Taiwania cryptomerioides) lineages occurring in Taiwan, the mainland of China (Yunnan-Myanmar border), and northern Vietnam, and tested the relative strength of neutral drift and divergent selection in shaping divergence of those populations and lineages. We quantified genetic and epigenetic variation, respectively, using amplified fragment length polymorphism (AFLP) and methylation-sensitive amplification polymorphism (MSAP). Analysis of 1413 AFLP and 462 MSAP loci using frequency-based genome scan methods and generalized linear models (GLMs) found no potential selective outliers when only Taiwanese populations were examined, suggesting that neutral drift was the predominant evolutionary process driving differentiation between those populations. However, environmentally associated divergence was found when lineages were compared. Thirty-two potential selective outliers were identified based on genome scans and their associations with environmental variables were tested with GLMs, generalized linear mixed effect models (GLMMs), and model selection with a model averaging approach. Ten loci (six AFLP and four MSAP) were found to be strongly associated with environmental variables, particularly monthly temperature variation and normalized difference vegetation index (NDVI) using model selection and a model averaging approach. Because only a small portion of genetic and epigenetic loci were found to be potential selective outliers, neutral evolutionary process might also have played crucial roles in driving lineage divergence, particularly between geographically and genetically isolated island and mainland Asia lineages. Nevertheless, the vast amount of neutral drift causing genetic and epigenetic variations might have the potential for adaptation to future climate changes. These could be important for the survival of Taiwania in different geographic areas.The question of what determines divergence both between and within species has been the central topic in evolutionary biology. Neutral drift and environmentally dependent divergence are predicted to play roles in driving population and lineage divergence. However, neutral drift may preclude adaptation if the rate of gene flow between populations is high. Here, we sampled populations of three Taiwania ( Both selective and neutral forces may be involved in population and lineage diversification. Elucidating their relative strength in driving biological variation is critical to understanding how these processes impact evolution at population level and early stages of speciation  is a coniferous species in the monotypic genus Taiwania of the cypress family Cupressaceae. While its fossil record indicates that it was formerly widespread across the northern Hemisphere  potentially shaping population and lineage divergence and its association with specific environmental variables. Additionally, AFLP and MSAP may have the advantage of revealing genetic and epigenetic variations that could also be useful for investigating population and lineage divergence and their association with specific environmental variables. Previous studies have indicated a high rate of gene flow among mainland China populations and also between the Chinese and the Vietnamese lineages  collected from south of Gaoligonshan, though not used in the present study, were marked by black circles in Figure n = 51), one along the Yunnan-Myanmar border (mainland China) (n = 18), and one in northern Vietnam (n = 33)  and M00 (5′-GATGAGTCCTGAGTAA-3′) primers were used in AFLP amplification procedure  , EcoRI-HpaII (eHpaII), and a combined error rate (eMspI + eHpaII – 2eIeHpaIIMsp) for each primer combination were calculated  at the 95% level and unbiased expected heterozygosity (uHE)  Nei,  within aHE) Nei, . Allele HE) Nei,  assumingHE) Nei,  was usednce Nei,  matricesprivate_alleles function of R package poppr. Multilocus linkage disequilibrium (LD) was assessed with index of association (IA)   with reduced maximum likelihood estimation was used to assess whether mean s Lenth, .FST, and Bayesian HICKORY θII  for dominant markers. The default settings for sampling and chain length parameters  were used in HICKORY. Four models, including a full model, f = 0 model, θII = 0 model, and f-free model, were assessed for the best model fitting to the genetic and epigenetic data. The f-free model was used to estimate f. Separate analyses of the population structure, including between populations and between lineages, were performed in HICKORY. The Deviance Information Criterion (DIC) was used to identify the best fitting model. A lower D + pD (model fit + model complexity) was used to assist determination of the best model if the difference between models was smaller than six units. We ran HICKORY twice for each analysis in order to check the convergence of parameters.AFLP, MSAP-m, and MSAP-u datasets were used for computation of the levels of genetic and epigenetic differentiation via analysis of molecular variance (AMOVA), across population neighborNet function of R package phangorn )  . DAPC, a multivariate method, was performed using the find.clusters and dapc functions of R package adegenet. DAPC first performed a principal component analysis (PCA) followed by a discriminant analysis that maximize the inter-group component of variation.To identify genetically and epigenetically homogeneous groups of Taiwania populations, we used the Bayesian model-based method implemented in STRUCTURE v.2.3  and an FDR of 1% with each run comprising 106 simulations. Both “neutral mean FST” and “force mean FST” were selected. FST outliers were removed to increase the reliability of calculating the global distribution of FST. BAYESCAN uses a reversible-jump Markov chain Monte Carlo algorithm based on a Bayesian likelihood approach to estimate the ratio of posterior probabilities of selection over neutrality [the posterior odds (PO)]. Parameters for running BAYESCAN were 100 pilot runs of 50,000 iterations followed by a sample size of 50,000 with thinning interval of 20 among 106 iterations. A locus with log10(PO) > 0.5 was considered to have substantial evidence for selection . Because genome scan methods may obtain low support value [low log10(PO)] in BAYESCAN and false positives in DFDIST . For the Samβada analysis, genetic and epigenetic markers were coded with “11” and “00” for presence and absence and tested for associations of allele frequencies with values of environmental variables  in global and pairwise lineage comparisons. Significant fit was identified comparing between models with and without environmental variables based on both Wald and G scores with an FDR cutoff of 0.01. When only Taiwanese populations were investigated, no FST outlier was found using DFDIST and BAYESCAN, and neither with Samβada for association between genetic and epigenetic variations with environmental variables. Therefore, we focused on lineage comparison in the following analyses.A total of 32 FST outliers with environmental variables  using the glmer function of R package lme4. In GLMMs, environmental variables were used as fixed effects and lineage as a random effect. To determine significant associations of environmental gradients with allele frequencies of FST outliers, profile CIs (95 and 99%) based on likelihood ratio test for fixed effects were used. A two locus exact test was used to assess pairwise LD between potential outlier loci using ARLEQUIN, and significance determined by 10,000 permutations.Generalized linear mixed effect models (GLMMs) were also used to test for the association of http://www.worldclim.org/) at 30-s spatial resolution (~1 km) . Ecological factors including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from moderate resolution imaging spectroradiometer (MODIS) dataset MOD13A2 (1 km resolution), and leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fPAR) obtained based on MOD15A2 dataset (500 m resolution). The annual total potential evapotranspiration (PET) was calculated based on MOD16A3 dataset (500 m resolution). All the MODIS datasets were acquired from Land Process Distributed Active Archive Center  during 2001–2013. Four tiles  were required to cover the entire study region. The monthly mean values of NDVI, EVI, LAI, and fPAR were computed using a maximum values composite procedures  using a universal spherical model of the Kriging method in ArcGIS  conducted in 1969–1986, were acquired from the Agriculture and Food Agency of Taiwan , cloud cover (CLO), time of sunshine (SunH), number of rainfall days per year (RainD), mean wind speed (WSmean), and soil pH. Monthly mean values of RH, CLO, SunH, RainD, and WSmean at spatial resolution of 1 km were obtained from the Data Bank for Atmospheric Research  were calculated using the adonis function of R package vegan  was used to test for environmental heterogeneity among Taiwania populations within Taiwan and among Taiwania lineages using the e Herve,  with 999prcomp function of R. The first two PCs with eigenvalues >1  explaining 51.63 and 28.12% of environmental variation were used in a redundancy analysis to assess the relative contribution of environmental variables explaining the total genetic and epigenetic variations using the varpart function of R package vegan, and significance tested using the anova.cca function with 999 permutations. AFLP, MSAP-m, and MSAP-u variations were, respectively, partitioned into four fractions explained by pure environmental variables (fraction [a]), geographically-structured environmental variables (fraction [b]), pure geographic variables (fraction [c]), and residual effects (fraction [d])  and performed the subsequent model averaging analyses based on Akaike information criterion with a correction for small sample sizes (AICc) . AICc was used to rank the models and to calculate the Akaike weights for each model  and the presence of a genetic or epigenetic outlier (response variable), and a marginal-R2 for the fixed effect of the most important environmental variable(s) explaining outlier variation was calculated using the r.squaredGLMM function.To further assess the relative importance of environmental variables influencing outlier genetic and epigenetic variations, functions within the R package MuMIn Barton,  were user = 0.387, P < 0.001; AFLP vs. MSAP-u, Mantel r = 0.388, P < 0.001; and MSAP-m vs. MSAP-u, Mantel r = 0.680, P < 0.001).In total, 1,413 and 462 loci were obtained, respectively, with a mean ± SD of 128.45 ± 10.46 and 57.75 ± 9.85, for AFLP and MSAP  than those of Yunnan-Myanmar (Np = 4) and Vietnamese (Np = 14) lineages (Table Np = 13) compared with Vietnamese (Np = 0) and Taiwanese  lineages at the population level. The Yunnan-Myanmar and Vietnamese lineages had similar number of MSAP-u private alleles  compared to populations of Taiwanese lineage . The average percentage of polymorphism was comparatively higher in Taiwanese lineage compared with Yunnan-Myanmar and Vietnamese lineages .The number of private AFLP alleles was comparatively higher in populations of the Taiwanese lineage . Post-hoc pairwise comparisons revealed significantly higher levels of AFLP, MSAP-m, and MSAP-u diversity in the Taiwanese lineage compared with the other two lineages, and significant differences in these diversity measures between the Yunnan-Myanmar and the Vietnamese lineages were also found . However, not all pairwise population comparisons showed significant differences in the level of uHE  analyzed using the total data  was, respectively, obtained at K = 6, 3, and 7, for AFLP, MSAP-m, and MSAP-u . When K = 3 and 4, no further differentiation power between lineages was found based on AFLP and MSAP-m data. Nonetheless, LEA for MSAP-u showed differentiation between Yunnan-Myanmar and Vietnamese lineages at K = 4 . Environmental heterogeneity was also found in pairwise lineage comparisons . Moreover, PERMANOVA revealed overall environmental heterogeneity based on the eight retained environmental variables when only the six populations of Taiwanese lineage were analyzed (P = 0.001). However, not all pairwise population comparisons showed significant environmental differences  were detected as potential outliers under selection based either on DFDIST or BAYESCAN  considering the redundancy between variables in different categories in explaining genetic and epigenetic variations and also because of only small amounts of genetic and epigenetic variations attributed to pure environment in the variation partitioning analysis Table . For allThe 95% CIs for coefficients of environmental covariates which did not overlap with zero suggest individual environmental variables acted on potentially adaptive genetic and epigenetic loci Table . ResultsWithin each lineage, the 32 potential selective outliers were subjected to two-locus exact test for LD  was attributed to geographically-structured environmental variables Table , suggestUsing the 32 potential outliers Table , resultsThe values of monthly temperature variation were larger in Yunnan-Myanmar and Vietnamese lineage compared with populations of Taiwanese lineage  and within Vietnamese lineage  . Higher allele frequency of aP9_133 was found in Yunnan-Myanmar and Vietnamese lineages in contrast to Taiwanese lineage Figure , and aP9Conservation genetics emphasizes the maintenance of the evolutionary capability of species adaptations to varying environments (Frankham et al., S-YH proposed, funded, and designed the research. PT, J-DC, and C-NW collected samples. Y-SL performed research. S-YH, Y-SL, and C-TC analyzed data. S-YH, Y-SL, C-TC, PT, and C-NW wrote the paper. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pinus pinaster and assessed whether such variation was associated with climate gradients. To that end, we characterized the resin duct system of 2-year-old saplings from 10 populations across the species’ distribution range. We measured axial resin duct features  and their inducibility in response to methyl jasmonate. Genotyping of single nucleotide polymorphisms allowed to account for the population genetic structure in our models in order to avoid spurious correlations between resin duct characteristics and climate. We found large inter-population variation in resin duct density and conductive area, but not in their inducibility. Our results suggest that population variation in the percentage conductive area of resin ducts likely arise from adaptation to local climate conditions. This study highlights the adaptive relevance of resin ducts and helps to shed light on the micro-evolutionary patterns of resin-based defenses in conifers.Resin ducts are important anatomical defensive traits related to biotic resistance in conifers. Previous studies have reported intraspecific genetic variation in resin duct characteristics. However, little is currently known about the micro-evolutionary patterns and adaptive value of these defensive structures. Here, we quantified inter-population genetic variation in resin duct features and their inducibility in  Several external stimuli have been reported to induce the formation of "traumatic" resin ducts, including insect attacks, pathogen infestation, or wounding alone (Endoconidiophora polonica (previously Ceratocystis polonica (Siemaszko) C. Moreau) or MJ application in Picea abies (L.) Karst trees enhanced traumatic resin duct production and biotic resistance to bark beetles and their associated pathogens in later infections  Carr. populations shaped genetic variation in resin duct characteristics. However, genetic variation includes not only adaptive variation resulting from diversifying selection in fitness-related traits, but also neutral variation due to processes such as genetic drift or gene flow  and their inducibility in Pinus pinaster Ait.) is a Mediterranean conifer species of high economic and ecological value in Southwest Europe and North Africa, inhabiting a wide range of heterogeneous environments, from wet-coastal to regions with prolonged summer drought  of the main stem at mid height was sampled and stored in FAA  and then transferred to 75% ethanol (v/v) as previously reported in ImageJ 1.51k image analysis software (2), the mean area of resin ducts (hereafter RD mean size – mm2), and the percentage of the total tissue area occupied by the lumen of resin ducts (hereafter RD conductive area – %)  coupled to a binocular stereoscope and analyzed using software . After csoftware Figure 2lmer function from lme4 package in R  and their response to MJ treatment (inducibility), we applied linear mixed models using the age in R . Populat2. The model of We obtained average climate data for the period 1950–2000 at the origin of populations from regional  and globLSmeans function from the homonym package in R . To that end, population least square means for each resin duct trait were obtained from the afore-mentioned model using the age in R .Q matrix) based on the genotyping of 126 SNPs from an independent set of clonal replicates (see more details in http://doi.org/10.5281/zenodo.1445313). The Q matrix assigns to each genotype a genetic membership coefficient for each of the five genetic clusters . However, RD mean size was a 10-fold higher in the phloem than in the xylem . Consequently, RD conductive area was higher in the phloem than in the xylem   and in the phloem . Moreover, RD density in the phloem was negatively correlated with RD mean size in the xylem  and vice versa, RD density in the xylem was negatively correlated with RD mean size in the phloem  (see RD density was negatively correlated to RD mean size both in the xylem (130) see Table S32 between populations. TAMR was the population with the highest RD density in the xylem whereas PLEU showed the lowest value for this trait . These results also agree with previous findings. In particular, Picea sp. Variation among Pinus sylvestris L. and P. nigra J.F. Arnold populations in resin duct characteristics has also been reported  . Abioticreported , althougP. pinaster populations suggests important implications in potential biotic resistance in this species. It has been suggested that resource allocation to resin ducts provides enhanced resistance to different antagonists. For instance, in North American pine species, trees that survived or avoided bark beetle attacks had more or larger resin ducts than trees that died (Picea sitchensis provenances previously characterized as resistant to the white pine weevil (Pissodes strobi) exhibited a denser network of resin ducts , which were analyzed in previous work  Voss introgression zone were found to be positively correlated with aridity and continentality at the origin of the populations . Phenotypic differences in resin duct characteristics among in ducts . Likewisulations .P. pinaster, like other European tree species, experienced severe bottlenecks during the ice ages . That seems to be the case for most plant secondary metabolites in P. pinaster, whose genetic differentiation among populations (STQ) was not different from that expected by genetic drift alone (STF)  .When making inferences in comparative ecology, not accounting for the genetic relatedness can lead to the misinterpretation of potential clinal patterns due to the non-independence of the populations . After aThe opposite situation would be those resin duct characteristics that were significantly correlated with climate even after accounting for the demographic history of the species. Such is the case of RD conductive area. In the xylem, this trait remained significantly and negatively correlated with the PC1 of Atlanticity after accounting for the neutral variation, suggesting that climate heterogeneity is contributing to inter-population differentiation of resin ducts in this tissue. In addition, a positive significant correlation between RD conductive area in the phloem and the PC2 emerged after accounting for the neutral variation. In both cases, adaptation to local climate conditions, alongside the demographic history of the species, seems to help explain the differentiation of populations in these traits. Current hypothesis on growth-defense trade-offs state that resource allocation to defenses is promoted in limiting environments to the detriment of growth capacity . Our resversus quantitative variation  in resin duct characteristics to broadly understand the evolutionary origin of this anatomical trait. Moreover, assessment of adaptive genetic variation in resin ducts should also be tested in mature trees, as previous studies showed that ontogeny plays an important role modulating resin-based defenses in conifers  which is likely associated to climate gradients .The SNP dataset analyzed in this study is publicly available in the Zenodo repository  grants FUTURPIN (AGL2015-68274-C03-02-R), AdapCon (CGL2011-30182-C02-01/02) and RESILPINE (RTI2018-094691-B-C33), and by the Xunta de Galicia-GAIN grant (IN607A2016/013). CVG received financial support from the MINECO FPI Grant program (MINECO-Spain BES-2016-076624). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Apis mellifera scutellata and A.m. capensis (the Cape honey bee) are western honey bee subspecies indigenous to the Republic of South Africa (RSA). Both bees are important for biological and economic reasons. First, A.m. scutellata is the invasive “African honey bee” of the Americas and exhibits a number of traits that beekeepers consider undesirable. They swarm excessively, are prone to absconding (vacating the nest entirely), usurp other honey bee colonies, and exhibit heightened defensiveness. Second, Cape honey bees are socially parasitic bees; the workers can reproduce thelytokously. Both bees are indistinguishable visually. Therefore, we employed Genotyping-by-Sequencing (GBS), wing geometry and standard morphometric approaches to assess the genetic diversity and population structure of these bees to search for diagnostic markers that can be employed to distinguish between the two subspecies.Apis mellifera scutellata possessed the highest mean number of polymorphic SNPs (among 2449 informative SNPs) with minor allele frequencies > 0.05 (Np = 88%). The RSA honey bees generated a high level of expected heterozygosity (Hexp = 0.24). The mean genetic differentiation  among the RSA honey bees revealed that approximately 93% of the genetic variation was accounted for within individuals of these subspecies. Two genetically distinct clusters (K = 2) corresponding to both subspecies were detected by Model-based Bayesian clustering and supported by Principal Coordinates Analysis (PCoA) inferences. Selected highly divergent loci (n = 83) further reinforced a distinctive clustering of two subspecies across geographical origins, accounting for approximately 83% of the total variation in the PCoA plot. The significant correlation of allele frequencies at divergent loci with environmental variables suggested that these populations are adapted to local conditions. Only 17 of 48 wing geometry and standard morphometric parameters were useful for clustering A.m. capensis, A.m. scutellata, and hybrid individuals.We produced a minimal set of 83 SNP loci and 17 wing geometry and standard morphometric parameters useful for identifying the two RSA honey bee subspecies by genotype and phenotype. We found that genes involved in neurology/behavior and development/growth are the most prominent heritable traits evolved in the functional evolution of honey bee populations in RSA. These findings provide a starting point for understanding the functional basis of morphological differentiations and ecological adaptations of the two honey bee subspecies in RSA.The online version of this article (10.1186/s12864-018-4998-x) contains supplementary material, which is available to authorized users. Apis mellifera L. (Hymenoptera: Apidae), are cosmopolitan eusocial insects that play an important role in the cultivation of various crops and maintenance of healthy ecosystems globally [Apis, which is comprised of nine honey bee species, is believed to have an evolutionary origin in Asia [A. mellifera have been identified: (A) African subspecies, (M) northern and western European