The effective protection and utilization of poultry resources depend on an accurate understanding of the genetic diversity and population structure. The breeding of the specialized poultry lineage “Yufen 1”, with its defined characteristics, was approved by the China Poultry Genetic Resource Committee in 2015. Thus, to investigate the relationship between the progenitor H line and other poultry breeds, the genetic diversity and population structure of “Yufen 1” H line (YF) were investigated and compared with those of 2 commercial chicken breeds, the ancestor breed Red Jungle Fowls, and 11 Chinese indigenous chicken breeds based on a whole-genome resequencing approach using 8,112,424 SNPs. The genetic diversity of YF was low, and the rate of linkage disequilibrium decay was significantly slower than that of the other Chinese indigenous breeds. In addition, it was shown that the YF population was strongly selected during intensive breeding and that genetic resources have been seriously threatened, which highlights the need to establish a systematic conservation strategy as well as utilization techniques to maintain genetic diversity within YF. Moreover, a principal component analysis, a neighbor-joining tree analysis, a structure analysis, and genetic differentiation indices indicated that YF harbors a distinctive genetic resource with a unique genetic structure separate from that of Chinese indigenous breeds at the genome level. The findings provide a valuable resource and the theoretical basis for the further conservation and utilization of YF. China is among the countries with the richest poultry genetic resources worldwide . With thIn recent years, with the reduction in sequencing costs, whole-genome sequencing technology has been applied to several aspects of agricultural practices, including molecular detection and breed conservation ,7. NumerIn total, 115 different chicken breeds are considered indigenous to China, which was shown to possess great advantages for genetic breeding. However, breeding methods in many parts of China still require considerable development, and several excellent poultry breeds have not yet been efficiently utilized. The matching line of the “Yufen 1” layer was authorized by the National Commission on Livestock and Poultry Genetic Resource in 2015. A closed breeding method was used to develop line H by crossing barred plumaged-original Gushi chicken (GS) with an egg-laying brown-shelled Babcock B-380 grandparent line C. As the progenitor, line H is characterized by early sexual maturity, a fast plumage line, a high egg production, and a Columbian feather pattern . HoweverHerein, whole-genome sequencing (WGS) data were obtained from ten YF chickens from the poultry germplasm resource farm of Henan Agricultural University. By combining the published whole-genome resequencing data of 2 commercial chicken breeds, 11 Chinese indigenous chicken breeds, and 1 wild chicken breed, the genetic structure of YF representatives was thoroughly evaluated using genetic diversity and population structure analyses. The results discussed constitute scientific and effective data for the development and conservation of YF genetic resources.A total of 154 individuals from 15 chicken breeds were included in the present study . Ten bloClean reads were aligned to the Gallus GRCg6a reference genome using the BWA-MEM alignment algorithm built into BWA v0.7.17) . Subsequ . SubseqTo determine the genetic diversity among all chicken populations, the average minor allele frequency (MAF), observed heterozygosity (Ho), expected heterozygosity (He), and runs of homozygosity (ROHs) were estimated using PLINK (v1.90). The parameters for the ROH analysis were as follows: a sliding window of 50 SNP slides along the chromosome to estimate homozygosity; each sliding window allowed no more than 1 heterozygote, with no more than 5 missing SNPs, a minimum length of ROH of 100 kb, a minimum density of 1 SNP/50 kb, and a maximum gap between consecutive SNPs of 1000 kb. To estimate individual genomic inbreeding coefficients (FROH) using the ROH data, the length of the genome covered by ROH was divided by the total chicken autosomal genome length covered by SNPs  . The avePrior to performing a population genetic analysis, the SNPs were identified using LD-based pruned data with PLINK (v1.90), and the parameter was indep—pairwise 25 5 0.2. Based on the pruned SNP data, a principal component analysis (PCA) was conducted using PLINK (v1.90) to explore the genomic repartition in 15 chicken breeds. A neighbor-joining (NJ) tree of the studied populations was constructed based on pairwise distances using PHYLIP (v3.697)  and MEGAThe samples were sequenced at a genome coverage depth from 5.17× to 32.63×. After quality control and the filtering of unqualified SNPs, a total of 8,112,424 autosomal SNPs were obtained for statistical analyses. After annotating the obtained SNPs, most of the variations were found in introns (29.84%), intergenic regions (10.18%), and transcripts (40.41%) . A densiThe genetic diversity parameters are shown in PCA showed that the contribution rates of the first two principal components (PCs) to explain the variance in the data were 16.53% and 13.30%, respectively A. PC1 isAn analysis of genetic diversity and population structure using genetic data can improve or drive animal selection toward maintaining genetic diversity . In thisThe genetic diversity indices  of the two commercial chicken breeds (WLH and RIR) were the lowest compared with those of the indigenous chicken breeds, indicating a lower genetic diversity among the commercial breeds. In addition, the slowest rate of LD decay was found among the two commercial chicken breeds, which could be explained by the fact that both breeds experienced intensive selection for egg production. This hypothesis can be also confirmed by the high FROH values, indicating that these two breeds underwent inbreeding to a considerably large extent . CompareAn effective population size and an appropriate selection and mating strategy are necessary components of species conservation plans. Studies have shown that the appropriate effective population size not only maintains population fitness but also facilitates species monitoring and management . Indeed,Furthermore, the population structure and historical admixture patterns were assessed for the 15 different chicken breeds. Based on PCA and an NJ tree analysis, each individual of YF clustered together, showing a consistent genetic relationship. Interestingly, the ML tree of the gene flow analysis showed that YF and RIR clustered in a single clade, which was also confirmed by PCA and the NJ tree analysis, thereby suggesting a possible genetic relationship between these two breeds. We believe that the close genetic relationship between YF and RIR is mainly due to their low genetic diversity and drift, which further emphasizes the urgency and importance of conserving the genetic resources of YF. The migration events not only indicated the genetic contribution of the wild chicken breed RJF to the commercial breed WLH but also suggested gene exchange between the commercial chicken breed RIR and the indigenous breeds GS, YF, and JH. Finally, an ADMIXTURE analysis revealed that, at K = 5, WLH, RIR, RJF, and the other 12 indigenous chicken breeds could be distinguished from one another, and YF was clearly assigned to a separate gene pool. These results confirm the reliability of the classification of YF as a separate breed.In conclusion, the genetic diversity and population structure of YF, two commercial chicken breeds, indigenous breeds, and the ancestor breed RJF were comprehensively studied based on genome-wide SNPs. The results show that the genetic diversity of YF was low, which may have undergone intensive selective pressure. Additionally, this study demonstrated at the whole-genome level that YF is a precious genetic resource and helped us to understand the genetic structure of YF. Based on the data described herein, conservation strategies should be implemented to maintain genetic diversity in YF, and the potential of this genetic resource should be further explored. In fact, we can control inbreeding through appropriate mating programs and develop in situ and off-site conservation programs to safeguard genetic variability and achieve specific genetic conservation plans for this breed. Moreover, the present data provide useful information about the genomics of Chinese and Western poultry breeds, which will likely assist in the development of a national project for the conservation and utilization of these breeds.


