The founder population of Newfoundland and Labrador (NL) is a unique genetic resource, in part due to its geographic and cultural isolation, where historical records describe a migration of European settlers, primarily from Ireland and England, to NL in the 18th and 19th centuries. Whilst its historical isolation, and increased prevalence of certain monogenic disorders are well appreciated, details of the fine-scale genetic structure and ancestry of the population are lacking. Understanding the genetic origins and background of functional, disease causing, genetic variants would aid genetic mapping efforts in the Province. Here, we leverage dense genome-wide SNP data on 1,807 NL individuals to reveal fine-scale genetic structure in NL that is clustered around coastal communities and correlated with Christian denomination. We show that the majority of NL European ancestry can be traced back to the south-east and south-west of Ireland and England, respectively. We date a substantial population size bottleneck approximately 10-15 generations ago in NL, associated with increased haplotype sharing and autozygosity. Our results reveal insights into the population history of NL and demonstrate evidence of a population conducive to further genetic studies and biomarker discovery. An analysis of the population genetic structure of Newfoundland and Labrador identifies close genetic links between this population and South England and South Ireland, with a strong bottleneck roughly 300 years ago. This NL population was historically small, with a census reporting 74,094 individuals in 18362, but has expanded to approximately 520,000 (Statistics Canada) today. Historical records suggest that the Irish, predominantly Catholic settlers, came from communities in the southwestern Irish counties of Waterford, Wexford, south Kilkenny, southeast Tipperary, and southeast Cork3. In the case of the English, the mainly Protestant settlers can be traced back to the counties of Dorset and Devon as well as the fishing ports such as Dartmouth, Plymouth, and Southampton in southwestern England4. Prior to and after European settlement of the region several Indigenous Peoples including the maritime Archaic peoples, Mi’kmaq, the Innu and the Inuit, and Beothuk, inhabited areas within the modern Province6. The modern population of NL includes peoples of Indigenous ancestry, primarily the Inuit, Innu, and Mi’kmaq. The Inuit and Innu are mainly located within Labrador, and the Mi’kmaq are found within Newfoundland, both with some level of admixture with people of European ancestry4.Newfoundland and Labrador (NL) is the most eastern Canadian province. It is comprised of Labrador on the Canadian mainland, and the island of Newfoundland located in the north Atlantic. The European-ancestry population is primarily derived from Irish and English settlers4. These migrants who settled into coastal communities (known as outports) were socially and geographically isolated from one another, rarely intermarrying and so experienced subsequent private or separate bottlenecks. This cultural and geographical isolation is mirrored in the genetic landscape of NL. Previous studies using blood antigen frequency or birth-record data found evidence of genetic isolation and elevated inbreeding coefficients8, though there is conflicting evidence of extended linkage disequilibrium9. These population characteristics have been successfully leveraged to identify both recessive and dominant Mendelian traits14. Recently, analysis of genome-wide SNP-array genotype data from 494 NL individuals confirmed evidence of genetic isolation in the NL population, and that the broad NL population structure could be described in terms of; (i) indigenous American ancestry and, (ii) Catholic versus Protestant background. However, with a set of 494 individuals, the extent of population structure within these three backgrounds was not explored in depth, nor the source of putative British and Irish ancestry examined.Migration of Catholic Irish and Protestant English settlers to NL peaked in the mid to late 18th century15 as well as increasing average haplotype length and general homogeneity16. Such efforts have been realised in other genetically isolated populations17, including island communities such as Sardinia in Europe18, the Ryukyu Archipelago south of Japan19, or the Shetlands in northern Scotland15. As isolated populations are increasingly leveraged in the study of rare or ultra-rare genetic variation15, an appreciation of their fine-scale genetic landscape is needed to account for the increased stratification of rarer genetic variation21. Although haplotype-based methods have demonstrated fine-scale genetic structure in many populations including the ancestral source populations of Britain and Ireland25, such approaches are yet to be applied within the context of NL. Furthermore, applying haplotype-based approaches to the NL context could also reveal insights into the population’s demographic history and isolation29.NL is therefore a genetically understudied community, and potentially of great value to genetic mapping efforts. Isolated populations with a history of bottlenecks are valuable communities for genetic mapping efforts as founder effects increase the frequencies of rare clinically relevant variants31, and finally (iii) characterise the extent that a history of bottlenecks has had on the haplotype diversity of NL compared to ancestral sources in Britain and Ireland.Given this context, we aimed to explore the non-Indigenous (European) settlement of the present NL population in unprecedented detail, studying a sample of 1807 individuals with NL ancestry from the Newfoundland and Labrador Genome Project (NLGP), together with ancestry source references from Britain and Ireland. We set out to; (i) characterise the fine-scale population structure in NL using haplotype-based methods and investigate how this structure relates to ancestry, religion, and geography, (ii) quantify the proportions of British and Irish ancestry in NL and map these to their regional sources in Britain and Ireland using ancestry references1,807” dataset) and 685,221 common SNPs for further analysis.To sample the genetic landscape of NL before modern economic migration in the latter 20th century onwards, we applied principal component analysis (PCA) to genotype data from 2446 participants from the Newfoundland and Labrador Genome Project  and world-wide ancestry references from HGDP or KGP3  state. This k = 22 level of clustering combines smaller, difficult to interpret, clusters together to summarise the predominant fine-scale structure present in NL 23. Measuring connectivity with average sharing of IBD segments supports this differentiation with comparatively low sharing between clusters not on the same fineSTRUCTURE branch , in agreement with previous work4. These differences are largely driven by Catholic membership within the Avalon peninsula , whose residuals contribute approximately 22% of the overall Χ2 statistic. Furthermore, PCA of the ChromoPainter co-ancestry matrix supported this observation by separating individuals with a Catholic or Protestant background along PC2, echoing previous observations4 . The two remaining clusters do not exhibit elevated levels of this component, but are grouped with putatively Indigenous ancestry clusters, and therefore difficult to further interpret. As our focus was to characterise the genetic structure of NL arising from the non-indigenous European settlers and given the lack of appropriate North American Indigenous references in addition to the small number of participants in these clusters (4% of the cohort), we made no further effort to characterise indigenous ancestry in subsequent analyses and focussed analysis on the remaining European-descent 18 clusters.The other major branch in the fineSTRUCTURE dendrogram separated out clusters of the remaining NL individuals and those with putative Indigenous ancestry Fig. . IndividST distances between these clusters show substantial differentiation between the neighbouring communities, consistent with genetic isolation  of United Church of Canada background, as does the Conception Bay W cluster (57%), compared to an average of 24% elsewhere. Considering the United Church of Canada post-dates settlement, this could reflect specific communities preferentially favouring one denomination.Beyond the south-eastern branch of the dendrogram, clusters demonstrate fine-scale structure among predominantly Protestant communities. These clusters align strikingly with the geographic features of individual NL bays. For example, the north-eastern Trinity and Conception Bays exhibit population structure not only between one another Fig.  but alsoAdmix NL cluster contains individuals with recent genealogies from across the island, and whose average copying vectors from the ChromoPainter coancestry matrix suggests haplotype sharing with different clusters across NL  on a combined dataset of 1807 individuals of NL ancestry and 4,408 ancestry reference individuals from Britain and Ireland, 1,808 of whom have geographic annotation . We identified 26 IBD-clusters across Ireland and Britain that confirm previous fineSTRUCTURE-based clustering patterns . Our rationale being that if different NL clusters carry different proportions of Irish or British ancestry, this will differentiate in the amount of IBD segments that they carry—thus creating different copying profiles.Historical records suggest that the European settlers of NL were predominantly from south-eastern Ireland and south-western EnglandfineSTRUCTURE-cluster target, as estimated by the nnls method. We first summed the contributions from general Irish or British regions, such as the south-west of England, or southern Ireland , and in general a high proportion of individuals with a Catholic background are associated with a high proportion of southern Irish ancestry   from the Wexford and Waterford regions of southern Ireland, primarily N&W Munster and SE Leinster. Both these English and Irish contributions to Irish-British IBD-segment sharing with NL are strikingly supportive of historical records which show the migrants who migrated to NL can be traced back to communities from these regions in Britain and Ireland3. Moreover, the contributions from each individual Irish/British cluster to the Irish or British ancestry profile in NL are in similar proportions across NL clusters. This is suggestive of a single source of the Irish and British ancestry in NL, i.e., that the Irish or British ancestry in NL is not from multiple waves from different regions in Ireland or Britain.Next, we investigated if any specific IBD-cluster of Irish or British individuals were driving these English/Irish signals. Considering individual source clusters that contribute substantially to any one NL target cluster , we indeed find specific regional affinity within the English and Irish ancestry components Fig. . The Engnnls method where we consider each NL or Irish-British cluster as a mixture  of NL to provide insight to past population bottlenecks. Using our IBD segment sharing data, we applied IBDNe27 to estimate historical Ne in NL clusters >100 individuals in membership, comparing to Irish and British regions  higher than Orkney and Wales. When compared to Orkney or Wales, NL clusters share slightly fewer segments on average even though the total length shared is comparable. This suggests that the increase of relatedness in NL is more recent than Orkney or Wales, or that on average the Ne is overall higher in NL. Elevated IBD levels within NL are also supported by IBD sharing patterns between NL clusters compared to between Irish or British clusters   Ireland/England, and (ii) other NL individuals (see “Methods”). An excess of NL allele sharing in both tests may indicate NL-specific drift independent of Ireland or England. We find that most NL clusters, but especially Conception Bay S 2 and Conception Bay W, present excess NL drift  Fig. , and whend) Fig. . Next, wift Fig. . In the 45). This mixture of south-western English and south-eastern Irish ancestry was distributed unevenly across NL, with Irish ancestry predominant in the south and south-east, and English ancestry predominant elsewhere. Post-migration, genetic structure shows geographic stratification around the bays of the island due to population isolation and then expansion. Genetic homogeneity was elevated with increased haplotype sharing both between and within individuals, resulting in NL-specific drift of allele frequencies. The St John’s region of NL in northern Avalon Peninsula is associated with wide-spread haplotype sharing with other regions of NL, suggesting that the urban area was a target of post-settlement migration and resultant admixture from across the province.We have performed a systematic analysis of the European ancestry within the Canadian province of NL. Irish and British settlers whose ancestry is shared predominantly with the southeast of Ireland and the southwest of England, respectively, underwent a genetic bottleneck approximately 10–15 generations ago . In addition, these results