Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination.We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling.Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations.The online version contains supplementary material available at 10.1186/s13059-021-02354-7. Triticum aestivum L.) production is a critical component of worldwide food security. About 21% of the calories and protein consumed by humans are from wheat, and demand is predicted to increase 60% between 2014 and 2050 [Bread wheat , North-West (NW) Europe (n = 1343), Europe (n = 2331), and Global (n = 4506), to understand how allele frequencies across subsets relate to our founders , landraces , and uncategorized/landrace germplasm (n = 1247), we conclude that NDM is representative of NW European wheat germplasm.We placed the 16 founders in the context of global wheat diversity by analyzing 113,457 genotyping array sites that vary among 4506 diverse global wheat accessions , of whicrope n = 331, and Haplotypes are sequences of linked genetic variants that are inherited together. In crop genetics, the term haplotype has at least three common usages: (1) long genomic blocks that tend to be found intact despite opportunities to break them apart by recombination. There is some evidence that breeders retain longer blocks than would be expected by chance , probablWe estimated this latter type of haplotypic diversity among the founders using the 1.13 M SNPs called in promoter and genic regions, at two length scales. Both analyses found only limited haplotype diversity. First, to give a gene-centric view, we defined haplotype blocks as the gene regions, plus the promoter associated with each gene. Within each gene-promoter locus, we identified haplotypes shared between founders using complete-link clustering. We defined two founders to carry the same haplotype when their genotypic similarity exceeded 95%. Of 73,982 gene-promoter loci that had SNPs, 38,535 loci (52% of loci with SNPs) had only two haplotypes, 61,438 loci (83%) had at most three haplotypes, and 70,602 loci (95%) had at most four , each descended from all 16 founders via a crossing funnel Fig. a. After We excluded 46 RILs excessively similar (> 92%) to other RILs, indicating possible errors during population development. We sequenced the remaining 550 RILs after 7 generations of inbreeding by low-coverage WGS (mean 0.304×), and called variants at the 1.13M founder SNP sites from sequence alignments. A further 46 RILs were excluded as their genotypic concordance with the initial 35 k array data was below 95%, leaving 504 RILs in 141 families (RILs in the same “family” are derived from the same 16-way cross), from which we based our main analyses.We imputed RIL genotypes using STITCH  to inferThe “founder haplotype” blocks found in each RIL are long relative to the length scale for haplotype identity in the founders  for 501 RILs varied between 20.9 and 42.7% (mean 27.8% sd 3.4%), as expected for 0.3X-coverage sequence data. A further three RILs were sequenced to higher depth  and had call rates of 79.9%, 90.0%, and 93.0%, respectively  to map −1 in year 1 and year 2, respectively) is substantially lower than that of the founders (5.43 and 5.10 t ha−1) whereas the maximum yield in the RILs (8.37 and 8.46 t ha−1) only slightly exceeds that of the founders (8.11 and 8.28 t ha−1). This suggests that recombination in the absence of selection is more likely to break up the favorable allelic combinations that have been selected for in the founders, rather than creating new beneficial combinations. Pleiotropy is common: all phenotypes have significant  correlations with at least one other phenotype  on both SNP and founder haplotype data, we mapped 136 QTLs across the 73 phenotype/year combinations that were genome-wide significant at the 5% level . Many QTLs for different phenotypes overlapped each other, clustering into 42 distinct genome locations. For 25 phenotypes that were measured in both years, we found 48 QTLs in year 1 and 49 QTLs in year 2, of which 28 were mapped to the same location and were genome-wide significant in both years. For example, in replicated trials lacking fungicide treatment, we mapped yellow rust (Puccinia striiformis) susceptibility to four QTLs in year 2  for QTL mapping. The pruned SNP set tags all other SNPs at somes 2A , 34, 2B somes 2A , 3B, andIn total, 126/136 QTLs at 40/42 genomic locations were mapped using SNP-based associations, whereas only 87/136 QTLs at 30/42 genomic locations were mapped using founder-haplotype-based association tests, which can reveal multi-allelic effects on trait variation, even if the underlying causal variants are not observed directly, albeit at reduced statistical power. Only 10 QTLs at two loci were identified solely by founder-haplotype-based association whereas 49 QTLs and 12 genomic locations were detected by SNP-based association only. The relative scarcity of evidence for multi-allelic QTL effects is consistent with the effectively biallelic gene-level haplotypic diversity observed among the founders.n = 10). To check mapping interval calibration, we confirmed that six high-confidence candidate loci with annotated reference genome locations  were wiPPD-D1 carried by the founder Soissons is favored in southern Europe to avoid the summer drought. The modern semi-dwarfing alleles at RHT-B1 or RHT-D1 that have been favored globally since the Green Revolution are absent from founders Banco, Bersee, Copain, Flamingo, Holdfast, Kloka, Spark, Steadfast, and Stetson.It is to be expected that most loci with strong effects colocalize with previously reported QTLs, since these large effects are commonly associated with adaptation of the founders to their individual geographic and temporal ranges, and therefore segregate in the NDM. For example, the early flowering allele at the photoperiod locus p < 0.05) than was initially used to establish genome-wide significance  of genic regions and 1095 (1.1%) of promoter-gene pairs, suggesting possible structural variations , least absolute shrinkage and selection operator (LASSO), and elastic nets (EN), using 50 replicates of cross validation. Within each replicate, a randomly selected training set comprising 90% of RILs was used to train a model, which was evaluated on the out-of-sample test set comprising the remaining 10%. We report the average prediction accuracy across 50 replicates. We found that LASSO and EN had almost identical prediction accuracies but EN used on average 26% more SNPs than LASSO  of the 193 non-zero LASSO SNP effects is shown in Fig. We used the LASSO genomic prediction models to explore the potential for selection in a much larger simulated population of 20,160 MAGIC RILs, 40 times larger than the real population. The simulated RILs were created by permuting the founder identities within the founder haplotype mosaics inferred in the real RILs, preserving linkage through the genetic map. Phenotypes were predicted for the test set of real RILs  and in the simulated RILs for all 50 prediction models (training/test set resamples). Figure Next, we predicted the most extreme phenotypic values it would be possible to create in theory from existing segregating variation, should unlimited recombination be possible. That is, we computed the phenotypic prediction in an imaginary line that carries all the alleles predicted to increase/decrease each phenotype. For this exercise, we trained the LASSO prediction models on the full set of 504 RILs so they differ slightly from those used to predict phenotypes in the test set.Figure −1 year−1 based on a regression of average yield on founder release year . However, high average grain yield (GY) is correlated with low average grain protein content (GPC) among the founders , Fig. p < 0.001, n = 504), suggesting antagonistic pleiotropy in the underlying genetic effects.For most crops, a trade-off is evident between yield and quality (here defined as percentage protein content), a problem that is well-known in wheat , 44. ThuZ-score normalization. Thus, lines with positive PYD fall above the negative trend between yield and protein. The distribution of PYD is shown in Fig. To investigate if any segregating genetic variation might break the trade-off between yield and protein, we defined a new phenotype that combines protein concentration and yield. The protein-yield deviation (PYD) is the perpendicular distance from the symmetrical Thiel-Sen regression between GPC and GY, after n = 504, R2 = 1) and other closely linked SNPs. Mixed model genetic association of PYD produced suggestive, but not genome-wide significant, QTLs at this locus in both years . These genetic associations are not conclusive because of the need to adjust for genome-wide significance . However, when interpreted solely as a phenotypic association with awns, the evidence appears stronger  and logP = 5.26 in year 2 . Moreover, using Fisher’s method to combine the p values from genome-wide association across both years gives a composite logP = 7.269, which exceeds both 1% significance thresholds , which were global samples from a natural population. Limited haplotypic variation in the UK wheat gene pool probably results from historical selection and population bottlenecks preceding the onset of twentieth century breeding programs [Based on SNPs called from genic and promoter capture on the 16 founders, no more than three haplotypes segregate at most genes. Indeed, most genes are biallelic at most: there is no detectable variation at about a quarter of genes on the A and B subgenomes, and at about half on the D subgenome. Nonetheless, the founders were chosen to capture genetic diversity from a panel of UK varieties released since 1935 and are representative of diverse NW European bread wheats. Our results suggest that deeper sampling from this gene pool will reveal few novel haplotypes. For comparison, much greater gene-level haplotype diversity was observed among the 19 founders of the programs , as wellprograms .Complete re-assembly and re-annotation of the 16 founders might yield more complete insights into the extent and impact of coding and regulatory variation: we could only make partial functional predictions from genic/promoter capture alone. Greater haplotypic diversity was recently reported among 15 wheat assemblies , 22. TheAcross phenotypes, we found a wide range of underlying genetic architectures. For a few simple phenotypes, large fractions of the phenotypic variance could be explained through one or two QTLs, but most quantitative traits, including yield, were polygenic with QTLs of smaller effect Fig. a. Most QWe compared the accuracy of ridge regression, LASSO, and elastic net genomic prediction models across phenotypes. The prediction accuracy of ridge regression is correlated with trait heritability Fig. c, as expWe found that LASSO genomic prediction models could achieve reasonable prediction accuracy with modest numbers of SNPs; the mean out-of-sample prediction accuracy was 0.43, using on average only 155 SNPs per phenotype. Here, rather than using low marker densities, we trained models that select a just a few hundred markers from ~ 55 