Reticulitermes grassei and R. banyulensis, two closely related termite species in southwestern Europe. We sought to clarify the current genetic structure of populations that formed following postglacial dispersal from refugia in southern Spain and characterize the gene flow between the two lineages over the last several million years. Each marker type separately provided a fragmented picture of the evolutionary history at different timescales. Chemical analyses of cuticular hydrocarbons and phylogenetic analyses of mitochondrial and nuclear genes showed clear separation between the species, suggesting they diverged following vicariance events in the Late Miocene. However, the presence of intermediate chemical profiles and mtDNA introgression in some Spanish colonies suggests ongoing gene flow. The current genetic structure of Iberian populations is consistent with alternating isolation and dispersal events during Quaternary glacial periods. Analyses of population genetic structure revealed postglacial colonization routes from southern Spain to France, where populations underwent strong genetic bottlenecks after traversing the Pyrenees resulting in parapatric speciation.A long‐standing goal of evolutionary biology is to understand how paleoclimatic and geological events shape the geographical distribution and genetic structure within and among species. Using a diverse set of markers , we studied  The Mediterranean basin, particularly the Iberian Peninsula, is one of the most biologically diverse areas in the world . Their population genetic structure might also be impacted by human‐mediated introductions at much shorter timescales , transects were established to perform more extensive sampling. Along each transect, collection points were located at least 15 meters apart to maximize the number of different colonies sampled. Sites A, B, and C in southwestern France and sites G, D, and F in Spain have been previously studied by DeHeer et al.  and VargR. grassei and R. banyulensis chemotypes, cuticular hydrocarbon profiles were characterized using gas chromatography (GC), which separated out the different cuticular compounds ) to perform PCA. Of the 98 chromatograms analyzed, 61 were new to this study; 37 were published previously by Kutnik et al.  using the relative proportions of the cuticular hydrocarbons were employed to differentiate clusters of chemical phenotypes within the species complex, as well as within each lineage. From the 98 interpretable chromatograms, we analyzed the 35 peaks shared by both species  and the mitochondrial cytochrome oxidase II gene .Amplified DNA was purified using the Qiagen PCR Purification Kit and cycle‐sequenced in both directions using an automated AB Rf6‐1, Rf15‐2, Rf21‐1, Rf24‐2, RS1, RS10, RS15, RS62, RS76, and RS78 . Alleles were scored using the computer program gene profiler 4.03 .To characterize population genetic structure, we used ten microsatellite loci: es Vargo  and R. sR. flavipes consensus sequence as outgroup. Finally, BI analyses were conducting using a partition‐by‐gene (COI and COII genes) scheme for combining the mitochondrial alignments. The most likely models for each gene were determined using MrModeltest v. 2.0  were concatenated to form single sequences that were then aligned using the ClustalW algorithm . We studied 16–33 colonies per site using one individual per colony. The program Genetix 4.05 , the multilocus genotypes described above for each of the intensively sampled 15 populations were evaluated for heterozygote excess using the program BOTTLENECK v. 1.2.02  were identified. However, we also obtained a profile that, visually, seemed to be intermediate  were indeed well separated and found on both sides of the first axis (30% of the variation), while the chemical profile (H‐2) of individuals from northern Spain were located between them. This intermediate profile previously reported by Kutnik et al. (R. grassei samples (H‐1 and H‐2), we were able to distinguish four different geographical clusters , which contains individuals from 43 locations, cannot be split into different subclades. In contrast, the M‐2 clade (R. banyulensis), which contains individuals from 19 locations, can be split into three well‐supported subclades, called M‐2a, M‐2b, and M‐2c  was the most variable; there were 47 informative sites for the 66 variable positions. The R. grassei clade (M‐1) had 26 informative sites for 49 variable positions. The differentiation between the two clades was also clearly established by 34 fixed substitutions. The Moroccan clade had 20 unique substitutions. Of the 34 substitutions distinguishing the two‐first clades, 12 are shared by the Moroccan and R. grassei clades and 22 are shared by the Moroccan and R. banyulensis clades.At the nucleotide level, the R. grassei (M‐1 clade), the haplotypes form a condensed