Walnut is an important horticultural crop, the production of which ranks second among all nut crops. Despite the significant demand in the domestic market in Russia, the industrial production of walnut fruits in Russia is currently underdeveloped. At the same time, there is a need to update the assortment with new highly productive varieties adapted to local agro-climatic conditions and having high quality nuts that are competitive at the world level. An important issue for the successful implementation of breeding programs is a comprehensive study of the gene pool. In this regard, within the framework of the study, the task was to evaluate promising varieties from the collection of the walnut gene pool of the Nikitsky Botanical Gardens and analyze genetic relationships based on microsatellite genotyping. On the basis of the performed phenotypic assessment, the study sample, which included 31 varieties, was divided into several groups according to the main phenotypic traits, such as frost and drought resistance, the start of the growing season, the ripening period, the weight and type of flowering, the weight of the fruit, and the thickness of the endocarp. Varieties with economically valuable traits that can be recommended as promising as initial parental forms in breeding work for resistance to abiotic stress factors have been identified, as well as varieties with increased productivity and large fruit sizes. Based on the analysis of eight SSR markers , an analysis of the level of genetic diversity was performed and genetic relationships were established in the studied sample of varieties. Six (for WGA089) to eleven (for WGA276) alleles per locus have been identified. A total of 70 alleles were identified for the eight DNA markers used, with an average value of 8.75. Analysis of SSR genotyping data using Bayesian analysis established the presence of two main groups of genotypes. Taking into account the fact that all the studied varieties are selections from local seed populations in different regions of the Crimean Peninsula, the revealed level of polymorphism may indirectly reflect the level of genetic diversity of the local walnut populations. Furthermore, the presence of two genetically distant groups indicates the presence of two independently formed pools of the autochthonous gene pool of the species Juglans regia L. on the Crimean Peninsula Walnut is one of the most important nut crops, which is secondonly to almonds in terms of production. The world leaders inthe production of walnuts are China, Iran, the USA and Turkey. Commercial production of walnutfruits in Russia is currently not developed, however, there arepositive trends in the establishment of industrial orchards. Atthe same time, there is a need to update the assortment withnew highly productive varieties adapted to local agro-climaticconditions and having high quality fruitsObviously, a comprehensive study of the walnut genepool is an important issue for increasing the efficiency ofusing genetic resources in solving breeding problems tocreate new generation varieties, as well as for preserving andreplenishing collections. At the same time, the assessmentof the level of genetic diversity, elucidation of the degree ofgenetic similarity, as well as DNA certification of collectionsamples occupy an important place. One of the most popularmethods for assessing the genetic diversity of the walnut isthe analysis of microsatellite loci of the genome . SSR markers currently widely used for the analysisof walnut polymorphism were developed both directly forthis species  and for the species Juglans nigra L. . Subsequently, these markers were effectively usedto study the interspecific diversity of J. regia due to the highlevel of cross-reproducibility within the Juglans genus. At thesame time, it is worth highlighting the SSR markers marked as“WGA”, which are the most commonly used . With the use of SSR markers for walnut, a significantamount of research has been carried out aimed at analyzing thegenetic structure of gene pool collections, including varieties,promising breeding forms, as well as selections from localpopulations of interest for breeding.The most large-scale studies of the genetic diversity ofcollections of genetic resources include the work performedby a team of authors from the INRA Research Center, inwhich, using 13 SSR markers, genotyping of 217 accessionsof walnut and 36 accessions of other species of the genusJuglans from the INRA collection was carried out. Basedon the SSR genotyping data, the presence of the main twogroups of the greatest genetic similarity was established,which for the most part corresponded to the ecological andgeographical origin of the varieties. The data obtained madeit possible to form a core collection of fifty samples, reflectingthe genetic polymorphism of the entire sample . It is noteworthy that a comparative analysis ofthe effectiveness of using 13 SSR markers in the above work and 364,275 SNP markers – dataobtained using the Axiom™ J. regia 700K SNP genotypingarray SNP chip, showed a close level of information content ofthe two approaches used in assessing the genetic structure ofcollections . A comparable studyof a sample of 189 varieties and breeding forms, representativeof gene collections from 25 regions in 14 countries of theworld, made it possible to establish the presence of two maingroups, including accessions from: (1) Europe and NorthAfrica and (2) Greece and the Middle East . Along with such large-scale studies, a wide range ofstudies was performed using microsatellites on gene poolcollections, as well as local populations in different regionsof the world: Europe , East Asia,Central and South Asia , the Middle East region , North America A number of studies are known in which, along withmolecular genetic analysis of polymorphism based on SSR markers, a comprehensive assessment of the phenotypicvariability of samples was performed ,or an assessment of individual groups of traits, such as fruitcharacteristics . This made it possible bothto compare the efficiency of using different approaches todetermine the groups of the greatest genetic similarity , and to identify selectively valuable accessions atthe first stage and subsequently to evaluate the heterogeneityof the selected groups of accessions based on microsatelliteanalysis data .Despite the ongoing breeding work on walnuts in thesouth of Russia  and the availability of research results on the studyof collections of varieties using molecular genetic methods, one should still note the limitationsof studies aimed at analyzing the level of genetic diversityand identifying the genetic structure of the gene pool in theSouth of Russia, including the Crimea and the North Caucasus.The Nikitsky Botanical Gardens (NBG-NSC) is one of theleading scientific organizations in the Russian Federation thatperforms breeding work on walnuts. The collection of geneticresources of the Nikitsky botanical walnut is represented by76 accessions. The basis is made up of varieties of selectionNBG-NSC (86 %). Among the introduced genotypes, 10 %of the total collection falls on varieties from Moldova and3 % each on accessions from Ukraine, Europe, the USA, andTajikistan . It is obvious thata comprehensive phenotypic assessment, the identification ofgroups of the most valuable genotypes, characterized by thepresence of several breeding-valuable traits at the same time,as well as the analysis of genetic relationships of valuablevarieties and forms, will improve the efficiency of the breedinguse of the gene pool in order to create new adaptive varietieswith increased productivity potential and high fruit quality.In the presented work, we set the task of assessingperspective varieties of walnut from the collection of thegene pool of the NBG-NSC according to economicallyvaluable traits, identifying groups of varieties with a complexof important characteristics and analyzing their geneticrelationships using microsatellite DNA markers.Phenotypic assessment was carried out in the collectionplantations of the laboratory of steppe horticulture (LSH) ofthe NBG-NSC in 2014–2022. 