GALNT2 loci as containing potential CpG sites that moderate the impact of genetic polymorphisms on triglyceride levels. Further work is needed to provide clear guidance on analytic strategies for testing SNP–CpG interactions, although leveraging prior biological understanding may be needed to improve statistical power in data sets with smaller sample sizes.In the search for an understanding of how genetic variation contributes to the heritability of common human disease, the potential role of epigenetic factors, such as methylation, is being explored with increasing frequency. Although standard analyses test for associations between methylation levels at individual cytosine-phosphate-guanine (CpG) sites and phenotypes of interest, some investigators have begun testing for methylation and how methylation may modulate the effects of genetic polymorphisms on phenotypes. In our analysis, we used both a genome-wide and candidate gene approach to investigate potential single-nucleotide polymorphism (SNP)–CpG interactions on changes in triglyceride levels. Although we were able to identify numerous loci of interest when using an exploratory significance threshold, we did not identify any significant interactions using a strict genome-wide significance threshold. We were also able to identify numerous loci using the candidate gene approach, in which we focused on 18 genes with prior evidence of association of triglyceride levels. In particular, we identified  Methylation plays a major role in gene regulation through epigenetic modifications at specific cytosine-phosphate-guanine (CpG) residues within the regulatory regions of genes and, consequently, may influence the transcriptional activity . In brieAs part of GAW20, we were provided access to a data set of methylation, SNPs, and triglyceride levels over 2 time periods, along with numerous related covariates. In particular, the study measured triglyceride levels before and after pharmaceutical intervention. Given the well-known relationship between triglycerides and many different cardiometabolic diseases, including cardiovascular disease , we chosThe sample consisted of 670 individuals from a pedigree sample provided as part of GAW20 for whom all analyzed variables were available. We considered 6 covariates  the majority of which were significantly associated with baseline triglyceride (TG) in this sample. The primary response variable of interest was TG level at baseline (visit 1 or 2). For variables with up to 2 measurements at baseline , we used the average value if both measurements were available, or the only available measurement if only one was available.p value> 1 × 10− 6, minor allele frequency > 1%, SNP missing data rate < 5%).The modeling process was done in 2 stages. The first stage model resulted in a single residual TG value for each person, while the second stage resulted in approximately 700,000 models ]. In cases where 2 separate TG measurements were available for the baseline, we natural-log (ln)-transformed the data before averaging. Baseline ln-transformed TG levels was predicted by the 6 covariates listed earlier and accounted for the familial relationships in the model through the use of the kinship matrix. We then saved the resulting “residual” value  from stage 1 based on the number of minor alleles  and methylation scores  with a separate model for each SNPj, CPGj pair using the lm function in R [SNPj, CPGj pair was:The second stage predicted the residuals  by using 5 different methods  to evaluate the potential impact of different ways of summarizing methylation evidence for each SNP. For the SNPs that demonstrated a significant interaction for more than one of the collapsing methods used, we then looked at the interactions between all CpG sites within the region and those SNPs.We followed this genome-wide analysis with a candidate gene study focusing on 18 gene regions (containing 423 unique SNP-CpG sites) that have been shown to be associated with TGs in previous genome-wide association studies via searches at p values were significant when using the conservative 5 × 10−8threshold. However, 58 SNP-CpG pairs showed significant interactions using the more liberal 1 × 10−4significance level. Table p value of the interaction term across all sites was 0.504 and a lambda value of 1.02, showing no inflation of test statistics.No interaction term In our data, there are 18 genes (containing 423 SNPs for which data was available) previously shown to be associated with TG levels. Table p < 0.05) evidence of statistical interaction between nearby methylation values and SNPs within the gene. The most significant SNP is in FADS3 (rs1675102) and has a minor allele frequency of 0.28. The interaction is such that additional copies of the minor allele lead to a decreased impact of methylation on changes in TG levels.Thirteen of the 18 candidate genes show at least modest  has a minor allele frequency of 0.026. The interaction results in an increased impact of methylation on TG levels for every additional allele.We identified 176 unique SNPs in significant interactions for more than 1 of the 5 different CpG collapsing methods as found in Table − 8), use of a more exploratory approach identified many genes previously shown to be associated with cardiometabolic traits (1 × 10− 4). A candidate gene approach, using a significance level of 0.05, identified loci in 13 genes with modest evidence for SNP-CpG interactions on baseline TG levels. Furthermore, by using the collapsing methods, we were able to identify potentially interesting SNPs for additional exploration. Using only these SNPs, our examination of all CpG sites within each gene region resulted in 176 significant unique SNP-CpG pairs. In every case, the SNP-CpGp value was smaller than both the SNP and CpGp values from the noninteraction model. This suggests that using SNP-CpG pairs may identify SNPs that would not be identified by traditional GWAS techniques. The gene GALNT2, had the most significant interactions with 69. SNPs in GALNT2 were previously identified as associated with TG levels, high- and low-density lipoprotein cholesterol [GALNT2 is associated with a higher risk of coronary heart disease [Although no significant SNP–CPG interactions were identified when using strict, genome-wide significance thresholds 5 × 10− , use of There are some limitations to our analysis. First, to manage computational resources, we began by predicting baseline TG levels by kinship and covariates, yielding residuals for each individual, which we used for assessing impact of methylation and genetic variation. Other alternatives to this methodology may exist. We used an exploratory significance threshold for the genome-wide analysis, relative to the vast majority of GWAS-type analyses published today. Although this can lead to more false-positive results, we did find a number of “subthreshold” loci of potential interest suggesting the need for studies with larger sample sizes and more sensitive statistical methods to draw out these loci of interest. The minimum method of summarizing methylation in a region nearby to a gene showed promise, although further work is needed to more fully evaluate the many options. Regardless, leveraging prior biological evidence  may be of potential effect when testing for SNP–CPG interactions.Even with “subthreshold” significance, our results go a long way toward showing the need for statistical models that leverage prior biological information. Our study shows that a mediated effect of SNPs on methylation is a possible explanation for changes in TG levels. With this knowledge, more studies with greater sample sizes can be performed as well as wet lab experimentation to confirm the relationship. As we learn more about the effect an individual’s genotype has on their health, there is greater opportunity for personalized medicine to be an effective treatment strategy.


Manihot esculenta) is the most important staple food crop that originated in this region. Although manioc is relatively well‐studied, little is known about the diversification of bitter and sweet landraces and how they were dispersed across Amazonia. We evaluated single nucleotide polymorphisms (SNPs) in wild and cultivated manioc to identify outlier SNPs putatively under selection and to assess the neutral genetic structure of landraces to make inferences about the evolution of the crop in Amazonia. Some outlier SNPs were in putative manioc genes possibly related to plant architecture, transcriptional regulation and responses to stress. The neutral SNPs revealed contrasting genetic structuring for bitter and sweet landraces. The outlier SNPs may be signatures of the genomic changes resulting from domestication, while the neutral genetic structure suggests independent dispersals for sweet and bitter manioc, possibly related to the earlier domestication and diversification of the former. Our results highlight the role of ancient peoples and current smallholders in the management and conservation of manioc genetic diversity, including putative genes and specific genetic resources with adaptive potential in the context of climate change.Amazonia is a major world centre of plant domestication, but the genetics of domestication remains unclear for most Amazonian crops. Manioc ( Among these, manioc  is a major staple food crop cultivated around the Tropics and is the main source of calories for about 800 million people  was domesticated from M. esculenta ssp. flabellifolia (Pohl) Cif.  . We sampled one wild manioc near a field of cultivated manioc and 18 individuals in the surroundings of the experimental station of the Federal University of Rondônia (UNIR), Rolim de Moura . We recognize that these groups of bitter, sweet and wild manioc are not “true populations.” However, along with clonal propagation, sexual reproduction still occurs in manioc  or NucleoSpin Plant II (Macherey‐Nagel) following the manufacturers' instructions. We inspected the quality and quantified DNA samples with electrophoresis in agarose 1% (w/v) gels stained with SYBR safe DNA (Invitrogen) by comparison with Phage lambda molecular size standards (Invitrogen). After quantification, we normalized DNA samples to 30 ng/µl.SbfI, and the resulting fragments were ligated to adapters containing unique barcodes (proprietary of Floragenex Inc.) for each sample. Individual samples were then pooled and randomly sheared, and DNA fragments ranging from 300 to 800 bp were size‐selected in 1.5% (w/v) agarose gel and recovered with a MinElute Gel Extraction Kit (Qiagen). An adapter complementary to the Illumina's flow cell was ligated to the fragments, and the library was enriched for RAD fragments through PCR. To increase sequencing throughput, RAD libraries were prepared for two sets of manioc samples, with 87 and 72 landraces, and each library was sequenced in two lanes of an Illumina HiSeq 2,500 (Illumina), with a single‐end (100‐bp) configuration.Library preparation using the restriction site‐associated DNA (RAD) technique  between each read and the reference genome. SNP discovery was performed using SAMtools v.0.1.19  to take advantage of their different models.A number of tests for the identification of loci putatively under selection (outliers) have been developed  compared to the expected FST‐EH relationships in an island model of migration  and alpha (locus‐specific) components using a logistic regression , considering the island model of migration and the uncertainty of allele frequencies  and generates a vector of z‐scores, which measure the relationships of each SNP marker to the first K principal components. Then, a Mahalanobis distance  to perform the test. We chose K = 3 because the addition of more components