Brassica napus lines are often unstable and infertile, unlike B. napus cultivars. We tested this hypothesis by characterizing 41 resynthesized B. napus lines produced by crosses between 8 Brassica rapa and 8 Brassica oleracea lines for copy number variation resulting from nonhomologous recombination events and fertility. We resequenced 8 B. rapa and 5 B. oleracea parent accessions and analyzed 19 resynthesized lines for allelic variation in a list of meiosis gene homologs. SNP genotyping was performed using the Illumina Infinium Brassica 60K array for 3 individuals per line. Self-pollinated seed set and genome stability (number of copy number variants) were significantly affected by the interaction between both B. rapa and B. oleracea parental genotypes. We identified 13 putative meiosis gene candidates which were significantly associated with frequency of copy number variants and which contained putatively harmful mutations in meiosis gene haplotypes for further investigation. Our results support the hypothesis that allelic variants inherited from parental genotypes affect genome stability and fertility in resynthesized rapeseed.Established allopolyploids are known to be genomically stable and fertile. However, in contrast, most newly resynthesized allopolyploids are infertile and meiotically unstable. Identifying the genetic factors responsible for genome stability in newly formed allopolyploid is key to understanding how 2 genomes come together to form a species. One hypothesis is that established allopolyploids may have inherited specific alleles from their diploid progenitors which conferred meiotic stability. Resynthesized  Polyploidy is the heritable condition of possessing more than 2 sets of chromosomes . The extTriticum aestivum), prevention of homoeologous crossovers is known to be regulated by the Ph1 locus (ZIP4 (TaZIP4-B2), a gene which is essential for homologous crossover formation as well as synapsis in wheat  and is the most economically important genus within this tribe (Brassica napus (genome AnAnCnCn) was spontaneously formed by recent allopolyploidy between ancestors of Brassica oleracea  and Brassica rapa  in the last 7,500 years and is thought to be polyphyletic in origin , for which a differentially expressed meiosis gene candidate RPA1C was identified  or Chinese kale , and A genome genotypes are yellow sarson (B. rapa ssp. trilocularis), oilseed turnip   and 16 B. oleracea lines including a core set of 64 cross combinations between 8 B. rapa and 8 B. oleracea lines. B. rapa was always the maternal parent in the crosses. Seeds from 41 resynthesized B. napus genotypes produced from crosses between 8 B. rapa  and 8 B. oleracea parent genotypes  via embryo rescue, chromosome doubling and self-pollination (S1 generation),and their B. rapa and B. oleracea parent genotypes were used in this study. Established Brassica cultivars were used as controls for fertility in our experiment: winter-type B. napus ‘Darmor’, spring-type B. napus ‘Argyle’, and semiwinter-type B. napus ‘Ningyou7', as well as B. rapa var. oleifera (unknown accession) and B. oleracea var. botrytis ‘NGB 1810.2'.The materials used in this study comprise resynthesized inensis) . Abel etB. napus lines are represented by codes in the form “A1C1,” where “A1” is the B. rapa parent genotype and “C1” is the B. oleracea parent genotype. In the present study, 3 seeds from each of 41 resynthesized genotypes and cultivars of established B. napus, B. rapa, and B. oleracea used as controls were sown in quick pots and seedlings transferred to small pots without vernalization between September and November 2017 under glasshouse conditions at Justus Liebig University (JLU). Eight B. rapa parent genotypes  as well as 6 B. oleracea lines  used to produce the resynthesized B. napus lines were likewise sown on 2019 September 12 under the same glasshouse conditions at JLU as the resynthesized lines. All lines except C38 successfully germinated and produced plants. Three plant replicates from each genotype were then isolated in bags at flowering to ensure self-pollination.The resynthesized B. napus genotypes with SNP genotyping information was assessed. Eight parent B. rapa, 5 double haploid (DH) lines  and 3 inbred lines , as well as 8 B. oleracea genotypes, 5 DH lines  and 3 inbred lines , were used to produce the resynthesized lines (B. rapa and B. oleracea (AA × CC) are expected to be completely homozygous. Therefore, individuals of the same progeny sets having the same maternal B. rapa (AA) or paternal B. oleracea (CC) parents are expected to be nonsegregating. We assessed purity using 2 criteria: (1) the absence of segregation pattern among progeny sets and (2) the absence of continuous blocks of heterozygous (AB) calls across the A and the C genomes in all individuals.The purity of 41 resynthesized Three parameters were scored to describe fertility in resynthesized lines: relative pollen viability (as estimated by stainability), total number of seeds produced per plant, and number of seeds produced per 10 pods. Pollen viability was assessed for 2 freshly opened flowers per plant, and pollen grains stained with 1–2% acetocarmine solution (http://qiagen.com/). SNP genotyping was carried out using the high-throughput Illumina Infinium 60K Brassica SNP array for the resynthesized lines. Hybridization protocols were performed according to the manufacturer’s instructions for all samples.Young leaf samples were collected in 2 mL microcentrifuge tubes at the 4–6 leaf stage of plant development. DNA was extracted for 41 resynthesized lines (123 plants) using the BioSprint 96 plant work station  according to the manufacturer's instructions (bzh version 8.1 (Brassica species sourced from Brassica carinata (BBCC) and B. oleracea (CC) genotype controls as well as SNPs which mapped to the C genome but amplified in Brassica juncea (AABB) and B. rapa (AA) genotype controls in >50% of the controls were filtered out. SNP markers with >50% heterozygous AB calls in all 5 B. napus homozygous control cultivars  as well as SNPs which showed >99% missing calls (NC) across all lines were removed. Further filtering steps included the removal of SNPs with ≥80% AB calls across individuals. After filtering, 21,938 SNPs were retained: 8,369 SNPs in the A genome and 13,569 SNPs in the C genome . All 52,149 SNPs were exported for the A and C genomes after application of the recommended “brassica60K” cluster file . The pipeline uses the log R ratios  to get the expected log R ratio  direction  that were very close together (<5 Mb) were merged (declared as deletion in the case that a deletion and a missing copy region were merged), and regions <2 Mb were also filtered out, so as not to overestimate CNV numbers based on noise in the data, especially since we only expect a limited number of nonhomologous recombination events per chromosome and hence smaller CNVs are less likely in this population . Therefore, using the abovementioned criteria, we assessed CNVs in the resynthesized B. napus lines as deletion, missing/reduced copy, and extra/higher copy, with 2 copies as the expected copy number (R ratio data of the resynthesized B. napus lines to check whether the same CNVs (inherited) are present in all progeny set derived from the B. rapa or B. oleracea parents.A copy number pipeline was developed in R  as well as 5 B. oleracea genotypes  using the https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and no further processing was found to be necessary. The parental reference genomes B. rapa cv. Chiifu version 3.0  was calculated. These gene depths were then normalized by dividing by the mean depth of all genes. As our lines are not the same genotypes as the reference genome, there was probably some mapping bias. If all lines from 1 parent species showed low (<0.5-fold relative to mean coverage) or high (>1.5-fold relative to mean coverage) mapping rates, these genes were excluded from the analysis. To avoid uneven distribution of sequencing depth along the genome, a sliding window was calculated for the median depth of 40 genes. Relative read coverage for the median depth of 40 genes was carried out for each of the sequenced enotypes .Arabidopsis gene annotation (TAIR) and additional published Brassica information  and 8 B. rapa genotypes  were analyzed using the following steps. Firstly, the SNP data of B. oleracea/B. rapa parents were read in. In the next step, all noninformative SNPs were removed: if 1 line had “missing information,” this SNP was excluded. Gene haplotypes were obtained from the SNP information. We then inferred the allelic state of the S1 resynthesized B. napus lines by combining the respective parents and subsequently matched these to the phenotype data . Next, one-way ANOVA was used to test for significant differences between haplotypes and total CNVs as well as total seed set. The P-values were corrected using the false discovery rate (FDR) test. Subsequently, Fisher's exact test for count data was used to check for significant differences between putatively “stable” and “unstable” lines.A list of genes annotated as having functions in meiosis was established according to B. rapa or B. oleracea parent or with the interaction between the 2 in the resynthesized lines. One-way ANOVA was used to test for significant differences in means followed by Tukey's Honest Significant Differences (HSD) test to assess differences between parent B. rapa and B. oleracea genotype groups using R v. 3.6.3 .Genotypic effects on fertility (self-pollinated seeds and seeds per 10 pods) and genome stability (as measured by number of CNVs) were tested for associations with alleles inherited from either B. napus genotypes . All individual lines were homozygous and identical in allele inheritance to other individuals in the same progeny set, as expected. However, we observed unexpected differences in allele inheritance between progeny sets with the same parental lines C46, C49, and A19. For progeny sets sharing B. oleracea parent line C46, segregating regions were observed on chromosomes C06 (∼1.4 Mb) and C08 (2.4 Mb). Progeny sets with parent C49 also showed segregation on chromosomes C01 (∼1.1 Mb) and C04 (0.3 Mb). A 1 Mb region on chromosome A04 was also segregating between progeny sets A19C37, A19C47, and A19C49. Each of C46, C49, and A19 was homozygous inbred lines, rather than doubled haploid; this is likely the origin of the small