31 samples of walnut selectionfrom the Nikitsky Botanical Gardens were chosen as theobject of observation . The LSH territory islocated 25 km north of Simferopol, in the village of Novy Sad, Republic of Crimea, Russia. Inthe system of agro-climatic zoning of the peninsula, it belongsto the central plain-steppe region, characterized by an arid climatewith a moderately hot growing season and mild, unstablewinters . Also, in the genotyping work,the Chandler variety of the USA selection was used. The reliefof the area on which the collection garden is located is flatand slightly wavy; the soil of the plot is southern carbonatelow-humus heavy loamy chernozem on red-brown Plioceneclays. The average annual air temperature is +10.5 °C, theaverage January does not exceed –1.0 °C, and the averageJuly, +21.9 °C. Walnut plants are planted according to thescheme 12×12 m, peach is used as a compactor. The aislesare kept under black fallow. The age of the trees is 30 years.Determination of the degree of frost resistance of varietieswas carried out according to the method developed in theNikitsky Gardens  and the method ofLapin and Ryabova (1982). The assessment of drought resistanceof walnut plants was carried out in accordance withmethodological recommendations .A modified CTAB method was used for DNA extraction. Genotyping of walnut varietieswas carried out using 8 SSR markers: WGA001, WGA376,WGA069, WGA276, WGA009, WGA202, WGA089,WGA054 . PCR wascarried out under the following conditions: the concentrationof PCR reagents of the mixture: Buffer 1X, dNTP – 0.24 mM,Taq 1U, SSR primers (forward and reverse) – 0.16 μM each,DNA – 40–50 ng. The following PCR parameters were used:3 min initial denaturation at 94 °C; the next 35 cycles: 20 secdenaturation at 94 °C, 30 sec primer annealing at 58 °C, 40 secelongation at 72 °C; final elongation for 10 min at 72 °C. Thesize of the reaction products was analyzed on a Nanofor 05automatic genetic analyzerThe data were processed using the GeneMarker V3.0.1program. The following genetic parameters were calculated inthe Microsoft Exel GenAlEx 6.503 macro: Na – number of alleles,Na (average) – average number of alleles, Ne – effectivenumber of alleles, I – Shannon diversity index, Ho – observedheterozygosity, He – expected heterozygosity, F – fixation index. The PCoA plot with the geneticsimilarity coefficient Dice was built using the Past 2.17 program based on a binary matrix. Clusteranalysis was carried out using the Structure 2.3.4 program.The optimal value of clusters for analysis was established inthe Structure Harvester online program Phenotypic evaluationBased on the comprehensive phenotypic assessment, thestudied varieties were combined into several groups accordingto the main economic and biological characteristics.By the degree of frost resistance: varieties with highfrost resistance, in which 60–100 % of generative andvegetative buds were preserved without damage – ‘Arkad’,‘Burlyuk’, ‘Orionid’, ‘Skaberi’, ‘Yuzhnoberezhniy’,‘Pozdnotsvetushchiy’; moderate frost resistance (from 40to 60 %) – ‘Bospor’, ‘Al’minskiy’, ‘Konkursniy’, ‘PamyatiPasenkova’, ‘Dolinniy’, ‘Zolotopolyanskiy’, ‘KrymskiySkoroplodniy’, ‘Zhemchuzhniy’, ‘Sokoliniy’, ‘Novikov’,‘Bulganak’, ‘Gurzufskiy’, ‘Sladkoyaderniy’, ‘Pioner Kryma’,‘Bel’bekskiy Ranniy’, ‘Partizanskiy’, ‘Dzerzhinskiy’,‘Bel’bekskiy’, ‘Komsomolets’, ‘Bomba Chkalovskaya’,‘Kollektivniy’; low frost resistance (less than 40 %) –‘Bubenchik’, ‘Kacha’, ‘Malosadoviy’, ‘Podlesniy’.By the degree of drought resistance: with highstability – Arkad’, ‘Burlyuk, ‘Orionid’, ‘Bel’bekskiyRanniy’, ‘Zhemchuzhniy’; with stability above average –‘Al’minskiy’, ‘Bospor’, ‘Konkursniy’, ‘Pamyati Pasenkova’, ‘Zolotopolyanskiy’, ‘Krymskiy Skoroplodniy’,‘Pozdnotsvetushchiy’, ‘Sokoliniy’, ‘Yuzhnoberezhniy’,‘Novikov’, ‘Bulganak’, ‘Gurzufskiy’, ‘Sladkoyaderniy’,‘Dolinniy’, ‘Pioner Kryma’, ‘Kollektivniy’, ‘Partizanskiy’,‘Dzerzhinskiy’, ‘Bel’bekskiy’, ‘Komsomolets’, ‘Malosadoviy’,‘Podlesniy’, ‘Skaberi’, ‘Bomba Chkalovskaya’; with stabilitybelow average – ‘Bubenchik’, ‘Kacha’.By maturity: early – Arkad’, ‘Bulganak’,‘Dolinniy’, ‘Komsomolets’, ‘Krymskiy Skoroplodniy’,‘Zhemchuzhniy’, ‘Orionid’, ‘Yuzhnoberezhniy’; middle –‘Al’minskiy’, ‘Novikov’, ‘Gurzufskiy’, ‘Sladkoyaderniy’,‘Zolotopolyanskiy’, ‘Pamyati Pasenkova’, ‘Burlyuk’,‘Pozdnotsvetushchiy’, ‘Sokoliniy’, ‘Dzerzhinskiy’, ‘Bospor’,‘Bubenchik’, ‘Kollektivniy’, ‘Kacha’, ‘Partizanskiy’,‘Podlesniy’, ‘Pioner Kryma’, ‘Bel’bekskiy Ranniy’, ‘BombaChkalovskaya’, ‘Skaberi’, ‘Kollektivniy’; late – Konkursniy’,‘Malosadoviy’.By type of flowering: protogeny  – ‘Al’minskiy’, ‘Novikov’, ‘Bulganak’, ‘Gurzufskiy’,‘Sladkoyaderniy’, ‘Dolinniy’, ‘Pioner Kryma’, ‘Bel’bekskiyRanniy’, ‘Bomba Chkalovskaya’, ‘Skaberi’; protandria – ‘Bubenchik’, ‘Kollektivniy’,‘Kacha’, ‘Partizanskiy’, ‘Konkursniy’, ‘Dzerzhinskiy’,‘Bel’bekskiy’, ‘Komsomolets’, ‘Malosadoviy’, ‘Podlesniy’;homogamy  – ‘Zolotopolyanskiy’, ‘Arkad’,‘Krymskiy Skoroplodniy’, ‘Zhemchuzhniy’, ‘PamyatiPasenkova’, ‘Burlyuk’, ‘Pozdnotsvetushchiy’, ‘Sokoliniy’,‘Yuzhnoberezhniy’, ‘Bospor’, ‘Orionid’.By fruit weight: large-fruited  – ‘Bomba Chkalovskaya’, ‘Bulganak’,‘Dolinniy’, ‘Pioner Kryma’, ‘Bel’bekskiy Ranniy’, ‘Skaberi’,‘Komsomolets’, ‘Malosadoviy’, ‘Podlesniy’, ‘Arkad’,‘Krymskiy Skoroplodniy’, ‘Burlyuk’, ‘Pozdnotsvetushchiy’,‘Sokoliniy’, ‘Bospor’, ‘Orionid’; medium-fruited (from6 to 12 g) – ‘Al’minskiy’, ‘Zolotopolyanskiy’, ‘PamyatiPasenkova’, ‘Yuzhnoberezhniy’, ‘Kollektivniy’, ‘Kacha’,‘Novikov’, ‘Partizanskiy’, ‘Konkursniy’, ‘Dzerzhinskiy’,‘Bel’bekskiy’, ‘Gurzufskiy’, ‘Sladkoyaderniy’,‘Zhemchuzhniy’; small-fruited (less than 6 g) – ‘Bubenchik’.In all varieties, with the exception of ‘Bomba Chkalovskaya’,the shape of the fruit is oval-round or ovoid.According to the thickness of the endocarp: thinshelled – ‘Zolotopolyanskiy’, ‘Yuzhnoberezhniy’;standard shell  –‘Bomba Chkalovskaya’, ‘Bulganak’, ‘Dolinniy’, ‘PionerKryma’, ‘Bel’bekskiy Ranniy’, ‘Skaberi’, ‘Partizanskiy’,‘Komsomolets’, ‘Malosadoviy’, ‘Podlesniy’, ‘Arkad’,‘Krymskiy Skoroplodniy’, ‘Burlyuk’, ‘Pozdnotsvetushchiy’,‘Sokoliniy’, ‘Bospor’, ‘Orionid’, ‘Bubenchik’, ‘Kollektivniy’,‘Al’minskiy’, ‘Novikov’, ‘Gurzufskiy’, ‘Sladkoyaderniy’,‘Pamyati Pasenkova’, ‘Zhemchuzhniy’; hard-shelled  –‘Kacha’, ‘Partizanskiy’, ‘Konkursniy’, ‘Dzerzhinskiy’.By the beginning of the growing season: early – ‘Dolinniy’,‘Komsomolets’, ‘Arkad’, ‘Zhemchuzhniy’, ‘Bel’bekskiyRanniy’; medium – ‘Al’minskiy’, ‘Novikov’, ‘Bulganak’,‘Gurzufskiy’, ‘Sladkoyaderniy’, ‘Pioner Kryma’, ‘BombaChkalovskaya’, ‘Skaberi’, ‘Bubenchik’, ‘Kollektivniy’,‘Kacha, ‘Partizanskiy’, ‘Dzerzhinskiy’, ‘Bel’bekskiy’,‘Malosadoviy’, ‘Podlesniy’, ‘Zolotopolyanskiy’, ‘PamyatiPasenkova’, ‘Burlyuk’, ‘Sokoliniy’, ‘Bospor’, ‘Orionid’,‘Yuzhnoberezhniy’, ‘Krymskiy Skoroplodniy’; late –‘Pozdnotsvetushchiy’, ‘Konkursniy’.The results of a long-term study of the walnut gene poolmake it possible to identify varieties with economically valuabletraits that can be recommended as initial parental formsin breeding work: for tolerance to abiotic stresses duringwinter-spring period – ‘Arkad’, ‘Burlyuk’, ‘Orionid’, ‘Skaberi’,‘Yuzhnoberezhniy’, ‘Pozdnotsvetushchiy’; for increasedand high drought resistance – ‘Arkad’, ‘Burlyuk’, ‘Orionid’,‘Bel’bekskiy Ranniy’, ‘Zhemchuzhniy’. For introductioninto production, varieties with complex resistance to adverseclimatic conditions are recommended – ‘Burlyuk’, ‘Bospor’,‘Arkad’, ‘Al’minskiy’, ‘Pamyati Pasenkova’, ‘Orionid’,‘Konkursniy’, as well as those characterized by high yieldand large fruits – ‘Bulganak’, ‘Dolinniy’, ‘Pioner Kryma’,‘Bel’bekskiy Ranniy’, ‘Skaberi’, ‘Partizanskiy’, ‘Komsomolets’,‘Malosadoviy’, ‘Podlesniy’, ‘Arkad’, ‘KrymskiySkoroplodniy’, ‘Burlyuk’, ‘Pozdnotsvetushchiy’, ‘Sokoliniy’,‘Bospor’, ‘Orionid’.Analysis