Vitis, including V. berlandieri. The rootstocks currently use in vineyards are derived from breeding programs involving very small numbers of parental individuals. We investigated the structure of a natural population of V. berlandieri and the association of genetic diversity with environmental variables. In this study, we collected seeds from 78 wild V. berlandieri plants in Texas after open fertilization. We genotyped 286 individuals to describe the structure of the population, and environmental information collected at the sampling site made it possible to perform genome–environment association analysis (GEA). De novo long‐read whole‐genome sequencing was performed on V. berlandieri and a STRUCTURE analysis was performed. We identified and filtered 104,378 SNPs. We found that there were two subpopulations associated with differences in elevation, temperature, and rainfall between sampling sites. GEA identified three QTL for elevation and 15 QTL for PCA coordinates based on environmental parameter variability. This original study is the first GEA study to be performed on a population of grapevines sampled in natural conditions. Our results shed new light on rootstock genetics and could open up possibilities for introducing greater diversity into genetic improvement programs for grapevine rootstocks.In grafted plants, such as grapevine, increasing the diversity of rootstocks available to growers is an ideal strategy for helping plants to adapt to climate change. The rootstocks used for grapevine are hybrids of various American  Vitis vinifera is used as the scion, to maintain grape yield and quality, but other Vitis species resistant to phylloxera are used as rootstocks. These rootstocks are derived from hybridizations between American species  and sometimes with V. vinifera , and V. riparia.Grapevine , Fercal (Berlandieri Colombard no. 1 B × 31 Richter), Gravesac (161‐49 Couderc × 3309 Couderc), and SO4 (Vitis berlandieri × Vitis riparia). The V. berlandieri genotypes used for hybrid creation were selected rapidly at the start of the twentieth century on the basis of surveys performed across the USA. Unfortunately, this genetic background has never been explored more deeply, and the diversity and genetic architecture of traits of interest in V. berlandieri in the wild remains unknown. This species is endemic to the Edwards Plateau area in Texas (USA), a dry, chalky region  exploration of the genomic features of this species in terms of the extent of linkage disequilibrium and genetic diversity and their variation according to genetic structure; (iii) study of deleterious allele accumulation in the different subpopulations; and (iv) identification of genes potentially involved in the adaptation of this species to the environment.In this study, we addressed the following objectives: (i) characterization of the genetic structure of 22.1V. berlandieri plants were ampelographically identified in Edwards Plateau  in Texas, USA. The coordinates and elevation of each sampling site were recorded. We harvested approximately 40,000 seeds from these plants after open fertilization, and about 5000 of these seeds were sown in a field at Geisenheim University, Germany. We selected 286 genotypes within this population on the basis of vigor, such that each initial “mother” plant was represented by four genotypes  to encompass the available genetic diversity.In 2005, 78 wild female 2.2V. berlandieri 10585”  from the collection of INRAE‐Bordeaux, . This genotype was selected on the basis of its rooting capacity and use in other experiments. “V. berlandieri 10585” leaves were harvested from the INRAE Bordeaux grapevine collection . Two young leaves with a width of about 5 cm were collected, frozen, and stored in a −80°C freezer for DNA extraction. DNA was extracted with the Tip 100 Qiagen Genomic kit, according to a slightly modified version of the manufacturer's protocol. We incubated 0.5 g of ground plant material with 9.5 mL of G2 buffer supplemented with 1% PVP‐40, 19 μL RNase A, and 500 μL proteinase K for 3 h at 50°C for lysis. The lysate was subjected to tip filtration and the DNA was precipitated with isopropanol, centrifuged for 15 min at 5000 g, washed with 70% ethanol, and resuspended in 50 μL TE buffer. The quality and molecular weight of the DNA isolated were checked. An A260/A280 ratio between 1.8 and 2.0 and an A260/A230 ratio between 2.0 and 2.2 were obtained, and an Agilent Genomic DNA Screentape analysis was performed. We used 10 μg of high‐quality DNA for sequencing. Samples were sequenced with Single‐Molecule Real‐Time PACBIO SEQUEL II HIFI long reads at the INRAE Clermont‐Ferrand GENTYANE platform (France).The reference genotype for this study was “DNA consensus call sequences obtained in BAM format were converted to Fastq format with the bam2fastq tool from the SMRTLink v11.0 PACBIO library. The HIFI sequencing DNA quality was checked with FastQC version 0.11.7. Paternal and maternal kmers were identified with yak‐0.1 software, using the parental reads. The outputs were then used in hifiasm‐v0.15.5 to bin long reads and to assemble the two haplotypes. For each haplotype, contigs were aligned with PN40024.v4 with minimap2 version 2.17. The best contig alignments were used to build an AGP file, and each pseudomolecule was then reconstructed. We refined the pseudomolecules by repeating the process, beginning with an alignment of each haplotype against the other, previously reconstructed haplotype. The embryophyta_odb10 lineage package from BUSCO 5.3.1 software was used in genome mode to estimate the completeness of all assemblies.2.3Leaves were sampled from all 286 genotypes at Geisenheim University, Germany. Two leaf discs of 1.5 cm diameter were sampled from each genotype and placed on ice. The leaf discs were then frozen in liquid nitrogen and freeze‐dried in a Martin Christ, Beta 2‐8LD freeze‐dryer. DNA was extracted from all lyophilized samples as described by Cormier et al.  in Corni2.4https://github.com/timflutre (demultipley.py) and cleaned with Cutadapt . VCFtools . A new run was then performed, with a burn‐in period of 100,000 and 100,000 MCMC iterations. Genotypes with a membership proportion greater than 0.8 for a population were attributed to the population concerned. Genotypes not attributed to a particular population were considered to be admixed. The STRUCTURE results were then compared with those of a k‐means clustering method described elsewhere  and from the visualization of within‐cluster sums of squares (K = 5).Two methods were used for the analysis of population structure. We first ran STRUCTURE v2.3.4  FST was calculated with VCFtools (‐‐weir‐fst‐pop option), using the default parameters. The V. berlandieri reference genome was annotated according to the Pinot noir reference genome , which is essentially GSR for the period between April and July. It can be used as an indicator of biotic pressure during the first few months of vegetative growth , the maximum air temperature , and day length coefficient (d) according to latitude, between April and September. This parameter may reflect the sugar‐producing potential of the plant during the vegetative period in a given context.The Huglin index  analysis was performed with the elevation of the mother plants reported during sample and additional environmental information extracted from the TerraClimate platform . We therefore included only GST49 and GSR49 in the PCA calculation. A GEA analysis was performed for elevation, and then for the first two principal components of the PCA performed with TerraClimate environmental parameters. Elevation was analyzed separately from the other environmental parameters because it was measured during the sampling campaign, whereas the other environmental variables were obtained by climatic interpolation.K = 2) was used as a covariate. In this model, kinship was derived from pseudo‐QTN