The Tunchang pig has characteristics such as tolerance of coarse feed, strong disease resistance, and delicious meat. In 2014, it was listed as one of the main populations of pigs in Hainan and included in the National Livestock and Poultry Genetic Resources Protection List. Protecting and developing the genetic resources of Tunchang pigs can effectively ensure the sustainable development of animal husbandry in Hainan Province. Whole-genome re-sequencing data were used to analyze the genetic diversity, population structure, and selection signals of Tunchang pigs. Compared to commercial pigs, Tunchang pigs have higher genetic diversity and are closer to indigenous Chinese breeds in terms of genetics. Through comprehensive analysis of multiple selection signals, candidate genes for meat quality, disease resistance, growth, and reproduction were identified.ST, θπ, and XP-EHH, were used to detect selection signals for three breeds of pigs: Tunchang, Duroc, and Landrace. A total of 2117 significantly selected regions and 201 candidate genes were screened. Gene enrichment analysis showed that candidate genes were mainly associated with good adaptability, disease resistance, and lipid metabolism traits. Finally, further screening was conducted to identify potential candidate genes related to phenotypic traits, including meat quality , immune regulation , growth and development , reproduction (LGALS13 and EPG5), vision (SLC9A8 and KCNV2), energy metabolism (ATP5G2), cell migration (EPS8L1), and olfaction (GRK3). In summary, our research results provide a genomic overview of the genetic variation, genetic diversity, and population structure of the Tunchang pig population, which will be valuable for breeding and conservation of Tunchang pigs in the future.Tunchang pig is one population of Hainan pig in the Hainan Province of China, with the characteristics of delicious meat, strong adaptability, and high resistance to diseases. To explore the genetic diversity and population structure of Tunchang pigs and uncover their germplasm characteristics, 10 unrelated Tunchang pigs were re-sequenced using the Illumina NovaSeq 150 bp paired-end platform with an average depth of 10×. Sequencing data from 36 individuals of 7 other pig breeds , and 3 commonly used commercial pig breeds ) were downloaded from the NCBI public database. After analysis of genetic diversity and population structure, it has been found that compared to commercial pigs, Tunchang pigs have higher genetic diversity and are genetically close to native Chinese breeds. Three methods, F Pork has a broad consumer market and has become the world’s second most consumed meat product due to its high production, good taste, and easy processing ,2. AccorAs one of the native pig population in China’s southernmost region, Tunchang (TC) pigs are well known for delicious meat, strong reproductive capacity, high stress resistance, and ability to tolerate coarse feed . In FebrLivestock exhibits rich diversity in appearance, reproductive ability, growth, adaptability, and other aspects to adapt to the environment and meet human needs. China has a significant advantage in the genetic resources of domestic pigs because it has over one-third of the world’s total pig breeds . With itCompared with another local pig breed in Hainan Province, the Wuzhishan (WZS) pig, there is little research on the TC pig. Therefore, to fully develop and utilize the breeding resources of TC pigs, it is necessary to accurately assess the genetic diversity and population structure of the existing TC pig population. Currently, the use of high-throughput sequencing technology to explore genetic markers at the individual or population level to investigate genetic diversity and reveal the genetic basis of certain animal species’ specific traits is already mature and widespread, as it has been applied in pigs, chickens, cows, sheep, and other animals ,13,14,1510 male TC individuals’ whole blood samples were sampled from the farm of TC pig . Following the library construction protocol, the genomic DNA of each sample was extracted from blood tissue using a commercial kit .https://www.ncbi.nlm.nih.gov/sra, accessed on 18 April 2022) and combined with the TC pigs’ re-sequencing data for population analysis. Therefore, based on the geographic distribution described in Qualified libraries were sequenced on the Illumina NovaSeq platform , generating paired-end reads of 150 bp in accordance with the manufacturer’s protocol. Whole-genome high-coverage sequencing was performed on these 10 individuals with an average coverage depth of 10×. In addition, to better reflect the diversity level of the TC pig population, re-sequencing data from 36 individuals of 7 other pig breeds were obtained from a public database (https://github.com/OpenGene/fastp), which ensured high-quality data by trimming adapters and removing low-quality nucleotides, unknown nucleotides (NS), and reads containing more than 10% N. Filtered reads from all individuals were aligned to the pig reference genome Sus scrofa11.1 using the Burrow–Wheeler aligner software program  [https://github.com/biod/sambamba) was used to discard duplicates and remove unmapped or low mapping quality score reads from the alignment results. The remaining reads were defined as good reads and used for further analysis. All parameters were set to default. Single-nucleotide polymorphism (SNP) calling was performed using GATK (V4.0.3.0) and filtered according to the parameters established by Xu et al. [The raw unprocessed data were merged and quality controlled (QC) using the software program FASTP  . The sofu et al. , which ou et al. . Finallyu et al. , and SNPST) was calculated using VCFTOOLS (v0.1.16), which reflects the degree of differentiation between the tested populations based on their genetic polymorphism data [ST heat map and FROH bar chart were inspired by Yang et al. [By completing the above steps, a total of 21,912,205 common SNP sites were obtained to infer the genetic information of these 8 pig populations. First, nucleotide diversity (θπ) , which cism data . The linism data . Data fiism data . The drag et al. .First, the software program MEGA (v 7.0) was used to construct a population neighbor-joining (NJ) tree, which reflects the developmental relationships between individuals at the whole-genome level based on the identity-by-state (IBS) distance matrix data . IBS wasST), polymorphism level statistics method (θπ), and cross-population extended haplotype homozygosity (XP-EHH). FST and θπ (DU or LR/TC) for TC and DU or LR breeds were calculated using VCFTOOLS (v0.1.16). The genome window and step size were set to 100 kb and 10 kb, respectively. Then log2 transformation was performed on θπ (DU or LR/TC) [http://kobas.cbi.pku.edu.cn/) was used to analyze the richness of gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways containing identified variants [p-Value p < 0.05 were further interpreted in this study. Finally, to understand the overlap information between the selected candidate regions and published QTL traits, pig QTL files were downloaded from the QTLdb database for comparison [Three genome selection feature detection methods were used to detect genome selection markers in TC, DU, and LR pigs. The three methods were the population differentiation coefficient method (Fr LR/TC) . XP-EHH r LR/TC) . Selscanr LR/TC) . The topvariants . Only GOmparison .To study the genetic variation characteristics of TC, whole-genome re-sequencing was performed on 10 individuals using the Illumina NovaSeq platform. The statistical results of the sequencing data are listed in After strict quality control and filtering, a total of 12,595,393 SNPs were detected in TC. Then the annotated file of all detected SNPs in TC was obtained using the gene annotation file downloaded from the NCBI database. It was evident that the highest number of SNPs were found in the intergenic region (45.03%) and introns (42.37%). Only 0.553% of the SNPs were located in the exonic region, including 22,342 (32.09%) nonsynonymous SNPs and 44,152 (63.41%) synonymous SNPs . We alsoPopulation genetic diversity can be reflected by the θπ value, with lower diversity indicating a higher degree of selection in the population. As can be seen from LD analysis using r2 values to measure LD decay provides information about the overall diversity level of each test population. DU showed the highest level of LD among all tested populations, while TC and WZS had the lowest LD levels. In addition, LD decay in JH and MS populations from other regions of China was found to be above that of the commercial pig breeds LR and LW B. The reST values for all populations  was higher than that of local Chinese pig breeds (0.190–0.383). TC had the highest FROH (0.3827) among local Chinese pig breeds. These results suggest that existing breeding programs have to some extent avoided inbreeding in the TC pig population. Then the ROH segments were divided into seven categories B. We fouTo explore the genomic similarities and differences between TC and other tested populations, neighbor-joining (NJ) tree, PCA, and population structure analysis were conducted . In the PCA results are shown in The results of the admixture analysis are shown in ST); nucleotide polymorphism level (θπ); and cross population extended haplotype homozygosity (XP-EHH)) were used to study the whole-genome selection signal. In order to reduce false positive candidate regions, regions with a top 5% threshold in at least two methods were selected as the selected region. ST, 0.622899; θπ ratio, 0.469371498; XP-EHH: 0.934944; ST, 0.560883; θπ ratio, 0.469371498; XP-EHH: 0.9196323; As one of the unique pig populations on Hainan Province in China, the TC pig possesses its own excellent traits. To explore the genomic information of TC, the genomic features of TC were further compared with those of two typical commercial pig breeds (DU and LR). Three complementary methods  in 77 GO terms and 8 KEGG pathways . We found that 2591 QTL data overlapped with the 2217 candidate regions . NotablyST, θπ, and XP-EHH methods  of the collected individuals were all greater than 93%, meaning that the percentage of bases with a quality value greater than or equal to 30 accounted for more than 93% of the total bases . After calling high-quality reads for SNPs and filtering, a total of 12 595 393 SNPs were identified in the TC population, and it was found that the distribution density of SNPs on chromosome 10 was the most concentrated, indicating that chromosome 10 could be a target chromosome for further study.ST between TC and other populations except for WZS ranges from 0.12 to 0.27, indicating a higher genetic similarity between TC pigs and local Chinese pig breeds. However, TC pigs still have some contamination by commercial pig bloodlines , and 3 commercial pig breeds . Generally, species with strong selection pressure have lower genetic diversity, while local breeds or varieties have higher levels of genetic diversity due to traditional breeding methods involving natural or random mating ,36. The oodlines .A study reported that FROH can estimate inbreeding coefficients more accurately , so FROHThe phylogenetic tree and principal component analysis results indicate that the population of TC pigs can be clearly distinguished from other pig breeds. They exhibit unique clustering and branching, indicating that the TC pigs’ genome has unique genetic characteristics after long-term domestication and breeding. Specifically, the population clustering in the phylogenetic tree and principal component analysis is highly consistent and has a certain correlation with the geographical distribution of the tested populations. All populations can be divided into local Chinese pig breeds and common commercial pig breeds, with local Chinese pig breeds further divided into eastern China , southern China , and southwestern China (RC) based on their geographic distribution. The results of the admixture analysis are similar to those of the phylogenetic tree and principal component analysis. When the number of common ancestors is set to 2 (K = 2), it is easy to distinguish between commercial pigs and local Chinese pig breeds. This differentiation is maintained even when increasing the number of common ancestors. In this study, we found significant differences between southern Chinese pigs and eastern Chinese pigs, while southwestern Chinese pigs exhibit a mixed state between southern China and eastern China. These findings are consistent with the results reported by Huang et al. , who stuST values, high θπ ratios, and high XPEHH values (top 5%) were selected. A total of 2117 overlapping regions were detected among the candidate regions, and 201 candidate genes were further identified in these regions. Gene ontology analysis showed that these candidate genes may play important roles in meat quality, immune regulation, and growth and development processes.As one of the native pig population in the southernmost part of China, TC pigs have many excellent traits. Therefore, TC pigs must have unique selection labels on their genome. By comparing different breeds, candidate regions with high FST, θπ, and XP-EHH three methods, among which were genes related to the genetic characteristics of TC pigs, such as SELENOV, TNNT1, TNNT3, VPS13A, PLD3, and SRFBP1. The SELENOV gene is a member of the selenoprotein family and plays an important role in selenium metabolism [SELENOV gene in mice increases fat accumulation, demonstrating that SELENOV can inhibit fat accumulation and promote energy consumption [TNNT1 and TNNT3, as muscle troponin T (TnT), regulate slow skeletal muscle TnT and fast skeletal muscle TnT, respectively. Proteomic studies have found that the expression of TNNT1 and TNNT3 proteins in Angus crossbred cattle and yaks is closely related to meat tenderness [VPS13A, one of the lipid transfer proteins, plays an important role in lipid metabolism and membrane homeostasis [VPS13A gene can regulate platelet secretion and aggregation under heat stress conditions [PLD3 has been identified as one of the key genes regulating lysophospholipid metabolism [SRFBP1 gene expresses a protein product called p49/STRAP, which is an auxiliary factor of serum response factor (SRF) that can assist SRF in regulating target genes [SRFBP1 can regulate early gene expression and cell metabolism, affecting cell skeleton morphology [SRFBP1 is a significantly correlated gene with fat deposition and is enriched in the insulin secretion pathway [We detected 28 genes by comparing the overlapping genomic regions found with the Ftabolism . Knockousumption . TNNT1 anderness ,46,47. Veostasis . The TC nditions . Triglycnditions ,51. Studtabolism . The SRFet genes . Data inrphology ,55. Stud pathway ,57.CD48, FBL, SLAMF6, and ZNF667. CD48 can promote the adhesion and activation of immune cells. For example, the interaction between CD48 and cis-CD2 can promote T-cell activation, while the interaction between CD48 and trans-CD2 can promote adhesion and co-stimulation of immunological synapses [CD48 plays an important role in hematopoietic recovery after sub-lethal irradiation in high-sunlight regions [FBL and its mediated RNA 2′-O-methylation (Nm) modification can inhibit innate immunity and promote viral infection. Down-regulation of FBL can block viral infection of macrophages and inhibit viral infection. Knocking out FBL can also increase the expression of ISG and reduce viral invasion [SLAMF6 is one of the signaling lymphocytic activation molecules, which can affect T cell function in regulating cancer immunity, but the role of SLAMF6 in T cell signaling is not clear yet and there are conflicting reports on whether SLAMF6 enhances or inhibits T cell activity [ZNF667  participates in cell growth, apoptosis, oxidative stress, and lipid accumulation. In a study on laryngeal squamous cell carcinoma (LSCC), it was found that ZNF667 is a tumor-suppressing gene [ZNF667 can regulate P-selectin expression in specific cells and leukocyte-endothelial adhesion after hypoxia [Several genes related to immune regulation were also detected, including synapses . Researc regions . FBL andinvasion . SLAMF6 activity ,62,63. Zing gene ,66,67,68 hypoxia .SMG9 gene is essential for growth and development, and cells lacking SMG9 exhibit global transcriptional dysregulation [SMG9 gene can result in severe developmental anomalies, including malformations or overgrowth of the brain, heart, face, or eyes, and developmental delay [LGALS13 is a placenta-specific gene that is crucial for the development and function of the human placenta [LGALS13 is highly expressed in the syncytiotrophoblast layer of the placenta and can induce apoptosis of cytotoxic T cells in maternal–fetal immunity [LGALS13 is also associated with gender, with higher expression in early male placentas [EPG5 gene related to complex birth traits, and the protein product of the EPG5 gene plays a critical role in the autophagy pathway, which is beneficial for the differentiation of early embryonic stem cells [EPG5 is involved in regulating nucleic acid transport and connects the autophagy pathway with innate and adaptive immunity [The gulation . Deletioal delay . LGALS13placenta . LGALS13immunity . The explacentas . A GWAS em cells ,76. EPG5immunity . TherefoST, and ROH, the TC pigs were found to have a high level of genome-wide genetic diversity. The population genetic structure analysis revealed that the TC pigs are closely related to indigenous Chinese pig breeds, particularly the WZS pig, which also originates on Hainan Island in China. The intersection of selection signals between the TC pigs and two commercial pig breeds identified potential genetic characteristics related to meat quality, disease resistance, growth, and reproduction. In summary, these findings contribute to a better understanding of the genetic information of the TC pig and provide data-based support for its conservation and improvement.This study analyzed the genetic diversity and population structure of TC pigs in comparison to commercial pig breeds and indigenous Chinese pig breeds. Based on genetic diversity parameters such as θπ, LD, F


Coriandrum sativum L.), hart’s pennyroyal (Mentha cervina L.) and pennyroyal (Mentha pulegium L.). The methodology involved direct observations and surveys (2002/2003 and 2011). The GE formula applied in Hammer’s studies was used to estimate genetic erosion. The main factors causing genetic erosion were the primary drivers of biodiversity loss: habitat loss, invasive species, and overexploitation influenced by human intervention such as the clearing of watercourses, vegetation control, grazing and desertification. The results indicate a reduction in individuals per species in Alentejo, with a net erosion loss of 11% for M. pulegium, 32% for M. cervina and 33% for C. sativum. The overall loss of accessions (genetic erosion risk) was higher in cultivated accessions (33%) than in wild accessions (11%), with an annual genetic erosion rate of 3.7% and 1.2%, respectively. The annual risk of genetic erosion for M. pulegium accessions collected in a natural habitat was 0.6%, which is much lower than the 3.7% for M. cervina. These results consolidate the importance of collecting and conserving genetic resources.The main goal of this work was to study the genetic erosion risk of plants with aromatic, medicinal and gastronomic applications in Portugal, particularly in the Alentejo region. The target species were coriander ( Genomic erosion is a pervasive—but frequently overlooked—consequence of the many threats faced by wild populations, such as overexploitation, invasive species, emerging infectious, diseases, hybridization, and habitat and environmental changes .Genetic erosion can be defined as the “loss of genetic diversity, in a particular location and over a particular period, including the loss of individual genes, and the loss of particular combinations of genes such as those manifested in landraces or varieties. It is thus a function of change of genetic diversity over time” . EstimatGenetic erosion, according to the European Environment Agency (CHM Biodiversity), is the loss of genetic diversity between and within populations of the same species over time, or the reduction of the genetic basis of a species due to human intervention, environmental changes, etc.Habitat destruction, degradation, fragmentation or conversion for agriculture, ranching, horticulture, mining, ecotourism, industry, population fragmentation, commercial over-harvesting to satisfy urban and export demands, and overgrazing are some of the human exerted pressures  on nativAs genetic erosion research has evolved, three main measurement targets have emerged. The quantification may be direct, or through proxies such as numbers of farmers or villages. These crop diversity analyses have been conducted at a wide range of geographic scales, from local , to community and agroecological landscapes, to country, regional and global scales. Intermediate time frame studies often compile and report diversity changes at standardized intervals, such as per decade. As with other parameters, the sources of information used to document change in crop diversity also vary widely and may be used in combination. Direct field observations provided the first lines of evidence for genetic erosion. Local knowledge, gathered through interviews with farmers and their families, community meetings, and surveys, have been widely used to assess change and document farmers’ perspectives ,14,15,16There is increasing evidence for the significant value and potential of medicinal and aromatic plants (MAPs) worldwide. Among other non-wood forest products, MAPs