Ootheca mutabilis) has lately emerged as a major bean pest in Uganda, causing devastating crop losses. Despite its importance, little is known about its population genetic structure. We developed microsatellite DNA markers and combined them with partial mito-chondrial cytochrome oxidase subunit I gene sequences as a marker to examine the spatial pop-ulation genetic structure of 86 O. mutabilis samples from 16 populations. We developed a set of five highly polymorphic microsatellite DNA markers. From both types of markers, nearly all the genetic variation occurred within populations and there was no evidence of genetic differentiation in both markers. There was no isolation by distance between geographical and genetic distances for both markers except in one of the agro-ecological zones for mtCOI data. This information will assist in the design of O. mutabilis control strategies.The bean leaf beetle (Ootheca mutabilis) has emerged as an important bean pest in Uganda, leading to devastating crop losses. There is limited information on the population genetic structure of BLB despite its importance. In this study, novel microsatellite DNA markers and the partial mitochondrial cytochrome oxidase subunit I (mtCOI) gene sequences were used to analyze the spatial population genetic structure, genetic differentiation and haplotype diversity of 86 O. mutabilis samples from 16 (districts) populations. We identified 19,356 simple sequence repeats (SSRs)  of which 81 di, tri and tetra-nucleotides were selected for primer synthesis. Five highly polymorphic SSR markers  50.13–83.14%) were used for this study. Analyses of the 16 O. mutabilis populations with these five novel SSRs found nearly all the genetic variation occurring within populations and there was no evidence of genetic differentiation detected for both types of markers. Also, there was no evidence of isolation by distance between geographical and genetic distances for SSR data and mtCOI data except in one agro-ecological zone for mtCOI data. Bayesian clustering identified a signature of admixture that suggests genetic contributions from two hypothetical ancestral genetic lineages for both types of markers, and the minimum-spanning haplotype network showed low differentiation in minor haplotypes from the most common haplotype with the most common haplotype occurring in all the 16 districts. A lack of genetic differentiation indicates unrestricted migrations between populations. This information will contribute to the design of BLB control strategies.Bean leaf beetle (BLB) ( Ootheca mutabilis (Coleoptera: Chrysomelidae)) is an economically important pest of the common bean (Phaseolus vulgaris L.) [Ootheca species have been classified as field pests of common bean (P. vulgaris L.) in Uganda’s Northern and Eastern regions [Ootheca mutabilis is the most common Ootheca species (70.3%) in all agro-ecological zones of Uganda [O. mutabilis comes in a variety of colours and is frequently confused with other Ootheca species such as O. proteus, O. bennigseni, and O. orientalis [Ootheca species have been re-described and re-assigned to other species, resulting in revisions to the morphological identification records [In East and Southern Africa, the bean leaf beetle (aris L.) . In the  regions ,3. The l regions . During  regions ,5. Bean  regions ,6,7. Ootf Uganda , despitef Uganda ,4,8. Howientalis . Some pr records . Present records  which, eMicrosatellite genetic markers, commonly known as simple sequence repeat (SSR) markers, are comprised of tandemly repeated nucleotide motifs of 1–6 base pairs (bp). They have been frequently employed in evolutionary genetic studies ,11 to inHigh throughput sequencing (HTS) enables cost-effective and rapid identification of a large number of SSR loci even when only a fraction of the sequencing run is used . The datO. mutabilis has revealed information on its distribution [Recent research on ribution ,8, morphribution , abundanribution ,8, and yO. mutabilis in Uganda using microsatellite DNA markers and partial mitochondrial DNA cytochrome c oxidase subunit I (mtCOI). Since there were no existing genetic resources specific to O. mutabilis that could be utilized to determine its population genetic structure, we developed SSR markers which we deployed as a genetic tool. Given the desirable attributes of microsatellites, such as codominance, high polymorphism level, and data reproducibility can be instrumental in revealing small-scale resolution of the demographic events [COI) sequences being a preferred method globally and successfully employed in our Ugandan laboratory for other agricultural pests  (21 beetles), M2 (O. mutabilis with black elytra) (39 beetles), and M3 (O. mutabilis with brownish elytra) (39 beetles)   . This toelytra) 3 beetles,songola) . Also, bFor DNA isolation, we used the Qiagen DNeasy Blood & Tissue Kit  in accordance with the manufacturer’s instructions. Following isolation, each DNA sample was quantified using an Agilent Technologies NanoDrop 2000 spectrophotometer  and the quality was confirmed using 1% agarose gel electrophoresis.COI partial gene primers. The PCR primers used were BLB-LCO: 5′-GGTCAACAAATCATAAAGATATTGG-3′ and BLB-HCO: 5′-TAAACTTCAGGGTGACCAAAAAATCA-3′, which amplify a 710 bp fragment (M. Otim unpublished). Each reaction was carried out in a 25 µL volume comprising 1 µL of template DNA, 1 µL of 10 pmol/µL primer, 2.5 µL of 10X DreamTaq Green buffer, 0.5 µL of dNTP (10 mM), 0.25 µL (1.25 units) of Taq DNA polymerase (5 U/µL), 2.5 µL 5% tween20, and 16.25 µL of nuclease-free water. The PCR conditions were as follows: 1 cycle of denaturation at 95 °C for 2 min, 35 cycles of 20 s at 95 °C, 30 s at 52 °C annealing temperature, 1 min at 72 °C, and a final extension cycle at 72 °C for 10 min, after which reactions were held at 4 °C. All samples were tested for amplification success on 1.3% w/v agarose in TAE buffer and stained with ethidium bromide as stated above. Sequences generated from mtCOI PCR products were processed with Pregap4 and Gap4 [O. mutabilis  and O. proteus .To confirm their identity, all samples selected based on colour were barcoded using the mtand Gap4  and compwww.genewiz.com was contracted to carry out high throughput sequencing (HTS). DNA was isolated from three ethanol-preserved BLBs denoted by the letters M1, M2, and M3 . Primers were designed for all di-nucleotide, tri-nucleotide, and tetra-nucleotide microsatellite loci identified. Microsatellite sequences were compared with microsatellite sequences in the NCBI GenBank using BLASTX and BLASTN [WebSat  was usedd BLASTN  to find d BLASTN ).O. mutabilis samples (at least one sample from every population). Each locus was amplified in a 12.5 µL PCR reaction containing 0.5 µL of 50 ng DNA template, 0.5 µL of 10 pmol/L primer, 1.25 µL of 10X DreamTaq green buffer, 0.25 µL of DreamTaq dNTP (10 mM), 0.125 µL of DreamTaq DNA polymerase (5 U/µL), 1.25 µL% Tween20, and 8.125 µL of nuclease-free water. The PCR conditions were as follows: initial denaturation (4 min 94 °C), 35 amplification cycles , and a final extension (10 min 72 °C). Then, the reactions were held at 4 °C. The amplicon was run on a 3% w/v agarose in 1X TAE buffer at first, then on 6% acrylamide gels for 6 h at 120 V with a 100 bp DNA ladder in 1X TAE buffer. Polyacrylamide gels were stained for at least 20 min in an ethidium bromide (0.5 g/mL) solution before being de-stained in distilled water. The U-genius gel documentation system was used to visualize fragment sizes . Each forward primer was labelled with 6-FAM or HEX fluorescent dyes (dna.macrogen-europe.com)  . The PCRoH, eH).Genotyping was performed using GeneMarker v2.6.3 , and allDuring data analysis, different districts were treated as different populations leading to a total of 16 populations as detailed . The eliCOI partial gene diversity components such as the number of haplotypes, haplotype diversity, nucleotide diversity, and polymorphic site estimates [DnaSP v6 was used to compute mtstimates .FST), among populations within groups (FSC) and among groups (FCT)) [FCT reached a plateau. The best K grouping was used to calculate AMOVA in Arlequin to estimate genetic differentiation.We investigated population genetic structure by AMOVA that partitions total variance into covariance components. It then verifies the hierarchical or non-hierarchical variation distribution ) . Analysis (FCT))  to obtaiO. mutabilis populations for both types of markers were investigated using the Bayesian approach implemented in STRUCTURE v2.3.4 [COI sequences were first processed so that only haplotypes are used in the inference as explained in the STRUCTURE user manual [O. mutabilis samples. The true value of K was estimated using the program STRUCTURE HARVESTER [Geographic structures of E v2.3.4 . STRUCTUr manual . The LOCARVESTER , as descARVESTER . CLUMPAKARVESTER  was usedCOI haplotype network was inferred using the minimum spanning network approach described in POPART [The mtn POPART  using 20Evidence of IBD for both types of markers was tested using the Mantel test  with 999A total of 272,853,156 raw reads were generated, with equal forward and reverse reads. The length of all sequences was 151 bp, with a GC content of 34%. QC results included: basic statistics passed; per base sequence quality passed; per tile sequence quality passed; per sequence quality scores passed; per base sequence content passed; and, sequence length distribution passed. There was no over-representation of the sequences. The assembly of all sequences in the selected part of the reads resulted in a total of 282,696 contigs. From the assembled contigs, a total of 19,356 SSR were identified, including (i) mononucleotides , (ii) di-nucleotides , (iii) tri-nucleotides , (iv) tetra-nucleotides , (v) penta-nucleotides , and (vi) hexa-nucleotides .Sixty-five of the 81 loci analyzed showed multiple fragments on agarose gels and were therefore removed from the analysis. On agarose gels, a total of 16 loci were observed as a single band, and they also appeared polymorphic on 6% acrylamide gels. Six of the 16 polymorphic loci were eliminated due to inadequate PCR amplification . Two of the ten loci had primers that overlapped the (GT) SSR units and were thus eliminated. We eliminated three of the remaining eight loci due to low repeatability. As a result, five loci were chosen for genotyping (representing a 6% success rate) and labelled with fluorescent dyes . The numA locus was judged to have low polymorphism if the PIC was less than 25% and high polymorphism if the PIC was greater than 50% . Locus BAt the population level, the average PIC based on all loci across all populations was 53.59% , while tO. mutabilis mtCOI partial gene sequences were analyzed, and 21 segregating sites (S) with an average of 0.827 nucleotide differences were found (Kt). We found 20 haplotypes with an estimated haplotype diversity  of 0.51ifferent ) of 0.00n = 60). The second most prevalent haplotype was haplotype 14, which was present in 5.81% (n = 5). Three haplotypes  had 2.30% with two individuals each. The remaining 15 haplotypes each had n = 1.  (n = 4) belonged to agro-ecological zone A  a. Haplotl zone A a.In the 16 populations, the number of segregating sites ranged from 0 to 6 with Lira, Amuru, and Bulisa having the highest number of haplotypes, haplotype diversity ranged from 0 to 0.9 among the districts with Lira district having the highest and nucleotide diversity was low in all the populations (districts) .Another minimum spanning haplotype network b for theO. mutabilis haplotypes with the O. mutabilis reference sequences  with high node support (100%), but with distinct intra-species differences  likewise with intra-species differences (The neighbour-joining phylogenetic tree constructed (with one sample (representative) from each haplotype) clearly clustered together all the ferences . The O. ferences .COI where all the genetic variation occurred within the populations (FCT = 0.02073 (p = 0.05), FSC = −0.01523 (p = 0.05), and FST = 0.00582 (p = 0.05)  for both types of markers resulted in all the genetic variation (100%) occurring within the populations. Non-hierarchical AMOVA for microsatellites had the highest genetic variation occurring within the populations and the lower genetic variation occurred among the populations  (SSR markers) and FST (−0.01605) (mtCOI), genetic differentiation was low.Based on the results from AMOVA for both markers, COI partial gene markers, STRUCTURE analysis identified K = 2 as the likely optimal number of ancestral genetic clusters, with all individuals categorized as one dominant cluster (blue colour) and different degrees of genetic contributions (orange colour) from a second minor genetic cluster  r = 0.107, p = 0.205; (C1) r = 0.204, p = 0.332; (D1) r = −0.038, p = 0.373; (E1) r = −0.073, p = 30.363  r = −0.044, p = 0.342; (B2) r = −0.128, p = 0.685; (C2) r = −0.050, p = 0.755; (E2) r = −0.153, p = 0.273) except in one of the populations ((D2) agro-ecological zones ((D). Agro-ecological zone D2) .Isolation by distance (IBD) was calculated for each agro-ecological zone independently. During this inference, agro-ecological zones were regarded as populations as detailed in = 30.363 ). IsolatPrior to designing robust and effective pest management strategies, information about the level of individuals’ interaction among locations , in addiO. mutabilis in Uganda. Based on both types of markers, we found that the highest genetic variation occurred within the populations, low among groups and none among groups within populations. While sample sizes could account for the low genetic variation detected among groups and none were detected among groups within-population level, this was likely also an indication that there was intermixing or interbreeding between individuals from different populations. This aspect can be attributed to the capabilities for migration of our studied species as beetles are reported to fly short distances, although such trivial flights can also lead to the covering of longer distances [Cerotoma trifurcata, from the mid-western United States [C. trifucarta communities in the mid-western United States facilitated dispersal and reduced geographical fragmentation and genetic differentiation [Our study is the first to report the spatial population genetic structure of istances ,56. Our istances . Anotherd States  exhibitentiation .O. mutabilis for both microsatellite and mtCOI partial gene molecular markers indicated by insignificant FST values. This suggests that migration occurs across O. mutabilis populations, particularly those separated by large geographic distances. Local and long-distance migrations of O. mutabilis may be aided by the presence of other host plants and staggered bean planting. Beans are grown at different times of the year in the study area as one of the cultural practices to manage BLBs [Spodoptera littoralis caterpillars while feeding on maize [There was no evidence of genetic differentiation for age BLBs , and thion maize .Bean leaf beetles oviposit in the soil, and the eggs hatch into larvae, pupae, and finally adults ,8. HowevO. mutabilis, which, crossed the Rift Valley, may have resulted in comparable incipient speciation as observed in the cassava whitefly Bemisia ‘SSA1’ species using a whole-genome analysis technique [COI gene, are ineffective for distinguishing between closely related subspecies [Our study areas were limited only to Uganda, however, BLBs have been reported to occur elsewhere including in countries from East Africa  and West Africa  . It woulechnique . Mitochobspecies ,62,63.O. mutabilis samples. Based on limited nuclear (SSR) markers and the maternally inherited mtCOI marker, our findings showed that the genetic composition of the current populations could be explained as the outcome of genetic contributions from two hypothetical ancestral O. mutabilis genetic clusters. This study detected no evidence of genetic differentiation indicating that the Ugandan population likely represents a single panmictic population. To gain a better knowledge of the evolutionary genetics and landscape adaptability of this major agricultural coleopteran pest complex, an in-depth population structure study based on whole-genome sequencing would be desirable.STRUCTURE analyses from SSR markers revealed genetic mixing from two ancestral genetic lineages in our O. mutabilis and O. proteus are important bean pest species that are morphologically indistinguishable and can only be separated by dissection and study of the male genitalia [COI gene can help identify morphologically challenging  and  and O. mg  includig . MolecuO. mutabilis has had little or no barriers to free migration in all populations. In research conducted by Krell et al. [C. trifurcata beetle travelled 4.9 km. These trivial flights in search of mates, oviposition sites, and food [Our IBD analysis results also showed that l et al. , a singland food  may contand food . BehavioO. mutabilis, occurred within populations which, we attributed to dispersal that facilitated genetic mixing between populations. Ootheca mutabilis were divided into two population groups by SAMOVA at K = 2. When inferred, the structure suggested by SAMOVA resulted in almost all the genetic variation occurring within the populations. There was no evidence of genetic differentiation as seen from insignificant FST and we attributed it to gene flow between different O. mutabilis populations. On inferring the Bayesian clustering, STRUCTURE at K = 2 categorized all the samples as one dominant cluster (admixture) and different degrees of genetic contributions from a second minor genetic cluster. Therefore, the Ugandan populations of O. mutabilis likely represent a single panmictic population with genetic contributions from two ancestral lineages. Our study provides a baseline for future evolutionary and functional genomic studies to generate a better understanding of host-plant adaptation, insecticide resistance management, and the development of integrated pest management control measures for this important pest. Future research on O. mutabilis should increase coverage of samples from other African countries via a whole-genome sequencing approach.This study found that almost all the genetic variation in our target species,


We have assessed the forensic parameters and population genetic structure for each group. Among the 493 unrelated individuals from four ethnic groups , 82 haplotypes were observed with haplotype diversity (HD) of 0.9906 in Baloch, 102 haplotypes with HD value of 0.9957 in Pathans, 80 haplotypes with HD value of 0.9924 in Punjabi, and 105 haplotypes with HD value of 0.9945 in the Sindhi population. The overall gene diversity for Baloch, Pathan, Punjabi, and Sindhi populations was 0.6367, 0.6479, 0.6657, and 0.6112, respectively. The results had shown us that Pakistani populations do not have a unique set of genes but share the genetic affinity with regional  populations. The observed low gene diversity (heterozygosity) values may be because of endogamy trends and this observation is equally supported by the results of forensic parameters which are mostly static across 4 combinations  of STRs in these four populations.The online version contains supplementary material available at 10.1186/s12864-022-09028-z. The genetic makeup of Pakistan’s various ethnic groups was forged by successive waves of immigration from Central Asia and South Asia since the end of the last Ice Age. Throughout its long ancient history, the Indus Valley has been known for welcoming different people, faiths, and cultures. The Indus was a region where early human ancestors encountered soon after they left Africa between 50,000 to 70,000 years ago. Evidence of these early humans can be found throughout Pakistan today at Soan, Rawat, Makli Hill, Bajaur, and Sanghao. Approximately 9000 years ago they began establishing cities such as Mehrgarh, which eventually expanded to represent the Harappan culture  in 3000 BCE (Before the Common Era), rivaling the early city-states of Mesopotamia. Harappans fused culturally with the Aryans, forming Indo Aryans and Indo Iranians, which today culminates in the native ethnic groups of Pakistan. It was through these various influences by Pakistani ethnic groups would be forged into its multi-ethnic society today .Pakistanis are divided genetically into 11 major distinct groups: Baloch, Brahui, Burusho, Hazara, Kalash, Kashmiri, Makrani, Parsi, Pashtun, Punjabi, and Sindhi . The uniMost of these studies focused on the control region sequencing of mtDNA. A limited number of studies are available related to Y Chromosomal analysis in Pakistani ethnic groups and most of their focus was only on allelic frequency analysis along with basic forensic parameters –12.