information. Bonferroni correction was applied to the calculated p‐values. The significance thresholds were, thus, set at 0.05/n and 0.01/n where “n” is the number of markers used. The GEA analysis was performed on the 281 genotypes with 88,804 SNPs after the recalculation of minor allele frequency in GAPIT.We performed GEA with the BLINK model in GAPIT with default settings. Before running GWAS, the SNPs dataset has been imputed with the snp.impute function available in GAPIT using the “Major” argument in order to impute missing data by the major allele of each SNP. Because the imputation procedure led to changes in the allele frequency at each position, mainly for SNPs with a large number of missing data, a new MAF > 0.05 filtration was applied excluding 15,574 SNPs. The BLINK model was used because of the ease with which false discovery rate can be controlled in this model  were found in V. berlandieri. This high‐quality assembly resulted in a highly reliable reference genome for these analyses.The long‐read sequencing of 3.1V. berlandieri population allowed us to get 104,378 relevant SNPs with an average of 5500 SNPs per chromosome .The e Figure . The sube Figure  with the, Figure  and acco5 Figure , a clear3.2r2 < 0.2) was calculated per chromosome; it ranged from 307 bp on chromosome 16 . The distribution of each category of SNPs was similar between chromosomes. The number of SNPs for each estimated effect is detailed in Table The extent of LD decay , 7 , and 15  for all subpopulations and was significantly higher for subpopulation 1 than for the other populations  of the Anatolia region, which is considered to be the center of origin of grapevine , probably due to the small size of the geographic area used for sampling. The significant IBD could explain the low FST observed in each subpopulation, as it could indicate existing gene flow between subpopulations. Moreover, the FST between the two subpopulations was 0.032. A low level of differentiation at regional level has also been observed in other perennial species, such as Fagus sylvatica . For this reason, despite the smaller size of the area sampled in this study than in previous studies on grapevine  and chr07_9106899 (detected in GEA for PC2), respectively—corresponding to strong genetic differentiation between subpopulations at these loci . The analysis of genome–environment association revealed genetic markers associated with the climatic conditions in the sampling areas. These associations should highlight genetic regions involved in the adaptation of plants to different environmental conditions. Wild genotypes constitute valuable resources that could be subjected to more precise selection and included in breeding programs. However, before their inclusion in breeding programs, these genotypes will need to undergo assessments of their agronomic performance.Our results confirm the existence of considerable genetic diversity in the The authors have no conflict of interest to declare.Appendix S1.Click here for additional data file.


Aedes (Hulecoeteomyia) koreicus (Edwards), a mosquito species native to East Asia, has spread to parts of Europe and Central Asia since 2008. In the last decade, Ae. koreicus has been shown to be a competent vector for chikungunya virus and Dirofilaria immitis. However, information about the current and potential distribution of Ae. koreicus is limited. Therefore, to understand the changes in their global distribution and to contribute to the monitoring and control of Ae. koreicus, in this study, the MaxEnt model was used to predict and analyze the current suitable distribution area of Ae. koreicus in the world to provide effective information.Invasive alien species are a growing threat to natural systems, the economy, and human health. Active surveillance and responses that readily suppress newly established colonies are effective actions to mitigate the noxious consequences of biological invasions.  Aedes (Hulecoeteomyia) koreicus (Edwards), a new invasive mosquito species in Europe, appears to have a daytime habit of feeding on human blood [Ae. koreicus was reported as early as 1967 [Ae. koreicus is most likely a competent vector of dog Dirofilaria immitis and is of potential relevance in the natural cycle of the parasite [Ae. koreicus specimens were able to transmit Zika virus and chikungunya virus under laboratory conditions [an blood ,2 and is as 1967 . It has parasite . In addinditions ,6; the mnditions . Regardinditions ,6.Ae. koreicus females are able to lay desiccated and cold-resistant eggs that can survive during the winter and hatch in the spring similar to other females of Aedes species, and Ae. koreicus can survive in cold and dry environments, which allows this species to avoid competition with other mosquito species and has considerable advantages in breeding and transmission [Ae. koreicus originated in North Korea, China, Japan, and Russia [Ae. koreicus in Italy was in 2011 [Ae. koreicus [Ae. koreicus were published in Hungary [Ae. koreicus in Slovenia was in 2013 [Ae. koreicus in Austria and Kazakhstan was in 2018 [Ae. koreicus in the Czech Republic and the Netherlands was reported [Ae. koreicus has established stable populations except in Switzerland, Slovenia, and the Netherlands. Several studies indicate that Ae. koreicus is well adapted to urban settlements, where a variety of artificial containers can be found as breeding sites in gardens and urban areas, and similar to other species of the genus Aedes, the female Ae. koreicus lays eggs in artificial containers, so the introduction of Ae. koreicus could be due to the trade of small containers, ornamental plants, or used tires and has spread throughout Europe through international trade [Ae. koreicus has now invaded Europe from Asia and settled in areas partially occupied by other mosquito species after Ae. albopictus, Aedes aegypti, and Ae. japonicus [Some studies have shown that at the end of the annual season, when the day becomes shorter, smission ,8. Most d Russia ,10, and d Russia , it has  in 2011 . Then, ikoreicus . In 2016 Hungary , the fir in 2013 , and the in 2018 . In 2021reported . In the Ae. koreicus is recognized as a potential vector of arboviruses due to its known role as a vector of CHIKV, ZIKV, and D. immitis under laboratory conditions. According to the WHO, a total of 89 countries and territories have reported evidence of mosquito-transmitted Zika virus infection, with the first local mosquito-transmitted ZIKV disease cases reported in Europe in 2019 and ZIKV outbreak activity detected in India in 2021 . Chikungunya was first identified in the United Republic of Tanzania in 1952 and subsequently in other countries, such as Africa and Asia, and has now been identified in over 110 countries in Asia, Africa, Europe, and the Americas . Meanwhile, the global expansion of exotic mosquito species has intensified with the development of international trade and increased movement of people; coupled with climate warming and urbanization, this trend will further increase in the future [Ae. koromikos not only has an impact on the biodiversity of the invaded areas but also poses a potential risk to local public health safety. For this reason, the possible involvement of a new mosquito species as a vector is crucial to set up the necessary preventive measures to contain the spread of infections. Understanding its distribution is important for the formulation of an Ae. koreicus control strategy in invaded areas.Although there is very little published evidence on its vector status, e future ,23,24,25Species distribution models (SDMs) are models that correlate information on species distribution samples with information on corresponding environmental variables to derive the relationship between species distribution and environmental variables and apply this relationship to the area under study to estimate the distribution