regions of allelic heterozygosity hypothesized to be present in these progenitor genotypes.SNP genotyping was carried out for all 41 resynthesized B. rapa A6 and A7 as A genome parents: lines were homozygous within progeny sets but showed inheritance of different alleles from the A6 and A7 parents between progeny sets. Hence, parent genotypes A6 and A7 were likely actually heterozygous instead of homozygous as expected, explaining why the A genomes of different resynthesized genotype combinations with A6 and A7 B. rapa parents were not all the same . Other progeny sets with B. rapa parental genotypes A4, A8, A9, A13, and A16 as well as B. oleracea genotypes C34, C36, C37, C38, and C42   , with a enotypes . Average = 0.68) , althoug = 0.68) .B. napus lines were assessed in order to detect whether maternal (B. rapa) or paternal (B. oleracea) genotypes independently influence fertility . The total number of self-pollinated seeds produced was significantly affected by B. rapa parent genotype   was observed for the combination of B. rapa and B. oleracea parent genotypes on the total number of self-pollinated seeds produced in B. napus resynthesized lines based on our linear model (Resynthesized .000539)  but not genotype . Neither 10 pods . Howeverar model .B. napus lines  were detected at a high frequency across the A and C genomes in the resynthesized us lines . The totividuals . No CNVsB. rapa and B. oleracea genotypes independently had no significant effect on the number of CNVs, the interaction between the 2 parent genotypes was significant, such that there were significant differences between resynthesized lines  was observed between total number of copy number variants per individual and total seed set per individual. This indicates that other factors apart from genome stability also contribute to fertility in resynthesized lines.The relationship between genome stability (as measured by number of CNVs) and fertility was assessed in the resynthesized B. napus lines. However, in order to carry out further analyses, 19 resynthesized B. napus lines, excluding cross combinations with heterozygous A6 and A7 parents, were classified into stable (more stable), intermediate, and unstable (less stable) using the following criteria and process. Firstly, we undertook pairwise comparisons of CNV data to determine which resynthesized lines were significantly different from each other  and 5 parent accessions of B. oleracea  were resequenced. However, B. rapa A6 and A7 accessions were found to be heterozygous and were subsequently taken out of the analysis. In the next step, the allelic variation was analyzed in a list of meiosis gene homologs. A total of 3,689 SNPs in B. rapa meiosis genes were detected, of which 832 were nonsynonymous and no splice variants were detected  association with total CNV number, which was our most important criterion for the selection of putative meiosis gene candidates. So these genes from B. rapa parents , although this subspecies also contributed to intermediate (8 out of 11) and unstable lines (3 out of 4)  as paternal B. oleracea parent were also either putatively stable or intermediate  . Resynthrmediate . AlthougB. rapa and B. oleracea would affect meiosis in newly resynthesized rapeseed lines. To this end, we analyzed relative genome stability (as measured by copy number variants) and measured fertility using self-pollinated seed set, seeds per 10 pods, and pollen viability in a set of lines of resynthesized B. napus with common parent genotypes after 1 generation of self-pollination as well as screened for variants of meiosis candidate genes possibly affecting genome stability in the lines. Our results show that allelic variants inherited from both diploid B. rapa and B. oleracea parents interact to affect genome stability and fertility in resynthesized B. napus lines. Resynthesized rapeseed lines from different genetic backgrounds also vary significantly in both fertility and genome stability. We identified 13 putative meiosis candidate genes which were significantly associated with frequency of copy number variants and which contained putatively harmful mutations in meiosis gene haplotypes for further investigation.In this study, we aimed to test the hypothesis that allelic variants inherited from parental genotypes of P = 0.04, Spearman correlation r = −0.24) in resynthesized B. napus. In natural B. napus, inheritance of unbalanced translocation events in mapping populations was also associated with a fertility penalty , both size and relative A–C genome homoeology/chromosomal location of the CNV would have large effects, although we were not able to resolve this level of detail in the current study.Our results show a negative correlation between genome stability (CNVs) and fertility as measured by total seed set  often show higher yield levels and outperform their diploid relatives . Further investigation would be needed to confirm this.We observed a wide range of genotype-dependent fertility across the resynthesized  S-locus , which ped lines . One genB. napus genotypes averaged >5 CNVs per plant across the A and C genomes. Several studies have shown that chromosomal rearrangements occur frequently in both resynthesized and natural B. napus  than either reduced (1 missing copy) or higher copy number (1 or 2 extra copies as predicted by the pipeline) variants. However, our inability to discriminate between 3 or 4 copies (the combined “higher copy number” category) prevents us from making conclusions about the prevalence of deletions relative to duplications. Also, in contrast to the abovementioned studies, which were all on established lines of B. napus or synthetic B. napus which had been through many generations of self-pollination, our resynthesized B. napus material has undergone only 1 self-pollination event (2 meioses). Hence, any novel variants which have arisen are unlikely to be “fixed” , as the products of a single homoeologous crossover event during meiosis rarely segregate together: 2 recombinant chromatids are produced from 1 homoeologous crossover, but these are usually separated into different gametes in the first meiosis. Subsequently, selection against unbalanced translocations may “fix” these events in self-pollinated progeny, resulting in balanced duplication/deletions.In this study, most of our resynthesized Brassica SNP chip used possibly lowered the average copy numbers across specific regions, leading to high false positive error rates  were present, leading us to use a combined “higher copy number” category. Our copy number pipeline may also be overestimating reduced and higher copy number CNV calls. Failed SNP calls from the 60K B. rapa and B. oleracea genotypes significantly affect genome stability  based on our linear model. This observation is likely due to interactions between specific allelic variants from the parent genotypes which influenced genome stability in the resynthesized lines. Hypothetically, different allelic variants present in B. rapa and B. oleracea may be present but only have an effect on meiosis after combination of these 2 genomes in a single cell. Such an effect was observed for genetic locus PrBn (B. napus (2n = AC) into “high-pairing” and “low-pairing” phenotypes based on meiotic behavior , but which was found to have no effect on meiosis in established allopolyploid B. napus (2n = AACC). As well, B. rapa and B. oleracea are mesopolyploid genomes which show a triplicated genome structure relative to Brassicaceae relative Arabidopsis (B. rapa and B. oleracea), which may explain the observed genotypic interaction effects we found (meiotic effect as a sum of working meiosis gene copies in both genomes). In support of this hypothesis, MSH4. Additionally, Brassica allohexaploid population. B. napus might have initially been unstable, but that alleles responsible for genetic control of meiosis inherited from 1 or both diploid progenitors may have been selected for over time, possibly by conferring improved seed set. In A. arenosa, selection of specific alleles of meiosis genes seems to be responsible for reduced crossover frequency, resulting in meiotic stability in the polyploid  is a eukaryotic, single-stranded DNA-binding protein made up of 3 subunits RPA1, RPA2, and RPA3 and plays important roles in almost all DNA metabolic pathways including S-phase genome replication, DNA recombination, and DNA excision repair  on the basis of their sequence conservation  × B. juncea  and homoeologous (less similar) chromosome sequences during meiosis, with 4 MutS homologs identified in RecQ helicases are involved in the processing of DNA structures arising during replication, recombination, and repair throughout all kingdoms of life  to have undergone any major gene fractionation: almost all  A and C genome gene copies in B. napus are still intact (and expressed similarly) relative to progenitor B. rapa and B. oleracea subgenomes (B. napus (before meiosis) relative to its parent genotypes: although differences between the subgenomes exist, these appear to be mainly inherited directly from the progenitor diploids (e.g. B. rapa and B. oleracea are themselves mesopolyploids, and not all meiosis genes have been reduced to single copy in these species (B. rapa and B. oleracea may contain allelic variants including loss-of-function mutations which could conceivably affect meiosis in resynthesized B. napus.Genetic variation in meiosis genes in general may cause large effects on genome stability in different plant lineages . Althougids e.g. . HoweverB. napus lines. Our study suggests meiotic stability in B. napus arose via selection of allelic variants from its diploid progenitor species and provides information that will be useful for breeders aiming to use resynthesized lines in breeding programs. The production of genomically stable resynthesized B. napus lines might be useful in the future as a germplasm resource to broaden the limited genetic diversity of established B. napus cultivars or for hybrid breeding (Our results show that some resynthesized lines are more genomically stable and fertile than others and suggest that allelic variation present in both of the diploid parents interacts to affect the chance of chromosome rearrangement and CNV events, but that the presence of such events may not always be detrimental to fertility in resynthesized jkad136_Supplementary_DataClick here for additional data file.