have furthered our understanding of European settler genetic history in NL which is of interest to genetic genealogy as well as historical research. Our data indicates that a Protestant or Catholic religious background still acts as a proxy for English or Irish ancestry even after 300 years of settlement. The fact that Irish was predominantly spoken in some of these communities until the early-20th century in Newfoundland is consistent with these observations. Whilst confounding factors may influence the co-stratification of Christian religion and population structure, we show that same-denomination cluster pairs do share more haplotypes in common than differing-denomination at the same distances - even in fine-scale on the Burin Peninsula where Catholic and Protestant clusters are geographically interspersed. It is likely that that genetic structure in NL is still consistent with a predominantly isolation-by-distance model—but where religious structure has also played an important role. Lastly, using detailed Irish and British ancestry references, we were able to trace these sources to the specific regions of southeast Ireland and southwest England, improving upon the observations of Zhai et al.4 This work also highlights the use of detailed reference datasets30 with geographic or regional data in elucidating human history.Our analysis has substantially expanded our understanding of the genetics of a population with evidence of genetic isolation and bottlenecks. Previous work on rare Mendelian conditionsfineSTRUCTURE in detecting population structure within the south of England22. This allowed us to genetically differentiate the communities in the centre and south of England which were grouped into a one equivalent cluster in a previous fineSTRUCTURE analysis22. This highlights the potential of relatively simple IBD-based clustering methods that not only scale to large datasets but provide insights where other more computationally intensive methods cannot. It is possible that the IBD-based and ChromoPainter-based approaches are accessing subtly different time-periods of coalescing genealogies, hence their differing power to detect structure in different regions of Britain. To further highlight this, although IBD-based methods were able to show structure in the south of England, fineSTRUCTURE methods were able to differentiate more clusters in north England than our IBD-based approach22.Surprisingly, within our analysis of Britain and Ireland to identify reference clusters for ancestry estimation, we found our IBD-network method provided greater resolution than D statistic analysis  is a general population cohort focused on the characterization of Newfoundland and Labrador’s population . The NLGP cohort consists of the initial 2,500 participants for whom medical history and saliva samples were obtained with informed consent under a study protocol approved by the Newfoundland and Labrador Health Research Ethics Board (Ethics Reference #: 2018.243). The NL Genome Project was based on the random recruitment of participants from General Practice clinics from across the province that were 18 years of age and over, possessed a valid NL health card, and provided written consent. This may bias towards individuals or communities with existing health-care access. As part of the participant’s self-reported data, information was collected on their religion and the birthplace of their parental ancestors. Each participant provided a saliva sample using the DNA Genotek Oragene OG-600 collection kit . DNA extracted from these samples was genotyped using the Illumina Global Diversity Array . A quality threshold of 99.2% SNP pass rate per sample resulted in 2,446 individuals and 1,721,246 SNPs passing initial genotyping QC. Marker genome positions were made available in both human genome builds 37 and 38.4 were accessed from the Gene Expression Omnibus (GEO) accession GSE74392, covering 442 individuals and 1.3 M SNPs and INDELS over the Affymetrix Axiom Genome-Wide Array platform.Previous NL reference genotypes23, Trinity Student31, and the People of the British Isles (PoBI)30 datasets. These three datasets were first merged in a combined dataset of 4,469 individuals and 419,033 common SNPs aligned to human genome build 37.Irish and British reference genotypes were assembled from the previously reported Irish DNA Atlas51 and the Human Genome Diversity Project (HGDP) were accessed from the gnomad v3.1 data release, filtering for autosomal bi-allelic SNPs. A total of 3,942 individuals were used to characterise global patterns of diversity.Genotypes from the combined worldwide references from the 1000 Genomes Project Phase 3 (KGP3)--hwe 1e-9 midp keep-fewhet filter, removing SNPs which violated the Hardy-Weinberg equilibrium at a p-value significance <1e–9, with a mid-p adjustment recommended52, as well as keeping markers which failed due to too few heterozygotes as this is expected from significant population stratification (see PLINK 2.0 documentation). After pruning SNPs in strong LD with the parameters --indep-pairwise 1000 50 0.2, we were left with a dataset of 228,761 common SNPs. All filtering was performed with using plink2.054. We projected the NL individuals onto the principal components calculated from the world-wide references using the --pca --pca-cluster-names option and removed ancestry outliers based on their separation with non-European or non-American ancestry clusters, leaving a dataset of 2,417 individuals with either European, Indigenous, or mixed European-Indigenous ancestry.To characterise global ancestry within the NLGP dataset, we combined the genotypes of 2,446 NL individuals with 3,942 individuals from the world-wide reference dataset. We selected individuals and SNPs with missingness proportions <5%, selecting non-A/T or G/C SNPs with a MAF > 2%. To remove markers poorly genotyped, we applied the −9. With this filtered set of individuals and genotypes we estimated familial relatedness with KING and the --related option of relatedness estimation – randomly removing one from each pair of individuals with a 3rd degree relationship or closer. We chose a higher threshold than commonly used for population-based analyses as the subsequent haplotype-based analyses have greater sensitivity to relationship between individuals than so-called “unlinked” analyses22. The final dataset consisted of 1,807 individuals and 685,221 common SNPs .With the ancestry dataset generated above, we re-extracted SNP genotypes, selecting non-A/T or G/C SNPs with missingness <5%, MAF > 2%, and removing SNPs that failed HWE at significance of <1eChromoPainter32 based haplotype painting, we first phased the genotypes into inferred haplotypes using SHAPEIT v455 with default parameters, using a recombination map from human genome build 38. Using bcftools56 and scripts provided by the authors of ChromoPainter, we then converted the phased haplotypes from vcf format to the input format required by ChromoPainter.To perform ChromoPainter phase and recombrates format, we utilised the fs utility which combines ChromoPainter and fineSTRUCTURE32 analyses in a single unified pipeline. We estimated the parameters Ne and mu for the calculation of the ChromoPainter co-ancestry matrix which records the number of haplotypes that an individual copies from other individuals. Using a random subset of chromosomes  we estimated Ne and mu in all NL1807 individuals. With this estimate (otherwise known as stage 1 in fs), we calculated the co-ancestry matrix, painting all NL1,807 individuals as a mixture of haplotypes donated from every other NL1,807 individual (stage 2). For stage 1 and 2, we set the expected haplotype chunks to define a region to 50 rather than the default 100 to reflect the expected homogeneity in the NL cohort22. With the resultant chunkcounts co-ancestry matrix we performed fineSTRUCTURE clustering (stage 3), using 2 million burnin and 2 million sampling iterations of fineSTRUCTURE’s MCMC clustering algorithm, sampling states every 4000 iterations. With the MCMC, having sampled the highest posterior probability we performed 200,000 additional tree-building steps (stage 4) to reach a final inferred maximum a posteriori (MAP) state and dendrogram of clusters.With haplotype data encoded in 1,807 dataset generated by fineSTRUCTURE. We processed and visualised the dendrogram in R in part using scripts provided by the authors of fs.We performed several additional analyses to further characterise the clustering of the NLi and j), we calculated the Euclidean distance . Then, ChromoPainter co-ancestry matrix by performing principal component analysis using R functions supplied by the authors of fs. To visualise the Indigenous ancestry component, and the structure of European ancestry across the province we visualised PC1 versus PC2, and PC2 versus PC3. We estimated genetic distance between clusters using the Hudson FST estimator57 implemented in the admixtools2 R package (manuscript in preparation), using the Hudson FST estimator on a set of 182,222 LD-independent SNPs identified through PLINK using the command --indep-pairwise 1000 50 0.2. We applied the chi-squared test to assess religious association with fineSTRUCTURE clusters, computing p-values with simulation because of low counts in the contingency table using the chisq.test function within R. To further enhance the visualisation of the co-ancestry matrix, we applied the t-SNE and the umap58 algorithms implemented in the R packages Rtsne and uwot, respectively. With the exception of setting the t-SNE perplexity to 10, we used default parameters to generate the lower dimensional space.We visualised the multi-dimensional sharing of the 1,807 dataset was combined with the 4,469 individuals of Irish and British ancestry (see Datasets). Combining these datasets and selecting SNPs with a genotype missingness <5%, MAF > 2% and removing SNPs which failed HWE at significance of <1e−6 resulted in a combined dataset with 178,603 common SNPs aligned to the human genome build 37. The combined dataset was then phased together using SHAPEIT v4 to generate a set of inferred NL, Irish, and British haplotypes. The results of a PCA of this dataset is shown in Supplemental Data To explore the genetic links between NL, and Ireland and Britain, the NLrefinedIBD59. We then combined adjacent segments putatively fragments of a true, larger, IBD segment broken up by genotyping or phasing errors with the merge-ibd-segments utility using default parameters.To estimate British and Irish ancestry in NL we used sharing of Identical-by-Descent (IBD) segments. We detected segments, > 1 cM in length, from phased genotypes using 23) we decided to cluster Irish and British individuals based on genetic sharing. We leveraged the Louvain community detection algorithm60 to cluster a network where each individual is a node, and each edge is the total length of IBD in cM that one individual shares with another. We excluded extreme outlier edges which shared >142 cM which are assumed to be more reflective of familial relationships. This approach has been successful in extremely large datasets38, and was used as it scales to our sample size where other methods such as fineSTRUCTURE become a computational burden. The Louvain clustering was performed hierarchically, with three rounds of clustering, each performed on each cluster detected in a previous hierarchal level of clustering. The first level generally split Ireland from Britain, the second level identified broad regions , and the third level identified the individual clusters reported in Fig. W England, East Anglia, Midlands, Corn.