The ratio of quality SNP to potential SNP ranged from 0.22 to 0.75. A higher frequency of transitions and transversions was observed more in the exonic than the intronic region, while indels were present more in the intronic region. C↔T (transition) was the most dominant nucleotide substitution, while in transversion, A↔T was the dominant nucleotide substitution, and in indel, A/- was dominant. (4) Conclusions: Detected SNP markers may be useful for linkage mapping; marker-assisted breeding; studying genetic diversity; mapping important phenotypic traits, such as adaptation or oil production; or disease resistance by targeting and screening mutations in important genes. Euphorbia tirucalli L. is well-known for its healing properties against disorders , medicine , fruits (Phyllanthus emblica L.), and insecticides  Muell. Arg.) [Euphorbiaceae is a diverse group of flowering plants distributed worldwide, particularly in tropical and subtropical regions. The family is well-known for both its medicinal and commercial relevance due to the presence of a large variety of unique secondary metabolites. For instance, d tumors ,2,3) as tung oil ,5,6. Som Crantz. , while sl. Arg.) .Ayurveda [Croton spp.). Ethnobotanical leads from a study in Samoa led to the development of the anti-AIDS  drug prostratin from Homalanthus nutans [The family is also an important source of traditional medicine to cure liver disease, sprains, snake bites, convulsion, asthma, tumors, and rheumatism, as documented in the Indian traditional system of medicine—Ayurveda . Other ms nutans . Due to In particular, expressed sequence tags (ESTs) provide valuable resources to develop gene-associated markers. Compared to other DNA markers, such as amplified fragment length polymorphisms (AFLPs) and randomly amplified polymorphic DNAs (RAPDs), EST-derived simple sequence repeat (SSR) markers are advantageous because they show the highest level of polymorphism and are co-dominant. They are, therefore, more informative and more conserved and are potentially transferable between genera ,12,13,14The traditional method of SNP identification is done by sequencing the selected DNA fragment and comparing it with a reference genome in cases wherein whole-genome sequences are available. But this method of SNP identification has limitations like a high rate of sequencing error, a high cost, and the limited availability of complete genome sequences. Therefore, the growing availability of ESTs represents an alternative in silico approach for de novo SNP discovery in species whose whole-genome sequences are not presently available. Moreover, because EST-derived SNPs are found in both coding and 3′-UTR regions, they are regarded as gene-derived SNPs .E. esula L. (leafy spurge), E. fischeriana Steud. (spurge), E. lagascae Spreng. (spurge), E. tirucalli L. (milk bush), H. brasiliensis Müll.Arg. (rubber tree), J. curcas L. (Barbados nut), M. esculenta Crantz. (cassava), R. communis L. (castor oil plant), and V. fordii Hemsl. (airy shaw), available at dbEST of NCBI. These species are of high agro-economic importance but lack robust molecular markers for breeding. The distribution of nucleotide substitution and the comparative distribution of these SNP markers in the exonic and intronic areas were both investigated. The proper analysis of SNP markers in EST sequences will provide a better insight into the genetic architecture and will correlate the genes with their functions, as well as with the physiological process involved. The revealed SNP markers may also be employed for genome-wide association studies, QTL mapping, and marker-assisted breeding programs for improving varieties with desired traits.EST sequences are frequently employed in quick and affordable ways to find new genes and as a resource for cDNA array construction and gene mapping . Many biA total of 291,693 EST sequences of nine selected species of the Euphorbiaceae family was retrieved from dbEST , hosted Utilizing the online server EGassembler , which oDuring the process of repeat masking, the query sequence was compared to one or more files of FASTA sequences (library for masking). Vectors and organelles were masked by the program Cross_Match . Finallyhttp://www.bioinformatics.nl/tools/snpweb/, accessed on 4 March 2022) pipeline was used for the extraction of SNPs [The Linux-based command-line program QualitySNP  was used. A standalone version was downloaded, and executables for the Linux platform were used for locating the exonic and intronic regions among the available contigs with SNPs. All available contigs of nine different species of the Euphorbiaceae family were processed through GENSCAN [For detecting the distribution of SNPs in the exonic and intronic regions, GENSCAN  and assembly detail is provided in The cleansed sequences were then assembled by Cap3 (similarity score 95) to obtain a total of 34,736 contigs and 77,341 singletons. The obtained contigs of each species were further analyzed individually by QualitySNP for the detection of potential SNP sites and then filtered to find high-quality SNPs. The detected high-quality SNPs were further screened to find their distribution in the exonic and intronic regions.E. esula. L. The highest number of SNPs per kbp was found to be 203.47 in the case of E. tirucalli L., and the lowest number of SNPs per kbp was found to be 0.03 in the case of E. fischeriana Steud. The ratio of transitions to transversions ranged from 0.59 to 2.57 and was found to be highest in E. fischeriana Steud. (2.57). The highest number of indels was found in R. communis L. (804), and the maximum indels per kbp was found in E. tirucalli L. (35.84). A summary of SNPs detection, indels, and other parameters is depicted in In this study, a total of 25,432 potential SNPs and 20,753 real SNPs were discovered. The real SNPs were further filtered , and, finally, based on the confidence score; the numbers were reduced to a total of 14,351 high-quality SNPs, including 2276 indel polymorphisms. The ratio of qSNP to pSNP ranged from 0.22 to 0.75 among the nine species under study. The highest (qSNP/pSNP) ratio was found to be 0.75 in the case of R. communis L. and V. fordii Hemsl., in which transversions are more frequent in the intronic region. The distribution of indels was found to be higher in the intronic region in most of the species, including H. brasiliensis Müll. Arg., J. curcas L., M. esculenta Crantz., R. communis L., and V. fordii Hemsl.A high frequency of transition was observed among all of the members of Euphorbiaceae under study. Out of 14,351 high-quality SNPs, only 8849 high-quality SNPs were found to be located in the exonic/orf region, while 5502 high-quality SNPs were present in the intronic region. The prevalence of exonic and intronic SNPs as per their occurrence is detailed in The details of nucleotide substitutions of all of the nine selected species belonging to the family Euphorbiaceae are shown in Since SNPs are present throughout the genome, both in the coding and non-coding regions, even though they are less informative than microsatellites due to their bi-allelic nature, they are still regarded as a highly reliable and valuable molecular marker system for genotyping and selective breeding. Low genetic variation is a key feature of many agro-economically important families like Euphorbiaceae ,41,42,43M. esculenta (5475), followed by E. esula L. (3270), R. communis (2574), H. brasiliensis (1635), J. curcas (1043), E. tirucalli (176), V. fordii (140), and E. fischeriana (31), and the lowest number was detected in E. lagascae (7), as shown in In this study, a total of 25,432 potential SNPs, and 14,351 high-quality SNPs, including 2276 indels, were detected from 26,0479 EST sequences of nine different species belonging to the family Euphorbiaceae. Maximum number of high-quality SNPs was found in E. fischeriana, E. lagascae, E. tirucalli, H. brasiliensis, J. curcas, M. esculenta, R. communis, and V. fordii, respectively  and in ecotype Columbia (1 SNP in every 6.1 kb) [H. brasiliensis Müll.Arg. and found that the average SNP frequency was 1 SNP in every 1.5 kb [SNP frequency was found to be higher, i.e., one SNP in every 3.61 kb, 2.08 kb, 8.09 kb, 1.87 kb, 3.62 kb, 2.28 kb, 3.21 kb, 2.24 kb, and 2.96 kb in ectively . Similar 6.1 kb) . Pootakhy 1.5 kb , while ty 1.5 kb , as welly 1.5 kb . This cay 1.5 kb . One proy 1.5 kb . Higher y 1.5 kb , and theThis study provides information about potential SNPs 25,432) and selects 14,351 high-quality SNPs, including 2276 indels from the EST datasets of nine agro-economically important species belonging to the family Euphorbiaceae. It also provides insight in the comparative distribution of high-quality SNPs in the exonic and intronic regions, as well as a comparison of nucleotide substitution among the members of Euphorbiaceae. The triple filtration strategy adopted in QualitySNP makes it a more stringent and efficient tool for SNP detection from EST. Although QualitySNP generated a reduced number of high-quality SNPs, the reliability of the results obtained is more than other software tools like PolyBayes, which needs quality files, or Pearl script AutoSNP. A study on SNP mining from EST in sea bass using different tools justifies the reliability of SNP discovery by QualitySNP over other tools . The rel,432 and The mining of ESTs revealed 25,432 potential SNPs and 14,351 high-quality SNPs, which includes 2276 indels from a total of 260,479 EST sequences from the nine agro-economically important species of Euphorbiaceae under study. SNP frequency was found to be high among all of the members, with an average of 3.33 SNPs/1000 bp. A high frequency of transition was observed among all of the members. The comparative distribution of SNPs  showed that both transition and transversion were present more in the exonic region than the intronic region, while indels were present more in the intronic region compared to the exonic region. C↔T (transition) is the most dominant nucleotide substitution among all the members, while in transversion, A↔T is the dominant nucleotide substitution, and in indel, A/− is dominant. The SNP markers detected may be useful for linkage mapping and marker-assisted breeding programs, as well as for studying genetic diversity among the members of Euphorbiaceae. They may also be used for mapping important phenotypic traits, such as adaptation, oil production, or disease resistance, by targeting and screening mutations in important genes.