− 4 to 7.44 × 10− 2) [In population genetics, the non-recombining region of the human Y chromosome (NRY) has attracted much attention for its unique inheritance characteristics , 13. The× 10− 2) , 14, and× 10− 2) , 16, reg× 10− 2) , and patIn the present study, we planned to assess the forensic parameters and genetic structure of four major ethnic groups from Pakistan on Y chromosomal STRs. For this, we have investigated four main ethnic groups  of Pakistan using AmpFlSTR Y-filer PCR Amplification Kit (Life Technologies). We also gathered 17 commonly used Y-STR loci data which is available at YHRD  . We calcBlood samples were collected from a total of 493 unrelated individuals, who are residents of respective provinces for at least three generations  across four provinces  of Pakistan. All participants gave their informed consent in writing after the study aims and procedures were carefully explained to them. The study was approved by the ethical review board of the University of Sargodha, Sargodha Punjab, Pakistan, and in accordance with the standards of the Declaration of Helsinki 1964.ReliaPrep™ Blood gDNA Miniprep System  according to the manufacturer’s instructions. The quantities of extracted DNA samples were determined using a NanoDrop spectrophotometer . These samples were diluted accordingly to make a final concentration of 2 ng/μl.All blood samples were stored at − 20 °C before DNA extraction. DNA was isolated using the ID v4.1 software (Life Technologies). We strictly followed the recommendations of the DNA Commission of the International Society of Forensic Genetics (ISFG) on the analysis of Y-STRs [Diluted DNA samples were genotyped at 17 Y-STRs using the AmpFlSTR Yfiler™ kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. PCR amplification was carried out using the Applied Biosystems® GeneAmp® PCR System 9700 thermal cyclers. AmpFlSTR Y-filer (Thermo Fisher Scientific) PCR amplifications were performed as recommended by the manufacturer, although using half of the recommended reaction volume (12.5 μl). Subsequently, separation and detection were performed using an Applied Biosystems™ 3500 Series Genetic Analyzer (Life Technologies). Internal controls (negative and the 9947A DNA positive control) were genotyped along with each batch of samples to ensure that the results were reproducible and accurate. Finally, the raw data were analyzed using GeneMapper f Y-STRs .Haplotype and allelic frequencies of these four ethnic groups  were calculated using the direct counting method. Gene diversity (GD), haplotype diversity (HD), and discrimination capacity (DC) were calculated using the following formulas:http://www.yhrd.org) based on pairwise Rst and Fst values. Reduced dimensionality spatial representation of the populations based on Rst values, was performed using multi-dimensional scaling (MDS) with IBM SPSS Statistics for Windows, Version 23.0 . A neighbor-joining phylogenetic tree was constructed for these four ethnic groups and the reference populations based on a distance matrix of Fst using the Mega7 software [http://www.nevgen.org). Using the program Network 4.1.1.2., the median-joining network was constructed from data of these four ethnic groups for 14 Y-STRs .Genetic distances between these four ethnic groups and reference population analysis of molecular variance (AMOVA) and multidimensional scaling (MDS) that exploit variations among populations were performed using YHRD online tools . Other forensic parameters such as polymorphic information content (PIC), matching probability (MP), and discrimination probability (DP) showed the same trends as we have observed for locus diversities (GD).Successfully generated genotypes at 17 Y-STRs from 493 male individuals  across four provinces  of Pakistan are summarized in Table S(Table ), the minimal 9 Y-STRs loci (MH-9), the extended 11 Y-STRs loci (SWGDAM-11), PowerPlex Y12 STRs loci (PPY-12), and Y-filer 17 STRs loci (Yfiler-17). A total of 82 haplotypes were observed at Y-filer 17 STRs loci with haplotype diversity (HD) 0.9906 and discriminatory capacity (DC) 0.6250 while among these 82 haplotypes 40.62% (52) were unique with random matching probability (RMP) 0.0171 for the Baloch population. When the number of STRs was reduced from 17 to 12 (PPY-12), we did not observe much change in the values of these forensic parameters. In the Pathan population, at Y-filer 17 STRs loci we have observed 102 haplotypes with haplotype diversity of 0.9957, a discrimination capacity of 0.8360, among these 102 haplotypes 73.77% (90) were unique with a random matching probability of 0.0125. After reducing the number of STRs to 12, 11, and 9 we did observe any change in any of these forensic parameters. In the Punjabi population, at Y-filer 17 STRs loci we have observed 80 haplotypes with haplotype diversity of 0.9924, a discrimination capacity of 0.7407, among these 80 haplotypes 58.33% (63) were unique with a random matching probability of 0.0168. When we reduced the number of STRs to 12, 11 and 9 number of haplotypes also reduced to 76, 76, and 75, respectively. In the Sindhi population, we have observed a static trend across 4 combinations of STRs. We have observed 105 haplotypes with haplotype diversity of 0.9945, a discrimination capacity of 0.7777, among these 105 haplotypes 65.92% (89) were unique with a random matching probability of 0.0129. The overall gene diversity for Baloch, Pathan, Punjabi, and Sindhi populations was 0.6367, 0.6479, 0.6657, and 0.6112, respectively. These low gene diversity (heterozygosity) values showed that these populations are endogamous and this observation is equally supported by the results of forensic parameters which are mostly static across 4 combinations of STRs in these four populations.We assessed the haplotype resolution at four levels Table , the min. The majority of Pakistani ethnic groups were located in the middle of the MDS plot, except for the Uthmankheil, Pashtun, Hazara, Saraki, and Gujjar populations, who were located on the plot’s boundaries. Among 23 Pakistani populations . In neighbor-joining trees, usually, an admixed population will always lie on the path between the source populations [Most of the Pakistani ethnic groups are thought to have a blend of Central Asian and European ancestors . UtiliziFig. . The majulations . Accordi. The genetic distances (Rst) between the Punjabi population and other reference are summarized in . Punjabi population showed genetic association with Baloch, Balochistan, Pakistan (0.0028) followed by the Iranian population from Iran (0.0038) while the Kazakh population from Altai, Xinjiang, China (0.0805) and Kazakh population from East Kazakhstan, Kazakhstan (0.1808)  Table S.Fig. 4Tw showed the bulk of R haplogroup. We also presented a stacked histogram with the haplogroup composition of these populations in Fig. 7.Ethnic groups which are situated in Punjab province  are admixture populations and determining their ancestry is challenging because of their admixture nature. Information about ancestry plays an important role in forensic genetic investigations. So we have to use NEVGEN software to calculate haplogroups from STRs. Only Six haplogroups  have accounted for 84% of these samples among 4 major ethnic groups from Pakistan. The median-joining network of haplotypes Fig.  showed tHaplogroup E is 9% of currently studied populations and is the most frequent haplogroup in West Asia and East Africa , 26. ThiHaplogroup H is 6% of currently studied populations and is the most frequent haplogroup in South Indians and Roma people. It also originated in 48,5KYA in the south and west Asia . The freHaplogroup I is 9% of the currently studied population. Subclades I1 and I2 are found in the majority of modern European people, with maxima in Northern and Southeastern European nations. Haplogroup I appear to have evolved in Europe, as evidenced by its presence in Palaeolithic sites across the continent , but notHaplogroup J accounts for 20% of currently studied populations and this haplogroup is predominately found in Arabian Peninsula. The origin of this haplogroup is from the Middle East area known as the Fertile Crescent, comprising the Palestine, Jordon, Syria, Lebanon, and Iraq 42,9KYA . This haHaplogroup L accounts for 5% of currently studied populations and this haplogroup is believed to have originated in the Middle East or Sub-continent 25-30KYA . The sprThis is the dominating haplogroup in Pakistani populations. Haplogroup R originated in the north of Asia about 27KYA years ago . It is the most frequent haplogroup in Europe and Russia and in some parts it is 80% of the population. Some believes its one branch originated in the Kurgan culture and their people were responsible for the taming of the horses and speaks the Indo-European languages . The frePakistan is a diverse nation where several different languages are used as first languages , 36. TheThe human Y-chromosome can be used for studying Y-STR haplotypes and determining their haplogroups which ultimately lead us to the ancient geographic origins of the studied population/individuals. In this study, allele frequencies and forensic parameters of the four Pakistani ethnic groups  were calculated. These four groups and 83 regional ethnic groups were analyzed, and their corresponding haplotypes were compared. Using Y-STRs and available information of haplogroups from the Y-DNA phylogenetic tree, the geographic origin was traced. Results of our study showed us that according to the genetic makeup of these four ethnic groups belong to at least thirteen specific haplogroups with thirteen different lines of ancestry and geographic origins. Above 84% of these ethnic groups belongs to only six different lines of ancestry and geographic origins. Overall, the 17 Yfiler STRs included in the Yfiler kit are slowly to moderate mutating and can be used in sexual assault cases, paternity casework involving male offspring, or missing person analysis. More studies on extended sets of STRs are required to better understand the genetic complexity of the Pakistani population. The recent inclusion of these data in the YHRD allows widespread use for forensic application and paternal population history reconstruction.Additional file 1: Supplementary Table 1. Raw genotypic data of 4 ethnic groups typed with Yfiler.Additional file 2: Supplementary Table 2. Allele Frequencies and Forensic Parameters 4 ethnic groups.Additional file 3: Supplementary Table 3. Pairwise Rst values  and their corresponding p values  between 4 ethnic groups and other reference Pakistani populations.Additional file 4: Supplementary Table 4. Pairwise Fstvalues  and their corresponding p values  between 4 ethnic groups and other reference Pakistani populations.Additional file 5: Supplementary Table 5. Pairwise Rst values  and their corresponding p values  between 4 ethnic groups and other reference Pakistani populations.Additional file 6: Supplementary Table 6. Pairwise Fst values  and their corresponding p values  between 4 ethnic groups and other reference Pakistani populations.