k tagging SNPs. In part, this may be a consequence of the design and construction of MAGIC populations, which eliminate rare alleles and create blocks of markers inherited from each founder. Previous crop and livestock studies have found very sparse markers can be sufficient for useful genomic prediction , 53, 54.We used our LASSO models to predict the potential for future phenotypic change from segregating variation. By simulating a larger NDM population of over 20,000 lines, we showed that blind-breeding a very large population in the hope of generating novel combinations of beneficial alleles is inefficient. As expected, the most extreme predicted values (both maximum and minimum) in the simulated RILs exceed than those in the real dataset because novel allelic combinations are generated in the larger simulated population. However, the average improvement in extrema between the real set and simulated phenotype predictions is only ~ 0.5 standard deviations Fig. c. This iThe dramatic genetic improvement witnessed over 70 years of wheat breeding has largely been achieved through the fine shuffling of a few haplotypes to recombine polygenic alleles of small effect, combined with the introduction of alien introgressions from wide crosses. There are a small number of loci with large effects on particular phenotypes, notably including the “Green Revolution” semi-dwarfing alleles introduced from Japan . The inthttp://www.jic.ac.uk/germplasm/).The 16 NDM founders were chosen to capture the greatest genetic diversity using PowerMarker genetic analysis software . They we7 stage when seed for phenotyping was multiplied in 1 × 1 m nursery plots. Additional file These founders were intercrossed in a balanced funnel crossing scheme, based on a Latin square field trial design, over four generations to create 16-way crosses with all the founders equally represented in their pedigree. First, all 120 possible 2-way crosses between founders were made in a half diallel scheme. Two-way plants were then crossed in 60 4-way combinations. Multiple plants from each family were used in crossing from 2-way onwards, in order to maintain maximum founder allelic diversity within the population. 30 crossing combinations were made between 4-way plants to create 8-way crosses, making between five and eight replicate crosses per combination using different plants. These were intercrossed in 15 combinations to create balanced 16-way crosses, with each combination replicated between six and fifteen times using different 8-way plants. This resulted in 174 16-way plants from which one to sixteen inbred lines per 16-way family were made through single seed descent (SSD). In total, 596 RILs were advanced to the F−2. In year 1, 596 lines were included in two replicates, the sixteen founders in four replicates and the commercial control variety “KWS Santiago” in 24 replicates in a randomized nested block design with 16 main blocks of 80 adjacent plots which comprised each row in the trial and eight sub-blocks of ten plots nested within each main block. In year 2 trials, 596 lines and the 16 founders were included in two and four replicates respectively but three control varieties  were all included in four replicates. Plots were again randomized in a nested block design but including additional plots making a larger trial, consisting of 20 main blocks of 115 adjacent plots, which comprised each row, and 23 sub-blocks of five plots nested within each main block.RILs from the population were phenotyped in field trials over multiple environments near Cambridge, UK. Yield trials were conducted in the growing seasons 2016–2017 and 2017–2018, hereafter year 1 and year 2 (phenotype suffix codes _1 and _2). Information on location, soil type, key dates, and inputs for both years are given in Additional file Puccinia striiformis f.sp. tritici) and were not experimentally challenged with pathogens. In both years, DOTs included two replicates of 596 RILs, four replicates of the 16 founders, and 68 additional replicates of the susceptible founder “Robigus.” Trial designs included two main blocks of 660 plots, with 11 sub-blocks of 60 plots nested within main blocks. All trial designs for both yield and disease observation trials were made using the package “blocksdesign” in R. Phenotyping of some traits was also carried out in 1 × 1 m seed nursery plots where lines were not replicated but the founders were in three replicates and randomized across the nurseries (phenotype code _N).Disease observation trials (DOTs) were conducted near Cambridge, UK, in the same years as the yield trials to assess resistance to crop diseases. These plots consisted of two 1.2-m length rows, treated with no fungicide but otherwise standard inputs. Due to local conditions, DOTs had a high natural likelihood of yellow rust infection  for each trait separately for each trial year using mixed effects models with ASRemL . Genotyp6). To account for genotyping failures and to ensure the accuracy of sample labels, 150 RILs were re-genotyped in the F7 generation along with a further replicate of each founder.All DNA extraction was performed using the Qiagen DNeasy Plant Kit on leaf tissue samples taken from emerging leaves of seedlings. First, genotyping was performed at the Bristol Genomics Facility using the Axiom 35 k wheat breeders’ array . InitialGenotype calling was performed using the Affymetrix Power Tools (v1.19) and SNPolisher R packages, following the recommended Axiom analysis pipeline. All samples except two-way crosses were given the standard inbreeding penalty, 4, which penalizes calling heterozygous genotypes. Four samples failed the “dish quality control” threshold (0.82) and a further 28 samples with call rates below 97% were excluded. Marker classifications were performed using “ps-classification” and “ps-classification-Supplementary” functions with options --species-type polyploid --hom-ro false. All calls were adjusted using the standard 0.025 confidence threshold using the Ps_CallAdjust function.Samples were compared to one another using the 14,935 markers classified as “PolyHighResolution” only. Overall, 46 RIL pairs were found to be > 92% similar (mean 98.5% genotype similarity), where all other comparisons between MAGIC lines were, at most, 84% similar (mean 67.8%). These apparently duplicated genotypes could indicate genotyping, labelling, or propagation errors so only one RIL from each pair was used for sequencing (550 RILs). To ensure pedigree accuracy, we chose the RIL in each pair that was genotypically most similar to other RILs derived from the same 16-way cross .7 generation. DNA for RILs that failed quality control were extracted again at the F8 generation (n = 50). Sequencing and library preparation was performed at Novogene, where libraries were generated from 1.0 μg DNA per sample using the NEBNext DNA Library Prep Kit. Sequencing was performed on a NovaSeq 6000 instrument (Illumina) to get at least 6 Gb of raw sequence data (2 × 150 bp paired end reads) per sample. One founder (Holdfast) was sequenced to 15.8× coverage using the same method.For whole-genome sequencing, DNA was extracted from 550 RILs at the FThe other founders were sequenced after capture using two recently designed probe sets targeting promoter and genic regions, respectively . CaptureAll reads were aligned to the bread wheat reference genome from cv. Chinese Spring (RefSeq v1.0)  using bwWe first called genotypes in the RILs at these 1.13 M SNP sites directly using GATK HaplotypeCaller in GENOTYPE-GIVEN-ALLELES mode, using the same options as above. We assessed the concordance between array genotypes and these direct calls (AD) at overlapping sites (see below). For 10 RILs, the directly called sequencing variants most closely matched genotyping array data for a different line than expected. These were excluded because the source of the discrepancy (sequence data or array data) cannot be established. The concordance between our genotyping array data and direct calls (AD) was below 95% for a further 36 RILs, which were excluded (mean AD 84.7% for removed lines), leaving 504 RILs. We estimated heterozygosity in these 504 RILs using only genotypes called from at least four reads. Of 2.6 M such genotype calls, only 0.67% were called as heterozygotes.We imputed genotypes at the 1.13 M SNP sites using the alignments and STITCH software (version 1.5.7) . Becausecerealsdb.uk.net) [For comparison against the sequencing dataset, we used all genotyping array markers. Replicates of founders and MAGIC RILs (where available) were used to make a consensus call where the most common genotype across replicates was taken as the consensus and only retained when more than 50% of the non-missing calls were in agreement. In addition, markers where one homozygous genotype was missing from all RILs were converted such that all heterozygous calls were assumed to be in the missing homozygous class. The failure to detect a homozygous class is likely to be a result of polyploidy, which can reduce differentiation between the three genotype classes and make them hard to distinguish. Finally, to get genome coordinates for the genotyped markers, BLASTn v2.2.30  was used.uk.net)  against n = 56,063). We used genotyping array calls for cv. Chinese Spring to determine reference/non-reference alleles on the genotyping array, ignoring sites called as heterozygous (n = 109) or missing (n = 306) in Chinese Spring. Seven of the MAGIC founders were also present in the global genotype set 7. The average concordance of the global genotype calls and our sequencing calls for these founders was 94.3% (sd 0.63%). We excluded 5491 (9.8%) sites that had mismatches across these founders, many of which are likely to reflect differences in the underlying genetic variation picked up by the different genotyping technologies. Two other founder variety names were in the genotyping array dataset7 (Banco and Holdfast) but the genotyping calls did not match , which may reflect differences in the seed stock used.To compare against global wheat diversity, we called founder genotypes at 113,457 genotyping array sites that were polymorphic among 4506 diverse global wheat accessions . We callFirst, we used the SNPs called within each promoter-gene pair to estimate haplotypic diversity among the founders. We calculated absolute (Manhattan) pairwise genetic distances between founders at each site and then used complete linkage clustering to define haplotypic groups using dist and hclust functions implemented in R statistical software (version 3.6.0) . This waSecond, we determined haplotype breakpoints using a dynamic programming algorithm. For each pairwise founder combination, our algorithm calculates a mosaic of genotypic similarity/dissimilarity akin to the Viterbi path from a hidden Markov model. Genotype matches and mismatches are allocated a score (1 by default). To prevent excessive switching between states, there is also a “transition penalty” for inferring a change between matching and mismatching states. Based on their pairwise matching/mismatching states, we then infer the total number of haplotypes inferred at each site. We repeat