of target species . MaxEnt The maximum entropy model (MaxEnt) is based on the maximum entropy theory, which treats species and their growing environment as a system, determines the stable relationship between species and the environment by calculating the state parameters when the system has maximum entropy, and estimates the distribution of species . SeveralAe. aegypti used MaxEnt ecological niche modeling in Tanzania [Ae. Aegypti will shift toward the central and northeastern parts with intensification in areas around all major lakes in the future. The MaxEnt model was also applied to predict the potential global distribution of Ae. albopictus and Ae. aegypti by Kamal [Ae. albopictus would expand in the northern United States, parts of southern Canada, and eastern Europe, and Ae. aegypti would expand to southeastern Australia. The predicted potential distribution of Ae. albopictus in China by Liu et al. [Ae. albopictus in China will expand westward due to future climate change under the SSP126 climate scenario. The distribution potential of the two species was different. The above examples show that the MaxEnt model can predict the potential distribution of species under variable climatic conditions by combining species occurrence data with natural environmental data and that its results are theoretically more accurate and reliable.The prediction of the potential habitat of Tanzania , showingby Kamal  et al., u et al.  using thAe. koreicus mosquitoes mainly focuses on a particular country or region, and there are very few studies on predicting the global distribution of Ae. koreicus mosquitoes, showing a gap in the understanding of changes in their global distribution. In addition, Ae. koreicus is recognized as a potential vector of arboviruses due to its proven role as a vector of the viruses CHIKV and ZIKV, which are important threats to humans. As the species continues to spread, new threats to public health will emerge, and analyzing the likelihood of further incursions by Ae. koreicus is therefore particularly important. In this study, we chose to use the MaxEnt model to predict and analyze the current suitable global distribution area of Ae. koreicus to provide effective information for Ae. koreicus monitoring and control.Current research on the monitoring and distribution of Ae. koreicus occurrence data were collected from two sources. First, a comprehensive and systematic literature retrieval was performed. We used the keyword “Ae. koreicus” searched in PubMed and CNKI  , which contains global occurrence records of many species, and we used “Ae. koreicus” as the keyword to include only species names “Ae. koreicus” or “Hulecoeteomyia koreica” distribution points and eliminated duplicates with those found in PubMed and CNKI. A total of 410 distribution records were integrated and entered into the corresponding Excel sheet in the model input file format (The ructure) . The sece format , with cohttps://www.worldclim.org/, accessed on 26 August 2023), with both current and future variables having a high resolution of 2.5 arcmin (approx. 5 km × 5 km). The 19 climate factors included the annual mean temperature, maximum temperature of the warmest month, minimum temperature of the coldest month, and annual precipitation  126 climate scenarios used in the model projections were downloaded from WorldClim  Import species distribution data and environmental variable data into maxent.jar software, and set the following parameters: the proportion of training data is 75%, the output format is selected as ASC raster format, the number of model repetitions is set to 1, and other parameters are set to the software defaults.(2) Open the GIS software, import the results of model in ArcMap10.4.1 to overlay the prediction map and define the projection, and open the Spatial Analyst tool inside the Arc toolbox to perform the reclassification process. The classification method is the natural breaks (Jenks) method, and the distribution area is divided into four levels: unsuitable region, lowly suitable region, moderately suitable region and highly suitable region. Finally, the map is created.(1) Import the species distribution data and current and future environmental variable data into maxent.jar software and set the parameters: the proportion of training data is 75%, the output format is selected as ASC raster format, the number of model repetition runs is set to 1, and other parameters are set to the software default.(2) Open the GIS software, input the results of the model in ArcMap10.4.1 and define the projection, and open the Spatial Analyst tool inside the Arc toolbox to perform the reclassification process. The classification method is the natural breaks (Jenks) method, and the distribution area is divided into four levels: unsuitable, marginally suitable, moderately suitable, and highly suitable. Finally, the map is created.To test the predictive ability of MaxEnt in this study, the receiver operating characteristic (ROC) curve was used. The AUC (area under the ROC curve) value is the area enclosed by the ROC curve and the horizontal coordinate; it indicates the accuracy of the model, and the value ranges from 0 to 1. A more accurate model is indicated by a higher AUC value.Ae. koreicus was 5,453,100 km2, of which 7,146,900 km2 was highly suitable  and was detected in 40.2% of places and in 37.3% of larval habitats monitored, in a range of altitudes from 173 to 1250 miles above sea level in northeastern Italy [Ae. koreicus is well established in northeastern Italy and confirmed the invasive potential of this mosquito. Then in the summer of 2013, for the first time, Ae. koreicus was found in the Swiss Italian border region of Ticino–Lombardy and southern European Russia [Ae. koreicus. Subsequently, the first occurrence reports of Ae. koreicus were published in Hungary (2016) [Ae. koreicus is indeed widening, under the influence of global warming and heavier international trade and travel in Europe. The predicted results of this study showed that under the SSP126 climate scenario, the highly suitable area of rn Italy . The resn Russia ,37. Two y (2016) , Austriay (2016) , and they (2016) . The disAe. koreicus was Bio1, followed by Bio6 and Bio3, all three of which are temperature variables. Mosquitoes are small-bodied poikilotherms, meaning that the ambient temperature is the main abiotic factor limiting their development, reproduction, ecological niche, and, therefore, geographical distribution [Ae. koreicus can survive at temperatures ranging from 10 °C to 28 °C [Ae. koreicus are mainly in northern Italy and southern Germany near the Alps in Europe, the Yangtze and Yellow River basins in China, and the area surrounding Mount Fuji in Japan. In 2011, Ae. koreicus was found in a small village of Belluno Province in northern Italy, and this is the first report in Italy of the introduction of the exotic mosquito Ae. koreicus. Then, in 2015, the first record of Ae. koreicus in southern Germany was reported. These areas for the establishment of Ae. koreicus were characterized by mid-high annual average temperatures, between 10 °C and 15 °C [Ae. koreicus can overwinter in the form of diapause eggs and can better adapt to urban environments [Ae. koreicus [Ae. koreicus is able to lay diapause eggs that survive the winter and hatch in the spring, allowing adult Ae. koreicus to persist from late summer into autumn [Ae. koreicus are poorly known, the results of our study on the potential distribution of Ae. koreicus may suggest that the distribution of this species seems to be more dependent on temperature changes.The most important environmental factor for the distribution of ribution ,39. Expeto 28 °C . The prend 15 °C . Ae. korronments ,42,43. Tkoreicus . These ro autumn ,44. For Ae. koreicus in the world. As we can see in the prediction results under the SSP1-2.6 scenario, the global total suitable area for Ae. koreicus will increase in 2021–2040 and 2041–2060, and the highly suitable area will remain distributed in northern Italy and southern Germany near the Alps in Europe, the Yangtze and Yellow River basins in China, and the area around Mount Fuji in Japan.