did not increase significantly the proportion of the variance explained by PCA  or to the divergence between bitter and sweet landraces  with outlier SNPs was performed using the online tools of Phytozome v12 . We used PhytoMine  .We obtained the predicted effects of outlier SNPs using 2.5MLGs), total number of alleles (A), number of private alleles (Ap), observed (HO) and expected (HE) heterozygosities and Wright's (1965) inbreeding coefficient (f). We used diveRsity  in Arlequin 3.5  obtained with MSA 4.05 .We evaluated the clustering of landraces and rivers with neighbour‐joining  reads per individual. Our restrictive filtering resulted in a final set of 2,013 SNP markers, with no missing data, a mean depth of 109.8 (SD ± 5.2) per individual and a mean per‐site SNP quality of 955 (SD ± 196). From these, we detected 216 candidate outliers with fsthet, 195 with pcadapt and only three with BayeScan. Larger q‐values (>0.1) increased the number of candidate outliers detected with BayeScan but resulted in less statistical power. Because all these three candidate outliers were also detected with pcadapt, we opted to maintain this very conservative result as evidence of selection. In total, 46 SNPs were considered outliers by at least two of these tests: 44 putatively under positive selection and two putatively under balancing selection , but only seven of them occurred in more than one predicted gene  was equal to the sample number for all groupings of manioc landraces. Bitter and sweet manioc had similar numbers of alleles, but bitter manioc had almost twice as many private alleles as sweet manioc , followed by the divergence between bitter and sweet manioc (ΦST = 0.086). Although we observed significant divergence among rivers (ΦST = 0.071), most of this was due to genetic differences between bitter and sweet manioc within rivers (ΦSC = 0.107). Based on pair‐wise FST, sweet manioc was a little more divergent from wild manioc (FST = 0.321) than bitter manioc (FST = 0.297), but these estimates were not significantly different from each other (Table FST < 0.1), with higher divergences restricted to comparisons between bitter and sweet landraces from different rivers (Table FST = 0.027) were greater than divergences among bitter manioc from different rivers (average pairwise FST = 0.003).AMOVAs suggested that most of the genetic variation was within groups Table . The higer Table . When cors Table . OverallIn the DAPC performed for the whole set of individuals, wild manioc formed a very distinct cluster from the cultivated landraces, while bitter and sweet manioc presented some overlap Figure a. ConsidIn general, the DAPCs performed separately for bitter and sweet manioc showed considerable admixture for the groups of landraces from different rivers Figure . The bitvice versa also exhibited this pattern in DAPC  and MYB DNA‐binding domains, which are typical of transcription factors. Oryza sativa Red pericarp (Rc)  were in predicted genes putatively involved in transcriptional regulation. Frequently, domestication traits are influenced by genes of regulatory factor families Gepts, . The nodOutlier SNPs putatively related to moisture (SNP 377) and drought stress (SNP 456), responses to pathogens (SNP 672) and plant defence (SNPs 601 and 1282) make sense in the context of adaptations to distinct ecological environments during domestication and dispersal. Tolerance to biotic and abiotic stresses is essential for crops to adapt to new environments after dispersals and for their deliberate breeding , and the same pattern occurred for these two groups across different rivers. Similar results were reported previously    from south‐western Amazonia into central and then eastern Amazonia  were probably introduced into Amazonia through the upper Negro and Solimões Rivers  was likely domesticated along the northern Peruvian–Brazilian and southern Colombian–Brazilian borders and was dispersed eastwards into Amazonia along the Solimões and Amazonas Rivers (Thomas et al., We found no major patterns of genetic structure along rivers, in accordance with our previous study based on SSR (Alves‐Pereira et al., flabellifolia, the great divergence between wild and cultivated manioc was expected. However, hybridization between wild and cultivated manioc may still occur (Duputié, David, Debain, & McKey, Given the antiquity of manioc domestication (Olsen & Schaal, Interestingly, the suggestion of GxE and hybridization between Amazonian manioc landraces may have broader implications. For example, in contrast to Amazonia, the toxicity of manioc landraces is a public‐health problem in Africa. Konzo is a health disorder caused by cyanide intoxication commonly reported in manioc‐dependent African communities. Imakumbili, Semu, Semoka, Abass, and Mkamilo  suggesteGenetic evidence is essential to advance our knowledge about plant domestication in Amazonia because archaeological and ethnobotanical information is still scarce in this region (Watling et al., None declared.AA‐P, CRC, DP‐R, JBP and MIZ designed the research. AA‐P, CRC, EAV, APS and MIZ got financial support. AA‐P, EAV, GD and SLFR performed data collection. AA‐P, CRC, APS and MIZ performed data analyses and interpretation. AA‐P wrote the manuscript and all authors contributed to its final form. Click here for additional data file. Click here for additional data file.


Sclerotinia stem rot caused by Sclerotinia sclerotiorum is a major disease of crop brassicas, with inadequate variation for resistance in primary gene pools. We utilized a wild Brassicaceae species with excellent resistance against stem rot to develop a set of B. juncea - B. fruticulosa introgression lines (ILs). These were assessed for resistance using a highly reproducible stem inoculation technique against a virulent pathogen isolate. Over 40% of ILs showed higher levels of resistance. IL-43, IL-175, IL-215, IL-223 and IL-277 were most resistant ILs over three crop seasons. Sequence reads (21x) from the three most diverse ILs were then used to create B. juncea pseudomolecules, by replacing SNPs of reference B. juncea with those of re-sequenced ILs. Genotyping by sequencing (GBS) was also carried out for 88 ILs. Resultant sequence tags were then mapped on to the B. juncea pseudomolecules, and SNP genotypes prepared for each IL. Genome wide association studies helped to map resistance responses to stem rot. A total of 13 significant loci were identified on seven B. juncea chromosomes . Annotation of the genomic region around identified SNPs allowed identification of 20 candidate genes belonging to major disease resistance protein families, including TIR-NBS-LRR class, Chitinase, Malectin/receptor-like protein kinase, defensin-like (DEFL), desulfoglucosinolate sulfotransferase protein and lipoxygenase. A majority of the significant SNPs could be validated using whole genome sequences (21x) from five advanced generation lines being bred for Sclerotinia resistance as compared to three susceptible B. juncea germplasm lines. Our findings not only provide critical new understanding of the defensive pathway of B. fruticulosa resistance, but will also enable development of marker candidates for assisted transfer of introgressed resistant loci in to agronomically superior cultivars of crop Brassica. Sclerotinia sclerotiorum, causing Sclerotinia stem rot, is a serious pathogen of oilseed crops including Brassica juncea (Indian mustard)3. This necrotrophic fungus attacks mustard crops at all crop growth stages, especially at flowering, causing yield losses of 10–90% and impaired oil quality4. Mustard is an important oil seed crop of India and worldwide5, but faces increasing challenges to its productivity from this pathogen. Moreover, wide host range of S. sclerotiorum and the long period of survival of its resting sclerotia together limit the option for control through crop rotation or tillage. Fungicide application, at best, provides inconsistent control and is expensive. Hence there is a need to better understand host-pathogen interactions for using host resistance. A plethora of pathogenicity factors; mainly oxalic acid7, hydrolases and poly-galacturonases8, small RNAs9 have been reported to facilitate Sclerotinia infection and colonization on rapeseed-mustard, soybean, pea and sunflower. These pathogen-associated molecular patterns act as the initial trigger for altered redox homeostasis of host tissue and switches on the necrotrophic mode of stem rot pathogen after oxidative burst10.Sclerotinia stem rot is via genetic improvement to enhance inherent resistance in the host through utilization of conventional/molecular breeding approaches.The only sustainable and economical way to manage 13. Partial resistance to S. sclerotiorum has been reported in some Chinese14 and Australian15 lines of oilseed rape, but complete genetic resistance to this pathogen does not occur in crop brassicas. However, high levels of disease resistances are present in genetically diverse wild allies of the family Brassicaceae such as Erucastrum gallicum, Capsella bursa-pastoris, E. cardaminoides, Diplotaxis tenuisiliqua, B. fruticulosa and B. oleracea. Many of these have been utilized in recent research into stem rot resistance in B. napus and B. juncea18. In addition, a number of QTL mapping studies with biparental mapping population have helped to identify chromosomal loci linked to Sclerotinia resistance in B. napus22, soybean23 and sunflower24. Integration analysis of previously known QTLs of the B. napus genome have allowed identification of conserved QTLs and clusters of nucleotide-binding-site, leucine-rich-repeat-containing candidate genes for Sclerotinia resistance on chromosomes A09 and C0625. However, low mapping resolution due to limited recombination events and allelic diversity of these QTL mapping studies constrained prediction of candidate gene(s) and development of marker assisted selection for disease resistance. Fortunately, genome-wide association studies (GWAS) allows improved mapping resolution by exploiting historical recombination events and high allelic diversity present in association panels27. Further, GWAS approaches are more cost effective for the identification of the minor QTLs with small effects that underlie many complex traits such as disease resistance31. A few success stories for candidate gene association with host infection as well as resistance have been reported in soybean36 and B. napus38 through SNP markers validation by c-DNA microarrays42, RT-seq43 and proteomic44 approaches. GWAS have also revealed association of significant SNPs on chromosomes A08, C04, C06 and C08 in B. napus to Sclerotinia stem rot resistance. These SNPs are close to genes involved in oxidative burst, lignin biosynthesis and jasmonic acid pathway38.Unfortunately, the complex genetic basis of resistance poses challenges in conventional breeding due to involvement of diverse signalling factors  associated with the host-pathogen interaction18, we demonstrated significant stem rot resistance through introgression of B. fruticulosa genomic fragments in to B. juncea. Molecular characterisation of developed ILs was carried out through SSR based linkage mapping and cytogenetic tools18. Although, ten significant marker-trait associations to disease resistance of ILs were discovered consistently over two years of study, there were large mapping intervals as a limited number of transferable SSR markers did not permit precise localization of resistance associated genes. To facilitate finer resolution in search of candidate genes, we now report extension of our previous research studies18through GBSof 88 ILs. This generated large numbers of marker trait associations (MTA’s) and highlighted 49 significant SNPs corresponding to thirteen loci on chromosomes A01, A03, A04, A05, A08, A09 and B05. Many of these MTA’s were consistent across crop seasons and explained significant phenotypic variation (7.34% to 16.04%), and helped to predict 20 candidate genes belonging to major disease resistance protein families. We discuss how discovery of such novel SNP marker associations to Sclerotinia stem rot resistance will not only facilitate novel candidates contributing to mustard immune response, but will also enable development of breeder-friendly markers for assisted introgression of resistant loci into agronomically superior cultivars of crop Brassica against Sclerotinia stem rot.In our previous study18.  