of genetic diversityIn order to establish genetic relationships within the studiedsample of varieties, analyze the level of genetic diversityand identify the groups of the closest genetic relationship,an analysis of the polymorphism of microsatellite loci wasperformed.As a result of microsatellite genotyping, DNA profilesspecific for all studied varieties were obtained. Six (WGA089)to eleven (WGA276) alleles per locus have been identified.A total of 70 alleles were identified for the eight DNA markersused, with an average value of 8.75. Analysis of the level ofgenetic polymorphism included the indicators presented inTable 1.The value of the Ne index varied from 2.873 (WGA089) to6.450 (WGA276). At the same time, in the group of markerswith the same number of identified alleles :WGA001, WGA376, WGA009, and WGA202, this indicatorvaried from 3.269 (WGA202) to 5.071 (WGA376), which maybe due to variation in allele frequencies. The lowest (1.337)and highest (2.060) values of the diversity index I werefound in the least polymorphic marker (WGA089) and themost polymorphic marker (WGA276), respectively. At thesame time, the highest value of the observed heterozygositywas found for the WGA001 marker, and the expectedheterozygosity, for the most polymorphic WGA276 marker.Based on the genotyping data of 32 walnut varieties foreight SSR markers, an analysis was carried out using theStructure 2.3.4 program. The range of analyzed clusterswas from 2 to 7. Based on the results of the analysis in theStructure Harvester online program, the optimal cluster valuewas calculated equal to 2. The results obtained with a valueof K = 2 are shown in Fig. 1.According to the predominance of the first or secondclusters, the studied Crimean varieties can be conditionallydivided into two groups. The first group (the predominanceof cluster 1): ‘Bulganak’, ‘Arkad’, ‘Dolinniy’, ‘Partizanskiy’, ‘Pioner Kryma’, ‘Sokoliniy’, ‘Bel’bekskiy’, ‘Bel’bekskiyRanniy’, ‘Malosadoviy’, ‘Podlesniy’. The second group (thepredominance of cluster 2): ‘Al’minskiy’, ‘Novikov’, ‘Bubenchik’,‘Gurzufskiy’, ‘Sladkoyaderniy’, ‘Zolotopolyanskiy’,‘Krymskiy Skoroplodniy’, ‘Kollektivniy’, ‘Zhemchuzhniy’,‘Konkursniy’, ‘Kacha’, ‘Pamyati Pasenkova’, ‘Pozdnotsvetushchiy’,‘Dzerzhinskiy’, ‘Bospor’, ‘Komsomolets’, ‘BombaChkalovskaya’, ‘Skaberi’, ‘Yuzhnoberezniy’, ‘Burlyuk’,‘Orionid’. Variety ‘Chandler’ was assigned to the second groupof varieties. It should be noted that some varieties of the secondgroup have a significant contribution from the first cluster(from 0.185 to 0.481), on the other hand, among the varietiesassigned to the first group, two varieties have a significantcontribution from the second cluster (0.215 and 0.448).For a detailed analysis of the genetic relationship of thestudied walnut genotypes, an analysis was carried out by themethod of principal coordinates (PCoA) in the PAST 2.17program .The distribution of varieties on the PCoA plot largelyreflects the grouping of varieties obtained in the Structureprogram. Varieties of the first group are concentrated atthe bottom of the graph. In turn, the varieties of the secondgroup are distributed in the middle and upper parts of thegraph. In the arrangement of varieties of the first group,subgroups can be distinguished: (1) varieties ‘Bel’bekskiyRanniy’, ‘Bel’bekskiy’ and ‘Podlesniy’, (2) ‘Partizanskiy’,‘Malosadoviy’, ‘Pioner Kryma’, (3) ‘Sokoliniy’, ‘Bulganak’.Two varieties from the first group were not included in oneof the subgroups: variety ‘Arkad’ occupies an intermediateposition between varieties of the first and second groups,variety ‘Dolinniy’ is equidistant from other varieties includedin the first groupVarieties of the second group are distributed on the graph lessorderly and do not form clear structures, however, it is worthnoting that the varieties ‘Kollektivniy’, ‘Zolotopolyanskiy’,‘Bomba Chkalovskaya’ and ‘Yuzhnoberezniy’ occupy anintermediate position between the varieties of the first andsecond groups. Variety ‘Chandler’ on the graph of principalcoordinates is spatially close to variety ‘Al’minskiy’.The SSR markers used in our work were previously widelyused to solve various problems in walnut genetics and breeding,including DNA fingerprinting and analysis of the geneticdiversity of cultivar collections, breeding-promising forms,and interspecific hybrids , study of traitrelatedcollections , clarification of the issues of gene pool formation within its natural habitats, aswell as distribution pathways in the process of domestication.Comparison of the average values of indicators characterizingpolymorphism, identified by the results of our work,and in studies conducted on collections of walnut varietiesfrom other regions of the world, makes it possible to comparethe heterogeneity of the studied samples of varieties withthat studied in this work. Thus, in a study of a collection of35 varieties of Chinese breeding using 10 SSR markers, theaverage Na and Ne values were 9.4 and 4.67, respectively,while the values of expected (He) and observed (Ho) heterozygositywere 0.77 and 0.62, respectively .In the work of M. Aradhya et al. , whenanalyzing the genetic polymorphism of a collection of 236 varietiesfrom different walnut growing regions using 15 microsatellitemarkers, the average number of identified allelesper locus was 11, while the average He and Ho values were0.699 and 0.536, respectively. It is worth noting that in thisstudy, markers WGA001, WGA202, WGA009, and WGA069showed higher polymorphism , while WGA089 was one of the leastpolymorphic  . We obtainedsimilar results in terms of the level of allelic polymorphismof markers (see Table 1).In an analysis of a sample of 189 varieties representingthe gene pool of 14 countries, an average of 11.5 alleles perlocus was identified, while the average values of observedand expected heterozygosity were 0.62 and 0.73 . In this work, the markers WGA001, WGA202,and WGA276, as well as in our study, were included in thegroup of more polymorphic ones, and the WGA068 markershowed a lower level of polymorphism. In the work of Turkishresearchers who performed genotyping of 30 elite breedingforms (candidates for varieties) using 21 SSR markers, anaverage of 6.15 alleles per locus were identified, while theobserved and expected heterozygosity was 0.64 and 0.62.Considering the works aimed at studying the geneticdiversity of natural populations and promising for breedingforms selected from them, one can also speak of a comparablelevel of polymorphism. For example, in a study by F. Shamluet al. (2018), when assessing polymorphism and geneticrelationships in a sample of 39 walnut accessions selected innatural populations in northeastern Iran, the average number ofidentified alleles per locus was 7.9, and the number of effectivealleles was 3.91. Expected and observed heterozygosityvalues were 0.74 and 0.93, respectively. At the same time,the diversity index was lower than the indicator we identified,1.34 .In studies devoted to the analysis of the structure of naturalpopulations, elucidation of the ways in which the gene poolspreads, and the formation of its local pools, the indicatorsof the number of alleles varied. In a study of the geneticdiversity of local populations in the Eastern Alps of Italy, the average number of alleles per locuswas 4.7 in a sample of 13 markers (WGA class) and 2.7 ina sample of seven EST-SSR markers, which is a rather lowfigure, especially given the sample size of about 200 samples.This can be explained by the possible isolation of the studiedpopulation, selected in mountainous areas, as well as in the flatarea limited by them . In studies of naturalwalnut populations in Southwestern Tibet, when analyzinga sample of 86 genotypes selected in five geographicallocations, the Na indicator was 9.92, but the Ne value was3.95, which may be due to an uneven distribution of allelefrequencies . In a study aimed at studyingthe ways of