Venturia inaequalis is an economically significant disease worldwide. The annual sexual reproduction of V. inaequalis leads to high variation, changes in the population’s genetic structure and adaptations to the changing environment, including overcoming the host’s resistance. The objective of this study is to characterise and assess the genetic diversity of V. inaequalis populations in two main apple-growing regions in Latvia. In total, 143 V. inaequalis isolates were collected from Latvia, six reference strains with known virulence were obtained from other countries, and all strains were genotyped by 12 SSR markers. The SSR markers were highly variable and informative, identifying 158 alleles that ranged from two to 29 per locus. The Bayesian clustering identified three genetic lineages among the Latvian isolates that did not correlate to the geographic origin, host genotype, organ (leaves or fruits) from which the pathogen was isolated, time of collection, and type of isolation (single conidium or ascospore). The possible relatedness to virulence was detected when reference strains with known virulence were included in the analysis. Our findings correspond with previous studies demonstrating that V. inaequalis in Europe has a high genetic diversity within populations, but low diversity among the populations.Apple scab caused by the ascomycete  Venturia inaequalis (Cooke) is an economically significant disease that affects apples  worldwide, causing deformations in fruit shape and size that may result in yield reductions of up to 70% [Apple scab caused by the ascomycete p to 70% . In areap to 70% . Fungicip to 70% ,4. Althop to 70% , it can p to 70% . In Latvp to 70% .V. inaequalis is a heterothallic pathogen with annual sexual reproduction and multiple asexual reproductions. The annual sexual reproduction leads to high variation, changes in the genetic structure of the V. inaequalis population, and adaptations to the changing environment, including forming new races and overcoming host resistance [Malus and V. inaequalis pathosystem, and 14 races of V. inaequalis virulent on host genotypes with corresponding resistant genes (Rvi) are known to occur in nature [V. inaequalis population is crucial for understanding the epidemiology of the apple scab, and it can provide growers with valuable information concerning disease management [V. inaequalis have been conducted in several countries in central and western Europe [V. inaequalis populations, such as random amplified polymorphic DNA (RAPD) markers, restriction fragment length polymorphism (RFLP), genome-wide SNPs, as well as microsatellite or simple sequence repeat (SSR) markers [sistance . Currentn nature ,11. The nagement . Studiesn Europe ,13,14, In Europe , centraln Europe , Turkey n Europe , Iran [9n Europe , United n Europe , Belarusn Europe , and Rusn Europe . Various markers ,22,23,24 markers .V. inaequalis in Latvia is lacking. Weather conditions in Latvia, where rain is prevalent from spring to late autumn, are conducive for apple scab development throughout the season. In addition, weather conditions during the dormancy period are usually suitable for forming the sexual stage of the pathogen, as it forms pseudothecia in leaf litter. In Latvia, a wide range of apple cultivars that are adapted to local climatic conditions are grown, including locally bred or introduced cultivars from several growing regions, such as Russia, Belarus, Lithuania, and Estonia. In the breeding of new cultivars, the donors of important agronomic traits and disease resistance from other countries and regions, such as Germany, Ukraine, Czechia, France, the United States, Canada, New Zealand, and Scandinavia, are used [V. inaequalis populations in Latvia in two main apple-growing regions using SSR markers.The information on the genetic structure of are used . Althougare used , they maare used . The objMalus genotypes, including locally bred Latvian cultivars and hybrids , widely grown commercial cultivars ), other introduced cultivars , and the ornamental apple, M. toringo.Samples were collected from two main apple-growing regions in the central  and North-western (Kurzeme) parts of Latvia from 2010 to 2016, and they were treated as separate populations in the data analysis . Apple lV. inaequalis strains originating from diverse localities and hosts in Latvia were genotyped. In addition, six V. inaequalis reference strains 1127, 147, 333, 1634, EU-B04, and 2408 for the races (1), (2), (4), (5), (6), and (8), which were found to be virulent on hosts with the resistance genes Rvi1, Rvi2, Rvi4, Rvi5, Rvi6, and Rvi8  containing streptomycin (50 μg/mL) to avoid bacterial contaminations. Single germinated spores were excised, transferred on potato dextrose agar , and isolates were preserved in 7 mL tubes at +5 ± 1 °C on PDA (Difco) and potato–carrot agar slants , and innd Rvi8 .Fungal isolates were grown in Petri dishes containing 25 mL of potato dextrose broth  for 21 to 28 days at room temperature (22 ± 2 °C) without shaking. The mycelia were harvested without agar plugs, placed in 50 mL Falcon tubes, washed three times with sterile distilled water, and harvested via centrifugation at 3.500× rpm for 5 min . The harvested mycelium was freeze-dried  and ground to a fine powder in liquid nitrogen using a mortar and pestle. DNA was extracted using a DNeasy Plant Mini kit (Qiagen), in accordance with the manufacturer’s instructions. The concentration of each DNA sample was determined on a spectrophotometer , in accordance with the manufacturer’s instruction. The PCR amplifications were performed with an Eppendorf Mastercycler EP Gradient thermocycler under the following conditions: 5 min at 95 °C, followed by 35 cycles of 30 s at 95 °C, 90 s at 58 °C (or 60 °C depending on the marker), and 30 s at 72 °C, with a final extension of 30 min at 60 °C. The annealing temperature used for each SSR primer pair is shown in The amplification products during the primer tests were separated by electrophoresis in 4% agarose gel (Applichem), stained with ethidium bromide, and visualised under UV light . The approximate sizes of the amplified fragments were estimated with an O’RangeRuler 20 bp DNA Ladder (Thermo Fisher Scientific). The final sizing of PCR fragments was performed via capillary electrophoresis with a 3130xl Genetic Analyzer ABI (Applied Biosystems) as an external service at Latvia State Forest Research Institute “Silava”.V. inaequalis multilocus genotypes. Before PCA analysis, the genotyping data were transformed in a binary format into a data matrix file, scoring the presence of an allele in each locus as “1” and its absence as “0”.The software GenAlEx 6.41  was usedV. inaequalis isolates based on allele frequencies per locus. Each run consisted of a burn-in period of 100,000 steps, followed by 100,000 Monte Carlo Markov Chain replicates, assuming an admixture model and correlated allele frequencies. No prior information was used for a cluster definition. The ad hoc measure of ΔK was applied to calculate the optimum number of populations (K) [For population structure analysis, Bayesian model-based clustering program STRUCTURE version 2.3.4.  was usedions (K)  using thions (K) . Twenty ions (K) , and theions (K) .V. inaequalis strains. Nineteen markers were excluded from the further analysis because 11 markers  were monomorphic, six  gave several amplification bands, and two markers  did not have any amplification product in ten of the tested strains (Data not showed).Initially, 31 previously published SSR markers ,31,32 weV. inaequalis isolates were genotyped in 12 SSR loci. All these markers were polymorphic and multiple alleles were noted (e) at each locus ranged from 1.01 to 14.31. Nei’s gene diversity index (h) ranged from 0.01 to 0.93. Shannon’s information index (I) ranged from 0.04 to 2.88, and the number of private alleles ranged from one (Vigt 10/ε and Vict 1/130) to 15 (1tc1g). Polymorphic information content (PIC) values ranged from 0.013 (Vigt10/ε) to 0.934 (Vitc2D). Vict1/130 marker (PIC = 0.405) had moderate polymorphism (0.25 < PIC < 0.5), whereas the other ten loci (PIC = 0.549–0.934) had high polymorphism (PIC > 0.5)  .V. inaequalis population structure analysis, the program STRUCTURE was used to reveal populations (K) based on the allele frequencies per locus without prior information for the cluster definition. The population structure analysis, including isolates that were only from Latvia, identified three divergent groups, K1–K3 , specific orchard, or host genotype (data not shown). Therefore, for s, K1–K3 , whereasincluded .V. inaequalis isolates that were of Latvian origin, were not related to the initially defined populations based on the apple-growing regions . The first group (K1) included 38% (n = 54) of genotyped V. inaequalis isolates that were derived equally from both regions , and from the majority of the host genotypes (n = 11 + three unknown) represented in this study. In this group, the majority of the strains were monoconidial isolates (n = 46) that were obtained before 2013 (n = 44), in the two main fruit growing centres in Latvia , from fruits (n = 33) in commercial orchards (n = 32) that had been subjected to fungicide treatments (n = 36). The second group (K2) comprised 28% (n = 40) of the strains, and they were mainly from Zemgale . The strains were collected on fruits or leaves in different years and from different types of orchards that had either been subjected to a fungicide treatment (n = 22) or had not (n = 18). Half of the strains in this group originated from commercial orchards (n = 20) and the cultivar ‘Lobo’ (n = 17). The third group (K3) included 34% (n = 49) of the V. inaequalis strains, of which 73% originated in Zemgale . The strains were obtained equally from fruits (n = 24) or leaves (n = 25), mainly as monoconidial isolates (n = 30) before 2013 (n = 33), and from various types of orchards that had (n = 21) or had not (n = 28) been subjected to fungicide treatment. Isolates in this group originated from 14 host genotypes, and half of them were from the cultivars ‘Huvitus’ (n = 10), ‘Beloruskoje Malinovoje’ (n = 9), and ‘Lobo’ (n = 5). The gene diversity (h) was comparable among the three genetic clusters (K), ranging from 0.53 to 0.64. Shannon’s information index (I), an estimate of diversity, ranged from 1.18 (K2) to 1.51 (K3)  .The AMOVA results pointed to higher levels of variation within populations (87%) than between populations (13%). The low genetic differentiation among the populations in Zemgale and Kurzeme was also supported by a low PhiPT value. Higher PhiPT values (0.128–0.147) were detected among groups that were identified by STRUCTURE analysis .e) by the marker Vitc2/D was higher in K3 (12.84) than in K1 (8.84) and K2 (8.51), ranging from 1.04 to 12.84, depending on the locus and group. In K3, the gene diversity index (h) ranged from 0.04 to 0.92 depending on the marker, and it was higher than in other groups. In addition, Shannon’s information index (I) (0.10 to 2.72) and the number of private alleles (Pa) (2.75) were also higher in K3 than in the other two groups , respectively, were included in K2 (n = 36). This group mainly comprised strains obtained as monoconidial (n = 14) or ascospore (n = 16) isolates from scabbed leaves that were collected in closely located and related  orchards in Zemgale (n = 30), particularly Dobele (n = 28), and were mainly from areas with the fungicide-free growing system (n = 28). These two groups, K2 and K3, included the highest