are considered a key element of sustainable forest management and economic development. As part of Mediterranean cultural heritage, these plants are a major driver of rural tourism and in many areas represent an important raw material for various bio-based industrial sectors. In addition to their economic value, MAPs enhance social integration and maintain gender balance as harvesting and processing MAPs is clearly a female-dominated task . DespiteAs the knowledge about traditional use of plants is disappearing, ethnobotany has an important role in saving the remaining information . The farMentha (Lamiaceae). In Portugal, Mentha pulegium L. is mostly known as “poejo” . Pennyroyal is a very aromatic, herbaceous species, frequent in wet meadows and pastures, in ravines, banks and water line’s dry beds, ponds, lagoons, and other temporarily flooded or flooded sites, which are sometimes nitrified. Pennyroyal has a preference for acidic soils, with permanent or seasonal edaphic humidity in mainland Portugal and on the Azores and Madeira archipelagos.Mint is the common name for most species of M. cervina , is also known in Portugal as “poejo-fino” , among other common names. It is a rare species from the Natura habitat 3130 pt5 “temporary deep ponds” [p ponds” . It appep ponds” . Hart’s Mentha ssp. is similiar, being mainly related to respiratory and digestive disorders.Pennyroyal is traditionally used in culinary decoration, in the well-known pennyroyal liquor, and as a culinary herb, primarily in the preparation of piso paste, and typical fish  and bread (açorda and migas) Portuguese dishes. The infusion and/or pennyroyal liquor is widely used as a digestive and/or to tackle colds. In Portugal, the medicinal use of Coriandrum sativum L., Apiaceae) leaves are widely used in the traditional and ancient recipes of various regions of Portugal, especially in Alentejo in its popular açorda and fish soup (shark). It is an annual plant of the Apiaceae family, very aromatic, with finely cut upper leaves and small white or pink flowers. Its very round brown seeds are very popular in cooking, giving a distinctive aromatic flavor. It is probably native to the Mediterranean basin where the Greeks and Romans used it in the preparation of dishes and drinks. In the Middle Ages, it was cultivated in monastery gardens. Its seeds and leaves are widely used in Indian and Arabian cuisine. In Portugal, the seeds are also used to make confetti and other sweets. The coriander seeds and leaves are widely used to season meat and vegetables. The essential oil is used in soaps, deodorants and toothpaste. With digestive, antiseptic, diuretic and soothing properties, it is used as a therapeutic resource in situations of high cholesterol, colic, conjunctivitis, diarrhea, toothache, migraine, fever, swellings, indigestion, neuralgia, rheumatism and measles [Coriander  of three taxa of aromatic, medicinal and condiment group species, Coriandrum sativum L.), hart’s pennyroyal (Mentha cervina L.) and pennyroyal (Mentha pulegium L.). The concept of crop genetic erosion, as applied in this research, is the loss of crop diversity in a defined area over a period of time, typically measured by the decline of species, varieties and/or within varieties [n = 28) and cultivated (n = 111) populations were studied, using indirect data (loss of accessions) and field observations to estimate the risk of genetic erosion and its causes.In 2002–2003, a survey on traditional ecological knowledge was carried out to assess the causes of genetic erosion of the studied taxa—coriander . The genetic erosion risk estimated for cultivated accessions was similar among the three studied species (32–33%)  . Accessi(32–33%) . Most of% . AccesThe main causes of genetic erosion identified by the informants in the 2002–2003 surveys were: desertification, soil erosion, excessive collection from natural habitats, overgrazing, shrub invasion of natural habitats, riparian vegetation cleaning, indiscriminate herbicide application and agricultural machinery use.Mentha species. M. cervina was considered more endangered by the informants and more causal factors for genetic erosion were identified for this taxon  were considered for genetic erosion assessment in the 2011 field surveys; the majority (57%) maintained their relative abundance in the natural habitat. M. cervina had a lower relative abundance in 25% of the places, whereas M. pulegium augmented its relative abundance in 20% of the accession’s sites, some of which had a high grazing pressure (M. cervina had the highest genetic erosion risk (33%) (M. pulegium disappeared in some places (5%) mainly due to mechanization and herbicide application. Seeds were dispersed by wind and natural vegetative cuttings were also dispersed by streams, as [Mentha spp. conservation, present results indicated that a relative abundance persisted after grazing.A total of 28 accessions (6 pressure . Only hask (33%)  due maineams, as  associatThe relationship between population size and loss of genetic diversity has been well established and quantified. Generally, smaller populations tend to lose genetic variation by genetic drift much more quickly than larger populations ,36,37 anThis relationship has also been examined at the species level, and various reviews have found that restricted or rare species are generally less genetically diverse than more common plant species ; althougC. sativum accessions collected in 2002 was 32% . Overall, genetic erosion risk from the accessions collected before 2002 was 38.9%. All C. sativum accessions from the 1991 to 1992 collections disappeared or were not found during resampling in 2011. Regarding the collection in 1996–1997, the genetic erosion risk was 20% , and for the collection in 1999–2000, the calculated genetic erosion risk was 36.4% . The annual genetic erosion risk for the M. pulegium accessions collected in the natural habitat in 2002 was 0.6%, which is much lower than the 3.7% for M. cervina. In Albania (1941 to 1993), the genetic erosion on cereal landraces was much higher than in the present results, representing 72.4% at the regional level and 79.2% at the country level; similarly, in Italy (1950 to 1983/86), vegetables showed an average genetic erosion of 82%, but Ocimum basilicum, a locally popular aromatic plant, only had a 33% genetic erosion rate [Overall, accession loss (genetic erosion risk) was higher in cultivated accessions (33%) than in wild accessions (11%) , with anion rate  estimateion rate , most ofTeklu and Hammer  estimateMentha pulegium resistance, whereas the vegetative propagated M. cervina had the highest rates of genetic erosion risk. The seed dispersion method and broader potential habitat of Mentha pulegium contributed to lower wild accession loss, while habitat specificity (Natura habitat 3130 pt5 ‘temporary deep ponds), lack of seed production and viability observed in M. cervina made this taxon especially sensitive to habitat destruction. In Portugal, like worldwide [Outcrossing is considered the best precondition for resisting genetic erosion since the whole range of genes will be naturally spread throughout the population , and theorldwide , ripariaThe data collected also underline the need to conserve this considerable biodiversity, using farm conservation methods, which would allow for a greater adaptation and evolution of traditional varieties in favorable conditions of the Mediterranean basin . SustainMentha spp. accessions in our study. As [Traditions supported by isolation in mountainous regions and a low living standard preserved at least a part of the traditional crop landraces in Albania and Italy . Tradititudy. As  have conThe main reason for the genetic erosion of cultivated plants in the Alentejo region  is the human depopulation of the region, with the rural population aging and abandonment of traditional farming, and the consequent loss of cultivated regional plant genetic resources.2 located in southern Portugal with the Tagus river (39°39′49″) as its northern border, and the Algarve region with the Monchique and Caldeirão mountains (37°19′08″) as its southern border. Spain (06°55′53″) is at the eastern border and the Atlantic Ocean (−09°00′16″) is at the western border. Its longest north–south distance is 260 km and the longest east–west distance is 181 km. Its altitude varies from zero at ocean level to 1027 m in northern Alentejo in the São Mamede Mountain [Alentejo is a region of 31 605 kmMountain . The reg2 in the country and only 14.5 inhabitants/km2 in southern Alentejo, with negative growth and people-aging tendencies [Its climate is typical of the Mediterranean , sub-humndencies . Nowadayndencies  the regiPeople’s employment depends mainly on: 1—wholesale and retail trade, 2—agriculture, livestock production and the forestry sector, and 3—manufacturing. The average farm area (51 ha) is the highest in Portugal (12 ha), being even higher in South Alentejo (66 ha) .The typical agriculture is multifunctional with cork oak agroforestry systems and permanent pastures for extensive livestock grazing . Livestock consists of cows (cattle and milk), pigs, sheep and goats. Horses are in the minority but are regionally important for cultural reasons. Cereals, olives and vineyards are also important regional crops. Family horticulture, where medicinal and aromatic plants are cultivated, is a minor activity in area (1593 ha), but important for supporting livelihoods and biodiversity conservation . In AlenMentha spp. potential sites were also gathered from herbarium vouchers; the accessions collection took place in home gardens and natural habitats. During project Agro 34 field missions, in 2002 and 2003, 30 accessions of coriander, 34 accessions of M. cervina and 31 accessions of M. pulegium were collected; the location of the collection sites was identified to better represent the Alentejo region, covering an area of 31,551 km2, representing 33% of mainland Portugal. Seed samples of M. pulegium and C. sativum and vegetative samples of M. cervina were also collected in 2002–2003 and sent to the Portuguese Gene Bank for ex situ conservation. Thirty-four accessions of coriander were previously collected within a joint mission from our team and the Portuguese Gene Bank team (BPGV): 11 in 1992; 9 in 1997; and 15 in 2000. Only seeds from local proveniences (landraces) were collected for coriander. Passport data were registered. At each site, accession passport data, soil samples and ethno-botanical data were collected. Each site was recorded with a GPS device and then downloaded to GIS (Geographic Information System) software to produce the interview location map.Accession passport files and queries from the previous 2002–2003 collection missions were used to track informant’s contacts and wild habitat location sites .The incomplete database of 34 coriander