& Devon), N Britain , Wales , Orkney (Orkney), SW Ireland , SE Ireland , NW Ireland .To explore regional Irish and British ancestry in an expanded sample of Irish and British genotypes . Our modification of the method generated two matrices of X, and Y. X is a matrix of n rows and m columns where each row is the total amount of IBD > 3 cm and <15 cM shared between the row individual ni and each column individual mj. Each row is divided by the sum of IBD summarised in that row so that each Xij entry is the proportion of IBD recorded in each row. The second matrix is of m rows and m columns, recording the same for each Yij individual pair. X summarises the IBD sharing between n target individuals and m source individuals, and Y summarises the IBD-sharing between the m source individuals and themselves. We use nnls to estimate the ancestry proportions explained by these IBD-sharing patterns, modelling each target individual as a mixture of the source individuals.To estimate ancestry, we performed a modification of the nnls method to estimate sharing proportions in each NL individual as a target and leveraged every Irish or British individual as a source of IBD sharing. This design modelled NL as a mixture of Irish and British sources, both estimating overall sharing proportions, as well as investigating if certain genetic regions of Ireland and Britain share excess haplotypes with NL, thus allowing the investigation of a specific source of Irish and British ancestry in NL. We used the cor.test function provided in R to test the significance and strength of the relationship between the Irish and British ancestry proportions with religious denomination. We tested the correlation between cluster-averaged estimated Irish or English ancestry proportion and the proportion of individuals in that cluster reporting a Catholic or Protestant domination (Anglican or United) background.We performed this modified ADMIXTURE33 analysis and to calculate Patterson’s D statistic43 between NL, Irish and British references, and world-wide outgroups, we merged the NLGP, Irish and British references, and KGP3 and HGDP reference genotypes from the gnomad v3.1 data release together. We selected non-AT/GC SNPs common to all three datasets filtering for SNPs with a missingness <5% and a MAF > 5%. We removed SNPs which violated Hardy-Weinburg Equilibrium (HWE) most likely due to genotyping error using the PLINK54 command --hwe --hwe 1e-9 midp keep-fewhet. We additionally removed SNPs in high LD using the plink command –indep-pairwise 1000 50 0.2 for both sets of analyses. In the supervised ADMIXTURE analysis, we first applied the ADMIXTURE algorithm to just, unrelated, KGP3 individuals (n = 2508), setting k ancestral populations to five to capture continental ancestry components. We then performed supervised ADMIXTURE analysis on the 1807 NL individuals, using the P matrix calculated from KGP3 allele frequencies.To perform supervised D statistics between populations, we used the qpDstat program (version 970) from the admixtools62 suite of tools. We only selected KGP3 Yoruban (YRI) reference individuals from the world-wide set, and re-filtered SNPs using the same parameters to reflect this exclusion on non-European or Yoruban ancestry. We defined the “English” reference as British individuals placed within the S&W England cluster, and “Irish” reference individuals as either NW Munster or SE Leinster Irish individuals as these British and Irish clusters contributed the primary signals of the English and Irish ancestry in haplotype analysis.To estimate e) over time using the IBDNe utility27. Using IBD segments >4 cM in length between individuals of the same population label, we estimated Ne in Ireland and Britain in the second level IBD clusters (see above). We chose this level as tested groups would have a large enough sample size (> 100 members) for Ne estimates to be robust.We used the IBD-sharing patterns of NL, Ireland, and Britain to estimate recent effective population size . For each individual, we averaged the number and total length of IBD that that individual shared with another placed in the same cluster. Then for each cluster we calculated the mean and confidence intervals, and then plotted the relationship between the number of segments shared and the total length in Irish and British clusters, and NL clusters.To visual haplotype sharing, we considered IBD-segments that were shared between individuals who were placed in the same third level IBD cluster  or refinedIBD, utilising segments > 1 cM in length. We detected total amounts of ROH in NL, Irish, and British individuals, first using all segments >1 cM in length  segments detected by Patterson’s D statistic for each NL cluster, using the same methodology in NL Irish-British Ancestry. One D statistic arrangement of populations tested the comparative allele shifting between Irish references and 500 random NL individuals not members of the tested cluster X ), and the other arrangement comparing English references to the same 500 random NL subset ) ) Fig. . An exce63 v4.1.3, using Rstudio. When calculating the average of a group we state whether we use the arithmetic mean or median. We calculate standard deviation using the R function sd, and the standard error from dividing the standard deviation from the squared root of the number of observations in the group . When we have randomly chosen observations we have used the sample function from R, sampling without replacement unless otherwise specified.The summary statistics presented in the study were all calculated in the statistical computing language RSupplementary InformationDescription of Additional Supplementary FilesTransparent Peer Review FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8


Analyses of the genetic diversity of indigenous cattle are essential for implementing conservation programs, promoting their sustainable use and maintaining productive advantages offered by these breeds in local conditions. The aim of this study was to investigate the genetic diversity and population structure of six Colombian cattle breeds: Blanco Orejinegro (BON), Costeño con Cuernos (CCC), Romosinuano (ROM), Sanmartinero (SAM), Casanareño (CAS), and Hartón del Valle (HDV). Two additional breed groups were included for comparison: Zebu (CEB) and a crossbreed of Colombian cattle breeds × Zebu. Genetic diversity within breeds was analyzed using expected heterozygosity (He), inbreeding coefficient (f), and runs of homozygosity (ROH). Population structure was assessed using model-based clustering (ADMIXTURE) and principal components analysis (PCA). Zebu cattle showed the lowest genetic diversity (He = 0.240). Breeds with the highest genetic diversity level were HDV and BON . Inbreeding was lower for Colombian cattle breeds ranging between 0.005 and 0.045. Overall, the largest average genetic distance was found among the group of Colombian cattle breeds and Zebu, while the smallest was found between ROM and CCC. Model-based clustering revealed some level of admixture among HDV and CAS cattle which is consistent with their recent history. The results of the present study provide a useful insight on the genetic structure of Colombian cattle breeds. Modern cattle descend from independently domesticated lineages of Taurine and Indicine cattle, which diverged more than 200,000 years ago, with numerous breeds having hybrid ancestry between these different lineages  or inbreeding coefficients, usually estimated from just few genetic markers , Romosinuano , Costeño con Cuernos , San Martinero , Casanareño , and Hartón del Valle . These animals belong to the Sistema de Bancos de Germoplasma de la Nación para la Alimentación y la Agricultura , which the Colombian Corporation for Agricultural Research (AGROSAVIA) guard and manage. A mating strategy (Nomura and Yonezawa n = 34) and the crossbred populations BON × Zebu (n = 61) and ROM × Zebu (n = 19) were obtained from commercial herds.This study included a total of 2182 individuals of Blanco Orejinegro . DNA quantification was made by a spectrophotometric analysis at 260 nm using a NanoDrop ND-2000 spectrophotometer  and diluted to 50 ng/μL. Subsequently, DNA quality was evaluated using the same equipment to estimate the absorbance ratio at 260 and 280 nm.P < 0.01), were also excluded from the analysis. Finally, the analysis included 44496 autosomal SNPs with an average marker density of 56.4 kb.Genotyping was performed with the Illumina Bovine SNP50 BeadChip , using a HiScan array scanner in the Molecular Genetics Laboratory of AGROSAVIAand following established protocols  and inbreeding coefficient (F) were calculated with the genome and het procedures of PLINK v.1.07 software . Then, the K value with the lowest cross-validation error was chosen as the best K value. A stacked bar plot of these results was obtained with the R© software (http://cran.r-project.org). FST values between breeds were used to infer population structure using a neighbor-joining (NJ) clustering method, and the relationship tree was constructed using the MEGA software version 5.0  was carried out to evaluate the genetic variability within and among Colombian Creole cattle breeds, and to explore possible mixture with Zebu animals.F values for all breeds except BON. The highest mean number of ROH per individual (MNROH) and the highest sum of all ROH segments per individual (SROH) were found in ROM, while the lowest MNROH and the lowest SROH were observed in HDV.The SNP average heterozygosity ranged between 0.310 for SAM and 0.350 for HDV  along with the highest homozygosity (0.75 ± 0.05). ROH were low for all creole breeds, with values ranging between 0.011 ± 0.01 for CAS and 0.015 ± 0.01 for SAM. The highest ROH value was obtained for Zebu (0.023 ± 0.015); however, it was considerably lower than the inbreeding value estimated for this breed.According to the PCA, there was a clear differentiation among groups. The first three principal components explained 14.19% of the variance . Moreover, creole breeds were separated into four groups, with the BON cattle displaying the highest variation (blue) and located relatively far from the other creole breeds in PC1  was identified with a CV value of 0.532. When K reached values of 4, some creole cattle groups showed differentiation and were represented by well-defined clusters for CCC, ROM, SAM, and BON. The CEB cluster showed a clear differentiation at K = 5 and similarly through K = 6 and K = 7. However, CAS and HDV individuals did not show a strong pure origin, a result that was consistent across all K values  values markedly decreased from 0.621 at K = 1 to 0.537 at K = 4, K = 5, K = 6, and K = 7. In all scenarios, Zebu and Colombian creole cattle breeds were in two different branches. The phylogenetic tree obtained with K = 6 and K = 7 shows a common node for ROM and CCC, and a common node for CAS and SAM. When K = 7, BON is derived from a node that diverges into both the branch that contains HDV and the common node for ROM and CCC.Additionally, Fst divergences calculated between populations and the neighbor-joining phylogenetic trees are shown in Fig. A whole dataset of SNP genotypes for six different Colombian creole cattle breeds was obtained to assess levels of inbreeding and genetic variability in these populations. Estimated heterozygosity was high for all creole breeds (> 0.31), while the lowest value was observed in Zebu. These genetic diversity levels are higher than those reported by Makina et al.  in indigA possible explanation of the higher genetic diversity of the Taurine breeds analyzed in this study could be the lower levels of selection pressure applied to the Colombian creole cattle, preventing these populations from undergoing heterozygosity erosion. On the other hand, and as discussed by Ben Jemaa et al. , the biaInbreeding levels estimated for Colombian creole cattle were low (0.056 ± 0.01 to 0.018 ± 0.). Inbreeding coefficients for these breeds had been calculated using pedigree information  for 50 k SNP chip data (≈ 0.21–0.26) than the pedigree inbreeding coefficient (FPED) (0.13); this might be explained by the fact that pedigree captures only relatively recent inbreeding since pedigree recording started. Different factors such as the minimum length of ROH, minimum number of SNP involved, or even the density of the beadchip used for genotyping make difficult the comparison of ROH and its statistics (MNROH and SROH) with other studies. Further analysis of ROH along with other statistics for each class of ROH length, the relationship between these statistics, and the coverage and distribution of ROH across chromosomes, is beyond the scope of this paper; however, they must be studied in depth for each Colombian breed to have a better understanding of the inbreeding history of these populations.Moreover, ROH values were lower than the inbreeding coefficients calculated in Colombian creole cattle using genomic information, and also lower than inbreeding levels previously estimated from pedigree information  is similar to that obtained by Rodriguez-Ramilo et al.  and the second cluster to herds brought from the oriental savannas (SAM) or from the southern part of the country (BON). Likewise, Ocampo et al. , b also FST was higher between the Colombian creole cattle and Zebu compared to the value obtained among creole breeds (11–18%), indicating a lower genetic structure differentiation in the latter. Similarly, Campos et al. . A similar genetic differentiation was observed by Weerasinghe et al. (As expected, the fixation index s et al.  observede et al.  between In addition, New World cattle such as Texas Longhorns, Corriente, and ROM breeds have showed evidence of having an admixed ancestry between African hybrid cattle and European cattle. However, the Indicine genomic component found in those breeds might be due to a recent introgression with Indicine cattle in the New World, rather than to an ancient admixture (Edea et al. The existence of six distinct Colombian Creole cattle populations was demonstrated with the use of a genomic analysis. These populations have high genetic diversity and low inbreeding levels compared with Zebu. However, a regular evaluation of these parameters is recommended as the population structures change over time. Finally, all Colombian creole cattle breeds must be essential and viable objects for conservation, since they are genetically unique and have particular characteristics that include its high capacity to adapt to the difficult conditions of the Colombian tropics, surviving under conditions of low food and water availability, and still showing high fertility and resistance to several pathogens.