this procedure with different transition penalty parameter choices  with option --indep-pairwise 500 10 0.99. The genotype dosages at each tagging SNPs were standardized to produce a 504 × 55,067 genotype dosage matrix G which was used to calculate the genetic relationship matrix (GRM) K = GG ’ /p. The phenotypic variance-covariance matrix for a given vector y of standardized phenotype values was modelled as V as A2 = V, and the mixed model transformation of the data performed, i.e., y → A−1y, G → A−1G, V → I , to remove the inflationary effects of unequal relatedness on genetic associations before association mapping.For mapping, we used the full set of 1,065,185 high-quality SNP sites called in 504 RILs after imputation and quality control filters. From these, we selected a subset of kelihood . The herp value than found across at least 950 phenotypic permutations), then we repeated the association test at all of the ~ 1.1 M SNPs on the chromosome with the strongest association signal . Mapping intervals were defined to include SNPs surrounding the peak SNP, with log10(p) values within d units of x using d = max {2, 0.1x} where x is the peak log10(p) value. The interval for founder-haplotype-based tests includes the range of sites that have log10(p) values within d units of x. SNP-based intervals were calculated using the same measure but then extended by the minimum of 5 Mb or the distance to the next SNP in either direction that the same “strain distribution pattern” [10(p) values within d units of x). The “strain distribution pattern” is the pattern of major/minor alleles across founders. This procedure is designed to capture the uncertainty in the positioning of relevant recombination events on either side of the QTL peak. We fitted QTLs in a stepwise manner by fitting the phenotype against the most strongly associated SNP (or founder haplotype dosage) whenever genome-wide significant QTLs were detected. The above association test procedure was then repeated using the phenotype residuals after fitting all previously identified QTLs. This allows closely linked QTLs to be detected when they have different patterns of causal variants among RILs. Where QTL associations were found for different genotypes, they were judged to be at the same locus if they had overlapping mapping intervals and at least one matching strain distribution pattern at highly associated SNP sites.We performed association tests at the level of both SNPs and founder haplotypes using R statistical software (version 3.6.0 ), using ppattern”  as any hFor 40 QTLs identified using SNP-based associations, we looked in the set of ~ 55 k SNPs that were called in 4500 global wheat accessions  for markn = 454) as a training set in each round to train the model, which was then used to predict the remaining 10% of RILs (n = 50)—the test set. For the three methods, the model equation can be written generally as y = μ + Gβ + ε, where y is the estimated trait value, μ is the model intercept, β is the vector of SNP effects, G is the genotype dosage matrix, and ε is the residual error. With appropriate choice of ridge parameter We evaluated the accuracy of trait prediction within NDM and estimate the extent of polygenic variation beyond genome-wide significant QTLs. We conducted genomic prediction across all phenotypes using three shrinkage-based methods: ridge regression (RR), elastic nets (EN), and least absolute shrinkage and selection operator (LASSO), using the R package glmnet , which eRILs n = —the test (BLUPs) . This exWe then predicted phenotypes in the test set by multiplying all SNP coefficient estimates by their corresponding genotypes in the test set (and adding the intercept term). We reported the training and test set prediction accuracy as the mean Pearson correlation coefficient of the predicted trait values and the actual phenotype values over 50 rounds of cross validation.We used these genomic prediction models to evaluate the potential for phenotypic change in a simulated NDM population of 20,160 RILs, assuming the same patterns of recombination as actually observed. We did this by simulating new breeding funnels. Thus, we permuted the population founder haplotype identities 40 times across the 504 RILs and then projected the permuted founder genotypes onto the new lines. This creates new genetic combinations while retaining the mosaic breakpoints, genetic map, and linkage disequilibrium found in the real population. We applied the LASSO models (trained as above on the 504 RILs) to predict phenotypes for the simulated MAGIC RILs. We further calculated the theoretical maximum and minimum phenotype values that are possible given the genomic prediction models and the variants segregating in the population, by summing the estimated effects for all positive or negative SNP coefficients, respectively.f and at each gene feature g, we computed a gene deletion index Dgf based on the number of reads aligning to the associated capture sequences, normalized by the overall coverage for that founder. The gene deletion score (GDS) for each MAGIC RIL i and feature j was computed as Hijf is the founder haplotype dosage for founder f in RIL i at gene j, as computed by STITCH. For each phenotype, a mixed model GWAS was performed, using the GDS in place of SNP dosages and with a genetic relationship matrix computed from the GDS  calculated in 10-Mb windows moved in 5-Mb steps. Within introgressions, carriers should have a high proportion of non-reference alleles due to the alignment of inter-specific genetic material to the bread wheat reference genome. Introgression boundaries were defined by the extent of 10-Mb windows where all introgression carriers had a higher proportion of non-reference alleles than all non-carriers. Within these regions, we then checked the relative coverage of carriers and the extent to which the alleles of carriers are over- or under-represented among the RILs. This evidence is summarized in Additional file Additional file 1: Table S1. Summary of NDM founder varieties and sequencing coverage. Table S2. Summary of overlap in sites called from wheat breeders’ genotyping array and SNP sites called in founders from sequencing data. Table S3. Summary of evidence for introgressions segregating among NDM founders. Table S4. Summary of trial inputs and conditions in 2016-17 (year 1) and 2017-18 (year 2). Table S5. Description of all phenotype measurement methods and timings. Table S6. Best Linear Unbiased Estimate for all phenotypes in 504 RILs. Table S7. Summary of all genome-wide significant QTL associations found for SNP-based and Haplotype-based mapping. Table S8. Gene-Deletion-Score (GDS) loci with GDS-phenotype associations with logP> 6. Table S9. Summary of crossing scheme.Additional file 2: Figure S1. Distribution of SNPs across chromosomes and founders. Figure S2. Minor Allele Frequency among ~ 4500 global wheats and the NDM founders. Figure S3. Haplotype block length in founders and MAGIC lines. Figure S4. Distribution of Best Linear Unbiased Estimates (BLUEs) across 73 phenotypic measurements. Figure S5. Correlations between phenotypes in the 504 RILs. Figure S6. Comparison between genomic prediction using LASSO and ELNET and between genomic prediction based on SNPs and gene deletion scores. Figure S7. Association mapping of the Protein Yield Deviation (PYD) compound phenotype.Additional file 3. Review history.


AA), 6,433 Charolais (CH), 1,129 Hereford, 8,745 Limousin (LM), and 1,698 Simmental. Genome-wide association analyses were undertaken using imputed whole-genome sequence data for each sex separately by breed. For each single-nucleotide polymorphism (SNP) that was segregating in both sexes, the difference between the allele substitution effect sizes for each sex, in each breed separately, was calculated. Suggestively (P ≤ 1 × 10−5) sexually dimorphic SNPs that were segregating in both males and females were detected for all traits in all breeds, although the location of these SNPs differed by both trait and breed. Significantly (P ≤ 1 × 10−8) dimorphic SNPs were detected in just three traits in the AA, seven traits in the CH, and three traits in the LM. The vast majority of all segregating autosomal SNPs (86% in AA to 94% in LM) had the same minor allele in both males and females. Differences (P ≤ 0.05) in allele frequencies between the sexes were observed for between 36% (LM) and 66% (AA) of the total autosomal SNPs that were segregating in both sexes. Dimorphic SNPs were located within a number of genes related to muscularity and/or size including the NAB1, COL5A2, and IWS1 genes on BTA2 that are located close to, and thought to be co-inherited with, the MSTN gene. Overall, sexual dimorphism exists in cattle at the genome level, but it is not consistent by either trait or breed.Sexual dimorphism, the phenomenon whereby males and females of the same species are distinctive in some aspect of appearance or size, has previously been documented in cattle for traits such as growth rate and carcass merit using a quantitative genetics approach. No previous study in cattle has attempted to document sexual dimorphism at a genome level; therefore, the objective of the present study was to determine whether genomic regions associated with size and muscularity in cattle exhibited signs of sexual dimorphism. Analyses were undertaken on 10 linear-type traits that describe the muscular and skeletal characteristics of both males and females of five beef cattle breeds: 1,444 Angus ( Sexual dimorphism is the phenomenon whereby males and females of the same species are distinctive in behavior, size, or appearance . This isLinear-type traits describing the muscular and skeletal characteristics of an animal are scored globally in both dairy  and beefhttp://www.icbf.com).Animal Care and Use Committee approval was not obtained for the present study as data were obtained from the existing Irish Cattle Breeding Federation (ICBF) national database (DHQ), inner thigh (DIT), and loin (DL), thigh width (TW), wither width (WOW), wither height (WH), back length (BL), hip width (HW), and chest width (CW) and depth (CD). The five muscular traits were scored (AA), Charolais (CH), Hereford (HE), Limousin (LM), and Simmental (SI) beef cattle scored between 6 and 16 mo of age between the years 2000 and 2016.Linear-type traits are routinely scored in both registered and commercial beef herds by trained classifiers from the ICBF as part of the Irish national beef breeding program . The type scored  on a scae scored  were scoData editing procedures and the justification for such edits are outlined in detail by Doyle et al. 2019, , b. AnimPrior to inclusion in the genome-wide association analysis, all phenotypes were adjusted within breed in ASREML  using thijkl is the linear-type trait, μ is the overall mean, HSDi is the fixed effect of herd-by-scoring date , AMj is the fixed effect of the age in months of the animal (j = 11 classes from 6 to 16 mo), DPk is the fixed effect of the parity of the dam , Animall is the random additive genetic effect of the animal where A is the numerator relationship matrix, and I is an identity matrix. The adjusted