accessions harvested before 2002 made it difficult to trace them. However, it allowed 15 of them to be traced. Nevertheless, they were not considered for estimating the risk of genetic erosion. This problem was even more severe for the 11 accessions collected in 1992, of which only two sites were found. Van de Wouw et al.  considerM. cervina; 19 cultivated and 22 wild of M. pulegium; and 48 cultivated of C. sativum  = 100%—GI (Genetic integrity)) was used by Hammer  for calcMost threats to biodiversity result from human actions, expressed in the overuse of natural resources for fuel, fodder, manure, grazing and collecting of plants in their natural habitats for ornamental or medicinal purposes. This overuse leads to the loss of genetic diversity including crop wild relatives. According to the FAO’s State of the World’s Plant Genetic Resources for Food and Agriculture , the maiOverall, accessions loss (genetic erosion risk) was higher in cultivated accessions (33%) than in wild accessions (11%) due to the human depopulation of the Alentejo region. The elderly rural population and the abandonment of traditional farming systems caused the loss of cultivated regional plant genetic resources.Mentha cervina in wild habitats had the highest genetic erosion risk (25%) mainly due to habitat destruction. Grazing is usually considered a threat for wild accessions. In grazed areas, the biomass of Mentha spp. was highly reduced, but the plants persisted, apparently indicating an adaptation to the grazing effect. However, overgrazing can be a serious genetic erosion risk.M. cervina) because apparently only four wild populations persist in the Alentejo region.So, complementary strategies of ex situ and in situ conservation should be implemented for the conservation of this rare species (Overall, the high risk of genetic erosion justifies further regional plant genetic resources collection, especially of cultivated plants, being necessary to employ more thorough sampling strategies in future collection programs ,51. Furt


The increase and stability of crop production call for further genetic progress. To maintain it, breeders need to preserve genetic diversity in their programs, although it is depleted by the selection. During the last century, large collections of genetic resources have been organized, representing significant diversity. However, using them for variety development has remained an unsolved problem due to their performance gap with the elite varieties. Advances in genotyping and statistical methods now allow their efficient use through genomic prediction-based bridging schemes and diversity monitoring. Our study provides a demonstration of a suite of new tools incorporated into new breeding schemes to effectively resurrect donor accessions, enabling them to contribute to the improvement of quantitative traits in elite populations. Genetic progress of crop plants is required to face human population growth and guarantee production stability in increasingly unstable environmental conditions. Breeding is accompanied by a loss in genetic diversity, which hinders sustainable genetic gain. Methodologies based on molecular marker information have been developed to manage diversity and proved effective in increasing long-term genetic gain. However, with realistic plant breeding population sizes, diversity depletion in closed programs appears ineluctable, calling for the introduction of relevant diversity donors. Although maintained with significant efforts, genetic resource collections remain underutilized, due to a large performance gap with elite germplasm. Bridging populations created by crossing genetic resources to elite lines prior to introduction into elite programs can manage this gap efficiently. To improve this strategy, we explored with simulations different genomic prediction and genetic diversity management options for a global program involving a bridging and an elite component. We analyzed the dynamics of quantitative trait loci fixation and followed the fate of allele donors after their introduction into the breeding program. Allocating 25% of total experimental resources to create a bridging component appears highly beneficial. We showed that potential diversity donors should be selected based on their phenotype rather than genomic predictions calibrated with the ongoing breeding program. We recommend incorporating improved donors into the elite program using a global calibration of the genomic prediction model and optimal cross selection maintaining a constant diversity. These approaches use efficiently genetic resources to sustain genetic gain and maintain neutral diversity, improving the flexibility to address future breeding objectives. Modern plant selection can be traced back to the 19th century with pioneering work by the de Vilmorin family . It clea4710Genetic diversity depletion within breeding populations can be slowed down using specific constraints. Methodologies to do so emerged initially in animal species for which the breeding population is directly used in production. In this case, excessive loss of diversity reduces production due to inbreeding depression, in addition to lowering additive genetic variance and therefore long-term genetic gain. A first approach is to impose restrictions on the number of progeny selected from any family . A more SI Appendix, Fig. S1). Current competitor materials adapted to the target environment are the least challenging for incorporation. The accessibility of such resources depends on regulation rules. Varieties under protection by the International Union for the Protection of New Varieties of Plants (UPOV) are accessible for breeding, which guarantees a straightforward use for inbred varieties . Patents and Plant Variety Protection (PVP) delay the access to competitor materials until patent expiration, giving to the owner a substantial advantage. Incorporation of UPOV-protected and ex-PVP materials has become a routine procedure in breeding programs. In contrast, the inclusion of landraces and lines from collections of patrimonial materials (Sustaining the efficiency of plant breeding programs therefore necessitates an appropriate diversity enrichment strategy. This strategy must i) identify relevant donors, i.e., individuals carrying favorable alleles not yet, or no longer, present in the breeding population and ii) incorporate these alleles into the elite population. Diversity sources in plant breeding can be categorized according to their readiness for use in an elite program. Two main factors impact this use: their “eliteness” and their environmental adaptation  in variety development, in a context where their overall gap in performance with elite materials increases over time. To do so, we consider as a general framework the scheme described in The objective of our study is to further improve the use of genetic resource collection materials . After 60 generations, all strategies led to a low level of genetic diversity. Note that attempting to maintain the elite diversity constant was not advantageous in the short term and it was too stringent so that long-term UCPC parental selection failed. In the following, we introduced external donors to maintain diversity. UCPC-based OCS with a controlled decline in diversity in a closed program was considered as a benchmark to evaluate the benefit of different open breeding strategiesAs expected, OCS and UCPC-based OCS with a controlled decline in diversity led to the highest long-term genetic gain, with only a short-term penalty.  to a one-stage bridging component and 75% (15 crosses per year) to the elite component. This allocation of experimental resources is referred as SI Appendix, Table S3). We used a Joint TS for the rest of our study. Keeping a constant elite diversity (He constant) led to lower We centered our study on the introduction of diversity from gene bank collections, using different strategies listed in ductions B. DiversSI Appendix, Table S4). We confirmed the superiority of the “He constant” for all options. Multiple-stage strategy with preselection of DE progeny appeared slightly superior than that without  in addition to donor × elite crosses . This waSI Appendix, Fig. S3 and Table S5), but this advantage decreased for the most advanced generations. With the one-stage bridging strategy, at generation 60, Donors were selected above based on UCPC of all possible DxE crosses 5B in , using tSI Appendix, Fig. S4 and Table S5). The best for long-term genetic gain optimization was using the We evaluated two additional experimental resource allocations between the bridging and elite component. We kept the same total number of individuals created each year as the initial allocation . For the first  led to the fixation or quasifixation of all quantitative trait loci (QTLs) at generation 60, including a substantial proportion of QTLs fixed for the unfavorable allele , the “He linear loss” diversity management allowed a rapid fixation of favorable alleles but the A closed elite program . Using a Joint TS instead of a Specific TS reduced the number of different donors incorporated in the elite population . Phenotype-based selection of donors, and to a lesser extent the use of a Joint TS, selected first donors in the best true breeding value (TBV) quintile and then progressively donors with lower TBVs . In this study, we focused on the use of adapted materials preserved in gene banks, created more than 20 y before the elite program was started. SI Appendix, Fig. S5 illustrates that this corresponds to a large genetic gap between donors and elite material, which increases over time as the elite program progresses. We show a clear benefit of enriching diversity using such genetic resources through a bridging component, i.e., by preselecting within the progeny of donor x elite crosses prior to their incorporation into the elite program. This confirms previous findings . This importance of phenotyping donors supports initiatives devoted to the phenotypic characterization of genetic resource collections, complemented by genomic prediction model training to facilitate prescreening  . OptimizGS models calibrated with a same global training set (Joint TS) should be used for evaluating all types of candidate crosses. With respect to genetic gain, this option appeared superior to selecting the crosses within the bridging and elite components with specific models (Specific TS). It also led to a higher incorporation rate of DE progeny in the elite germplasm . This suggests that one major interest of the bridging component of the program is to create and phenotype intermediate individuals between donors and elites that make it possible to evaluate contrasts between alleles of the elite pool and newly introduced alleles.SI Appendix, Table S8). This might be explained by differences in the variability of the DE progeny