Solanum accessions based on 52 single nucleotide polymorphisms (SNPs) markers together with 17 morphological traits. This core collection was further used to analyze the genetic associations of eggplant morphological variations. A large variation was found among the evaluated eggplant accessions for some agro-morphological traits. Stem prickles and leaf prickles showed a significant positive correlation (r = 0.83***), followed by days to flowering and days to maturity (r = 0.64***). A total of 114,981 SNPs were filtered and used for phylogenetic tree analysis, population structure analysis, and genome-wide association study (GWAS). Among the agro-morphological traits, significantly associated SNPs were found for six traits. A total of 377 significantly associated SNPs with six agro-morphological traits were identified. These six traits and the number of SNPs were: days to maturity (51), flower size (121), fruit width (20), harvest fruit color (42), leaf prickles (38), and stem prickles (105). The largest fraction of significant SNPs (11.94%) was obtained on chromosome Ch01, followed by Ch07 and Ch06 with 11.67% and 10.08%, respectively. This study will help to develop markers linked to the most important agro-morphological traits of eggplant genetic resources and support the selection of desirable traits for eggplant breeding programs.Eggplant is one of the most economically and nutritionally important vegetables worldwide. The study of the association of phenotypic traits with genetic factors is vital for the rapid and efficient identification and selection of eggplant genetic resources for breeding purposes with desired traits. The eggplant resources (587) collected from different countries, including Korea, were used for establishing the core collection. A total of 288 accessions were selected from 587  Solanum melongena L.), a member of the Solanaceae family, is a popular vegetable in Africa, Asia, and Southern Europe [Eggplant  collected from 50 countries, including 80 resources in the Philippines, 44 resources in China, and 16 resources in Korea, were used for establishing the core collection. A total of 288 eggplant resources were selected from 587 Phenotypic characterization of 17 qualitative and quantitative agro-morphological traits was performed  for 288 The correlation between agro-morphological characteristics is shown in Principal component analysis (PCA) plot was generated using the phenotypic data of 288 eggplant accessions . The firThe GBS library was constructed from 288 eggplant accessions and sequenced using the Illumina Hiseq 2000 platform  and generated approximately 2.2 billion reads with an average mapping depth of 25.41× for a single accession. The population structure of the 288 eggplant genetic resources was inferred using STRUCTURE (v. 2.3.4) software . Admixture model-based simulations were carried out by varying K from 1 to 10 with 10 iterations. The estimated likelihood (lnP (D)) was greatest for K = 3 , suggestp > 6.34) was used as a cut-off to identify marker-trait associations. A total of 377 significant SNPs associated with six agro-morphological traits were identified. These six traits (number of SNPs) were: days to maturity (51), flower size (121), fruit width (20), harvest fruit color (42), leaf prickles (38), and stem prickles (105). All SNPs significantly linked to six agro-morphological traits are presented in p-value for six agro-morphological traits are presented in A genetic association study was conducted to identify SNPs associated with qualitative and quantitative agro-morphological traits. The GWAS results of 17 agro-morphological traits were visualized in Manhattan  and QQ pThe number of significant SNPs associated with leaf prickles were seven on Ch02, six on Ch01 and Ch05, four on Ch04 and Ch06, three on Ch03, Ch08, and Ch10, and one on Ch09 and Ch11. Following flower size, the second highest number of significantly associated SNPs were found for stem prickles and located across all 12 chromosomes. The numbers of significantly associated SNPs with stem prickle found on Ch01, Ch08, Ch07, Ch10, and Ch05 were 14, 12, 11, 11, and 10, respectively. A relatively high number of significantly associated SNPs (121) were found for flower size across all 12 chromosomes. Of these SNPs, 15 were on Ch01, 14 on Ch04, 13 on Ch06 and Ch07, 12 on Ch03, and 11 on Ch10 and Ch11. Regarding fruit width, significantly associated SNPs were found only on a few chromosomes: Ch01 (three), Ch02 (one), Ch04 (one), Ch05 (two), Ch07 (nine), Ch09 (three), and Ch11 (one). Among the nine SNPs associated with fruit width located on Ch07, two of them were located in the intergenic region, and the other two were on genes that encode proteins with unknown functions. Among the SNPs associated with harvest color, seven were on Ch06, five on Ch01 and Ch07, and four on Ch03, Ch05, Ch08, and Ch10. Also, two SNPs were located on chromosomes Ch02, Ch09, Ch11, and Ch12. One SNP associated with harvest color was found in a gene that encodes sbt3, a subtilisin-like protease SBT3. Significantly associated SNPs with days to maturity were found on all chromosomes. Eight SNPs were located on Ch08, seven on Ch10, and six on Ch03 and Ch07 each. Relatively few SNPs associated with days to maturity were found on Ch05, Ch11, Ch01, Ch09, Ch12, and Ch02.Solanum accessions for efficient germplasm management and further studies. The greater the genetic diversity of germplasm, the greater the likelihood of success in breeding desirable traits. Studying and understanding the association of agro-morphological trait variations with genetic variable sites may assist in the selection and transformation of desirable traits to develop new cultivars through breeding programs. Diverse agro-morphological variations (fruit and leaf) of eggplant germplasm were found in previous studies [The genetic diversity of plant genetic resources (PGRs), which provide useful alleles linked to plant development and improvement, is critical for the conservation and utilization of germplasm conserved in a gene bank ,22. DNA  studies ,36,37. SS. melongena. Population 1 (Pop1) and 2 (Pop2) were mainly germplasm belonging to S. melongena, and a few unknown (S. spp.) species were also clustered. As presented in the PCA and DAPC, the first two clusters did not separate from each other entirely. A few germplasms from one to five genotypes belonging to other species were clustered in Group 3 (42 germplasms). The possibility of genetic material hybridization  and migration of genetic resources from place to place could be the reason for creating subpopulations within the same species.SNP markers are regarded as potentially promising breeding tools for use in genetic mapping and marker-assisted selection since they can be scored in parallel experiments at a low cost . SNP marSolanum viarum Dunal indicated that they may be initiated by GTs or triggered by GT-derived signals [S. viarum revealed several transcription factors (TFs) that may be involved in prickle development [Pl locus on chromosome 6, and produced a 0.5 kb presence/absence variant marker for prickleless eggplant selection [Genome-wide association studies have proved its efficiency in finding genomic regions linked with economically important agronomical features in several crops, including wheat ,40,41,42 signals . Transcrelopment ,50. In telopment ,8,51,52.election . Interestingly, one SNP on Ch01 was found to be strongly linked with fruit color at harvest and was situated in a gene that produces the acetylserotonin O-methyl transferase (ASMT) enzyme. ASMT was also involved in a variety of plant growth and development dynamics. ASMT is the final enzyme in melatonin biosynthesis and may have a rate-limiting role in plant melatonin production. Several studies in recent years have confirmed that tryptophan decarboxylase (TrpDC), tryptamine 5-hydroxylase (T5H), serotonin N-acetyltransferase (SNAT), and acetylserotonin-O-methyltransferase (ASMT) are involved in melatonin synthesis in plants ,55. Sun In a previous study, it was indicated that the width and length of each flower organ affect the entire flower size . Also, aThe number of days needed until maturity is an important agronomic trait to determine and select early and late mature crops. The early flowering plant had a shortened maturity period as supported by a strong positive correlation of days to flowering and days to maturity r = 0.64***). In previous studies, several SNP markers associated with days to maturity have been found in different crops, such as Kersting’s groundnut . In this***. In pA total of 587 eggplant resources collected from 50 countries, including 80 resources in the Philippines, 44 resources in China, and 16 resources in Korea, were used to establish a core collection. These germplasms belong to different species. The eggplant seedlings (eight to ten in triplicates) were planted in the National Agrobiodiversity Center (NAC) greenhouse at the Rural Development Administration (RDA), Jeonju, the Republic of Korea in 2021. The eggplants were cultivated according to the RDA-recommended eggplant cultivation method.To establish a core collection, 52 microsatellite markers (single sequence repeats: SSRs) were used along with 17 morphological traits. Among 587 eggplant resources, a representative 288 resources were selected as a core collection based on the advanced maximization (M) strategy using a modified heuristic algorithm implemented in PowerCore software . This coA total of 17 agro-morphological traits were assessed. This includes hypocotyl anthocyanin, stem anthocyanin staining, growth habit, plant height (cm), stem-prickle, leaf prickle, calyx prickle, flower size, flower color, fruit length (cm), fruit width (cm), fruit shape, immature fruit color, mature fruit color, harvest fruit color, days to flowering, and days to maturity. Except for the quantitative parameters, the scales or scores were used to assess the agro-morphological differences of eggplant genetic resources. The agro-morphological characterization descriptions are presented in DNA was extracted from the samples using a Genomic DNA Prep Kit  following the manufacturer’s instructions. The GBS libraries were created using the restriction enzyme ApeKI (5′-GCWGC-3′) and a protocol modified from previous research . OligonuSolanum melongena L. (https://solgenomics.net/ accessed on 19 September 2022). Mapping was a preliminary step to detect raw SNPs (In/Del) between the S. melongena genome (Eggplant genome consortium V4.1) and sequenced samples. Barcode sequences were used to perform demultiplexing. Adapter trimming was done using cutadapt (version 1.8.3) , and seqClean reads were mapped to the reference genome sequence, and the obtained SAM files were used to discover raw SNPs using SAMtools (0.1.16)  and extrSolanum melongena L), the designated SNP positions were defined as “intergenic or genic regions”, and the genic region was further classed as “CDS or intron regions”. An integrated SNP matrix was obtained between samples to assess SNPs between the assessed objects. A list of unions was generated by comparing each sample’s raw SNP sites to a standard template, and a non-SNP locus was filled in from the sample’s consensus sequence. The final SNP matrix was formed by filtering out the miscalled SNP sites using SNP comparison among samples. Based on the position, SNPs were classified as homozygous (SNP read depth ≥ 90%), heterozygous (40% ≤ SNP read depth ≤ 60%), etc., . Based on the location information of the reference genome sequence , is shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were calculated using the maximum composite likelihood method and are expressed in terms of the number of base substitutions per site. The analysis included 288 eggplant nucleotide sequences with the final dataset containing 114,981 SNPs. MEGA6  was used−7 (0.05/114981). The candidate genes were identified using the BLAST searching tools for each SNP in the eggplant genome database. Association analysis was performed using 114,981 union SNP datasets using a mixed linear model (MLM) . The sigData summarization and descriptive statistics on agro-morphological data were performed using the Microsoft Excel program. PCA, DAPC, and correlation were all performed using the R program (version 4.2.1). Other software programs used in this study are discussed in the Crop breeding schemes require important agro-morphological characteristics. Phenotypic characterization is a time-consuming process and may not be precise enough for selection when a huge germplasm pool is available for breeding. The study of genetic association with morphological variation and the identification of SNPs associated with key agro-morphological and yield-related traits are important for assisting the selection process with rapid and accurate prediction. This study provides a comprehensive result on the association of genetic and agro-morphological traits of eggplants and significantly associated SNP markers for six eggplant agro-morphological traits are presented. A total of 377 significantly associated SNPs were found for stem prickles, leaf prickles, flower size, fruit width, fruit color at harvest, and days to maturity. These SNPs can be used for further research and to identify markers with high efficiency. We recommend further exploring the genes’ functions where SNPs are found for a better understanding of the molecular mechanisms of agro-morphological variation in eggplant germplasm.