phenotype used in the subsequent analysis was the raw phenotype less the fixed-effect solutions of HSD, AM, and DP. This approach of pre-adjustment of phenotypes was undertaken  so as to generate a better estimate of especially the contemporary group effect that would have contained nongenotyped animals.where yWGS) as part of a larger data set of 638 662 multi-breed genotyped animals (SNPs) with a known chromosome and position on UMD 3.1, and SNPs with a call rate ≥ 90% within the panel were retained for imputation. Of the phenotypic data set of 81 200 animals, 19 449 animals from the five beef breeds  were imp animals . These 6 animals , b. The PAR) and non-PAR regions of the X chromosome was undertaken separately. The non-PAR region was imputed for males and females separately. The PAR region of the X chromosome was defined from 143,861,798 to 148,823,899 bp (MAF) ≤ 0.002 were not considered further  panel  were used to construct the genomic relationship matrix (GRM) for each sex within each breed as per Whole-genome association analyses were performed within each sex in each breed separately using a mixed linear model association analysis in GCTA . Autosomy is a vector of preadjusted phenotypes, µ is the overall mean, x is the vector of imputed genotypes, b is the vector of additive fixed effects of the candidate SNP to be tested for association, G is the genomic relationship matrix calculated from the HD SNP genotypes, and I is the identity matrix and where t-test:For each SNP that was analyzed in both sexes , the difference between the allele substitution effect sizes for each sex, in each breed separately, was calculated using a bx is the allele substitution effect in males (m) and females (f), SE is the estimated standard error of the allele effect, and n is the respective sample size. The presence of dimorphism was determined at each SNP based on the calculated P-value from the t-test statistic. An SNP with a P-value ≤ 1 × 10−5 was assumed to have a suggestively different allele effect in the two sexes, whereas an SNP with a P-value ≤ 1 × 10−8 was assumed to have a significantly different allele substitution effect in the two sexes. These levels of significance are generally consistent with recommended levels  regions that were dimorphic in more than one trait or more than one breed, each chromosome was split into 1 kb genomic windows and windows containing at least one suggestive (P ≤ 1 × 10−5) or significant (P ≤ 1 × 10−8) SNP were compared across the traits and breeds.To identify quantitative trait loci (P ≤ 1 × 10−8) dimorphism were detected for some traits, while suggestively (P ≤ 1 × 10−5) dimorphic SNP were detected for all of the traits in all five breeds; however, these SNPs differed both by trait and by breed.The scale of measurement, mean, and standard deviation of the linear-type traits in each sex in each breed is in P ≤ 1 × 10−8) was evident for a total of seven SNPs across just three traits  was evident for between 31 (DHQ) and 1,254 (HW) SNPs depending on the trait (P = 3.45 × 10−9) located within the ADGRA3 gene on Bos Taurus autosome 6 (BTA6) and was associated with DIT (P = 9.86 × 10-10), located on BTA8 that was associated with HW , between HW and TW (one window on BTA8 located at 66.264 Mb), and between HW and DL (two windows on BTA27 at 18.141 and 18.403 Mb). Only one 1-kb genomic window was suggestively associated with three skeletal traits  existed for between 51 (DIT) and 3,051 (CW) SNPs depending on the trait (P ≤ 1 × 10−8) was evident in all but three traits . Of the muscular traits, the most significantly dimorphic SNP was rs110487743 (P = 1.36 × 10−10), an intronic SNP located within the NAB1 gene on BTA2 which exhibited dimorphic associations for DL  was evident for fewer SNPs in all of the traits, ranging from 39 (DIT) to 256 (CW) SNPs depending on the trait; there was no evidence of significant dimorphism , an intronic variant located within the NR5A2 gene on BTA16 with an allele effect of +0.42 (SE = 0.13) in males but −0.61 (SE = 0.14) in females , whereas three traits  had evidence of significant dimorphism , an intergenic SNP located on BTA1 that had a dimorphic association with DHQ (P = 3.63 × 10−11), located on BTA11, that had a dimorphic association with CW (P = 5.71 × 10−7) had an MAF of 0.493 in males and 0.486 in females, but the allele substitution effects were just −0.07 (SE = 0.01) in males and +0.06 (SE = 0.02) in females.A total of 18,056,913 SNPs were segregating in the LM males and 17,767,237 SNPs were segregating in the LM females. Of these, 17,482,131 SNPs were segregating in both the male and female animals. Between 46 (TW) and 1105 (CW) SNPs were suggestively dimorphic , whereas no SNP was significantly dimorphic , an intron variant located within the GPC5 gene on BTA12 that had a dimorphic association with DIT; the allele substitution effect in the males was −0.85 (SE = 0.34), whereas the allele substitution effect in the females was +1.76 , that had a dimorphic association with CD (P = 2.20 × 10−7), located within the MMRN1 that had a dimorphic association with CW had an MAF of 0.439 in males and 0.472 in females with an effect size of +0.14 (SE = 0.03) in males and −0.13 (SE = 0.04) in females.A total of 18,257,175 SNPs were segregating in the SI males, and 17,814,297 SNPs were segregating in the SI females, whereas 17,319,250 of these SNPs were segregating in both the males and females. Between 87 (BL) to 679 (CD) SNPs were suggestively dimorphic , DHQ and WOW (102.906 Mb on BTA6), and WH and BL (145.410 Mb on BTAX).Few 1-kb windows containing a suggestive SNP overlapped among the muscular and skeletal traits. A single 1-kb window on BTA2, approximately 0.1 Mb from the morphism , and no morphism . Three 1morphism ; these wP ≤ 0.05) in allele frequencies between the sexes were observed for between 36% (LM) and 66% (AA) of the total autosomal SNPs that were segregating in both sexes.The numerically smaller breeds of AA, HE, and SI had approximately 1.5 times more SNPs segregating in only one sex than the numerically larger breeds of CH and LM. Of the total autosomal SNPs that were segregating in both sexes, between 86% (AA) and 94% (LM) had the same minor alleles in both sexes. However, differences (n = 1), on BTA12 between 87.128 and 87.303 Mb and containing the ENSBTAG00000032038 gene (n = 6), and on BTA24 between 31.661 and 31.671 Mb (n = 2). Also for CW, a single 1-kb window on BTA6 approximately 0.1 Mb from the SLC34A2 gene displayed dimorphic associations in both the AA and the CH. A single 1-kb window containing the ENSBTAG00000046311 gene on BTA10 had dimorphic associations with HW in both the AA and SI. In the CH and the SI, two common windows, one at 91.126 Mb on BTA7 and one at 70.827 Mb on BTA9, exhibited dimorphism with CD in both breeds.The vast majority of SNPs and 1-kb windows associated with any one trait were breed specific. The most windows displaying dimorphic characteristics in common for more than one breed was for CW in both the CH and LM with nine 1-kb windows common to these breeds occurring on BTA2 at 89.527 Mb  can be regarded as separate traits which are genetically correlated . In manyP < 0.05) for BL, WH, and DHQ in the CH, with no differences observed in the LM  containing dimorphic SNPs associated with multiple traits are thought to either be in strong linkage disequilibrium with MSTN (MSTN that was associated with muscularity in beef cattle (MSTN gene has already been documented as being responsible for muscular hypertrophy in cattle (PDHX gene on BTA15 that contained dimorphic SNPs for 3 of the muscularity traits in CH and has previously been associated with carcass quality traits in beef cattle (SNPs exhibiting sexually dimorphism were located within or close to a number of different genes that have previously been associated with muscularity and/or size in cattle , b. Threith MSTN  or have f cattle . The MSTn cattle  and is wn cattle . Anotherf cattle .Although many significantly and suggestively sexually dimorphic SNPs associated with the muscular and skeletal type traits were identified in the present study, the location and effect sizes of these tended to be both trait specific and breed specific. Both the allele substitution effect sizes and the allele frequencies of the dimorphic SNPs also differed by sex. This indicates that although sexual dimorphism exists in cattle at a genome level, it occurs at a low frequency but also differs both by trait and by breed.skab070_suppl_Supplementary_Figures_S1_S2Click here for additional data file.skab070_suppl_Supplementary_Table_S1Click here for additional data file.skab070_suppl_Supplementary_Table_S2Click here for additional data file.


Esox masquinongy) is the largest and most prized game fish in North America. However, little is known about Muskellunge genetic diversity in Iowa’s propagation program. We used Whole-Genome Sequencing of 12 brooding individuals from Iowa and publicly available RAD-seq of 625 individuals from the St. Lawrence River in Canada to study the genetic differences between populations, analyze signatures of selection, and evaluate the levels of genetic diversity in both populations. Given that there is no reference genome available, reads were aligned to the genome of Pike (Esox lucius). Variant calling produced 7,886,471 biallelic variants for the Iowa population and 16,867 high-quality SNPs that overlap with the Canadian samples. Principal component analysis (PCA) and Admixture analyses showed a large genetic difference between Canadian and Iowan populations. Window-based pooled heterozygosity found 6 highly heterozygous windows in the Iowa population and Fst between populations found 14 windows with fixation statistic (Fst) values larger than 0.9. Canadian inbreeding rate (Froh = 0.32) appears to be higher due to the inbreeding of Iowa population (Froh = 0.03), presumably due to isolation of subpopulations. Although inbreeding does not seem to be an immediate concern for Muskellunge in Iowa, the Canadian population seems to have a high rate of inbreeding. Finally, this approach can be used to assess the long-term viability of the current management practices of Muskellunge populations across North America.Muskellunge ( Esox masquinongy) is a species of freshwater fish native to North America and is the largest species of the Esocidae family. Moreover, Muskellunge is considered the most prized esocid by anglers. Originally, the species could be found in large lakes and rivers ranging from central Canada, east in the waters and branches of the Saint Lawrence River and even reaching south into Tennessee [Muskellunge  and 625 RAD-seq individuals from Saint-Lawrence river in Canada available on SRA (Sequence Read Archive) . The CanEsox lucius) was used as a reference. The Northern Pike is closely related to Muskellunge and is the most frequently studied member of the Esocids [n = 50) [Esox lucius * Esox masquinongy) which are considered valuable trophies by anglers. Nonetheless, Northern Pike is known to have a wider distribution than Muskellunge, inhabiting rivers, lakes and brackish water that range from North America to Europe and