were to: 1) investigate the mode of inheritance of the FFN trait in pepper; 2) identify the genomic regions correlated with variation in FFN; and 3) identify candidate genes and SNP markers linked to the FFN trait in pepper.Thus, in the present study we have used a combined BSA and SLAF-seq strategy to identify genomic regions linked to the FFN trait in DNAs pooled from distinct FFN phenotypes in an F2 mapping population comprising 249 plants was derived from a cross between the Capsicum annuum pepper lines Z4 and Z5, which were bred in Beijing Vegetable Research Center of the Beijing Academy of Agriculture and Forestry Sciences. Plants from line Z5 tend to be tall and have a high FFN , while those from line Z4 tend to be of medium height with a low FFN . Parental and F2 plants were grown in the greenhouse at the Beijing Vegetable Research Center of the Beijing Academy of Agriculture and Forestry Sciences in Beijing, China.The first flower node was measured as the number of nodes on the primary axis from the node of the cotyledon to that of the first flower. An FPhenotypic data were statistically analyzed using Microsoft Excel  and data were plotted using SigmaPlot 10.0 .2 plants using the cetyl trimethyl ammonium bromide (CTAB) method [2 population.Total genomic DNA was isolated from young leaves of the both parental lines and F) method . Two DNACapsicum annuum cv. ‘Criollo de Morelos 334’ (CM334)  was used as the reference genome in the present study. Genomic DNAs from both parents and the H-pool and the L-pool were digested with the restriction enzyme HaeIII  after optimizing restriction enzyme digestion completeness to obtain even genome coverage. Single-nucleotide A overhangs were polished from these DNA fragments using Klenow fragment (New England BioLabs), and fragments were then ligated to dual-index sequencing adaptors [http://www.biomarker.com.cn). To check the reliability and validity of sequencing and screening processes, the genome of rice  was selected as a control to undergo in parallel the same library construction and sequencing processes as performed for the pepper mapping population.The genome of adaptors . Target Raw reads were filtered for quality and trimmed to remove adaptors, and then sequence quality was assessed based on sequencing quality scores and guanine-cytosine (GC) content . The prohttps://www.broadinstitute.org/gatk/guide/best-practices.php. To ensure the accuracy of SNPs identified using GATK, SAMtools software also was used to detect SNPs [http://peppergenome.snu.ac.kr/download.php, version 1.55). Before performing association analysis, SNPs were filtered using the following criteria: SNPs with multiple alleles were filtered out; SNPs with sequencing depths of less than 4× in each pool or parent were excluded; SNPs with the same genotypes among pools were removed; and SNPs with recessive alleles that were not inherited from parents with recessive genotypes in pools were filtered out. Ultimately, a collection of high-quality SNP markers was obtained for use in association analysis.Single-nucleotide polymorphisms (SNPs) were detected primarily using GATK software . Using cect SNPs . The intect SNPs  based onA, C, G, T) indicates the frequency of its corresponding DNA nucleotide. ED values were then squared to decrease the effects of noise and increase the effects of large ED measurements based on distance measurements raised to a power (EDx). Data were then fitted using Loess regression [Association mapping was performed using either Euclidean distance (ED)  or the Sgression . In addiThe SNP-index algorithm is valuable for finding significant differences in the frequencies of genotypes between DNA pools. The frequency of genotypes in the SNP-index algorithm is denoted by SLAF depth . The SNPhttp://circos.ca/).Finally, ED, SNP-index, and ΔSNP-index values were plotted and the intersections between the candidate regions for FFN that were identified using the ED and SNP-index methods were designated as the final candidate FFN-related regions. A circular graph representing the distribution of chromosomes, genes, SNPs, ED values, and ΔSNP-index values was then plotted using CIRCOS 0.66 software (http://www.uniprot.org/), the Gene Ontology , the Cluster of Orthologous Groups of proteins , the Kyoto Encyclopedia of Genes and Genomes , and the NCBI non-redundant protein database  using blastp with default parameters.To verify the predicted genes in the target region, we compared these candidate genes to the CM334 reference genome using blast. Putative functions of candidate genes were predicted based on sequence alignments with annotated genes in the databases at Swiss-Prot , a homolog of SEC22 in Arabidopsis thaliana that encodes a 25.3 kDa vesicle transport protein that takes part in both the ‘phagosome’ pathway  might be involved in the protein ‘ubiquitination’ pathway. Hu et al. (2003) reported that ATL52 is expressed in Arabidopsis flowers [CA12g15390 matched a purine permease 1, which is highly expressed in leaves, stems, and flowers, but not in roots [CA12g15160 is homologous to At1g48120 from Arabidopsis, which encodes a serine/threonine-protein phosphatase 7 long form homolog that is expressed in the SAM, root tips, hydathodes, leaf vasculature, and mature flowers [We predicted that 23 protein-coding genes , includiperones’ ; Fig 4.  