Prunus avium L.), quantitative trait loci have been identified for fruit maturity, colour, firmness, and size to develop markers for marker-assisted selection. However, resolution is usually too low in those analyses to directly target candidate genes, and some associations are missed. In contrast, genome-wide association studies are performed on broad collections of accessions, and assemblies of reference sequences from Tieton and Satonishiki cultivars enable identification of single nucleotide polymorphisms after whole-genome sequencing, providing high marker density. Two hundred and thirty-five sweet cherry accessions were sequenced and phenotyped for harvest time and fruit colour, firmness, and size. Genome-wide association studies were used to identify single nucleotide polymorphisms associated with each trait, which were verified in breeding material consisting of 64 additional accessions. A total of 1 767 106 single nucleotide polymorphisms were identified. At that density, significant single nucleotide polymorphisms could be linked to co-inherited haplotype blocks (median size ~10 kb). Thus, markers were tightly associated with respective phenotypes, and individual allelic combinations of particular single nucleotide polymorphisms provided links to distinct phenotypes. In addition, yellow-fruit accessions were sequenced, and a ~ 90-kb-deletion on chromosome 3 that included five MYB10 transcription factors was associated with the phenotype. Overall, the study confirmed numerous quantitative trait loci from bi-parental populations using high-diversity accession populations, identified novel associations, and genome-wide association studies reduced the size of trait-associated loci from megabases to kilobases and to a few candidate genes per locus. Thus, a framework is provided to develop molecular markers and evaluate and characterize genes underlying important agronomic traits.In sweet cherry ( Prunus avium L.) is a vegetatively propagated perennial crop grown for its tasty fruits. It is a diploid species in the Rosaceae with a relatively small genome of approximately 340 Mb (2n = 16) [Sweet cherry ( Mb 2n = 6 1]. Th. ThPrunu2n = 16) .It is a challenge to breed new sweet cherry cultivars because of relatively small numbers of progeny compared to number of pollinated flowers. For example, Quero Garcia  reportedPrunus species, including peach   was identified in a region ranging from 12.131 to 12.220 Mb on chromosome 3 . Accessions with homozygous genotype AA had significantly stronger firmness of skin and firmness of flesh than those with TA and TT allele combinations , homozygous genotype GG was associated with higher value of penetration than that of the AG genotype. Accessions with homozygous genotype GG for SNP marker chr4_16229770  had reduced fruit bruiseness, as indicated by higher values on a nine-point classification scale than those of accessions with allele combinations AA and AG  and chr1_6099740 , respectively, indicated relatively high bruiseness. In addition, marker chr7_25384148 was associated with fruit bruiseness as well as firmness of skin.Eighteen SNP markers  were assrrelated . The strinations ; Fig. S7A and AG . By contfruit length, fruit width, fruit thickness and weight of fruit , chr6_34823266 , chr8_27088406 , and chr4_5976608 , respectively   was very strongly associated with fruit width  had smaller fruit thickness than GA heterozygotes and AA homozygotes, with ~2 and ~ 3.5 mm thedifference, respectively. For weight of fruit, associated SNPs were identified, for example, on chromosomes 2 and 6. Accessions with homozygous genotypes GG for marker chr2_29787028  and CC for marker chr6_16582783  had lower weights than those of other variants  (Supplementary Data). Blocks ranged from 2 to 307 SNPs and from 10 bp to 58.6 kb in size. Medial size of assigned haplotype blocks was 10.1 kb. Thirteen remaining SNPs were not part of any co-inherited SNP group. Nine SNPs directly hit the region of candidate genes between start and stop codon. Number of candidate genes in individual haplotype blocks ranged from one to nine. A total of 141 candidate genes were identified, and homology-based annotation indicated function for 104 . One of the haplotype blocks  included three different SNPs associated with three different traits, all linked to fruit size . Four geThe GWAS results were verified using a second set of 64 additional accessions (denoted BM), which were processed in the same way as CGR accessions up to SNP calling. In a PCA of all 299 samples (CGR + BM), subpopulations were not identified, but BM accessions were slightly shifted compared with CGR accessions . Most SNFigs. S2, S4, S7, time of harvest, the difference in minimal values between BM and CGR was 8.63 d. The phenotypic values suggested loss of allele combinations for early ripening in BM accessions. Accordingly, no BM accession had the AT allele combination for marker chr4_4 523 736, which was associated with early time of harvest  were associated with loci previously described as QTLs for those particular or similar traits . Furthermore, three  SNPs associated with two different but highly correlated traits were associated with published QTLs. Additionally, SNPs associated with fruit colour marked unambiguously deletion of a region containing five MYB transcription factor genes, with MYB10.1 responsible for differences in sweet cherry skin colour [Relevance of SNPs was also confirmed by comparing genomic positions with previously known QTLs for particular traits. Twenty-two of the SNPs colocalized with known QTLs (Tables S2–S5), and especially, all SNPs most significant for individual traits (except n colour .time of harvest (if viable), could provide very early and late fruit ripening, respectively, and therefore extension of the harvest period.Molecular markers for valuable sweet cherry traits are rare and thus need to be developed and validated. Previously, the most precise approaches to identify QTLs associated with important traits used the cherry 6 + 9 K SNP array , 27, wittime of harvest NAC transcription factors [fruit weight PavCNR12 [The GWASs were performed using 235 accessions (CGR). The effects of the identified SNPs were then verified on anindependently evaluated set of 64 additional accessions (BM).Significance of markers was confirmed on trees with fundamentally different growing habits. While 20- to 30-year-old CGR trees were planted on vigorous mazzard rootstock, very young BM trees were grafted on semidwarf rootstock Gisela 5, with all expected consequences including those for tree height.Colocalization of most SNPs (including those with the highest significance) and known QTLs provided additional independentevidence of robustness of results. On the other hand, some candidate genes proposed in previous studies were missed by the GWAS in this study . Howevep-value, chr4_16 353 619, was mapped into the region of HD_Y4_56 [m region [m [time of harvest collocated with known QTLs.The most significant SNPs identified by the GWAS were on chromosome 4, which is a hot spot for harvest and ripening traits according to previous studies , 12, 13.Although NAC transcription factors (NAC5 and NAC25) are proposed as candidate genes , 13 basetime of harvest, chr4_4 523 736, shared the haplotype block with four genes (FUN_032302 to FUN_032305), with the most interesting aquaporin NIP1–1 (FUN_032302). Aquaporins form membrane water channels that are critical in controlling cell and tissue water content. Twenty-eight putative genes coding for aquaporins have been identified in the sweet cherry genome, and 16 are differentially expressed in developing fruits [NIP1–1 , which belongs among the MYBs with R2R3 domains. The MYB proteins constitute a large family of transcription factors that control secondary metabolism, development, signal transduction, and resistance to biotic and abiotic stresses, among other processes , 36. SevMYB10.1 that determines fruit colour described by Jin et al. [Prunus avium Myb family, according to Sabir et al. [The Myb10 V1–2 gene identified in this study, however, differs from the n et al. . Sequencr et al. . Moreovefruit colour of accession SB077 had the highest value of 9 (dark red) each year, which indicated the complete dominance of the non-deleted allele and confirmed previous findings [Notably, yellow-fruited accessions possessed the deletion in homozygous constitution; whereas the parent of one of the accessions (namely SB077) was heterozygous in the region . Howeverfindings .fruit colour) and in haplotype blocks co-inherited with markers chr3_19315249 (associated with fruit colour) and chr3_24544649 (associated with flesh colour). Downstream of SNP chr1_41003304, anthranilate synthase beta subunit 1 (FUN_004911) was identified. The gene protein product is a part of a complex that catalyses biosynthesis of anthranilate, an intermediate in the biosynthesis of L-tryptophan, which is an amino acid scarce in cells and only infrequently used in protein synthesis [Mangifera indica L.) [flesh colour) is a member of a large family of modular RNA-binding proteins, which facilitate processing, splicing, editing, stability, and translation of RNAs [Cucumis melo L.) is CmPPR1, which likely affects carotenoid accumulation [Citrullus lanatus (Thunb.) Matsum. & Nakai), ClaPPR140 cosegregates perfectly with red flesh colour [Fragaria vesca L.) accessions with white fruits, PPRs are among the genes significantly downregulated, compared with accessions with red fruits [Other candidate genes for colour traits were found close to chr1_41003304 (associated with ynthesis . Howeverynthesis . Anthocyynthesis . The genmulation . In wateh colour . In woodfirmness of skin and firmness of flesh (chr4_16000421) and for fruit bruiseness (chr4_16229770) were localized between markers ss490552880 and ss490552942 that delimit the QTL for fruit firmness identified by Cai et al. [m identified by Calle and Wunsch [firmness of skin (chr1_31855038) was closely mapped to ss490546759  that were also consistent with published QTLs. The most significant SNPs for s chr4_169770 wered Wunsch . Anothers chr4_169770 weree et al. .firmness of skin and firmness of flesh (chr4_16000421). The gene is among 25 candidate genes for fruit firmness located in the qP-FF4.1 locus on sweet cherry chromosome 4 [firmness of skin (chr1_31855038).The NAC transcription factor 56 (FUN_033984) emerged downstream of the most significant marker identified for both mosome 4 . In applmosome 4 ) is a stmosome 4 . Most otmosome 4 . In stramosome 4 . Additiovalue of penetration, and the SNP was localized in one of its introns. Polygalacturonase is also known as pectin depolymerase or hydrolase degrading polygalacturonans, which are significant carbohydrates in the pectin network in cell walls. Polygalacturonases soften and sweeten fruit during the ripening process, including in strawberry [value of penetration. Three genes encoding ubiquitin-conjugating enzyme E2 (UBC) should also be considered. One was upstream of SNP chr3_19735173 , and two were in the haplotype block of chr7_25384148 . Two UBC enzymes are connected with ripening in strawberry fruit. One has a positive effect on fruit development, ripening, and softening by upregulating anthocyanin accumulation and significantly increasing expression of maturity-related genes, whereas the other increases fruit firmness [Polygalacturonase  was the only gene in the haplotype block of chr2_43495800 associated with rawberry , peach [rawberry , banana rawberry , and apprawberry . Moreoverawberry –61. Putrfirmness .Firmness of skin and firmness of flesh shared the most significant marker and had also the highest Pearson correlation coefficient. Conversely, value of penetration, the only firmness trait measured under standard laboratory conditions, showed lower correlation to all other traits  are expected to contribute to fruit bruiseness. The traits classified as fruit firmness are influenced by such parameters as water content [While all four characteristics contributed to overall fruit firmness, they were correlated at different level. r traits . The dis content , chemica content  or cell  content ). These fruit length, fruit width, fruit thickness and weight of fruit were used to evaluate fruit size and weight. Although all four phenotypes were highly correlated, SNPs usually differed among the parameters. Such differences were not surprising because size characteristics were not in “mutually fixed” ratios. Instead, different ratios reflected heterogeneous fruit shapes. Nevertheless, three markers from chromosome 2 associated with fruit length, fruit thickness and weight of fruit collocated in a single haplotype block, indicating it was a very strong candidate to determine fruit size. Overall, SNPs for individual size-related traits were identified on all chromosomes except chromosome 7, although the most significant associations were found on chromosomes 1 and 2. This result is consistent with known QTLs associated with fruit size and weight. The SNP marker with the lowest p-value  was collocated with multiple QTLs, i.e. qP-FW2.1m [fruit length), chr2_29776725 (the most significant for fruit thickness), and chr2_29787028 (the most significant for weight of fruit) was in regions of qP-FW2.1m [Traits P-FW2.1m  and FW_GP-FW2.1m , and QTLP-FW2.1m . In addiP-FW2.1m  and the P-FW2.1m . SimilarP-FW2.1m , 23, 25.PavCNR12 has been proposed [PavCNR12 was not among them. Nevertheless, another promising candidate appeared among the three collocated genes. Nucleoside diphosphate kinase 1  has a major role in the biosynthesis of nucleoside triphosphates (other than ATP) [Arabidopsis thaliana (L.) Heynh. [The cell number regulator gene proposed  as a canproposed . Howeverhan ATP) . Nucleoshan ATP) . Its hom) Heynh. . To deteLagenaria siceraria (Molina) Standl.) accession with big fruits than in one with small fruits [A nearby haplotype block (290 SNPs) with SNP markers chr2_29776624, chr2_29776725, and chr2_29787028  provided additional candidate genes. Cyclin-U4–1-like (FUN_010712) is involved in the G1/S phase transition during cell division, and it has significantly higher expression in a bottle gourd (l fruits . Similarl fruits . Anotherfruit width and weight of fruit). The RGLG5, together with RGLG1, mediates the ubiquitination and subsequent degradation of phosphatase PP2CA, a major inhibitor of ABA signalling [Fragaria vesca fruit development, auxins and gibberellins are responsible for fruit growth (supporting both cell division and enlargement), and ABA accumulation is suppressed. With onset of fruit ripening, auxin and gibberellin levels decrease, leading to an abrupt increase in ABA that drives fruit ripening. Therefore, ABA may be negatively correlated with fruit size [Another gene potentially associated with fruit size and weight was E3 ubiquitin-protein ligase RGLG5 (FUN_003761), which was found in a haplotype block (221 SNPs) surrounding another significant SNP marker, chr1_33914270  were used from the Collection of Genetic Resources (CGR) of the Research and Breeding Institute of Pomology Holovousy Ltd. (RBIPH). Accessions included commercial and older historic cultivars, important breeding hybrids, and traditional Czech landraces. Germplasms were established at RBIPH in 1989. Holovousy  has an annual average temperature of 8.4°C and annual precipitation of 663.5 mm [time of harvest, fruit colour, flesh colour, firmness of skin, firmness of flesh, value of penetration, fruit bruiseness, fruit length, fruit width, fruit thickness, and weight of fruit. Assessment of fruits was done directly after sampling and no cooling was applied on fruit before evaluation. Fruit was kept in ambient temperature of 24°C.All accessions were phenologically and phenotypically evaluated by using modified descriptors for sweet cherries , 75 fromTime of harvest - date, when mature fruits were collected. Determined with respect to the first ripening reference cultivar Kisinevskaja (SA993) to minimize seasonal perturbations, mainly weather, over the years. All values for SA993 were set to “0” and values for other accessions were standardized to it. Kisinevskaja usually ripens 0–2 days before well-known cultivar Früheste der Mark (syn. Krüppers Frühkirsche or Rychlice nemecka).Fruit colour and flesh colour were visually determined on ten fruits by skilled evaluators using 9-point classification scale . Firmness of flesh represented sensory assessment of the firmness of the pulp when biting into the flesh . Fruit bruiseness was assessed as the fruit resistance to bruising due to compression by squeezing the fruits between the fingers. Squeezing with fingers do not cause visible damage or change in colour of the fruit. Fruit bruiseness was sensory evaluated as a measure of fruit elasticity or how solid the fruit appears to the touch . Each characteristic was evaluated for fifteen fruits.n et al.   and on the right side of the fruit suture with a 3 mm tip of FTA-Fruit Texture Analyzer, , and expressed in kg.cm−2. An average value of ten fruits per cultivar was recorded. Fruits from BM set were measured in years 2012–2020 manually using a digital firmness tester AGROSTA 100X  with Tip of 25 (6 mm) and expressed in index value of firmness (1–100). Five fruits per genotype were measured by compression on cheek, on the left and the right side of the fruit suture and average value of penetration was noted. Undamaged fruit was used for each penetration experiment.Weight of fruit including the stalk was determined with a digital balance  and calculated as an average from a weight of one hundred cherry fruits. Fruit length, fruit width, and fruit thickness were measured in millimetres with a calliper and calculated as an average from 10 fruits (0 fruits .https://www.r-project.org/) was used to determine and display Pearson correlation coefficients between phenotypes.Final values of each phenotype and sample were means of values over all trees and years . Those vGenomic DNA was isolated from 100 mg of shoot phloem using an Exgene Plant SV isolation kit (GeneAll) according to the manufacturer’s instructions. Bark from 1- to 2-year-old shoots was peeled off by scalpel, and phloem was scraped and then homogenized in liquid nitrogen using a mortar and pestle. An Invitrogen Qubit Fluorometer was used to assess DNA quality. The 50 ng DNA was fragmented in 50 μl using a Bioruptor Plus (Diagenode); five cycles were run for 30 s at the high setting. Libraries for sequencing were prepared using a NEBNext Ultra™ II DNA Library Prep Kit for Illumina (New England Biolabs) with three cycles of amplification. Libraries were sequenced on a NovaSeq 6000 (Illumina), producing 2 × 150-bp paired-end reads. Simple sequence repeat markers were used to analyse pedigrees of yellow-fruited cherries following the protocol of Clarke and Tobutt .P. avium “Tieton” Genome v2.0 [MarkDuplicates (http://broadinstitute.github.io/picard/). Resulting alignments were processed using GATK v4.0.12.0 [p < 1e−6 were removed.For SNP calling, the reference sequence from the ome v2.0 . Trimmedome v2.0 , and dup4.0.12.0  software4.0.12.0 . Missing4.0.12.0 , and allhttps://www.rstudio.com/). Kinship matrix was generated from the SNP data set by the VanRanden algorithm method [Principal component analysis was used to determine number of subpopulations with GAPIT3 software (Genome Association and Prediction Integrated Tool) . The PCAm method  and visup-values obtained from association analysis. The FarmCPU model was most suitable for the data set. According to BIC values generated using GAPIT, the PCA parameter in following analysis was set to 0. Final GWASs for all phenotypes were performed using FarmCPU, and markers above the 5% Bonferroni multiple test threshold values were considered significant. Effects of alleles for each SNP marker were determined based on phenotype values, and SNP calls and corresponding box plots were created using Rstudio.Associations between molecular markers and phenotypic data were computed for the CGR set in GAPIT3. Two models were used in a preliminary analysis: Bayesian-information and linkage-disequilibrium iteratively nested keyway (Blink) and Fixed and random model circulating probability unification (FarmCPU). Model performance was compared based on a genomic inflation factor (lambda) calculated from https://www.rosaceae.org/Analysis/9262820); 2) a BLASTP search against nonredundant databases of Viridiplantae proteins at https://blast.ncbi.nlm.nih.gov; and 3) assignment to protein families using InterProScan at https://www.ebi.ac.uk/interpro/.The software PLINK v1.90 was used to select 100-kbp-long regions surrounding associated SNP markers, including allele combinations for all filtered SNP markers. Haploview v4.2  was usedwww.rosaceae.org, www.ncbi.nlm.nih.gov, Cherry Genome DataBase (kazusa.or.jp), or other publications . Sequences of primers or amplicons (in cases of SSR and CAPS markers) and surrounding sequences  were searched by BLASTN against the P. avium “Tieton” v2.0 genome [www.rosaceae.org to obtain unique positions of analysed markers. In the absence of more specific markers, the nearest outer markers delineating a QTL in genetic maps were used to assign the region of a particular QTL.Locations of significant SNPs identified in GWASs were compared with positions of known QTLs. To find positions of SSR, CAPS, and SNP markers used to define QTLs, relevant sequence information on particular markers and genetic maps was obtained from MYB10.1 gene determining yellow colour of sweet cherry fruits, a region of chromosome 3 was studied in detail. Paired-end reads from accessions with yellow fruits , four randomly selected accessions with red fruits , and Tieton [MarkDuplicates. Non-uniquely mapped reads were filtered out using SAMtools v1.9 [To characterize the deletion around the ols v1.9  with theols v1.9  subcommaWe thank Petra Chudobová, Lenka Křivohlávková, Lenka Tůmová, Lucie Zálešáková, and Helena Tvardíková for outstanding technical assistance. This work was supported by the Ministry of Agriculture of the Czech Republic (project QK1910290). Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA CZ LM2018140) supported by the Ministry of Education, Youth and Sports of the Czech Republic.J.B., J.C., L.Z., P.S., R.C. designed the research; K.H., J.C., P.S., J.S., R.C. performed the experiments; K.H., J.B., J.C., R.C. analysed and interpreted the data; K.H., J.C., J.B., P.S., J.S. wrote the manuscript; all authors revised and approved the manuscript.Raw data have been submitted to the Sequence Read Archive (SRA) under study number PRJNA813711.The authors declare no conflicts of interest.Horticulture Research online.Web_Material_uhac233Click here for additional data file.