subspecies, (C) North Mediterranean subspecies, (O and Z) Middle Eastern subspecies, and (Y) in Ethiopia [Within the insect family Apidae, western honey bees, globally , 2. The  in Asia . From th in Asia , 5. Six Ethiopia , 6–8.A. mellifera subspecies distributed across the continent with substantial geographical variability among the areas in which the lineages are endemic [A.m. scutellata from the Savannah areas of central and southern Africa, and A.m. capensis from the southern part of the Western and Eastern Cape of the Republic of South Africa (RSA), are of particular interest due to the behavioral characteristics they present [Africa, specifically, is home to at least 11  endemic . It has  endemic . Two subA.m. scutellata is referred to as the “African,” “Africanized”, or “killer” bee of the Americas where it is considered invasive. A.m. scutellata and its hybrid populations have spread throughout South America, Central America and the southern parts of North America [Apis mellifera scutellata exhibits several behaviors that beekeepers consider undesirable, but that are biologically important to the bee. These include excessive swarming, absconding, aggressive usurpation and heightened defensiveness [A.m. scutellata in the Americas has not been quantified but is likely significant.Outside of its endemic range,  America –14. Apissiveness , 12, 15.Apis mellifera capensis is a facultative social parasite that can reproduce thelytokously . These bees are characterized by a unique set of genetic, behavioral and physiological traits expressed by the worker bees [A.m. capensis workers  [capensis calamity” in the RSA when A.m. capensis colonies were moved by beekeepers to areas where A.m. scutellata were indigenous. Once there, A.m. capensis workers drifted into A.m. scutellata colonies and became social parasites of these colonies, thus leading to widespread colony collapse and the recognition of the threat A.m. capensis pose to non-capensis colonies [ker bees , 16–19. ker bees , 21. Furker bees , well-deker bees , shorterker bees , and theker bees , 26. The female) , 28. Thicolonies .A.m. scutellata and A.m. capensis makes them ideal model organisms to investigate the genetic variability and population structure of African honey bee subspecies. Wild honey bees in sub-Saharan Africa are believed to have low levels of genetic differentiation which may be due to a high degree of panmixia and large dispersal capacity of colonies [A.m. scutellata and A.m. capensis is occupied by hybrids of the two subspecies [The behavioral and ecological diversity of colonies , 29. Thecolonies . Additiobspecies .A.m. capensis and A.m. scutellata and the stability of the hybrid zone over time is still incipient. Recent progress in the development of next-generation sequencing (NGS) platforms has enabled scientists to genotype large groups of individuals using a genotyping-by-sequencing (GBS) approach [Knowledge concerning the population structure of approach . GBS is approach , 32. Theapproach –35.A.m. capensis, A.m. scutellata and hybrid honey bees collected from 69 different apiaries, representing 28 geographical regions across the natural distribution of honey bees in the RSA. We also determined if allele frequencies at divergent loci were significantly correlated with environmental variables, in an effort to identify regions of the genome under natural selection. We further measured wing geometry and standard morphometric parameters to evaluate the differentiation pattern between A.m. capensis and A.m. scutellata, given that morphometrics is the current tool utilized to separate the subspecies [A.m. capensis and A.m. scutellata bees.Herein, we investigated genetic differentiation and population structure within 464 bspecies  and the bspecies , 38. TheA.m. capensis, A.m. scutellata and hybrids of the two in the RSA. The number of bees examined per region/apiary and geographic information are reported in Table A. mellifera samples collected from honey bee colonies located at Honey Bee research Extension Laboratory, University of Florida were included in the genetic analysis for reference purposes. The RSA honey bees were collected into 50 ml vials containing absolute ethanol, imported into the US per USDA APHIS protocol and approval, and stored at − 20 °C prior to morphological and molecular analyses.We collected samples of worker honey bees from 1000+ managed colonies across the RSA in 2013 and 2014. These collections spanned the native geographical distribution of Lateral images of the thorax and hairs on abdominal tergite 6 (A6) were taken of each bee prior to dissection. Four different parts of each bee’s body  were dissected to facilitate imaging of the morphometric characteristics. Forewings and hindwings were removed from the thorax using forceps. The sternites and tergites were removed by tearing the connective tissue between them. The dissected sternite A4 was cleaned with a paint brush and soaked in KOH to remove any remaining tissue. It also was stained with Bioquip double stain (6379B) and dried on a Kim-tech wipe. Excess stain was removed with ethanol. All body parts were dried on a Kim-tech wipe and mounted on slides using Euparal mounting medium. Mounts were made on 25 × 75 mm Fisher glass slides (S17466A) and cover glass , warmed at 60 °C for 3 days on a slide warmer , and imaged individually using Leica M205 light microscope equipped with a Leica MC170 camera (1600 × 1200 pixels) with its related software.Ten standard morphometric characters were chosen based on information published by , 22. TheForewing geometric landmarks were chosen based on the information published by Francoy et al. . These iAfter morphometric analysis, total DNA was extracted from the dissected honey bee thoraces in accordance with the protocol outlined in . DNA quaEcoT22I, a type II restriction endonuclease which recognizes a degenerate 6 bp sequence (ATGCAT) , by incubation at 37 °C for 2 h. The digested DNAs were ligated with an equal amount of a different barcode-containing adapter and the same common adapter. The 474 barcode sequences were pooled (5 μl each) and purified with QIAquick PCR Purification Kit . The sequence of barcodes used for EcoT22I GBS library construction was published by [EcoT22I library was sequenced on an Illumina HiSeq 2000  in one sequencing flowcell lane (100 bp single-end sequencing) at the Cornell University Life Sciences Core Facility.We constructed a GBS library containing 474 honey bee DNA samples (5 × 96 plate), including a negative control (no DNA) in accordance with the methods outlined by , 41. Eacished by , 41. Theished by . The ampished by . The conEcoT22I library were quality-filtered by removing adapter sequences and enzyme recognition sites, followed by trimming by quality score utilizing the GBS analysis pipeline as implemented in TASSEL v3.0 [A. mellifera reference genome [EcoT22I library reads produced the average individual depth of 38.75  with the average site depth of 27.88  across all genotypes. All submitted samples generated sufficient genotypes for analysis and the effectiveness of the GBS method using EcoT22I digestion genomic library was previously shown by [The raw Illumina DNA sequence reads for the SEL v3.0 . We retae genome  using the genome . We furtshown by .Prior to the population genetic analyses, stringent filtering strategies were performed to obtain the most informative SNPs using VCFtools . We applFST-based methods including FDIST [FST values with significant P-value for each locus. This method computes the distribution between FST and expected heterozygosity (HE) using two options “neutral mean Fst” and “force mean Fst” to detect genes under selection. A total number of 2449 SNPs were analyzed based on the following parameters: 50,000 simulations, confidence interval of 0.95, false discovery rate of 0.1, attempted FST ≥ 0.9, and mutation model of infinite alleles. We considered the FST values higher than expected neutral distribution as directional selection and FST values lower than expected neutral distribution as balancing selection.Two different coalescent-based simulations were used to detect SNP loci deviating from neutrality. With these approaches, we expected to detect low levels of genetic differentiation under balancing selection  and high levels of differentiation under directional selection (divergent loci). We used two different ng FDIST  and hierng FDIST . FDIST wng FDIST . LOGISTAFST and heterozygosity. Loci with FST values above the 0.99 limits of neutral distribution were considered as putative outliers under the divergent selection [FST values were considered as neutral SNPs. All procedures reduced the bias and kept the highly diverged loci between individuals of subspecies.The hierarchical method is a modification of the FDIST approach performed using an Arlequin package ver. 3.5.1.2 . We usedelection . The remhttps://www.ncbi.nlm.nih.gov/assembly/GCF_000002195.4/). The DAVID gene accession conversion tool was used to identify homologous genes from Apis mellifera and D. melanogaster as listed in BeeBase and FlyBase [The closest gene to each of the 83 divergent SNPs was determined using bedops v 2.4.22 in vcf2bed and closest features , relativ FlyBase . Functio FlyBase , 54.http://www.worldclim.org/). These bioclimatic variables  . GPA anap-distance model with 1000 bootstrap value across entire SNP loci. Population genetic diversity indices such as observed heterozygosity (Hobs), expected heterozygosity (Hexp), proportion of polymorphic SNP (Np), and inbreeding coefficient (FIS) were calculated for SNP loci using the statistical package R. Departure from Hardy-Weinberg equilibrium (HWE) was assessed by an exact test using Genepop 4.2 [FST) [p-values for the mean and pairwise FST values. FST varies from 0 (lack of genetic structure and no sign of population subdivision) to 1 (distinct population structure or extreme segregation), with FST of up to 0.05 indicating a moderate genetic differentiation [We computed the pairwise evolutionary divergence among regions using MEGA7  based onepop 4.2  based onepop 4.2 . Analysiepop 4.2  with 100epop 4.2 . The ove.2 [FST)  were det.2 [FST) . We permntiation .lasig.epfl.ch/sambada). Samβada examines the associations between all environmental variables and allele frequencies of divergent SNP loci across sampling locations by a logistic regression approach. Models are selected through the examination of the significance of regression coefficient across environmental variables. The significance value of each model was evaluated with likelihood G and Wald scores. The Bonferroni-corrected threshold was considered as α = 0.05, indicating a significant association between loci and the environmental variables.We examined the association of divergent SNP loci with environmental characteristics using an individual-based spatial analysis as implemented in Samβada program  (availabK) with variable proportions of allele frequency of the ancestral population. This approach allows the characterization of an ancestral population using admixed bees [n = 2449 loci and q subset of divergent loci (n = 83) using an admixture model and by applying a putative number of clusters (K) varying from 1 to 10. The analysis was performed without prior information of population identity by a simulation of 50,000 pre-burn steps and 100,000 post-burn iterations of MCMC algorithm for each run. We performed 10 independent runs for each K to estimate the most reliable number of distinct genetic clusters (K) using the likelihood of the posterior probability (LnP (N/K)) [K partition. Posterior probability changes with respect to K between different runs are assigned as a method for determination of the true K value [K was identified based on average log likelihood, Ln P (D) using Evanno’s ∆K method [We applied a model-based Bayesian clustering approach to characterize the existence of distinct genetic clusters among regions of both subspecies as implemented in STRUCTURE 2.3.4 . The genxed bees . We ran P (N/K))  and ad h K value . The