Zingiber kawagoii populations using methylation-sensitive amplification polymorphism (MSAP) and investigated the associations of methylated (mMSAP) and unmethylated (uMSAP) loci with 16 environmental variables. Data regarding genetic variation based on amplified fragment length polymorphism (AFLP) were obtained from an earlier study. We found a significant positive correlation between genetic and epigenetic variation. Significantly higher mean mMSAP and uMSAP uHE  per locus than that estimated based on AFLP (uHE = 0.104) were found. Genome scans detected 10 mMSAP and 9 uMSAP FST outliers associated with various environmental variables. A significant linear fit for 11 and 12 environmental variables with outlier mMSAP and uMSAP ordination, respectively, generated using full model redundancy analysis (RDA) was found. When conditioned on geography, partial RDA revealed that five and six environmental variables, respectively, were the most important variables influencing outlier mMSAP and uMSAP variation. We found higher genetic (average FST = 0.298) than epigenetic  differentiation and higher genetic isolation-by-distance (IBD) than epigenetic IBD. Strong epigenetic isolation-by-environment (IBE) was found, particularly based on the outlier data, controlling either for geography  or for genetic structure . Our results suggest that epigenetic variants can be substrates for natural selection linked to environmental variables and complement genetic changes in the adaptive evolution of Z. kawagoii populations.We epigenotyped 211 individuals from 17  Adaptation to changing environments is a fundamental process for the survival of populations and species, especially during fast-paced environmental changes ,2,3,4. EEpigenetic variation can be classified into (1) obligatory dependence of epigenetic variation on genetic variation, (2) facilitated epigenetic variation represents partial independence of epigenetic variation from genetic variation, and (3) pure epigenetic variation characterizes complete independence of epigenetic variation from genetic variation . In natuZ. kawagoii [Z. kawagoii [Z. kawagoii [Z. kawagoii [In an earlier study, using data on amplified fragment length polymorphism (AFLP), a latitudinal pattern of environmentally dependent adaptive genetic divergence was found to be highly correlated with the annual temperature range in kawagoii . It is pkawagoii  may havekawagoii . Additiokawagoii  may resukawagoii ,26. Studkawagoii ,32,33,34kawagoii ,9,10. Tekawagoii ,32,33,34kawagoii ,34,35,36Z. kawagoii. Epigenetic variation in association with environmental gradients can be important for plant adaptations to various environments apart from adaptive genetic divergence [Z. kawagoii. First, we explored the inter-population correlation between epigenetic distance and genetic distance using the Mantel test. Second, we intended to understand the level of population epigenetic diversity and epigenetic structure relative to the level of genetic diversity and genetic structure. Third, we assessed the correlations between all MSAP loci and environmental variables using a latent factor mixed model (LFMM) [FST outliers were detected using genome scan methods including BAYESCAN [FST outliers identified using both BAYESCAN and DFDIST were further examined for their correlations with environmental variables using a Bayesian logistic regression approach [Z. kawagoii, (2) understand how environmental variables influence population evolutionary epigenetic divergence and local adaptation, and (3) investigate if there is an environmentally dependent epigenetic divergence when controlling for the presence of any genetic structure.Epigenetic variation can be quantified with methylation-sensitive amplification polymorphism (MSAP)  that refvergence ,14,15,16vergence ,32,33,34l (LFMM)  and Samβl (LFMM) . Fourth,BAYESCAN  and DFDIBAYESCAN . Fifth, approach . Sixth, approach . SubsequZ. kawagoii examined in an earlier investigation [HpaII (1 U) and MspI (1 U) as frequent cutters separately with rare cutter EcoRI (1 U). Restriction digestion was performed in a total 10 µL reaction volume with 10X CutSmart buffer  for 1.5 h at 37 °C and then deactivated at 65 °C for 15 min. Restricted DNA products were ligated to MSAP adaptors (5 µM) with 5 U T4 DNA ligase  and 5X ligation buffer (Thermo Scientific) at 22 °C for 1 h in a 10 µL ligation reaction mixture.This study used the same 17 populations of tigation   was mixed with 12 µM EcoRI primer , and 10X PCR buffer (Zymeset) in a 20 µL total volume. Pre-selective amplification was performed with an initial holding at 72 °C for 2 min and pre-denaturation at 94 °C for 3 min, followed by 25 cycles of 30 s at 94 °C, 30 s at 56 °C, and 1 min at 72 °C, with a final 5 min holding at 72 °C. Nine primer pair combinations with additional selective bases added at the ends of E00 and HM00 were used for selective amplification , 10X PCR buffer (Zymeset), and diluted pre-selective amplified products (1∶19 dilution with ddH2O) in a 20 µL total volume. Subsequently, selective PCR was performed with an initial holding at 94 °C for 3 min, followed by 13 cycles of 30 s at 94 °C, 30 s at 65–56 °C (decreasing the temperature by 0.7 °C each cycle), 1 min at 72 °C, and then 23 cycles of 30 s at 94 °C, 30 s at 56 °C, and 1 min at 72 °C, with a final 5 min holding at 72 °C. Selective amplification products were electrophoresed with an ABI 3730XL DNA analyzer .Pre-selective amplification was performed using a polymerase chain reaction (PCR). Adaptor ligated product , EcoRI-HpaII (eHpaII), and a combined error rate (eMspI + eHpaII − 2eMspHpaIIIe) for each primer combination was calculated [MspI and eHpaII, with a combined error rate of 4.18% . Low peak fragments and those scored higher than 99% or less than 1% of individuals were removed. Additionally, fragments within one nucleotide in a ± 0.8 base pair window were recognized as the same fragment. To determine the DNA methylation status of every locus, the “mixed scoring 1” transformation scheme in the R function SAP-calc  in the RSAP-calc  was appllculated . We remolculated . The meaof 4.18% .mean)  .uHE) [PPL) based on allele frequencies using the settings of the Hardy–Weinberg equilibrium and a non-uniform prior distribution. Per locus uHE was estimated using ARLEQUIN v.6.0 [uHE per locus between three types of markers . AFLP data were obtained from an earlier study [lmer function in the R package lme4 [Anova function in the R package car based on type II Wald χ2 statistic [uHE per locus between marker types were assessed using the lsmeans function in the R package emmeans [t-test was used to assess the differences in mean uHE between the three marker types at the population level. Population genetic (AFLP) and epigenetic (mMSAP and uMSAP) distance matrices were calculated using Nei’s genetic distance [nei.dist function in the R package poppr [mantel function in the R package vegan [mantel.partial function in the R package vegan, was also used to assess the inter-population relationship between genetic and epigenetic variation controlling for the geographic effect. The geographic effect was computed using the coordinates of the sample sites.The software AFLP-SURV v.1.0  was useduHE)  and the IN v.6.0 . A lineaer study . In LMM,age lme4 . Signifitatistic . Tukey’s emmeans . Additiodistance  using thge poppr . Mantel ge vegan . A partiΦST) using the poppr.amova function in the R package poppr. Significance was tested using the randtest function in the R package ade4 [FST was computed using ARLEQUIN, and significance was tested with 10,000 permutations. Pairwise population FST values were used to calculate the level of divergence for each population from the remaining populations as the mean value of pairwise FST for each population against the rest of the populations (denoted as the population mean FST). Epigenetic homogeneous groups of individuals were assessed using discriminant analysis of principal components (DAPC) [find.clusters and dapc functions in the R package adegenet [K = 1–10. The Bayesian information criterion (BIC) in DAPC was estimated to determine the optimal number of clusters.Analysis of molecular variance (AMOVA) was used to estimate the level of genetic differentiation between populations (age ade4  with 999s (DAPC) . The finadegenet  were useFST outliers were identified using BAYESCAN and DFDIST. BAYESCAN v.2.1 [10(PO)). Two hundred pilot runs of 50,000 iterations followed by a sample size of 50,000 with a thinning interval of 20 among 106 iterations were performed in BAYESCAN. A logarithmic scale of log10(PO) > 1 was used as strong evidence (posterior probability > 0.91) for selection over neutrality for a locus under directional selection [FST versus uHE, and loci under selection were identified by comparing them to a simulated neutral distribution. In DFDIST, we set critical frequency = 0.99, Zhivotovsky parameter = 0.25, trimmed mean FST = 0.3 , smoothing proportion = 0.04, 500,000 resamplings, and critical p = 0.05. MSAP loci with observed FST against uHE falling above the 95% confidence level of the simulated distribution were recognized as FST outliers under directional selection.To detect signatures in selection on MSAP loci, AN v.2.1  was usedelection . DFDIST p-values were adjusted using the genomic inflation factor [p-value using the qvalue function in the R package qvalue [p-value adjustment, to assess the fit of the model with environmental variables against the null model without environmental variables.The associations between all epigenetic loci and environmental variables were estimated using LFMM and Samβada. In LFMM, we considered population epigenetic structure as a random factor using a latent factor of 1 and 4, respectively, according to the DAPC results (see Results) for mMSAP and uMSAP. Matrices of mMSAP and uMSAP variation were used as fixed factors. Ten LFMM runs with 10,000 iterations of the Gibbs sampling algorithm and a burn-in period of 5000 cycles were performed for each environmental predictor. Z-scores for each environmental predictor were obtained by combining the results of ten independent LFMM runs, and n factor . Moreovee qvalue . A multie qvalue  were usestan_glm function in the R package rstanarm [FST outliers that were identified with both BAYESCAN and DFDIST. The weakly informative priors following a student’s t distribution with mean zero and seven degrees of freedom were used in stan_glm, and the scale of the prior distribution was 10 for the intercept and 2.5 for the predictors. We ran all the stan_glm models using 4 chains, each containing 2000 warm-up and 2000 sampling steps, and a 95% credible interval was determined using the posterior_interval function in the R package rstanarm. In the stan_glm analysis, we obtained values for the effective sample size and convergence diagnostic statistic representing good priors applied and stable estimates obtained for each predictor.Moreover, a Bayesian logistic regression analysis, implemented with the rstanarm , was useenvfit function in the R vegan package. In the envfit analysis, a full RDA model was used to estimate the independent effect of the environment  fitting to the amount of outlier variation with 999 permutations and represented by the squared correlation coefficients (R2). All p-values were adjusted for multiple comparisons using 5% FDR. Environmental variables with significant fit to the outlier epigenetic variation were then used in a pRDA analysis. pRDA was analyzed in order to assess the correlations between environmental variables and the first two axes conditioned on the geographic effect. Significance of the pRDA ordination axes was assessed using the anova.cca