The method allows us to compare the genomic differences and similarities between groups of individuals from many breeds. We show that there are distinct regions of DNA that are specific to the modern Bullmastiff breed. By focusing on these DNA regions, we can understand some of the characteristics that define the breed and use the information to help us understand how some diseases may be more common in this breed.Purebred dogs form distinct genetic subpopulations, and there are more than 400 of these recognized by breed clubs around worldwide. Their gene pool is limited by the number of dogs used to develop the breed and how the dogs have been bred. The total makeup of DNA in a breed determines the characteristics that we identify as typical of the breed. However, the limited genetic variation within a breed can also contribute to health issues arising from inherited faulty genes or because of complex interactions of many genes. Many studies have been completed in recent yearsso genetic information is now available for thousands of dogs. Together, this makes dog populations informative subjects for analysis. In this study, we applied a relatively new method which combines three ways to measure variation in the DNA of groups of animals, named FDog breeds represent canine sub-populations with distinctive phenotypic features and limited genetic diversity. We have established a resource to study breed-specific genetic diversity. Utilising genetic resources within our laboratory biobank, public domain genotype data and the phylogenetic framework of 23 breed clades, the primary objective for this study was to identify genomic regions that differentiate the Bullmastiff breed. Through application of a composite index analysis (CSS), genomic signatures were identified in Bullmastiffs when compared to the formative breeds, Mastiffs and Bulldogs, and to 22 other breed groups. Significant regions were identified on 15 chromosomes, with the most differentiated regions found on CFA1, CFA9, and CFA18. These regions may reflect genetic drift following establishment of the breed or the effects of selective breeding during development of the modern Bullmastiff. This was supported by analysis of genes from the identified genomic regions, including 458 genes from the multi-clade analysis, which revealed enriched pathways that may be related to characteristic traits and distinct morphology of the breed. The study demonstrates the utility of the CSS method in breed-specific genome analysis and advances our understanding of genetic diversity in Bullmastiff dogs. Canis familiaris) is considered the first domesticated animal arising from interactions of their wild ancestors and humans. Over many centuries, dogs have been selected for an extensive range of phenotypes, including a wide range of body size, specific morphological traits , coat color, and behavioural characteristics  [The domestic dog (ionship) ,2. Thereionship) ,4. Dogs ionship) .One group of breeds that has attracted some attention is the Mastiff-like dogs. Within this group are several breeds that share certain morphological features, including a characteristic skull shape described as brachycephalic, but they also exhibit a range of body sizes and susceptibility to some diseases. One example of this group is the Bullmastiff. This breed was derived by crossing Bulldogs (40%) with Mastiffs 60%) in Britain in the mid-19th century. The breed was popular with gamekeepers and was developed as a working dog to apprehend or ward off poachers [0% in BriWe have established a Bullmastiff resource to study genetic diversity of the breed in Australia ,7. Utilin = 747) were from four breeds: Bullmastiff, German Shepherd Dogs (GSD), Kelpie, and Border Collie [n = 250) [An in-house CanineHD BeadChip  genotyping dataset was merged with those downloaded from previously published datasets derived from dogs from across the world. The resulting pooled dataset is summarised in r Collie ,11,12,13n = 250) ,17,18,19Each of the datasets was generated independently, and they needed to be unified to address potential incompatibilities between genotypes arising from different sources, such as reference genome assemblies, labelling of X, Y, and MT chromosome, and the type of SNP identification used . The standard maps of CanineHD BeadChip SNP markers  were sourced from Illumina and applied to ensure that the different datasets were uniform. The SNPs that differed between genome assemblies (CanFam2 vs. CanFam3) were excluded. The genotypes assembled from different studies also employed different versions of the CanineHD BeadChip, with different strand orientations for different versions (TOP vs. BOTTOM) . This waFive studies had only raw SNP outputs available (idat file format) generated by the Illumina Canine High-Density Beadchip  and were downloaded from seven GEO database files. These raw data were processed with GenomeStudio 2.0 software , and each dataset was fit into a given cluster. Standard canine cluster files were retrieved from Illumina with chromosome, position, and genotype for each SNP. The files (map/ped) containing the information used in PLINK for each SNP were obtained from the PLINK Input Report Plug-in v2.1.4 in GenomeStudio 2.0 software .The combined genotype dataset was assigned genomic chromosome and positions according to the CanFam 3.1 assembly. Further pruning was conducted in the PLINK 1.9 software to exclude samples with over 10% of missing data and SNPs with less than 0.01 minor allele frequency to give a final sample size of 151,901 SNPs across 38 autosomes in 8005 dogs representing 250 breeds. The high-quality SNP dataset was subset using PLINK v.1.9  with thehttps://faculty.washington.edu/browning/beagle/beagle.html, accessed on 30 October 2020) was employed.The SNP array yields unphased genotypes. To generate the haplotype phase from the unphased genotype data, the HMM-based sampling approach implemented in the software Beagle 5.0 ,28 . The haplotype information obtained was applied in XP-EHH calculations. st, ∆DAF, and XP-EHH) were used. The description of these tests is provided in the following sections. CSS analysis is comprised of several steps described as follows. First, ranking of the SNPs based on estimates from individual tests were obtained, and the ranking was scaled to a fractional ranking (between 0 and 1). Secondly, the fractional scores were transformed into z-scores with the inverse normal cumulative distribution function (CDF). The z-scores were assumed to follow a single normal distribution. Then, the mean of the z-scores from individual tests were obtained for each SNP. This mean z-score follows a normal distribution  and was directly used to generate p values. The CSS value is defined as the logarithmic (−log10) transformed p value. To capture significant genomic regions and account for linked SNPs, the raw CSS scores were smoothed using mean values of CSS scores within a 0.5 Mb sliding window on both sides of each SNP. The top 0.005 fractions (0.5%) of smoothed CSS scores were considered as significant regions. The average CSS value was the mean of smoothed CSS values of all significant SNPs in the region [The composite selection signals (CSS) approach was used in this analysis. CSS combines different test statistics to generate rank-based empirical values. In this CSS analysis, three constituent statistical tests  were extracted using the SELSCAN package . The reqst) was first introduced 70 years ago [st statistic presented by Weir and Cockerham [st value (0~1) for specific locus means high reproductive isolation levels (fixed) or strong positive selection in one of the populations. It has been shown to be suitable for detecting selection signatures using SNP array data. The Fst scores for each SNP were computed by a pairwise comparison within all sets of the Bullmastiff target group and reference groups. The fixation index (Fears ago  and subsockerham  was appln = 10) [Multiple studies have found evidence that the most modern breeds primarily share European ancestry ,33. Hencn = 10) . target − DAFreference. The wolf SNP information was used as ancestral. The ancestral SNPs were assigned for the DAF calculation. The normal distribution of derived allele frequency was estimated, and the ∆DAF values were transferred to Z scores . In the present study, the major alleles in wolves were assigned as ancestral alleles, following the approach used previously [The derived allele frequency difference was computed depending on the following formula, ∆DAF = DAFeviously ,15,36 aneviously . The majhttp://genome.ucsc.edu/, accessed on 9 April 2021). The list of genes was then used for functional enrichment analysis. The Database for Annotation, Visualisation and Integrated Discovery (DAVID) v6.8 was used for analysing functional classification, gene ontology, pathway involvement, and for understanding high-level functions and contribution to biological systems from a large-scale molecular dataset  [https://www.kegg.jp/, accessed on 13 September 2021) and Gene Ontology  knowledge base database resources. The Benjamini-adjusted p-value ≤ 0.05 and FDR ≤ 0.05 were used as thresholds for declaring statistically significant differences.Annotated genes within significant genomic regions were identified and used for analyses. These genes were collated and annotated using RefGene (Ensembl) downloaded from the UCSC genome browser CanFam 3.1 (er 2021) ,38 via tp ≤ 0.05 and kappa score ≥ 0.4.Visualisation by Cytoscape software (Version 3.8.2)  with anaA total of 26 distinctive peaks were found from two breed pair comparisons (Bullmastiffs vs. Mastiff and Bullmastiffs vs. Bulldogs) scanned using a 1 Mb sliding window CSS analysis. These were found on chromosomes CFA1, CFA3, CFA7, CFA8, CFA9, CFA11, CFA18, CFA20, CFA23, CFA25, CFA26, CFA32, CFA35, and CFA37, with CSS values ranging from 2.10 to 2.81 . Out of Further analysis was conducted based on the genes within the identified regions. A total of 2681 significant SNPs were identified for the comparison of Bullmastiff with the two formative breeds (Mastiff and Bulldog). These SNPs were mapped to 487 genes from the pairwise comparison of Bullmastiff and Mastiff and 415 genes for the Bullmastiff/Bulldog comparison . Gene-seSix of these significant regions overlapped with the results described in the previous section . When both comparisons were collated, there were 490 genes  within significant regions that were immediately flanking. A gene-set and pathway analysis of these genes using DAVID captured a total of 144 GO terms  and four KEGG pathways . FurtherDog breeds have previously been grouped into clades based on genomic analysis ,42. Genon = 458) found within these regions and associated annotations are provided in Genes  chain) and ADAMTSL2 (ADAMTS-like protein 2) on CFA9 (48.92–51.87 Mb). Collagen is a key structural protein of the connective tissue including skin and supports the connective tissue matrix to maintain shape, strength, and the ability to resist deformation [COL5A1 gene are associated with loose skin, and in extreme cases, with a clinical presentation of Ehlers–Danlos syndromes (EDS) in dogs [ADAMTSL2 is a secreted extracellular matrix protein. Variants in this gene have been associated with the hereditary disorder Musladin-Lueke Syndrome (MLS) in Beagles, characterised by muscle and skin fibrosis leading to stiff skin and joint contractures [ADAMTSL2 are seen in cardiomyopathies [Bullmastiffs were originally created by crossing dogs from the Mastiff and Bulldog breeds to produce a breed that was intermediate in size and temperament. The Bulldog can be identified by its large head with wedge-shaped body, as well as short and folded ears. Other morphological features of Bulldogs include a stocky build with deep furrows of the skin and face, short or corkscrew tail, short thick legs with equally broad paws, and a moderate temperament. Relatively, Mastiffs have a more placid temperament, and are one of the largest and heaviest of dog breeds, weighing up to 100 kg . The strormation . Genetic in dogs . ADAMTSLractures . In humaA9 48.92–.87 Mb. CNKAIN2 (Sodium/potassium-transporting ATPase subunit beta-1-interacting protein 2) gene. This gene plays a role in hair hypopigmentation, craniofacial and limb formation, eye development, and macrocephaly [PRDM8 (PR domain zinc finger protein 8) gene. PRDM8 has been linked to coat length in dogs [ZNF608 (Zinc Finger Protein 608) gene, which is associated with body mass in dogs [MSRA (peptide-methionine (S)-S-oxide reductase) gene. This gene is involved in methionine metabolism and repair of oxidative damage of proteins. It has been associated with adiposity and fat distribution in humans [The genome-wide scan for the pairwise comparison of Bullmastiff and Mastiff identified a region on CFA1 (62.56–64.02 Mb), which includes the ocephaly . Similar in dogs ,53. This in dogs . A signin humans , and thrn humans . BullmasBMP3 (bone morphogenetic protein 3) affects bone growth and development and is associated with skull morphology. The FGF5 (Fibroblast growth factor 5) gene and ANTXR2 (anthrax toxin receptor-2) are associated with coat length in dogs. The PRKG2 (cGMP-dependent protein kinase) and RASGEF1B (RasGEF domain family member 1B) genes have been identified as positional candidate genes for growth restriction, aggression, self-injurious behaviours, and mental retardation in affected German Shepherd dogs [A region identified on CFA32 (3.99–5.7 Mb) overlapped growth-related genes that may be associated with morphological traits. erd dogs .Not surprisingly, the results of pairwise comparison of Bullmastiff with a combined reference group consisting of all other breeds in the European Mastiff clade showed substantial overlap with the comparison to Bulldog and Mastiff breeds. Some regions were consistently present in Bullmastiffs, but there were no outstanding gene candidates that could be associated with breed-specific morphological variation, which may require comparison of extreme phenotypes . InsteadBMP3 and other genes, was highlighted in many of the pairwise comparisons of Bullmastiffs and dogs from 22 non-Mastiff clades. There were also a number of commonly found enriched gene pathways and GO categories evident in many comparisons, e.g., those associated with neurological, nervous and immune system development, metabolism, and organ growth. This points to the potential of early developmental pathways that may have broad phenotypic effects. As recently highlighted, variation in many of these fundamental pathways may have arisen prior to modern breed formation and arisen from selection pressures on ancestral populations [The region identified on CFA32, mentioned previously, and containing ulations . A good ulations . Neural ulations . SeveralThis study demonstrates the utility of applying the composite index method, CSS, to identify genomic signatures in purebred dogs. The results define regions containing variants that are found at higher frequency in the Bullmastiff breed when compared to other dog breeds. Analysis of annotated genes and related pathways found within these regions contributes to understanding diversity in the breed and may underpin further studies of breed health and disease.