Screening of ILs was extended for another year during 2017–18. Continuous phenotypic variations for stem rot resistance confirmed the quantitative nature of Sclerotinia stem rot resistance in B. juncea - B. fruticulosa ILs. Frequency and disease score distribution graphs of 86 introgression lines along with susceptible and resistant checks are presented in Fig. B. fruticulosa were 11.2, 11.8, 10.38, 11.13 and 0.60, 0.30, 1.53, 0.81 cm of lesion length during seasons I, II and III and pooled, respectively. Lesion length scores differed over seasons. Of the ILs investigated, 57 lines fell under the resistant category in season I, 65 in season II and 74 in season III, respectively. Among these, 40% of genotypes were highly resistant with mean lesion length < 2.5 cm during season III, whereas in seasons I and II, proportions of such genotypes were 17% and 31%, respectively  value (>3) and clumping of SNPs, thirteen significant loci scattered across seven chromosomes  were considered , belonging to the PLAT/LH2 domain-containing lipoxygenase family. A group of five SNPs (A01_23456622-875) clustered in a small genomic region (253 bp) was also identified on A01 with indicative association with SOT18 that encodes desulfoglucosinolate sulfotransferase protein. A SNP on A01 (A01_23756215) explained 8.18% of phenotypic variation. The nearest gene to this MTA was At1g34047, which encodes defensin-like family protein. Single SNP locus identified on chromosome A03 was recorded close to PHOS32 encoding adenine nucleotide alpha hydrolases-like superfamily protein. Also present, at a short genomic distance, was the gene THE1. This gene encodes a Malectin/receptor-like protein kinase family protein. A group of 13 SNPs on chromosome A04 (A04_19791849-2116), covering a small genomic region of 267 bp were present close to the gene MPKKK17. Out of four associated loci, detected on chromosome A05, one locus comprising five SNPs (A05_2460823-1061) and spanning a genomic region of only 238 bp was located adjacent to AT2G43620 and CHI encoding proteins belonging to the chitinase family  on chromosome A05 appeared associated with the gene CYP76C3 encoding cytochrome p450 protein. SNP locus (A08_3147142) on chromosome A08, shared genome space with the gene LECRK91 encoding Concanavalin A-like lectin protein kinase family protein. Chromosome A09 harboured three SNPs  in a small genomic interval of 62 bp. These were present near to RPS4, AT5G45060 and AT4G19530 belonging to the disease resistance protein family  of significant and annotated SNP regions was analysed using software Haploview. LD blocks were identified on chromosomes A01, A04 and A05, which showed maximum MTA’s. Region in the LD blocks (black) indicated strong LD between pairs of SNPs , followed by on chromosomes A01 and A05 with less than 1 kb of region.The pairwise LD . Some of the identified SNPs were in heterozygous state. All this information is included as a supplementary data set  from five advanced generation lines , being bred for Sclerotinia stem rot in a superior agronomic base is a major plant breeding goal in crop brassicas. Although the studies on B. napus45 and B. oleracea46 have previously helped to identify resistance related QTLs in A, B and C genomes of monogenomic and digenomic Brassica species, these could not be commercially exploited due to poor levels of resistance. While secondary and tertiary gene pools of Brassicaceae exhibit enormous genetic diversity against S. sclerotiorum16, the knowledge of putative gene candidates that contribute to basal/innate immunity remains scarce. B. fruticulosa is one of the wild relatives of Brassica that exhibits high resistance to this devastating pathogen. We successfully utilized next generation strategies of allele mining and candidate gene identification through SNP based approach to characterize genomic introgressions of B. fruticulosa in order to identify potential candidates for stem rot resistance. This study is significant as it mapped 13 significant loci on seven chromosomes . In addition, annotation of the genomic region around identified SNPs allowed prediction of 20 candidate genes belonging to major disease resistance protein families, including TIR-NBS-LRR class, Chitinase, Malectin/receptor-like protein kinase, defensin-like (DEFL), desulfoglucosinolate sulfotransferase protein and lipoxygenase. Importantly, our findings not only provide critical new understanding of the defensive pathway of B. fruticulosa resistance, but will also enable development of marker candidates for assisted transfer of introgressed resistant loci into agronomically superior cultivars of crop Brassica.Incorporation of resistance to SNC1, RPP4). Tight regulation of gene expression for fine tuning defensive responses is necessary, otherwise it may impair the fitness of the host plant48. Such reprogramming of immune response is either mediated by pathogen infection or through intracellular signals whose downstream players alter gene expression through chromatin remodelling19, or transcriptional or post translational modification. HUB1, located close to 3 SNPs, identified on chromosome A05, is one such novel gene that is responsible for histone modifications . This is a large effect QTL, explaining 14.5% of the phenotypic variation. Mutant studies have revealed that histone 2B monoubiquitinylation through HUB1 activity induces expression of various R genes/R gene complex including SNC1, RPP5 in Arabidopsis against necrotrophic fungi like Botrytiscineria and Alternaria brassicicola50. Upstream regulation of HUB1 by hormonal signals  controls chromatin architecture of SUPPRESSOR OF NPR1-1 and CONSTITUTIVE1 (SNC1). These are key regulators of jasmonate-salicylate (JA-SA) crosstalk during systemic and local defences. We found association of 3SNPs on A05 (ERF13) and 1SNP on B05 (ERF 91) with ATERF2 family. ATERF2 are plant specific transcription factors (TFs) that belong to B3 sub-cluster of APETALA2/ethylene response factor (AP2/ERF) family. They play a crucial role in transcriptional regulation of JA-ethylene (ET) responsive defence genes  and thus integrate the hormonal crosstalk against necrotrophic pathogens53. Association of ERF13 with Sclerotinia stem rot resistance in the present study is supported by previous reports on its overexpression with MeJA51 and A.brassicicola51 and chitin54 in Arabidopsis for transcriptional activation of JA-induced disease resistance.A well-orchestrated network involving diverse defence signalling cascades may arise during plant-pathogen interaction in stem rot Fig. . It initPRR) with intracellular kinase domain also fine tune the temporal responses through expression of defensive genes in pathogen-challenged plants. We identified 15SNPs, located to chromosomes A03, A04 and A08 that were strongly associated with Sclerotinia resistance in ILs through kinase signalling cascade. Out of these, 13SNPs formed an LD block on A04 close to the gene encoding mitogen-activated protein kinase, MAPKKK17. However, SNPs corresponding to other receptor kinases LECRK IX.1/LECRK91 and THESEUS1 (THE1) on chromosomes A08 and A03, were also observed. These contributed up to 24.03% of phenotypic variation for disease resistance. Transcriptome studies in B. napus56 have emphasized the role of BnaMAPKKK17 (an orthologue of AtMAPKKK17), Bna MAPKKK18 and its downstream associated kinases  in abiotic and biotic stress tolerance. THE1, a CrRLK1 like kinase may sense cell wall integrity and mediate defence against necrotrophic fungi like B. cinerea in Arabidopsis through elicitation of GEF4 signalling network57. Further, the overexpression of THE1 caused increased deposition of callose, H2O2 accumulation and also the upregulation of PR2 and PDF1.2 transcript levels in B. cinerea treated plants as compared with the wild type57. Studies on LECRK9159 have addressed their role in plant innate immunity through cell wall associated defences and jasmonate signalling. Mutation in LECRK91 caused susceptibility to fungal and bacterial pathogens in Arabidopsis, while its overexpression reversed the effect, thereby enhancing immune response by cell death60.Aside, TFs an arsenal of cell surface-localized pattern recognition receptors (ACX1) that has a role in JA biosynthesis. These explained 11.75% of phenotypic variation. Three additional SNP markers on chromosome A01 and A03 appeared associated with lipoxygenase LOX1, LOX5 and hydrolases PHOS32. In vitro assays have confirmed phosphorylation of PHOS32 by AtMPK3 and AtMPK3 upon elicitation; and suggested its role in mediating downstream kinase signalling events61. Both LOX1 and LOX5 encoding 9-lipoxygenases, were responsible for hydro-peroxidation of fatty acid component of plant membranes. Increased activity of lipoxygenase has already been reported in a number of plant pathogen systems63, including Arabidopsis64, where loss of function of LOX1 and LOX5 mediated resistance occurs against Fusarium head blight. Their hormonal regulation with JA, salicylate (SA) in addition to other stress like wounding suggests that LOX control balance of JA-SA signalling. We observed a major locus with large effect (14.06% phenotypic variation) on chromosome A05, with significant SNPs near to candidate gene of the Chitinase family. Chitinases, also known as pathogenesis related (PR) proteins, are generally induced in the plant system in response to fungal, bacterial or viral infections. The chitinase genes At2g43620 and CHI belong to class IV chitinases and are involved in cell wall biogenesis. These are induced upon pathogen infection66 and hormones like ethylene67. We also identified a MTA (A01_23756215) close to another PR protein, defensin. Inductions of these defensins or defensin-like proteins represent a host innate immune response which is triggered upon pathogen infection. These cysteine rich cationic peptides modify fungal membrane permeability and inhibit growth of a broad range of fungal pathogens68.In our ILs, 7SNPs in a genomic interval of 1.4Kb on A01 appeared very close to the gene encoding Acyl–CoA oxidase  within a 62Kb region, seemed important. R genes like RPS4 recognise fungal effector-host protein complex and activate ETI against both fungal and bacterial pathogens. RPS4, alone or in pair with RRS1 (R protein complex), can act as a dual resistance system against the hemibiotrophic fungus Colletotrichum higginsianum in Arabidopsis70. Downstream signalling after activation of TIR-NBS-LRR involves genes EDS1 and EDS1 related proteins (PAD4 and SAG 101) with JA-SA regulation of immune responses. Present study also confirmed the role of NIP3-1 as reported previously18. This locus was in strong LD with the SNP A01_ 12606026. Thus search of putative candidates through systematic annotation in identified genomic region following GWAS helped us to identify genomic regions that likely contributed to Sclerotinia resistance in B. juncea-B. fruticulosa ILs. A majority of the significant SNPs could be validated using whole genome sequences (21x) from five advanced