function in the R package vegan with 999 permutations. The arrows pointed in the direction of maximum variation in the value of environmental variables, and the degree to which the variables correlated with pRDA axes was represented by the length of the arrows.A multivariate approach in an RDA analysis  was useddist function in R. To test IBE, Mantel and partial Mantel tests (controlling for the geographic effect) were used to assess the relationship between the epigenetic and environmental distance matrices, respectively, using the mantel and mantel.partial functions implemented in the R package vegan. IBD and IBE were also performed using the MMRR (multiple matrix regression with randomization) function in R [βD), IBG (βG), and IBE (βE) were obtained, and the significance was determined after 999 permutations.The Mantel function in the R package vegan (999 permutations) was used to test IBD based on the genetic and epigenetic distance matrices against the geographic distance matrix calculated using the ion in R . In addiMSAP-calc script [PPL was 58.8% in mMSAP and 33.6% in uMSAP. The average uHE was 0.160 in mMSAP and 0.119 in uMSAP . Pairwise comparisons between marker types revealed that the mean uHE per locus was significantly smaller in AFLP (mean uHE per locus = 0.104) compared to mMSAP (mean uHE per locus = 0.223) and uMSAP (mean uHE per locus = 0.131) , but it was significantly lower compared to mMSAP epigenetic uHE . mMSAP uHE was significantly higher compared to uMSAP uHE .Overall, 9 primer pairs resolved a total of 481 unambiguous bands ranging from 22 to 107 , and thec script  were 424in uMSAP . The LMM= 0.131) . At the FST based on all loci were 0.044 and 0.106, respectively, for mMSAP and uMSAP (ΦST) of 0.071 and 0.181, respectively, for mMSAP and uMSAP based on the total data  or with the uMSAP population mean FST .The overall values of nd uMSAP . The AMOtal data . The aveariation . Using t in DAPC . No dist in DAPC a, but fo in DAPC b. However = 0.484, p = 0.002; AFLP vs. uMSAP: Mantel r = 0.596, p = 0.001; and mMSAP vs. uMSAP: Mantel r = 0.832, p = 0.001), and the linear fitting lines are displayed in r = 0.388, p = 0.007; AFLP vs. uMSAP: Mantel r = 0.460, p = 0.004; and mMSAP vs. uMSAP: Mantel r = 0.814, p = 0.001).A pairwise population Nei’s distance matrix was used in a Mantel test to investigate the inter-population correlation between genetic and epigenetic variation. We found significant inter-population correlations between genetic and epigenetic distance  > 1.0 as a criterion for strong evidence in the identification of outliers using BAYESCAN, most of the outliers identified with BAYESCAN had a log10(PO) > 2 (decisive evidence for selection corresponding to a probability > 0.99) [ΦST was 0.284 in mMSAP and 0.404 in uMSAP based on the outlier loci  were identified as being outliers . The BAY > 0.99) , except ier loci .R2 = 0.273, F = 5.917, p = 0.001; uMSAP: adjusted R2 = 0.390, F = 9.414, p = 0.001). The full RDA model analysis with envfit suggested that outlier mMSAP and uMSAP variation, respectively, correlated significantly with 11 and 12 environmental variables   .R2  than the other environmental variables in the envfit analysis  [r = 0.126, p = 0.001; uMSAP: Mantel r = 0.184, p = 0.001) and MMRR   and MMRR . Moreover, a significant IBE can be inferred based on the total  and outlier  datasets when controlling for geography using the partial Mantel test. MMRR also revealed a significant IBE when controlling for the geographic effect based on the total  and outlier  variation.The Mantel test revealed a significant genetic IBD based on the total AFLP data (Mantel = 0.001) . A signi= 0.001) . A signir = 0.091, p = 0.031; uMSAP: Mantel r = 0.121, p = 0.001) and outlier  data. However, MMRR revealed non-significant IBE based on the total mMSAP variation when controlling for genetic structure , but a significant IBE was found when controlling for genetic structure based on the total uMSAP variation . Nonetheless, MMRR revealed a significant IBE based on the outlier mMSAP and uMSAP variation when controlling for the genetic structure .Partial Mantel tests revealed strong correlations between mMSAP and uMSAP variation and environmental heterogeneity when controlling for genetic structure based on the total . Nonetheless, significant epigenetic IBD was detected with both the Mantel test and MMRR based on the total and outlier data (Neutral epigenetic divergence may occur via drift, and inter-population correlation between genetic and epigenetic variation may involve no functional link ,69. The r plants ,63,64,70ier data , suggestuHE per locus compared to mean epigenetic uHE per locus (Z. kawagoii adaptation. This may enable higher survival in a population encountering novel environments until the acquisition of beneficial genetic mutation.Epigenetic changes can be triggered by biotic and abiotic stresses in natural environments and provide potential for novel heritable variation . Environer locus , reinforZ. kawagoii. The results of pRDA analyses in this study suggest outlier mMSAP variation is associated mainly with slope, soil pH, NDVI, PET, and BIO12, whereas outlier uMSAP variation is mainly associated with aspect, slope, MI, NDVI, soil pH, and BIO12 (The current study extends the earlier research  by examind BIO12 . Howevernd BIO12  suggest L. racemosa from salt marsh populations display a shrub-like phenotype with lower levels of DNA methylation, whereas individuals of riverside populations with a tree-like phenotype had higher levels of DNA methylation [Prunus mume [Carpobrotus edulis [V. cazorlensis [Phragmites australis and Lilium bosniacum [Although it is unclear whether environmentally dependent epigenetic changes at distinct loci are the direct consequence of environments, local environments may play a role in mediating heritable epigenetic divergence for certain loci that are responsive to environmental conditions ,72,76. Thylation . In thisnus mume  and Carps edulis , flower orlensis , and inhosniacum ,80. Epigosniacum ,33,81 anosniacum ,33. Epigosniacum ,82, NDVIosniacum ,33. Topoosniacum ,33. TherZ. kawagoii populations. We identified that outlier MSAP loci associated strongly with specific environmental variables that may have played important roles in the local adaptation and survival of Z. kawagoii populations despite significant IBD. The local adaptation involving epigenetic changes in Z. kawagoii is also evidenced by the finding of significant IBE based on the outlier epigenetic variation when controlling for the geographic or genetic effects. Nonetheless, this study suggests that selection may be involved in population divergence at epigenetic sites that are partly linked to genetic sites in Z. kawagoii. Our findings highlight that methylation changes can be substrates for natural selection associated with environmental variables and may have a long-term consequence on the adaptation and survival of Z. kawagoii populations. This study also emphasizes the importance of investigating population epigenetic variation in association with environmental gradients apart from adaptive genetic divergence.Genetic adaptation to environmental changes is crucial for the conservation and survival of species. In addition, researchers in evolutionary biology are increasingly interested in epigenetic adaptations to different environments. Both genetic and epigenetic variations may be sources of variation that play important roles in adapting to environmental heterogeneity and, hence, are important for understanding how the environment shapes natural population diversity in a changing environment. In the present study, epigenetic variation provided no distinct regional substructuring, particularly based on the total mMSAP data. This study revealed low epigenetic population differentiation, indicating that larger epigenetic variation occurs mainly at shorter spatial scales. Epigenetic variation may have compensated for the lack of genetic variation and played an important role in the adaptive divergence and local adaptation in


Tea, the second largest consumer beverage in the world after water, is widely cultivated in tropical and subtropical areas. However, the effect of environmental factors on the distribution of wild tea plants is unclear.Camellia gymnogyna was higher than that from the Carbonate Rock Classes of Camellia tachangensis. In addition, the genetic diversity of wild tea plants from the second altitude gradient was significantly higher than that of wild tea plants from the third and first altitude gradients. Two inferred pure groups (GP01 and GP02) and one inferred admixture group (GP03) were identified by population structure analysis and were verified by principal component and phylogenetic analyses. The highest differentiation coefficients were determined for GP01 vs. GP02, while the lowest differentiation coefficients were determined for GP01 vs. GP03.A total of 159 wild tea plants were collected from different altitudes and geological types of the Guizhou Plateau. Using the genotyping-by-sequencing method, 98,241 high-quality single nucleotide polymorphisms were identified. Genetic diversity, population structure analysis, principal component analysis, phylogenetic analysis, and linkage disequilibrium were performed. The genetic diversity of the wild tea plant population from the Silicate Rock Classes of Camellia tachangensis with Carbonate Rock Classes at the first altitude gradient and Camellia gymnogyna with Silicate Rock Classes at the third altitude gradient. Geological environment, soil mineral element content, soil pH, and altitude markedly contributed to the genetic differentiation between Camellia tachangensis and Camellia gymnogyna.This study revealed the genetic diversity and geographical distribution characteristics of wild tea plants in the Guizhou Plateau. There are significant differences in genetic diversity and evolutionary direction between The online version contains supplementary material available at 10.1186/s12870-023-04239-2. Camellia sinensis (L.) O. Kuntze) is the second largest consumer beverage in the world after water, and is widely cultivated in tropical and subtropical areas [Tea  and evaluated the genetic diversity in C. annuum germplasm. Meanwhile, Niu et al. [Molecular markers are essential for breeding major crops in current agriculture industry, and many molecular marker techniques have been developed and applied to the detection of genetic diversity and environmental adaptability of various crops –8. Singlu et al.  recognizCamelliaceae) [Populus szechuanica var. tibetica Schneid) was found to have the highest genetic diversity at low altitude and the lowest genetic diversity at high altitude [Local adaptation is the basis of segregation of most phenotypic variations within species, with various environmental factors shaping the patterns of genetic variation and differentiation of wild germplasm resources . Environliaceae) . Differeliaceae) , 18. Thealtitude . TherefoCamellia tachangensis and Camellia gymnogyna. C. tachangensis was predominantly distributed in the high-altitude carbonate rock area in the southwest of the Guizhou Plateau, while C. gymnogyna was predominantly distributed in the low-altitude silicate rock area in the north of the Plateau. C. tachangensis and C. gymnogyna have obvious differences in tree type, leaves, flowers, and fruits [C. tachangensis and C. gymnogyna adapt to the evolution in different altitudes and geological environments of the Guizhou Plateau. Wild tea plants  were collected from different altitudes and geological environments in Guizhou Plateau, and GBS was used to identify the significant SNPs of these plant