network, particularly those from northern and central Spain  are more scattered and cluster into three phylogenetic subclades  found in 10 other colonies from southeastern France and northeastern Spain  with the N2b phylotype rather associated to R. banyulensis  are shown in Figure K = 2 to K = 7 are provided in Figure S2. The most likely number of populations was estimated at K = 6 using the calculation of different criteria as per Evanno et al.  and R. banyulensis (E and H to O). For K = 3, K = 6, and K = 7, R. banyulensis colonies were split into several populations along the Mediterranean coast . In contrast, and despite the geographical distance, R. grassei colonies formed a single population until K = 5, at which point the French  and Spanish  sampling sites clustered into two distinct populations . It is worth mentioning that neither mixed‐species colonies nor hybrid individuals were found R. grasseo et al. . For K =R. grassei sampling sites  under the two mutation models (IAM and TPM), as expected for the French sites. As for the Spanish sites, only site D had significant P‐values for the three tests.A more detailed examination of the genetic parameters yields further insights into population structure and biogeographical history. For all the samples and loci, mean heterozygosity (He) was equal to 0.47, with an average allele number of 3.21. However, allele number and heterozygosity declined along a south–north gradient Fig. . At the Reticulitermes worldwide  of the Moroccan populations and clarify their phylogeographic relationships with the European Reticulitermes termites. The R. grassei‐like nuclear genome of the Moroccan colonies might be the result of recent introductions, incomplete lineage sorting, or convergent evolution of the ITS regions. In any event, these findings indicate that intimate relationships exist among termites found on both sides of the Strait of Gibraltar as in other insect species , resulting in a gradual decline in genetic diversity , a distance of over 400 km  and might even extend up the Rhône valley to the Gard (department of Gard), over a distance exceeding 300 km . In addition to their dispersal by natural means, the two lineages have also had their ranges artificially extended by human‐mediated dispersal. For example, R. grassei colonies have been found in Devon, England , the third largest city in France, approximately 200 km north of the Mediterranean habitat where it is naturally found. Introduction events in the natural environment can be difficult to detect, especially if they occurred long ago and close together  and microsatellite markers . The population structure of these two lineages has no doubt also been influenced by isolation and dispersal processes that occurred during the Pleistocene's multiple glaciation/warming cycles, as is the case for other species in the region .Table S2. Table with Genbank numbers for each site, the lab codes used in Kutnik et al. (k et al. , and theClick here for additional data file.


Tsuga dumosa) experienced a recolonization event during the Quaternary period; however, the specific dispersal routes are remain unknown. Recently, the least cost path (LCP) calculation coupled with population genetic data and species distribution models has been applied to reveal the landscape connectivity. In this study, we utilized the categorical LCP method, combining species distribution of three periods  and locality with shared chloroplast, mitochondrial, and nuclear haplotypes, to identify the possible dispersal routes of T. dumosa in the late Quaternary. Then, both a coalescent estimate of migration rates among regional groups and establishment of genetic divergence pattern were conducted. After those analyses, we found that the species generally migrated along the southern slope of Himalaya across time periods and genomic makers, and higher degree of dispersal was in the present and mtDNA haplotype. Furthermore, the direction of range shifts and strong level of gene flow also imply the existence of Himalayan dispersal path, and low area of genetic divergence pattern suggests that there are not any obvious barriers against the dispersal pathway. Above all, we inferred that a dispersal route along the Himalaya Mountains could exist, which is an important supplement for the evolutionary history of T. dumosa. Finally, we believed that this integrative genetic and geospatial method would bring new implications for the evolutionary process and conservation priority of species in the Tibetan Plateau.Himalayan hemlock ( The study of measurement and modeling of dispersal of organisms associated with genetic data is of growing interest in the field of landscape ecology  region, several cold‐inferring forest species that belong to T. dumosa as our model to identify its dispersal routes. There are three ideal reasons