Bemisia tabaci  whitefly species are vectors of more than 500 known plant-viruses and harbour highly diverse endosymbionts communities. To date, however, whitefly–endosymbiont interactions, community structure and their spatio-temporal changes are still poorly understood. In this study, we investigated the spatio-temporal changes in the composition and diversity of bacterial endosymbionts in the agricultural crop pest whitefly species, Bemisia tabaci sub-Saharan Africa 1-subgroup 1 and 2 (SSA1-SG1 and SSA1-SG2). 16S rRNA amplicon sequencing analysis was carried out to characterise endosymbiont compositionsin field-collected SSA1 (SSA1-SG1 and SSA1-SG2) populations infesting cassava in Uganda in 1997 and 2017. We detected Portiera, Arsenophonus, Wolbachia, Hamiltonella and Hemipteriphilus, with Arsenophonus and Wolbachia infections being predominant. Hemipteriphilus and Hamiltonella frequencies were very low and were detected in seven and two samples, respectively. Bacterial diversity based on three independent parameters including Simpson index, number of haplotypes and Bray–Curtis dissimilarity matrix was significantly higher in 1997 than in 2017. This period also coincided with the advent of super-abundant cassava-whitefly populations on cassava crops in Uganda. We discuss how endosymbionts may influence the biology and behaviour of whiteflies leading to population explosions.Sap-sucking insects, including whiteflies, are amongst the most devastating and widely distributed organisms on the planet. They are often highly invasive and endosymbiont communities within these insects help them adapt to new or changing environments.  Portiera aleyrodidarum, for instance, is the primary endosymbiont of Bemisia tabaci  is highly prevalent in sub-Saharan Africa. Based on the partial mitochondrial cytochrome oxidase I gene (mtCOI) marker, this species was divided into five subgroups  and from various different locations in Uganda.The infection dynamics of S-endosymbionts in n Uganda . Ghosh eaci SSA1 . Arsenop cassava . In addieir host . Hamiltofections . In thisB. tabaci adults on cassava were collected and preserved in ethanol for subsequent molecular analysis. GPS coordinates collected in 2017 were matched with the names of villages in the 1997 samples using QGIS v.2.18.17 online softwareA total of 65 young cassava leaves having eggs, nymphs and pupae were collected from Uganda in 1997, and stored at −80°C at NRI, University of Greenwich, United Kingdom. Samples were collected in 14 locations (~ 5 Km apart) in three districts of Mityana, Kampala and Masaka. Another sampling was carried out in 2017 in 13 locations in a slightly larger area of seven districts . A total of 48 mtCOI gene was carried out in 25 μl volumes consisting of 2 μl of DNA template, 0.4 μM of each primer, 0.15 mM of dNTPs, 1 × DreamTaq Green buffer and 0.5-unit DreamTaq Green DNA polymerase . The PCR amplification began with a denaturation step at 94°C for 3 min, followed by an amplification consisting of 38 cycles at 94°C for 30 s, an annealing step for 54 s, 72°C for 1.5 min and a final extension for 7 min at 72°C. PCR products were visualised on 1% agarose gels containing RedSafe nucleic acid staining solution . PCR products were then visualised under UV light (402 nm) and fragments of the expected size (~ 867 bp) were purified and sent for sequencing .For this study, the DNA was extracted from 121 whiteflies . Of thesEcherichia coli to control bias from multiple 16S rRNA gene copies or other sequencing errors was then prepared. Single amplicons were combined in equimolar ratios was purified using the same Gel and PCR purification kit. Quantification of pooled DNA was performed using NanoDrop 2000 . The pooled samples were sequenced by Fasteris Ltd., Switzerland by Illumina sequencing.The Hi-Seq Illumina platform with 466 bp paired-end reads was used for sequencing the V4–V5 region of the 16S rRNA gene as previously described . A dupliPaired-end reads were demultiplexed according to their barcode/primers using the software Metafast_BCsorting version 2.10; , and theSequences were subjected to a second filtering process using the DADA2 plugin  which waDADA2 generated Amplicon Sequence Variants (ASVs) and reported their relative abundance within each sample. VSEARCH consensus taxonomy classifier  was usedPortiera, it was excluded from the analysis to avoid skewing the results. Therefore, only S-endosymbionts were used in this analysis. Then, Vegan Library of R  was used to investigate mean differences between Simpson index and the number of observed haplotypes based on the 2 years of collection.Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), a robust methodology of differential abundance for microbial absolute abundances, was used to further confirm the date effect on S-endosymbionts composition . PermutaB. tabaci SSA1-SG1 and SSA1-SG2 which are the same species  and beta diversity level (ADONIS: p = 0.06). The results presented here are, therefore, solely from both the temporal and spatial changes.All individuals in this study were identified as  species . Moreovep = 0.0036 . Haplotypes average changed from 3 in 1997 to 7 in 2017, while the Bray-Curtis matrix measuring S-endosymbiont structure was significantly different  reads were assigned to S-endosymbionts and non-endosymbiotic bacteria  and 2017  samples . Of the bacteria . Amongstquencies , whereasquencies . Their aArsenophonus Wolbachia, Hemipteriphilus and Hamiltonella were detected in our samples with different prevalences , 49 samples in 1997 and 20 in 2017 (Arsenophonus was the most abundant S-endosymbiont followed by Wolbachia (OP161006–OP161013) which was observed in 5 whitefly samples with relative abundance ranging between 25 to 91% in 1997 and 28 samples in 2017 having a relative abundance ranging from 0.2 to 100% (Hemipteriphilus (OP160998 to OP161005) was least abundant in both years present in four samples in 1997 with relative frequency ranging between 3 and 47% and only present in one sample from 2017 with relative frequency reaching 7% (Hamiltonella (OP160970) was present in one whitefly sample only from the 2017 collections  occurred in less than three samples. Wolbachia prevalence was different between dates but the diversity did not change over time  but represented 5% of reads in 2017 . Reads with number of expected errors higher than 2 were discarded. Nevertheless, other errors or misinterpretations can still persist, notably, 16S rRNA gene copies that can occur in some bacteria species, such as polyploidy . In thisPortiera within B. tabaci SSA1. We discovered six prevalent Portiera haplotypes whose relative frequency was stable between the two dates of investigation. Portiera 16S rRNA was also found to contain a highly homologous sequence  in B. tabaci SSA1 using a simple PCR and sequencing approach, while we found a new and different combination of S-endosymbionts . Hemipteriphilus was found for the first time in seven samples and Hamiltonella in a single sample in African whiteflies. This is the first study to detect Hemipteriphilus in African whiteflies. Fritschea and Cardinium, however, were not detected in this study. Previous studies did not find Hemipteriphilus in B. tabaci SSA1, whilst Rickettsia infection was less than 1% , GroEL (269 bp) and GltA  were present on both tested dates. While the two least prevalent Arsenophonus haplotypes were present only in 1997, they were not detected in 2017 specimens. Three Wolbachia haplotypes were highly prevalent among the eight detected, but they did not change between the two dates. In this study, three Hemipteriphilus haplotypes were also found in only 1997. Little is known about the intraspecies diversity of Hemipteriphilus in African whiteflies, which was first characterised as Candidatus Hamiltonella defensa (B. tabaci. In that study using both 16S rRNA and gltA genes suggested that Hemipteriphilus is clustered within the Alphaproteobacteria subdivision of Proteobacteria. Here, we found that Hemipteriphilus sequences were clustered with one strain of Rickettsia. Another study in Burkina Faso also identified Hemipteriphilus similar to Rickettsia using 16S rRNA gene in Illumina sequencing technology in MED-Q1, MED-Q3 and ASL genetic groups (Hemipteriphilus and Rickettsia are so similar that they are difficult to differentiate especially using 16S rRNA primers. This issue could be due to the specificity of the primers used to detect Hemipteriphilus. When a mismatch within V4-V5 sequences occurs, it reduces the thermal stability of the primer-template duplex, thus affecting PCR specificity. Nevertheless in this study, some Hemipteriphilus sequences were not differentiated from Rickettsia, the use of other housekeeping genes (Hemipteriphilus in African whiteflies. These observed similarities and differences between Hemipteriphilus and Rickettsia imply that the group is not well defined and probably in need of a taxonomic update.The two most prevalent  defensa  in Chinac groups . The seqng genes  is thereWolbachia and Rickettsia were influenced by geography and host plants (Rickettsia was also shown to boost its invasive ability within B. tabaci by the increase of infection frequency from 1% in 2000 to 97% in 2006 in Arizona (In this study, we found that S-endosymbionts diversity and composition were not significantly different across sites in SSA1 whiteflies. In previous work, it was reported that site-to-site variations subsequently influence the prevalence of endosymbionts in a given whitefly population . Endosymt plants . These d Arizona .Wolbachia changed in SSA1 over time. Dual infection of Arsenophonus and Wolbachia also changed from 9 to 28%. In contrast, single infections of Arsenophonus changed from 91 to 68%. Wolbachia and Arsenophonus are reported to coexist in other insect species such as brown planthopper (Nilaparvata lugens; Portiera genome is highly reduced and lacks genes involved in the synthesis of certain vitamins, cofactors, and some essential amino-acids, suggesting that there is a metabolic niche that could be filled by secondary endosymbionts (Arsenophonus and Wolbachia has been found in different whitefly species from China