mosK method  from theK method . The popK method .A principal coordinate analysis (PCoA) was performed with both sets of SNPs and the entire and divergent SNP loci to visualize the divergence pattern among individuals between two subspecies using TASSEL v3.0 and JMP Pro v12 . The PCoA analysis enables us to visualize the relatedness of individual honey bees across individuals/regions in multidimension scales.P < 0.05). The linear stepwise discriminant function analysis based on all examined variables placed all individual bees into the three groups with a high percentage of classification (87%). The cross-validation test misclassified 61 (13.11%) out of 464 bees. At the regional level, a linear stepwise discriminant function analysis of all wing geometry and standard morphometric variables included six out of ten standard variables , 12 out of 38 forewing coordinates , and seven out of 20 hindwing coordinates  in the significant classification model for the honey bee populations (P < 0.05). A linear stepwise discriminant function analysis based on all examined variables placed all individual bees into the three groups with an average percentage of classification (53%). The cross- validation test misclassified 217 (47%) out of 464 bees.The 19 forewing landmarks created 38 Cartesian coordinates for each specimen and the ten hindwing landmarks generated 20 Cartesian coordinates for each specimen. At the subspecies level, the linear stepwise discriminant function analysis of combined wing geometry and standard morphometric variables incorporated six out of ten variables , 11 out of 38 forewing coordinates , and ten out of 20 hindwing coordinates  in the statistically significant classification model for the honey bee populations  explained 88% of the variance and Canonical Vector 2 (CV2) explained 12% Fig. . Twenty-An analysis of the honey bees grouped into the 28 geographical regions where they were collected revealed an incongruent and overlapped clustering pattern in the Canonical scatter plot of CV 1 33%) and CV 2 (12%). The ovariole count (0.54) and abdomen hair length (− 0.47) were significant characteristics in the first canonical function and the forewing landmark coordinate 5X (0.76) and forewing angle O (0.51) in the second canonical function. The least influential characteristics were forewing coordinate X7 (0.007) and hindwing coordinate X2 (0.007) in the first canonical function, and forewing coordinate X7 (0.03) in the second canonical function . The cluster analysis showed a consistent pattern of overlap with hybrids located between the two subspecies.A dendrogram constructed by hierarchical cluster analysis of the squared Euclidian distances across all individuals revealed six major morphological groups  are presented in Table F values generated significant differences among all three groups except for characteristics 4, 7, 8, 11, 12, 14, 20, 21, 22, 24 and 25 as depicted in Table The mean values of wing geometry and standard morphometric variables for three groups (F = 6.84), 2Y (F = 6.9) and 4Y (F = 6.39) show the highest between group variability and statistical difference among mean values (P ≤ 0.05), while angle N (F = 1.85) and hindwing characteristic 10X (F = 1.31) have the lowest variability and are not significantly different from other parameters (P > 0.05) (data not shown).At the regional level, forewing characteristics 1Y  was the highest across all regions (Hobs = 0.26) except for the PT region (Hobs = 0.21). The inbreeding coefficient (FIS) was negative in BD, MF, OD and TR regions for A.m. capensis and in the KR region for A.m. scutellata. Within the hybrids, two regions, BW and KL, generated negative values that indicate an outbreeding outcome in these regions. The FIS value estimated for the 2449 SNP loci revealed the inbreeding outcome occurred across the regions. The percentage of polymorphic SNPs (Np) ranged from 60.27 to 95.51% among regions. The mean percent of polymorphic SNPs was higher in A.m. scutellata than in A.m. capensis and hybrid populations . The minimum value was observed in two regions of A.m. capensis (BD and TR), and one region of A.m. scutellata (KR) (0.06). A net pair-wise evolutionary divergence among regions ranged from 0.01 to 0.08, with an average of 0.09 , resulting in 2,028,130 reads with 98,134 SNPs. After evaluating the dataset with informative pipelines , we filtered 70,475 SNPs with a mean individual depth of 38.7. By applying stringent additional post filtering SNP criteria, we kept 2449 high quality SNPs out of 70,475 total to analyze among the 474 individual bees in the final data set. A test for HWE departure indicated that all SNPs and regions are in HWE after the sequential Bonferoni correction. At the regional level, ten of 29 regions were in HWE  and LOGISTAN identified 132 divergent SNPs  and LOGISTAN identified 45 divergent SNPs .We retrieved 52 non-redundant BeeBase Genes IDs for known gene products most proximal to each of the 83 divergent SNPS, as well as gene names and functions, as listed in Table A.m. capensis  generated a total of 2961 models, within which 25 SNPs were significantly correlated (Bonferroni-corrected P < 0.05) with one or more environmental parameters   generated 1533 non-missing genotypes with a total of 1323 models, of which four SNPs were significantly correlated (Bonferroni-corrected P < 0.05) with one or more environmental parameters  (Table Within K method suggested three genetic groups (K = 3) with inclusion of European subspecies of A. mellifera , two genetic groups were observed according to the delta K calculation as determined by the Harvester method (K = 2)  confirmed the occurrence of distinct genetic groups, and variable degrees of genetic heterogeneity were observed across its natural distribution in the Cape region , with lesser contributions from K2 (21%) and K3 (K3). The hybrid population appropriately contained roughly equivalent proportions of the two primary Ks: 44% K1, 55% K2 and 1% K3. The PCoA analysis confirmed these results, positioning A.m. scutellata and A.m. capensis into two clusters with hybrids placed at intermediate positions  Fig. . Apis meons Fig. . In A.m.ion Fig. . Hybrid ion Fig. . When weons Fig. . EuropeaA. mellifera in the analysis, A.m. scutellata and A.m. capensis revealed a similar delta K value consistent with the pattern produced with the 2449 SNP set, supporting three distinctive genetic clusters (K = 3) across individuals , when European A. mellifera were excluded. In contrast, the PCoA analysis displayed increased resolution with the reduced marker set, capturing 14.95% and 67.96% of the variance in the first and second components respectively  was observed within each group, estimated at 0.035 for A.m. capensis and 0.04 for A.m. scutellata.Using the set of 2449 SNP loci, a low level of population differentiation (P < 0.05). The lowest FST values observed among other pairs of regions ranged from 0.02 to 0.09, P < 0.05 , while only 3% was attributed across populations (P < 0.001).We observed the highest significant pairwise genetic differentiation between BD and all other regions  [A.m. capensis and A.m. scutellata, though there were no individual variables that alone predicted this separation. Our study demonstrated that 17 wing geometry and standard morphometric parameters can be used to separate the bees into three clusters coinciding with their subspecies and hybrid distributions. We found that honey bee populations from several regions fell outside of the confidence ellipses and instead positioned at the intermediate regions, as expected for hybrids [Morphological studies revealed two distinct morphoclusters of honey bee colonies in RSA , 71, 72. number) . We foun number) . Our win hybrids , 9.A.m. capensis and A.m. scutellata in research and production settings. GBS provided a total of 2449 highly informative SNP markers based on very stringent quality criteria. We found the maximum evolutionary divergence between the African subspecies of honey bees and the European honey bees we tested.With the aid of high-throughput sequencing technologies, it has become possible to genotype a large number of samples economically, this in order to determine genetic diversity, population structure and degree of introgression in different honey bee populations , 74. HerA.m. capensis, A.m. scutellata and hybrids (Np = >80%) was higher than that previously reported for each subspecies of A. mellifera (Np = 40%) [Np = 84.5% vs 30%) [The majority of the SNPs identified in the examined honey bees exhibited a high degree of polymorphism. The average level of polymorphic SNP markers observed in p = 40%) . The ave vs 30%) . The larA.m. capensis, A.m. scutellata and hybrids, with an average of Hexp = 0.23. The highest level of heterozygosity previously reported in the honey bee literature was observed in African bees (Hobs = 0.12) [Foxo, GB48301) gene was greater in savannah honey bees (likely A.m. scutellata) than in desert honey bee populations (likely A.m. yemenitica and A.m. simensis) in Kenya.Genetic diversity based on expected heterozygosity was the highest in almost all regions of  = 0.12) . This is = 0.12) –79. The  = 0.12) , 29. The = 0.12) . In one A.m. capensis and A.m. scutellata, supporting the idea of two subspecies with distinctive physiological and behavioral differences [The clustering pattern from the STRUCTURE analysis of 2449 SNPs illustrated shared ancestry correlating to the two known subspecies in the RSA, and was mirrored by the PCoA. These methods distinguished between ferences . In contferences , 81–83. ferences . In thatA.m. scutellata in savannah regions can be distinguished from other honey bees based on the phylogenetic analysis of complete mitochondrial genome sequences [Our success in genetically identifying the two subspecies is likely due to the sizable number of individuals we analyzed, and the use of SNP markers specifically derived from the target populations , 81, 82.equences .A.m. capensis and A.m. scutellata. Our STRUCTURE and PCoA analyses using 83 divergent loci enhanced the resolution power of SNPs used to discriminate the two subspecies. We suggest that the accuracy and robustness of these markers should be determined on randomly collected samples from RSA. This could validate the discriminatory power of the divergent loci.The performance of outlier approaches to differentiate honey bee populations has been investigated by others , 82. HerA.m. scutellata could be due to the fact that A.m. scutellata differs in genetic characteristics from A.m. capensis. Unique clustering of A.m. scutellata was also observed in phylogenetic analysis based on whole genome data of honey bee populations in Kenya [A.m. capensis, allowing reliable assignment of individuals to the population of origin and providing effective tools to identify pure A.m. capensis colonies in RSA. We propose that these 83 markers may be utilized effectively for the identification of both subspecies, a critical application for both research and agricultural efforts. These 83 divergent loci may be under natural selection for physiological and behavioral characters adaptive to the native environments of both subspecies.The detected signature of admixture within in Kenya . We founApis mellifera scutellata and A.m. capensis are the two divergent honey bee subspecies that exhibit distinct biological functions in RSA [A.m. scutellata and thelytoky in A.m. capensis are the most heritable traits supporting the functional behavior in this study [Drosophila melanogaster [A functional analysis highlighted processes involved in neurology/behavior and growth/development which were among the most rapidly evolving genes identified in the two subspecies. s in RSA , 16–19. s in RSA , 27, 28.is study , 15, 17.nogaster , 86.Honey bees exhibit a suite of diverse behaviors in social environment and several hundred genes have been closely associated with brain function and physiological behaviors in bees . SeveralWe found several genes of the divergent loci shown to impact embryonic development