depending on the donor. Such differences were observed in nested association mapping population in maize and were poorly predicted by the genetic distance between parents for traits with a complex genetic architecture . During this phase, crosses between DE progenies can also reveal complementarity between different donors. Vanavermaete et al. . We observed that DE progenies were crossed to new E individuals essentially in the elite component, and their recycling in the bridging component was rarely used . An alternative diversity management with a weaker constraint on the elite diversity promoted the ExE crosses in the elite component at the expense of DExE crosses. It led to more frequent cycling of DE progeny in the bridging but without improving the final genetic gain . This confirms the impact of the diversity management on DE progeny improvement dynamics. Further investigations could be useful to find an optimal diversity management to ensure DE progeny improvement and maximize genetic gain in the elite component.For donors with low performance, several cycles of crossing to the elite are needed to generate progeny competitive with elite lines. Pedigree examination showed that, with UCPC-based OCS, the elite diversity constraint allowed to preserve a part of introduced DE progeny and improve it by several crosses in the elite component . More generally, we recommend performing a systematic assessment of the joint evolution of genetic gain, genetic variance, and genetic diversity, to detect situations, in which diversity erosion has a negative impact on genetic gain and calls for a reenrichment. . Gene editing can also be considered to create de novo allelic series for genes involved in agronomical interest traits  using the 57 Iodent lines as founders. Progeny was obtained using doubled-haploid (DH) technology. Recombination events along chromosomes were determined using a Poisson distribution, as suggested by the MoBPS user guide. We assumed 3 y to produce genotype and phenotype DH progeny. We considered overlapping generations with the selection of generation T parents from generations T-3, T-4, and T-5 to represent situations encountered in actual breeding programs. To initiate E progeny, 20 random founders were arbitrarily crossed with replacement during the first three generations, to create every year 10 families of 80 DH each. During  the next 17 generations, the 50 E progeny with the best phenotypic values from the generations T-3, T-4, and T-5 were selected and randomly intermated to create 20 biparental families of 80 DH each. Phenotypic values were inferred from the genotype at QTLs and an error variance corresponding to a trait heritability of 0.7 in the founder population, mimicking an usual multitrial phenotypic evaluation with a single replicate in 4 environments and a repeatability of 0.37. Starting from the initial elite program, different breeding strategies were conducted for 60 generations, including or not including diversity donor  introductions using a bridging component. We replicated the procedure 10 times.Closed breeding programs were implemented after the burn-in phase, with parental cross list selection guided by either i) the mean parental phenotypic values, ii) the mean parental GS predicted values (GEBV), iii) the cross UC, , iv) a cFor other options, E progenies were genotyped for the 2,000 neutral markers and selection was based on a GBLUP model trained on generations T-3, T-4, and T-5 . To reduce computation time, genome-based estimated breeding values (GEBVs) were computed using the “direct approach” of MoPBS, which considers a fixed error variance. Marker effects were estimated by backsolving . We perfSI Appendix, SI Text. For all options, 80 DH were derived for each cross .where y. As in , we consposed by . In the SI Appendix, SI Text) with an elite GS model  or a GS model calibrated on both components . DxE bridging crosses were selected among all possible crosses between D  and preselected E progeny using a bridging specific GS model or the joint GS model. This selection was performed conditionally to the elite crosses from the same generation to complement the elite component by the bridging component . Note that calibrating bridging-specific or joint GS models required three generations after the burn-in phase, so that they could first be used at generation 24. Before this, the bridging cross selections were performed using the elite specific GS model.We simulated different breeding programs with donor introduction through a bridging component , keepingTo implement the potential recycling of donors within the bridging component, we adapted previous settings to allow DExE crosses in addition to DxE crosses in bridging. Two options were considered to determine DE candidate parents: i) all DE progeny from generations T-3, T-4, or T-5  or ii) the preselected fraction considered for introduction in the elite component . We did not apply a direct constraint on the proportion of selected DxE versus DExE crosses. The same DE progeny could be selected both for introduction in the elite component and for recycling in bridging.SI Appendix, SI Text). We also investigated strategies with a preliminary selection of donors based on an H criterion aiming at complementing E progeny from the T-5, T-4, and T-3 generations with favorable haplotypes . H is the sum of overlapping haplotype segment values and reflects the maximum DH line performance expected after several generations of intercross and selection of the donor and elite lines . Twenty candidate donors were selected for each generation using this criterion.For some studied bridging strategies, candidate DxE crosses were selected using donor phenotypes. In this case, diversity panel lines were phenotyped only once considering the same environmental variance as for bridging and elite progeny evaluation. Phenotypes were centered and shrunk using the observed heritability to be comparable to values predicted with a GS model. As phenotype-based evaluation of donors hinders the possibility of predicting the variance of DxE progeny, crosses were selected using a classical OCS procedure Click here for additional data file.


A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed. The concept of pangenome  traces its origin in the molecular study of Prokaryotes, as it mostly refers to the entirety of the transcribed genetic units in all available lineages of a monophyletic  group ,2,3. GatOn the other hand, multicellular Eukaryotic organisms usually contain multiple fragments of DNA (chromosomes) as well as multiple copies of the same DNA strand in each cellular nucleus (polyploidy) ,11. ThesThe pangenome concept, as introduced in The pangenome concept can be formalized by focusing on its different components, which aim to provide a better understanding of the structure and dynamics of the gene content of a population. It can be tied to notions of static and dynamic genome composition and evolution, each of which has its own advantages and limitations. Static formulations of the pangenome can analyze the gene content of a population at a given time or location and are typically used to describe the core and accessory components of the pangenome ,43. A foThe concept of pangenome has recently been extended to higher-order organisms, and is being applied most notably to the human species ,50,51,52So, the pangenome provides a powerful tool for understanding the complexity of human cells and for developing novel therapies for the treatment of different human diseases. This concept is particularly relevant to the field of personalized medicine, as it allows for a much more targeted approach to diagnosis and treatment. By considering the individual’s genetic composition at different levels, a better assessment could be provided for the likelihood of a patient developing a particular condition, or how well a particular treatment will work for them. In addition, the concept of pangenome has important implications for the field of biotechnology. By understanding the pangenomic composition of different cell types, it is possible to develop new technologies that are tailored to work with a particular set of genetic material. For example, gene-editing techniques such as CRISPR-Cas9 can be used to modify the genetic material of a particular cell type in order to perform lab screening, treat a particular condition, or to create a new type of cell with desired characteristics ,68. ThisStructural variation (SV) refers to the large-scale changes in the structure of the genome, such as deletions, duplications, inversions, and translocations . These cRecent advances in the field of genomics have facilitated the identification of single nucleotide polymorphisms (SNPs), which are the most common type of genetic variation in the human genome ,86,87. SIn the context of SNP-phenotype relationship elucidation, genome-wide association studies (GWAS) have been increasingly important for deciphering the complex relationships between genes and physical characteristics ,106,107.Overall, the pangenome concept represents a powerful tool for increasing the discovery and resolution of single nucleotide polymorphisms (SNPs), as well as structural variants, in the human genome. By providing a more comprehensive analysis of the genetic code of an individual, as well as providing insights into the population-specific genetic makeup, the pangenome is a valuable tool for researchers studying the genetic diversity of human populations.The concept of the pangenome has opened up the possibility of looking into the genome as a non-linear object . This isThe prospect of a human pangenome holds tremendous potential for applications ranging from personalized medicine to forensics and population genetics. However, the technical implications of such a project should not be overlooked ,50. Inde