generation lines being bred for Sclerotinia resistance as compared to three susceptible B. juncea germplasm lines. We now hope to develop breeder friendly KASPar markers for effective integration of these defensive components into a superior agronomic base, initially in B. juncea and subsequently in B. napus and other vegetable Brassica species.Overexpression of defensin genes in transgenic 18. Eighty eight ILs were raised in a randomized block design with two replications over three crop seasons against an aggressive and virulent strain (PAU-4) of S. sclerotiorum at Punjab Agricultural University, Ludhiana, India. Analysis of variance (ANOVA) was undertaken and standard deviation and standard errors were estimated through Multivariate General Linear Model (M-GLM) using software, SPSS17.0.Genetic materials and stem inoculation procedures were same as described earlier71, with the chloroform-isoamyl alcohol purification step repeated twice to assure good quality DNA. RNase was added between the two chloroform: isoamyl alcohol solvent extractions by incubating the samples at 37° Celsius for 15 minutes to allow for a single DNA precipitation step at the end of the protocol. Quality of the extracted DNA was assessed using a NanoDrop® 2000 spectrophotometer and 1% (w/v) agarose gel. Sample DNA having 260/280 absorbance ratio of 1.8-2.0, and with no evidence of substantial band shearing or contamination (either RNA or polysaccharide), was used for library construction.Genomic DNA of ILs and their parents were isolated from young leaf tissue using a CTAB (cetyltrimethyl ammonium bromide) extraction methodB. juncea v1.5 was used for reference-based alignments of whole genome sequences (21x) from the four most prominent ILs, using software bowtie2. Initially, one introgression line was aligned into reference genome and SNP called by NGSEP-GBS pipeline. Total SNPs with base quality more 30 were replaced in background genome reference (B. juncea v1.5) using a perl script, pseudomaker.pl implemented in SEG-Map72 to construct the first step of mock-up pseudomolecule(s), which were then used as a reference for subsequent construction of pseudomolecules of sequential replacement of SNPs for remaining three ILs in the same way as described above.Direct resequencing (21x) of four ILs and genotyping by sequencing of 88 ILs were outsourced. For this, high quality DNA of each sample was digested with appropriate combination of restriction enzymes based on in-silico evaluation. This was followed by several rounds of PCR amplification. All samples were then individually pooled and then size-selected for the required fragments to complete the library construction. High quality libraries with appropriate insert sizes were subsequently used for pair-end sequencing on Illumina® HiSeq platform, with the read length of 150 bp at each end. The sequences and corresponding sequencing quality information were stored in a FASTQ file format. The adapter sequences were removed from raw reads using the software Cutadapt. The available reference genome of 73. Resultant marker dataset comprised 31,896,811 SNPs. These were then filtered to include only quality SNPs for further analysis. Filtering parameters were: minimum mapping quality (30), minor allele frequency (0.08), bi-allelic SNVs, minimum number of sample genotyped (70), maximum observed heterozygosity (30%) and maximum missing calls (30%). After filtering, 88,624 SNPs remained. These were imputed using fcGENE and Beagle softwares74.Resultant mock-up reference was then used for aligning sequence tags from 88 ILs. SNPs identification was then carried out by using NGSEP-GBS pipelineSclerotinia resistance lesion length (cm) data were first normalized through rank and log transformation. GWAS was carried out using transformed values of lesion lengths and 88,624 SNPs. Kinship matrix and Covariates data was generated through ‘MVP.Data’ function of the software R (MVP-package). Discriminant Analysis of Principal Components (DAPC) was implemented in R software package “adegenet”. PC and DAPC values were then used as covariates in different GWAS analysis algorithms to reduce false positives by accounting for the effects of population stratification. Manhattan plots were generated with multi-model plotting using MVP tools. GLM, MLM and FarmCPU methods were implemented in the software R (MVPpackage) (https://github.com/XiaoleiLiuBio/MVP), using default settings to identify marker trait associations. Identified markers were subsequently confirmed using alternate algorithms as implemented in TASSEL5.0 with DAPC (in adegenet) as covariate for GWAS with model; GLM + DAPC [GLM(T)] and MLM + kinship [MLM(T)]. Visualization of the significant QTLs and SNPs was done using Manhattan plots, generated using the R package qqman75. We used unadjusted p-values < 0.001, to declare significance as the Bonferroni and FDR tests are considered far too conservative for association studies involving thousands of candidate variants at very low minor allele frequencies76. Bonferroni correction also assumes independence of every association test, an assumption which is rarely met due to linkage disequilibrium between SNP markers. We screened 50 kb flanking regions on each side of resistance associated peak SNPs to predict candidate genes using Blast2GO v5.2.5 tool . Arabidopsis protein database was probed for gene finding as well as blast search. The gene ontologies of Arabidopsis orthologues were used for this analysis as these are better curated than those of B. juncea. Positions of the predicted candidate genes w.r.t. the SNPs, were projected through blast search of their sequences against B. juncea mock-up pseudomolecules. Predicted candidate genes were then verified for their function from literature for their relevance to the target trait.Supplementary Fig. S1-5Supplementary Table S1.


Pyrus spp.) lags far behind other fruit and nut crops in terms of employment of available genetic resources for new cultivar development. To address this gap, we designed a high-density, high-efficiency and robust single nucleotide polymorphism (SNP) array for pear, with the main objectives of conducting genetic diversity and genome-wide association studies.Both a source of diversity and the development of genomic tools, such as reference genomes and molecular markers, are equally important to enable faster progress in plant breeding. Pear (PolyHighResolution). We further used the Axiom Pear 70 K Genotyping Array to construct high-density linkage maps in a bi-parental population, and to make a direct comparison with available genotyping-by-sequencing (GBS) data, which suggested that the SNP array is a more robust method of screening for SNPs than restriction enzyme reduced representation sequence-based genotyping.By applying a two-step design process, which consisted of the construction of a first ‘draft’ array for the screening of a small subset of samples, we were able to identify the most robust and informative SNPs to include in the Applied Biosystems™ Axiom™ Pear 70 K Genotyping Array, currently the densest SNP array for pear. Preliminary evaluation of this 70 K array in 1416 diverse pear accessions from the USDA National Clonal Germplasm Repository (NCGR) in Corvallis, OR identified 66,616 SNPs  as high quality and polymorphic  contains supplementary material, which is available to authorized users. Single nucleotide polymorphisms (SNPs) and insertion/deletions (INDELs) are the most abundant classes of genetic variation in plant genomes . Recent Pyrus sp.) is one of the most important fruit tree crops in temperate climate regions. Despite the existence of a number of pear breeding programs internationally , and ree et al. –24, peare et al. , 26. In e et al. , 28 and e et al. , 29–32. e et al. , 33–37. e et al.  and by We et al.  the LD des sizes , 38, 39.Pyrus species . This collection provides a valuable source of diversity for exploitation in pear breeding programs. Subsets of this collection have been genotyped with microsatellite markers, enabling synonym identification, elucidation of relationship patterns [P. communis genetic diversity [The USDA National Clonal Germplasm Repository (NCGR) in Corvallis, OR, maintains 2300 clonal pear accessions and 364 seed lots with origins in 55 countries, representing nearly every known patterns –42, and iversity . NeverthPyrus SNP array that is informative for both the genetic characterization of the diverse NCGR collection, as well as GWAS and QTL mapping analysis. Among the various technologies available, SNP arrays have the advantage of providing complete and reliable genotypic data without requiring a preliminary complex bioinformatics processing, as required for GBS methods [In this study, our objectives were to develop a genome-wide high-density  methods –46. SNP  methods , pea [48 methods , to cite methods , 49, ros methods , strawbe methods ). Here, Pyrus species and interspecific hybrids , according to the Affymetrix default parameters for diploid samples: these are highly polymorphic SNPs exhibiting all three genotypic classes with a good cluster resolution. After adjusting the genotypic calls with a more stringent confidence threshold, the number of PHR SNPs was reduced to 315,642  version of the ‘Bartlett’ v2.0 genome , 580,621 exhibited a unique alignment, covering 208 (out of ~ 1400) contigs and 470 (out of ~ 500) Mbp. Among those, 181,022 were robust PHR (after exclusion of 7690 A/T and C/G SNPs). Division of these 181,022 SNPs into eight classes of polymorphism based on their minor allele frequency (MAF) values, as reported in Table P. communis was evaluated using the anchored portion of the first version of the ‘Bartlett’ genome [P. x bretschneideri ‘Dangshansuli’ genome [P. bretschneideri, respectively; in both genomes, chromosome 1 was the one with less SNPs, and chromosome 15 the one with the most. Chromosome 15 is also the longest one in both genomes.A total of 111,224 SNPs was left after removal of those in high LD. This list of SNPs was sorted in order of priority, based on their distribution across the scaffolds of the ‘Bartlett’ v2.0 genome (using again the FP strategy), as well as their SnpEff prediction and classification into degrees of polymorphism, and was submitted to Affymetrix  failed at sample QC when screened with the Axiom Pear 70 K Genotyping Array and were excluded from genotyping  gave rise to similar results to the PCA run just on the screening panel with the 71,363 tiled SNPs  for the Axiom data set . These observations were consistent among segregating types  fell into one of these classes of polymorphism (Table NoMinorHom (NMH), ABvarianceY and OffTargetVariants were worth undergoing additional analysis, while the majority of the SNPs in the other classes had poor-quality clusters. NMH SNPs usually have good cluster resolution, but one of the two homozygous clusters is missing. A total of 32,634 SNPs was classified as NMH. With the objective of eliminating any possible genotyping error, we filtered these SNPs with stringent metric thresholds, as used previously for the robust PHR, and we checked their Mendelian error rate in the 20 true trios. A total of 23,124 SNPs passed the stringent filter , and only 30 of them had more than 5% Mendelian errors. The ABvarianceY SNPs (with high heterozygous cluster variance in the y dimension) are similar to the OffTargetVariance SNPs, which display an additional cluster at a low hybridization intensity just below the AB cluster  and ABvarianceY (8566) SNPs (which we refer to as OTV), we generated new genotypic calls for the fourth, additional cluster (samples are coded as − 2). On using a similar approach as for the NMH SNPs, a total of 15,719 SNPs passed the