“Shared socioeconomic pathways” (SSPs) are a group of new emissions scenarios driven by different socioeconomic assumptions. Many of the new SSP scenarios were used for CMIP6, providing scientists with a wider choice to model the potential future. SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 are different scenarios showing the annual carbon dioxide emissions up to 2100 under this scenario . Under tAe. koreicus-transmitted disease has been reported, Ae. koreicus is recognized as a potential vector of arboviruses due to its proven role as a vector of CHIKV, ZIKV, and D. immitis. Moreover, the cold-tolerant biology of Ae. koreicus gives it an advantage in terms of spreading and establishing its population. Between 2007 and 2012, many autochthonous cases of dengue and chikungunya were reported in Europe [Ae. koreicus, which highlights the potential risks of invasion of Ae. koreicus into North America. As a defined vector of CHIKV, it is impossible to completely ignore the role it may play in the future spread of the virus. Furthermore, the predicted results in this study showed that given the risks associated with the presence of Ae. koreicus, we cannot ignore the need to monitor and control this species.Although no case of n Europe . CHIKV wn Europe . In 2013n Europe . Some scn Europe ,51. The Ae. koreicus population has been found to be resistant to etofenprox and permethrin with possible resistance to deltamethrin but was susceptible to chlorpyrifos in a recent study [Ae. koreicus in the field discovered that both Wolbachia and Asaia bacteria were detected in Ae. koreicus collected in northeastern Italy, as Asaia and Wolbachia are considered new tools for symbiotic control, which, given the increasing development of resistance in Aedes mosquitoes, also supports their use for innovative control strategies against invasive Ae. koreicus [In addition, the nt study . A studykoreicus . Ae. koreicus in some places  may have originated from one or a few initial infections, suggesting that its further spread may be under control if certain control measures are taken. Research on changes in the distribution of Ae. koreicus is lacking, and studying changes in its distribution can provide effective information for its monitoring and control. Based on the results of this study, it is possible to reduce the risk of spread and public health risks by implementing effective control measures in areas where Ae. koreicus populations are already established and by initiating regular surveillance in risk areas where Ae. koreicus may spread.The rapid spread of Ae. koreicus. According to this study, the areas suitable for Ae. koreicus survival are currently distributed mainly in in China, Japan, North Korea, South Korea, and some areas of Europe, and most of the highly suitable areas are distributed in China. Northeastern North America is the marginally and moderately suitable area for Ae. koreicus. Under the SSP1-2.6 climate scenario, the total global suitable area for Ae. koreicus will increase in the next forty years. Therefore, the species might further spread in the absence of control measures. For this reason, investigations on the main modes and routes of dispersal of this invasive species and an increase in monitoring efforts should be prioritized.This study provides, to our knowledge, the first experimental data on the global distribution of the invasive mosquito


Developing resilient cultivars ensuring adequate productions will be the agriculture industry’s primary challenge in the coming decades to ensure food security, especially with climate change and a growing global population. Massive efforts were made over the past 50 years to develop solutions for crop management and conservation. This resulted in the creation of germplasm repositories that can preserve and reproduce millions of accessions for thousands of extant plant species both ex situ and in situ. This relatively untapped biodiversity is one of the essential components for hastening the discovery of fresh sources of variation with the potential to raise crop yield and resilience. Several technologies in the field of biology offer the chance to thoroughly examine genetic resources at various depth scales, making the comprehension of the mechanisms regulating the complex basis of traits easier, as well as the discovery of novel alleles for breeding programs.Oryza sativa L.) [This Special Issue (SI) was launched with the intention to collect articles focusing on the investigation of the still unexplored crop diversity through advanced genomics and phenomics approaches. The SI consists of 11 original research contributions focusing on diverse crops including cereals  ,3,4, legO. sativa L. var. indica), screening for drought tolerance was performed in mini-hoop facilities projected to avoid rainfall [In indica rice  under different cultivation conditions was performed, in order to define strategies to address micronutrient deficiencies in grain when it is grown in poor soils [An assessment of zinc and iron concentration in different types of Maize . . A totalAmong Leguminosae phenotyping, a strategy was applied in bean pods, which are important both for nutrition and human health due to the high content of dietary fibers and vitamins as well as for the benefits given by flavonoids and phenols. García-Fernández et al.  built a Vigna unguiculata L.). This crop has a high level of tolerance to harsh heat and drought conditions, thus playing a key role in generating income for farmers of marginal areas. Gumede et al. [Application molecular markers were reported in two studies aimed to assess genetic diversity of cowpea germplasm  [Diplotaxis gene pool. The study provides valuable tools for the community for the genetic and genomic characterization of rocket salad species and for comparative studies with other Brassicaceae crops.A combination of microsatellites and SNPs was also applied in a collection of 100 genotypes of wild and garden rocket (tiva L.) . The stuThe application of phenotypic and molecular investigation strategies was also described in fruit trees. Toward the establishment of effective conservation and management strategies, a comprehensive analysis of 1442 sweet cherry accessions held in the German National fruit gene bank was reported by Reim et al. . The anaThe genotyping by sequencing (GBS) approach was used to examine the genetic structure of the local variety ‘Cipolla Rossa di Acquaviva’ . Given tVanilla planifolia, to investigate its evolutionary mechanisms that shaped the genome of cultivated vanilla in Mexico, its center of origin. The genomic structure of V. planifolia was compared with the available reference genome ‘Daphna’, evaluating heterozygosity, ploidy, synteny, and SNP correlation at the genome level. Whole genome sequencing was used to compare genome complexity between 15 accessions from different regions and gene pools. High levels of heterozygosity were found in all accessions except one, which showed a low level. The results supported the hypothesis that cultivated vanilla is the result of hybridization and that multiple domestication events underpin the development of several local varieties. These findings may help improve breeding and conservation efforts aimed at preserving the genetic diversity of this crop.Ellestad et al.  conducteThe Special Issue aims to provide new knowledge the broad scientific community involved in the exploitation of the potentialities of crops. We wish to thank all the authors for their important contribution to the research topic and the staff members at MDPI for their support during the editorial process.