materials. Subsequently, population structure analysis, principal component analysis (PCA), and phylogenetic analysis were conducted, and the 159 wild tea plants were divided into three groups . In addition, linkage disequilibrium (LD), genetic diversity, and soil nutrient characteristics of the different populations were analyzed. Findings from the study provide valuable information for future tea breeding and genetic analysis.In our previous study, we investigated the agronomic characteristics, soil nutrient status, and ecological environment of wild tea plants in Guizhou, China and discovered significant differences in the altitude and ecological environment regarding growth of d fruits –22. In tC. tachangensis plants and 58 C. gymnogyna plants, were collected based on the different altitudes and geological types of Guizhou Plateau . SNPs were then detected and genotyped by GATK (version 3.7.0) based on the reference genome [To explore the genetic variation of the wild tea plant population of Guizhou Plateau, 159 wild tea plants, comprising 101 e genome . In totaPi), observed heterozygosity (Ho), minor allele frequency (MAF), and inbreeding coefficient (Fis) are recognized indicators of genetic diversity. In this study, Pi, Ho, MAF, and Fis of the 159 wild tea plants were 0.230, 0.085, 0.149, and 0.644, respectively  is used as a measure of population structure; Fst values of 0–0.05 and 0.05–0.15 indicate little and moderate divergence, respectively [Fst value of C. gymnogyna and C. tachangensis was 0.075, suggesting that the moderate divergence occurred in the wild tea plant population between C. gymnogyna and C. tachangensis  was for II vs. IV, while the lowest pairwise GD was for III vs. IV ’ or ‘GP01’ from now on) contained 67 accessions, including 55 (82.0%) C. gymnogyna and 12 (18.0%) C. tachangensis, which all came from the SR Classes (46 (68.6%) tea accessions from area III, and 21 (31.4%) tea plants from area IV)  or ‘GP02’ from now on) contained 45 C. tachangensis, which came from the CR Classes (including 11 (24.4%) accessions from area I and 34 (75.6%) accessions from area II), with 39 (86.6%) plants from H1 and 6 (13.4%) plants from H2. The remaining 47 accessions formed an admixed group (referred to as ‘I and H2 of C. tachangensis’ (I H2 C. tachangensis) or ‘GP03’ from now on) that, comprised 44 (93.6%) C. tachangensis and 3 (6.4%) C. gymnogyna, with 37 (78.7%) plants collected from area I and 10 (21.3%) individuals collected from area II, and 42 (89.3%) and 5 (10.7%) of the accessions were distributed in H2 and H3, respectively . Analysis of cross-validation error (CV error) revealed that the CV error changed little as K increased from 1 to 9, and CV error was minimal at K = 2  rapidly decayed with increasing physical distance. The maximum r2 value was 0.226 for the LD decay of all 159 accessions. As r2 decayed to half maximum (0.113), the corresponding physical distance was 0.116 Kb  was detected for GP01 vs GP03 and GP02 vs GP03, while the lowest Nm was detected for GP01 vs GP02. Therefore, there are more gene exchanges between GP01 and GP03, GP02 and GP03 and less gene exchange between GP01 and GP02 , no variants were detected in other populations such as GP01, GP02, and GP03, indicating that these populations have the same genetic basis. Most of the variants were detected within population (91.5%) and 8.5% were found among population Table .Table 6AC. tachangensis were significantly higher compared with those of C. gymnogyna, while the exchangeable aluminum (Al) content and MAT of C. gymnogyna were significantly higher than those of C. tachangensis. The total nitrogen (TN), exchangeable Ca content and pH of H1 were significantly higher than those of H2 and H3, while the exchangeable Al content and MAT were significantly lower compared with those of H2 and H3. The SOM, AN, TP, AK, exchangeable Mg, pH, and MAR of H1 and H2 were significantly higher than those of H3. The SOM, TN, AN, TP, AK, exchangeable Mg, exchangeable Ca, pH, and MAR of CR were significantly higher compared with those of SR, while the contents of AP, exchangeable Al and MAT were significantly lower compared with those of SR. The content of exchangeable Al in soil I was significantly lower than that in soils II, III, and IV, and the content of AK was significantly higher than that in soils II, III, and IV. The SOM, AN, TP, exchangeable Mg, exchangeable Ca and MAR in soils I and II were significantly higher compared with those in soils III and IV. In addition, the SOM, TN and pH of GP02 were significantly higher than those of GP03 and GP01, and the SOM, TN, AN, TP, exchangeable Mg, exchangeable Ca content, pH, and MAR of GP02 and GP03 were significantly higher than those of GP01.Statistical analyses were conducted on soil nutrient content, pH, mean annual temperature (MAT) and mean annual rainfall (MAR) of different species, altitude, rock types, and groups Table . The anaPrior research has highlighted the influence of environmental factors on plant genetic differentiation –30. In tIn this study, 29,393,327 initial SNPs and 98,241 high-quality SNPs after applying filtering criteria were used to detect the genetic diversity of 159 wild tea plants from the Guizhou Plateau. In our previous report, 1,001,372 initial SNPs and 79,016 high-quality SNPs were obtained from 415 tea plants, which are lower than the high-quality SNPs in our material study under the same filtering conditions . This deHo and MAF of the C. gymnogyna population were significantly higher than that of the C. tachangensis population, while Fis of the C. tachangensis population was significantly higher than that for C. gymnogyn, suggesting that there was rich genetic diversity in the C. gymnogyn population. The SOM, AN, TP, AK, exchangeable Mg and Ca contents, and pH of the C. tachangensis population were significantly higher compared with those of the C. gymnogyn population. Previous studies have revealed that C. gymnogyn may have evolved from C. tachangensis [C. tachangensis is relatively primitive, which may adapt to the natural environment with high soil nutrient content, pH and low temperature in Guizhou Plateau for a long time. C. tachangensis has conducted frequent inbreeding, reduced communication with other tea plants, and has low genetic diversity.Life-history or geographic traits play an important role in genetic diversity . In geneangensis . C. tachEarlier research has uncovered that a suitable environment will promote gene exchange between plants and increase the genetic diversity of plants. The favorable and mild environmental conditions at mid-altitude mean plant populations at this altitude exhibit greater diversity compared with those at low and high altitudes , 35, 36.Fst has been widely used as a measure of population structure. The Fst values for wild tea plant populations of C. tachangensis and C. gymnogyn, H1 and H3, H2 and H3, areas II and III, and areas II and IV were 0.05–0.15, indicating that moderate divergence occurred in wild tea plant populations between C. tachangensis and C. gymnogyn, between H1 and H3, between H2 and H3, between areas II and III, and between areas II and IV [In addition, it has been shown that positive Tajima’s D indicated population bottlenecks and/or balancing selection . In our I and IV , 26.Environmental factors, including geological environment, altitude, temperature, rainfall, and light, significantly affect the growth of tea plants. In particular, altitude and geological environment played important roles in the distribution, population structure, and evolutionary direction of germplasm resources of wild plants , 42. ZhaC. gymnogyn distributed in the SR Classes, while the GP02 and GP03 populations were II H1 C. tachangensis and I H2 C. tachangensis, respectively, distributed in the CR Classes. CR soil is rich in calcium and has a high content of organic matter , including soil TN, AN, TP, AK and exchangeable Mg, while SR soil is rich in AP and exchangeable Al [C. tachangensis and I H2 C. tachangensis, while the lowest gene flow and the highest genetic distance and genetic differentiation were observed between III H3 C. gymnogyn and II H1 C. tachangensis. Therefore, III H3 C. gymnogyn and II H1 C. tachangensis have the least gene exchange, and they assume different evolutionary directions due to different soil nutrient contents. With the spread of wild tea plants in the Guizhou Plateau, the primitive II H1 C. tachangensis gradually differentiated into III H3 C. gymnogyn in the process of transmission from CR to SR, so as to better adapt to the lower soil nutrients and meet the demand for P and Al. It indicated that rock types and soil nutrients developed from rock played an important role in the genetic differentiation of C. tachangensis and C. gymnogyn.Geological heterogeneity is known to impact species diversity . The mineable Al . We obseC. tachangensis population was predominantly distributed in area II of the CR Classes, the I H2 C. tachangensis population was mainly distributed in area I of the CR Classes, and the III H3 C. gymnogyn population was predominantly distributed in area III of the SR Classes. The II H1 C. tachangensis were relatively primitive tea plants. In this study, the II H1 C. tachangensis growing area II has evolved into I H2 C. tachangensis growing area I, and then evolved into the H3 III C. gymnogyn growing area III in the process of communication by spreading. The II H1 C. tachangensis were growing in relatively higher pH soil from dolomitic limestone, the I H2 C. tachangensis were growing in relatively medium pH soil developed from dolomite, and the III H3 C. gymnogyn were growing in relatively lower pH soil from clastic rock. Ca and Mg ions alkalize soil, while Al ions acidify soil [C. tachangensis and medium pH I H2 C. tachangensis have a moderate of differentiation, while high pH II H1 C. tachangensis and low pH III H3 C. gymnogyn have a higher of genetic differentiation. Soil pH plays an important role in the propagation and differentiation of wild tea plants in the Guizhou Plateau and maintains the different evolutionary directions of the plant populations.The pH values of soil developed from different rocks are distinct. The pH of soil developed from rocks with high calcium and magnesium contents was higher than that from rocks with high aluminum content , 47. MorC. tachangensis populations were mainly distributed at higher altitude with low temperature, the I H2 C. tachangensis populations were predominantly distributed at middle altitudes with high temperature, and the III H3 C. gymnogyn populations were mainly distributed at lower altitudes with high temperature. We observed the highest gene flow and the lowest genetic distance and genetic differentiation between III H3 C. gymnogyn and I H2 C. tachangensis, higher gene flow and lower genetic distance and genetic differentiation between II H1 C. tachangensis and I H2 C. tachangensis, and the lowest gene flow and the highest genetic distance and genetic differentiation between III H3 C. gymnogyn and II H1 C. tachangensis. Thus, the genetic differentiation between III H3 C. gymnogyn and II H1 C. tachangensis is the largest. Due to the difference of temperature at different altitudes, the genetic differentiation between C. tachangensis and C. gymnogyn was caused, which was congruent with previous studies [C. tachangensis distributed in higher altitude areas gradually spread to middle altitude areas, and then evolved into I H2 C. tachangensis under the adaptation of suitable temperature and soil rich in soil nutrients developed by dolomite. Subsequently, continued spreading of the I H2 C. tachangensis to the lower altitude resulted in their evolution into III H3 