number of isolates from the greatest variety of genotypes (14 out of 19). Strains isolated before 2013 , as monoconidial isolates from fruits collected in the fungicide-treated orchards, were dominant in groups K3 and K4. The highest effective number of alleles (Ne), the gene diversity index (h), and Shannon’s information index (I) were observed in groups K2 and K3, which included reference strains (a) was highest in K2 (2.00) and K3 (1.83) , specific orchard, or host genotype, six irulence ,30 were ectively ,30. Thisi5, Rvi6 ,30, and  strains . The num3 (1.83) .AMOVA results showed that 85% of the genetic variation was distributed among individuals within populations, and only 15% of the variation was attributable to differences among the populations . The greV. inaequalis population into four  or three  genetic groups, revealed by the population structure analysis, was generally confirmed by PCA and DAPC  and two  in the DAPC of populations (K = 4) exposed in the bi-dimensional plot  elucidatV. inaequalis , plants are infected with the newly released ascospores each spring; thus, the pathogen population acquires new genotypes, ensuring the high potential for genetic diversity and adaptation ability. The diversity and population structure of V. inaequalis has caught the attention of research groups in Europe and other regions for decades [V. inaequalis population diversity in Latvia based on the genotyping within twelve microsatellite loci. We used a highly diverse collection of strains, collected in different years, in two main apple-growing regions from various habitats , host genotypes, and plant protection systems.Plant pathogens with mixed reproduction systems  pose the greatest risk for host adaptation and breaking down the resistance . Due to  decades ,13,15,31 decades ,18,42. TV. inaequalis population. In our study, markers Vitc2/D and 1tc1g revealed the greatest polymorphism that coincided with results from other studies. Similarly, in studies from the United Kingdom [V. inaequalis population diversity in the United States and the North Caucasian region in Russia, this marker showed one of the least amount of polymorphism  [V. inaequalis in Europe [V. inaequalis population studied in Turkey [V. inaequalis population in Belarus, only the Vitc2/16 marker was monomorphic for all of the studied strains [The SSR markers used in this study were highly variable , and markers Vitc 2D , 1tc1g , and 1tc1a  were the most informative in terms of identifying the genetic diversity of the  Kingdom , South A Kingdom , five Eu Kingdom , and Bel Kingdom , the Vitn Europe  but it wn Turkey . Out of n Turkey ,19,20,22n Turkey . In the V. inaequalis populations in Latvia. Our results from both analyses showed that the populations were highly similar in both regions, and the geographical location made no contribution towards the population differentiation of V. inaequalis. No differentiation between these regions could be expected due to the distribution of the propagation material from the two main germplasm collections and nurseries located in these regions , a relatively short distance (about 100 km) between both germplasm collections, and a long history of material exchange between them. The germplasm collections in both regions have been Latvia’s main cultivar introducers and planting material distributors for decades. The AMOVA analysis showed only 5% of the variance among the populations confirming the frequent gene flow between the regions. Our findings correspond with previous studies demonstrating that V. inaequalis in Europe has high genetic diversity within populations but low diversity among populations, which increases with the expansion of spatial distance [V. inaequalis, and the great diversity of grown cultivars. Recombination occurs via sexual reproduction, thus leading to the high variation and diversity in fungi, and changes in the genetic structure of populations [V. inaequalis in orchards [We used two complementary analysis methods, STRUCTURE and PCA, to study the population structure and relationships between ce about 0 km betwdistance ,31. Simidistance ,16,19,43ulations ,24,44. Torchards ,42. Our orchards ,19,42.Rvi1, Rvi4, and Rvi8 hosts. Over 50% of Latvian V. inaequalis isolates, clustering in this group, were from Zemgale, commercial orchards with fungicide applications, and isolated from fruits before 2013. The clustering of the strains was possibly related to the virulence, and this is supported by our previous findings. In Zemgale, the V. inaequalis races (1), (4), and (8) are established and capable of overcoming the Malus resistances genes Rvi1, Rvi4, and Rvi8 [Rvi2, Rvi5, and Rvi6 hosts, clustered together (K2) with the V. inaequalis isolates that were mainly obtained from leaves and fungicide-free growing sites  in the Dobele region . In both groups, K3 and K2, the number of host genotypes  was higher than in other groups (K1 n = 8 and K4 n = 10). The segregation of the V. inaequalis population in the orchard, depending on the virulence, has been demonstrated in Germany, where the studied population was split into two subpopulations based on the strain sampling from host genotypes with or without the Rvi6 gene [Malus resistance genes Rvi2, Rvi5, and Rvi6 had not yet been observed in closely located orchard with V. inaequalis races differential hosts in Dobele; however, the first signs of overcoming the Rvi6 resistance gene were noticed in organic farms in other regions of Latvia [Rvi6, have been grown in small quantities since 2011 [Rvi5 and Rvi6 genes, has been carried out in Latvia since 1989 [Rvi5 or Rvi6 hosts among the genotyped isolates from Latvia, the virulent pathogen could infect other genotypes without resistance genes, and such sites without scab management , near commercial orchards or nurseries, may be the source of the diversity. The great diversity of the apple cultivars that have different resistance, or orchards only with resistant cultivars, may still require fungicide applications that can contribute to the reduction of virulence build-up in the pathogen’s population and avoid the risk of the breakdown of resistance in genes in resistant cultivars. Although no or insufficient use of plant protection measures, including leaf litter sanitation, may pose a threat in terms of virulence formation in the V. inaequalis population [The STRUCTURE analysis identified three genetic lineages among the Latvian isolates (K1-K3) that were also supported by DAPC. In general, the distribution of the isolates in these lineages did not clearly correlate with geographic origin, the cultivar from which the pathogen was isolated, organ (leaves or fruits), time of the collection, and type of the isolation (single conidium or ascospore). Four genetic lineages were identified when six reference strains, with known virulence, were included in the population structure analysis to clarify the possible reasons for the grouping of isolates regardless of their origin. The segregation of the strains in the identified groups indicated the possible relatedness to the virulence. The largest group (K3) included reference strains virulent on the and Rvi8 . This fiand Rvi8 ,46, whervi6 gene . The abif Latvia ,48. In cnce 2011 ,50. Morence 1989 ,52. Althpulation  and grad


Triodanis perfoliata, a widespread mix‐mating annual plant in the contiguous US. By integrating population genomic data with spatial analyses and modeling the relationship between a breeding system and genetic diversity, we illustrate the complex ways in which these forces shape genetic variation. Specifically, we used 4705 single nucleotide polymorphisms to assess genetic diversity, structure, and evolutionary history among 18 populations. Populations with more obligately selfing flowers harbored less genetic diversity , and we found significant population structuring (FST = 0.48). Both geographic isolation and environmental factors played significant roles in predicting the observed genetic diversity: we found that corridors of suitable environments appear to facilitate gene flow between populations, and that environmental resistance is correlated with increased genetic distance between populations. Last, we integrated our genetic results with species distribution modeling to assess likely patterns of connectivity among our study populations. Our landscape and evolutionary genetic results suggest that T. perfoliata experienced a complex demographic and evolutionary history, particularly in the center of its distribution. As such, there is no singular mechanism driving this species' evolution. Together, our analyses support the hypothesis that the breeding system, geography, and environmental variables shape the patterns of diversity and connectivity of T. perfoliata in the US.Both intrinsic and extrinsic forces work together to shape connectivity and genetic variation in populations across the landscape. Here we explored how geography, breeding system traits, and environmental factors influence the population genetic patterns of  T. perfoliata.Both intrinsic and extrinsic forces work together to shape connectivity and genetic variation in populations across landscapes. We explicitly examined how these factors influence the landscape genetics of a widespread cleistogamous species. Our analyses support the hypothesis that breeding system, geography, and environmental variables all shape the patterns of diversity and connectivity of  While gTriodanis perfoliata (Campanulaceae), a widespread, annual native to North and South America  and isolation by environment (IBE) will be the strongest factors structuring genetic diversity. Both geographic distance and variance in biotic and abiotic factors can limit gene flow or shape the potential for gene flow through particular corridors. Therefore, we explicitly discern the roles of geographic isolation (IBD) and environment (IBE) in shaping observed patterns. At local spatial scales, we predict that populations with a greater allocation to cleistogamy will exhibit reduced genetic diversity and high population genetic structuring (average Fst) due to increased inbreeding and that the breeding system will be a more important factor influencing population‐level genetic patterns. Finally, we use habitat suitability models to predict routes of dispersal among contemporary populations. Incorporating genetic data into these analyses provides a framework for understanding corridors of gene flow among our study populations.Integrating genetic data with landscape and environmental parameters can better describe the range of factors driving or maintaining patterns of genetic diversity among populations  Nieuwl. (Campanulaceae) is a small, common, annual herb native to North and South America. This weedy annual grows in a variety of conditions including disturbed areas, along rocky outcrops, dry open habitats, and prairies  spanning the contiguous US  to serve as an outgroup for phylogenetic analyses. We used a CTAB protocol  sites observed >90% of the sequenced individuals of 2.3K; analyses were run under the admixture model and assuming correlated allele frequencies. To determine the most likely value for K, we assessed values of ΔK , as per the Evanno et al., FST was calculated via the R packages Adegenet  were calculated using Arlequin 3.5.2.2 . Breeding system data for the OCN population (Otter Creek North Carolina) were derived from Ansaldi, Franks, & Weber, π, number of polymorphic sites) and pCH via a linear regression using the lm function in R