Eurasia [Given the lack of a reference genome of Muskellunge to align sequence data, the reference genome of Northern Pike ( Esocids . Both sp[n = 50)  and are  Eurasia ,15. ThisIowa’s Department of Natural Resources routinely samples Muskellunge populations as part of their hatchery operations through humanely netting random individuals each spring with the number of males and females averaging 165 and 130, respectively. On average, 35 males and 15 females are used to produce the milt and eggs needed to reach production quotas of fry. As part of this standard sampling, they obtain very small fin clips to estimate the age of the fish and other projects. Whole-genome sequence was produced from samples of a total of 12 individuals from 2 lakes  with Illumina paired-end sequencing, performed by Neogen . Ten of the twelve fish sampled were used that season to produce fry. As shown in Esox Lucius) version fEsoLuc1.pri was used to align the reads. Alignment was performed with BWA mem 0.7.17 [For all reads (Iowan and Canadian samples) read quality assessment was performed with FastQC 0.11.5 . Then, rm 0.7.17  using dehttp://samtools.sourceforge.net, accessed on 3 February 2021) was used to remove duplicate reads and low-quality mapped reads (q < 20), while BCFtools 1.10.2  was used to call and filter variants. In the case of samples from Iowa, a minimum depth of 10x and quality score of at least 20 were the parameters required to retain a variant for both the Iowa and Canada populations. For both datasets, only biallelic SNPs were retained to minimize the risk of including alleles from Northern pike in downstream analyses. Additionally, monomorphic alleles were removed for downstream analyses.SAMtools 1.10  and Pooled Heterozygosity (Hp) analyses were performed for all individuals from Iowa together, males only and females only since PCA showed clustering between sexes. Hp was used to calculate whole-genome distribution of heterozygosity, averaged over 0.5 Mb sliding windows, with 50% overlapping. For each window, Hp values were calculated using the following formula ,24:(1)HpFst score for each SNP was estimated using the --Fst flag in Plink 1.9, followed by the calculation of mean Fst values (mFst) in 500 Kbp windows with 50% overlapping using an in-house script as performed by Bertolini et al. . Mean FsGene ontology analyses of the genes contained in the windows of interest for Hp and mFst analyses were performed using FishEnrichr ,27.ROH, a percentage of homozygosity was calculated by summing ROH >1 Mb across the covered genome and dividing by the total base pairs represented in the SNP data obtained by calling SNPs from the Canadian and Iowan populations simultaneously. Runs of homozygosity were called using the --homozyg flag in Plink 1.9. To be considered ROH, segments had to be at least 1 Mb in length and have a maximum gap between SNPs of 500 Kb. However, several levels of stringency for other criteria were used to calculate ROH segments: Three sizes of window were examined  with 1, 2, and 3 heterozygotes per window allowed. These 6 levels of stringency were used to calculate Froh.Given the lack of pedigree information for the individuals included in this research, inbreeding coefficient (F) was estimated from runs of homozygosity (Froh) . This waAfter removing duplicate reads, whole-genome sequencing of the 12 individuals from Iowa produced an average of 217,503,897  reads per individual, out of which 96.27% were considered of high quality  and retained for further analyses. Here, an average of 86% (±0.57) of the reads mapped to the reference Northern Pike genome. This produced an average depth of 26.26× that ranged from 8× to 49× presumably for differences in sample quality. In the case of the samples from the Canadian population, 86% of the reads were considered high quality, 63% of the high-quality reads were successfully aligned to the Northern Pike genome and a depth of 11.44× was obtained for the sequenced sections of the genome. Details on the sequence data of each individual from Iowa and the averages for the Iowa and Canadian populations are shown in Breadths of coverage at different depth thresholds are shown in The variant calling pipeline produced three large sets of Single Nucleotide Polymorphisms (SNPs) that were used in further analyses. When variants were called for the twelve whole-genome sequenced samples from Iowa, a total of 36,627,942 biallelic SNPs were called out of which 8,218,039 were not monomorphic. Given the large number of SNPs, all SNPs that did not have a call rate of 100% were dropped, resulting in a final set of 7,886,471 SNPs that was used for all analyses involving only individuals from Iowa. The variant calling of Canadian samples produced 128,213 biallelic SNPs, out of which 16,059 were not biallelic.The transitions/transversions ratio (Ti/Tv) was calculated for the two set of samples, and the results were of 1.09 and 1.29 for samples from Iowa and Canada, respectively. When combining the two Canadian and Iowan dataset, a total of 108,132 biallelic SNPs were called with 22,705 SNPs not being monomorphic. After retaining only SNPs with a call rate larger or equal to 90%, a total of 16,867 SNPs were kept for further analyses. The number of Iowa-specific biallelic SNPs and biallelic SNPs called for Iowa and Canada along with the density of SNPs per megabase are shown in Principal Component Analysis (PCA) results for the Iowa and Canadian populations are shown in Admixture analyses confirmed the findings from PCA. Results from admixture analyses are shown in Regardless, the subgroup of the Iowa population that was considered for pooled heterozygosity (Hp) analyses , the same windows were seen to be highly heterozygous in all cases, as shown in amh) is responsible for sex determination in Northern Pike (Esox lucius), the genotypes of the individuals from the Iowa population were manually examined. As shown in Although PCA showed a clear clustering according to sex in the Iowan population, Fst analyses did not provide any insight on the differences. The highest mFst value found was of 0.09 and the overall Fst value between sexes was of 0.05 Shown in . NonetheAs shown, population stratification analyses in Iowa and Canada have markedly different genetic backgrounds, and this is reinforced by the mFst values obtained for the comparison between both populations, where the overall Fst value was 0.24. Window-based Fst results are shown in Inbreeding coefficients ranged from 0.00 to 0.44 depending on the level of stringency considered to call a segment as a run of homozygosity. Nevertheless, out of the six different stringency levels at which runs of homozygosity were analyzed, the level that was considered to produce the most realistic levels of inbreeding was with windows that included at least 20 SNPs while allowing a maximum of three heterozygotes. Individual details for the Iowa population and the average for the Canadian population are found in n = 50). There is evidence that using a highly related species is a valid option in mammals [Equus anus) reads were aligned to the Horse  genome and this approach has been used previously in Muskellunge [One of the main limitations of the present study is the absence of a reference genome for Muskellunge. Therefore, the traditional bioinformatics pipeline used in whole-genome sequencing analyses had to be adapted, to map the reads against the reference genome for Northern Pike which was available. Northern Pike is an esocid species closely related to Muskellunge with the same number of chromosomes , which was deemed as the master sex determinant gene in Northern Pike is located in chromosome 8 on the newest reference genome for the species, which was used in the present research. As shown in amh gene consistently show different genotypes in males and females. Although further analyses are needed for confirmation, these findings suggest that the amh gene may play a key role in sex determination in Muskellunge. In teleost fish, sex determination is achieved through a wide variety of mechanisms that include genetic, environmental and social factors . The cluEsox lucius is not thoroughly annotated many genes located in the windows with high mFst values were unannotated and thus caused gene ontology analyses to be unsuccessful. This being said, although not significant, a large number of enriched GO terms were related to growth and cellular differentiation, which highlights differences between both populations and perhaps their adaptation to the different environmental conditions present in their geographical locations. Additionally, after manual verification, several genes were found to be linked to congenital disorders and other fitness effects. Genes linked to congenital and behavioral disorders included dynein axonemal intermediate chain 1 [Several windows were found to have mFst scores of 1 when the populations from Iowa and Canada were compared. This reinforces the findings of PCA and Admixture analyses and was expected since both populations have distinct origins and have been isolated from each other to the best of our knowledge. Previous research has shown the presence of private alleles in the majority of populations of esocids in the Great Lakes ,39,40,41 chain 1 , Rho GTP chain 1 , ATPase  chain 1  and 5-Hy chain 1  while ge chain 1 .Given the type of genomic data available, runs of homozygosity were deemed the most appropriate method to estimate some level of inbreeding. However, given the arbitrary method in which stringency levels are set up to identify ROH segments , severalIn the case of the Canadian population, the markedly higher inbreeding coefficient is in line with previous research, confirming that the genetic structure of these population represents bottlenecked subpopulations of the overall St. Lawrence River population. These bottleneck events could have been caused by the small number of founders from the populations used to stock these locations . Given tThis genomic study is the first of its kind to focus on the Muskellunge population in Iowa. The results of the study provide the following conclusions:Although special attention is needed to filter variants appropriately, using the genome of a closely related species (Northern Pike) as a reference for alignment is a valid approach to perform population genomic analyses when no existing reference genome is available. Despite genetic differentiation based on sex, no major locus has been detected.Muskellunge from Canada and Iowa represent two clearly distinct populations with different estimated rates of inbreeding.Inbreeding does not seem to be an immediate concern for Muskellunge in Iowa.Apparent isolation of subpopulations has caused levels of homozygosity to be higher in the Canadian Muskellunge population.These results provide insight into the validity of using genomes of closely related species to perform genomic analyses of species that have no reference genome assembly available. Additionally, these approaches can be used to develop assessment procedures to monitor the long-term viability of the management practices of Muskellunge in US and Canada.