formation and distribution of the walnut gene pool from the centers of its origin in Eurasia, as a result ofSSR genotyping of a sample of about 2000 genotypes using14 SSR markers, an average allele number per locus was foundto be 14.21 .In general, considering the work on the study of the geneticpolymorphism of the walnut gene pool, both cultural forms andlocal populations, including natural ones in regions related tothe centers of origin of the species J. regia L., we can makea conclusion about the high level of polymorphism of thesample of varieties studied by us. Given the fact that all thevarieties studied are selections from local seed populationsin different regions of the Crimean Peninsula, this level ofpolymorphism may indirectly reflect the level of geneticdiversity of the local walnut gene pool. This is confirmed toa certain extent by the results obtained by us in the courseof cluster analysis. Based on the data obtained, it can beconcluded that the autochthonous gene pool of the walnutprobably comes from two hypothetical populations. Thedivision of the studied sample of varieties into two groupsbased on the results of Bayesian analysis is consistent withthe high level of polymorphism, since the presence of twogenetically distinct groups may contribute to a higher levelof genetic heterogeneity, including allelic polymorphism ofDNA markersIn the work, the groups identified during clustering werecompared according to a number of population geneticparameters (Table 2).The value of the average number of alleles per locus in thesecond group is two times higher than this indicator in thefirst group; such indicators of genetic diversity as the effectivenumber of alleles and the Shannon diversity index also reflecta greater allelic polymorphism of microsatellite markers inthe second group of varieties. The observed heterozygosity inthe groups has a similar value of 0.583 and 0.588, in turn, theexpected heterozygosity is higher in the second group. Thefixation index has a low positive value in the second groupof varieties; in the varieties of the first group, the parametertends to zero.At the genetic level, the differences between the groups,in addition to the specific allelic composition of SSR loci,characteristic of each sample of varieties, are also expressedin the degree of allelic diversity. The second group of varietiessignificantly exceeds the first group of varieties in terms ofa number of genetic parameters that reflect the degree of allelicdiversity. It can be assumed that the first group of varietiesis represented by the most isolated part of the autochthonousgene pool of the Crimean Peninsula. In turn, the geneticdiversity of the second group of varieties was influenced bythe genetic contribution of the introduced gene plasma broughtinto the region from outside. An additional confirmation of thisassumption is the assignment of the ‘Chandler’ variety, USselection, to the second group. The value of the fixation indexin the first group of varieties is typical for populations in a stateof panmixia and the absence of genetic barriers that increasethe number of observed homozygotes. The data obtained inthe Structure 2.3.4 program indicate a genetic relationshipbetween the groups, expressed in the presence of samples witha comparable contribution of two clusters. Since the studiedvarieties are forms selected in the walnut population that existswithin the borders of the Crimean Peninsula, it can be assumedthat the high values of the observed heterozygosity reflect thesignificant size of the walnut gene pool, which contributesto panmixia. In general, the first group of walnut varieties ischaracterized by high yields and large fruits (more than 12 g).Based on a comprehensive phenotypic assessment of samplesfrom the collection of the walnut gene pool of the NikitskyBotanical Gardens, groups of cultivars with a complex ofeconomically valuable traits were identified. When performingmicrosatellite genotyping, a high level of genetic diversity andthe presence of two genetically distinct groups of varietieswere established. One of the groups includes predominantlylarge-nut cultivars with increased productivity potential,which actualizes the use of them as breeding material, andtheir genetic remoteness from the rest of the gene pool canincrease the effect of heterosis during hybridization.The authors declare no conflict of interest.Antyufeev V.V., Vazhov V.I., Ryabov V.A. Reference Book on the Climateof the Steppe Department of the Nikitsky Botanical Garden.Yalta, 2022. (in Russian)Aradhya M., Woeste K., Velasco D. 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Diversity under threat:connecting genetic diversity and threat mapping to set conservationpriorities for Juglans regia L. populations in Central Asia. Front.Ecol. Evol. 2020;8:171. DOI 10.3389/fevo.2020.00171.Guney M., Kafkas S., Keles H., Zarifikhosroshahi M., Gundesli M.A.,Ercisli S., Necas T., Bujdoso G. Genetic diversity among some walnut(Juglans regia L.) genotypes by SSR markers. Sustainability.2021;13(12):6830. DOI 10.3390/su13126830.Gunn B.F., Aradhya M., Salick J.M., Miller A.J., Yongping Y., Lin L.,Xian H. Genetic variation in walnuts : species distinctions, human impacts, and the conservationof agrobiodiversity in Yunnan, China. Am. J. Bot. 2010;97(4):660-671. DOI 10.3732/ajb.0900114.Hammer Ø., Harper D.A.T., Ryan P.D. PAST: Paleontological statisticssoftware package for education and data analysis. Palaeontol. Electron.2001;4(1):1-9.Ikhsana A.S., Topc H., Sütyemez M., Kafkas S. 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(in Russian)Magige E.A., Fan P.-Z., Wambulwa M.C., Milne R., Wu Z.-Y.,Luo Y.- H., Khan R., Wu H.-Y., Qi H.-L., Zhu G.-F., Maity D.,Khan I., Gao L.-M., Liu J. Genetic diversity and structure of Persianwalnut (Juglans regia L.) in Pakistan: implications for conservation.Plants. 2022;11(13):1652. DOI 10.3390/plants11131652.Orhan E., Eyduran S.P., Poljuha D., Akin M., Weber T., Ercisli S. Geneticdiversity detection of seed-propagated walnut (Juglans regiaL.) germplasm from Eastern Anatolia using SSR markers. FoliaHort. 2020;32(1):1-10. DOI 10.2478/fhort-2020-0004.Peakall R., Smouse P.E. GenAlEx 6.5: genetic analysis in Excel. Populationgenetic software for teaching and research – an update. Bioinformatics.2012;28(19):2537-2539. DOI 10.1093/bioinformatics/bts460.Pollegioni P., Woeste K., Major A., Scarascia G., Malvolti M. Characterizationof Juglans nigra (L.), Juglans regia (L.) and Juglans × intermedia(Carr.) by SSR markers: a case study in Italy. Silvae Genet.2009;58(1):68-78. DOI 10.1515/sg-2009-0009.Pollegioni P., Woeste K., Olimpieri I., Marandola D., Cannata F., MalvoltiM.E. Long-term human impacts on genetic structure of Italianwalnut inferred by SSR marker. Tree Genet. Genomes. 2011;7:707-723. DOI 10.1007/s11295-011-0368-4.Pollegioni P., Woeste K.E., Chiocchini F., Olimpieri I., Tortolano V.,Clark J., Hemery G.E., Mapelli S., Malvolti M.E. Landscape geneticsof Persian walnut (Juglans regia L.) across its Asian range.Tree Genet. Genomes. 2014;10:1027-1043. DOI 10.1007/s11295-014-0740-2.Pollegioni P., Woeste K.E., Chiocchini F., Del Lungo S., Olimpieri I.,Tortolano V., Clark J., Hemery G.E., Mapelli S., Malvolti M.E. Ancienthumans influenced the current spatial genetic structure of commonwalnut populations in Asia. PLoS One. 2015;10(9):e0135980.DOI 10.1371/journal. pone.0135980Pollegioni P., Woeste K., Chiocchini F., Del Lungo S., Ciolfi M.,Olimpieri I., Tortolano V., Clark J., Hemery G.E., Mapelli S., MalvoltiM. 