C. gymnogyn by natural selection on the clastic rock most suitable area to meet the demand for Al. We detected the highest and lowest genetic diversity in I H2 C. tachangensis and II H1 C. tachangensis, respectively. It is possible that the I area at medium altitude may be due to the high content of soil nutrients, suitable temperature, and frequent genetic exchanges among wild tea plants, which have increased their genetic diversity. In contrast, the II area at higher altitude may be suitable for local adaptation of tea plants owing to the lower temperature and rich soil nutrients, thereby reducing genetic diversity of the wild tea plants.The II H1  studies –51. Lori studies . In thisWang  believedC. tachangensis group (GP03) growing in I areas at middle altitude, while the II H1 C. tachangensis group (GP02) growing in the II areas at high altitude had the lowest genetic diversity. In the Guizhou Plateau, the suitable temperature in the middle altitude and the higher nutrient content of soil developed from dolomite promote the formation of high genetic diversity, while the low temperature at high altitude and the high pH of soil developed from limestone with dolomite promote the formation of low genetic diversity. Temperature and, soil nutrients and pH are the main factors affecting the genetic diversity of wild tea plants in the Guizhou Plateau. Findings from this study will facilitate understanding of the adaptive evolutionary characteristics of wild tea plants in the Guizhou Plateau, and will provide reference suggestions for further research on wild tea germplasm resources.The highest genetic diversity was observed for the I H2 Camellia tachangensis (F.C.Zhang) and 58 were Camellia gymnogyna Chang according to the classification system reported by Chen [In total, 159 wild tea germplasm were used in this study  and is mapped using GIS techniques. Professor Suzhen Niu from the Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region, Guizhou University, Ministry of Education, Institute of Agro-Bioengineering, associate professor Jie Yin and lecturer Qinfei Song of the College of Tea Science of Guizhou University, and researcher Zhengwu Chen of the Institute of Tea Science, Guizhou Academy of Agricultural Sciences conducted morphological identification on all sampled tea plants, and stored them in the tea germplasm resource garden of Guizhou University  and mean annual rainfall (MAR) over 5 years  according to the manufacturer’s instructions. The genomic DNA was digested with the restriction endonucleases SacI and MseI , Ipswich, USA), then the adapters “SacAD and MseAD”, which have unique barcodes, were ligated to the DNA fragments. Fragments of 500-–550 bp were selected for amplification and sequenced on an Illumina Hi Seq platform . The length of the original paired-end sequence is 150 bp , 59.http://tpia.TeaPlant.org/) using BWA-MEM v. 0.7.10 (https://sourceforge.net/projects/bio-bwa/files/) with default parameters [https://github.com/vcftools/vcftools) [https://rdrr.io/cran/CMplot/) [Barcodes were used to multiplex raw DNA reads, and the adapters were removed using custom Perl scripts. Only 5 read quality values > were retained. The reads were mapped to the reference genome (rameters . SNPs werameters  and McKerameters . The SNPcftools) . The SNPCMplot/) . A totalhttp://dalexander.github.io/admixture/download.html) was used to speculate the proportions of admixtures among the wild tea populations, and the number of ancestries (k) was in the range of 1–9. The score threshold was set to 0.8 to distinguish between pure and admixture groups [https://tassel.bitbucket.io) [https://www.megasoftware.net/dload_win_gui) to run 500 bootstrap replicates. The phylogenetic tree was displayed by iTOL [https://github.com/BGI-shenzhen/PopLDdecay) with default parameters, the LD was calculated statistically based on the correlation coefficient (R2) of the unpruned pairwise SNPs throughout the genome [ADMIXTURE v. 1.30 (e groups . PCA wascket.io) . The max by iTOL . Using Pe genome . The VCFe genome .Ho, Pi, MAF, Fis, and Tajima’s D. Ho, MAF, and Fis of each inferred population were calculated using Plink v. 1.90 (https://www.cog-genomics.org/plink2/) [Pi and Tajima’s D of each inferred population and Fst of the paired inferred population [Fst was then used to calculate Nm according to the formula Nm = (1 − Fst)/4Fst [GD for the pairwise inferred populations. Significant differences between these indexes and the single factor analysis of variance (ANOVA) of soil nutrients in different groups were calculated using SPSS v.25  [The genetic diversity indexes included plink2/) . VCFtoolpulation . Fst wasst)/4Fst . MEGA v.NY, USA) . AnalysiNY, USA) ) was conNY, USA)  to deter2 (20 m × 30 m). Next, five points were set on the diagonal of each sample plot, and one soil profile was dug at each point by removing 0–5 cm dead leaves from the surface layer and then collecting 10–60 cm of the soil layer. The soil samples collected at the five points were mixed and impurities and stones were removed. Approximately 1 kg of soil samples were subjected to the quartering method, spread on white paper indoors, and allowed to dry, naturally, then plant roots and stones were picked out. The resulting samples were ground with wooden sticks and passed through a nylon sieve [4-H2SO4 method. AK and AP were determined by flame emission spectrometry and the Olsen method, respectively. Exchangeable Ca and exchangeable Mg were measured by continuous source atomic absorption spectrometry. The pH of the soil was tested with a glass electrode (ratio of soil to water = 1:2.5).In the distribution area of wild tea germplasm resources, select the area with the largest population density of wild tea plants and set it at 600mon sieve . The soion sieve . BrieflyAdditional file 1: Table S1. Information of 159 wild tea accessions used in this study. Table S2. The quality control data of 159 wild tea accessions. Table S3. Statistics of Heterozygosity Rate of 98,241 SNPs in 159 wild tea accessions. Table S4. SNP density. Table S5. Statistics of the number and ratio of the accessions of species, rock classes and geologically suitable area in three inferred populations. Table S6. Statistics of the number and ratio of the accessions of altitude gradient in three inferred populations. Table S7. The soil nutrient content and altitude factor of 159 wild tea accessions.Additional file 2: Figure S1. Geographic distribution of 159 materials collected at different altitudes and rock types. Note: (A) Geographical position. (B) Distribution map of altitude, rock type and sample point in Guizhou Plateau. Figure S2. Graph for CV error in the range of K=1-9 of 159 wild tea accessions. Figure S3. ML tree of four geologically suitable areas. Note: I Dolomite sub-suitable area, II Dolomitic limestone suitable area, III Clastic rock most suitable area, IV Purple clastic rock suitable area.Additional file 3: Table S1. Sampling localities of 159 wild tea accessions.Additional file 4: Table S1. Genotyping of 98,241 SNPs based on GBS in 159 wild tea accessions.


Fusarium graminearum, is one of the most destructive wheat diseases worldwide. FHB infection can dramatically reduce grain yield and quality due to mycotoxins contamination. Wheat resistance to FHB is quantitatively inherited and many low-effect quantitative trait loci (QTL) have been mapped in the wheat genome. Synthetic hexaploid wheat (SHW) represents a novel source of FHB resistance derived from Aegilops tauschii and Triticum turgidum that can be transferred into common wheat (T. aestivum). In this study, a panel of 194 spring Synthetic Hexaploid Derived Wheat (SHDW) lines from the International Maize and Wheat Improvement Center (CIMMYT) was evaluated for FHB response under field conditions over three years (2017–2019). A significant phenotypic variation was found for disease incidence, severity, index, number of Fusarium Damaged Kernels (FDKs), and deoxynivalenol (DON) content. Further, 11 accessions displayed < 10 ppm DON in 2017 and 2019. Genotyping of the SHDW panel using a 90 K Single Nucleotide Polymorphism (SNP) chip array revealed 31 K polymorphic SNPs with a minor allele frequency (MAF) > 5%, which were used for a Genome-Wide Association Study (GWAS) of FHB resistance. A total of 52 significant marker-trait associations for FHB resistance were identified. These included 5 for DON content, 13 for the percentage of FDKs, 11 for the FHB index, 3 for disease incidence, and 20 for disease severity. A survey of genes associated with the markers identified 395 candidate genes that may be involved in FHB resistance. Collectively, our results strongly support the view that utilization of synthetic hexaploid wheat in wheat breeding would enhance diversity and introduce new sources of resistance against FHB into the common wheat gene pool. Further, validated SNP markers associated with FHB resistance may facilitate the screening of wheat populations for FHB resistance.Fusarium head blight (FHB), caused by The online version contains supplementary material available at 10.1186/s12870-023-04306-8. Cereal crops have played a major role in shaping societies and providing food, feed, and raw materials for industrial uses. Food security, however, is compromised by a growing human population, shortages of water and nutrients, as well as biotic and abiotic stresses. Worldwide, 400 million people faced food insecurity during 2015–2019 .−2 for 12 years [Recently, the annual increase in wheat yields has slowed down . In the 12 years , 5. This12 years . In addi12 years .Fusarium species that cause FHB, F. graminearum is the predominant species in North America that produces deoxynivalenol (DON) as a secondary metabolite [Among tabolite . FHB wastabolite  and FHB tabolite . Breedintabolite –11.Triticum turgidum, AABB) and diploid wild goat grass  [The development of FHB resistance varieties, which is widely regarded as the most effective way of controlling FHB, requires the incorporation of diverse sources of resistance into breeding programs. To increase the diversity in the bread wheat gene pool, CIMMYT has developed thousands of synthetic hexaploid wheat (SHW) accessions from crosses between tetraploid durum wheat  . In ordehii, DD) .Aegilops tauschii into SHWs resulted in diverse populations with resistance or tolerance to environmental stresses including resistance to stripe rust and Septoria leaf blotch [A. tauschii into an SHW population improved resistance against FHB by reducing disease severity (18.3%) compared with tetraploid counterparts [Incorporation of the D genome from f blotch . Wild whf blotch , 15. Altf blotch , 16. Thef blotch . On the f blotch . IncorpoResistance to FHB is a quantitative trait with a complex nature. Since the first QTL study of FHB resistance in 1999 , approxiFhb1 from Chinese germplasm is stable in different wheat backgrounds and environments without a negative effect on yield [Fhb1, Fhb2, Fhb4, Fhb5, and Qfhs.nau-2B QTL, are the most important sources of FHB resistance worldwide [The majority of FHB resistance QTL are population-specific and non-stable in different environments. In contrast, on yield , 23, 24.orldwide , 25, 26.orldwide . In addiThe main goals of this study were to evaluate FHB resistance in an SHDW spring wheat population, identify new sources of resistance to FHB, determine SNP markers associated with FHB resistance, and identify FHB resistance candidate genes.An evaluation of 194 SHDW for FHB symptoms was conducted for three years 2017–2019) under artificial inoculation. These evaluations revealed variable results for FHB field traits  and post-harvest traits (FDKs number and DON content) among wheat lines  were