stringent filter , 2113 of which had a Mendelian error rate higher than 5%. We finally displayed the cluster plots for the 13,606 good OTV SNPs, using different colors for each species, and we observed that Asian Pyrus species were more likely to fall into the fourth OTV cluster  the duplicated nature of the Pyrus genome; and iv) the availability, at the beginning of our study, of a draft, fragmented reference genome constructed from the highly heterozygous ‘Bartlett’. Because of all these issues, we anticipated a high probability of error both at the read-alignment and the SNP calling phases, which could have been only partially controlled with the Quality filter. In contrast, SNPs selected from the initial set based on their performance in genotyping a screening panel would be expected to be more reliable. The low proportion of robust PHR SNPs from the 700 K array (30%), and the subsequent very high proportion from the final 70 K array (93%) eventually confirmed our hypotheses. In comparison, the Axiom Apple480K Array, which was designed from the high-depth re-sequencing of 63 Malus x domestica cultivars, had a proportion of 74% PHR SNPs, 54% of which were further classified as very robust and validated PHR SNPs [The Axiom Pear 70 K Genotyping Array is currently the largest SNP array for pear, and one of the densest for fruit tree crops, second only to the Axiom Apple480K Array . It is aPHR SNPs . AlthougApproximately 10% of the samples screened with the 70 K array failed at genotyping, mostly at the QC CR step. According to the Axiom genotyping design, samples are clustered twice and those with < 97% QC call rate during the initial round of clustering are eliminated. While often sample failure can be attributed to bad quality DNA, low CR at clustering can also occur because of the high divergence from the reference genome used to design the probes. This possibility is discussed more in detail later on. With caution, the QC CR default threshold could be lowered in cases of wide diversity germplasm.in primis the characterization of the large and diverse Pyrus germplasm and the performance of GWAS within the USDA-NCGR collection, but also for QTL mapping in bi-parental populations. The choice of accessions in the discovery and the screening panels was fundamental in our achievement of these objectives. When selecting the samples to re-sequence, we gave preference to the main founders of pear breeding programs, also trying to include a wide diversity of species. Even though the read-alignment and variant calling for samples that are highly divergent from the reference genome were more prone to errors, they were also necessary for discovery of polymorphic sites in species other than P. communis.The objective of this study was to design a high-density and highly efficient SNP array that could serve for multiple downstream applications, P. communis, and our breeding programs are based mainly on crossing with P. communis cultivars. We therefore accepted a bias in favor of SNPs that were highly polymorphic within the Group Communis, which finally made up 45% of the 70 K array  were highly polymorphic within at least one of the other groups of species, as well as for the identification of genomic regions that might have been under selection, with 16% discriminative SNPs , the non-redundancy of their genetic information (SNPs in high LD within the same FP window were removed) as well as their degree of polymorphism in the screening panel Fig. . About hP. communis [P. x bretschneideri [P. bretschneideri; however, this is in line with the different proportions of the assemblies that are currently anchored to chromosomes . While the SNP-density was variable over the length of each chromosome, we could not observe any gaps, nor relevant differences among the chromosomes.The SNPs included in the pear 70 K array were well-distributed across the 17 chromosomes of both the communis , 54 and hneideri  genomes Pyrus USDA-NCGR collection. This large genotypic dataset enabled the observation of strong population structure that is consistent with the geographical-based subdivision in groups of species performed by Challice and Westwood [P. communis; some West Asian species of Group 2 appeared to be related to a number of Group 1 species; and several degrees of relationship were observed between species of Groups 3 and 4.The Axiom Pear 70 K Genotyping Array displayed a high degree of polymorphism across the diverse genotyped accessions from the Westwood  , or into the A0 or B0 clusters   was chosen according to the final proportions in the germplasm collection  in Kearneysville, WV  Group 1, including species that are considered wild relatives of P. communis; iii) Group 2, including Middle East/Central Asia arid-adapted species; iv) Group 3, including East Asian “pea” pear species; v) Group 4, including East Asian large-fruited cultivars and wild relatives; and vi) Group Hybrids, including all supposed interspecific hybrids . Samplend Earle : i) GrouP. x bretschneideri ‘Dangshansuli’ genome [Variants were subjected to two filters, the Quality and the Affymetrix filters, to remove artefacts and guarantee a final set of high-quality SNPs Fig. . The Qua’ genome . Based o’ genome  and Li e’ genome  Fig. 1)P. x brethttps://www.rosaceae.org/species/pyrus/pyrus_communis/genome_v1.0) were used to estimate the impact of SNPs on proteins. In the SnpEff output, SNPs were divided into four categories: i) HIGH impact, ii) MODERATE impact, iii) LOW impact, and iv) MODIFIER. All SNPs with HIGH or MODERATE impact were kept; all the LOW impact SNPs were also kept, except for those with all homozygous in silico genotypes . A total of three technical replicates of ‘Bartlett’ (CPYR 38.001), one per plate, two of P. pyrifolia ‘Dan Bae’ (CPYR 2623.001) and two of the DH were also included. DNA was extracted and evaluated as described above and sent to Affymetrix to be genotyped with the Axiom Pear 700 K Genotyping Array.A number of 284 diverse pear accessions was selected as the screening panel to be genotyped with the Axiom Pear 700 K Genotyping Array . Samples that did not pass these thresholds were excluded from the analysis. Genotypic calls and SNP QC performed at Affymetrix were then modified by applying more stringent thresholds. First, the function Ps_CallAdjust in the R package “SNPolisher” v1.5.2 was used to decrease the confidence threshold from the default 0.15 to 0.01 , and then the functions Ps_Classification and Ps_Classification_Supplemental were run to divide the SNPs into 13 different categories based on their QC metrics  6.6; Homozygous FLD (HomFLD) 14.3, Heterozygous Strength Offset (HetSO) 0; n_AB ≠ 0; all other metric thresholds were left as default. These SNPs were defined as robust PHR.The genotypic data from the screening panel were analyzed using particularly stringent metrics, to identify the most robust SNPs Fig. . The defcs Table  and usin” v3.8.0  in “Bioce−20. Afterwards, FPs were placed every 100 Kbp on the new assembly and SNPs in high LD (r2> 0.85) within each 100 Kbp FP window were removed. Afterwards, the SNPs inside each FP window were prioritized according to their SnpEff prediction  and to their degree of polymorphism. For this last point, values of MAF across the entire screening panel and within each group of species were used to identify rare (MAF 0.2 and MAF 0.8) and common SNPs (0.2 ≤ MAF ≤ 0.8), and to divide the SNPs into classes of polymorphism , and only one was kept for subsequent analysis. Putative sampling errors were verified by SSR fingerprinting the original trees from the NCGR collection ; two pear rootstocks collected from a commercial orchard in Ukiah, CA; three and seven founders of the AFRS and the INRA-Angers pear breeding programs, respectively; 12 samples from Fondazione Edmund Mach (FEM), including nine historical Italian varieties and three different clones of ‘Bartlett’; one haploid ‘Bartlett’ and two different samples of the DH of ‘Bartlett’ from the INRA of Angers  with the SNP markers segregating as backcross type for each parent, following the double pseudo-testcross strategy [An interspecific segregating population (P16.009) was developed by crossing two advanced selections from the PFR cultivar breeding program, and raising 57 seedlings. DNA was extracted from the two parents and the offspring using the Qiagen DNeasy™ Plant Mini Kit . Samples were genotyped with the Axiom Pear 70 K Genotyping Array. Genetic map construction was carried out using JoinMap 5.0®   number, the inventory lot identifier, the assigned taxon and common plant name, the group to which the species belongs , the soAdditional file 2:http://snpeff.sourceforge.net/SnpEff_manual.html). (XLSX 10 kb)Explanation of the SnpEff categories for all annotated variants. The number of variants for each type of predicted impact on the gene and the explanation of the effects are shown, as reported in the SnpEff manual  number, the inventory lot identifier, the assigned taxon and common plant name, the origin, the group to which the species belongs , the soAdditional file 4:PolyHighResolution SNPs of the Axiom™ 700 K Pear Genotyping Array. (XLSX 12 kb)List of trios used for Mendelian check. A total of 22 trios  were included in the screening panel and were used for the Mendelian test on the Additional file 5:P. communis ‘Bartlett’ v1.1 [P. x bretschneideri ‘Dangshansuli’ [Distribution of the SNPs of the Axiom™ 70 K Pear Genotyping Array on the pear genomes. The number of SNPs in 500 Kbp bins was plotted over each chromosome length for the tt’ v1.1 , 54 . Putative sampling errors were verified by SSR analysis. (XLSX 11 kb)Additional file 7:PolyHighResolution (PHR) SNPs on the screening panel. (B) PCA performed with the SNPs tiled on the Axiom Pear 70 K Genotyping Array on the screening panel. (C) PCA performed with the PHR SNPs of the Axiom Pear 70 K Genotyping Array on all genotyped accessions, including both the screening and the genotyping panel. A different color is used for each Pyrus species. Group Communis = P. communis; Group 1 = P. communis wild relatives; Group 2 = Middle East/Central Asia arid-adapted species; Group 3 = East Asian “pea” pears; Group 4 = East Asian large-fruited cultivars and wild relatives; Group Hybrids = interspecific hybrids. (PNG 1146 kb)Principal Component Analysis (PCA) plots of the PC pairs for the first four PCs. (A) PCA performed with all robust Additional file 8:Pyrus accessions genotyped with the Axiom™ 70 K Pear Genotyping Array. For each sample the table shows the accession’s Plant Introduction (PI) number, the inventory lot identifier, the assigned taxon and common plant name, the origin, the group to which the species belongs , the soAdditional file 9:Parental genetic maps of the F1 population P16.009 constructed with the Axiom™ Pear 70 K Genotyping Array. Genetic map of the female parent is on page 1, that of the male parent on page 2. (PDF 157 kb)Additional file 10:Statistics about the parental genetic maps of the F1 population P16.009. The number of markers, the length in cM, the average distance between markers (in cM) and the length of the largest gap (in cM) are reported for each Linkage Group (LG) and for the two maps. (XLSX 11 kb)Additional file 11:Comparison of SNP array and GBS data error rate. The total number of Axiom™ 70 K Pear Genotyping Array and GBS-based SNP markers segregating in a backcross manner in the P493 population are reported. The total numbers of data points and erroneous data points were counted in 16 offspring for each segregation type. The total numbers of heterozygous × homozygous (Het × Homo) and homozygous × heterozygous (Homo × Het) segregation types were calculated for both Axiom and GBS SNPs. (XLSX 11 kb)Additional file 12:ABvarianceY and an OffTargetVariant SNP of the Axiom™ Pear 700 K Genotyping Array. The SNP on the left-hand side was classified as ABvarianceY, the SNP on the right-hand side as OffTargetVariant. Samples are from the screening panel. (PNG 629 kb)Cluster plots of an Additional file 13:ABvarianceY or OffTargetVariant) and were processed with the OTV_Caller function in “SNPolisher”. Samples are from the screening panel and different colors are used for each Pyrus species. Species in green color gradients belong to Group Communis (P. communis) or Group 1 (P. communis wild relatives); species in red color gradients belong to Group 2 ; species in purple/pink color gradients belong to Group 3 (East Asian “pea” pears); species in blue color gradients belong to Group 4 (East Asian large-fruited cultivars and wild relatives); species in yellow color gradients belong to Group Hybrids (interspecific hybrids). (PNG 1213 kb)Cluster plots of two OTV SNPs of the Axiom™ Pear 700 K Genotyping Array with samples colored by species. Both SNPs were classified as OTV Number of samples for each species in the screening panel and in the USDA-NCGR collection. The number of samples for each