Since the introduction of genomic selection in plant breeding, high genetic gains have been realized in different plant breeding programs. Various methods based on genomic estimated breeding values (GEBVs) for selecting parental lines that maximize the genetic gain as well as methods for improving the predictive performance of genomic selection have been proposed. Unfortunately, it remains difficult to measure to what extent these methods really maximize long-term genetic values. In this study, we propose oracle selection, a hypothetical frame of mind that uses the ground truth to optimally select parents or optimize the training population in order to maximize the genetic gain in each breeding cycle. Clearly, oracle selection cannot be applied in a true breeding program, but allows for the assessment of existing parental selection and training population update methods and the evaluation of how far these methods are from the optimal utopian solution. Over time, plants have been cultivated and selected based on morphological characteristics to improve favorable traits. Initially, phenotypic information was used to guide the selection of parental lines in plant breeding. Contemporary plant breeding methods select parents based on molecular markers such as single nucleotide polymorphisms (SNPs). Based on the idea that a phenotypic trait is controlled by many quantitative trait loci (QTLs) or genes, molecular markers can serve as proxies for these QTLs, assuming that they are in strong linkage disequilibrium with at least one QTL . GenomicA variety of methods aiming to maximize the long-term genetic value have been proposed. This problem can be tackled by optimizing the parental selection and by improving the prediction of the GEBVs. Different methods to update the training population (TP) have been proposed , but Neyscoping  as parents (truncation selection) . This ofscoping  and deep methods  were pro methods , the max methods  and the  methods , it remaOracle selection is a hypothetical frame of mind in which a value of interest  is maximized using the ground truth. Therefore, oracle selection can only be used in silico, allowing for the assessment of different selection algorithms. By assuming that oracle selection represents the optimal selection, the progress of existing parental selection methods and TP update methods can be assessed. Additionally, by exploiting the insights resulting from oracle selection, new (non-oracle) selection methods could be developed in the future.In this paper, an oracle parental selection method and an oracle TP update method are proposed. Both oracle methods are compared with existing, state-of-the-art selection methods. In the case of TP updates, each method is assessed using a simulated breeding population with a narrow as well as a broad genetic variation. Different numerical characteristics such as the genetic value, predictive performance and genetic relationship of a breeding population using different TP update methods including oracle selection will be assessed. Finally, based on these insights, new approaches for parental and TP update methods are discussed.2vulgare) from the University of Minnesota (UMN) and the University of North Dakota (NDSU), counting respectively 384 and 380 six-row spring inbred lines with 1590 biallelic SNP loci.The base population and breeding scheme in this paper are adopted from 2.1The recurrent breeding scheme is depicted in In the first breeding cycle, 50 individuals with the highest phenotypic values of the NDSU dataset are coupled with 50 individuals with the highest phenotypic values of the UMN dataset. In all subsequent breeding cycles, 100 parents are selected and coupled according to one of the parental selection methods that are described further. From this point onwards, the parents are selected solely based on the genomic estimated breeding values(GEBVs). The GEBVs are predicted using a linear mixed effects model that has initially been fitted using the base population and incorporates both phenotypic and genotypic information. During each breeding cycle, the TP is updated using one of the TP update methods (described further). These methods add and remove individuals from the TP. Added individuals are selected from the current breeding population.The different parental selection methods are compared over 50 breeding cycles, whereas the different TP update methods are compared over 15 breeding cycles. All results are averaged over 100 simulation runs.2.2Truncation selection selects 100 individuals with the highest GEBVs and crosses them randomly. This method is generally associated with a high short-term genetic gain and is therefore often used in breeding programs.The optimal genomic mating (OGM) method, on the other hand, selects parents that minimize an objective function combining the inbreeding coefficient, the coancestry coefficient and a usefulness criterion . ParentsThe scoping method has been proposed by the present authors and consists of two steps . First, Z containing the genotypes introduce new QTL alleles in the breeding population through the use of a genebank. First, truncation selection is used for five breeding cycles (DSBC5) to simulate a reduction in genetic variation in the breeding population due to intensive breeding. Next, a genebank is used to (re)introduce genetic variation in the breeding population. Because the genebank is characterised by a broad genetic variation and individuals with a low genetic value, the breeding population is divided into different layers allowing for the genetic values to gradually increase over different breeding cycles before introducing these individuals into the elite population. A complete overview of the deep scoping method is reported in 2.3Oracle parental selection is a hypothetical frame of mind that allows to reveal the full potential of parental selection. Classical selection methods only have access to information extracted from molecular markers that are linked to the QTLs that underlie the trait of interest. In contrast, oracle parental selection is allowed to exploit knowledge of the actual QTL effects. Intuitively, the method selects individuals with the highest number of favorable QTL alleles, giving priority to QTL positions that have not yet been selected in the parental population, thus preventing the loss of rare favorable QTL alleles. Formally, each individual receives a score between 0 and 2.4Different methods to update the TP will be evaluated. Each method selects individuals from the current breeding population and adds them to the TP. The top TP update method selects individuals with the highest GEBVs, the tails TP update method selects individuals from both tails of the normally distributed GEBVs and the random TP update method selects individuals at random. A complete description of these methods can be found in 2.5We propose the oracle TP update method to construct an optimal TP that maximizes the predictive performance of the genomic prediction model. The oracle TP update method is again a hypothetical frame of mind to study the characteristics of an optimally selected TP. In contrast to the oracle parental selection method, the oracle TP update method only requires the phenotypic and genotypic values of each individual. QTL information is thus not needed. The oracle TP update method can be used in combination with classical  parental section methods that rely on GEBVs. The difference is that by using an optimally selected TP, the GEBVs are better predicted.In the oracle TP update method, individuals are added and removed from the TP iteratively. First, the contribution of each individual towards the predictive performance is calculated. The predictive performance is expressed as the Pearson correlation between the predicted and true genetic values of the whole breeding population. To avoid overfitting, individuals of the breeding population that have been accepted in the TP are not used to calculate the predictive performance. The individual that maximizes the predictive performance after its addition to the TP is accepted in the TP. Second, the impact of removing each individual separately from the TP is assessed and the individual that maximizes the predictive performance after its removal is eliminated from the TP. In total, up to 50 individuals can be added and removed from the TP. An individual will only be added to or removed from the TP if that action increases the predictive performance. It is, however, possible that each individual in the TP has a positive contribution towards the predictive performance and that no individual is removed. Therefore, the size of the TP could vary, depending on the addition and removal of individuals.2.6In the first breeding cycle, the complete base population is used as TP. In the subsequent breeding cycles, 150 individuals are phenotyped and added to the TP according to the tails method, selecting 75 individuals with the highest and 75 individuals with the lowest GEBVs . AccordiIn the case of the oracle TP update method, only 100 randomly selected individuals of the base population are used as TP. To compare the different TP update methods, in subsequent breeding cycles, only up to 50 individuals are added to the TP. Additionally, the oracle TP update method can also remove up to 50 individuals from the TP if the removal