for choosing this species: first of all, according to its previous phylogeographical work, a series of conclusions inferred from molecular evidence have revealed that this hemlock species underwent range expansion after the largest glaciation (0.8–0.6 Ma) in the HHM region  to identify the dispersal routes during the late Quaternary in the HHM region and assess their reliability from various evidence, and (2) to compare the concordance/discordance in dispersal corridors across time periods and molecular markers; (3) we expected to reveal some valuable implications for the evolutionary history and conservation priority of plants in the Tibetan Plateau (TP).In this study, we chose a widespread subalpine species T. dumosa, which populations have shared haplotypes needed to be firstly determined. According to the previous phylogeographical work from Cun and Wang , the last glacial maximum  based on the Community Climate System Model (CCSM) and the last interglacial . Environmental data in this study, including altitude and 19 bioclimate variables (Table S1), were obtained from the WorldClim database  were retained for subsequent analysis (Table S2).We used environmental and occurrence data to build species distribution models, identifying areas that http://data.gbif.org), the Chinese Virtual Herbarium  and field surveys in the year of 2013. For SDMs to be performed well, occurrence data require to be spatially independent, so the elimination of spatial clusters of localities is important for model calibration and evaluation. Because when spatial clusters of localities exist, models are often over‐fit toward environmental biases  and model performance values are inflated  analysis for 19 bioclimate variables, and then we calculated the mean standard deviation of the first three climate PCs. Finally, the remaining 65 localities together with low correlated environmental layers were used for species distribution modeling.For the second necessary data set for SDMs, species occurrence localities were mainly gathered from literature , number of iterations (500) and localities of occurrence divided into testing data sets and training data sets . In addition, we carried out pairwise comparison of the binary SDMs for the three periods  to predict range shifts since the last interglacial period.In this study, we used the maximum entropy algorithm in MAXENT v3.3  group, the eastern Hengduan Mountains (EHe) group, the western and central Hengduan Mountains (W&CHe) group, and the southern Hengduan Mountains (SHe) group. To assess the level of gene flow among the four groups, a maximum‐likelihood estimation of migration rates based on coalescence theory was implemented using the software Migrate v3.6.4 . Because in the study of Cun and Wang .Estimation of gene flow among regional groups has been able to provide a fine proof for investigating dispersal corridors of species. As mentioned above, evidence suggests plants migrated from the Hengduan Mountains to the Himalaya, we hypothesized there was higher gene flow along this colonization route. To verify the hypotheses, according to the publication of Cun and Wang , we divi4 Beerli . Here, oand Wang , they diDA; Nei and Li DA) were mapped at the midpoints between populations and finally a continuous surface  was created using an inverse distance weighted interpolation algorithm.Geographical barriers usually lead to genetic divergence and hinder species dispersal. So a genetic divergence pattern could indicate which areas represent geographical barriers, and which areas with low values of genetic divergence could be suitable for dispersal. Here, we mapped the pattern of genetic divergence using the Genetic Landscape GIS Toolbox  from the last interglacial to the last glacial maximum, species distribution in the Himalaya mainly presented an east‐westward contraction and little downstream movement within the Himalayan valleys, in the meanwhile, for the EHe regional group, the species expanded northward  afard Fig. B; (3) asard Fig. C.T. dumosa is partially overlaid with the Yarlung Zangbo valley; we assumed this valley might be another important historical dispersal corridor.Putative dispersal corridors in the three periods were visualized based on three genomic markers Fig. . When coAccording to gene flow patterns estimated from coalescent analyses based on cp/mt DNA Fig. , the patT. dumosa in the late Quaternary. In the TP, one directional dispersal approximately from the Hengduan Mountains to the Himalaya has been repeatedly assumed by several phylogeographical studies , integrated with SDMs and molecular data, we have been able to identify the dispersal corridors of Tsuga pollen and pollen of other coniferous species found within the valley  for the non‐eliminated bio‐climate predictors in the three periods.Click here for additional data file.