Microhaplotypes have the potential to be more cost-effective than SNPs for applications that require genetic panels of highly variable loci. However, development of microhaplotype panels is hindered by a lack of methods for estimating microhaplotype allele frequency from low-coverage whole genome sequencing or pooled sequencing (pool-seq) data.We developed new methods for estimating microhaplotype allele frequency from low-coverage whole genome sequence and pool-seq data. We validated these methods using datasets from three non-model organisms. These methods allowed estimation of allele frequency and expected heterozygosity at depths routinely achieved from pooled sequencing.https://www.github.com/delomast/mhFromLowDepSeq.These new methods will allow microhaplotype panels to be designed using low-coverage WGS and pool-seq data to discover and evaluate candidate loci. The python script implementing the two methods and documentation are available at The online version contains supplementary material available at 10.1186/s12859-023-05554-z. As the cost of obtaining genetic information has decreased, more applications for this information have been created. Genotypes are now used across medicine, forensics, agriculture, and natural resource management to inform decisions –5. For aGenotyping microhaplotypes instead of SNPs can increase the variability of a given genetic panel without increasing the number of loci genotyped. A microhaplotype locus contains multiple SNPs that are close enough to be genotyped in the same sequencing read, and so genotyping a microhaplotype locus via sequencing  uses the same resources as genotyping a locus with one SNP using the same technology. Because microhaplotypes can display more than two alleles, they can achieve higher variability than a biallelic SNP. The use of microhaplotype panels has been demonstrated to increase power compared to SNP panels for applications in forensics , geneticSeveral strategies have been used to identify and estimate allele frequencies for microhaplotypes in order to design genotyping panels. One opportunistic strategy is to select single SNPs for an amplicon sequencing panel and incorporate any additional SNPs that happen to be in the targeted amplicons . A more When information on a large fraction of the genome is needed but individual information is not, two main techniques have been previously utilized. The first is to sequence a mixed DNA sample derived from a single population, often referred to as pool-seq . AlternaWe developed two related methods for estimating microhaplotype allele frequencies. The “individual” method addressed low-coverage WGS datasets where reads can be assigned unambiguously to individuals. The second, “pool” method addressed pool-seq datasets where reads are not able to be assigned to individuals. Both methods utilized mixture models to infer allele frequencies. In the individual method, the individual was the unit of observation, and the genotype was a latent variable. Component weights were genotype frequencies and were linked to allele frequencies by assuming Hardy–Weinberg equilibrium (HWE). In the pool method, the read was the unit of observation and the allele that it was derived from was a latent variable. Component weights were allele frequencies.When sequencing reads can be assigned to individuals but the depth of sequencing is too low to definitively call genotypes, methods have previously been used that take genotype uncertainty into account to infer SNP allele frequencies through a maximum likelihood approach . We exteRi be the set of all sequencing reads for one individual  that cover one or more SNPs in the target locus. The likelihood of Ri  until all y SNPs were considered in the final iteration. In the final iteration, no alleles were dropped regardless of estimated frequency as this would not increase computational efficiency. An example can be found in Additional file x and c were set to 1 and 0.001, respectively.The method implemented here was to iteratively estimate allele frequencies within successively larger subsets of a given locus while removing alleles with estimated frequency close to zero at the end of each iteration. For a locus containing https://www.github.com/pysam-developers/pysam) [https://www.github.com/delomast/mhFromLowDepSeq.These methods were implemented as a python script that takes as input one or more bam files, a file containing the known positions of substitution SNPs, and optional user specified parameters. Methods of SNP discovery for both data types considered here exist , 35–37, s/pysam) , 39, nums/pysam) , and nums/pysam)  and is aCrassostrea gigas from Weihai, China [Salmo salar from the St. John River strain [These methods were tested on three publicly available paired-end sequencing datasets from non-model organisms. Two were WGS datasets containing 25 Pacific oysters i, China  and 53 Ar strain  while thr strain . Resultshttps://www.github.com/delomast/mhFromLowDepSeq/tree/main/testVCFcalc) relying on the htsjdk API (https://www.github.com/samtools/htsjdk). These statistics calculated from the full data are referred to throughout as “observed” allele frequencies and expected heterozygosity. Imitation low-coverage sequencing datasets with mean individual depth of 0.1, 0.5, 1, and 2 were then created by randomly subsampling aligned reads using samtools [For each dataset, we first called genotypes and performed short-range, read-based phasing. The resulting genotypes were used to calculate allele frequencies and expected heterozygosity for microhaplotypes with a length of 60 bp using a custom java program  which is a wrapper for cutadapt [http://broadinstitute.github.io/picard/) and samples were genotyped using GATK HaplotypeCaller followed by GenomicsDBImport and GenotypeGVCFs with default settings [Whole genome sequencing data from 25 Pacific oyster i et al.  was downcutadapt  then alicutadapt  in end-tcutadapt . Alignmecutadapt . Reads wsettings , 49. Thesettings , 51 withSalmo salar samples from the St. John River strain described by Gao et al. [Whole genome sequencing data from 53 Atlantic salmon o et al.  were dowo et al.  and thenEntosphenus tridentatus described by Hess et al. [Due to the relative scarcity of whole-genome sequencing datasets containing a large number of samples in non-model species, we additionally tested the methods using a RAD-seq data set for Pacific lamprey s et al. . From ths et al.  and the s et al. . The gens et al. , 51 usinhttps://www.github.com/delomast/mhFromLowDepSeq/blob/main/filterLamp.py). This left only SNPs that would be sequenced by the read in a pair that did not start at the cutsite. Because the insert size was variable in this library, the positions of these reads are variable and therefore a closer approximation of what is expected in a WGS dataset.A major way that single-enzyme RAD-seq data deviates from whole genome sequencing data is that in paired-end RAD-seq data, one read from each pair starts at a restriction enzyme cutsite. This results in multiple reads that all start at the same position. In a WGS dataset, one would expect reads to be closer to randomly distributed. To account for this, we excluded any SNPs from consideration that were within one read length of a cutsite in the reference genome using a custom python script . Outliers were observed with errors close to the maximum error possible at very low sequencing depths (less than 10 reads)  often does not benefit from the economies of scale present for humans, model species, and terrestrial livestock. Potential benefits from the application of modern genetic techniques to these fields have been well documented –63, inclPool-seq and low-coverage WGS have both been popular methods for cost-effective acquisition of genome-wide data, resulting in a multitude of existing datasets , 64–68. https://www.github.com/delomast/mhFromLowDepSeq.These new methods facilitate cost-effective microhaplotype panel design by allowing the use of low-coverage WGS and pool-seq data for estimation of allele frequencies and expected heterozygosity in candidate loci. The python script and documentation implementing the two methods described here are available at Additional file 1: Example of the pruning algorithm.Additional file 2: Distribution of error in estimated allele frequencies.Additional file 3: Mean square error in estimated allele frequencies.Additional file 4: Distribution of error in estimated expected heterozygosity.Additional file 5: Mean square error in estimated expected heterozygosity.