statistics v 4.1.1 . Model fit was assessed via the omission rate, area under the curve (AUC), and model feature class complexity  are estimated by inverting the SDM  to function as a friction layer, characterizing the cost of dispersal through each pixel in the landscape; areas of high suitability have a lower dispersal cost compared to areas of low suitability . This scenario describes the role of likely dispersal corridors in shaping the genetic structure seen across the sampled landscape. Connectivity among clades. In the third scenario, dispersal networks were estimated among major phylogenetic clades. Here we subjectively split the phylogeny into subclades by placing a vertical line near the base of the tree . To remove the explicit effects of geographic distance from our two IBE matrices, we performed a linear regression in which each IBE distance matrix was a response variable and our geographic distance matrix was the predictor variable , as our corresponding IBE distance matrices in separate MMRR analyses. In each MMRR analysis, genetic distance was used as a response variable, whereas the IBD and IBE matrices were each considered individually as a single predictor variable. To assess the statistical significance of each MMRR analysis and predictor variables, we used 10,000 permutations in each analysis  analyses in R  in aligned sequence sites for T. perfoliata. These SNPs were presumably random except for their proximity to the Sbf1 restriction site that was used to generate the library. There is no reason to believe there is systematic selection occurring on the eight bases that constitute the Sbf1 target sequence (CCTGCAGG). The final, stringent list of variants contained data for 97% of genotypes; 3% of genotypes were not sequenced or found to have low quality, and therefore were considered missing. Total heterozygosity was relatively low (16%) and a potential signature of high inbreeding in this species. The number of private alleles, π, and polymorphic sites varied widely among the 18 populations, indicating considerable variation in genetic diversity   Tables  and S2. K = 4 and K = 17 genetic clusters among the 18 populations of T. perfoliata , populations generally segregated into broad geographic regions of the US including (1) the central Midwest and Western states, (2) the southern Midwest and Gulf states, (3) Eastern states and (4) populations from NY that form a separate cluster , individuals generally segregated into clusters by population , and some populations from the Eastern US also clustered together  was 0.48 ± 0.07 [N = 9; mean ± 1 SE; Table FST was significantly negatively correlated with pCH; populations producing relatively more CL flowers exhibited greater overall population substructuring . These strong associations support the hypothesis that breeding system strongly influences population genetic patterns. Populations with a greater allocation to CL flowers exhibit reduced genetic diversity and increased population substructuring, as predicted by reduced gene flow among highly selfing populations.Similar to previous work, we found that breeding systems are highly variable across populations of 3.4Our final SDM had a high predictive performance with an Omission Rate (OR) < 0.01 and a moderate discriminatory ability with an AUC = 0.62 . Using genetic clusters imposes key roles for factors such as demography and lineage history in shaping the genetic landscape in addition to IBD and IBE  was the strongest predictor of our observed genetic diversity across the sampled distribution (4Triodanis perfoliata (Campanulaceae). Specifically, we found that populations with a greater proportion of cleistogamous (obligately selfing) flowers had reduced genetic variation compared to populations with relatively more open flowers . Greater inbreeding also tends to increase population structure and reduce gene flow among populations  produced in populations; suggesting greater gene flow among populations with a greater allocation to CH compared to CL (closed) flowers. Overall this species exhibits a relatively high global FST (0.48), a wide range of population pairwise FST values among populations and genetic clusters (Tables K = 4 out of 18 populations). Genetic divergence among populations of T. perfoliata is also influenced by both geographic distance and environmental factors. Therefore, we also explored models of population connectivity to better understand the factors shaping the genetic landscape of this widespread, mixed‐mating species.Here we explicitly demonstrate how a variety of factors, both intrinsic  and extrinsic , drive patterns of genetic diversity and population divergence in the mixed‐mating annual, s Lande, . In concs Tables  and S2, Reproductive systems can influence population dynamics in complex ways, driving patterns of genetic diversity, gene flow, and demographics, and influencing fitness . However, strong correlations between the extent of cleistogamy at the population level and metrics of genetic diversity suggest a meaningful relationship that warrants subsequent investigation. Inbreeding depression is an obvious potential negative consequence of greater inbreeding  analyses, a method that uses SDMs to find predicted corridors . In addition, ongoing work aims to quantify how the breeding system explicitly influences patterns of demography and fitness among populations. Overall, our analyses of T. perfoliata illustrate the influence of the breeding system, geography, and the environment in shaping population genetic patterns in this widespread, mixed mating wildflower.Our study encompasses a wide geographic area, and our data reflects both the implications of factors more predominant at the population level  and factors affecting landscape‐scale patterns such as geographic distances ; data curation ; formal analysis ; investigation ; methodology ; project administration ; writing – original draft (lead); writing – review and editing . Colette Berg: Conceptualization ; data curation ; investigation ; project administration ; writing – review and editing . Taylor Simmonds: Writing – review and editing (supporting). Olivia Lopez: Data curation (supporting); investigation (supporting). Jason Brown: Conceptualization (supporting); formal analysis ; methodology ; writing – original draft (supporting); writing – review and editing . Robert Ruggiero: Data curation ; formal analysis (supporting); writing – original draft (supporting). Jennifer Weber: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation (lead); methodology ; project administration (lead); supervision (lead); visualization ; writing – original draft ; writing – review and editing .The authors claim no conflict of interestThis work was supported by startup funds from Southern Illinois University, Carbondale to J. Weber, and additional research and startup funds from Southeast Missouri State University to J. Weber and R. Ruggiero.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Figure S1Click here for additional data file.Figure S2Click here for additional data file.


Glyptemys insculpta) population graph, we introduce BRIDES as a new tool to evaluate populations for conservation purpose without focusing solely on individual nodes. BRIDES characterizes different types of shortest paths among the nodes of a subgraph and compares the shortest paths among the same nodes in a complete network. The main objectives of this study were to (1) introduce a BRIDES selection process to assist conservation biologists in the prioritization of populations, and (2) use different centrality indices and node removal statistics to compare BRIDES results and assess gene flow among wood turtle populations. We constructed six population subgraphs and used a stepwise selection algorithm to choose the optimal number of additional nodes, representing different populations, required to maximize network connectivity under different weighting schemes. Our results demonstrate the robustness of the BRIDES selection process for a given scenario, while inconsistencies were observed among node-based metrics. Results showed repeated selection of certain wood turtle populations, which could have not been predicted following only genetic diversity and distinctiveness estimation, node-based metrics and node removal analysis. Contrary to centrality measures focusing on static networks, BRIDES allowed for the analysis of evolving networks. To our knowledge, this study is the first to apply graph theory for turtle conservation genetics. We show that population graphs can reveal complex gene flow dynamics and population resiliency to local extinction. As such, BRIDES offers an interesting complement to node-based metrics and node removal to better understand the global processes at play when addressing population prioritization frameworks.Genetic diversity within and among populations is frequently used in prioritization processes to rank populations based on their vulnerability or distinctiveness, however, connectivity and gene flow are rarely considered within these frameworks. Using a wood turtle ( The conservation of units below the species level, commonly populations, is widely used in wildlife management, as it provides critical information to support species survival and evidence of within-species differences. Populations show important local adaptive variations that are essential for species adaptation to changing environments –3. FurthConsidering that two populations may be ranked in the same category even when faced with entirely different threats, and the limited financial resources available for conservation biology, prioritizing populations arose as an essential concept to target the most effective conservation actions. Multiple prioritization frameworks have been developed to provide objective criteria during decision-making processes, and either estimate species extinction risks  or priorOncorhynchus spp.) and adapted for all freshwater fish by Clarkson and colleagues  containing the numbers of each type of paths estimated by the algorithm  components of the network, similar to cut nodes that represent stepping-stones among isolated populations. Equal paths are corresponding to redundant connections among populations for dispersal and gene flow. In contrast, impasses and roadblocks are detrimental to network connectivity by separating populations in isolated components. These latter types of paths are to be avoided in the prioritization process.n to maximize the connectivity of Xn+1 , only considered shortcuts, whereas the second model, B3S1 = , and third model, B1S3 = , also accounted for shortcuts and breakthroughs, but with distinct weights. The fourth model, B1R1D1S = , considered the negative impact of roadblocks and detours with respect to breakthroughs and shortcuts. For the fifth model, B3D1E2S3 = , we used an ecological interpretation of BRIDES in which breakthroughs, detours, equals and shortcuts were all considered to increase the connectivity of the network, but with different weights.In the present study, five distinct weighting schemes were used for the six types of paths  to select optimal solutions (maximum scores) under different scenarios, with positive weights (denoted with superscripts) attributed to preferred types of paths, negative weights (denoted with subscripts) attributed to rejected types of paths, and null weights (zero) attributed to types of paths ignored in the final score computation. The first weighted model, SE), estimated population size, and