Vulpes vulpes) has the widest global distribution among terrestrial carnivore species, occupying most of the Northern Hemisphere in its native range. Because it carries diseases that can be transmitted to humans and domestic animals, it is important to gather information about their movements and dispersal in their natural habitat but it is difficult to do so at a broad scale with trapping and telemetry. In this study, we have described the genetic diversity and structure of red fox populations in six areas of north-eastern Poland, based on samples collected from 2002–2003. We tested 22 microsatellite loci isolated from the dog and the red fox genome to select a panel of nine polymorphic loci suitable for this study. Genetic differentiation between the six studied populations was low to moderate and analysis in Structure revealed a panmictic population in the region. Spatial autocorrelation among all individuals showed a pattern of decreasing relatedness with increasing distance and this was not significantly negative until 93 km, indicating a pattern of isolation-by-distance over a large area. However, there was no correlation between genetic distance and either Euclidean distance or least-cost path distance at the population level. There was a significant relationship between genetic distance and the proportion of large forests and water along the Euclidean distances. These types of habitats may influence dispersal paths taken by red foxes, which is useful information in terms of wildlife disease management.The red fox (The online version of this article (doi:10.1007/s13364-014-0180-2) contains supplementary material, which is available to authorized users. Gene flow between populations or groups of animals is dictated by a multitude of internal  and external  factors , Białowieża E 23° 56′ N 52° 63′ , Drygały E 22° 02′ N 53° 78′ , Jedwabno E 20° 69′ N 53° 43′ , Knyszyn E 23° 64′ N 53° 05′ , and Łomża E 22° 08′ N 53° 04′ . The approximate latitude and longitude geographic coordinates of capture were available for all individuals.Kidawa and Kowalczyk  describeGenomic DNA was extracted from all samples using the QIAGEN DNeasy Tissue Kit, according to the standard protocol for animal tissue. We tested 15 published canine microsatellite loci: FH2001, FH2010, FH2054, FH2079, FH2088, FH2096, FH2137, and FH2140 , except for locus FH2054 that showed a compound repeat pattern, making it difficult to manually assign bins. We used the automated binning software Tandem , observed heterozygosity (HO), and expected heterozygosity (HE) using the R package ADEGENET 1.3.6 (Jombart FIS) for each population were calculated using FSTAT 2.9.3 . The null hypothesis of homogeneity in allele frequencies among all the subpopulations was tested with both the chi-squared test and the Fisher’s exact test  after the drift process. This process was repeated 1,000 times and the proportion of significant (p < 0.05) tests was used as an estimate of power. The type-I error was estimated with the same parameters but no generations of drift, to give an expected FST of zero.We used the simulation program POWSIM 4.1  that best fit the data by grouping individual genotypes such that HWE and linkage disequilibrium (LD) are minimized. We used Structure 2.3.4  iterations, discarding the first 100,000 as burn-in. We used Structure Harvester . These values are sufficient to achieve high statistical power  and the confidence intervals about r were estimated by bootstrapping with replacement (N = 9999). Significant spatial genetic structure was inferred when r exceeded the null distribution for a given distance class and the confidence intervals did not overlap zero. The results were plotted in a correlogram and the overall significance was evaluated with the heterogeneity test  distance  (European Environment Agency 1990–ongoing). The CLC06 classes were grouped into six land cover categories Table . HabitatFST/1 − FST; Rousset FST/1 − FST) and the proportion of given habitats along the Euclidean distances for the populations examined. When a significant relationship was observed between genetic distance and a particular habitat proportion, we also tested this given habitat in combination with each of the non-significant given habitats to test if this strengthened the relationship with genetic distance  between populations  either failed to amplify or were difficult to score. VWF amplified reliably and was easily scored, but only one allele was detected among 23 individuals in the pilot study Table . Three os Figure .Table 2CA) ranged from 4.444 in Łomża (LOM) population to 5.281 in Białowieża (BIA) population (Table HO) values were not significantly different from expected heterozygosity (HE) values in each population. Inbreeding coefficient (FIS) was greatest in the Białowieża population (0.071) and smallest in Jedwabno (JED) population (0.009) .Allelic richness (on Table . Observe9) Table . FIS vaFST (0.019) and Jost D (0.056) estimates between Drygały (DRY) and Jedwabno (JED)  values and higher variability among the replicate runs at K > 1.Genetic differentiation between the six sample locations was low to moderate, with the highest pairwise D) Table . These tD) Table . Łomża  showed a pattern of decreasing relatedness with increasing distance in the first four distance classes (0 to 18 km), with an x-intercept at 21 km  based on the permutation test, which is more conservative . There was no significant correlation between genetic distance (FST/1 − FST) and either Euclidean distance  or least-cost path distance  at the population level. In general, the mean resistance values for LCP lines were much lower than the resistance values for Euclidean lines  and water  and Bayesian clustering revealed weak differentiation across the study area  is indicative of a large effective population size  with increasing forest cover. Open or mixed habitats offer a higher abundance of food such as voles ; the Central European (CE) cluster and the North Eastern European (NEE) cluster. These two groups appear to be separated by the Vistula River, indicating limited movements of the host species across the river. Smaller rivers presumably do not impede gene flow to the same extent, especially when they are frozen in winter  in an oral rabies vaccination (ORV) zone in TX, USA, where genetic data for 469 foxes at five microsatellite loci indicated local subpopulations were connected at distances greater than 30 km, beyond the established ORV zone buffer of 16–24 km .Information on the genetic structure of carnivore populations can assist the management of wildlife disease Below is the link to the electronic supplementary material.