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Latin America and the Caribbean (LAC) is one of the most urbanized and violent regions worldwide. Homicides in youth  and young adults (25-39yo) are an especially pressing public health problem. Yet there is little research on how city characteristics relate to homicide rates in youth and young adults. We aimed to describe homicide rates among youth and young adults, as well as their association with socioeconomic and built environment factors across 315 cities in eight LAC countries.This is an ecological study. We estimated homicide rates in youth and young adults for the period 2010–2016. We investigated associations of homicide rates with sub-city education and GDP, Gini, density, landscape isolation, population and population growth using sex-stratified negative binomial models with city and sub-city level random intercepts, and country-level fixed effects.The mean sub-city homicide rate per 100,000 in persons aged 15–24 was 76.9 (SD = 95.9) in male and 6.7 (SD = 8.5) in female, and in persons aged 25–39 was 69.4 (SD = 68.9) in male and 6.0 (SD = 6.7) in female. Rates were higher in Brazil, Colombia, Mexico and El Salvador than in Argentina, Chile, Panama and Peru. There was significant variation in rates across cities and sub-cities, even after accounting for the country. In fully adjusted models, higher sub-city education scores and higher city GDP were associated with a lower homicide rate among male and female (rate ratios (RR) per SD higher value in male and female, respectively, 0.87 (CI 0.84–0.90) and 0.90 (CI 0.86–0.93) for education and 0.87 (CI 0.81–0.92) and 0.92 (CI 0.87–0.97) for GDP). A higher city Gini index was associated with higher homicide rates (RR 1.28 (CI 1.10–1.48) and 1.21 (CI 1.07–1.36) in male and female, respectively). Greater isolation da was also associated with higher homicide rates (RR 1.13 (CI 1.07–1.21) and 1.07 (CI 1.02–1.12) in male and female, respectively).City and sub-city factors are associated with homicide rates. Improvements to education, social conditions and inequality and physical integration of cities may contribute to the reduction of homicides in the region.10.13039/100010269Wellcome Trust [205177/Z/16/Z].The  Although the estimated number of homicide victims is decreasing globally, homicide rates remain unacceptably high in many countries, including in Latin America and the Caribbean (LAC), characterized by a high level of urbanization and inequality. Yet few studies have examined how city-level factors are related to urban homicides. Identifying and understanding these factors and their relationships with homicide in youth and young people is essential to support urban policies to reduce homicides in the region.Our study uses unique harmonized data to characterize homicide rates among youth and young adults in about 85% of all cities of 100,000 inhabitants in eight LAC countries. We found very high rates overall: the mean sub-city homicide rate per 100,000 in persons aged 15–24 was 76.9 in male and 6.7 in female, and in persons aged 25–39 was 69.4 in male and 6.0 (SD = 6.7) in female. We also observed important heterogeneity not only between countries but also across cities within a country and across sub-city areas within a city. Cities with a better social and built environment such as higher education levels, higher GDP, lower income inequality, and lower landscape isolation had lower homicide rates in youth and young adults.The high levels and the large variability of homicide rates across cities in youth and young adults in cities of the region highlight the importance of homicides as a public health and social problem. The associations we observed also suggest that urban policies related to education, income inequality, and urban planning may play an important role in reducing the very high homicide rates in cities LAC.Violence is a major public health and social problem across the globe. Worldwide, there are more than 1.3 million deaths annually as a result of violence including self-directed, interpersonal and collective violence, corresponding to about 2.5% of total global mortality.The impact of violence varies by age and gender. Violence is the fourth leading cause of death in youth and young adults (15–44 years old) worldwide. Homicide, or interpersonal violence, accounts for about a third of all violent deaths for men in this age group.,Latin America and the Caribbean (LAC), is one of the most urbanized regions in the world, with 80% of its population living in cities, and is also among the most violent: eight out of the 10 most violent countries in the world are in LAC.,Despite the high levels of homicides observed in Latin America and in particular in cities of the region, few studies have examined how various city-level factors are related to levels of urban violence particularly among youth and young adults, the group with the higher rates among all ages.We used harmonized data from over 300 cities in the region to: (1) describe levels and variability in homicide rates among youth and young adults across all cities with 100,000 residents or more in eight countries of Latin America; and (2) investigate how city and sub-city social, economic and environmental characteristics are related to homicide rates.Salud Urbana en America Latina) project. The study identified all cities of 1000,000 residents or more in 2010 in participating countries. Cities were defined as agglomerations of country-specific administrative units  that encompassed the urban extent, identified using satellite images.,This study is part of the SALURBALWe obtained data on homicide deaths from the vital registration systems of each country. Homicides were identified using the Global Health Estimates (GHE) codes 1580 (violence) and 1600 , corresponding to International Classification of Diseases version 10 (ICD10) codes X85-Y09, Y871 and Y35 .13 InjurWe focused on youth (ages: 15–24) and young adult (ages: 25–39) homicide deaths. We aggregated homicide counts at the sub-city level from 2010 to 2015 for Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico and Peru; from 2012 to 2016 for Panama and from 2010 to 2014 for El Salvador. As denominators, we used postcensal population projections or intercensal population estimates obtained from national census bureaus or equivalent.,We investigated features of city social and built environments . MeasureBuilt environment (BE) indicators included measures of four dimensions: city population density (to capture heterogeneity with cities) and city isolation of urban development, city population size, and city population growth. Population density was measured by dividing the total population by the built up area in 2010.We computed the undercounting-corrected homicide rates per 100,000 inhabitants for each sub-city, sex , age category and year. We also computed aggregated homicide rates per 100,000 inhabitants pooling across all years. We then examined the distributions of sub-city rates over time, by country and sex.To assess variability in homicide rates within cities, between cities within countries and between countries, we fitted a sex-stratified multilevel linear model with the logarithm of homicide rates (pooling across ages) as the outcome and with random intercepts at the city and country level. The variability across levels was then evaluated by calculating the percent of variance at each level.We used sex-stratified negative binomial models to estimate the association of city and sub-city social and built environment characteristics with homicide rates. We modelled the count of deaths in each age group and each sub-city unit summed across all years as a function of city and sub-city characteristics with a random intercept for each city and sub-city. Models included dummy variables representing the young adult age group and country-level fixed effects. The logarithm of the population multiplied by the undercounting correction factor, divided by 100,000 and summed across all years was used as an offset.We fit a series of models: the first included each exposure separately, then two models including all SOE and BE exposures in the same model, respectively, and a final model including all exposures together. All exposures were standardized to have a mean of 0 and a standard deviation