persistence probability (EO ranks). These scenarios were used to generate different subgraphs Xn onto which candidate nodes were added in turn to construct subgraphs Xn+1. For each parameter, pairs of networks (subgraphs) were generated by selecting nodes with either the largest or smallest values, representing nodes that prioritize a proactive (P) or a reactive (R) conservation perspective, respectively. For the first pair of networks, populations with higher (Scenario AP) and lower (Scenario AR) heterozygosity values (HE) were selected as possible subgraphs for the prioritization process. For the next pair of networks, nodes with higher (Scenario BP) and lower (Scenario BR) estimates of population sizes were selected. Finally, EO ranks were used to select nodes for the fifth (Scenario CP) and sixth (Scenario CR) networks based on their persistence probability for a period of time. The five weighting models were then applied to the six scenarios to examine their impact on the node selection process.In addition to the five weighting schemes, six scenarios encompassing three different parameters important for management purposes were considered based on their frequent use in conservation studies: genetic diversity (HThe wood turtle population graph constructed with cGD resulted in 19 nodes and 37 edges (19% of the edges in a saturated network) after the pruning procedure. Eight nodes were located on the north shore and eleven nodes on the south shore of the St. Lawrence River . Sample The population graph was analyzed with different node-based metrics to determine which sites contributed most to network connectivity . One nodE values greater than (Scenario AP) or lower than 0.72 (Scenario AR). Those based on estimated population sizes were created by including populations with more (Scenario BP) or less than (Scenario BR) 75 individuals. Networks based on element occurrence included populations with EO ranks greater than BC (Scenario CP) or lower than B (Scenario CR)  . The fivario CR) .n (1) that only accounted for shortcuts (complexity of 1), three out of six scenarios returned no solution as no shortcuts were possible by adding candidate nodes to the subgraph. In three other cases, one or two candidate nodes were required to reach a maximal score. Models B3S1 and B1S3 considered shortcuts as well as breakthroughs (complexity of 2), but with different weighting schemes, and the number of nodes required to reach the maximum score varied from one to three. Model B1R1D1S1 contained contrasting roadblocks and detours with respect to breakthroughs and shortcuts (complexity of 4), and two to five candidate nodes were required to reach a maximal score. Finally, Model B3D1E2S3 accounted for breakthroughs, detours, equals, and shortcuts (complexity of 4), and three to six nodes were required to reach a maximal score. In some cases, multiple solutions with equal scores were available at any given step of the procedure. To represent these equally multiple solutions, we depicted alternative paths with different types of edges in corresponding graphs . For that reason, BRIDES may be used jointly with complementary tools, such as phylogenetic networks developed by Volkman and colleagues [Important ranking information is largely provided by node-based metrics, as nodes can be compared across a multitude of indices operating at different scales and patterns of connectivity. Cross and colleagues  argue thlleagues  to accouIn the context of wood turtle conservation genetics, the results we obtained with population graphs are consistent with previous landscape genetics analyses , howeverE, population sizes, and high EO ranks.The BRIDES algorithm was applied in this study to demonstrate how it may be used to select wood turtle populations with either a reactive or proactive prioritization procedure. To do so, we considered six hypothetical scenarios and five different weighting schemes, and applied a node selection procedure to prioritize populations. For example, the first scenario only included populations with large heterozygosity values. Even when neutral genetic diversity is not correlated to functional diversity , heterozRegardless of their genetic diversity, the topological relationships among nodes within the network may be of importance for connectivity . PeripheR, 3D1E2S3, 3D1E2S3, In Québec, riverbeds and riverbanks which are part of wood turtle habitats are protected in public land, but stronger management actions are still needed to ensure long-term survival of populations . To do sSubgraph constructions in our study were based on proactive and reactive procedures to illustrate the method, but it is entirely up to the user to select populations in the starting subgraphs. The subgraphs could also be based on characteristics independent of the species, such as land ownership or jurisdictions involved allowing for nesting sites or for hibernacula that can be easily protected and managed. Because the choice of populations in subgraphs greatly influences the subsequent selection of candidate nodes, we suggest to use practical conservation criteria to select the nodes of a subgraph, especially when BRIDES is applied for population prioritization.E, notably, may not be the most relevant criteria since it was very similar in several populations. In the present case, subgraphs were created by removing nodes and their corresponding edges from the complete population graph among 19 wood turtle populations. Considering that the graph pruning method is based on the removal of redundant edges that do not contribute to the overall genetic covariance of the network, population graphs should be re-estimated every time a node is removed.Uncertainties about genetic connectivity, genetic differentiation, isolation by distance, and population assignment may all be addressed with a single heuristic approach: the network. Moreover, many levels of connectivity can be considered at a different time and spatial scales when using evolving networks, especially those analyzed with BRIDES. Network analysis also offers an alternative model to population genetic analysis when populations are not considered to be at a mutation-drift balance and depart from the Hardy-Weinberg equilibrium , 72, 73.Given the specific, historic life traits of long-lived organisms with low recruitment and late sexual maturity, the genetic characterization of recent perturbation events can be challenging . As netwS1 Fign are depicted by empty circles, whereas candidate nodes and corresponding edges in Xn+1 are depicted by blue circles.In each case, networks with optimal scores while minimizing the number of candidate nodes are presented. Nodes from the subgraph X(TIF)Click here for additional data file.S1 TableFor each node, the initial presence of a population in the subgraph is identified by (x) and its selection (1) or not (0) in at least one of the optimal solutions is indicated. Scenarios for which no nodes were selected (-) are also shown.(PDF)Click here for additional data file.


All the loci were found to be in Hardy-Weinberg Equilibrium (HWE) after applying a Bonferroni correction and no pairwise loci showed prominent linkage disequilibrium. The combined matching probability (CMP) and the combined power of discrimination (CPD) are larger than 1.8089 × 10−11 and 0.99999999995, respectively. Principal component analysis, multi-dimensional scaling plots and Neighbour-Joining tree among 65 worldwide populations indicated that Sichuan Hui and Han are genetically close to Hmong-Mien and Tai-Kadai-speaking populations, and Sichuan Tibetan and Yi bear a strong genetic affinity with Tibeto-Burman-speaking populations. The model-based genetic structure further supports the genetic affinity between the studied populations and linguistically close populations.Forensic parameters of 30 insertion-deletions (InDels) in 444 individuals from four populations are reported, which showed abundant genetic affinity and diversity among populations and high value in personal identification.Genetic similarities existed between the studied populations and ethnically, linguistically close populations.Sichuan Hui and Han are genetically close to Hmong-Mien and Tai-Kadai-speaking populations.Sichuan Tibetan and Yi bear a strong genetic affinity with Tibeto-Burman-speaking populations.Sichuan Province is located at the transitional junction regions of the Qinghai-Tibet Plateau and the low-altitude plains. It also serves as the corridor of Sino-Tibetan-speaking population migration and expansion since neolithic expansion of Proto-Tibeto-Burman populations from Middle/Upper Yellow River during Majiayao period (3300–2000 BC). However, the population structure and the corresponding genetic diversity of forensic-related markers in this region remain unclear. Thus, we genotyped 30 insertion-deletion (InDel) markers in 444 samples from four ethnic groups  from Sichuan Province using the Investigator Insertion-deletion (InDel) polymorphisms has been a promising and powerful supplementary tool in forensic personal identification cases and kinship testing, since they bear the advantages of lower mutation rate, smaller amplicons and absence of stutter peaks compared with traditional gold standard short tandem repeats (STRs) .Lying in Southwest China, backland of the Eurasian continent, Sichuan Province owns a complex topography spanning Qinghai-Tibet Plateau, Hengduan Mountains, Yunnan-Guizhou Plateau, Qinba Mountains and Sichuan Basin. Evidence of both archaeology and linguistics subsequently suggested that these regions play an important role in the formation of modern Sino-Tibetan-speaking populations, especially for Tibeto-Burman-speaking populations spreading southwards and westwards from the middle and upper basins of Yellow River during Yangshao period (about 7 000–5 000 years BP) with the millet cultivation expansion , 7.http://tjj.sc.gov.cn/tjxx/zxfb/201903/t20190319_277119.html), which is nearly equivalent to Germany’s population. As a multi-ethnic province, Sichuan is the biggest agglomeration for the Yi ethnic group, the second-largest community of Tibetan nationality and residence for large number of Hui group. However, Han group is still the largest population here (93.53%). Over the past hundreds of years, the ethnic groups have been the largest immigrant population inflow and accounted for an indispensable part in the construction of genetic variability in the province. In this study, we use the Investigator® DIPplex kit  to investigate population genetics and forensic efficiency in these four Sichuan ethnic groups.Nowadays, the permanent population of Sichuan has exceeded 80 000 000 according to Sichuan Population Statistics Bulletin 2018  after obtained participants’ written informed consents with the approval of the Ethics Committee of the Institute of Forensic Medicine, Sichuan University (K2015008).