E,Z)-2,6-nonadienal, 53384 transcripts were also detected through RNA sequencing. The differentially expressed genes analyses included catalytic activity, transferase activity, adenosine diphosphate binding, transcription factor activity, and oxidoreductase activity. α-Pinene content and expression of the differentially expressed genes involved in terpenoid metabolism and enzyme activities in the terpenoid metabolic pathways gradually increased during the maturity of the fruit, and had maximum values at day 8 of storage. Moreover, the integrative analyses revealed potential molecular insights of mango development and aroma formation in the fruit.This study aimed to evaluate the changes in aromatic components and other chemical properties of Tainong mango during fruit development, ripening, and storage. As the volatiles of Tainong mango and their related molecular mechanisms remain unclear, volatile profile, metabonomics, and transcriptome analyses were applied to investigate the molecular determinants of the synthesis of aroma components in mango during fruit development and storage. Total acids, total sugar, total carotenoids, enzyme activities of the mango pulp samples were also determined. Volatile components of the mango pulp samples were identified using a gas chromatography-mass spectrometric method. Ribonucleic acid (RNA) sequences of the samples were analyzed by real-time polymerase chain reaction. The results showed that 181 volatiles were isolated and identified in the fruit at seven stages. Compared to the other stages, mango collected on day 8 and day 12 had higher concentrations of 17 volatile components, especially ( Mangifera indica L.), the king of tropical fruit, is native to South Asia (Mango (uth Asia . It is puth Asia . The quauth Asia . The volR)-linalool, -2,6-nonadienal, and terpinolene are the essential aromatic compounds determined in yellow Thai Keaw mangoes (E)-2-decenal, hexanal, (E)-β-ocimene, (E)-2-hexenal, γ-terpinene and (Z)-3-hexenal have been detected as the main aromatic components in Thai Khieo Sawoei mangoes -2,6-nonadienal, (E)-β-ionone, and (E)-2-nonenal have been potentially identified as the most important aroma compounds in the 20 cultivars of mango -2,6-nonadienal, (E)-β-ionone, β-damascenone, (E)-2-nonenal, ethyl butanoate, and ethyl 2-methylpropanoate are the odor-active compounds of Corazon mangoes  since 6 years ago. The ambient temperatures of the nursery ranged between 25 and 28°C, and the altitude is 110 m. Mango samples were collected from the nursery at different stages of fruit development . Fruit samples harvested at 90 days after the flowering were also kept for 4, 8, and 12 days in the laboratory at a controlled temperature of 25°C and a humidity level of 95%. Seven mangoes of an average weight of 100 ± 10 g were sampled. The fruits were picked from different trees at the same positions on the day of fruit collection. Mango peels were removed, and the pulps were directly used for volatile analysis. All pulp samples were stored at –80°C before extraction. Data of triplicate analyses were obtained for each analysis.Total acid content, total sugar content, and carotenoid content of the mango pulp samples were measured according to the methods described by D-xylose-5-phosphate reductase (DXS), 1-deoxyxylose-5-phosphate synthase (DXR), geranyl pyrophosphate synthetase (GPPS), geranylgeranyl pyrophosphate synthetase (GGPPS), pyruvate carboxylase (PC), diacylglyceryl transferase (DGAT), farnesyl diphosphate synthase (FPS), and hydroxymethyl glutarate monoacyl CoA reductase (HGMR) were measured using plant ELISA kits, where the all the ELISA kits were purchased from a local chemical supplier . A microplate reader  was used to obtain the absorbance readings of each test; the analyses were performed according to the instructions provided by the manufacturers. The results were calculated based on the formula provided in the manufacturers’ instructions.Enzyme activities of 1-deoxy-Exactly 2.5 g of homogenized mango pulp was added with 2.5 mL of saturated sodium chloride and 100 μL of internal standard solution  in a 20 mL headspace bottle . The bottle was sealed with a crew cap fitted with a PTFE/silicone septum. After a 15-min agitation at 50°C and 250 rpm, volatile compounds were extracted with 50/30 μm DVB/CAR/PDMS as the extraction fiber, which was later exposed to the headspace for 40 min.m/z 20–400.The gas chromatography-mass spectrometry (GC-MS) analysis was performed using a GC-mass spectrometer  to quantify the volatile components of mango pulp samples. Separation of the volatile compounds was performed using a DB-Wax column . The extract was injected in splitless mode and desorbed at 260°C for 5 min. Helium was used as the carrier gas with a constant flow rate of 1 mL/min. The initial oven temperature was 40°C, and then programmed at 5°C/min to 220°C, followed by an increase of 20°C/min to 250°C, and finally held at 250°C for another 2.5 min. Electron ionization (EI+) was set at 70 eV, and the data were recorded in scan mode of Based on the MS fragmentation patterns and linear retention indices, volatile compounds were identified and quantified through comparisons with the NIST14 library. The differential volatiles in each group were screened according to the following criteria: fold change ≥ 1.5 or fold change ≤ 0.67; variable importance in project (VIP) ≥ 1.The real-time quantitative PCR (qRT-PCR) was performed using the fluorescent intercalating dye SYBR Green in a detection system , and MiACT was used as a standard control . The two® spectrophotometer and Agilent 2100 bioanalyzer, high-quality mRNA was enriched by poly-T oligo-attached magnetic beads. The library was constructed using the NEBNext® UltraTM RNA Library Prep Kit for Illumina® . After the fragmentation of the purified mRNA by divalent cations at elevated temperature, the first-strand complementary DNA (cDNA) was generated using a random hexamer primer. The second strand of cDNA was generated using DNA polymerase I of M-MuLV reverse transcriptase (RNase H-) and RNase H . After methylating the 3’ ends of DNA fragments and ligating the adaptor for hybridization, the library fragments were purified using AMPure XP beads . Under the action of high-fidelity DNA polymerase, Universal PCR primers, and Index (X) primer, PCR was performed, and the PCR products were purified with AMPure XP system. The list of primers used is showed in TM platform .RNAprep pure plant plus kit  was used to purify total ribonucleic acid (RNA) in mango pulp samples. The purification steps were done in accordance with the manufacturer’s instructions. After a quality checked using NanoPhotometerAfter the quality check and adaptor trimming, clean reads were assembled using the Trinity software, and the transcripts were generated. The reads contained unknown and over 50% low-quality nucleotides (Qphred ≤ 20) were removed. Quality of the transcripts was evaluated using the Benchmarking Universal Single-Copy Orthologs. The coding sequences (CDs) were predicted through comparisons with the NR and Swissprot protein libraries, and using ESTScan v3.0.3 software.The clean reads were mapped to the transcripts using RSEM v1.1.17 software . The gen2 (fold change) ≥ 1 and padj < 0.05.Differential expression analysis was done using the DEGSeq2, which was according to the criteria as follows: log1.The gene ontology (GO) enrichment analysis of differentially expressed transcripts in mango pulp samples was performed using the KOBAS 2.0, GOseq, and GO databaseThe correlation analyses of differential volatiles and differential expression transcripts are achieved using the Pearson correlation analysis (SPSS version 15.0).The mango morphology was observed. Total acid, total sugar, and carotenoid contents of the fruit pulp were determined at different stages of fruit development and storage. As shown in As shown in p < 0.05). However, the highest significant difference in the numbers of differential volatiles was determined between 12 DAP and 0 DAP samples (p < 0.01).Volatile profiles of mango pulp samples were determined based on the headspace solid-phase microextraction method and GC-MS. The differential volatiles in mango pulp samples of different groups were screened according to the following criteria: fold change ≥ 1.5 or fold change ≤ 0.67; VIP≥ 1. The numbers of significantly different volatiles in each group are shown in D-limonene, (E)-2-hexenal, and hexanal; three up-regulated volatiles found in the 80 DAF vs. 40 DAF group were (E)-2-hexenal, hexanal, and 1,3-bis-benzene; ten up-regulated volatiles determined in the 12 DAP and 0 DAP group were (2E)-nonenal, (2E)-(2-pentenyl)furan, geraniol, -2,6-nonadienal, ethanol, butanoic acid, p-mentha-1,5,8-triene, (E)-3,7-dimethyl-2,6-octadienal, (Z)-3,7-dimethyl-2,6-octadienal, and (6Z)-nonen-1-ol; seven up-regulated volatiles determined in the 8 DAP vs. 0 DAP group were (E)-butanoic acid 3,7-dimethyl-2,6-octadienyl ester, geraniol, linalool, ethanol, -2,6-nonadienal, α-pinene, and (E)-2-hexen-1-ol. These up-regulated volatiles might be the dominating aromatic components of Tainong mango.The data determined for the 12 DAP and 0 DAP group included ten up-regulated volatiles and four down-regulated volatiles. As shown in D-limonene, β-myrcene, γ-terpinene, 2-carene, 3-carene, 4-carene, copaene hexanal, and (E)-2-hexenal (Dataset 1). In this study, the amounts of (E)-2-nonenal, (2E)-(2-pentenyl)furan, geraniol, -2,6-nonadienal, ethanol, butanoic acid, p-mentha-1,5,8-triene, (E)-3,7-dimethyl-2,6-octadienal, (Z)-3,7-dimethyl-2,6-octadienal, and (6Z)-nonen-1-ol in the mango pulp samples of 12 DAP were higher than that of the sample at baseline (0 DAP) (E)-3,7-dimethyl-2,6-octadienyl-butanoate, geraniol, linalool, ethanol, -2,6-nonadienal, α-pinene, and (E)-2-hexen-1-ol in the mango pulp samples of 8 DAP were higher than the sample at baseline.As shown in Dataset 1, 181 volatiles were isolated and identified. The major volatiles detected in the ripen fruit samples are α-pinene, β-phellandrene, β-ocimene,  (0 DAP) , whereasMolecular insight of volatile biosynthesis in mango pulp during the fruit development and storage can be obtained from RNA sequencing (RNA-Seq) analysis. Seven cDNA of the fruit samples, collected after 40, 60, 80, and 90 DAF, and at 4, 8, and 12 days of storage were constructed and large-scale sequenced. 