Ailanthus altissima var. erythrocarpa is an A. altissima variety with high economic, ecological and ornamental value, but there have been no reports on the development of SSR primers for it. According to the SSR primer information provided by the transcriptome of A. altissima var. erythrocarpa, 120 individuals with different redness levels were used to screen polymorphic primers. Transcriptomic analysis revealed 10,681 SSR loci, of which mononucleotide repeats were dominant (58.3%), followed by dinucleotide and trinucleotide repeats  and pentanucleotide repeats (0.2%). Among 140 pairs of randomly selected primers, nineteen pairs of core primers with high polymorphism were obtained. The average number of alleles (Na), average number of effective alleles (Ne), average Shannon’s diversity index (I), average observed heterozygosity (Ho), average expected heterozygosity (He), fixation index (F) and polymorphic information content (PIC) were 11.623, 4.098, 1.626, 0.516, 0.696, 0.232 and 0.671, respectively. Nineteen EST-SSR markers were used to study the genetic diversity and population structure of A. altissima var. erythrocarpa. The phylogenetic tree, PCoA, and structure analysis all divided the tested resources into two categories, clearly showing the genetic variation between individuals. The population showed high genetic diversity, mainly derived from intraspecific variation. Among nineteen pairs of primers, 4 pairs  could effectively distinguish and be used for fingerprinting of the tested materials. This study is of great significance for genetic diversity analysis and molecular-assisted breeding of A. altissima var. erythrocarpa. Ailanthus altissima (Mill) Swingle, belonging to Simaroubaceae and Ailanthus, is widely distributed on all continents except Antarctica and is a pioneer tree used in greening and barren mountain afforestation. It has high economic, medicinal, ecological and ornamental value7. Wild related species often become important resources for innovative plant breeding due to their higher resistance and richer genetic characteristics than those of this species. Ailanthus altissima var. erythrocarpa is a variety of A. altissima (Mill) Swingle; it is usually recognized as a single population or multiple very small populations in the northern region of China. A. altissima var. erythrocarpa has significantly better physical and chemical properties8 and medicinal and ornamental value than A. altissima (Mill) Swingle due to its higher contents of flavonoids and other substances. The fruiting period of A. altissima var. erythrocarpa is from June to September. Because the fruit is red and persists for months, it has high ornamental value and a high profile8. There are significant differences in the appearance time, duration and chroma of fruit color (data not yet published). Genetic composition and the environment can lead to stability or variability in plant morphology9, which necessitates scientific identification and evaluation of germplasm of A. altissima var. erythrocarpa.12. Compared with g-SSRs, EST-SSRs are more efficient, require less time and have a lower cost. They also have stronger transferability among species19 and have been widely used in plant variety identification, genetic structure analysis and fingerprint development24. To date, due to the lack of genomic and genetic information, there have been few studies on the genetic diversity of A. altissima (Mill) Swingle in China and elsewhere. The research that has been performed concerned the development of nuclear microsatellite markers for Ailanthus on Mediterranean islands and in eastern Austria; population structure and genetic diversity analyses of North American A. altissima (Mill) Swingle; patterns of regional expansion and genetic differentiation among populations of Japanese highland A. altissima (Mill) Swingle; the origin, expansion and genetic structure of Chinese squash introduced from Austria; the development of 10 cpSSR markers and 13 EST-SSR markers; and genetic diversity analysis of 14 population groups in different regions of China32. These findings have promoted the application of SSR labeling in A. altissima (Mill) Swingle. Nevertheless, there are few reports on the genetic diversity of A. altissima var. erythrocarpa and no reports on the development of polymorphic SSR primers or fingerprints.Expressed Sequence Tag-SSRs (EST-SSRs) are derived from mRNA transcription sequences and are applicable to genetic linkage map construction, genetic diversity analysis, population genetic structure research, and studies of species evolution and variation in plants with or without reference genomesA. altissima (Mill) Swingle is limited, and EST-SSRs of A. altissima var. erythrocarpa have not been reported. Therefore, transcriptome sequencing was used to provide SSR marker information for A. altissima var. erythrocarpa. We developed a total of nineteen pairs of EST-SSR markers and conducted genetic diversity and population structure analyses on the basis of 120 wild individuals of A. altissima var. erythrocarpa from 7 regions in China, constructing fingerprint maps. This study will provide technical support for resource collection and protection, germplasm bank establishment and management, germplasm identification and intellectual property protection in the future and will lay a theoretical foundation for breeding and germplasm innovation of A. altissima var. erythrocarpa and A. altissima (Mill) Swingle.The number of available expressed sequence tags in A. altissima var. erythrocarpa was performed, and an SSR search of the spliced and assembled unigenes revealed 9828 accurate SSRs and 853 compound SSRs, totaling 10,681 SSR loci. The EST-SSR types of A. altissima var. erythrocarpa were abundant, and their distribution showed  ranging from 7 to 20 (mean 11.632), number of effective alleles (Ne) from 1.550 to 11.942 (mean 4.098), observed heterozygosity (Ho) from 0.191 to 0.931 (mean 0.516), expected heterozygosity (He) from 0.355 to 0.916 (mean 0.696), unbiased expected heterozygosity (uHe) from 0.356 to 0.920 (mean 0.699), Shannon’s information index (I) from 0.797 to 2.675 (mean 1.626), and polymorphism information content (PIC) from 0.340 to 0.912 (mean 0.671). The marker with the most alleles was p33 (20), and the markers with the fewest alleles were p30, p40 and p95 (7). The PIC values of the nineteen EST-SSR markers ranged from 0.340 to 0.912, with an average of 0.671. All markers exhibited high polymorphism, with the exception of p30 and p49 (PIC > 0.5).Based on capillary electrophoresis, correlation analysis of 7 natural populations of sm Table . There wFis and Fit were − 0.018 and 0.199, respectively, indicating that the number of homozygous individuals was greater than that of heterozygous individuals in the seven natural populations of A. altissima var. erythrocarpa. Among the seven natural populations, the Fst differentiation coefficient of the nineteen EST-SSRs showed a variation range of 0.084–0.456, with an average value of 0.237, which is between 0.15 and 0.25. The variation range of gene flow at polymorphic sites was 0.298–2.737, with an average value of 1.056.As shown in Table PIC values and more genotypes were selected to distinguish 120 A. altissima var. erythrocarpa accessions. According to the PIC values of the SSR primers, the test materials were added successively from high to low and identified gradually until they were completely separated. Table A. altissima var. erythrocarpa were imported into QR code software to generate fingerprint QR codes, wherein the primers p33, p15, p46 and p92 were represented by A, B, C and D, respectively  > NX (Ne = 3.330) > SHX (Ne = 3.224) > HN (Ne = 3.202) > BJ (Ne = 2.829) > TJ and SHD (Ne = 1.421). In contrast, the level of genetic diversity in SHD and TJ was low, which may be due to the small number of individuals. The fixation index was less than 0 in populations TJ, BJ and SHD, indicating that the number of heterozygotes was greater than that of homozygotes, while the number of homozygotes was greater than that of heterozygotes in the other four populations. The average paired FST coefficient among the 7 populations of A. altissima var. erythrocarpa was 0.163. The differentiation between TJ and SHD was the highest (0.600), which may be related to too few samples. The differentiation between BJ and SHD was the second highest (0.342), and the differentiation coefficient between NX and SHX (0.016) was the lowest, but their Nm was the highest (15.401), indicating that the genetic differentiation between the NX and SHX populations was small and that gene exchange is frequent  continued to increase, there was no inflection point, and the Delta K values could be used to determine the optimal K value .According to the genetic similarity coefficients among the test materials of als Fig. a, which sis Fig. d the val33 identified 219 SSR loci based on the chloroplast genome of A. altissima (Mill.) Swingle in 2018. In 2021, 13 EST-SSRs were developed, and 10 polymorphic chloroplast microsatellite (cpSSR) markers were constructed based on the transcriptome of A. altissima (Mill.) Swingle and 219 cpSSR markers. The distribution characteristics of cpSSRs and EST-SSRs were similar, but cpSSRs did not show hexanucleotide and compound nucleotide repetitions. In this study, 10,681 potential microsatellite loci were obtained based on transcriptome sequencing results of tissue-cultured seedlings from young leaves of A. altissima var. erythrocarpa, which was far lower than that reported by JosphatK. Saina et al.18 , and the EST-SSR distribution characteristics were similar. The SSR markers developed in this study had a high effective amplification rate, which is helpful for analyzing the genetic background and performing auxiliary breeding of A. altissima var. erythrocarpa.In recent years, SSR molecular markers have been widely used in studies of species genetic diversity due to their advantages, such as high species specificity and cross-related species transfer, and primer development is their primary use. To date, only JosphatK. Saina et al.Stephanandra incisa23, Bougainvillea cultivars21, Cocos nucifera L.34, A. altissima (Mill.) Swingle18, Pinus bungeana11, Ulmus pumila L.35, and A. altissima var. erythrocarpa. The results of the transcriptome analysis showed the same pattern, with these three types accounting for 90.12% of the total SSRs. AG/CT is the most frequent dinucleotide repeat motif in most plants, and the homopurine–homopyrimidine extension is often found in the 5′ untranslated region, which plays an important role in gene expression and nucleic acid metabolism regulation in plants. AAG/CTT is the main nucleotide repeat type in dicotyledons41. In this study, the dinucleotide AT/AT repeat type had the highest frequency, which was consistent with the results for Pinus bungeana42, Bougainvillea cultivars21. The majority of the trinucleotide repeats were AGA/TCT, followed by AAG/CTT. Among the 140 pairs of primers screened and synthesized, 106 yielded amplification bands, and the effective amplification rate reached 75.71%, which was similar to that in A. altissima (Mill.) Swingle (80%)18 and Cinnamomum chago (70.59%)43. The polymorphism rate of the primers was 13.57%, which was significantly lower than that reported by Saina et al.18 (65%), which may be caused by the use of more randomly selected primers, small genetic differences between samples and different environmental selection pressure. Therefore, EST-SSRs are still the most economical and effective tool for developing a large number of primers at the same time using transcriptome data. The chloroplast genome of A. altissima is a circular molecule with a size of 160,815 base pairs and a tetrad structure. The chloroplast genome size of A. altissima was similar to that of Arabidopsis thaliana (L.) Heynh. (120–217 