® DIPplex PCR Amplification Kit (Qiagen) following the manufacturer’s protocols and analyzed with Applied Biosystems 3130 Genetic Analyzer and Gene Mapper v3.2 (Thermo Fisher).Genomic DNA was extracted with the DNA Blood Mini Kit (Qiagen) and quantified using the NanoDrop-2000 Spectrophotometers  according to the manufacturer’s instructions. InDels amplification was carried out using the Investigator2O (Qiagen) were amplified and genotyped along the population samples as the positive and negative control, respectively. All experiments were carried out in our laboratory which has been accredited by the China National Accreditation Service for Conformity Assessment (CNAS) and ISO 17025.Control DNA 9948 (Qiagen) and ddHSupplementary Table S1.To comprehensively explore the patterns of genetic affinity between our studied populations and worldwide reference populations, we merged our newly obtained population data with those of 61 worldwide populations. The final database used in the population comparison study included 6483 East Asian individuals from 38 populations , 8–26, 4http://cmpg.unibe.ch/shiny/STRAF/) [https://www.r-project.org/). Multi-dimensional scaling analysis (MDS) was performed by SPSS software v25.0 . Principal component analysis (PCA) was conducted using the MultiVariate Statistical Package . Neighbour-joining tree (N-J tree) was carried out by the Molecular Evolutionary Genetics Analysis  on basis of the Nei’s standard genetic distances (Rst) using the Phylogeny Inference Package . The genetic structure composition analysis was performed by the STRUCTURE programme v2.3.4 (https://web.stanford.edu/group/pritchardlab/structure.html) [Allele frequencies and forensic efficiency parameters were calculated using online software STRAF (/STRAF/) . The heare.html) .P ≤ 0.0017) (see raw genotype data in Supplementary Table S2). Allele frequencies, forensic parameters, and efficiencies in the four Sichuan populations are shown in Supplementary Table S3. The highest power of discrimination (PD) loci were HLD88 (PD = 0.6519) in Sichuan Han, HLD40 (PD = 0.6294) in Sichuan Yi, HLD124 (PD = 0.6405) in Sichuan Tibetan and HLD88 (PD = 0.6607) in Sichuan Hui. The combined matching probability (CMP) and the combined power of discrimination (CPD) ranged from 1.8089 × 10−11 to 4.6696 × 10−11, 0.99999999995330 to 0.99999999998191, respectively. The highest and lowest combined powers of paternity exclusion (CPE) were 0.9919 in Sichuan Hui and 0.9861 in Sichuan Tibetan. These observed values indicated that the InDel system can serve as a powerful supplementary tool for existing STR system.All InDel markers were successfully amplified and genotyped in the 444 samples with no significant deviation from Hardy-Weinberg Equilibrium (HWE) and no significant linkage disequilibrium after performing a Bonferroni correction  and others. The further separation happened between Asian, European and American roughly. In most Asian populations, which is contrasted with other populations, HLD111, HLD39 and HLD122 loci show prominent low insertion frequencies and HLD118, HLD99, HLD64 and HLD81 loci perform distinct high frequencies.To further explore the genetic similarities and differences between populations, 61 worldwide population data of 30 InDel markers were collected into the reference database mentioned above , 5, 8–38st generated from PHYLIP v.3.6. (Supplementary Table S4). As is shown in MDS , population stratification along the continental geographical boundaries is identifiable. Genetic similarities and differences were further dissected using PCA  follows a pattern of ethnic, geographical and linguistic affinity. Asian populations are close to each other on the large scale, while the Sichuan Tibetan group remains close to other 10 Tibetan groups, and Sichuan Hui group stays next to Xinjiang Hui in the picture. Guangdong Han is considerably closer to the geographically close Tai-Kadai or Hmong-Mien-speaking populations .The evolutionary genetic relationship revealed by the N-J tree (The STRUCTURE analysis was used to dissect the genetic structure of the four Sichuan populations we studied and other 32 populations on the basis of their raw genotypes, which were shown in ® PCR Amplification Kit has been proved to be extremely useful as a supplement of STR to apply in forensic case works in the four populations we studied.According to the obtained results, Han, Yi, Tibetan, and Hui group of Sichuan were scattered among the Asian groups in terms of genetic distance, evolutionary relationship and genetic structure. The least significant differences in the genetic relationship were found between studied populations and ethnically, linguistically close populations. Furthermore, based on the acquired values, the Investigator DIPplexClick here for additional data file.


Varanus acanthurus. Specifically, we tested whether chromosome rearrangements are indicators of genetic barriers that can be used to identify divergent populations by looking at gene flow within and between populations with rearrangements. We found that gene flow was present between individuals with chromosome rearrangements within populations, but there was no gene flow between populations that had similar chromosome rearrangements. Moreover, we identified a correlation between reduced genetic variation in populations with a higher frequency of homozygous submetacentric individuals. These findings suggest that chromosomal rearrangements were widespread prior to divergence, and because we found populations with higher frequencies of submetacentric chromosomes were associated with lower genetic diversity, this could indicate that polymorphisms within populations are early indicators of genetic drift.Chromosomal rearrangements are often associated with local adaptation and speciation because they suppress recombination, and as a result, rearrangements have been implicated in disrupting gene flow. Although there is strong evidence to suggest that chromosome rearrangements are a factor in genetic isolation of divergent populations, the underlying mechanism remains elusive. Here, we applied an integrative cytogenetics and genomics approach testing whether chromosomal rearrangements are the initial process, or a consequence, of population divergence in the dwarf goanna,  Drosophila melanogaster), when Alfred Sturtevant crossed a wild female to a male with mutations on the second chromosome. He observed that when a F1 female was backcrossed, there was no recombination in any offspring for the mutant alleles that had previously had a crossover frequency of 37% , which is unique among varanids in having within-species chromosome polymorphisms  in which progeny has shown severe lethality due to failure in meiotic chromosome malsegregation  situated between Tennant Creek in the Northern Territory and Mount Isa in Queensland  for genotyping 49 individuals including the 34 individuals that were karyotyped Table . DArT isFst analysis were performed for each population to further characterize the distribution and frequencies of alleles between populations . We assigned karyotypes to each individual and plotted the analyses  were AA:6, and 37.5%  were heterozygous (MA:6), and there were no individuals with the homozygous submetacentric (MM:6) morphology. In the south, we found the opposite, 62.5% of individuals were MM:6, and the remaining 37.5% were heterozygous (MA:6), and there were no individuals with the AA:6 morphology. In the west, there was an even distribution of the two homozygous karyotypes; AA:6 individuals (38.5%) and MM:6 individuals (38.5%) and only 23% were heterozygous. In the east, all five individuals karyotyped had a MM:6 karyotype.We observed enlarged microchromosomes in only three out of 34 individuals from two localities (east and north). In the north, 12.5% (one of eight) karyotyped individuals had an enlarged acrocentric microchromosomes pair 9 Fig. . In the n = 9) had enlarged W chromosomes.We observed size variation in the sex chromosomes in the north Fig. . All eigThe unfiltered DArTseq dataset contained 301,738 SNP loci   Fig. . To furt Fig. . T53) Fig. . In the 53) Fig. , however53) Fig.  had admi53) Fig. . All popFst, and fixed differences. First, PCA analysis of the 46,189 SNP loci shared between all populations showed four distinct genotype clusters with no observable genetic admixture between the north, east, west, and south subpopulations representing 37.9%, 11.5%, 7.1%, and 2.5% of the total genetic variation on PCA axes 1, 2, 3, and 4, respectively   Fig. .Fig. 4a y = 0.084 + 2.2e-07, R2 = 0.314, p = 0.001) and all three karyotypes clustered together   Fig. .Fst and private alleles , the second fewest number of monomorphic loci  and 301 fixed differences with the east , and 111 fixed differences with the south . The west also shared 4508 SNP loci with the east and 3891 loci with the south, indicating that the east and south populations have diverged from the west more recently than the north.The south population was dominated by homozygous submetacentric karyotypes and had the fewest number of SNP loci 7733), the highest number of monomorphic loci 38,439), and the fewest private alleles in all pairwise comparisons to the other populations indicating a genetic bottleneck  Fig. .Allele shuffling and parallel divergence of thousands of loci were observed in all populations. Submetacentric chromosomes were associated with increased monomorphic loci and fewer polymorphic loci (drift), whereas acrocentric chromosomes were associated with fewer monomorphic loci and increased SNP loci. When we plotted the flow of private and fixed alleles between populations pairwise and with the metapopulation Table , it was Varanus acanthurus. We investigated intraspecific chromosome rearrangements within four geographically distinct populations of V. acanthurus to determine if chromosome morphology was indicative of population structure, i.e., to determine if chromosomal rearrangements drive population divergence or result from it. We discovered that there was no obvious population structure associated with chromosome rearrangements; however, chromosome rearrangements were widespread prior to the genetic divergence of populations.In this study, we used a “chromosomics” approach integrating populations genetics data with molecular cytogenetics Claussen  to test V. acanthurus revealed three different chromosome arrangements: (i) polymorphism in chromosome 6, described previously  , where three genetically distinct conservation units were identified from the five main Indonesian islands (Iannucci et al. V. acanthurus that utilized SNP markers, morphometric data and weather data, identified a need for taxonomic revisions and considered V. acanthurus a species complex with at least one undescribed species from the Cape Crawford area, which is sympatric with our north population (Pavón-vázquez et al. To determine the level of genetic diversity among four geographically distinct but chromosomally polymorphic populations of Drosophila persimilis and D. pseudoobscura in which restriction of gene flow is only isolated to the rearranged region of the chromosome polymorphism (Fuller et al. V. acanthurus genome (Zhu et al. Although the within-population structure analysis did not indicate an abrupt sink of gene flow, there were some fixed allele differences between the two homozygous karyomorphs that could indicate recombination suppression between the fixed AA and MM morphs in the western population. This would be similar to the observations between V. acanthurus which is likely a complex of multiple cryptic species.In conclusion, cytogenetic only data was inconclusive for defining population structure; however, when combined with the molecular data, it clearly provided evidence that chromosome rearrangements were widespread prior to the genetic divergence of these populations. The population genetics data provided this additional resolution to define fine-scale population structure that could not be resolved with cytogenetics alone. However, combining both cytogenetic and molecular data sets enabled the reconstruction of the history of chromosome transitions within