MEAM1 and MED (Arsenophonus and Wolbachia influence whiteflies nutritionally by synthesising vitamin B (B. tabaci SSA1 should further be investigated.Our results demonstrate the dynamic nature of bacterial endosymbionts. Both single and dual infections of ymbionts . Similar and MED  and Eastitamin B . Their eWolbachia changed from 0 to 5%. This change may be an indication of the emerging role of Wolbachia in B. tabaci SSA1 or the beginning of a new invasion by these Wolbachia species. Wolbachia is well known to influence the reproduction of their host by reproductive incompatibility within and between insect species including B. tabaci SSA1 and B. tabaci SSA1-SG2. The latter harboured Wolbachia (Wolbachia infections over time in field-collected whiteflies can have implications on their reproduction that should be investigated further.In this study, a single infection of olbachia , suggestB. tabaci SSA1 individuals changed over time, coinciding with the advent of whitefly outbreaks in Uganda. This whitefly upsurge was estimated at more than 1,000 adults per top five leaves (B. tabaci MEAM1 by Rickettsia produced more eggs and females with higher survival to adulthood which led to its invasion across the southwestern United States (Arsenophonus or other hidden S-endosymbiont in B. tabaci SSA1 whiteflies may be responsible for improved fecundity and survival as suggested in previous studies (Arsenophonus and high haplotype diversity were detected from SSA1 in this study, suggesting that this S-endosymbiont can potentially affect whitely behaviour and abundance. In one reciprocal crossing experiment, Arsenophonus, the most prevalent S-endosymbiont in Ugandan B. tabaci SSA1, influenced whitefly reproduction (A total of 12 and 17% of the samples were S-endosymbionts free in 1997 and 2017, respectively. Similarly, e leaves . The read States . The pre studies . Both a oduction .Using a population dynamics technique, we looked at endosymbiont diversity and composition. We demonstrated how these discoveries are helping us get closer to our aim to better understand factors changing endosymbiont communities in whiteflies. This research, therefore, adds to our understanding of how S-endosymbiotic communities change over time. Further research is now needed to understand in more depth the role played by the diverse endosymbiont communities in African cassava whitefly outbreaks.The data presented in the study are deposited and available in the GenBank repository, accession number from OP160970 to OP161013.JC, HD, and MM: conceptualisation. HH, CO, and HA: data collection. HH, H-LW, SB, HD, and MM: data analysis and interpretation. HH: writing—original draft. MM and HD: writing, review and editing. JC, MM, and CO: funding acquisition. All authors contributed to the article and approved the submitted version.This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [Grant Agreement OPP1058938]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Oidium spp. in tomato, is a global concern that leads to diminished yield. We aimed to evaluate previously reported DNA markers linked to powdery mildew resistance (PMR) and identify novel quantitative trait loci (QTLs) for PMR through a genome-wide association study in tomato. Sequencing analysis of the internal transcribed spacer (ITS) of a PM strain (PNU_PM) isolated from Miryang, Gyeongnam, led to its identification as Oidium neolycopersici. Thereafter, a PM bioassay was conducted for a total of 295 tomato accessions, among which 24 accessions (4 S. lycopersicum accessions and 20 accessions of seven wild species) showed high levels of resistance to PNU_PM. Subsequently, we genotyped 11 markers previously linked to PMR in 56 accessions. PMR-specific banding patterns were detected in 15/22 PMR accessions, while no such bands were observed in the powdery mildew-susceptible accessions. The genome-wide association study was performed using TASSEL and GAPIT, based on the phenotypic data of 290 accessions and 11,912 single nucleotide polymorphisms (SNPs) obtained from the Axiom® Tomato SNP Chip Array. Nine significant SNPs in chromosomes 1, 4, 6, 8, and 12, were selected and five novel QTL regions distinct from previously known PMR-QTL regions were identified. Of these QTL regions, three putative candidate genes for PMR were selected from chromosomes 4 and 8, including two nucleotide binding site-leucine rich repeat class genes and a receptor-like kinase gene, all of which have been identified previously as causative genes for PMR in several crop species. The SNPs discovered in these genes provide useful information for understanding the molecular basis of PMR and developing DNA markers for marker-assisted selection of PMR in tomato.Powdery mildew (PM), caused by  It reduces production by more than 50%, depending on the growth stage of the infected plants and the surrounding environment. The major pathogens involved in PM are Oidium neolycopersici, Oidium lycopersici, and Leveillula taurica, which belong to Ascomycota   and FiguS. peruvianum), which carries the resistance gene Ol-4. The remaining 223 accessions were susceptible , among which 204 lines were S. lycopersicum, including susceptibility controls, PNU-PMS (S. lycopersicum) and Moneymaker as well as one S. habrochaites accession (PI247087) carrying the resistance gene Ol-5 , including LA2172 , 28 accessions that showed sensitivity , and 6 controls . PMR-spend KNU17 .Ol-qtl1 was observed in five wild accessions  and LA1969 (S. chilense) carrying the Lv gene. Ol-qtl3 was found in 10 wild accessions  including the Ol-4-derived accession LA2174 (S. peruvianum). The accessions that displayed PMR in the bioassay but did not present a PMR-specific marker genotype included LA1141 , LA1272 (S. pennellii), LA1963 and LA2932 (S. chilense), LA2181 and LA2184 (S. pimpinellifolium), A1161, A1162, A1164, and A1216 (S. lycopersicum), and LA0458 (S. chilense), an accession carrying the Lv genes.In the case of the PMR QTLs, ® Tomato Genotyping Array, resulting in the raw data for 51,214 SNPs. Following that, for SNP quality evaluation in the wild species, Dish QC (DQC), call rate, and heterozygosity rate were analyzed for 182 accessions, excluding the 160 cultivated tomato accessions . In the wild species, the DQCs of S. cheesmaniae, S. galapagense, and S. pimpinellifolium, which are known to exhibit high crossing compatibility with S. lycopersicum , was located in QTL-2 of chromosome 4, while the most significant one predicted by TASSEL, AX-95810925 [−log10(P) = 4.57], was located in QTL-4 of chromosome 8  revealed 22 genes in the QTL-2 region  gene (Solyc08g074980.4.1)  and QTL-4 , which harbored AX-95771531 and AX-95810925, the most significant SNPs identified by GAPIT and TASSEL, respectively, were searched for the existence of candidate PMR genes. Assessing the annotation information from the SL4.0 (ITAG4.0) assembly of Sol Genomics Network  . Two SNP980.4.1) . In the 980.4.1) .O. neolycopersici.PM is a common disease that affects a wide range of host plants worldwide. To date, GWASes have been conducted on several crops such as barley, wheat, and oats, to explore PMR-related QTLs ,27,28. ITy1,3, Ty2, Ty4, ty5, and Ty6; fusarium wilt resistance genes I-1, I-2, I-3, and I-7; late blight resistance genes Ph-1, ph-2, ph-3, and Ph-5; bacterial spot resistance genes Rx-4 and RXopJ4; and nematode root-knot resistance gene Mi-1 have been derived from wild species   , blight  = 9.69] , and gra = 9.69] . In genellection . Two majpulation ,41.Ol-4 and Ol-6, which induced rapid resistance to O. neolycopersici through a single cell hypersensitive reaction, have been also presumed to be a homolog to the Mi-1 locus in which a number of R genes encoding NBS-LRR proteins form a cluster [Mla in barley [REN1 in grape (Vitis vinifera) [MtREP1 in alfalfa (Medicago sativa) [In this study, genes annotated as NBS-LRR and receptor-like STPK were selected as potential candidate genes for PMR. The gene encoding the NBS-LRR protein is a representative disease resistance gene (R gene) that activates a pathogen-specific defense system together with receptor-like kinase ,43,44. I cluster ,6. Othern barley , REN1 ininifera) , and MtR sativa) .Pto encoding serine/threonine kinase in tomatoes, Pto kinase recognizes the elicitor generated by the avrPto of the pathogen Pseudomonas syringae pv. tomato and activates another serine/threonine kinase, Pti1, in addition to interacting with the NBS-LRR gene, Prf, to induce resistance responses such as hypersensitivity reactions and oxidative bursts [Stpk-V) cloned from wheat (Dasypyrum vilosum) is located within the PMR locus Pm21 and reduces haustoria formation of the PM pathogen Blumeria graminis f. sp. tritici [10(P) value in TASSEL, was located in the intron region of the gene encoding RSTK. Moreover, there was a clear distinction in the phenotypic distribution based on the genotypes of SNPs AX-95781958 and AX-95809404 found in the exons of this gene, thereby suggesting that the SL-STPK gene may be a putative candidate for PMR in tomato.In addition to NBS-LRR, receptor protein kinase, another protein taxon involved in plant disease resistance, and receptor-like STPK belonging to the receptor-like kinase family, are also involved in signal transduction processes required for disease resistance, through phosphorylation of serine and threonine residues . In the e bursts ,49,50,51 tritici . In our Slmlo1 for ol-2, is used for targeted mutagenesis such as RNAi-silencing and CRISPR/Cas9 technology [Slmlo1-transgenic plants demonstrated improved PMR and can be useful breeding resources for expedited development of new PMR cultivars. In addition, a putative gene (ShORR-1) for Ol-1 was recently identified by complementary cDNA-amplified fragment length polymorphism (cDNA-AFLP) analysis of the resistant cultivar S. habrochiates G1.1560, carrying the Ol-1, which can be another target gene for genome editing mutagenesis [PMR gene cloning is essential for rapid breeding processes based on the plant transformation. The first cloned PMR susceptibility (S) gene, chnology ,54,55. TIn conclusion, PM bioassay and PMR-linked marker tests performed on a natural population with a diverse genetic