The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population’s intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome. CurrentThere are several genotyping platforms for obtaining SNPs throughout the genome, which have provided an infinity of sequencing information with remarkable improvements in coverage, time, and costs, making it possible to genotype thousands of samples with many markers , with SNBeyond crop-specific SNP arrays, NGS-based platforms are adaptable to various crops, regardless of prior knowledge of genomics, genome size, organization, or ploidy . GenotypThere are many crops of unique relevance to developing countries, essential for the food, nutritional, and economic security of these countries, which still do not have a reference genome . These cRecent studies have compared the performance of genotyping platforms and how this choice affects genomic studies regarding genetic diversity studies , genome-Although some reports are available, there still needs to be a consensus on how different platforms provide the results. Thus, we conducted a full study on this topic with a robust germplasm panel. For this, we compared different genotyping scenarios from the beginning of the breeding process with the approach of genetic diversity and population structure, advancing to the formation of heterotic groups and choice of testers to the synthesis and prediction of single crosses. Thus, this information will be valuable to leverage genomic studies and accelerate the development of cultivars in minor crops without a reference genome. Therefore, our goals were to verify whether the source of SNP can influence the assessment of the population structure of parental lines, to ascertain if the source of SNP can affect the determination of heterotic groups and the prediction of single crosses performance, and check if the GBS and the mock genome efficiently performs the SNP calling in orphan crops (without reference genome).2To facilitate the understanding of the analysis carried out in this study, a workflow is described in 2.1https://data.mendeley.com/datasets/5rtc89t7v5/1).We used maize as a model species in this study because it is already well-established regarding SNP array, with several array options available , and GBS2.2−1 and ideal N with 100 kg N ha−1. Each location × year × N level combination was defined as an environment.The phenotypic dataset consists of 903 maize single crosses  derived −1), plant height , and ear height . The plots were harvested manually, and the grains were harvested with a moisture content of approximately 18%. Subsequently, grain yield was corrected for 13% moisture, according to Each plot consisted of 7 m rows spaced 0.50 m apart. Conventional fertilization and weed and pest control were carried out. The traits evaluated were grain yield  for each genotype, and later, the adjusted means of these across the environments evaluated by the following mixed model were estimated:y is the vector of phenotypic values of single crosses and checks; l is the vector of fixed effects of the environment (site × year × N level combination); b is the vector of random effects of block nested within environments, where b ~ N; c is the vector of fixed effects of checks; g is the vector of fixed effects of single crosses; i is the vector of fixed effects of the interaction checks × environments; ε is the vector of random residual effects, where ϵ ~ N. An unstructured covariance matrix across environments was assumed for the residual term (De) due to the contrasting doses of N. Q, S, T, U, and V are the incidence matrices for l, b, c, g, and i. The analysis was performed using the ASReml-R (where ASReml-R .2.4® Axiom Maize Genotyping Array (SNP array) and 2) GBS method following the protocol described by PstI and MseI, to reduce the genome complexity uniformly. Subsequently, the samples were included in a sequencing plate, performed on the Illumina NextSeq 500 platform .The 360 lines belonging to the public tropical maize diversity mentioned above were genotyped using two SNP high-density genotyping platforms: 1) AffymetrixThe raw GBS data were used for two purposes: the first was to perform the SNP calling using the B73 line of temperate germplasm as a reference genome. The second purpose was to build a simulated reference genome (mock genome) according to the GBS-SNP-CROP pipeline proposed by Further analysis was performed considering three SNP datasets: 1) SNP array, 2) GBS with SNP call using B73 as the reference genome (GBS-B73), and 3) GBS with the simulated genome being used as the reference genome (GBS-Mock). For GBS datasets, according to standard parameters, SNPs were scored from raw data using the TASSEL 5.0 GBSv2 pipeline . With th2), and values greater than 0.99 were removed from the datasets using the SNPRelate package (As two genotyping platforms (SNP array and GBS) were performed, the parental lines that showed a very contrasting genotypic profile between the two platforms were removed from the analysis to obtain a fair comparison. Thus, between sequencing errors and divergences in genotypic profiles between platforms, 330 parental lines remained, among which 45 parental lines make up the diallel, which generated 751 single crosses. The number of markers concerning the raw data was 18,413 (SNP array), 131,350 (GBS-B73), and 46,9126 (GBS-Mock). All SNP sets underwent quality control, in which low call rates (<90%) and non-biallelic markers were removed from the datasets. The remaining missing data were imputed by the Beagle 5.0 algorithm . Pairwis package , resultiin silico) to build an artificial single-cross genomic matrix. In addition, duplicate markers between chromosomes were removed to avoid overparameterization caused by multicollinearity. Finally, markers with minor allele frequency (MAF)<0.05 were removed from the single-cross genomic matrices, resulting in 11,884 (SNP array), 10,361 (GBS-B73), and 4,801 (GBS-Mock) SNP markers to perform the remaining analysis.Subsequently, new quality control was performed, in which heterozygous loci in at least one individual were removed. High-quality polymorphic SNPs from the parental lines were combined  from the 330 parental lines were used to assess the population structure of the panel. In these analyses, in particular, heterozygous loci and rare variants (MAF< 0.05) were considered to capture all diversity and variability to perform principal component analysis (PCA) and determine the relatedness between parental lines.K-means clustering was applied, using the total within-cluster sum of square (WSS) method to determine the optimal number of clusters so that the total intra-cluster variation is minimized . The facsuperheat R package  and specific combining abilities (SCA). For this, the following diallel model was adjusted:To find out how diversity and population structure can influence the formation of heterotic groups, it was necessary to construct y is the adjusted phenotypic data vector of the single crosses for the trait, gP is the random effects vector of the GCA captured by the markers of the parental lines, and h is the random effects vector of the SCA that denotes the interaction effects across the parental lines. ZP and ZH are incidence matrices that relate y to gP and h to gp ~ N  and h ~ N , where σ2P and σ2H are variance components associated with GCA and SCA, respectively. GP and H are relationship matrices for the parental lines and single crosses, respectively. Finally, ε ~ N , where σ2ε is the variance associated with the residuals.where P relationship matrix was calculated using the SNP markers according to  design were considered for the following analyses. The number of single crosses changed according to the configuration of heterotic groups for each trait in the three SNP scenarios.After the parental lines were divided into heterotic groups, only the single crosses corresponding to interpopulation crosses For the genomic prediction of the single crosses, an additive–dominance genomic best linear unbiased prediction (GBLUP) model was used, as described below:N ; d is the vector of dominance effects, where d ~ N ; ε is the random effects vector of the residuals, where ε ~ N . Z is the incidence matrix for a and d. σ2a is the additive genomic variance, σ2d is the dominance genomic variance, and σ2ε is the residual variance. Ga and Gd are the additive and dominance genomic relationship matrices, respectively, of the single crosses, where ip is the frequency of an allele at locus i and W is the matrix incidence of markers  and the narrow-sense (h2) was also estimated by the equations below:To evaluate the model performance, we used the CV-α cross-validation with five folds and four replicates . The prewhere 33.1According to the WSS method, for all datasets, the optimal number of clusters among the 330 parental lines that minimized within-group variance and maximized between-group variance was six Figure 2Concerning PCA, the SNP datasets showed similar performances regarding the variance explained by the principal components. The first principal components hold the highest percentage of explained variance (2). However, the other eigenvectors showed less similarity between the captured variance patterns  from all SNP datasets sampled similar groups and subgroups, with slight differences between them Table 1.3.22) followed the same tendency.Broad- and narrow-sense heritabilities were higher for EH, followed by PH and GY Table S2Ga) across the single-crosses, SNP array, GBS-B73, and GBS-Mock showed high Mantel correlations (Gd) showed lower correlations than Ga. The correlations between the dominance relationship matrices (Gd) were lower but still from medium to high. GBS-Mock stands out with a correlation of 0.72 with GBS-B73.Regarding the additive genomic relationship matrices  was L023, and for heterotic group two (HP2), it was L006. As for PH and EH, L001 was elected as the HP1 tester and L003 as the HP2 tester. The correlations between the GCAs confirm this result, with maximum correlations  showed high correlations among SNP array, GBS-B73, and GBS-Mock, indicating that these approaches capture similar additive variance patterns. GBS-Mock captures additive relationships in single crosses similar to standard procedures, SNP array, and GBS-B73 (Gd) were lower but still from medium to high. In both Ga and Gd, the correlations between SNP array and GBS-Mock were lower, which can be explained by the fact that these SNP datasets use different reference genomes to perform SNP calling.The PH and EH traits showed higher proportions of additive variance captured by the  than GY Table S2 than GY . AccordiSCA reflects the action of non-additive gene effects, indicating intra-allelic interactions, is one of the most important parameters in identifying superior single crosses, and is an indicator of genetic distance between parents . Thus, uIn addition to presenting distinct heterotic groups, a well-established breeding program also offers good testers. When crossed with parental lines, these provide information about the genetic value of the lines when evaluating the ability to combine between them since it is associated with the additive effects of alleles and additive-type epistatic actions . The corThe genotyping approaches produced very similar results but not the same as previous results regarding the study of the population structure of parental lines; it was expected that this would somehow influence the formation of heterotic groups and the choice of testers. However, given the results, the genotyping platform and, more specifically, the approach that uses the simulated genome as a strategy, the GBS-Mock, produced similar results to the standard procedures.4.3Assessing the performance of all single-cross combinations of parental lines that excel in a breeding program is impractical in most cases, given that the number of combinations grows exponentially as the number of elite parents increases. Thus, obtaining estimates of the genetic values of single crosses not evaluated became viable with the increased availability of molecular markers and genomic prediction models . TherefoHowever, few studies still address how genotyping platforms influence single crosses’ prediction and, more specifically, regarding the mock genome as a tool for more sophisticated analyses, such as genomic prediction. Only one recent study shows the mock genome’s efficiency in predicting maize single crosses, which may be an alternative for crops that do not yet have a reference genome . HoweverPredictive ability and prediction accuracy are closely related measures. Therefore, we will only discuss it based on predictive ability. GY showed the lowest predictive abilities in all SNP datasets, and EH has the highest in the additive–dominance GBLUP prediction model Table S2Selection intensity must be chosen thoughtfully, as genetic variability can be drastically reduced with high selection pressure. The choice of appropriate selection intensities depends on the size of the population and the duration of the breeding program, whether short-term or long-term. In general, selection intensities ranging from 10% to 40% are used in plant breeding, the highest being applied at the beginning of a breeding program . For theDespite the apparent differences between SNP datasets, the general message is that these approaches perform comparably in the analyses performed in this study, even accessing different types of genomic sequences. While SNP array is derived from exome capture and therefore focused on coding sequence variation, the GBS data represent a wider diversity survey in genomic regions associated with low levels of DNA methylation, which may also include many genes and gene regulatory regions . However4.4Until recently, only the main commercial crops benefited from state-of-the-art technologies. However, the development of the GBS platform emerged as an alternative for using such technologies to be viable for orphan crops. Approaches like this can convert orphan crops into crops rich in genomic resources and substantially reduce the breeding process .Previously, this process was much slower than nowadays. Rice, for example, took almost 20 years to stop being an orphan crop and become a basic model for cereals . IntroduDespite initiatives and investments, not all crops will benefit, so they cannot take advantage of modern breeding tools. While these advances are being consolidated, mock genomes can be an alternative, where the absence of a reference genome presents a barrier to the efficient use of GBS data . In the These advantages of using a mock genome in genomic studies must consider some caveats; for example, diploid crops with smaller genomes are preferred over cross-pollinated or polyploid orphan crops, as they have genomes that are too complex to be sequenced. However, genome size will become less of a barrier with advances in sequencing technologies and bioinformatics tools . AnotherGiven what has been shown, it is possible to infer and recommend that a mock genome constructed from the population’s polymorphisms to perform the SNP calling is an excellent strategy to support plant breeders in studies of diversity, population structure, the definition of heterotic groups, choice of testers, and genomic prediction in species that still do not have a reference genome available, which is an alternative for the rapid advancement of orphan crop improvement. This approach will play a key role in improving the genetic potential of orphan crops and helping develop sustainable food systems.https://data.mendeley.com/datasets/5rtc89t7v5/1, DOI:10.17632/5rtc89t7v5.1.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: IM was responsible for writing the report, analysing data, interpreting results, and creating tables/figures. JD was responsible for supervising and analyzing data regarding genomic prediction. FS contributed to the manipulation of the raw genomic data and mock genome assembly. RF-N contributed extracting data and designed the work that led to the submission. JD, FS and RF-N played an important role in interpreting the results and contributed to the report review. All authors contributed to the article and approved the submitted version.