genotyped using Illumina’s iSelect 90 K SNP chip. Genotypic data were filtered for missing data > 10%, MAF < 5%, and heterozygosity > 50%, which resulted in 31 K high-quality SNPs. The missing data were then imputed. Of these variants, 14,622, 18,299, and 5470 SNPs were located in A, B, and D genomes, respectively  and cryptic relatedness (K*) as covariates to reduce false positive signals. Using this approach, 52 significant marker-trait associations (MTAs) for FHB resistance were identified  in the current population. While 61% of the candidate genes were involved in a broad range of physiological processes including defense response, the remaining candidate genes (39%) had unknown functions. In the group of candidate genes with identified functions, reverse transcriptases and zinc ion binding proteins had the highest frequency (4%), while protein kinase genes and genes encoding for protein binding had the next highest frequencies (3%). The P-loop containing nucleoside triphosphate hydrolases occurred with a frequency of 2%. Genes encoding HSP40/DnaJ peptide-binding, hydrolase, nucleic binding, pectinesterase inhibitor, protein dimerization, protein transporter, structural constituent of ribosome, sucrose transmembrane transporter, and transferases had frequencies of 1%. Some of the notable candidate genes with frequencies of less than 1% included gibberellin-regulated protein, photosystem II protein, and the stress-induced protein Di19. In particular, SNPs linked to 9 genes on chromosome 2B were associated with both disease severity and disease index in 2019  can introduce new sources of genetic resistance against abiotic and biotic stresses into accessible germplasm for the wheat breeding program. In general, it offers opportunities to broaden the wheat gene pool.The narrow genetic diversity in the wheat gene pool poses major challenges to breeding new wheat varieties with desirable traits. The production of SHWs from crosses between modern durum wheat and its wild relative . In 2018, FHB pressure was low due to low precipitation. In 2017 and 2019, however, the SHDW lines exhibited significant variations in FHB incidence and severity. The variability of the responses of these inbred lines from one year reflected the variability in the establishment of the FHB disease conditions  from yeaAe. tauchii and 13 modern tetraploid wheat parents, and later were backcrossed onto adapted hexaploid lines. The previous study [R2 = 3.48) than the tetraploid parents (R2 = 2.75). In this study, the genetic structure of the SHDW was not found to be related to the origin of Ae.tauschii or the tetraploid parents.Results from the current study found that the panel of 194 SHDW lines consisted of three sub-panels. This differed from a previous analysis of the same materials that suggested five subgroups . The disus study  revealedOur results identified marker-trait associations with resistant allele effects between 0.69%-71.54% for FHB field and post-harvest traits. This is an important resource for FHB resistance marker development, which improves the efficiency of selection for FHB resistance traits for variety development . In line(QFhb.hbaas-2DS and QFhb.hbaas-4DS) that decreased FHB severity by 69.9% and 55.5%, respectively, were identified in a doubled haploid population of a cross between moderately resistant Jingzhu 66 and susceptible Aikang 58 [The D genomes of disparate SHW populations display higher nucleotide sequence diversity compared with the D genome of bread wheat , 36. In ikang 58 . In addiikang 58 .F. graminearum isolate [TaUGTs in a susceptible line (Fielder) resulted in a lower DON content than the wild type [TraesCS3B01G017200, TraesCS7D01G117800, TraesCS4A01G445700, and TraesCS5B01G059400) in the SHDW panel , and glutathione-S-transferases in DON-treated barley spikes [F. graminearum spores and DON treatment were accompanied by the upregulation of cytP450s [In this study, we detected 395 candidate genes in regions spanning 100 kbp on both sides of the SNP markers associated with FHB resistance traits. These marker-trait associations during our three-year experiments included 56 genes for DON (2017), 19 for FDK (2017), 59 for FDK (2019), 28 for FHB incidence (2019), 33 for FHB index (2017), 38 for FHB index (2019), 95 for FHB severity (2017), and 67 for FHB severity (2019)  [TraesCS3B01G008200, TraesCS3B01G008300, and TraesCS3B01G008400  . Therefot spikes .TraesCS7A01G159800 may constitute part of a defense mechanism to restrict sugar availability and proliferation of F. graminearum but may equally be important for other known developmental processes including glucose efflux from the tapetum for pollen growth [F. oxysporum [In the SHDW panel, a SWEET gene n growth . The SWEn growth , 62. Ovexysporum .F. graminearum [TraesCS7A01G634900LC, which encodes a TRAF-like protein  are prominent plant defense-related lectins that are associated with disease resistance, abiotic stress signaling, wounding, and insect damage [F. graminearum [Fhb1 is a major genetic determinant of FHB resistance [Lectins, including LRR lectins, are carbohydrate-binding proteins involved in defense against insects as well as viral, bacterial, and fungal pathogens . Severalminearum . It is it damage , 67. Forminearum . Furthersistance . The sigAn LSM domain-containing proteins (with an FDF domain of unknown role) are part of a complex in the mRNA de-capping machinery . In ArabBased on the comparison of genomic regions associated with FHB resistance in the current study and previously reported QTL, seven unique genomic regions were identified. These regions include two genic regions on 3B for FHB severity and FDK, respectively; two regions on 5B for FHB severity; one region on 7A for FHB severity, and one region on 7D for FDK. The specific details of these unique genomic regions are shown in Table In this study, 7% of the significant marker-trait associations were located on chromosome D which was less than the contribution of chromosomes A and B. While the D genome does contribute to genetic diversity in wheat, our study showed that it does not provide significant resistance to Fusarium head blight (FHB). Instead, resistance to FHB is largely attributed to genes present in the A and B genomes in the SHDW panel. Therefore, breeding programs focused on developing FHB-resistant wheat varieties typically prioritize genes found in the A and B genomes over those found in the D genome. However, it is important to note that the D genome can still contribute to other desirable traits in wheat, such as drought tolerance , diseaseF. graminearum growth and DON production. Once validated, the markers associated with FHB resistance traits can be utilized for DNA marker-assisted selection. The incorporation of SHDW lines into wheat breeding schemes will offer a novel approach for the introgression of disease resistance into the conventional wheat gene pool and may mitigate the impact of FHB on wheat production.This study provides new insights into the genetic basis of FHB resistance in SHDW lines. Candidate genes encoding lectins, ABC transporters, cytP450, UGTs, knottin, 60S acidic ribosomal protein, and LSM domain-containing proteins may be involved in a defense network to suppress Ae. tauschii and 13 tetraploid accessions with synthetic degrees of 2–5 [nd- and 5th-degree synthetic hexaploid-derived wheat lines [A set of 200 spring wheat lines consisting of 194 accessions of spring Synthetic Hexaploid Derived Wheat (SHDW) from CIMMYT and six check cultivars were planted for three years (2017–2019) in two replications in an FHB nursery at the Elora research station . The check cultivars consisted of Sable (highly susceptible), Norwell (susceptible), Carberry (moderately susceptible), AAC Scotia (moderately resistant), Hoffman (susceptible), and Pasteur (susceptible). The SHDW panel was derived from crosses between 19 s of 2–5 . Geneticat lines . The expF. graminearum isolates from Ontario, Canada, consisting of 3ADON, 15ADON, and an undetermined chemotype was used for field inoculations. The F. graminearum inoculum was prepared for artificial inoculation in the field as described previously [2 disc of F. graminearum mycelium on potato dextrose agar (PDA) was cut and transferred to a sterilized medium consisting of five g of chopped wheat straw in 125 ml water. The inoculated medium was placed on a shaker and grown for 14 days at 120 rpm in the dark at 25 °C. The macroconidia were harvested and counted using a hemocytometer . Before dusk, wheat plants were sprayed three times at two days pre-anthesis, anthesis, and two days post-anthesis with the F. graminearum spore suspension . Plots were mist irrigated (1–2 h/day) to generate approximately 70% relative humidity across the FHB nursery.A mixture of three eviously . A 0.5 cFHB incidence and severity were evaluated 21 days post-inoculation. In each plot, FHB incidence was determined based on the number of infected wheat heads in 100 heads. FHB severity was evaluated based on the disease progress in each wheat head (0–100%) as described previously . The disFor each wheat line, FDK (%) was determined in a sample of 100 seeds in two replications. For DON measurement, a five g seed sample was ground to a fine powder, and DON was extracted and quantified by a Neogen Veratox 5/5 ELISA kit  according to the manufacturer’s instructions. Phenotypic data  were analyzed using PROC MIXED  with block as random and year and genotypes as fixed effects. Normality was tested using a Shapiro-Wilks test in the PROC UNIVARIATE. For principal component analysis (PCA), PROC PRINQUAL was conducted to produce bi-plots. PROC CORR was used to create a correlation table. For correlation matrix analysis, the PerformanceAnalytics package was used in RStudio.DNA was extracted using DNeasy Plant Mini Kit  according to the manufacturer’s instructions. All accessions were genotyped using Illumina’s iSelect 90 K SNP chip  at the N2 and D´) was performed using all SNPs and LD decay was calculated using PopLDdecay [Population structure was estimated using fastStructure . Five rupLDdecay .p-value  was used to establish a significance threshold [p-value distributions of markers  were calculated in Q-Q plots. Manhattan and Q-Q plots were drawn using CMplot. The WheatMine database (Wheat IWGSC RefSeq v1.0 data) was used to identify candidate genes associated with the SNP markers within a region extending to 100 kbp at either side of the peak marker. The allele effect of each QTL was calculated based on the average of each FHB trait.GWAS was conducted using the rMVP package in R  utilizinhreshold . The p-vAdditional file 1: Additional Figure 1. Correlations among FHB resistance traits in SHDW lines in 2017 (A), 2018 (B), and 2019 (C).Additional file 2: Additional Figure 2. A principal component analysis (PCA) [is (PCA)  performeis (PCA)  shows thAdditional file 3: Additional Figure 3. LD Decay.Additional file 4: Additional Figure 4. Manhattan plots of associations between SNPs and FHB traits in the SHDW panel across three years (2017-19). A) Deoxynivalenol content (DON ppm), B) The average of Fusarium Damaged Kernels (FDKave), C) Fusarium Head Blight Incidence (FHBINC), D) Fusarium Head Blight Index (FHBINX), and E) Fusarium Head Blight Severity (FHBSEV).Additional file 5: Additional Figure 5. Q-Q plots of expected and observed associations between polymorphic SNPs and FHB traits across three years (2017-2019).Additional file 6: Additional Table 1. Marker-trait associations for the FHB resistance traits and selected genes associated with the QTL identified in the present study.Additional file 7: Additional Table 2. Eleven accessions displayed < 10 ppm DON in 2017 and 2019.