and growth. Eusocial insects have remarkably diverse exocrine gland functions and produce many novel glandular secretions, including pheromones, brood food, and antimicrobial compounds , 90.Genes involved in caste differentiation, worker development and reproduction in both subspecies are the most prominent examples of gene families gaining diverse functions through the social interactions . Our resA.m. scutellata prefers warm and dry climates while A.m. capensis prefers cooler wetter ones [A.m. scutellata SNPs and precipitation for A.m. capensis ones. These findings explain the population distribution of the two subspecies along the west-east axis, and supports the occurrence of adaptive divergence related to environmental parameters. Such signals of local adaptation to environmental variables were previously observed in Iberian honey bee populations [A.m. capensis and A.m. scutellata as demonstrated by Hepburn and Radloff [The distribution and clear differentiation of the two subspecies suggests that they may have been separated by a permanent barrier historically, a barrier likely influenced by environmental conditions such as temperature and precipitation. This is consistent with the literature, which suggests that ter ones , 15. Indulations . We beli Radloff , Jaffé e Radloff  and Onio Radloff .FST = 0.06) could be related to a substantial level of gene flow between populations [FST values could be attributed to the large population size of A.m. capensis and A.m. scutellata [FST values observed in this study for A.m. capensis (0.035), A.m. scutellata (0.04) and hybrids (0.04), is lower than previously reported for honey bees in Africa [A.m. capensis [A.m. scutellata) migrate over large distances to environments with more resources in order to withstand reduced nutrient intake during winter seasons better [The low level of differentiation between these two subspecies in our study Additional file 2:Figure S1. Canonical variance analysis factor loadings of wing geometry and standard morphometric measurements onto Canonical Vector 1 (CV1) (A) and Canonical Vector 2 (CV2) (B), based on subspecies classifications, for 464 measured honey bees collected from the Republic of South Africa. CV1 and CV2 factor loadings all generated the varied sign and contributed in positive and negative values. (TIF 8470 kb)Additional file 3:Figure S2. Canonical variance analysis factor loadings of wing geometry and standard morphometric measurements onto Canonical Vector 1 (CV1) (A) and Canonical Vector 2 (CV2) (B), based on regional classifications, for 464 measured honey bees collected from the Republic of South Africa. CV1 and CV2 factor loadings all generated the varied sign and contributed in positive and negative values. (TIF 8470 kb)Additional file 4:Figure S3. The hierarchical clustering structure of 464 honey bees collected from 28 geographical regions in the Republic of South Africa. The colors indicate different subspecies: blue = Apis mellifera scutellata (N = 73), red = A.m. capensis (N = 337) and green = hybrids (N = 54). (TIF 622 kb)Additional file 5:Table S2. A pair-wise evolutionary divergence matrix based on a corrected p-distance nucleotide model among honey bees in 29 geographical regions in the Republic of South Africa and a reference European Apis mellifera. The geographical abbreviations are explained in Table Additional file 6:Figure S4 A, B. Identification of putative divergent SNP loci under directional selection for Apis mellifera capensis based on FST outlier approaches. (A) Hierarchical structure model using Arlequin 3.5. FST: locus –specific genetic divergence among the populations; Heterozygosity: measure of heterozygosity per locus. The significant loci are shown with red dots (P < 0.01). (B) Finite island model (fdist) by LOGISTAN. Loci under positive selection above 99% percentile are shown in the red area. Loci shown in the gray area are neutral loci and those in the yellow area are under balancing selection. (JPG 592 kb)Additional file 7:Figure S5 A, B. Identification of putative divergent SNP loci under directional selection for Apis mellifera scutellata based on FST outlier approaches. (A) Hierarchical structure model using Arlequin 3.5. FST: locus –specific genetic divergence among the populations; Heterozygosity: measure of heterozygosity per locus. The significant loci are shown with red dots (P < 0.01). (B) Finite island model (fdist) by LOGISTAN. Loci under positive selection above 99% percentile are shown in the red area. Loci shown in the gray area are neutral loci and those in the yellow area are under balancing selection. (JPG 272 kb)Additional file 8:Table S3. Estimated posterior probabilities and delta K for each K partition. (JPG 571 kb)


IS > 0.05), whereas a high excess heterozygote value (FIS = –0.098) was observed from the Njombe ecotype. Two percent of the genetic diversity was due to differences among ecotypes, and the rest was due to differences among individuals within the ecotypes. Despite the overall low genetic differentiation, both fragment and sequencing analyses depicted a high allelic and genetic variability across 10 chicken ecotypes. These results therefore, underscore the importance of establishing appropriate conservation and management strategies to capitalize on observed variability and maintain genetic flexibility across diverse production environments.Unraveling the genetic diversity of livestock species is central to understanding their value and importance for conservation and improvement in diverse production environments. In developing countries, information on genetic attributes of many livestock species is unfortunately scanty to support well-informed decision-making upon relevant management strategies. This study aimed at investigating allelic variability, genetic diversity, and genetic relationships of 10 indigenous chicken ecotypes from Southern Highlands of Tanzania using the Major Histocompatibility Complex-linked LEI0258 marker. A total of 400 DNA samples, 40 per ecotype, were genotyped by capillary electrophoresis. Thirty different alleles with sizes ranging from 197 to 569 bp were determined. The number of alleles ranged from 17 (Itunduma) to 21 (Mbeya), with an average of 19.20 alleles per ecotype. Allelic polymorphism was further evaluated through genotyping by Sanger sequencing. Thirty-three DNA samples with different fragment sizes were re-amplified and their alleles sequenced to depict polymorphism based on a combination of two repeat regions at 12 and 13 bp, respectively, and flanking regions with SNP and indels. The repeat region at 13 bp appeared 1 to 28 times, whereas the region at 12 bp appeared 3 to 19 times in all sequenced fragments. The numbers of indels and SNP determined were 7 and 9, respectively. From capillary electrophoresis, the Chunya and Msimbazi ecotypes exhibited the highest genetic diversity (0.937), whereas the lowest value (0.910) was observed from the Mbarali ecotype, with an average of 0.925. The Namtumbo and Wanging’ombe ecotypes showed high inbreeding coefficients (F Consequently, characterization of AnGR constitutes one of the key priorities on appropriate management practices and is one of the key strategies for a global plan of action for the management of AnGR  form an integral part of highly variable agro-ecological zones and farming systems, and are the most adaptable and geographically widespread livestock. Currently, there are about 84.6 million chickens, producing approximately 4.15 billion of eggs and 99,540 metric tons of meat annually  region has proven to be useful for the analysis of population genetic diversity, structure, distinctiveness, and relationships , and its alleles show high polymorphism with large numbers, large size ranges, and composition  plus several sequence polymorphisms in the flanking regions , which allow easy identification of each allele using direct polymerase chain reaction (PCR) and sequencing . Each district represented an independent IC ecotype  and 0.8% agarose gel electrophoresis. Eluted DNA from each sample was subsequently diluted to a working concentration of 20 ng/μL using milliQ H2O.Total DNA was purified from FTA discs using an FTA Purification Reagent  as per the FTA manufacturer's instruction. One FTA disc (of 1.2 mm in diameter) was punched per sample and purified accordingly. The DNA was eluted from the FTA discs in 50 μL of milliQ H5′-CACGCAGCAGAACTTGGTAAGG-3′ and LEI0258-R: 5′-AGCTGTGCTCAGTCCTCAGTGC-3′ . The mixture was denatured in a PCR machine at 95°C for 3 min and snap-chilled on ice for 5 min. The products were then electrophoresed using an ABIPRISM 3730xl automated sequencer . Allele size scoring was performed twice, including the optimization stage, using the GeneMapper software version 4.1  to ascertain the correct size and number of alleles.TAA TAC GAC TCA CTA TAG GGC ACG CAG AAC TTG GTA AGG-3′and Reverse: 5′-ATT TAG GTGACA CTA TAA GCT GTG CTC AGT CCT CAG TGC-3′ (with the underlined T7 and SP6 promoter sequences). Therefore, a subset of DNA samples in both homozygous and heterozygous forms and with private alleles were selected for amplification and sequencing. The PCR products were tagged with T7 and SP6 to enable direct sequencing of amplicons. The amplifications were performed in a 50 μL reaction volume containing 60 ng of DNA, 0.1 μM of both primers, 1X Bioneer AccuPower PCR PreMix, and 19 μL of milliQ water. The PCR cycling conditions were as for the genotyping profile with 5 additional cycles. The PCR products were purified with the QIAquick PCR Purification Kit  and QIAquick Gel Extraction Kit  for homozygous and heterozygous alleles, respectively. Sanger sequencing was carried out by the Bioneer Sequencing Service (Korea).Allelic polymorphism was further analyzed by Sanger sequencing. A total of 33 out of 400 DNA samples with different fragment sizes were amplified and sequenced successfully. One previously identified allele by capillary electrophoresis was represented by at least one DNA sample to depict the repeat regions with a combination of motifs or fragments (repeats 12 and 13) and flanking regions with SNP and indels. Homozygous samples were selected when possible, but heterozygous samples were used for DNA with alleles in heterozygote forms. T7 and SP6 promoter tagged-LEI0258 primers used included Forward: 5′-(PIC) were assessed using the PowerMarker Software V.3.25  and evaluate the heterozygote deficiency or excess per ecotype and pooled sample  using the GenAlEx 6.41 software   was also performed with the GenAlEx 6.41 software using allele frequencies in order to assess genetic relationships among the ecotypes. The first 2 principal components (PC) were used to identify possible population clusters.Genetic variations within and between populations were also established by analysis of molecular variance , 465 (Songea), 460 (Namtumbo), and 440 bp (Msimbazi). The frequencies of private alleles were low, below 5% each. On the other hand, 9 alleles (with their frequencies in parentheses): 221 (10.63%), 315 (9.88%), 327 (8.13%), 209 (8.00%), 300 (7.38%), 363 (5.63%), 277 (5.50%), 426 (5.50%), and 263 (5.38%) were the most frequent across chicken ecotypes. A total of 8 alleles were common to all 10 ecotypes, and 16 alleles were shared by at least 2 chicken ecotypes. The number of alleles per ecotype ranged from 17 (Itunduma) to 21 (Mbeya), with an average of 19.20 alleles per ecotype  and R12 (CTTTCCTTCTTT) and indels and SNP along both flanking regions. The polymorphisms observed in the present study were 7 deletions (indels) and 9 SNP in the upstream and downstream regions. The deletions of TT were observed at positions –29 to 30 bp, i.e., 29 bp before the R13 repeats along the upstream region. On the other hand, no large deletion or insertion (indel) was observed along the downstream sequence. The conserved region, which could make an indel along the downstream sequence, consisted of 8 bp (ATTTTGAG) and was located at positions +23 to +30. The sizes of sequenced alleles, which reflected the combination of the