The expansion of bean genome technologies has prompted new perspectives on generating resources and knowledge essential to research and implementing biotechnological tools for the practical operations of plant breeding programs. This study aimed to resequence the entire genome (whole genome sequencing—WGS) of 40 bean genotypes selected based on their significance in breeding programs worldwide, with the objective of generating an extensive database for the identification of single nucleotide polymorphisms (SNPs). Over 6 million SNPs were identified, distributed across the 11 bean chromosomes. After quality variant filtering, 420,509 high-quality SNPs were established, with an average of 38,228 SNPs per chromosome. These variants were categorized based on their predicted effects, revealing that the majority exerted a modifier impact on non-coding genome regions (94.68%). Notably, a significant proportion of SNPs occurred in intergenic regions (62.89%) and at least one SNP was identified in 58.63% of the genes annotated in the bean genome. Of particular interest, 7841 SNPs were identified in 85% of the putative plant disease defense-related genes, presenting a valuable resource for crop breeding efforts. These findings provide a foundation for the development of innovative and broadly applicable technologies for the routine selection of superior genotypes in global bean improvement and germplasm characterization programs. Its grains are gluten-free and serve as a valuable nutritional source, abundant in proteins, minerals, vitamins, and bioactive compounds3. Additionally, they are high in fiber, low in fat, and rich in slow-digesting carbohydrates, which are associated with a considerably reduced risk of heart disease, obesity, and diabetes5. Despite its numerous health benefits, bean cultivation presents several challenges, including enhancing yield in the presence of biotic and abiotic stresses, as well as improving grain quality and nutritional value7. To overcome these challenges, plant breeders have been empowered by the knowledge and understanding of the common bean's genomic resources to conduct more targeted and specific studies that align with global requirements8.Among pulses, the common bean stands out as one of the most significant food crops globally, with a profound socioeconomic impact10. For common bean, WGS projects aim to uncover novel insights into evolution and diversity, contributing to the generation of valuable genomic resources that accelerate common bean improvement and enhance its efficiency12.The generation of a substantial amount of genomic information has led to a significant transformation in the overall field of plant breeding. The availability of the sequence of the entire genome allows researchers to understand and exploit the genetic variations and their control over complex traits, enabling plant breeders to conduct more targeted and specific studies aligned with the global demands. Among the approaches available for generating a vast amount of genomic information, whole-genome sequencing (WGS) provides an opportunity to analyze and identify genetic variations at the base level throughout the entire genome, such as single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations15. In addition, this integration has provided valuable insights into the evolutionary and domestication history of plants, as well as the genetic mechanisms of adaptations and selective breeding that have contributed to the environment’s adaptation16. This has empowered plant breeders to propose and implement new breeding approaches, such as genome-wide selection (GS) and marker-assisted selection (MAS) specifically aimed at selecting traits of high economic importance19. The availability of the new genomic tools has increasingly provided improvements in terms of speed, efficiency, accuracy, cost-effectiveness of plant breeding processes opening new ways to improve agronomic traits20, addressing the demands of the productive sector26.By integrating genomic knowledge, such as genomic structure, function, diversity, with quantitative genetics, a solid foundation has been established for detailed dissection of the genomic architecture of genetic variationIn this study, we performed a comprehensive analysis of genomic variations using WGS in 40 important breeding lines/cultivars from the Middle American and Andean gene pools, developed by Brazilian and international breeding programs. The objective was to assist in the genetic studies of important agronomic traits, addressing the challenges faced in common bean cultivation.27 (Supplementary Table The WGS of the 40 common bean lines (Supplementary Table S1) generated 7.4 billion paired end read fragments, the equivalent to a raw data set of 1.18 Terabases (Tb), ranging from 5.14 billion (Rosinha G2) to 210.47 billion base pairs (BRS Pérola) (Supplementary Table –9). The estimated transition/transversion ratio (Ts/Tv) was 1.29.More than 6.5 million SNPs were identified in the dataset. After filtering out SNPs in repetitive regions and applying quality metrics, 420,509 (6.37%) with a minor allele frequency (MAF) of ≥ 0.5 were maintained Table . The avenon-reference SNPs was 14.21%, while it was 49.24% for Middle American accessions . Among the SNPs identified in genes, the majority were in introns (58.11%), followed by exons (30.99%), 3′ Untranslated region (UTR) (6.17%), and 5′ UTR (4.73%) regions  were detected in intergenic regions, while 23.44%  were found in gene regions , accounting for a total of 14.05% of the effects. Furthermore, 2.48% of the effects were in the 5′ and 3′ UTR regions of genes, indicating potential in regulatory regions (Table The predicted effects of SNP were categorized based on the variants as follows: modifier (94.68%), moderate (2.69%), low (2.59%) and high (0.05%) Table . Most SNst) based on common SNPs  was 0.27. For the SNPs exclusive to the Middle American germplasm  and Andean germplasms  the Fst values were 0.31 and 0.85, respectively. The average Fst for the total set of SNPs  was 0.45, with 63,366 differentiating the two groups and resulting in an average Fst of 0.97. Chromosome 2 exhibited the highest number of differentiating SNPs (8520), followed by chromosomes 9 (8274) and 8 (7548)  value for the entire sample set was estimated to be 0.36, with 0.29 for the Andean and 0.28 for the Middle American gene pools. The Middle American gene pool exhibited higher nucleotide diversity  compared to its Andean counterpart . Tajima’s D values were estimated as D = 2.46 for the Middle American and D = 0.93 for the Andean germplasms. Phylogenetic analysis based on a set of 30,503 SNPs with a resolution of one bin per 10 kb, revealed a clear division between the Andean and Middle American gene pools, consistent with the well-established classification in the common bean , with a predominance of NB-ARC, TIR, CC, kinase, LRR, Serine/threonine-LRR and Kinase-LRR domains. Additionally, 45 genes were classified as putative disease resistance functions, including zinc finger transcription factors and dirigent-like proteins , while chromosome 9 had only two annotated genes, as shown in Fig. The SNPs identified in disease resistance genes were classified according to their predicted effects. The majority, 75.35%, were categorized as modifier type (impacting non-coding regions), followed by 15.71% classified as moderate (involving non-synonymous substitutions), 8.60% as low (involving synonymous coding/start/stop effects) and a small percentage, 0.34%, fell into the high category  (Supplementary Table The Gene Ontology (GO) enrichment analysis was performed for the 27 SNPs predicted to have a high impact, considering both biological process (BP), and molecular function (MF) as shown in Supplementary Fig. The analysis of annotated terms showed that the 27 R-genes were overrepresented and played important roles in “protein biding”, ADP biding” and “signal transduction”  associated to disease resistance and providing valuable markers for use in marker-assisted breeding programs.E of 0.334 (± 0.008) for the entire collection of SNPs. The genotypes from the Middle American gene pool exhibited higher heterozygosity (0.27 ± 0.009) compared to the Andean genotypes (0.19 ± 0.010). The coefficient of gene differentiation between the gene pools (Fst) was 0.386 (± 0.001).Out of the 438 SNPs associated with R-genes that were evaluated, a total of 356 non-redundant R-genes were sampled , Fusarium Wilt (FW), and Cowpea mild mottle virus (CPMMV). QTL positions were identified on chromosomes Pv04, Pv07, and Pv08 at specific positions as reported in the respective references. A total of 110 SNPs were identified in silico within the genomic window containing the QTLs, and 8% of them were annotated to disease genes, with the aim of approaching the disease-causal variants  and P. coccineus L. leading to the identification of over 40 million genomic variants providing comprehensive resources for molecular breeding and further understanding of the genetic diversity within common bean populations. In a more extensive study, Wu et al.12 conducted resequencing of 683 accessions from the National Crop Genebank of China and successfully identified variants associated with yield components, of high value for breeding purposes. These collaborative initiatives in the scientific community are significantly contributing to the establishment of a comprehensive knowledge base, paving the way for advancements, facilitating gene discovery, and enhancing our understanding of key agronomic traits.In the present study, we conducted resequencing of 40 agronomically important common bean breeding lines of Middle American and Andean origin, resulting in the identification of nearly 500,000 SNPs. These germplasms have been extensively studied worldwide for their resistance/susceptibility to various diseases, such as anthracnose, angular leaf spot, fusarium wilt, common bacterial blight, bean golden mosaic virus, and cowpea mild mottle virus. In a related study, Lobaton et al.32. Moreover, a remarkable number of SNPs with MAF ≥ 0.05  and MAF ≥ 0.1  were identified, of which 63,366 SNPs effectively contributed to differentiate the Andean and Middle American groups (Fst ≥ 0.97), confirmed by the phylogenetic analysis. These extensively characterized set of SNPs covering the common bean genome holds great potential of applications, mainly if combined with datasets generated by other research groups33, as they enable the accurate selection of SNPs for candidate genes, as well as those distributed across the entire genome.The analysis of the common bean accessions' raw data in the present study revealed an extensive collection of SNPs, exceeding 6.5 million within the dataset. Among these SNPs, 4.07 million were identified in non-repetitive regions, representing a significant increase of approximately 68% compared to the 1.3 million SNPs reported in non-repetitive regions by Lobaton et al.33 . The displayed higher genetic diversity in this study correlates with the wide range of genetic backgrounds of the sampled accessions, which was crucial for capturing a broader spectrum of genetic variations across the entire genome. The Ts/Tv ratio (1.29) observed exhibited minimal variation compared to other relevant bean-related studies35, aligning with our expectations36 and affirming the high quality of the data. Despite the substantial presence of introgressions in our study, only 99 of them exceeded 1 Mb in