of such individuals increases the predictive performance.The GEBVs are predicted by fitting a linear mixed effects model:y a vector of phenotypic values, 1n a vector of size with with The linear mixed effects model in Eq. (2) is fitted using the package rrBLUP in R . Even th2.7g) with The simulation study was built upon the work of pulation . The varwith The phenotypic value of an individual is defined as the average over the three environments. A comprehensive overview of the simulation study can be found in To track the fixation of unfavorable QTL alleles, the maximum reachable genetic value is calculated as the sum of the QTL effects that are fixed (both favorable and unfavorable) and the sum of the favorable QTL effects that are not yet fixed. It represents the maximum genetic value that could still be reached, taking into account the fixation of unfavorable QTL alleles. The maximum reachable genetic value and the mean genetic value are rescaled such that the maximum reachable genetic value, when no unfavorable QTLs are fixed, has a value of 1. As in 33.1Oracle parental selection is a hypothetical frame of mind that uses the knowledge of QTL positions and QTL effects to demonstrate the effect of an almost perfect parental selection on the genetic value. In the initial population, some of the QTLs are already fixed for one of the two possible alleles. Some of these alleles have a negative contribution to the genetic value. This explains why the maximum reachable genetic value is slightly lower than 1 for the initial population see Figure 2The scoping method 3.2The oracle TP update method is a hypothetical frame of mind that selects individuals to construct a TP using their genotypic and phenotypic values. The optimal TP is then used to (re-)train the mixed effects model that predicts the GEBVs of the current breeding population. In turn, these GEBVs are used to select parents for the next breeding cycle. Assuming that the oracle TP update method results in a good prediction of the GEBVs, it can be used to assess the current progress of other TP update methods.The TP update methods were compared using a breeding population where the parents are either selected according to truncation selection or according to the scoping method. Truncation selection prioritizes individuals with the highest GEBVs, whereas the scoping method will also select individuals that maximize the genetic variation of the parental population. Therefore, both approaches may require a different TP update strategy. The results for truncation selection (left panel) and the scoping method (right panel) using different TP update methods are shown in At the start of the simulation, each method results in the same mean genetic value of the top-10 individuals. After only two breeding cycles, the oracle TP update method already yields a higher genetic gain compared to the other TP update methods. At breeding cycle 15, a difference of 15 and 21 percentage points is observed between the oracle and top TP update methods for a breeding population using truncation selection and the scoping method, respectively. It is clear that the oracle TP update method results in a better prediction of the GEBVs, optimizing the parental selection and thus leading to higher long-term genetic values. An overview of the mean genetic value of the top-10 individuals and of the maximum reachable genetic value using different update methods is listed in The top, random, tails, PEVmean and CDmean TP update methods  yield ap44.1The oracle selection method is a theoretical concept that uses QTL positions, QTL effects, and genotypic and phenotypic information to select an optimal parental population or TP. This method allows for the comparison of currently existing methods. If a method obtains a similar genetic value as observed for the oracle method, then this means it is optimal and cannot be further improved upon. However, as observed in 4.2in vivo, only genetic markers are available to guide parental selection. Although oracle selection is able to maximize the genetic gain by greedily selecting the favorable QTL alleles in the parental population, when the parental selection process relies on genetic markers that are putatively linked to the causal QTL effects, greedily selecting individuals (as observed for truncation selection) often results in a premature convergence of the genetic value. In other words, by only preserving the marker alleles that have a positive estimated marker effect, the loss of favorable QTL alleles cannot be prevented. Preserving both marker alleles in the breeding population prevents the elimination of poorly estimated QTL alleles resulting in higher long-term genetic values compared to a greedy strategy like truncation selection.Oracle parental selection was developed to study the effects of using a modified truncation selection scheme in which the frequency of all favorable QTL alleles is maximized. Oracle parental selection assumes knowledge of the actual QTL effects and is therefore only of conceptual interest; 4.3The favorable QTL alleles are not always abundantly present in the initial population and many breeding cycles may be needed before fixation occurs. This explains the relatively slower increase of the fixed genetic value compared to the mean genetic value of the top-10 individuals see Figure 44.4The main goal of breeding is to maximize the genetic value of various traits of interest and this both in the short and the long term. Unfortunately, the genetic value cannot be measured, and thus selection is often based on the phenotype. Because the phenotype is also influenced by the environment, its use to guide parental selection will result in a lower genetic gain compared to the use of genetic values. This is shown in Aside from environmental effects and prediction errors, selecting the parents with the oracle selection method results in a higher long-term genetic value than when the parents are selected with truncation selection based on the true breeding values. Truncation selection selects individuals with the highest genetic value. Because the genetic value is calculated as the sum of all the QTL effects, an individual with a high genetic value can still possess unfavorable QTL alleles. In other words, truncation selection based on genetic values can still result in the loss of favorable QTL alleles. Oracle selection prevents, just like the (deep) scoping method, the loss of these favorable QTL alleles, resulting in an overall higher long-term genetic value.4.5At the first breeding cycle, the oracle TP update method adds approximately 30 individuals to and removes 30 individuals from the TP, replacing the randomly chosen individuals one by one. In subsequent breeding cycles, the number of individuals that are removed from the TP is reduced, allowing for an increase in the size of the TP. Over time, fewer individuals are added to the TP, resulting in the same number of individuals that are added to and removed from the TP (steady state). This means that the predictive performance is not maximized by consistently increasing the TP size.For truncation selection, the size of the TP starts to converge at breeding cycle 15. At that point, fewer than 25 individuals are added to the TP. This observation seems to indicate that using huge datasets to fit a prediction model may not be the best strategy.4.6The top and tails TP update methods rely on GEBVs. Because the top TP update method selects only the individuals with the highest GEBVs, the mean genetic value of the TP is higher compared to that of the breeding population  can be found below: DV, SM, JF, and BDB conceived and supervised the study. DV designed and performed the experiments, and wrote an early version of the manuscript. All authors contributed to the article and approved the submitted version.


Pocillopora acuta Lamarck, 1816 displays varying levels of asexual versus sexual reproduction, with strong repercussions on genetic diversity, connectivity and genetic structuring within and among populations. For many geographic regions, baseline information on genetic diversity is still lacking, particularly in the Andaman Sea. The region suffered a massive heat-induced bleaching event in 2010 with high coral cover loss of branching coral species such as P. acuta. A subsequent bleaching in 2016, however, revealed a mild bleaching response in pocilloporids compared to other coral taxa in the region, suggesting that rare, heat tolerant genotypes had been selected by the 2010 bleaching event. In order to test whether this potential ‘evolutionary rescue’ event has led to a low genetic diversity, we conducted a population genetic survey covering a total of nine different P. acuta populations  along a 50 km coastal stretch around Phuket Island, Thailand. We used six microsatellite markers to assess genotypic diversity and to determine the prevalent mode of reproduction .The widespread Indo-Pacific coral species P. acuta populations, the majority of corals in this study adopted a sexual reproduction mode . At the same time, substantial regional gene flow was observed around Phuket Island with strong genetic differentiation as indicated by three genetic clusters that were separated by only a few kilometers. Patterns of isolation by distance over 0.7 – 40 km suggest small-scale genetic barriers, such