kb) and Populus trichocarpa (155–159 kb), so it was relatively easy to develop cpSSR markers for A. altissima. Compared with cpSSRs, EST-SSRs exist in the gene regions of the nuclear genome and are highly polymorphic44. In this study, EST-SSRs were combined with molecular markers of A. altissima var. erythrocarpa to study the point mutations in A. altissima var. erythrocarpa, providing important resources for subsequent germplasm resource protection and evolutionary research in populations. Phenotypic traits combined with molecular markers could be used to analyze the correlations of traits, providing guidance for the utilization of germplasm resources and breeding of new varieties.Most plants show mononucleotides, dinucleotides and trinucleotides as dominant repeat motifs, such as PIC can measure the polymorphism of primers: PIC > 0.5 indicates highly polymorphic, 0.25 < PIC < 0.5 indicates moderately polymorphic, and PIC < 0.25 indicates weakly polymorphic45. In this study, 19 pairs of clearly polymorphic primers with the expected size were screened, and the average PIC value was 0.671, lower than the average PIC value of A. altissima (0.819)18. PIC > 0.5 indicated that the identified SSR loci had high genetic resolution, making them conducive to population genetic analysis and revealing that the studied A. altissima var. erythrocarpa accessions had high genetic diversity. This may be related to the complex living environment of wild A. altissima var. erythrocarpa. The materials of wild A. altissima var. erythrocarpa used in this experiment are widely distributed in Ningxia, Shanxi, Shandong, Henan, Tianjin, Beijing, Hebei and other places. The sampling points were mostly on hillsides, on roadsides and in other similar places. The diversity of the geographical environment and the characteristics of climate variability jointly determine the diversity of the living environment of A. altissima var. erythrocarpa. Therefore, this study further reveals the genetic diversity and structure of this species.A. altissima var. erythrocarpa from 7 natural populations was 0.509, similar to the results of Saina et al.18, which may be related to the wide distribution and complex living environment of wild A. altissima var. erythrocarpa. The mean He was slightly lower than that reported in previous studies30, which may be related to the fact that A. altissima var. erythrocarpa is a variety of A. altissima and originated in the same place. In the study of Saina et al.18, the mean Ne of A. altissima (Mill.) Swingle is 3.757. In this study, the mean Ne of A. altissima var. erythrocarpa is 2.838. This may be due to the fact that the population size (14) and sample size (165) of A. altissima (Mill.) Swingle are larger than that of this study (7 and 120), and the distribution range (99–120° N and 29–37° E) is wider than that of A. altissima var. erythrocarpa (105–117° N and 33–41° E). Among the seven natural populations, HB  had the highest genetic diversity, TJ  and SHD  had the lowest genetic diversity. This may be because each group has different site conditions , and its growing environment is different; Samples from city streets, there is human interference. These all influence the measurement of genetic diversity. The FST comparison between nineteen pairs of EST-SSRs and 7 natural populations  showed a high degree of genetic differentiation  were screened to distinguish 120 tested A. altissima var. erythrocarpa materials and construct a unique molecular ID card. The establishment of fingerprinting for A. altissima var. erythrocarpa is of great significance for its subsequent breeding, as the resource can be used to identify varieties and excellent genes as well as their variation and transmission in offspring, and lays a foundation for the establishment and improvement of fingerprinting databases of A. altissima var. erythrocarpa variety resources, genetic resource management and intellectual property protection. However, in this study, there were few test materials, the sample collection area was only in northern China, and the fingerprint background of the "core germplasms” of A. altissima var. erythrocarpa lacked in-depth research. As a result, further verification of the reliability of the results is required.DNA fingerprinting technology analyzes the genetic material itself, avoiding the influences of environmental factors, sample morphology, material source and other factors, and identifies the germplasm genotypes as indicators at the molecular level. It has been widely used in grain, oil, vegetable and fruit crops, flowers, shrubs, arbors and other plantsA. altissima var. erythrocarpa. Using four pairs of SSR core primers, a total of 120 wild individuals from 7 regions of A. altissima var. erythrocarpa were 100% distinguished and assigned unique identification IDs. The experimental resources of A. altissima var. erythrocarpa have rich genetic variations, mainly derived from intraspecific variations, and do not exhibit geographical variation. The research results presented in this article provide a public resource of SSR primers in A. altissima var. erythrocarpa, in turn providing a theoretical basis for effective management, protection, and utilization of its germplasms. The excellent varieties studied according to the SSR primers of A. altissima var. erythrocarpa can be introduced by biotechnological means and can also be preserved ex situ or in situ, which would be conducive to subsequent research. The findings also provide genetic resources for an in-depth understanding of population genetics and evolutionary history, joint genomics research, the selection of dominant parents for the cultivation of new varieties, and intellectual property protection in A. altissima var. erythrocarpa.In this study, nineteen pairs of EST-SSRs revealed high genetic diversity and significant genetic structure in Ailanthus altissima var. erythrocarpa selected based on four indicators were collected from 7 provinces in China . The geographical locations of the sample spanned 106.00–117.72 east longitude, 33.76–41.12 north latitude, and 34.7–1858.2 m altitude, and the local soil was mostly red soil. When sampling, the latitude, longitude and altitude of the sample were recorded with a GPS device. The HN, TJ, BJ and SHD sampling sites were distributed discontinuously, with 6–10 plants per hectare. The number of individuals was small., the distribution was scattered, and the distance between the trees collected was not less than 50 m to prevent half-sib relationship between the samples. . After quality determination, the DNA was diluted to 30 ng/μl in each tube. A total of 5 tubes (150 μl/tube) were used as working liquid. The original liquid and working liquid were stored in a freezer at − 20 °C for later use.Fresh and tender Due to the lack of a reference genome sequence, leaves of tissue-cultured plantlets were sent to Wuhan Matwell Biotechnology Co., Ltd., for transcriptome sequencing and primer design. SSR primer design data were provided by Professor Dukejiu of Hebei Agricultural University. A total of 140 pairs of primers were randomly selected from 10,681 pairs of primers by Excel, Sequon Bioengineering (Shanghai) Co., Ltd., and Tsingke Biotechnology Co., Ltd., were commissioned to synthesize primers.2O 6.5 µl. The thermal cycling parameters used in the PCR procedure were 95 °C predenaturation for 5 min, followed by 33 cycles of 95 °C for 1 min, 55 °C annealing for 30 s, and 72 °C extension for 45 s, with a final elongation step at 72 °C for 8 min. The samples were then stored at 4 °C. The initial screening conditions for polymorphic primers were as follows: 2% agarose gel electrophoresis, voltage 110 V, 30 min, real-time photography; screening and verification with 8% nondenatured polyacrylamide gel electrophoresis, 200 V, 100 mA, 20 W, 1 h and 300 V, 100 mA, 20 W, 2.5 h; silver staining (AgNO3); and photo storage for subsequent analysis.Nondenaturing polyacrylamide gel electrophoresis. The optimized PCR system was carried out in a volume of 20 µl: DNA template 1.5 µl (30 ng/μl), 2 × Taq PCR Master Mix 10 μl, upstream and downstream primers 1 μl (10 µmol/µl), and ddHFluorescent capillary electrophoresis The PCR systems were similar to those used for nondenaturing polyacrylamide gel electrophoresis, with the only modification being the addition of 1 μl (10 µmol/μl) fluorescent primer (FAM/TAMRA/HEX/POX) to each plate system. The procedure was as follows: 95 °C predenaturation (5 min), followed by 25 cycles of 95 °C denaturation (1 min), 58 °C annealing (30 s), and 72 °C extension (45 s) and then 10 cycles of 95 °C denaturation (1 min), 58 °C annealing (30 s), and 72 °C extension (45 s), with a final elongation step at 72 °C for 8 min. The products amplified by PCR were sent to Taihe Biotechnology Co., Ltd., for testing.First, the mixtures of 8 samples were used as templates to screen out primers with clear and bright bands that matched the size of the product. Second, primers with clear bands and good polymorphism were selected for amplification in 6 samples. Finally, the core primers were screened from 120 samples, which were verified with the primers.Na), average effective number of alleles (Ne), Shannon's information index (I), average observed heterozygosity (Ho), average expected heterozygosity (He), and fixation index (F). Hardy–Weinberg equilibrium tests were performed51. The polymorphism information content (PIC) was calculated using Cervus 307 software51. In GenAlExv6.5, F and Nm (Nm = (0.156/SHUA)2 statistical calculations  and principal coordinate analysis (PCoA) were performed in combination with Microsoft Excel. The paired heat map of Fst and Nm was constructed in R 4.2.2. Used Populations v1.2.32 to calculate genetic distances, based on genetic similarity coefficients, a phylogenetic tree was drawn using MEGA752. Mixed analysis based on Bayesian models was used for population structure analysis in Structure v2.3.453. Then, the number of Markov chain Monte Carlo (MCMC) iterations after burn-in was set to 100,000, with a run length of 100,000 and a genetic uniform cluster number  between 1 and 10. Each analysis consisted of 10 repeated runs. The optimal K value was determined using the best-K method in StructureHarvester54. Then used CLUMPP Windows.1.1.2b software55 to found the real K value, replaced the K value in STRUCTURE, and generated a genetic structure map.The fluorescence capillary electrophoresis detection results were viewed using GeneMarker V2.4.0 software and collated in Excel. GenAlEx 6.502 software was used to calculate the average number of alleles (Permission to collect plant samples was obtained for this study. Voucher specimens were identified by Alfred Rehder and deposited in the Chinese Field Herbarium (CFH). This study complied with local and national guidelines.Supplementary Information.