The amplitude of auditory steady-state responses (ASSRs) generated in the brainstem of rats exponentially decreases over the sequential averaging of EEG epochs. This behavior is partially due to the adaptation of the ASSR induced by the continuous and monotonous stimulation. In this study, we analyzed the potential clinical relevance of the ASSR adaptation. ASSR were elicited in eight anesthetized adult rats by 8-kHz tones, modulated in amplitude at 115 Hz. We called independent epochs to those EEG epochs acquired with sufficiently long inter-stimulus interval, so the ASSR contained in any given epoch is not affected by the previous stimulation. We tested whether the detection of ASSRs is improved when the response is computed by averaging independent EEG epochs, containing only unadapted auditory responses. The improvements in the ASSR detection obtained with standard, weighted and sorted averaging were compared. In the absence of artifacts, when the ASSR was elicited by continuous acoustic stimulation, the computation of the ASSR amplitude relied upon the averaging method. While the adaptive behavior of the ASSR was still evident after the weighting of epochs, the sorted averaging resulted in under-estimations of the ASSR amplitude. In the absence of artifacts, the ASSR amplitudes computed by averaging independent epochs did not depend on the averaging procedure. Averaging independent epochs resulted in higher ASSR amplitudes and halved the number of EEG epochs needed to be acquired to achieve the maximum detection rate of the ASSR. Acquisition protocols based on averaging independent EEG epochs, in combination with appropriate averaging methods for artifact reduction might contribute to develop more accurate hearing assessments based on ASSRs. Auditory steady-state responses (ASSRs) are brain oscillations locked to the periodic properties of acoustic stimuli , 2. AudiTypically, ASSR are elicited by the continuous presentation of amplitude modulated (AM) tones. The extraction of the auditory response from the measured signal essentially relies on averaging epochs of the EEG, time-locked to the stimulus . Such a Evidence supporting the adaptation of auditory evoked potentials (AEP) has been provided by analyzing the effect of the stimulation rate on the amplitude and latency of transient responses. Those studies show that, as the presentation rate of acoustic stimuli increases, the amplitude of AEPs obtained in both humans and rodents decline –19. WhenTraditionally, it has been argued that the ASSRs primarily result from the linear superposition of transient AEPs elicited by the high presentation rate of acoustic stimuli , 23. NevSeveral studies have analyzed the time course of the ASSR amplitude, describing the changes in amplitude resulting from the time-domain averaging of sequentially acquired EEG epochs, i.e., averaging epochs containing auditory responses elicited by continuous acoustic stimuli –26. TheyThe estimation of the ASSR during the time-domain averaging of epochs is a cumulative process, in which the response amplitude computed from a given epoch relies on those obtained in the preceding EEG segments. Consequently, the ASSR computed at a given time after the stimulus onset might not necessarily correspond to the instantaneous ASSR amplitude -as subsequent epochs of a recording cannot be considered statistically independent. Therefore, the standard time-domain averaging of subsequently acquired epochs does not allow to differentiate between methodological and physiological related variations in the amplitude of the ASSR . For assSince computing the amplitude of evoked potentials at the level of single trials might be controversial, we analyze the stability of ASSR using the traditional methodology proposed by Ritter et al.  for quanThe adaptation of ASSR might have implications in the clinical practice, especially when recording protocols are based on averaging a reduced number of sequentially acquired epochs , 28, 29.Possible shortcomings in the computation of ASSRs resulting from adaptation might be overcome by implementing stimulation protocols which prevent the suppression of the ASSR amplitude over time. In practice, the acquisition of unadapted ASSR can be achieved by replacing the continuous acoustic presentation of tones commonly used to elicit ASSRs by a discrete presentation mode -in which segments of AM-sounds of a few seconds in length are presented with a given inter stimulus interval (ISI). Using an adequately long ISI would imply that consecutive epochs can be considered statistically independent events, i.e., they are different, independent measures of the same variable. From a physiological point of view, this means that the auditory response embedded in any unaveraged epoch is not affected by the preceding stimulation. In such a situation, the neural population synchronously responding to the incoming stimulus would be equal or only slightly different in size compared to the number of neurons that responded to the preceding stimulation , 32. ConAdditionally, a better estimation of the response might result from the implementation of averaging procedures that attenuate the effect of motion and muscular artifacts, i.e., using weighted and sorted averaging methods –35. WeigAs mentioned above, we have previously demonstrated that the ASSR generated in the brainstem of rats adapts to the sustained stimulation . HoweverOn the other hand, both weighted and sorted averaging are artifact reduction protocols which have been implemented considering stationary auditory responses. Therefore, the efficacy of those algorithms needs to be re-evaluated in scenarios in which changes in SNR are associated not only with noise variability, but also with the dynamics of the response. Consequently, we analyzed the validity of weighted and sorted averaging for detecting adaptive responses, and the improvement in the ASSR detection resulting from averaging statistically independent epochs as a function of the averaging protocol. We discussed the results based on a comparison with existing paradigms designed to optimize the detection of ASSR. Additionally, we addressed the discrepancies between the brainstem ASSR adaptation obtained in rats  and the Auditory responses were obtained from 8 adult Wistar rats. Animals were housed in a standard bio-clean animal room under a 12-h light-dark cycle at 22–24°C, with free access to food and tap water. To perform the recordings, animals were anesthetized with ketamine  and diazepam . Supplemental doses of anesthesia were administered during the experiment at a level sufficient to maintain the animal in an areflexic state. Atropine sulfate  was administered to decrease the mucosal secretions. Body temperature was maintained at 37.0±0.1°C by a body temperature control system . Due to the experimental procedure, the sacrifice of the animals was not necessary. They were returned to the colony after recovering from anesthesia. The present study was performed under approval of the Animal Research and Ethics Committee of the Cuban Neuroscience Center, conformed to the guidelines of the National Center for Animal Breeding of Cuba.Continuous tones of 8 kHz sinusoidally-modulated in amplitude (95% depth) at 115 Hz were generated using the ASSR software module  of the A4 and band-pass filtered–cutoff frequencies of 10 and 300 Hz. Output of the filter was digitized at 16 bits of resolution and sampled at 920 Hz. Segments with peaks of electrical oscillations exceeding 50 mV were rejected online. Typically, less than five segments per recording were rejected and they were randomly distributed across epochs. Data acquisition continued until completing 60 artifact-free epochs of 4.45 s (4096 time-points each). Thirty recordings were acquired from each animal. During the experimental session, every recording was preceded by a no-stimulation period of ten minutes.Electrophysiological responses were recorded differentially using stainless-steel needle electrodes inserted subdermally . Recordings were amplified with gain 1.2x10Processing of the data was performed using in-house Matlab codes . The 60 sequential epochs of the 30 recordings were re-arranged offline into a data matrix of 30 rows (recordings) and 60 columns (epochs within recordings) . We used an FFT length of 4096 time points, which corresponded to the length of an epoch (4.45 s) and led to a spectral resolution of 0.22 Hz. A windowing technique was not implemented. The amplitude of the ASSR was defined as the spectral amplitude obtained at 115 Hz (frequency of the amplitude modulations of the acoustic stimuli). The amplitude of the 30 FFT bins at each side of the frequency of the auditory response were vector averaged to calculate the residual noise level (RNL). The selection of such spectral region for the computation of the RNL is based on the relation between the amplitude and the frequency of the EEG. It is well-known that the spectrum of EEG background oscillations (o resting EEG) is characterized by a non-linear decrease with increasing frequencies, such that higher amplitudes are found for lower frequencies. Therefore, computing the RNL in spectral regions much lower than that of the ASSR might led to overestimate the RNL, making the statistical detection of the ASSR more conservative , 48. TheThe column-wise averaging of epochs removes the background noise in every column separately, making the ASSR amplitudes comparable to those obtained with the classical procedure of averaging many epochs within a recording. Such manipulation allowed to reliable compute “instantaneous”, not accumulative ASSR amplitudes, since subsequently acquired epochs corresponding to the same recording were not averaged. Therefore, plotting such “instantaneous” ASSR amplitudes as a function of column index, i.e., the number of the acquired epoch, allowed us to explore the evolution of the ASSR amplitude during the stimulation period. It is worthy of note that such evolutions were represented in previous studies using the stimulation time as the abscise in graphs , 40. HowOne-way ANOVAs (p<0.05) and the corresponding post-hoc analyses  were performed to analyze the changes of the “instantaneous” ASSR amplitude and the RNL in the stimulation period. Since the ASSR generated in the brainstem of rats completely adapts 30-s after the stimulus onset [We analyzed the effect of the standard, weighted and sorted averaging on the ASSR computed by using the classical method of averaging subsequently acquired epochs, when the ASSR