repeats/motifs and indels for sequenced alleles, ranged from 249 to 552 bp. Numbers of repeat regions (R13 and R12) differed across sequenced alleles. R13 appeared 1 to 28 times, whereas R12 appeared 3 to 19 times depending on the allele sizes. The upstream region was at positions −78 to −1, and the downstream region at 1 to 88 bp. The majority of sequenced alleles showed combinations of only one R13 motif with several R12 repeats (28 out of 33 combinations). The following R13 and R12 combinations were the most frequent across all 33 sequenced alleles: one with 13, one with 16, and one with 11  and sequencing (consensus sizes) did not exactly match. The size differences ranged from 2 to 17 bp. On the other hand, results from NCBI blasting of all sequenced alleles indicated 19 alleles to be 100% similar to their corresponding accessions from the GenBank database. However, similarities of 14 sequences against their corresponding accessions in GenBank were slightly less than 100% Table . All the(Ho) and expected (He) heterozygosity were 0.898 ± 0.02 and 0.925 ± 0.00, respectively. The Njombe ecotype showed the highest Ho (1.00) and the Wanging’ombe ecotype showed the lowest Ho (0.775). The Chunya and Msimbazi ecotypes showed the highest He (0.937), whereas the Mbarali ecotype showed the lowest value (0.91). The Chunya, Ileje, Mbarali, Mbeya, Namtumbo, Songea, and Wanging’ombe ecotypes had lower Ho than He, whereas the reverse was true for the Njombe, Itunduma, and Msimbazi ecotypes  were low and, therefore, insignificantly different from zero. However, the Wanging’ombe and Namtumbo ecotypes showed high and positive FIS values , which were significantly different from zero. On the other hand, the Njombe ecotype showed a high and negative FIS value (–0.098), which was also significantly different from zero. The remaining ecotypes showed low FIS values (<0.05), which were not significantly different from zero. Five ecotypes with a significant deviation from HWE exhibited deficient heterozygotes, except the Itunduma ecotype, which showed an excess heterozygote  and gene differentiation (FST) indices among studied ecotypes. Both DA and FST indices ranged from 0.000 for the Mbeya and Songea ecotypes to 0.726 and 0.045 for the Mbarali and Njombe ecotypes, respectively. On the other hand, Figure Table This study aimed at assessing the allelic and genetic diversity as well as relationships of 10 chicken ecotypes from the Southern Highlands of Tanzania using the MHC-linked LEI0258 marker in order to ascertain the worthiness of these ecotypes for improvement and conservation. Genotyping by both fragment and Sanger sequencing analyses was employed.From capillary electrophoresis, a total of 30 distinct alleles were scored from 10 selected ecotypes, with sizes ranging from 197 to 569 bp. Out of 30 alleles, 4 alleles were private and existed in only one ecotype each, whereas 8 alleles were observed in all 10 chicken ecotypes. Nine alleles were the most frequent across chicken ecotypes. The presence of many common and frequent alleles across chicken ecotypes suggests close relationships in all studied ecotypes. The minimum and maximum numbers of alleles per ecotype were 17 and 21, respectively. The large numbers and big range of allele sizes at this marker implicate high allelic polymorphism across studied ecotypes. Differences in allele numbers among ecotypes might be an indication of differences of chicken populations depending on their origins, dispersion, production environments, and level of interactions within and between ecotypes.The average number of alleles per ecotype in the present study is slightly lower than the mean numbers reported previously for the Tanzanian Kuchi (22) and Medium (23) ecotypes  regions. The upstream region showed 7 insertions (indels) and 7 SNP, whereas the downstream region exhibited only 2 SNP. The TT indel position was at position –29 to 30 bp, i.e., 29 bp before the R13 repeats, instead of the –31 to 32 bp position, which was previously reported in the North American and European layer-type chickens  with similarity levels slightly less than 100%. All these alleles might be novel for Tanzanian chicken populations. However, this observation needs further verification. On the other hand, the fragment lengths by capillary electrophoresis did not exactly match those obtained by sequencing for unclear reasons. Some environmental factors might have affected electrophoresis, or capillary electrophoresis might have shown false-positive results. Moreover, LEI0258 is a composite VNTR, which is characterized by repetition of 2 tandem and conserved short sequences of 13 and 12 bp (R13/R12 regions) plus several sequence polymorphisms in the flanking regions (SNP and indels). It is common for a compound microsatellite to contain different repeating units with the same size of the fragments. Therefore, fragment analysis alone without combining with sequence analysis at this marker may lead to poor or inadequate estimation of actual divergence among studied chicken ecotypes  to 93.7% (Msimbazi and Chunya). A high level of genetic diversity observed in this study could be attributed to high antigenic diversity and other stresses that are prevailing in the free range production environments in which these chickens have evolved and are kept. The frequency of heterozygosity at the MHC is also expected to be higher in outbred populations with high gene flows and exposure to all kinds of infectious agents. Selected populations were managed extensively with free movements and high admixtures from different agro-ecological conditions.The range of genetic diversity observed in the present study is relatively higher than that of 86.4 to 88.2% and 84 to 88%, which were reported in both the Kuchi and Medium ecotypes from eastern Tanzania , standard genetic distance (DA), and PCA. The mean coefficients of genetic differentiation observed in this study demonstrated that only 2% of the total genetic variation was accounted for by between-ecotype differences, whereas the rest (98%) was attributed to differences among individuals within chicken ecotypes. These findings signify the overall genetic diversity of 10 selected ecotypes from the Southern Highlands chicken populations to be highly influenced by heterogeneities of individual birds within and across populations unlike differences between chicken ecotypes. The observed overall level of genetic differentiation among ecotypes was lower than that reported (4.8%) by Lyimo et al. (One of the main objectives of this study was to evaluate the genetic relationships of 10 promising IC ecotypes in order to determine if they merit to be considered as distinct ecotypes for conservation, promotion, and improvement. The evaluation was based on coefficients of genetic differentiation (Fo et al.  in otherST and DA values were also relatively small, indicating low levels of genetic differentiation and distance, respectively, between the IC populations. Low genetic differentiation and distances between IC ecotypes are attributed to high gene flows, admixture, and interbreeding in the study area as well as sharing a common ancestry among the ecotypes. According to Laval et al. (The majority of pairwise Fl et al. , migratiThe results from PCA supported the close relationships among the chicken ecotypes. These observations further signify that the IC ecotypes are highly admixed or share a common ancestry and that their separation might be only recent. The findings of the present study are consistent with the results that have been reported by Moioli et al.  in the IAll 10 IC ecotypes depicted large genetic variations and hosted multiple and highly variable MHC-linked alleles. High allelic and genetic diversities observed at the MHC region of the studied chicken ecotypes support their claimed value of being hardy, adaptive, and resilient to various production environmental challenges, including diseases and parasites. This observation offers a basic step towards well-informed decision-making on relevant management strategies for improvement, development, and conservation without compromising the existence of each unique chicken genetic resource in its present environment. Routine assessment of genetic attributes of native AnGR is worthwhile for present and future uses. However, the utilization of the VNTR LEI0258 marker alone is only an indicator of MHC diversity because the mutation rate of the LEI0258 alleles might be a source of error in estimating variations with this marker.


Glossypium klotzschianum and allotetraploid cotton accessions, including Glossypium barbadense, Glossypium darwinii, Glossypium tomentosum, Glossypium ekmanianum, and Glossypium stephensii, from Santa Cruz, Isabella, San Cristobal, Hawaiian, Dominican Republic, and Wake Atoll islands. A total of 111 expressed sequence tag (EST) and genomic simple sequence repeat (gSSR) markers produced 382 polymorphic loci with an average of 3.44 polymorphic alleles per SSR marker. Polymorphism information content values counted 0.08 to 0.82 with an average of 0.56. Analysis of a genetic distance matrix revealed values of 0.003 to 0.53 with an average of 0.33 in the wild cotton collection. Phylogenetic analysis supported the subgroups identified by STRUCTURE and corresponds well with the results of principal coordinate analysis with a cumulative variation of 45.65%. A total of 123 unique alleles were observed among all accessions and 31 identified only in G. ekmanianum. Analysis of molecular variance revealed highly significant variation between the six groups identified by structure analysis with 49% of the total variation and 51% of the variation was due to diversity within the groups. The highest genetic differentiation among tetraploid populations was observed between accessions from the Hawaiian and Santa Cruz regions with a pairwise FST of 0.752 (p < 0.001). DUF819 containing an uncharacterized gene named yjcL linked to genomic markers has been found to be highly related to tryptophan-aspartic acid (W-D) repeats in a superfamily of genes. The RNA sequence expression data of the yjcL-linked gene Gh_A09G2500 was found to be upregulated under drought and salt stress conditions. The existence of genetic diversity, characterization of genes and variation in novel germplasm collection will be a landmark addition to the genetic study of cotton germplasm.This study evaluated the genetic diversity and population structures in a novel cotton germplasm collection comprising 132 diploids, including  Gossypium and has broad phenotypic diversity, which includes more than 50 species [Gossypium ekmanianum (AD6) and Gossypium stephensii (AD7) [n = 2x = 26) and two are allotetraploids (2n = 4x = 52). Global cotton production is manifested from the two allotetraploid species Gossypium hirsutum and Gossypium barbadense [The leading natural fiber in the world is a product of cotton crops. Cotton is placed in the taxonomic order with the genus  species ,2,3. Theii AD7) ,5,6. Amo ,5,6. Amrbadense ,9.Gossypium herbaceum (A1 genome) and the New world cotton Gossypium raimondii (D5 genome) as a consequence of subsequent diploidization and domestication [ossypium hirsutum, also called “Upland cotton”, represents 90% of global cotton fiber production [Gossypium barbadense  is valued for its extra-long staple fiber source, is domesticated in North-West South America, has its native origin in Egypt, and contributes around 8% of total world lint [Gossypium darwinii originated from Galapagos Island and, relative to G. barbadense, also has good fiber fineness characteristics and is a rich source of resistance to fusarium and verticillum wilts [d-genome Gossypium klotzschianum, having glabrous seed coverings, evolved through long-distance dispersals, is endemic to Galapagos Island, and is considered a New-World d-genome diploid along with G. raimondii. Gossypium tomentosum is drought-tolerant, native to a Hawaiian Island, and has a more diffuse population structure falling typically as scattered individuals and small populations on