47.29-79.61 million raw reads and 46.48-78.33 million cleaned reads were generated in each sample . The Q20After assembled using the Trinity software, the transcripts of each sample were acquired. The frequency and numbers of transcripts and unigene in the corresponding length are shown in Hierarchical clustering analysis of Pearson correlation according to the level of gene expression levels revealed that a high correlation was found for the gene expression in mango pulp samples between different development stages . BesidesThe differentially expressed transcripts in the groups of 60 DAF vs. 40 DAF, 80 DAF vs. 40 DAF, 90 DAF vs. 40 DAF, 80 DAF vs. 60 DAF, 90 DAF vs. 60 DAF, 90 DAF vs. 80 DAF, 4 DAP vs. 0 DAP, 8 DAP vs. 0 DAP, 12 DAP vs. 0 DAP, 8 DAP vs. 4 DAP, 12 DAP vs. 4 DAP, and 12 DAP vs. 8 DAP were identified based on the DEGSeq analysis for obtaining an overview of the interesting genes. The numbers of differentially expressed transcripts in each group are shown in The GO analysis was performed to show the comprehensiveness of functions of differentially expressed transcripts. As shown in E)-butanoic acid, geraniol, hexanal, 6-methyl-5-hepten-2-one, linalool, ethanol, α,α,4-trimethyl-benzenemethanol, -2,6-nonadienal, α-pinene, and (2E)-hexen-1-ol; 420 differentially expressed transcripts determined in the group of 4 DAP vs. 0 DAP were either positively or negatively correlated with α,α,4-trimethyl-benzenemethanol and -2,6-nonadienal; 601 differentially expressed transcripts determined in the group of 12 DAP vs. 0 DAP were either positively or negatively correlated with (2E)-nonenal, hexanal, (2E)-(2-pentenyl)furan, geraniol, (2E)-hexenal, -2,6-nonadienal, ethanol, (E)-3,7-dimethyl-2,6-octadienal, (Z)-3,7-dimethyl-2,6-octadienal, butanoic acid, α,α,4-trimethyl-benzenemethanol, (6Z)-nonen-1-ol, p-mentha-1,5,8-triene, and β-ocimene; 277 differentially expressed transcripts determined in the group of 80 DAF vs. 60 DAF were either positively or negatively correlated with α,α,4-trimethyl-benzenemethanol, 1-(4-methylphenyl)- ethanone, 1-methyl-4-(1-methylethenyl)-benzene, p-mentha-1,5,8-triene, alloaromadendrene, and 3-hexenal; 381 differentially expressed transcripts determined in the group of 60 DAF vs. 40 DAF were either positively or negatively correlated with D-limonene, ethanol, copaene, linalool, α-phellandrene, (2E)-hexenal, hexanal, and β-myrcene (Dataset 3).Correlation analysis was performed to explore the transcripts related to aroma biosynthesis during the development and storage of Tainong mango (p < 0.05). It indicated that α-pinene is one of the key aroma components of mango. The results showed that a total of eight genes involved in the diterpenoid metabolism pathways -furanone was an important aromatic compound detected in mango cultivars Haden, White Alfonso, Praya Sowoy, Royal Special, and Malindi . Howeveran mango . Some ofan mango .E,Z)-2,6-nonadienal. Moreover, three up-regulated volatiles in both groups of 60 DAF vs. 40 DAF and 80 DAF vs. 40 DAF, and about 180 volatile components identified in the mango pulp samples might be the potential aromatic compounds.The mango stored at these two stages had a more intense aroma than the other five stages. It could be due to a gradual increase in the total sugar content and a gradual decrease in total acid content during fruit maturity . The resThe transcriptome studies have put forth important information concerning the development of mango of different cultivars, such as Zill , Langra In this study, RNA-Seq was performed to explore the molecular mechanism of aroma compounds biosynthesis in mango during the fruit development, ripening, and storage. A large number of differentially expressed transcripts involved in multiple pathways have been identified among these samples. Quantitative RT-PCR analysis also showed that the relative expression patterns of eleven genes were consistent with the RNA-Seq data. The results indicate that the transcriptome data are reliable. The number of differentially expressed transcripts in the samples consisted of over 50,000 up-regulated and down-regulated transcripts. These genes are related to the development of mango. Only three genes were highly expressed and these genes are closely related to the biogenesis of aromatic components during the fruit development. These two genes, E5.5.1.13 and KAO, have never been reported in the transcriptome analysis and metabolic profiling of mango. Another gene that was also found to be highly expressed in the mango pulp samples of 8 DAP. K15095 was the only gene found to be highly expressed during the storage of mango.A large number of transcription factors (TFs) has been identified in the fruit samples as these factors are known to regulate the fruit development and formation of the aroma of the fruit . These TE,Z)-2,6-nonadienal, were the key aroma active compounds in Tainong mango. RNA-Seq and comparative analysis showed a large number of DEGs during development and after picking. These involved in catalytic activity, transferase activity, ADP binding, transcription factor activity, and oxidoreductase activity. The content of α-pinene, expression of genes involved in terpenoid metabolism, and enzyme activities in terpenoid metabolic pathways gradually increased after picking and generally attained their maximum levels on day-8. The integrative analyses also revealed potential molecular insights into fruit development and aroma formation. This study provides important cues for future work on mango quality improvement.Mango is favorable due to its pleasant sensory quality and high nutritional values. Although some of the aromatic components have been identified in different cultivars of mango, little is known about the volatile profile in the Tainong mango. 181 volatiles were isolated and identified in fruits collected at seven stages. These components, especially ethanol and (https://www.ncbi.nlm.nih.gov/), and the data accession number is PRJNA697524.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: (CL, MX, PY, and YT performed the experiments. CL conceived and designed the research. LL and JS analyzed data. MX and XH prepared the manuscript. CL, JS, and HEK checked and revised the manuscript. All authors contributed to the article and approved the submitted version.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.


Brassica napus, AACC, 2n = 4x = 38), which is a natural hybrid formed from a cross between turnip  and cabbage , that there is significantly more crossovers occurring along the entire A chromosomes in allotriploid AAC (crossbetween B. napus and B. rapa) than in diploid AA or allotetraploid AACC hybrids. We demonstrated that these allotriploid AAC hybrids are highly efficient to introduce new variability within oilseed rape varieties, notably by enabling the introduction of small genomic regions carrying genes controlling agronomically interesting traits.The selection of varieties more resilient to disease and climate change requires generating new genetic diversity for breeding. The main mechanism for reshuffling genetic information is through the recombination of chromosomes during meiosis. We showed in oilseed rape  and that it is linked to AAC allotriploidy and not to polyploidy per se. To that purpose, we compared the frequency and the distribution of crossovers along A chromosomes from hybrids carrying exactly the same A nucleotide sequence, but presenting three different ploidy levels: AA, AAC and AACC. Genetic maps established with 202 SNPs anchored on reference genomes revealed that the crossover rate is 3.6-fold higher in the AAC allotriploid hybrids compared to AA and AACC hybrids. Using a higher SNP density, we demonstrated that smaller and numerous introgressions of B. rapa were present in AAC hybrids compared to AACC allotetraploid hybrids, with 7.6 Mb vs. 16.9 Mb on average and 21 B. rapa regions per plant vs. nine regions, respectively. Therefore, this boost of recombination is highly efficient to reduce the size of QTL carried in cold regions of the oilseed rape genome, as exemplified here for a QTL conferring blackleg resistance.Meiotic recombination is the main tool used by breeders to generate biodiversity, allowing genetic reshuffling at each generation. It enables the accumulation of favorable alleles while purging deleterious mutations. However, this mechanism is highly regulated with the formation of one to rarely more than three crossovers, which are not randomly distributed. In this study, we showed that it is possible to modify these controls in oilseed rape ( Meiotic recombination shuffles parental alleles to produce new allelic combinations in the progenies, hence producing new genetic diversity at each generation. This biological mechanism is a key evolutionary process that is commonly used in plant breeding to accumulate favorable alleles and purge deleterious mutations ,2,3. ThiArabidopsis, the others giving rise to non-crossovers (NCOs) [A. thaliana , thereby limiting the overall number of crossovers generated per meiosis [A. thaliana, for which 80% of crossovers formed are observed within less than 26% of the genome [However, crossover formation is under tight genetic control . Typicals (NCOs) ,6. In th meiosis ,8,9,10.  meiosis , maize [ meiosis  or potat meiosis . Fine ch meiosis ,15,16,17 meiosis ,19,20, o meiosis . Conseque genome .Brassica AAC allotriploids (2n = 3x = 29). Indeed, in these viable and fertile hybrids, resulting from the cross between the rapeseed B. napus  and its B. rapa diploid progenitor , crossover frequency obtains an unprecedent boost on the A07 chromosome when compared to the results obtained from diploid or allotetraploid hybrids [Brassica AAC allotriploids are currently being investigated but are yet to be deciphered. Nevertheless, it is feasible to recover an AACC genomic structure from AAC plants [B. napus and its B. rapa diploid progenitor, combined with reshaped homologous recombination, would facilitate small introgressions of targeted valuable loci deriving from B. rapa while preserving the agronomic value of B. napus cultivars. Recently, simulations conducted with AA and AAC Brassica genetic maps tend to confirm this putative benefit [per se in this recombination pattern, as well as the true potential of using allotriploids compared to allotetraploids in oilseed rape breeding programs. Moreover, the potential of using AAC allotriploids  in reducing the size of a quantitative trait loci (QTL) identified in B. napus remains to be assessed.Despite the strong regulation of meiotic recombination, extensive variations are observed for crossover number and distribution both between and within species ,23. Fact hybrids . The A c hybrids . MoreoveC plants , enablin benefit ,40. The Brassica AAC allotriploid hybrids can be used for oilseed rape breeding. Firstly, we tested the impact of polyploidy per se vs. AAC allotriploidy by comparing the recombination profile in AA, AAC and AACC hybrids carrying an identical A genotype. We confirmed that the modification of meiosis control only occurs in AAC allotriploid hybrids. Secondly, compared to a previous study [B. rapa introgressions observed along each A chromosome, depending on the hybrid ploidy level. Finally, we highlighted the interest of using allotriploids in breeding programs, by taking the example of a large resistance QTL against Leptosphaeria maculans [In the present study, we analyzed how this unique recombination pattern observed in us study , we densmaculans  present Brassica hybrids presenting three different ploidy levels but sharing a genetically identical A genome sequence (n = 2x = 20) F1 hybrid, which was obtained by crossing the diploid AnAn (2n = 2x = 20) genotype extracted from the French variety B. napus cv. Darmor AnAnCnCn (2n = 4x = 38) [B. rapa ssp. pekinensis cv. Chiifu-401-42 , was backcrossed with the Korean variety B. napus cv. Yudal and gave rise to 329 plants; (2) the allotriploid hybrid AnArCn (2n = 3x = 29), which was obtained by crossing the allotetraploid B. napus cv. Darmor AnAnCnCn (2n = 4x = 38) and the pure inbred line B. rapa ssp. pekinensis cv. Chiifu , was then backcrossed with the Korean variety B. napus cv. Yudal and gave rise to 109 plants. This plant material was complemented by backcrossing the AnArCn hybrid (2n = 3x = 29) to B. napus cv. Darmor (131 plants selected among 234). To produce the allotetraploid hybrid AnArCnCo (2n = 4x = 38), we first crossed the pure inbred line B. rapa cv. Chiifu  with the doubled haploid B. oleracea cv. HDEM  and then performed embryo rescue on the obtained amphihaploid  as described in [n = 4x = 38), hereafter referred as ChEM. This latter plant was crossed with B. napus cv. Darmor as female, and the AnArCnCo F1 hybrid gave rise to 213 plants after backcrossing with B. napus cv. Darmor as male. All parental accessions were provided by the Biological Resource Center BrACySol .We compared crossover rate between homologous A chromosomes of