of 1, with city size also being log-transformed prior to standardization. All analyses were conducted with the package glmmTMB in the R software environment.Drexel University Institutional Review Board with ID #1612005035 and by appropriate site-specific IRBs.The SALURBAL study protocol was approved by the The funding sources had no role in the analysis, writing or decision to submit the manuscript.The association of social environment variables, education, GDP and Gini, with homicide rates did not change when all three social environment variables were adjusted for each other in males or in females . In maleIn the fully adjusted model , we founWe studied the distribution of homicide deaths among persons 15–39 years across 1205 sub-cities in 315 cities of 100,000 persons or more in eight countries of Latin America. This represents 85% of all cities with 100,000 residents or more in these countries, as of 2010. As expected, the homicide death rate was substantially higher in males than in females but there was important heterogeneity across cities and across sub-city units within cities. For both sexes, higher levels of sub-city education and city GDP were associated with lower homicide rates, while higher city income inequality and higher city landscape isolation were associated with higher homicide rates.,,,,We found a high overall homicide rate for residents aged 15–39 years in the eight LAC countries with little difference between youth and young adults. These rates were much higher than the European average and the global rates for 2012,,,machismo. As a result, most homicides among women are femicides, that is, the perpetrators are often their male partner or relatives and the crimes occur inside homes.,,We also found that homicide rates among males were about 11 times higher than in females in the same age group. However, it is important to highlight that our data is based on biological sex, which may not fully reflect the nuances of gender-based violence that permeate homicides on those who self-identify as women. The intersection of a lack of economic opportunities, interactions with law enforcement, and a culture of toxic masculinity, which imposes on men the need to demonstrate their force and power, may contribute to higher homicide rates among men.,,,,,,Although the age and sex patterns were approximately consistent across all countries, we also observed significant heterogeneity in levels of homicide not only between countries but also across cities and sub-city units within countries. Moreover, we found that city and sub-city characteristics were significantly associated with levels of homicide, often similarly, in males and females. The association between better socioeconomic conditions and homicide rates is consistent with other work showing that better living conditions are linked to lower homicide rates.. We add to this work by demonstrating that higher city GDP is associated with lower homicide rates even after adjusting for education, inequality and other city and country factors. The mechanisms that could link higher GDP to lower homicide rates include access to job opportunities and better living conditions. We also found that larger city income inequality was associated with a higher homicide rate. This is consistent with previous observations showing that homicides tend to occur more frequently in areas in which both wealthy and extremely poor individuals can be found.,,,,,,,,,We also found that a higher city GDP was associated with lower mortality rates, even after adjusting for country factors. This is consistent with other studies showing a negative association between GDP and homicide mortality in LAC, including Colombia,,After adjustment for all other covariates, we found that higher isolation of development was associated with higher homicide rates among both males and females. Isolated areas may be socially and economically disadvantaged,,,,,,,Although other associations of built-environment factors with homicide rates were not statistically significant, point estimates did suggest that larger cities have higher homicide rates independent of other characteristics in both males and females consistent with previous studies.An important strength of our study is that it encompassed nearly all cities of 100,000 residents or more in eight LAC countries, representing a wide variety of social and economic conditions and leveraged unique harmonized data on homicides as well as social and built environment data from a large multicentric study.We found high rates of homicides in cities of Latin American countries, with especially high rates being observed in males and in cities of El Salvador, Brazil, Mexico, Colombia and Panama. Even after accounting for country, we found wide variations in homicide rates across cities. In general, cities with higher education levels, higher GDP, lower income inequality and lower landscape isolation had lower homicide rates. Our findings point towards the possible role of urban policies related to education, income inequality, and urban planning in reducing the very high homicide rates in cities of LAC.ADR, WTC, OLS, MA, and AALF obtained funding for the study. AALF, WTC, UMS and ADR conceived the study. UMS conducted the statistical analyses with support from AALF, WTC, ADR and UB. AALF and UMS wrote the first version of the manuscript, WTC and ADR revised it. All authors helped in the interpretation of results, reviewed the manuscript providing critical inputs, and approved the manuscript. Members of the SALURBAL Group contributed to the overall conduct of the study as well as to data collection, data processing and data harmonization for this paper.salurbal@drexel.edu.The SALURBAL project welcomes queries from anyone interested in learning more about its dataset and potential access to data. To learn more about SALURBAL's dataset, visit the SALURBAL project website or contact the project at None of the authors had any conflict of interest.


Rhizophora mucronata population in Thailand, we utilized 10× Genomics technology to obtain a comprehensive whole-genome sequence, and restriction site associated DNA sequencing (RAD-seq) to genotype the population. Using SNPs discovered from the R. mucronata genome sequence, we detected moderate levels of genetic diversity and differentiation across 80 R. mucronata accessions collected from the coastal regions of Thailand. Both population structure and principal component analysis (PCA) converged on a clustering of two subpopulations. However, the results of two genetic groups did not correspond to the Gulf of Thailand or the Andaman Sea. Several factors could have influenced the R. mucronata genetic pattern, such as hybridization and anthropogenic factors.In order to examine the genetic diversity and population structure of the Rhizophora mucronata, a member of the Rhizophoraceae family, is prevalent in the mangrove forests of Thailand. R. mucronata’s population structure and genetic diversity have received scant attention. Here, we sequenced the entire genome of R. mucronata using 10× Genomics technology and obtained an assembly size of 219 Mb with the N50 length of 542,540 bases. Using 2857 single nucleotide polymorphism (SNP) markers, this study investigated the genetic diversity and population structure of 80 R. mucronata accessions obtained from the mangrove forests in Thailand. The genetic diversity of R. mucronata was moderate . Two subpopulations were observed and confirmed from both population structure and principal component analysis (PCA). Analysis of molecular variance (AMOVA) showed that there was more variation within populations than between them. Mean pairwise genetic differentiation (FST = 0.09) showed that there was not much genetic difference between populations. Intriguingly, the predominant clustering pattern in the R. mucronata population did not correspond to the Gulf of Thailand and the Andaman Sea, which are separated by the Malay Peninsula. Several factors could have influenced the R. mucronata genetic pattern, such as hybridization and anthropogenic factors. This research will provide important information for the future conservation and management of R. mucronata in Thailand.Unique and biodiverse, mangrove ecosystems provide humans with benefits and contribute to coastal protection.  