several islands. G. tomentosum (AD3), G. darwinii (AD5), G. ekmanianum (AD6), and G. stephensii (AD7) are wild and are not grown commercially [G. darwinii accessions from six islands using 17 isozyme markers and identified a high genetic diversity level within these accessions and relationships with G. barbadense and G. hirsutum genomes. This classic study suggested that G. darwinii and G. barbadense are separated and each has a distinct genome [Data on allotetraploid cotton evolution indicates that the seven tetraploid cottons evolved about 1.5 million years ago by hybridization of the Old world cotton tication ,10,11,12oduction , while Gum wilts . The d-gercially ,16. WendGossypium species is widespread, covering large geographic and ecological niches. It is a vital source of conserved genetic diversity in situ in Mexico for cotton origin [The genetic diversity of different plant species is an essential element for crop production in agriculture, including cotton. Genetic variation in the n origin ,19 and in origin .The narrow genetic background of Upland cotton has become a major concern as low genetic diversity gives rise to stagnant yield and quality of breeding. The elite breeding programs cannot make robust inferences without using the unexploited standing genetic variation of archaic cultivars typically associated with wild accessions ,21,22. TThe DNA-based markers used for determining genetic diversity in cotton include restriction fragment length polymorphisms (RFLPs) , random Gossypium davidsonii and G. klotzschianum. Similarly, a study of gene diversity, their functionality, and especially the diagnosis of uncharacterized domains of proteins in developing the evolutionary relationship among cotton accessions will be fruitful for exploring the mystery of cotton evolution. Among all protein domains with a unique structure and functions, nearly more than 20% are currently described as “domains of unknown function” (DUFs). They are often overlooked as irrelevant as many of them are found in only a few genomes. Approximately 2700 DUFs exist in bacteria as compared to eukaryotes, which have only 1500. More than 800 DUFs have been found to be common in bacteria and eukaryotes, and about 300 of these are also present in archaea. Evolutionary conservation suggests that many of these DUFs are important in biology as they mostly represent single-domain proteins, clearly establishing the biological importance of DUFs [Although data from several studies implicates the marker-based estimation of genetic diversity in cotton, the majority of those remain bound to the number of accessions included or the number of markers used to describe genetic diversity . Recentl of DUFs .The importance of prioritizing DUFs has been recognized in various experimental and/or computational characterization efforts ,46,47,48The objectives of this study were to explore the genetic diversity and evolutionary relationship among the domains of uncharacterized proteins in natural diploid and allotetraploid cotton germplasm resources and to analyze the population structures to maximize estimations about the accessions of cotton present in a wild nursery of China for their efficient utilization in cotton-breeding programs.G. hirsutum accessions is listed in Among a total of 853 SSR primer pairs used for genotyping 132 accessions, 205 primer pairs were found to be polymorphic with a polymorphism rate of 24%. Accessions with more than 5% missing data were removed and 94 SSRs were dropped; the selected 111 SSR primer pairs can be scored confidently and read clearly on PCR products. Data for monomorphic loci were also excluded from the analysis. Data generated from the selected 111 SSR primer pairs was analyzed. Among 132 accessions, a total of 382 SSR alleles were detected as marker loci with an average of 3.44 alleles per SSR ranging from 2 to 8. All 382 SSR loci were found to be polymorphic. The average polymorphism information content (PIC) value for SSRs was 0.555 with a range of 0.078 to 0.821, and the major allele frequency was 0.738 ranging from 0.541 to 0.959 for the complete panel. Seventy-six (68.468%) SSR markers in total were found to be highly informative with a PIC value ≥0.50, 29 (26.126%) were moderately informative with PICs value ≥0.25 and <0.50, and 6 (5.405%) were least informative with a PIC value <0.25. A summary of marker statistics for G. ekmanianum genotypes  of unique alleles, which were collected from the Dominican Republic, National Plant Germplasm System (NPGS) USA  USA . These uG. hirsutum as fixed. The SSR marker DPL0330-A showed the maximum number of common alleles (124) among all tetraploid cottons except for G. klotzschianum, which is diploid, while DPL0249-C showed the minimum number of common alleles, which were only found in G. barbadense, G. klotzschianum, and G. hirsutum. The number of common alleles ranged from 21 to 123 in all six species of Gossypium. In this investigation, a total of 459 common loci were observed. Eighty-eight, 101, 82, 91, 67, and 30 loci having common alleles specific to G. hirsutum were observed in G. barbadense, G. darwinii, G. tomentosum, G. ekmanianum, G. stephensii, and G. klotzschianum, respectively  obtained from a Hawaiian Island. Group 3 was composed of 14 accessions and was demarcated with the accession of G. darwinii (AD5) collected from Isabella Island but also including one from the China Wild Cotton Germplasm Nursery. Group 4 had 10 accessions of G. ekmanianum (AD6) collected from the Dominican Republic, NPGS, USA. Group 5 contained 19 accessions of G. darwinii that were collected from San Cristobal. Group 6 had 17 accessions of G. barbadense (AD2); of them, two were collected from the China Wild Cotton Germplasm Nursery  revealed highly significant variation between the six groups identified by the structure analysis, with 49% of the total variation contributing to between-group differences. However, a larger amount of variation (51%) was due to diversity within the groups having different populations  are closer to accessions from the San Cristobal (Pop 5) and Santa Cruz regions (Pop 6) as compared with the Hawaiian accessions. The highest genetic differentiation was observed among tetraploid populations between accessions from the Hawaiian (Pop 2) and Santa Cruz (Pop 6) regions with a pairwise FST of 0.752 (p < 0.001)  .A cluster analysis clearly discriminated diploid wild-type cotton from other tetraploid wild-types. These accessions were collected from different locations, namely the Galapagos Islands, Hawaii, the Dominican Republic, Wake Atoll, and the Wild Cotton Germplasm Nursery of China. The dendrogram was truncated at a genetic distance level of (0.05) and divided 132 cotton genotypes into seven clusters .Gossypium arboreum (258), G. raimondii (258), G. hirsutum (513), and G. barbadense (488) from the cotton functional genomic database (www.cottonfgd.org). These were the best-fit matched homologue genes having the highest similarity with the four cotton species. Out of 1517, only 116 genes differentially expressed in experiments and belonging to PF00400 and PF05684 were identified among G. arboreum, G. raimondii, G. hirsutum, and G. barbadense. A total of 24 uncharacterized genes identified to diagnose their evolutionary relationship with these cotton species. A phylogenetic tree consisting of 115 genes out of 116 expressed genes, including uncharacterized genes, in different experiments was constructed for the sorted PF00400 (105) and PF005684 (11)  was found on chromosome D11, and the minimum number (4) was found on chromosome 2 , and salt treatments and different developmental stages of different tissue organs, such as calyx, leaf, petal, pistil, root, stamen, stem, and torus tissue (The study was extended to dissect the evolutionary relationship among the uncharacterized genes because these are considered to be highly conserved and have an important role in biology. DUF819 is a family containing proteins PF005684) found in 532 species with a total of 756 sequences. A total 1517 genes were found among 684 (11) . The promosome 2 . The codmosome 2 . The maj84 found mosome 2 . Finallys tissue . The gens tissue . Among tGossypium hirsutum. Consistent results were found with 3.93 alleles per locus by Bardak and Bolek [darwinii from six islands. However, other studies showed a variable allele number per locus. For example, 2.13, 2.20, 5.46, and 7.64 alleles per marker have been found in several genetic diversity assessments for the cotton germplasm [In this study, 111 SSR primer pairs generated 382 polymorphic loci in the 132 tested accessions. An average of 3.44 alleles amplified per marker was observed for all accessions, ranging from 2 to 8 alleles. This value is comparable with the findings of Dahab et al.  on allelnd Bolek  for assend Bolek  detectedermplasm ,59,60,61ermplasm ,62,63. TG. barbadense, G. darwinii, G. tomentosum, G. hirsutum, G. ekmanianum, G. stephensii and G. klotzschianum, respectively. These are higher than the percentages reported in the earlier report [G. hirsutum. The unique alleles may be related to unique characteristics, such as extra-long fibers in G. barbadense and drought and salt tolerance in G. tomentosum and G. darwinii, and be similar to other wild accessions.Our study results determined an average PIC value of 0.555 with a range of 0.078 to 0.821, which completely corresponds to the literature-cited average PIC value for cotton SSRs, which ranges from 0.122  to 0.71 r report . It is aGossypium species during evolution. The results are supported by previous studies and the hypothesis that G. hirsutum has a single evolutionary lineage because all of the species in this study have common alleles with reference to the fixed alleles of G. hirsutum. G. tomentosum is considered to be a sister of G. hirsutum, while G. barbadense originates from a geographically overlapping region. G. darwinii has the same origin, the Galapagos Island, as G. barbadense. The G. klotzschianum diploid cotton is considered endemic to the New World and has an origin similar to that of G. raimondii. The two new species G. ekmanianum and G. stephensii are sister clades even though they make distinct groups with G. hirsutum, but G. ekmanianum and G. stephensii are monophyletic to G. hirsutum which strengthens the hypothesis of gene flow from different species to G. hirsutum having a single-lineage evolution [G. hirsutum and G. barbadense genomes using the chromosome positions as markers.The common alleles were estimated to understand the gene flow mechanism of the volution ,13,15. Tvolution  and Hinzvolution  who descPopulations from different islands are isolated distinctly, indicating general correspondence to Wedel and Percy’s investigations . Due to A phylogenetic tree constructed based on genotypic data completely validated the distinct clustering of the accessions detected. The results are quite congruent to prior taxonomic studies ,3,66. ThAn evolutionary relationship among the genes was also developed. It has been estimated that the majority of genes are linked to responses towards biotic and abiotic stress conditions. For example, the damage-specific DNA binding protein 2 (DDB2), Autophagy-related protein 16 (ATG16), WD repeat-containing protein LWD1, Denticleless protein homolog (DTL), Protein FIZZY-RELATED FZR2, FZR3, Pre-mRNA-processing factor 17 (CDC40), Diphthine methyltransferase (DPH7), Chromatin assembly factor 1 subunit FAS2, DNA excision repair protein ERCC-8, Flowering time control protein FY, Guanine nucleotide-binding protein subunit beta-like protein (GB1), Myosin heavy chain kinase B (mhkB), WD-40 repeat-containing protein MSI1, MSI4, F-box/WD repeat-containing protein sel-10, U5 small nuclear ribonucleoprotein 40 kDa protein (SNRNP40), THO complex subunit 3 (THO3), WD repeat-containing protein VIP3, WDR25, WDR5A, WDR5B, and U3 snoRNP-associated protein-like YAO belong to the superfamily of WDR and are involved in repairing damaged DNA under various stress conditions ,70. SimiGossypium. Genetic diversity within