background provided information about genetic resources that could be useful in MAS for PMR. Our GWAS identified nine significant SNPs from five novel QTL regions, which led to the selection of 3 PMR candidate genes from the two QTL regions where the most significant SNPs were located. The disease resistance locus information and resistance-related SNPs identified in this study will contribute to understanding the molecular basis of PMR and developing new PM-resistant cultivars. Further studies are needed to carry out gene-function analysis to verify the association of the putative candidate genes identified in this study with PMR.S. lycopersicum) obtained from the Asian Vegetable Research and Development Center (AVRDC), Tomato Germplasm Research Center at the University of California Davis (TGRC), Germplasm Resources Information Network, (GRIN), Gangneung-Wonju National University (GWNU), and Plant Genetics and Breeding Research Center at Pusan National University (PNU). Among them, four lines were reported as PM-resistant, including LA1969, LA0458 , LA2172 , and PI247087  [S. lycopersicum) and another accession reported as being susceptible to PM, PNU-PMS (S. lycopersicum), were used as controls for the bioassay [The genetic resources used for the PMR bioassay included a total of 295 accessions . This acchaites) ,58, as wbioassay .g for 1 min, following which the supernatant was discarded and the spores collected at the bottom were collected.A spore suspension was prepared for the isolation and identification of PM pathogens that occurred naturally in the greenhouse at Pusan National University in 2020. The PM spores were separated from those leaves of “Moneymaker” that showed symptoms of PM, by immersing them in double distilled water and then collecting the water after filtering it out using gauze. Thereafter, the suspension was placed in a 2.0 mL tube, centrifuged at 13,000× g for 10 min. Following that, 400 μL of the supernatant was collected and transferred to a new 1.5 mL tube. For DNA precipitation, 2.5 μL of glycogen (10 mg/mL) and 600 μL of isopropanol were added to the supernatant, and it was centrifuged at 13,000× g for 10 min. The obtained supernatant was discarded, and 300 μL of 70% ethanol was added to the generated DNA pellet, to remove the residual salt. The obtained solution was again centrifuged at 13,000× g for 10 min. After the DNA pellet was completely dried, it was resuspended in 60 μL of Tris-EDTA buffer, and quantified with a NanoDrop™ 1000 Spectrophotometer .Thereafter, 400 μL of sodium dodecyl sulfate (SDS) was added to the spore pellet and incubated with it for 50 min, in a water bath maintained at 65 °C, to release the genomic DNA. To separate the cell debris and SDS solution from the DNA, 200 μL of 7.5 M ammonium acetate was added to the solution, mixed evenly, left on ice for 15 min, and centrifuged at 13,000× Sequencing of the ITS region was performed to identify the isolated pathogen (named PNU-PM in this study). PM pathogen-specific primers PMITS1 (5′-TCGGACTGGCCTCAGGGAGA-3′) and PMITS2 (5′-TCACTCGCCGTTACTGAGGT-3′)  were usePCR was carried out using the Veriti™ 96-Well Thermal Cycler (Thermo Fisher Scientific) and the following PCR conditions; 1 cycle of pre-denaturation at 94 °C for 1 min, 35 cycles of denaturation at 94 °C for 1 min, annealing at 65 °C for 1 min, and extension at 72 °C for 2 min, followed by 1 cycle of final extension at 72 °C for 7 min. The PCR amplicons were electrophoresed on a 1% agarose gel, at 200 V for 45 min, and purified from the gel using the Expin™ Gel SV Gel Extraction Kit . Direct sequencing of the purified PCR amplicons was performed using the dye-terminator method at Genotech .L. taurica ITS sequence (accession number: MH698492.1) obtained from NCBI (https://www.ncbi.nlm.nih.gov/ accessed on 15 August 2022), by means of pairwise/multiple alignments using Clustal W [https://blast.ncbi.nlm.nih.gov/Blast.cgi accessed on 15 August 2022), to confirm the pathogen species with the highest concordance rate.The ITS region sequence of PNU-PM was analyzed for similarity to 27 ITS sequences of different fungal species used for phylogenic analysis in Kiss et al.  and the lustal W  in Moleclustal W . Phylogelustal W , with thlustal W  set as t4 spores/mL concentration was sprayed 3 times, at intervals of 2 weeks, using a manual sprayer, to the extent that the plants were soaked.PM bioassay was performed in March 2020 in the greenhouse of Pusan National University. For a total of 348 accessions, 10 seeds per accession were sown into 50-cell trays filled with seedling medium  and the seedlings were grown under the conditions of 26 ± 5 °C and 40–70% relative humidity. The spore suspension for pathogen inoculation was prepared as described in The disease severity index (DSI) was estimated by observing PM symptoms from 1 to 6 true leaves, in the 3rd week after completion of the last inoculation. The DSI was established based on the following criteria. 0: leaf area symptoms in up to 6 true leaves = 0%, 1: <10% leaf area symptoms, 2: 10–30% leaf area symptoms, 3: >30% leaf area symptoms . Later, For each accession, while referring to the PDI values of MM and PMS as controls, PDI < 10% was scored as resistance (R), PDI in the range of 10–30% as moderate resistance (MR), and PDI > 30% as susceptibility (S).A total of 56 accessions, including 22 that were highly resistant (R), 28 highly sensitive (S), and the PMR and PMS controls were genotyped for a total of 10 previously reported PMR-linked markers . For PCRPCR was performed using a Veriti™ 96-Well Thermal Cycler and the following conditions: 1 cycle of pre-denaturation at 95 °C for 1 min, 35 cycles of denaturation at 95 °C for 30 s, annealing at Tm (°C) in ® Tomato Genotyping Array (Thermo Fisher Scientific) was performed by DNA Link Inc. .For SNP genotyping, for use in the GWAS analysis, genomic DNA was extracted for the tomato accessions used in the PM bioassay. Fresh leaves of 3rd–4th true leave stage were collected from three individual seedlings per accession and pooled. Genomic DNA extraction was conducted using the SDS extraction method, with a partial modification, following which the quantity and quality of the purified DNA were measured as described in ® Tomato Genotyping Array, 125 cultivated species and 57 wild species accessions were selected and the DQC, call rate, and heterozygosity rate for the 51,214 raw SNPs in them were analyzed. Correlation analysis between these three factors was carried out by calculating the Pearson correlation coefficient and significance probability  using SPSS Statistics 25.0.0 .In order to check whether the wild species accessions used in this study are well applied to the Axiom® Tomato Genotyping Array were obtained from the reference genome assembly of S. lycopersicum and five wild species in NCBI  that were used in SNP genotyping data QC.To confirm sequence homology, DNA sequences corresponding to the 70 bp probe sequence for each of the 51,214 SNP loci of the 55K Axiom in NCBI . Sequenc® Tomato Genotyping Array were selected and converted into CAPS or dCAPS markers (http://nc2.neb.com/NEBcutter2/index.php accessed on 15 August 2022) and dCAPS finder 2.0 (http://helix.wustl.edu/dcaps/ accessed on 15 August 2022), respectively. The primers were designed using Primer3 v0.4.0. (https://bioinfo.ut.ee/primer3-0.4.0/ accessed on 15 August 2022). PCR, restriction enzyme treatment and electrophoresis were performed in the same manner as that for the CAPS marker, as described in To verify the reliability of the SNP genotyping results, six of the SNPs genotyped on the Axiom markers . The phyS. lycopersicum and 46 accessions of wild species) that showed relatively low standard deviation in the bioassay (GWAS for PMR was performed using the PM bioassay and SNP genotype data from 290 accessions (244 accessions of bioassay . SNP filhttp://taylor0.biology.ucla.edu/structureHarvester/ accessed on 15 August 2022). The genetic relationship among the accessions was analyzed using the VanRaden kinship algorithm [Population structure analysis was performed based on the admixture model of STRUCTURE v2.3.4  using thlgorithm  on the Rp-value < 0.001 were considered significant SNPs associated with PMR, and only SNPs that were common to both TASSEL and GAPIT software were reselected and used for subsequent analysis.GWAS was performed based on the mixed linear model (PC matrix + kinship matrix) and applied to the analysis using two programs, Traits Analysis by aSSociation, Evolution and Linkage (TASSEL) 5.0  and R-ba10(P) value in TASSEL and GAPIT were selected from the SL4.0 assembly (ITAG4.0) in SGN. The genes related to disease resistance were then selected as candidate genes based on the gene function annotation information.For the identification of putative candidate genes for PMR, genes located within 50 kb on each flaking region of the SNPs with the highest −logAlle experimental procedures described in the section ‘4. Materials and Methods’ were schematically represented in