Subsequent colonization events followed, some recognized regionally. Here, we assess the Y-chromosome structure of Cyprus in context to regional populations and correlate it to phases of prehistoric colonization.The archeological record indicates that the permanent settlement of Cyprus began with pioneering agriculturalists circa 11,000 years before present, . However, analyses of molecular variance (AMOVA) of haplogroups using 92 binary markers genotyped in 629 Cypriots revealed that the proportion of variance among the districts was irregularly distributed. Principal component analysis (PCA) revealed potential genetic associations of Greek-Cypriots with neighbor populations. Contrasting haplogroups in the PCA were used as surrogates of parental populations. Admixture analyses suggested that the majority of G2a-P15 and R1b-M269 components were contributed by Anatolia and Levant sources, respectively, while Greece Balkans supplied the majority of E-V13 and J2a-M67. Haplotype-based expansion times were at historical levels suggestive of recent demography.Analyses of Cypriot haplogroup data are consistent with two stages of prehistoric settlement. E-V13 and E-M34 are widespread, and PCA suggests sourcing them to the Balkans and Levant/Anatolia, respectively. The persistent pre-Greek component is represented by elements of G2-U5(xL30) haplogroups: U5*, PF3147, and L293. J2b-M205 may contribute also to the pre-Greek strata. The majority of R1b-Z2105 lineages occur in both the westernmost and easternmost districts. Distinctively, sub-haplogroup R1b- M589 occurs only in the east. The absence of R1b- M589 lineages in Crete and the Balkans and the presence in Asia Minor are compatible with Late Bronze Age influences from Anatolia rather than from Mycenaean Greeks.The online version of this article (doi:10.1186/s13323-016-0032-8) contains supplementary material, which is available to authorized users. Evidence from both site excavations and genetics support it being a threshold from which maritime colonists commenced entry to the Mediterranean basin and southeast Europe [The island of Cyprus is located t Europe  as well Major phases of prehistoric settlement in Cyprus based on material culture are summarized in Additional file Some extant Cypriot Y-chromosome data exists but it is either restricted to particular haplogroups  or of liAll aspects of sample selection and anonymity protections were managed by the Greek-Cypriot coauthors at the Molecular Medicine Research Center (MMRC) of the University of Cyprus. The Cyprus National Bioethics Committee approved the research program and the informed consent process. Most samples were collected through organized blood donation activities island-wide. Supplement DNA samples came from the DNA Biobank at the MMRC of the University of Cyprus. Specifically, 105 DNA samples came from the Biobank resource and the remainder from volunteers recruited during blood donations. All donors gave their signed consent for analyzing their DNA anonymously, according to the procedures approved by the Cyprus National Bioethics Committee. Genomic DNA was isolated from the whole peripheral blood  from 629Genotyping was conducted in both Nicosia and Marseille. Samples were screened hierarchically, using 92 biallelic markers whose specifications are given in Additional file We genotyped Y-STRs in 574 of the 629 samples, 572 of which pertain to haplogroups  using 17 loci: DYS19, DYS385 a/b, DYS389 I, DYS389 II, DYS390, DYS391, DYS392, DYS393, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635 (Y-GATA-C4), and Y-GATA-H4 in the AmpFLSTR® Yfiler® PCR Amplification Kit  following the manufacturer’s instructions. We also genotyped three additional Y-STRs: DYS388, DYS445, and DYS461 in the same 574 samples. A Y-STR based phylogenetic network of E-V13 haplotypes defined by 15 loci was constructed using the program Network 4.6.1.1 (Fluxus-Engineering) with the median joining algorithm. The 15 loci used were DYS19, DYS389 I, DYS389 