sequence . To that4x = 38)  with theribed in . This hyB. oleracea BoB014O06 BAC [Chromosome numbers were assessed in leaves by flow cytometry as described by . For the4O06 BAC  as “genoTM HD system (Fluidigm technology) and KASParTM chemistry , as well as from the Brassica 15K Illumina Infinium SNP array . The context sequences of each SNP marker were physically localized on the reference genome B. rapa Chiifu v1.5 [B. napus cv. Darmor-bzh v10 [−20) [B. rapa and BB in B. napus, or vice versa) were selected for either the establishment of genetic maps or assessment of introgressed regions from the diploid parent into the B. napus genome.Genomic DNA was extracted from lyophilized young leaves with the sbeadex maxi plant kit  on the oKtopure robot at the GENTYANE platform . Genotyping data were obtained using the 202 SNP markers already defined , and revifu v1.5  and B. n-bzh v10  using BL10 [−20)  and by kThe first genetic maps were established separately for the AnAr, the AnArCn and the AnArCnCo populations using the CarthaGene software . EstablIntrogressions were determined by two methods, if either one SNP or at least two consecutive SNP markers were detected as heterozygous in each plant from the backcross progeny. To estimate the length of the introgresssed regions detected by only one SNP and to prevent the underestimation of the smallest introgressions, we considered the position of the consecutive previous and following SNPs. The positions of the first and last heterozygous SNPs were used to infer the size of introgressed regions defined by at least two consecutive SNPs, preventing the overestimation of introgressions. Each SNP can be present in introgressions of different sizes in different plants of a progeny and we calculated per population for each SNP position the average size of the introgressions carrying this marker.Leptosphaeria maculans) resistance [B. napus cv. Darmor A01 chromosome was selected for its pericentromeric localization. The context sequences of the SNP markers (derived from Brassica 60 K Illumina infinium array) flanking this QTL were retrieved and physically localized on the B. napus reference genome Darmor-bzh v10 [A quantitative trait locus (QTL) involved in blackleg (sistance  and pres-bzh v10  using BL-bzh v10 .B. napus cv. Darmor-bzh v10 [−20). The pericentromeric borders for each chromosome were inferred by using the mean gene density along the chromosomes  agai-bzh v10  and by eented in ) and defThe heterogeneity of crossover rates among progenies was assessed for every interval between consecutive SNP markers using a 2-by-2 chi-squared analysis considering a significance threshold of 5%. Additionally, the heterogeneity of crossover rates among progenies was evaluated at chromosome and genome scales using 2-by-2 chi-squared tests. For these tests, a conservative Bonferroni-corrected threshold of 5% was applied, using the number of intervals between adjacent SNP markers per A chromosome or for the A genome (as described in ).The relationships between the relative size of introgressions normalized per A chromosome (%) vs. their relative distance from the centromeres (%) were studied by regression analyses, using the Spearman rank correlation, for linear (y = ax + b) and order 2 polynomial (y = ax² + bx + c) regressions (as described in ).Brassica, we generated AnAr, AnArCn and AnArCnCo F1 hybrids that present the same A genome nucleotidic sequence originating from B. napus cv. Darmor (AnAnCnCn) and B. rapa cv. Chiifu (ArAr)  . The dipu (ArAr)  and obseu (ArAr) a. To proivalents . These rivalents b with raivalents c. After 1 hybrid d and pro used in , allowinp = 0.18). However, significant variations were detected for 3x vs. 2x and 3x vs. 4x. In both cases, A genome-wide crossover rate was on average 3.6-fold higher in the allotriploid hybrids . After anchoring the polymorphic SNPs on the B. rapa cv. Chiifu v1.5 Ar genome, crossover distribution was analyzed by assessing heterogeneity of crossover rates among progenies for every interval between adjacent SNP markers. Similar recombination landscapes were thus identified between the AnAr and AnArCnCo hybrids as they evidenced significant differences in crossover rate in only two intervals out of 192 (1%), spanning 3.19 and 1.53 Mb on chromosomes A02 and A06, respectively . However, comparison of AnArCn with AnAr and AnArCnCo hybrids revealed significant differences in crossover rate in more than 60%  of the intervals (p < 0.05) (p < 0.05). Therefore, we concluded that recombination landscapes along A chromosomes of the allotriploid hybrid differed in a similar way, in regard to pericentromeric regions, to those of the diploid and allotetraploid hybrids.In total, cumulated genetic maps of the 10 A chromosomes of AnAr, AnArCn and AnArCnCo hybrids showed 830.2, 2902.3 and 790.8 cM, respectively. Pairwise comparison indicated a similar level of recombination at the whole A genome scale between the AnAr and AnArCnCo hybrids  . ImportaB. napus cv. Darmor-bzh v10 A genome, increasing the number of SNPs used for the comparison by more than ten times. On average, a polymorphic marker was observed every 150 kb along each A chromosome. Using this larger number of markers, we obtained genetic maps of 3045.4 and 827.7 cM for AnArCn and AnArCnCo hybrids, respectively. We thus improved the accuracy of our comparison and confirmed a higher recombination frequency in AnArCn (3.7-fold) compared to AnArCnCo hybrids (p < 2.2 × 10−16).To obtain a finer analysis of the crossover distribution in the allotriploid and allotetraploid hybrids, we then analyzed their progenies using the 15K SNP Illumina infinium array. A total of 2340 polymorphic SNP markers were physically anchored on the B. rapa genetic diversity introduced per generation is similar in allotriploid and allotetraploid hybrids (50% and 48%). However, as expected, with a larger number of crossovers occurring per chromosome pair and per meiosis, we observed that the mean introgression sizes were significantly smaller when arising from the meiosis of allotriploid compared to allotetraploid hybrids, with an average of 7.6 Mb vs. 16.9 Mb, respectively   per plant vs. nine regions in the backcross progeny of the allotetraploid hybrid  (p < 2.2 × 10−16). For both AnArCn and AnArCnCo hybrids, these results demonstrate that the size of introgressions increases toward the centromeres, which is in agreement with the crossover distribution along the chromosome arms: the higher rates of recombination result in smaller sizes of introgressions. However, the significant binomial regression unraveled in the AnArCnCo hybrid translates to a less continuous gradient of introgression size from the telomere to the centromere. Strikingly, 70% of the chromosome arms carried introgressions, representing about 40% of the chromosome size in this hybrid. Analyses conducted at the scale of individual A chromosomes revealed similar results  a. As a s× 10−53) b. Intere× 10−53) . In an A results . The onlBrassica AAC allotriploids for breeding purposes, we chose as an example a QTL responsible for blackleg resistance that is present in the A01 pericentromeric region [B. napus cv. Darmor-bzh v10 reference genome. The comparison of the recombination profiles from backcrossed progenies of allotriploid and allotetraploid hybrids revealed that significantly more crossovers were formed during the meiosis of the AnArCn hybrid compared to the AnArCnCo hybrid in this particular region, with 131 vs. 24 crossovers (corresponding to 58 and 16 different haplotypes), respectively . At least one recombination event was detected in 70.23% vs. 11.27% of the AnArCn and AnArCnCo progenies, respectively, highlighting the highest diversity of gametes produced by the AnArCn hybrid compared to the AnArCnCo hybrid. It allowed us to break down the QTL into five regions of 0.02 to 10.7 Mb in the AnArCnCo population, and into 13 regions of 0.004 to only 4.96 Mb in the AnArCn population  compared to allotetraploids, and, more interestingly, with the formation of crossovers all along the A chromosomes even in usually cold regions such as pericentromeres. These results are in agreement with previous data [per se can deeply modify the homologous recombination frequency and distribution.The production of ous data  comparinous data , who preous data  observedB. rapa: either direct crosses between B. napus and B. rapa or a bridge with a resynthesized oilseed rape. For the latter, it has been clearly demonstrated that homoeologous pairing occurs during the first meiosis of synthetics [B. napus breeding . Indeediew, see . Similariew, see , diseaseiew, see  or even iew, see . The maj disease ,67. HoweB. napus and B. rapa significantly improve the genetic shuffling of diversity all along the A genome. This strategy has also been largely used to generate new oilseed rape lines by backcrossing and/or selfing AAC hybrids [B. rapa in Asian B. napus breeding programs may partially explain why Asian oilseed rape varieties appear as a specific group of oilseed rape varieties and why it is still possible to detect B. rapa introgression in this material [B. napus and B. rapa have been performed to introduce in B. napus major resistance genes to clubroot [On the contrary, we showed that direct crosses between  hybrids ,69, prod hybrids . The frematerial . Similarclubroot  or blackclubroot .B. rapa but also to reduce the B. napus QTL size. Tourrette et al. [Brassica allotriploids [B. rapa to the region of interest as well as to favor the cloning of QTL carried by B. napus in a cold recombinant region by breaking down the linkage disequilibrium. Thus, it will be of particular interest to more frequently apply this strategy in order to rapidly and more efficiently identify the genes underlying the different QTL identified for disease resistance or other important traits for B. napus . Takingsemblies ,75, it bB. napus and its other diploid progenitor B. oleracea, remains to be deciphered (currently in progress). Even if these later CCA hybrids are more difficult to generate [B. napus breeding programs. In fact, different studies revealed that it could be useful to introduce into B. napus new diversity from B. oleracea [B. napus to B. oleracea [B. rapa) or CCB (B. carinata × B. oleracea) hybrids, and thus possibly be useful for Brassica breeding.Whether the reshaping of homologous recombination observed in AAC allotriploids similarly occurs in CCA allotriploids, resulting from the cross between generate , improveoleracea , such asoleracea , and conoleracea . It willoleracea , e.g., eB. rapa to B. napus compared to AACC hybrids, with a decreasing gradient of the introgression size from the centromere to the telomere in both hybrids. For breeders, the introgression of smaller genomic regions highlights the interest in using AAC allotriploid hybrids to further break the linkage disequilibrium. Additionally, the unique recombination landscape observed in AAC allotriploid hybrids will facilitate the identification of candidate genes underlying QTL of agronomical interests, most particularly in the normally cold pericentromeric regions.Meiotic recombination is a key process that generates new genetic diversity and enables the combination of favorable alleles. However, it is strictly regulated, both in frequency and distribution. In this study, we were able to demonstrate that an AAC allotriploid hybrid presents a higher recombination rate and modified distribution compared to AA or AACC hybrids (with the latter hybrids presenting a similar recombination landscape). The boost of recombination observed in AAC hybrids allows the introduction of more and smaller genomic regions from