was elicited by continuous stimulation . The direction of the averaging is represented by the horizontal line in the left panel . The ASSThe FFT parameters used for computing the ASSR within recordings were the same used for the computation of the response amplitude during the column-wise averaging of epochs, described above. Similarly, the ASSR amplitude and the RNL were defined as the spectral amplitude obtained at 115 Hz and the average of the spectral amplitude computed in the adjacent 30 frequency bins on both sides of the response, respectively.According to the adaptive behavior, the ASSR amplitudes embedded in the first unaveraged epochs of a recording (unadapted responses) will be higher than those embedded in later unaveraged epochs (adapted responses). Therefore, it might be expected that the ASSR amplitudes computed with the classical sequential averaging of epochs within a recording (row-wise averaging) will vary as epochs with unadapted responses are included or not in the averaging. Therefore, we analyzed the evolution of the ASSR amplitudes during the averaging of a fixed number of epochs corresponding to the same recording , when the first 1, 2, 4, 8, 16, and 32 epochs of the recordings were excluded from the averaging. The exclusion (rejection) procedure consisted in starting the averaging not from the first epoch of the recording (first element in the rows) but a given number of epochs after . The rejection of epochs was performed in the original, weighted and sorted datasets.A three-way ANOVA (p<0.05) and the corresponding post-hoc analyses  were conducted to compare the ASSR amplitudes obtained at the end of the averaging, using as factors the averaging method, the number of averaged epochs, and the number of rejected epochs (number of epochs acquired at the beginning of the recording that were not included in the averaging). In the experimental design, the factor “averaging method” had three levels: standard, weighted, and sorted; the factor “number of averaged epochs” had: three levels: 2, 4, and 8 epochs; and the factor “number of rejected epochs” had seven levels: 0, 1, 2, 4, 8, 16, and 32 epochs.One of the main aims of this study is testing the conditions in which the averaging of independent EEG epochs  results in higher ASSR amplitudes than those obtained by averaging a combination of epochs containing unadaptated and adapted auditory responses. To this end, the first epochs in each recording  were concatenated to form a synthetic recording . As mentFor comparative purpose, the evolution of ASSR amplitude associated with the averaging of independent epochs in the synthetic recordings were contrasted with that obtained analyzing the first of the 30 original (first row in the datasets). This selection allowed us using the first epochs of the original recording also as the first epochs of the synthetic recording. Thereby, the initial ASSR amplitude computed from both types of recording using standard and weighted averaging were equalized. We performed that comparison separately for each of the three averaging methods . In each case, a two-way ANOVA (p<0.05) and the corresponding post hoc analysis  was performed, using as factors the type of epoch  and the number of averaged epochs .The ASSR amplitudes obtained by the classical sequential averaging of epochs within a recording (first row in the datasets) and those obtained by the sequential averaging of epochs within the synthetic recordings (formed by independent epochs containing only unadapted responses) were subject to statistical detection. For this end, the ASSR computed after averaging a given number of epochs was compared with the corresponding RNL, using the Hotelling's T2 multivariate test, which considers both the amplitude and phase of the oscillations . The staThe upper panels in The adaptation of the ASSR was evident by analyzing the original and weighted datasets. In both cases, the ASSR amplitudes decreased over the first four EEG epochs and remained steady afterward . However, the adaptive behavior of the ASSR was not detected in the sorted dataset . As expected, the RNL resulting from the column-wise averaging of epochs was similar in the original, weighted and sorted data-sets. In all cases, the RNL did not vary as a function of the location of epochs within the recording, i.e., the RNL was constant among columns  is represented in th element of the rows  confirmed that, when using the standard and weighted averaging methods, the ASSR amplitude computed by averaging only two consecutive EEG epochs within a recording significantly decreased as the number of epochs excluded from the averaging increased up to eight  was calculated for each parameter combination, using the equation:Ampmax represents the maximum amplitude of the fitted curve and Ampadapt represents its asymptotic value.Results presented in As shown in After analyzing the error rate, the detection rate and the recording length of ASSR, Luts et al.  recommenAveraging a fixed number of EEG epochs , we corrConsequently, the ASSR amplitude computed at the end of the averaging decreased as more epochs containing unadapted responses are excluded . From a The benefits of estimating the ASSR by averaging independent EEG epochs were evident when analyzing the amplitude and the detection rate of the response Figs  and 6. ADue to the experimental design used in this work, we cannot make any statement about the minimum ISI required for enhancing the amplitude of the ASSR. Future experiments addressing that question are needed. Those studies should also focus on the effect of two other aspects of the stimulation strategy: the variability of the ISI and the presentation of broadband noise between consecutive stimuli. As described by Zacharias et al. , a semi-The result presented in Figs A hypothetical issue regarding the feasibility of the discrete stimulation paradigm is the possible attenuation of the averaged ASSR amplitudes due to variations in the phase of the neural oscillations from one trial to another. Futures studies need to address this topic experimentally. Nevertheless, the ASSR phase estimated from independent epochs might be less variable than expected, as previous studies have reported a regularity in the expected phase of the human ASSR , 64, 65.In this study, ASSRs were evoked by AM-tones with standard sinusoidal envelopes. Modifications of the spectral composition and the amplitude envelopes of these standard stimuli have been proposed for optimizing the detection of ASSR , 66, 67.


This research aimed to measure the extent of linkage disequilibrium (LD),effective population size ( According to Rashidi et al. (2008), the sheep industrycontributes about 35 % of the meat production in Iran. As the supply ofmeat production from sheep does not satisfy the demand, a breeding planseems reasonable to enhance the profitability of sheep production byimproving such traits as litter size, body conformation, lamb weight, andother economically important traits . TheZandi breed is one of the major, indigenous, fat-tailed sheep breeds of Iranthat has adapted to survive in harsh weather conditions and poor andmountainous pastures in the central regions of the country . Probably because of its large population in Iran (about 2 million heads),there is much interest in genetically improving Zandi sheepfor growth traits .The adoption of such new technologies as genomic selection can play an essentialrole in satisfying the demand for meat production. One advantage of genomicover traditional selection methods is that selection candidates can beevaluated more accurately at very early ages, which increases the rate ofgenetic gain . Also, the accuracy of genomicselection is regularly higher than that of traditional pedigree-basedselection . So, there is potential to increasegenetic gain in Zandi sheep by adopting genomic selection technology;however, the success of genomic selection relies on the level of genome-widelinkage disequilibrium (LD) between markers and causal mutations . The nonrandom association between alleles of differentvariants throughout the genome in the population is known as LD. The mainreason for this association, or correlation between alleles of differentmarkers, is their physical closeness. However, some other factors, such asgenetic drift, migration, and, more importantly, either artificial or naturalselection, also affect the LD and its pattern . Measuring theamount of genome-wide LD in a population can help us estimate the number ofsingle-nucleotide polymorphism (SNP) markers required to have robust genomicevaluations  and genome-wide association studies(GWASs) .The extent of genome-wide LD can also help us estimate past effectivepopulation sizes . The samples were genotyped usingIllumina Ovine SNP50 BeadChip for 54 241 SNP markers; the version of theovine assembly used in our research was Ovis_aries_v4.0 obtainedfrom the SNPchiMP v3 database . The filtering process wascompleted using PLINK V1.9 . Of 99 genotyped animals,three samples were excluded due to low genotype quality , separately for each ovine autosome. Missing genotypes afterquality control (0.018 %) were also imputed during haplotype phasing. Thephased haplotypes were read into HAPLOVIEW v4.2  tocalculate LD. For each autosome, 2.3The 2.4ROHs in the genotyped sheep were found using PLINK V1.9 according to thefollowing criteria. Each ROH had at least one SNP per 100 kb and a minimumlength of 4 Mb. The sliding window under examination was allowed to containup to one heterozygous SNP, and the minimum number of SNPs for a given ROH wasdetermined using Eq. (4) :33.1After quality control, the final dataset consisted of 96 animals genotypedfor 49 257 SNPs covering 2.6452 Gb of the ovine genome. The averagedistance between adjacent SNPs was 3.2The reduction in LD . Hence, these chromosomes had the mostand the minimum number of ROHs, respectively. We found a strong correlation. In the Zandi breed, LD remained at a moderatelevel (The average of The The power of quantitative trait locus (QTL) mapping and the accuracy ofgenomic predictions largely depend on the amount of LD between SNPs andquantitative trait nucleotides (QTNs) . Generally,when the extent of LD between markers decreases rapidly by increasing theinter-marker distances, the number of markers required for a robust genomicstudy should be kept high. Hayes and Goddard (2001) demonstrated that theaccuracy of genomic predictions for dairy cattle reached 85 % when theaverage The level of LD in Zandi sheep was less than it is in cattle  and pigs but close to the level of LD reported for othersheep breeds . For example, Qanbari et al. (2010) reported For the very recent generations , theaccuracy of estimating We observed that the One of the main factors affecting autozygosity estimates is the density ofSNP chips applied to generate the data for ROH studies. Marras et al. (2015),in a study of ROHs applying a medium-density chip, reported that whenheterozygous SNPs were allowed, the number of longer ROHs increaseddramatically and suggested not using them in the ROH. Ferenčakovićet al. (2013) reported that the 50K SNP panelled to the overestimation of short-lengthROHs. Since the Zandi population issubject to selection programs , the occurrence or runs of homozygosity in this breed can be attributedto both inbreeding effects and selection pressure. In the ROH analysis ofZandi sheep, around 37.8 % of the detected ROH was relatively long(5We found that in Zandi sheep, the amount of LD was relatively small betweenthe adjacent SNPs in Illumina Ovine SNP50 BeadChip (10.5194/aab-62-143-2019-supplementThe supplement related to this article is available online at: https://doi.org/10.5194/aab-62-143-2019-supplement.