They are primarily found in Asia (38.7%), Africa (20%), and Latin America and the Caribbean (20.3%) [Mangroves are one of the most prolific marine ecosystems on Earth . They pe (20.3%) . Forest  Asia 38.%, Africa (20.3%) . Over th (20.3%) . As habi (20.3%) ,12. Unde (20.3%) .Rhizophora is the most representative genus of the mangrove family Rhizophoraceae, and has a wide distribution over the Atlantic–East Pacific region, as well as the Indo-West Pacific region [R. mucronata, also known as loop-root mangrove, red mangrove, or Asiatic mangrove, is a plant in Thailand’s mangrove forests that is extremely valuable to the community as a source of both wood and nonwood items [R. mucronata plant have been used medicinally for centuries as an astringent, antiseptic, and hemostatic. This extract also possesses antibacterial, antiulcerogenic, and anti-inflammatory properties [Avicennia and Rhizophora species in other Southeast Asian countries [c region . R. mucrod items , as wellod items . Bark anoperties . Althougountries ,18,19,20ountries ,22.Over the past decade, the advances in sequencing technologies have provided genomic information to study population genetics in mangroves. A number of reference genomes of species in the family Rhizophoraceae have been reported ,27,28,29R. mucronata was sequenced and assembled as a reference genome. SNP markers that were generated from RAD-seq were utilized to determine the genetic diversity of R. mucronata individuals, as well as the population structure of these individuals along the coast of Thailand. This research we conducted will contribute to a better understanding of the genetic variability and current status of R. mucronata populations in Thailand.In this study, the whole genome of R. mucronata was chosen as a representative species for reference genome sequencing in this work. The sample was taken from a natural mangrove forest in Ranong province (9°52′36.1″ N 98°36′11.5″ E) under the supervision of Thailand’s Department of Marine and Coastal Resources. The morphology of R. mucronata is presented in One R. mucronata trees were collected from the mangrove forests in 18 provinces along the Andaman and Gulf of Thailand coasts in Thailand: Phetchaburi , Chanthaburi , Chachoengsao , Chumphon , Nakhon Si Thammarat , Narathiwat , Phang-nga , Phuket , Prachuap Khiri Khan , Pattani , Ranong , Samut Sakhon , Samut Songkhram , Samut Prakan , Satun , Surat Thani , Trat , and Songkhla   . Prior tR. mucronata accessions were used to extract genomic DNA by using the standard CTAB (Cetyl Trimethyl Ammonium Bromide) method, followed by cleaning up using a DNeasy Plant Mini Kit . DNA concentration can be determined with the Qubit fluorometer  and a Qubit DSDNA BR Assay Kit . One R. mucronata accession was used to generate a reference genome sequence using the 10× Genomics technology with linked-read sequencing, a microfluidics-based technique, making it possible to extract long-range information from short-read sequencing data . The 10× Genomics library was constructed from approximately 1 ng of high-quality, high-molecular weight DNA, following the manufacturer’s instructions for the Chromium Genome Library Kit and Gel Bead Kit v2, the Chromium Genome Chip Kit v2, and the Chromium i7 Multiplex Kit (10× Genomics). The sequencing was performed using the Illumina HiSeq X Ten, generating paired-end reads at 150 bp.Fresh leaves of all TaqI restriction enzyme was used to digest genomic DNA, then fragments were ligated with a unique barcoded adapter. The samples were pooled in an equimolar amount and subjected to PCR and quantification. Paired-end sequencing at 150 bp per read was performed on the MGISEQ-2000RS following the manufacturer’s protocol (MGI Tech).To construct a RADseq library, approximately 1 ug of genomic DNA was used following the MGIEasy RAD Library Prep Kit Instruction Manual . Briefly, Rhizophora mucronata was performed using SuperNova v2.1.1 with default settings (https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running (accessed on 5 January 2023)). Annotation was performed by following the pipeline reported in [R. mucronata under accession SRR5384658, and a transcriptome assembly was performed using PASA v2.4.1 [Oryza sativa), Mimulus guttatus, sesame (Sesamum indicum), black cottonwood (Populus trichocarpa), and flooded gum . The ab initio prediction program Augustus v3.3.3 [O. sativa, M. guttatus, S. indicum, P. trichocarpa, and E. grandis. All gene prediction evidence was then combined by EvidenceModeler to generate consensus gene models using the following weights for each evidence type: PASA2—1, GMAP—0.5, AAT—0.3, and Augustus—0.3. To obtain high-confidence gene sets, we cross-checked the positions of annotated genes with those of known repeats, then we excluded any gene that shared more than 50% of its sequence with those repetitious in the list. Using OmicsBox v2.0.10 (https://www.biobam.com/download-omicsbox/ (accessed on 16 January 2023)), the remaining predicted genes were functionally annotated. UniProtKB/Swiss-Prot (swissprot v5) and the GenBank nonredundant database (nr v5) were chosen to align with the protein-coding sequence using local BLASTP, with an e-value threshold of 10−5. Gene ontology (GO) terms were extracted and assigned to R. mucronata sequences, while enzyme codes (EC) were extracted and mapped to Kyoto Encyclopedia of Genes and Genomes pathway annotations.Following Illumina’s protocol for 150 bp paired-end sequences, the linked-reads DNA from 10× Genomics  was sequenced on an Illumina HiSeq X Ten . The whole genome assembly of orted in . We idenorted in  by combiA v2.4.1 . The AnaA v2.4.1  was utils v3.3.3  was traihttp://www.repeatmasker.org/RepeatModeler/ (accessed on 10 January 2023)) was used to construct a de novo repeat library for transposable element (TE) family identification in an unannotated genome assembly. RECON v1.08 and RepeatScout v1.0.5, which are both de novo repeat-finding programs, were used to identify the boundaries of repetitive elements and to build consensus models of interspersed repeats. To verify that repeat sequences in the library did not contain large families of protein-coding sequences that were not TEs, they were aligned to GenBank’s nr protein database using BLASTX, with an e-value threshold of 10−6. Repeat masking was performed on the assembled genome by RepeatMasker v4.0.6 (http://www.repeatmasker.org/ (accessed on 10 January 2023)) with the custom species-specific repeat library generated by RepeatModeler.RepeatModeler v2.0.1  to the assembly using minimap2 v2.17  for DNA R. mucronata accessions were used to identify single nucleotide polymorphisms (SNPs) following the Genome Analysis Toolkit (GATK) pipeline. Paired-end sequence reads from each accession were mapped to our genome assembly of R. mucronata (GenBank accession number JAJHUU000000000) using BWA v0.7.17-r1188 153 (https://github.com/lh3/bwa (accessed on 12 January 2023)) with default settings. GATK v4.1.4.1 with HaplotypeCaller mode was used for SNP calling. Finally, SNPs were filtered according to the following criteria: (a) a minor allele frequency greater than 0.1, (b) depth coverage ranging from 10× to 200×, and (c) less than 5% missing data. The remaining SNP markers were used to explore the genetic diversity and population structure of the 80 R. mucronata accessions.The RADseq data obtained from 80 R. mucronata. Twenty replicates were performed for each K value between 1 and 10, with a burn-in duration of 10,000 and a run length of 