length, reflecting the findings of Lobaton et al.32, who reported 100 introgressions longer than 1 Mb in the evaluated germplasm. In general, a greater enrichment of the Middle American germplasm was observed, with 92.65% of the introgressions consisting of Andean germplasm segments. These findings are consistent with the history of bean domestication in Brazil, where Middle American germplasm prevails, but there has been a notable focus on hybridization with Andean germplasm to introduce target genes and enhance genetic variability in the development of breeding lines and cultivars38. Interestingly, the TO cultivar, which has a Middle American background exhibited a larger proportion of Andean introgression (21.63%), as shown on the phylogenetic tree , we observed higher estimates of nucleotide diversity  compared to those reported by Delfini et al.33. Variation in non-coding regions have an established role in plant diseases and regulatory mechanisms, with growing evidence from many areas, including yield, abiotic stresses, and adaptation to environment41. Genes conferring resistance to bean diseases have been extensively documented in the literature43. In this study, a total of 501 putative disease resistance genes were selected from the common bean genome obtained from phytozome, surpassing the previously reported numbers for the Andean (376 NB-LRR) and Middle American (234 NBS-LRR) germplasms44. Moreover, the discovery of SNPs in R-genes (7841 SNPs in 85.03% of the R-genes) uncovers significant polymorphisms associated with the defense response in beans enabling the development of genotyping arrays that target and explore multiple genomic regions45. These SNPs can have a functional impact in terms of phenotypic resistance or susceptibility to certain diseases.Among the 98,584 SNPs identified, 58% fell in introns, as also observed by Delfini et al.Colletotrichum lindemuthianum, pathogen causing anthracnose46, and Phaeoisariopsis griseola which causes angular leaf spot11, have demonstrated their importance as valuable sources of resistance genes for breeders in the bean community. The validation and testing of this set of specific SNPs, associated with disease genes, using SNP arrays serves as the foundation for genome association studies and whole-genome-based selection in common beans. There is a growing demand for breeding programs to incorporate these SNPs into genotyping panels for diversity analysis, associative mapping, QTL mapping, and genomic selection47. The inclusion of SNPs associated with resistance genes in these genotyping panels enhances the value of molecular tools, enabling the detection of genetic variation in target genes. Our work provides a set of operationally evaluated SNPs that are readily suitable for incorporation into any genotyping chip.The genotyping of 438 SNPs we conducted aimed to operationally assess a set of SNPs associated with resistance genes annotated in common bean. This research contribution is significant and unprecedented as it provides allelic variants located within resistance genes derived from 38 genotypes of great scientific value  tolerant cultivars led to the identification of SNPs associated with target QTLs49, aiming to increase the likelihood and identifying more accurate molecular tools for MAS. Recently, Yan et al.50 refined four major QTLs for oil content in Brassica napus by integrating resequencing data and transcriptomics. In soybean, whole-genome sequencing allowed the identification of genomic variations and candidate resistance genes for soybean mosaic virus (SMV), opening possibilities for the development of resistant varieties51. Additionally, genotyping these target SNPs can optimize genomic selection approaches, enhancing the accuracy of predictions, as demonstrated for fusarium head blight resistance and yield-related traits in wheat52.Continuing within the scope of this study, the resequencing of inbred lines that incorporate crosses in which important QTLs were mappedP. vulgaris publicly available, we can achieve a more comprehensive understanding of the genetic variation within this species. This expanded dataset allows for the identification of both common and rare genetic variants, as well as a deeper understanding of their frequencies, enabling more robust analyses and accurate predictions of agronomic trait mechanisms. Ultimately, the availability of this valuable genetic information contributes to the development of personalized breeding approaches that can lead to improved crop varieties with desired traits. The integration of this dataset with other valuable omics data available for common bean can also drives advancements in various research areas beyond genomics. Additionally, it creates new opportunities for scientific advancement and interdisciplinary collaborations, accelerating progress in common bean genetics, genomics, and related fields.In conclusion, by re-sequencing and making more variants of A germplasm panel comprising 40 common bean lines/cultivars was carefully selected for this study. The genotypes were chosen based on their significance in breeding programs worldwide. Some of these have served as essential background accessions for modern lines  and have been cultivated in Brazil for many years . To represent the historical breeding genetic diversity of common bean, we sequenced a combination of modern elite cultivars  and older varieties . We also included lines associated with important disease resistance  as well as members of differential series used to differentiate races of pathogens causing anthracnose and angular leaf spot diseases . Additionally, we carefully selected lines with other desirable agronomic traits, such as specific plant architecture and darkening of the tegument ; adapted to different regions and exhibiting variations in grain types ; from diverse institutions of origins and representing both the Andean and Mesoamerican gene pools . The seeds utilized in this study were obtained from the germplasm bank of Embrapa Rice and Beans (Supplementary Table ® DNA flex kit was employed. DNA sequencing was carried out on the Illumina HiSeqX platform using a PE150 (paired-end 150) strategy.Total genomic DNA was extracted from leaves at the V4 growth stage (4th trifoliolate unfolded at node 6 and branching), using the Invisorb Spin Plant Mini Kit  following the manufactures’ instructions. For genomic DNA library preparation, the Nextera53 for quality trimming (SLIDINGWINDOW:10:30) and adaptor sequences removal (TruSeq3-PE-2), keeping reads with a length of at least 50 base pairs (bp). The filtered reads were subsequently aligned to the reference bean genome27 (https://phytozome-next.jgi.doe.gov/info/Pvulgaris_v2_1) using BWA mem software54 using default parameters.The raw sequence data obtained were processed using Trimmomatic software v. 0.3955 and VCFTools software v.0.1.1656 was utilized for variant filtering, applying the following parameters: Min_Alleles = 2, Max_Alleles = 2; Min_Mean_Depth = 10; Max_Mean_Depth = 750; Mindp = 3; Maf = 0,01, Mac = 1; Perc_Missing = 0.4; Min_Qual = 10.0.For variant calling we used FreeBayes v.1.3.127. SNPs located within these repetitive regions were filtered out using VCFTools software56 with the “–exclude-bed” command. SNPs with a Minor allele frequency (MAF) ≥ 0.05 were retained. Ultimately, a hard filtering approach was applied to ensure quality control using the vcfR package57 in the R environment58, following the parameters: call_rate ≥ 0.8; Allele frequency for each alternative allele (AF) >  = 0.1 and <  = 0.9; QUAL ≥ 30; Strand balance probability for the alternate allele (SAP) > 20; Strand balance probability for the reference allele (SRP) > 20; End placement probability (EPP) > 20 and Depth(DP) ≤ 1500.To identify the chromosomal coordinates of repetitive DNA we accessed Phytozome platform using the Andean common bean reference genome59, genotypes for each accession were determined using the VCF Summary Stats command. The genotypes were classified into three categories for a biallelic site in a diploid individual: (i) Homozygous and identical to the reference allele, (ii) heterozygous, and (iii) homozygous but different from the reference . SNP predictions of functional impact were performed using the command VCF Summary Annotate at NGSEP software59.Using the NGSEP software v.4.0.160 and Quick GO web-based tool61. A significance level of p-value < 0.05 was applied, and the results from GO and KEGG were visualized using R with the ggplot2 package62. The enrichment in the protein domain was established using the protein family (PFAM) analysis tool available at InterPro website63. Additionally, gene–gene interaction prediction was conducted based on the protein sequences alignment (genes and domains) using the resources available at Phytozome.For SNPs with putative effect predicted to be high, a functional enrichment Tusing the MeSH over-representation analysis (ORA), including gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis, were performed using the function g:PROFILER in PythonE) was calculated using the complete set of SNPs, filtered at every 50 kb interval using the hierfstat v.0.5-1064 and pegas v.1.1 packages65 in the R environment58. A Venn diagram depicting the distribution of SNPs within and between gene pools was generated using the JVENN tool66. Polymorphic SNPs that were found exclusively or common among the gene pools were used to estimate fixation indices (Fst)67, nucleotide diversity (π) and Tajima’s D68 within a 100 kb sliding window using the VCFtools software56.Genetic diversity . The General Time Reversible (GTR) model coupled with a gamma-distribution (G) rate, was applied. Branch consistency was accessed through 10,000 bootstraps. The resulting tree was visualized using FigTree program v.1.4.4 accessible at http://tree.bio.ed.ac.uk/software/figtree/.Phylogenetic analysis was conducted using the SNPs grouped into bins of 10 kb employing the RAxML v.8.259 was utilized for this purpose considering non-overlapping windows of 50 SNPs.Introgressed analysis was performed to identify genomic regions that have undergone introgression between Andean and Middle American. The VCF Introgression Analysis module in the NGSEP software70 was conducted. SNPs located within 5 kb upstream/downstream regions of the UTR 5' and 3' of the R-genes were filtered using VCFtools56. Pearson's correlation coefficients were calculated in R software58 to assess linear relationships between key variables56.Genes associated with disease resistance, specifically R-genes, were obtained from the Phytozome v.2.1 platform (Phytozome (doe.gov). Furthermore, a search for conserved putative domains for R-genes, well documented in the literatureGenexa—Adn evolutivo) facility for genotyping. Overall genetic parameters were calculated using GenAlex v. 6.50271.A set of 438 SNPs was selected from a total of 7841 SNPs  previously identified in the R-gene sequences Fig. . Genomic28, BRS Notável X BRS Supremo29, BRS Cometa X Sel 130830, and BRS Sublime X CNFCT 1620731(Table P. vulgaris reference genome (https://phytozome-next.jgi.doe.gov/). Additionally, functional annotations were gathered for each SNP, describing the type of genetic variant found, such as intergenic variants, variants located in gene promoter regions, synonymous variants, and intronic variants.The identification of SNPs flanking the disease QTL in four bi-parental crosses Rosinha G2 X BRS Cometa731Table  was perfSupplementary Information.