Recombination is a fundamental part of mammalian meiosis that leads to the exchange of large segments of DNA between homologous chromosomes and is therefore an important driver of genetic diversity in populations. In breeding populations, understanding recombination is of particular interest because it can break up unfavourable linkage phases between alleles and produce novel combinations of alleles that could be exploited in selection. In this study, we used dense single nucleotide polymorphism (SNP) genotype data and pedigree information to analyse individual and sex-specific variation and genetic architecture of recombination rates within and between five commercially selected pig breeds.RNF212, SYCP2 and MSH4. Two of the identified regions included the PRDM7 and MEI1 genes, which are known to be involved in meiosis but have not been previously associated with variation in individual recombination rates.In agreement with previous studies, recombination rates were higher in females than in males for all breeds and for all chromosomes, except 1 and 13, for which male rates were slightly higher. Total recombination rate differed between breeds but the pattern of recombination along the chromosomes was well conserved across breeds for the same sex. The autosomal linkage maps spanned a total length of 1731 to 1887 cM for males and of 2231 to 2515 cM for females. Estimates of heritability for individual autosomal crossover count ranged from 0.04 to 0.07 for males and from 0.08 to 0.11 for females. Fourteen genomic regions were found to be associated with individual autosomal crossover count. Of these, four were close to or within candidate genes that have previously been associated with individual recombination rates in pigs and other mammals, namely RNF212, SYCP2 and MSH4 genes in pig. In addition, we have found two novel candidate genes associated with the trait, namely PRDM7 and MEI1.This study shows that genetic variation in autosomal recombination rate persists in domesticated species under strong selection, with differences between closely-related breeds and marked differences between the sexes. Our findings support results from other studies, i.e., that individual crossover counts are associated with the The online version contains supplementary material available at 10.1186/s12711-022-00723-9. Meiotic recombination is the event in meiosis where double strand breaks are resolved as crossovers, resulting in recombined homologous chromosomes. Thus, recombination leads to variation in haplotypes by breaking up linkage disequilibrium and creating novel combinations of alleles for selection to act upon. In addition, recombination has an important function in the proper segregation of homologous chromosomes during meiosis, and its absence can often lead to nondisjunction and aneuploidy in the resulting gametes –4. HenceRNF212, RNF212B and REC8 [During the last decade, studies of variation in recombination rates have been conducted in a number of mammal populations, including model species such as mice , 12, dom in pigs  to 46% i in pigs . In addi in pigs . Some loand REC8 , 19, 20,and REC8 , 15, 21.In breeding populations, understanding recombination is of particular interest because it can break up undesired linkage phases and produce novel combinations of alleles that could be exploited in selection. A higher recombination rate may help quantitative traits to respond to selection faster , as it cSus scrofa) to study the genetic architecture and variation in individual autosomal crossover count (ACC). Our objectives were to: (a) construct high-density linkage maps to characterize sex-specific recombination landscapes; (b) characterise the genetic architecture of ACC by estimating its heritability and identifying genomic regions associated with its genetic variation; and (c) examine differences in ACC and its genetic architecture between breeds and the sexes.In this study, we used genotype data at more than 50,000 single nucleotide polymorphisms (SNPs) and extensive pedigrees for more than 250,000 pigs from five domestic pig breeds  and Large White (LW); and three boar breeds, Duroc (DU), Synthetic (SY) and Pietrain (PI).2 > 600). The sex chromosomes were not included in the study. An overview of the number of SNPs and animals from the five populations is in Table Genotypes were available from two medium-density SNP chips: an Illumina GeneSeek custom 80K SNP chip and an Illumina GeneSeek custom 50K SNP chip, which had 50,705 SNPs in common. The physical positions of the SNPs were determined based on the Sscrofa11.1 reference genome. The genotype data were filtered for each breed separately to remove SNPs with minor allele frequencies lower than 0.01, a genotype call rate lower than 0.95, and/or those that showed strong deviations from the Hardy Weinberg equilibrium , along with their parents and offspring. This family structure allows for phasing of the FID and offspring genomes and determining the positions of crossovers that occurred in the gamete transmitted from a FID to its offspring. A FID can be in multiple full-sib families , but each individual gamete from a FID is only counted once. An example of a three-generation full-sib family is illustrated in Fig. filtering2 module was run to filter markers based on segregation distortion, with dataTolerance = 0.01, as recommended by Lepmap3 for multi-family datasets. The Separatechromosomes2 module of the LepMap3 software was used as a filtering step (rather than to identify the linkage groups from scratch) by running the SNPs from a given chromosome based on the physical map through the module and excluding SNPs that were not assigned (LOD score < 5) to the largest linkage group. The number of SNPs in the final linkage map is provided in Table Ordermarkers2 module of LepMap3 was used to calculate the centimorgan (cM) positions for the SNPs using the Haldane mapping function. The ordering of the SNPs was based on their physical positions on the reference genome. Recombination rates from linkage map data were defined in cM per megabase (Mb) [The markers were physically mapped to the Sscrofa11.1 reference genome by extracting flanking sequences for each marker from the chip manifest files and aligning them to the 11.1 reference genome using the bwa software . Breed-sase (Mb) .Table 3EorderMarkers2 module of LepMap3 for offspring, and assigned to the appropriate parent, i.e., the FID in which the meiosis took place.Individual recombination rates were measured as the autosomal crossover count (ACC). Crossovers were counted from the gamete phase from the output of the –het function in PLINK1.9 [We estimated variance components for individual ACC with the following repeatability model using the average information (AI) algorithm for the restricted maximum likelihood (REML) method in the DMU v6 software :\documenPLINK1.9 , \documefastGWA module implemented in GCTA [–make-bK-sparse option of GCTA using a cut-off value of 0.05 based on the full genomic dataset, i.e., off-diagonal elements below 0.05 were set to 0 [Genome-wide associations were conducted using the 600K datasets with the  in GCTA , using tset to 0 . The proAll analyses and data manipulation where no other software is mentioned were carried out in R 3.6.3 .The sex-specific autosomal linkage maps spanned a total length of 1731.1 cM (PI breed) to 1887.1 cM (LW breed) for males and of 2231.3 cM (DU breed) to 2515.4 cM (LW breed) for females (Table The average ACC per gamete ranged from 16.3 (PI) to 18.2 (LW) in males and from 21.3 (LR) to 24.4 (LW) in females. The ACC was close to normally distributed  and 1.13  Mb away from the RNF212 gene, which is known to be associated with individual recombination rates in several other mammals, including humans [SYCP2  gene, while the top SNP for LW females was 0.03 Mb away from the SYCP2 gene. Chromosome 6 had two significant regions for LR females. For the first region, the most significant SNP (P = 1.36 × 10–14) was 2 kb from the PRDM7 (PR domain-containing protein 7) gene. The second region on chromosome 6 was also significant in the SY females, with the top SNPs 0.03 (LR) and 0.29 (SY) Mb removed from the MSH4 (MutS homolog 4) gene. LW females also had a significant region on chromosome 6, but with no clear candidate gene in close proximity. Chromosome 5 had a significant region for LR females, with the top SNP (P = 1.54 × 10–8) located within the MEI1 (meiotic double-stranded break formation protein 1) gene. The LR, LW, and DU breeds had a significant region on chromosome 7 for females but with no candidate genes in immediate proximity to any of the top SNPs. However, it should be noted that chromosome 7 includes the REC8 and RNF212B genes, which have been associated with individual recombination rates in several other mammal species [We found 14 genomic regions that were significantly associated with mean ACC in females and one genomic region in males Table . Base pag humans , cattle g humans , and pig species , 20, 32,RNF212 gene strongly influences individual crossover rates for all breeds and for both sexes. We also identified ~ 13 other genomic regions that are associated with individual crossover rates in one or some of the breeds, or only for one sex. Some of these genomic regions were in or near genes with known functions in meiosis, including SYCP2, PRMD7 and MSH4. In the following sections, we discuss these results in greater detail, provide context to how they may aid in better understanding the genetic mechanisms behind recombination rate, and their implications for commercial breeding populations.In this study, we confirm previous findings by Tortereau et al.  and JohnThe average genetic map lengths (in cM) estimated in our study are more in line with those reported by Tortereau et al. than witArabidopsis [In this study, we observed a substantial difference in recombination rates between sexes for all five breeds, with females showing ~ 1.28 times more crossovers than males. This direction of heterochiasmy is common in mammalian recombination, and while there has been great interest in determining the mechanisms that underlie this sexual dimorphism, there are few compelling explanations for it , since tbidopsis . There mbidopsis , sexual bidopsis , and thebidopsis , but empbidopsis .The heritability estimates of individual crossover counts were low but significantly different from zero, in agreement with Johnsson et al.  and LozaRNF212 gene, which has been found to be associated with individual recombination rates in several mammalian species, including other pig breeds [RNF212 gene has a dosage sensitive effect on crossover rates and that RNF212-knockout mice are sterile, implying a critical role in chromosome segregation and fertility. For the most highly associated SNP close to the RNF212 gene, in the LR and PI breeds the frequency of the allele associated with increased ACC was low . The distribution of crossover count per gamete plotted for the LR breed as an example. The x-axis is the autosomal crossover count for a gamete and the y-axis is the count of how many gametes had a particular crossover count.Additional file 2: Table S1. Effect of inbreeding on ACC. NFID are the total number of unique FID, mean F is the mean inbreeding coefficient, and E_BLUE F is the estimated effect of inbreeding with standard errors in parenthesis.