10,000 iterations. The number of subpopulations was determined by the ΔK method [FST) was performed using ARLEQUIN v3.5 [R. mucronata defined by STRUCTURE, using GenAlEx v6.502 [FIS). Polymorphism information content (PIC) values for SNP markers were calculated using PowerMarker v3.25 [We employed STRUCTURE v2.3.4 with a Bayesian model-based approach  to analyK method , which wK method . Based oK method  was usedUIN v3.5 . PrincipUIN v3.5 . The levx v6.502  to estimer v3.25 .R. mucronata, one mature individual located in the protected mangrove forest in Ranong province was selected. High-quality genomic DNA was extracted and used for 10× Genomics linked-read library preparation. The sequencing of the genome produced 100.78 Gb from a total of 335,963,870 paired-end reads. The de novo assembly generated from the linked-read data was 219 Mb and contained 12,956 scaffolds. The longest scaffold was 4,666,015 bp and the N50 length was 542,540 bp  of the eatScout . Of theseatScout .R. mucronata accessions were sequenced and genotyped using RAD-seq. In total, we obtained approximately 755,820,058 raw reads, of which 710,562,039 (94.01%) were successfully mapped onto an assembled genome. The average number of reads generated per individual is 8,882,025, ranging from 958,901 to 95,664,876. Following the completion of the data preprocessing, the GATK pipeline uncovered an initial total of 1,065,643 SNPs. Following high-quality filtering, which enables no more than 5% missing data per locus, and minor allele frequency (MAF) more than 10%, a total of 2857 SNPs were retrieved for further analysis.To generate SNP data, 80 R. mucronata population structure. The results of the STRUCTURE analysis revealed that the value of K = 2 had the highest ΔK  from 2857 SNP markers in order to obtain an accurate picture of the ghest ΔK A, which ghest ΔK B and conghest ΔK C, contaiariation C. The prFST values between clusters one and two. The FST values were moderate , and the estimated gene flow among populations (Nm) was 2.528 on average. The comparatively high degree of gene flow corresponded to the relatively low level of genetic differentiation. These findings indicated that there was significant divergence within both clusters one and two. The AMOVA analysis was conducted based on two clusters that were inferred by the STRUCTURE method, and the results showed that the majority of the total variation occurred within populations and accounted for 90.90% of the total, whereas the variation between populations was only 9.10% of the total , gene diversity (GD), heterozygosity (Ho), and minor allele frequency, were calculated for the entire population , observed heterozygosity (Ho), and expected heterozygosity (He) were 0.573, 0.619, and 0.391, respectively. The number of effective alleles (Ne) averaged 1.701, ranging from 1.701 to 1.702. Cluster one had genetic diversity values  similar to cluster two . The average percentage of polymorphic loci was 99.09%, ranging from 98.19% in cluster two to 100% in cluster one. The inbreeding coefficient (FIS) values ranged from −0.538 (cluster one) to −0.451 (cluster two) with an average of −0.494 . This is in contrast to previous studies on other mangrove species in Southeast Asia, which suggested that the Malay Peninsula served as a land barrier between populations occurring on its coasts, resulting in significant genetic differentiation. For instance, the genetic structure of Bruguiera cylindrica in Thailand was evaluated, and showed that their genetic structure corresponded to the Gulf of Thailand and the Andaman Sea according to the land barrier of the Malay Peninsula [R. apiculata using SNP markers, it was reported that the Gulf of Thailand population separated from the Andaman population [Ceriops tagal [Bruguiera gymnorrhiza [Excoecaria agallocha [Sonneratia alba [Avicennia marina [Avicennia alba [R. mucronata species. The results were consistent with the previously reported R. mucronata population structure in Southeast Asia, in which genetic differentiation was not found across the coasts of the Malay Peninsula [R. mucronata across the Malay Peninsula, and found that the Malay Peninsula is not a barrier to the gene flow of this species. In addition, Wee et al. [A. alba, S. alba, and B. gymnorhiza, but not in R. mucronata. In our study, we found that the FST values among the two clusters were not strong (FST = 0.09). According to Wright [R. mucronata. Possible factors could have contributed to the breakdown of the population structuring in R. mucronata across the Malay Peninsula, such as hybridization between isolated populations within a species that results in genetic admixture [Rhizophora mucronata and Rhizophora apiculate, since these species are widespread and predominate among mangrove trees. Furthermore, Rhizophora mangroves are hardy, rapidly developing, and produce viviparous propagules that are comparatively more viable than those of other mangrove species [R. mucronata has the largest propagule with one of the longest flotation periods [R. mucronata propagules can remain buoyant and viable in seawater for several months [Avicennia alba, Sonneratia alba, Bruguiera gymnorhiza, and R. mucronata, which differ in propagule size and buoyancy. The results showed that significant east–west genetic differentiation across the peninsula was observed in A. alba, S. alba, and B. gymnorhiza, and the effect was most pronounced for the two species with lower dispersal potential . In contrast, the two species with higher dispersal potential (B. gymnorhiza and R. mucronata) exhibited much higher proportions of recent inter-population migration along the Malacca Strait. Thus, the high dispersal potential of propagules could be expected to result in high levels of population connectivity, and little genetic differentiation over large distances. Therefore, the combination of factors such as human activities, sea or ocean currents, and the dispersal ability of seeds may have influenced the genetic structure of R. mucronata, resulting in the R. mucronata population that did not correspond to their geographical separation.Understanding genetic diversity requires an understanding of population structure. In this study, we aim to evaluate the genetic structure of the mangrove K = two; A, in accK = two; B, but theninsula . In addipulation . Moreoveps tagal ,55, Brugnorrhiza ,57, Excogallocha , Sonneratia alba , Avicenna marina , and Avieninsula . Ng et aeninsula  evaluateo Wright , the Nm dmixture . Hybrididmixture . Human admixture . In Thai species ,67. Thes species . These a periods . R. mucrl months . This col months . For insR. mucronata population. The PIC values of the SNP markers ranged from 0.16 to 0.38, with an average of 0.31. According to Botstein, et al. [B. parviflora [R. apiculata [With the rapid loss of mangrove areas, understanding the genetic diversity of mangrove species is important for conservation and management. Several studies on mangrove species have reported genetic variation based mainly on SSR markers ,71,72,73, et al. , the PICrviflora  and R. apiculata .R. mucronata collected from mangrove forests in Thailand. Our results showed that the genetic diversity of R. mucronata was moderate  and differentiation based on SNP markers, with the majority of variations occurring primarily within the population. The STRUCTURE and PCA analyses both indicate that R. mucronata populations can be divided into two genetically distinct groups. However, the results of two genetic groups did not correspond to the geographical separation between the Gulf of Thailand and the Andaman Sea. Several factors could have influenced the R. mucronata genetic pattern, such as hybridization and anthropogenic factors. This research will provide better information for the future conservation and management of R. mucronata in Thailand.In the present study, we investigated the genetic diversity and population structure of