Actinidia eriantha is a characteristic fruit tree featuring with great potential for its abundant vitamin C and strong disease resistance. It has been used in a wide range of breeding programs and functional genomics studies. Previously published genome assemblies of A. eriantha are quite fragmented and not highly contiguous. Using multiple sequencing strategies, we get the haplotype-resolved and gap-free genomes of an elite breeding line “Midao 31” (MD), termed MDHAPA and MDHAPB. The new assemblies anchored to 29 pseudochromosome pairs with a length of 619.3 Mb and 611.7 Mb, as well as resolved 27 and 28 gap-close chromosomes in a telomere-to-telomere (T2T) manner. Based on the haplotype-resolved genome, we found that most alleles experienced purifying selection and coordinately expressed. Owing to the high continuity of assemblies, we defined the centromeric regions of A. eriantha, and identified the major repeating monomer, which is designated as Ae-CEN153. This resource lays a solid foundation for further functional genomics study and horticultural traits improvement in kiwifruit.The online version contains supplementary material available at 10.1186/s43897-023-00052-5. A. eriantha filled most gaps and greatly improved the assembled genome quality, for the first time revealing the structure of centromeres and telomeres, laying the foundation for a better understanding of the structure and function of the kiwifruit genome.The telomere-to-telomere and haplotype-resolved genome of The genome sequencing data and transcriptome sequencing data have been deposited at Sequence Read Archive database in NCBI and the accession numbers is PRJNA905539.A. chinensis , A. eriantha , which provided a valuable resource for facilitating kiwifruit breeding and studies of kiwifruit biology. However, due to the limitations of past technologies, these genomes still contain a large number of gaps, leaving centromeres, telomeres, and other highly repetitive regions unfinished, even though these regions contain a lot of important information. Telomeres are highly repetitive DNA sequences at the ends of chromosomes that protect them from degeneration during cell division  have been released  derived from the hybrid progenies between “White” and “MHX-1”, which has the advantages of large fruit and high yield . We consequently obtained two preliminary chromosome-level genomes that contain 8 gaps and 17 gaps, respectively. Meanwhile, the polished ONT reads were used to fill gaps using the TGS-GapCloser  and 251.48 Mb (41.11%) of repetitive sequences were found Table  and showA. eriantha genome was sufficiently covered by the assembly; (iii) Hi-C interaction matrices displayed a diagonal pattern for the intra-chromosomal interactions in all pseudochromosomes, indicating the right ordering and orientation  The assembly spectrum copy number plots plotted by the KAT program  TheSyntenic analysis revealed highly consistent sequence orders between the two haplotypes Fig. a. Genomia/Ks value between allelic gene pairs. The results revealed that the majority of allelic genes had clearly experienced purifying selection (Ka/Ks < 1), with only a small number of allelic pairs showing possible positive selection (Ka/Ks > 1)  Fig. e.A. eriantha genome were coordinately expressed. These genes showed functional enrichment in biological processes such as regulation of lipids, activation of protein kinase activity, and cellular components including polysomal and vesicle tethering complexes based on the gene ontology (GO) annotation  across different tissues . A total of 2904 allelic genes showed different expression patterns   , which contribute to the assembly of highly repetitive regions. Using Hi-C scaffolding, short-read polishing, and manual curation, we elevated the reference genome of A. eriantha to a higher quality level. Thanks to the highly continuous genome, we have predicted more genes than “White” and “wild”, and a lot of these extra genes correlate with some important biological processes. The result showed the integrity and accuracy of two haplotypes were superior to other A. eriantha reference genomes.Over the past few years, the complete telomere to telomere (T2T) assembly of A. eriantha genome. Based on the new haplotype genomes, we first defined the locations of centromeres in A. eriantha. Although centromeric regions are highly repetitive and have a relatively low gene density as compared to other parts of the chromosome, there are still some new genes found in these regions. We found that the repeat type of centromeric regions in kiwifruit was relatively complex, but the main centromeric monomer in A. eriantha and A. chinensis was relatively conservative. These results laid the foundation for the sequence identity and functional analysis of centromere regions in kiwifruit.As we all know, parental alleles are randomly selected or collapsed during genome assembly  and A. eriantha “MHX-1” , was used in this study. High-quality genomic DNA was extracted from fresh young leaf tissue from “Midao 31”, growing in Hefei, Anhui Province, China, and separately packaged for PacBio HiFi, ONT ultra-long, and Hi-C sequencing. Tissue materials from leaves, stems, and fruits were used for RNA-seq and genomic annotation.“Midao 31”, a hybrid between The kiwifruit genomic DNA . The ONT reads were polished by Pilon  with default parameters   values of FPKM between two alleles were greater than 1 and the P value < 0.05. The ASE patterns (consistent and inconsistent expression pattern) was identified using the same methods as the tea plant Camellia sinensis genomes project  using the normalized and unified sequence “AAACCCT” for search. A pipline containing TRF tools, and CD-HIT tools was used for the identification of the centromere region. The TRF tools were used to identify whole-genome tandem repeats and monomers. The CD-HIT was used to cluster these monomers to reduce sequence redundancy. Those continuous and high-frequency regions were regarded as candidate centromere regions. At last, combine the result of gene density, TE number with candidate regions to predict the most likely centromere location.Arabidopsis  and GO(b) enrichment of newly predicted genes in MDHAPA. Supplementary Fig. 4. KEGG(a) and GO(b) enrichment of newly predicted genes in MDHAPB. Supplementary Fig. 5. K-mer spectrum analysis. The plots are colored to illustrate how many times of special K-mers from the hifi reads appearing in the assembly. Supplementary Fig. 6. BUSCO assessment of A.eriantha genome assemblies. Supplementary Fig. 7. GO(a) and KEGG(b) enrichment of ASES. Supplementary Fig. 8. Consistent and in consistent allele-specific expression (ASE) pattern across six sample. (a) Consistent ASEs. (b) inconsistent ASEs. The color bar represents log2(FC) values. FC indicates fold change of FPKM values between allele A and allele B. Red color suggests that expression in allele A is significantly higher than allele B and blue color means that expression in allele B is significantly higher than allele A. DAF means day after fruiting. Supplementary Fig. 9. Sequence alignment of A. eriantha and A.chinensis representative centromere (153bp) monomers.Additional file 2:Supplementary Table 1. Summary of the data sequenced by multiple technologies. Supplementary Table 2. The remaining gaps in MDHAPA and MDHAPB. Supplementary Table 3. Comparison of repetitive elements between MDHAPA and MDHAPB. Supplementary Table 4. Statistics of genomic variation releated to new predicted genes between MDHAPs and White. Supplementary Table 5. Mapping rate of Illumina,HIC,HIFI,ONT reads for MDHAPA and MDHAPB. Supplementary Table 6. Statistics of genomic variation between MDHAPA and MDHAPB. Supplementary Table 7. Functional impact of the identified SNPs and InDels between MDHAPA and MDHAPB. Supplementary Table 8. The identified telomeres in MDHAPA. Supplementary Table 9. The identified telomeres in MDHAPB. Supplementary Table 10. The identified centromeres in MDHAPA and MDHAPB. Supplementary Table 11. New predicted genes in the centromere region of MDHAPA. Supplementary Table 12. New predicted genes in the centromere region of MDHAPB. Supplementary Table 13. The identified monomers in MDHAPA. Supplementary Table 14. The identified monomers in MDHAPB. Supplementary Table 15. The 10 RNA-seq reads used in this study.