as changing currents throughout each monsoonal season, potentially contributing to locally restricted dispersal of P. acuta larvae.In contrast to other Indian Ocean The occurrence of distinct genetic clusters within short coastal stretches suggests that the 2010 bleaching event has not led to extreme genetic impoverishment. While more in-depth genomic analyses are necessary to investigate changes in genetic diversity following extreme bleaching events, our results will help guide conservation efforts to maintain genetic diversity of a coral species that likely will be dominant in future, warmer Andaman Sea reefs.The online version contains supplementary material available at 10.1186/s12862-023-02153-7. Pocillopora colonies exhibit a high recovery potential  + 30 x  + 72 °C for 5 min (adapted from [Na ± SE) and the mean number of private alleles (Np ± SE) were calculated in R Studio v1.3.1093 with the package poppr [P. damicornis colonies in Thailand for the microsatellite markers PV2 and Pd2-006. Studies showed that including third alleles in their analyses for the locus Pd3-EF65 had no effect on the outcome [P. acuta corals from reef sites around Hawai ‘i.DNA was extracted from coral tissue samples using an Ethanol precipitation protocol . DNA quated from ). Thermoge poppr . Five loge poppr , 129 or ge poppr , 130. Noge poppr ) found m outcome , 110. Se outcome , somatic outcome  or chime outcome , 131. Fu outcome , the cli outcome ) providepoppr [Therefore, and since peaks are likely to decrease in size the longer the repeat, a third or fourth peak were accepted if it was at least 0.25 RFU of the highest peak that was already scored for the same sample. The fraction of samples showing more than two alleles varied across populations, however, it was never zero. Thus, and since certain analyses do not allow for polyploidy, the samples exhibiting more than two alleles were excluded from further analyses. The remaining dataset thus contained 217 samples, all of which were biallelic. Among the 217 colonies and six loci retained, missing data represented 24.12%  were calculated in GenAlEx v6.503  for each population.Locus characteristics  / (N—1), with N, the number of sampled colonies in a population and NMLG, the number of MLGs identified in the considered population) and genotypic diversity G  were estimated for each population for the per-individual dataset as described by [G is indicative of the reproductive mode, ranging from 1 in predominantly sexual to 0 in clonal populations [R denotes whether all individuals within a population share the same genotype (R = 0) or all genotypes differ from each other (R = 1). The diversity and evenness of clonal populations  were estimated with GenoDive v3.05 [G = Go/Ge) and genotypic evenness (Go/NMLG) was investigated to determine the ratio of asexual to sexual reproduction present among the populations using R core packages in RStudio v1.3.1093.Identical MLGs among one v2.0 . Furtherone v2.0  implemenone v2.0 . The proone v2.0 . Subsequone v2.0 ) was conribed by . G is inulations . Similarve v3.05  for each6 chain length after a burn-in of 105 assuming admixture and correlated allele frequencies. Two sets of runs were conducted, first using no prior and second using location information as prior [n = 196). The optimal number of genetically homogeneous clusters was chosen from four different sets of runs  using the ΔK method [pophelper [poppr [FST [FST values were calculated with 1000 permutations in GenoDive v3.05 [FST/(1—FST) plotted against the shortest geographic distances between sites avoiding landmasses (Table Shttps://github.com/afiesinger/PopGen_Pacuta_MSATS_Phuket_2023).Population genetic structure analyses were performed on both the per-individual  and the per-genotype (i.e. keeping only one representative of each MLG) dataset. As discussed in depth in , keepingas prior . To checK method  in R Stuophelper . Then, cophelper ) was perr [poppr . Pairwisppr [FST . Signifive v3.05 . IsolatiAdditional file 1.


Data described in this article refer to molecular characterization and assessment of genetic diversity within a wide collection of pomegranate genotypes including both selections and cultivars from different geographical origin/disseminations by using microsatellite  markers. Supplied datasets refer to a set of 63 genotypes including 55 accessions (landraces) from Italy, Turkmenistan, Japan, and USA and 8 cultivars from Israel, established at the pomegranate repository of the Fruit Tree Unit of the Department of Soil, Plant and Food Science at University of Bari “Aldo Moro”, Italy. Pomegranate accessions differed for end-use purpose  and some morpho-pomological traits including juice taste, inner tegmen hardness, and skin/seed color. Molecular data were opportunely employed to build a similarity matrix to establish phylogenetic relationships (genetic similarity and distances) among pomegranate accessions and compare genetic clustering to morpho-pomological classification.The present data article provides detailed information and methodological protocols on SSR markers, PCR amplification and banding profiling aimed to molecular characterization of pomegranate collection. This latter was conducted by amplifying a set of informative polymorphic SSR markers on the genomic DNA of each pomegranate accession, and then comparing the different molecular profiles by capillary electrophoresis. The banding patterns obtained from microsatellite markers were used to build a binary matrix containing the scores for each individual SSR fragment, which was transformed into a similarity matrix and finally used for cluster analysis and dendrogram building based on the UPGMA algorithm. This paper supplies data potentially useful for the identification of polymorphic markers suitable for varietal identification and traceability, or discrimination between tightly related pomegranate accessions with very high morphological similarity and/or geographical identity.Punica granatum L.) selections and cultivars” [1].Data described in this paper support the published original research article titled “Exploiting DNA-based molecular tools to assess genetic diversity in pomegranate ( Specifications Table•Data described in this article support original research. The paper supplies detailed methods, data, and references allowing research reproducibility.•Datasets in this article are clearly, comprehensively, and adequately presented and are suitable to be re-used by scientific community.•Detailed information on microsatellites markers and molecular patterns derived from their PCR amplification could be employed for genetic characterization of pomegranate collections from other geographical areas of the world. Data on genetic variability could be useful to identify SSR markers able to discriminate between synonyms and homonyms genotypes and distinguish even closely related accessions with very high morphological similarity and/or geographical identity.•etc.).Genetic diversity assessment by microsatellite markers can supply a robust and reliable molecular tool for varietal identification. This could serve to selection of superior pomegranate genotypes to be employed directly or as donors in breeding programs for developing novel varieties endowed with improved agronomical, commercial, and nutritional properties  lists 52 microsatellite primer pairs used to assay genetic diversity within a pomegranate collection of 63 genotypes including 55 accessions (landraces) and 8 cultivars Supplementary Table 1, publicily available at the Mendeley Data repository 3Datasets supplied in this article refer to the assessment of genetic diversity and phylogenetic relationships within a comprehensive collection of 63 pomegranate genotypes including 55 accessions (landraces) from Italy, Turkmenistan, Japan, and USA and 8 cultivars from Israel Genomic DNA was isolated from 100 mg of fresh young leaf samples of pomegranate plants by using the DNeasy Plant Mini Kit (Qiagen) following the manufacturer's instructions. DNA quality was spectrophotometrically checked at a Nanodrop device by reading the A260/A280 ratio with a value of 1.8-2.0 indicating good quality. Genomic DNA of all samples was adjusted to a 25 ng/µL final concentration to be used in following PCR reactions. Amplifications of microsatellite markers were performed on 100 ng high-quality gDNA from each pomegranate accession, by using a set of 52 SSR primer pairs derived from relevant literature in the field Amplification products were preliminarily checked for size and quality by a standard electrophoresis on 1.8% (w/v) agarose gel , 4, thusOnly primer combinations giving specific polymorphic amplification products were employed for genetic characterization of pomegranate collection . Among tThe work meets the ethical requirements for publication in Data in Brief. The work does not involve studies with animals and humans.Angelica Giancaspro: Methodology, Investigation, Formal analysis, Visualization, Data curation, Writing – original draft. Stefania Lucia Giove: Methodology, Investigation, Formal analysis. Ilaria Marcotuli: Investigation. Giuseppe Ferrara: Conceptualization, Resources, Project administration, Funding acquisition, Supervision, Writing – review & editing. Agata Gadaleta: Conceptualization, Supervision, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.