Bemisia tabaci) and breeding for natural resistance, which requires the identification of the loci involved and the development of molecular markers for linkage analysis. In this work, we mapped a monogenic locus for resistance to CVYV in cucumber by using a Bulked Segregant Analysis (BSA) strategy coupled with whole-genome resequencing. We phenotyped 135 F3 families from a segregating population between a susceptible pickling cucumber and a resistant Long Dutch type cucumber for CVYV resistance. Phenotypic analysis determined the monogenic and incomplete dominance inheritance of the resistance. We named the locus CsCvy-1. For mapping this locus, 15 resistant and 15 susceptible homozygous F2 individuals were selected for whole genome resequencing. By using a customized bioinformatics pipeline, we identified a unique region in chromosome 5 associated to resistance to CVYV, explaining more than 80% of the variability. The resequencing data provided us with additional SNP markers to decrease the interval of CsCvy-1 to 625 kb, containing 24 annotated genes. Markers flanking CsCvy-1 in a 5.3 cM interval were developed for marker-assisted selection (MAS) in breeding programs and will be useful for the identification of the target gene in future studies.Cucumber vein yellowing virus (CVYV) causes severe yield losses in cucurbit crops across Mediterranean countries. The control of this virus is based on cultural practices to prevent the presence of its vector ( Potyviridae) that is transmitted in a semi-persistent manner by the whitefly Bemisia tabaci. CVYV was first reported in Eastern countries of the Mediterranean basin  later expanding to Western Mediterranean countries including Spain, Portugal, France, and Tunisia (reviewed in Cucumis melo L.) and cucumber (Cucumis sativus L.), it causes a typical severe vein clearing often followed by generalized chlorosis and necrosis. In cucurbit-producing areas of heavy B. tabaci infestation, it can cause epidemics with massive yield losses and dramatic economic consequences. Although significant diversity has been reported for this virus, epidemics in Western Mediterranean countries seem to be associated to genetically uniform virus populations (e.g.Cucumber vein yellowing virus (CVYV) is an ipomovirus , a CVYV-susceptible pickling cucumber, was crossed with accession CE0749 (hereafter PR), a CVYV-resistant Long Dutch type cucumber, to obtain the F1, F2 and F2:3 segregating populations used to perform the genetic mapping of the resistance trait. PS, PR, F1, F2, and F2:3 plants were inoculated mechanically with CVYV-AILM  according to the manufacturer’s protocol. For NGS sequencing, we pooled equimolar concentrations of DNA from 15 homozygous resistant F2 plants (R-Bulk), and from 15 homozygous susceptible F2 plants (S-Bulk). Twenty µg aliquots of each bulked DNA and both parental lines were sent to the National Centre for Genomic Analysis  for library construction and sequencing. Libraries of 300 bp and 500 bp average insert size for bulks and parents, respectively, were sequenced with Illumina HiSeq 2000 , generating 2 × 100 bp paired-end reads for both datasets.Young leaves from the parental lines and the FB method , adding R and PS from the resequencing data (www.lgcgroup.com) was used for genotyping a subset of 72 individuals, with the 172 SNPs converted to KASP markers, following the protocol of LGC Genomics. A genetic map was constructed using JoinMap® 5 , with 172 SNP markers data and the phenotypic data used as another marker (CsCvy-1) due to the monogenic inheritance of the trait. Three F2 individuals and one SNP marker were excluded because of the high amount of missing data. Two more SNP markers were excluded for being identical  to other nearby markers. Linkage analysis and marker order were performed with the regression mapping algorithm, and genetic distance was calculated using the Kosambi mapping function (® (Kyazma B.V.) using both interval mapping and Kruskal-Wallis (KW) analysis.A set of 172 polymorphic cucumber SNP markers, distributed across the seven chromosomes of the cucumber genome, were selected between Pfunction . QTL ana2 individuals were chosen depending on their phenotype. 15 homozygous resistant F2 individuals (R-bulk) and 15 homozygous susceptible F2 individuals (S-bulk) were selected for pooling and sequencing. Paired-end Illumina sequencing data from parental lines and the two bulks were trimmed  using Trimmomatic (www.bioinformatics.babraham.ac.uk/projects/fastqc/). Trimmed data were aligned versus the ChineseLong 9930 v3 assembly ftp://cucurbitgenomics.org/pub/cucurbit/genome/cucumber/Chinese_long/v3/) using the BWA-MEM algorithm  with default parameters. After removal of unmapped reads and marking of PCR duplicates, variant calling was performed with samtools  analysis between the two parents was conducted using DELLY  and PindAnnotation of the functional effect of the variants was done using snpEff , was done through a simulation analysis of 10,000 replications for two bulks that were randomly generated from the population and calculating, after each iteration, the SNP-index and the corresponding Δ(SNP-index) in the two simulated bulks.BSA-seq analysis was performed using the R package QTLseqr . More spS, PR, and F1 plants in a time course experiment (S plants as soon as 6 dpi (data not shown) and were conspicuous by 9 dpi, with plants showing obvious vein yellowing in all leaves and chlorotic mosaic in fully-expanded leaves  were found among the three accessions at 16 dpi in basal leaves and at 23 and 30 dpi in intermediate leaves  with a 1:2:1 segregation ratio. We propose the symbol CsCvy-1 for this monogenic incompletely-dominant resistance gene. For the BSA-seq analysis, two pools containing 15 F2 individuals homozygous for resistance and susceptibility to CVYV, respectively, were used.To determine the resistance genotype of F2 mapping with a subset of the population including the CsCvy-1 locus as a phenotypic marker. We used 172 polymorphic SNPs covering the cucumber genome: 24 SNPs in Chr01, 18 SNPs in Chr02, 34 SNPs in Chr03, 36 SNPs in Chr04, 21 SNPs in Chr05, 27 SNPs in Chr06, and 12 SNPs in Chr07  for mapping. The 72 F2 individuals were genotyped, and most of the markers fitted the expected 1:2:1 segregation ratio. However, a group of markers on Chr05 showed a distorted segregation. This distortion was also found for the phenotypic marker CsCvy-1, due to the selection of individuals, with almost all of them being homozygous for this trait. The genetic map consisted of eight linkage groups (LGs) spanning 628 cM, with Chr03 split into two linkage groups  , S-bulk and R-bulk were resequenced with the Illumina HiSeq2000 sequencer. In total, 274,709,874 paired-end clean reads were mapped, after trimming and adapter removal  value was -0.528 at position 7,678,525 bp whereas the average Δ(SNP-index) was found to be -0.523 , generated 186,433; 215,735; 933,846 and 915,524 variants (SNPs and INDELs) for PCsCvy-1 locus, fine mapping was performed using the linked SNPs obtained by BSA-seq on Chr05 to design 19 SNP markers . RDRs are critical players in RNA silencing pathways; they are the key enzymes in the process of amplification of double-stranded RNAs that activate gene silencing after nuclease processing. A role for RDRs in antiviral immunity has long been acknowledged, in particular for members of the RDR1 and RDR6 clades  exists. From the point of view of resistance stability when confronted to different CVYV strains  with the project accession number PRJEB34274.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher. Variation data were deposited to European Variation Archive (EVA: MA, JG-M, TJ, and MP conceived and designed the research. PM, MM and MA provided the plant material and performed the tests with CVYV. A-SF conducted the conventional mapping. KA performed the bioinformatics analysis of the BSA-seq. MP conducted marker development, mapping analysis, and wrote the manuscript with important contributions from MA and KA. All authors read and approved the final manuscript.Authors A-SF and TJ were employed by company Semillas Fitó S.A.The remaining 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.