Durum wheat landraces represent a genetic resource for the identification and isolation of new valuable genes and alleles, useful to increase the crop adaptability to climate change. Several durum wheat landraces, all denominated “Rogosija”, were extensively cultivated in the Western Balkan Peninsula until the first half of the 20th century. Within the conservation program of the Montenegro Plant Gene Bank, these landraces were collected, but without being characterized. The main goal of this study was to estimate the genetic diversity of the “Rogosija collection” consisting of 89 durum accessions, using 17 morphological descriptors and the 25K Illumina single nucleotide polymorphism (SNP) array. The genetic structure analysis of the Rogosija collection showed two distinguished clusters localized in two different Montenegro eco-geographic micro-areas, characterized by continental Mediterranean climate and maritime Mediterranean climate. Data suggest that these clusters could be composed of two different Balkan durum landrace collections evolved in two different eco-geographic micro-areas. Moreover, the origin of Balkan durum landraces is discussed. Triticum aestivum L., 2n = 6x = 42, BBAADD) and durum wheat (Triticum turgidum L. subsp. durum (Desf.), 2n = 4x = 28, BBAA). Historically, the cultivation of wheat consisted in the sowing of diverse plant assemblages to lower the risks of crop failure and increase food security, because traditional farmers had a limited capacity to control spatially heterogeneous and temporally unpredictable environments [Wheat cultivation accounts for nearly 220 million ha in the world . The speronments .Triticum monococcum L.) dating into the early VI millennium BC have been found in Serbia [Triticum turgidum subsp. dicoccum (Schrank ex Schübler) Thell.), durum, and turgidum (Triticum turgidum subsp. turgidum) was largely extended to the Balkan Peninsula. The territory near Podgorica (Montenegro), especially, was known for the cultivation of durum landraces, all named “Rogosija” [Wheat was introduced into the Balkan Peninsula through human migrations from Anatolia in the Neolithic era. Charred remains of einkorn wheat  during the vegetation season 2018–2019.The use of molecular methods to assess genetic diversity proved to be consistently useful for a correct classification of different genotypes and for the identification of duplicates, quite often present in gene bank collections ,12,13. TThe aims of the present research were (1) to study the genomic diversity and population structure of the Rogosija collection deposited in the Montenegro Plant Gene Bank by using morphological and SNP markers, and (2) to investigate correlation between genetic clusters and eco-geographic conditions of the Western Balkan Peninsula.Multiple Correspondence Analysis (MCA) based on 17 morphological descriptors split the Rogosija collection in two clusters which included 79 (Cluster A) and 9 (Cluster B) durum accessions . The firThe Rogosija collection and the four additional foreign durum cultivars were analyzed with a high-throughput genotyping system based on the 25K Illumina SNP wheat array. A total of 6915 high-quality SNPs were retained after filtering and mapped on the 14 chromosomes of the durum genome . IdentitThe SNP markers were unequally distributed across genomes and chromosomes. A higher number of polymorphic SNPs was recorded on the B genome (3656) than the A genome (3259) a. The hiNei’s gene diversity of SNPs ranged from 0.01 to 0.60 a and oveThe Principal Components Analysis (PCA) showed three different genetic durum clusters . In partThe phylogenetic tree was consistent with the PCA results. The Rogosija collection was split in two clusters, separated from the foreign durum cultivars .Ne) (1.443), Shannon’s information index (I) (0.416), diversity index (h) (0.266), and percentage of polymorphic loci (PPL) (88.24%). Cluster 2 showed the highest values for all indices .The Analysis of Molecular Variance (AMOVA) revealed that 16% of the total variation was due to differences among the clusters, whereas the remaining (84%) variance was within clusters . The genThe geographic positions of the Western Balkan durum accessions, split according to the genetic clusters defined by polymorphic SNPs, are reported in A total of 51 out of 60 durum accessions analyzed in this study were localized in Montenegro; there were 24 mapped around Skadar Lake and 27 in the coastal area. Therefore, the 51 Montenegrin durum accessions were used to explore relationships between the two genetic clusters and climatic regions. According to the Köppen climatic classification , the MonDurum wheat landraces, collectively known as Rogosija, were the main cereal of Western Balkan Peninsula until mid-20th century, covering over 80% of arable land of the Montenegro and Herzegovina littoral zone . After tThe common tools used for diversity studies of plant genetic resources are morphological markers ,25. TheySingle nucleotide polymorphism (SNP) markers have become fundamental for both genetic studies and breeding programs. In addition, the development of wheat high-density SNP array provided the most innovative tool for genetic diversity and the population structure estimation in durum collections, including cultivars and landraces ,33,34,35According to the IBS analysis, we discarded 29 Western Balkan durum accessions considered duplicated and/or synonymous. This is not surprising because historically a traditional exchange of seeds occurred between Balkan farmers. Moreover, our result confirms that the SNP markers are an efficient tool to identify duplicate accessions stored in gene banks , especiaThe 6915 high quality markers generated by SNP genotyping were suitable to define the genetic structure of the Rogosija collection. The PCA and phylogenetic analyses revealed three clusters: two distinguished clusters from the Western Balkan durum accessions, and a third cluster comprising foreign durum cultivars . This result confirms the reliability of the applied analytical approach, suggesting that the durum germplasm known as Rogosija and stored in Montenegro Plant Gene Bank likely consists of two different durum landrace groups, with no relation to the Italian durum cultivars . Considering the maritime trades between the Italian and Balkan Peninsulas, it was suggested that Balkan durum landraces had an Italian origin . Indeed,The AMOVA analysis revealed a higher genetic variance within, rather than among clusters of Western Balkan accessions, suggesting high and frequent rates of seed trade off among farmers in this area.T. turgidum subsp. dicoccum collection representing a wide geographic range of emmer accessions, using DArTseq was split into four distinguished clusters in accordance with their eco-geographic origin [To estimate whether the genetic clusters detected are related to the Balkan eco-geographic regions, the durum accessions were geo-referenced and evaluated according to the ecological data of the Rogosija collecting sites. Interestingly, 24 Montenegrin durum accessions contained in the Cluster A were collected around Lake Skadar, while 27 included in the Cluster B were sampled in the Montenegrin littoral coast. Continental Mediterranean climate and maritime Mediterranean clime are the ecological conditions of Lake Skadar zone and littoral coast, respectively. This result supported that the Rogosija collection stored in the Montenegro Plant Gene Bank enclosed two different landraces, evolved in two different eco-geographic micro-areas. Similar results were observed in a Tunisian durum collection, where the genetic clusters showed a strong genetic stratification from the north to the south of Tunisia site . A largec origin . Relatioc origin , lentil c origin , and dilc origin  confirmi2 and standard cultivation practices were applied. According to guidelines by the International Union for Protection of New Varieties of Plants [http://factominer.free.fr/ (accessed on 2 March 2023)).The Rogosija collection, including 89 Western Balkan durum accessions provided by the Montenegro Plant Gene Bank, was analyzed. The experiment was carried out in the 2020–2021 season at the Research Unit in Danilovgrad (Montenegro). Twenty seeds of each durum accession were sown at 5 cm depth in plots consisting of 1 m rows, 60 cm apart. During the growing season, 10 g of nitrogen per mf Plants , a panelThe Rogosija collection and four durum cultivars , provided by the Institute of Biosciences and Bioresources, National Research Council , were genotyped. Seeds collected of each accession were germinated in jiffy pots and grown under controlled conditions . Genomic DNA was extracted from 100 mg of fresh leaves of a bulk of 10-day-old seedlings using DNeasy™ Plant Mini Kit  following the manufacturer’s instructions. Genomic DNA quality was checked using 1% agarose gel electrophoresis. The DNA of each sample was used for SNP genotyping using an optimized wheat 25K Infinium iSelect array . The array contains 24,145 SNPs combining 17,229 markers from the Illumina 20K Illumina Infinium array , 6916 neNe), Shannon’s information index (I), heterozygosity observed (h) and percentage of polymorphic loci (PPL) were estimated using GenAlex v.6.5 software [Physical SNP positions were obtained by alignment of the 25K array design file to the reference genome of the durum wheat cultivar Svevo . The SNPsoftware .The geographic coordinates were taken for all accessions as shown on Google maps . The cliThis study showed morphological and SNP diversity in the Western Balkan accessions. In the Rogosija collection, the SNPs represent a powerful tool to study the genetic structure of wheat collections, and to detect duplicate or redundant durum accessions in comparison to morphological descriptors. These results suggest that the SNPs might be useful to define efficient strategies for the conservation of durum germplasm in gene banks.According to SNP genotyping, the old Balkan durum collection Rogosija was split into two distinguished clusters related to two different eco-geographic micro-areas of Montenegro. These results indicated that the Rogosija collection likely consists of two different durum wheat landraces.The genetic diversity observed in the durum Rogosija collection deserves further investigations. The durum Rogosija accessions can be included in association mapping studies to identify new alleles to contrast the effect of climate change. Finally, accurate phenotyping assays could be performed to select accessions useful for durum breeding programs.