the group was highest for the Isabella group and lowest for the Hawaiian group (p > 0.001) being explained by the population structure of the wild cotton germplasm  are very high, indicating high genetic distance and diversity. PCoA plots separated tetraploid cottons from diploid plants, supporting the AMOVA results. All these results are in good agreement with Noormohammadi et al.’s genetic diversity analysis [The current study is highly associated with the pedigree information recently provided by Gallagher et al.  after moan group . The genermplasm . Such hilability . The FSTanalysis  between Thus, our results could help breeders to determine the selection of appropriate parental combinations in germplasm enhancement programs and conserve genetic diversity and the evolutionary relationship among the genes of uncharacterized functions. The presence of profound population differentiation could pose a challenge to successful Genome-Wide Association Mapping (GWAS) studies in the Upland cotton germplasm for traits that are associated with population structures. The power of structure-based association studies to detect the effects of a single gene would be reduced if a large fraction of variation was explained by the population structures ,73. In sThis study was conducted at the Institute of Cotton Research , Chinese Academy of agricultural Sciences (CAAS), Anyang, China. The cotton accessions were obtained from six islands, namely Santa Cruz, San Cristobal, Isabella of Galapagos Island, a Hawaiian Island, Dominican Republic, and Wake Atoll. The screening of this unique collection was carried out using microsatellite markers for the detection of a polymorphism among these accessions.G. klotzschianum (D3), two G. hirsutum (AD1), 20 G. barbadense (AD2), 32 G. tomentosum (AD3), 59 G. darwinii (AD5), 10 G. ekmanianum (AD6), and four G. stephensii (AD7), from six islands and the Wild Cotton Germplasm Nursery of China. Among the 132 accessions, 32, 25, 16, 32, 10, 4, and 12 were obtained from Santa Cruz, Isabella, San Cristobal, a Hawaiian island, the Dominican Republic, Wake Atoll , and the Wild Cotton Germplasm Nursery of China, respectively (w/v) agarose gel electrophoresis.We sampled a total of 132 accessions belonging to different species, including five ectively . Seedlind-genome) polymorphic primers from the Cotton Marker Database  were used in the SSR analysis. The reaction contained 5 µL 2× Taq Master Mix , 2 µL primers, 1 µL DNA, and 2 µL H2O. The PCR reaction was performed using a together TP 600 thermal cycler  and then followed by silver staining according to a previous method described by Zhang et al. [A total of 853 randomly selected SSR markers, including 200 DPL, 310 MonCGR, 48 NAU, 41 MUCS, and 254 SWU, were surveyed for their polymorphisms in 132 genotypes belonging to seven cotton species. Then, 111 EST and genomic SSR . The cluster analysis was carried out using the unweighted pair group method using arithmetic average (UPGMA) and the dendrogram resulting from these calculations was plotted using MEGA 6.0 to visualize and edit the dendrogram. The basic summary statistics for biallelic data were calculated using the POWERMARKER software package version 3.25 [ijP is the frequency of the allele for locus i and the summation covered n patterns.Pairwise genetic distances between accessions were calculated using the Powermarker software package ver.3.25 by Nei et al.  DA distaion 3.25 . The polion 3.25  based onOA) using XLSTAT, 2014 [A PIC value of 1 indicates that the marker can differentiate each line, and 0 indicates a monomorphic marker. The informative potential of a marker is high if its PIC value is more than 0.5, moderate if its PIC is between 0.5 and 0.25, and only slightly informative if its PIC value is below 0.25. Other statistics calculated were the number of alleles and availability and gene diversity for each marker. Further analysis of genetic structure was done by means of Principal co-ordinate analysis  were downloaded from the pfam database (http://pfam.xfam.org/). The dendrogram was constructed by using Molecular Evolutionary Genetics version 7.0 [www.cottonfgd.org) [The complete sequence of Markers SWU15000–SWU15194 mapped for chromosome 6 was downloaded from the Genome database of aimondii  and blasu et al. . Similaru et al. . The uncsion 7.0 . The funfgd.org) . The evoThus, 115 genes out of 116 were grouped into 6 major clusters and their evolutionary history was inferred using the Neighbor-Joining method . The opthttps://cottonfgd.org/search/), While GO functional classification was done using Agrigo ver. 2.0 software acquiring Gossypium hirsutum as the reference genome. The analysis of RNA expression data inferred was then carried out to construct a heatmap using the R statistical software package.The explored genes were analyzed for their gene features, protein characteristics, and RNA expression using the cotton functional genome database (G. barbadense (AD2), G. tomentosum (AD3), G. darwinii (AD5), G. ekmanianum (AD6), and G. stephensii (AD7), are a rich source for the creation of genetic diversity in upland cotton.SSR markers can be used to describe the degree of differentiation between populations and to control the conservation of genetic resources. The study concludes that the evaluated cotton accessions have a broad genetic basis. The recurrent use of these accessions as parents will produce significant results. The genetic diversity and evolutionary relationship recognized among the uncharacterized genes and population structures established in this study would be informative to select parental accessions for breeding and genetic analysis as well as for efficient management and conservation of allotetraploid cotton genetic diversity. We identified DUF819 (PF005684), which is not only highly conserved but also plays an important role against biotic and abiotic stress, among four sequenced cotton species by using the WDR (PF00400) superfamily as reference genome-sequenced proteins. Additionally, the current diversity panel of semi-wild cottons will be invaluable as a community resource for measuring linkage disequilibrium (LD) and for fine-scale mapping of traits through LD mapping or a Genome-Wide Association Study (GWAS) that can be streamlined for genomics-assisted plant breeding programs. Our findings suggest that allotetraploid cotton species, including


Indigofera pseudotinctoria Mats is an agronomically and economically important perennial legume shrub with a high forage yield, protein content and strong adaptability, which is subject to natural habitat fragmentation and serious human disturbance. Until now, our knowledge of the genetic relationships and intraspecific genetic diversity for its wild collections is still poor, especially at small spatial scales. Here amplified fragment length polymorphism (AFLP) technology was employed for analysis of genetic diversity, differentiation, and structure of 364 genotypes of I. pseudotinctoria from 15 natural locations in Wushan Montain, a highly structured mountain with typical karst landforms in Southwest China. We also tested whether eco-climate factors has affected genetic structure by correlating genetic diversity with habitat features. A total of 515 distinctly scoreable bands were generated, and 324 of them were polymorphic. The polymorphic information content (PIC) ranged from 0.694 to 0.890 with an average of 0.789 per primer pair. On species level, Nei’s gene diversity (jH), the Bayesian genetic diversity index (BH) and the Shannon information index (I) were 0.2465, 0.2363 and 0.3772, respectively. The high differentiation among all sampling sites was detected , and instead, gene flow among accessions (mN = 1.1819) was restricted. The population genetic structure resolved by the UPGMA tree, principal coordinate analysis, and Bayesian-based cluster analyses irrefutably grouped all accessions into two distinct clusters, i.e., lowland and highland groups. The population genetic structure resolved by the UPGMA tree, principal coordinate analysis, and Bayesian-based cluster analyses irrefutably grouped all accessions into two distinct clusters, i.e., lowland and highland groups. This structure pattern may indicate joint effects by the neutral evolution and natural selection. Restricted mN was observed across all accessions, and genetic barriers were detected between adjacent accessions due to specifically geographical landform.  As a fundamental component of biodiversity, genetic diversity is indispensable to the population sustainability and evolution of plant species in changing environments. Its levels and patterns reflect the evolutionary history of the species  and the interactions of various processes, such as genetic drift, breeding system, selection and gene flow ,2. ConseAlthough limitation of gene flow remains the main reason to spatial genetic structure, historical events, eco-geographic conditions and anthropic effects can also influence the spatial distribution and structure pattern of a species ,10. TypiKarsts represent distinctive landforms and hydrological systems on Earth. Karst regions are extensive in southern China and have generated the extraordinary floras present on these landscapes ,17. The Indigofera L., a large pantropical genus belonging to the Fabaceae, contains 750 species worldwide and occurs on all major land masses, most abundantly in Africa and Asia  significantly decreased with increasing geographic altitude , indicating a pattern of lower genetic diversity at higher altitudes. The [STF/(1 − STF)] was not correlated to annual precipitation  and annual mean temperature .Among the 324 AFLP loci, the election . A spatiI. pseudotinctoria populations comprising two native populations in Japan and one population transported from China using 14 microsatellite primers and found no LD in any of the three studied populations, while 10.73% LD was detected in our study. Theoretical studies have noted that many interrelated factors are involved in the formation of patterns of LD, including genetic drift, population admixture, and natural selection  and geographic altitude revealed a strong and significant negative correlation  between within-accession diversity and site elevation, i.e., lower diversity at highland sites and higher diversity at lowland sites. As an insect-pollinated plant species, I. pseudotinctoria is thought to breed by xenogamy, which requires pollinators for outbreeding  and annual mean temperature  in this study. In fact, temperatures decline along with rising elevation gradients; thus, the extremely low temperature at peaks can threaten local life, decreasing population diversity and size and even resulting in extinction  and environment parameters were calculated using “ade4” package [A spatial autocorrelation analysis was conducted using SAM version 4.0  to detec package  in R proI. pseudotinctoria. The gene diversity of I. pseudotinctoria was high and decreased with increasing elevation. The accessions from lowland and highland areas clustered into separate groups and the structure pattern of I. pseudotinctoria was affected by the joint effects of neutral evolution and natural selection. Higher gene diversity and differentiation occurred in lowland accessions than in highland accessions. Restricted gene flow was observed at different spatial scales, whereas genetic drift was detected in adjacent accessions due to geographic barriers. Suffering from the degeneration due to climatic changing and human activities, the accession size and density of I. pseudotinctoria gradually reduce, thus we should protect the local environment for maintaining the diversity of I. pseudotinctoria. Furthermore, the very high proportion of genetic diversity residing within locations encourages the intensive selection and collection of elite genotypes from each location for the genetic improvement of I. pseudotinctoria.The results of this study demonstrate that environmental conditions in different habitats are a major factor influencing the hereditary characteristics of