pathway  and the  pathway . Five of flowers . CA12g15 flowers . Additioin roots , 46. Thiin roots . CA12g15 flowers . Because flowers –47 partiPhytophthora root rot to pepper chromosome 10 [Cucumber mosaic virus resistance in the physical interval from 152.87 to 153.20 Mb on chromosome 02 in pepper using this integrated strategy [SLAF-seq, a recently developed strategy for SNP discovery and large-scale genotyping , has beeosome 10 . Guo et strategy .We obtained a total of 76.24 Mb of clean reads from 153.82 Mb of raw reads with an average read length of 100 bp, and evaluated sequencing depth and sequencing quality scores. For successful SLAF-seq, Sun et al. (2013) suggested that sequencing depth should be greater than 6× and quality scores should not be lower than Q30 . The res2 progenies [SNPs are important tools for molecular genetic analysis, due to their high frequency, wide distribution , 54, higrogenies . AlthougSince the CM334 reference genome was released in 2014, it has been available for comparisons of high-throughput sequencing results from other crosses to determine the distribution of DNA polymorphisms across the pepper genome . FFN, anFA, CaJ, Ca-ANANTH, Ca-LEAFY, Capsicum annuum S, and CaBLIND were found to promote flower development [CaRNA-binding protein and Ca-APETALA2 were found to suppress flower formation [Arabidopsis APETALA2 and CLF [Arabidopsis. CA12g15160, which is homologous to Arabidopsis At1g48120 and encodes a serine/threonine-protein phosphatase 7 long form homolog, is particularly interesting because it is closely related to development of the SAM [CA12g15160 is an important candidate gene for FFN in pepper. Additionally, CA12g15390, CA12g15370, and CA12g15130 are also highly expressed in Arabidopsis flowers [CA12g15360 indicates that it could be related to xylem and phloem development. The protein encoded by CA12g15130 might take part in both the ‘phagosome’ pathway and the ‘SNARE interactions in the vesicular transport’ pathway. Although these genes that were annotated based on the reference genome are homologous to genes related to flower or stem development in Arabidopsis, none of previously known flowering genes have been found within the candidate regions. On the one hand, the constructed reference genome is still not perfect enough to find all the genes controlling pepper flowering due to the large pepper genome (3.48 Gb) [Among the 23 candidate genes for which annotations were found in the GO, COG, KEGG, and Swiss-Prot databases, some might be related to FFN, and should be useful for future gene isolation and functional testing studies. elopment –5, whileormation , 8. Tan  and CLF  as relat and CLF , 10. Bas the SAM . Due to  flowers –46, and 3.48 Gb) . On the 3.48 Gb) . AdditioCapsicum annuum lines Z4 and Z5). This method proved efficient for mapping genes related to the FFN trait in the reference genome (Capsicum annuum line CM334). Ten candidate regions within a 3.98-Mb interval on chromosome 12 were detected and associated with expression of the FFN trait. Five out of 23 candidate genes, in particular CA12g15160, were chosen for further analysis based on their annotations. The functions of the genes correlated to FFN will be further examined in future studies using transformation and mutation approaches.In the present study, we combined the SLAF-seq technology and BSA to successfully identify genomic regions involved in control of the FFN trait in pepper Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fighttp://circos.ca/).The first circle represents the 12 pepper chromosomes. The second circle represents the genes distributed along the pepper chromosomes. The third circle represents the SNP density distribution. The fourth circle represents the distribution of Euclidean distance values. The fifth circle represents the distribution of ΔSNP-index values. Data were graphed using the Circos program ((TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigBlue boxes represent all of the known enzymes that participate in the ‘phagosome’ pathway and the red box indicates the enzyme associated with the annotated match to the candidate gene.(TIF)Click here for additional data file.S6 FigBlue boxes represent all of the known enzymes that participate in the ‘SNARE interactions in the vesicular transport’ pathway and the red box indicates the enzyme associated with the annotated match to the candidate gene.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.