II, DYS390, DYS391, DYS392, DYS393, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635 (Y-GATA-C4), and Y-GATA-H4. For the multi-copy short tandem repeat or microsatellite (STR) DYS389I,II, the DYS389b value was DYS389I subtracted from DYS389II. We also included nine E-V13 haplotypes from Anatolian Greeks—Phokaia and Smyrna [R [K = 2 to K = 10 groups, K = 15, and K = 20 groups.We examined the population structure within Cyprus by using four approaches: genetic distance, spatial autocorrelation analysis, spatial analysis of molecular variance (SAMOVA), and analysis of molecular variance (AMOVA). First, Nei and Takezaki’s genetic distance  between R . Second,R . Third, R . SAMOVA Last, Y-chromosome haplogroup frequencies were used to test four historical models through an analysis of molecular variance (AMOVA) . In ordeTo assess Cyprus in a broader regional context, we gathered Y-chromosome haplogroup frequencies from 36 populations totaling 4666 males, from relevant surrounding regions, Levant-Middle East , Caucasus (Armenia), continental Balkans , Mediterranean northern coast , Central Europe , and Mediterranean Islands  , 26–32. We then investigated the genetic affinities among the populations by principal component analyses (PCA) using XLSTAT 7.5.2.mY estimators with 100 replications as implemented in ADMIX 2.0 [m estimators of Anatolia, Danube Balkans, Greece, and Levant to the six fore mentioned Cypriot haplogroups. The lineages were composed of 11 loci . All input files for both ADMIX packages were built using the AdFiT v1.7 tool [R2 means that the allelic frequencies in the hybrid population can be explained by the allelic frequencies in each of the parental population.Based on the pre- and historical events in Cyprus DMIX 2.0 , 35–40 aDMIX 2.0  to measu1.7 tool . A high We estimated the time of expansion (TIMEX) of E-V13, G2a-P15, I2-M423, J2a-M67, J2b-M12, and R1b-M269 lineages in Cyprus and their time of divergence (TD) from Anatolian, Balkans, Greek, and Levantine sources. TIMEX and TD date lineages assuming that the observed variance has arisen indigenously within a given population as a result of a unique founder-based migration from an outside source. In practice, this means measuring the variation between the source and sink population assuming that it has accumulated since the time of settlement.μ)2 [μ)2 = 2ωτ, where ω is the mutation rate and τ is the number of generations since isolation. Note that these approaches are sensitive to both multiple founders during a particular migration as well as subsequent population gene flows, both of which inflate STR variance and the age estimate of the event. Since the choice of mutation is debatable 2  and implμ)2 . Under te e.g., , 46), we, weμ)2 [e  known sites outside the Levant colonized by Neolithic peoples [In Cyprus, haplogroup G2a-U5 (12.9 %) is widely distributed. While only 0.3 % distribute to the U5* paragroup, the PF3147 component (2.5 %) includes lineages like L91 (1.3 %), also seen in Asia Minor and Crete  and attr peoples . Within  peoples . Lastly, peoples  and as w peoples .ca. 3.5 % in our sample. Overall, haplogroup R1 presence was 15.1 %. The total frequency of associated R1a-M449 and R1b-M415 sub-haplogroups was 4.5 % and 10.7 %, respectively. The paragroup R1b-M269*(xL23) lineage is present (2.5 %). Furthermore sub-branches reflecting distinctive European versus Asian divergences similarly occur in both R1a and R1b. Within R1b, the central/west Europe M412 constituent (2.2 %) is offset by the western/central/south Asia Z2105 fraction (5.4 %) that was previously reported as paragroup L23*(xM412) [Unlike samples from the present day interior Levant, such as Palestinians, Bedouins, and Jordanians , 54, J1-*(xM412) . Similar*(xM412) . Lastly,K = 4 with northern, eastern, and southern regions separating . The inland/coast partition model did not significantly pull apart the variance . Neither the Central South West versus East partition (Pottery Neolithic to Early Bronze Age versus Late Bronze Age occupation site model) nor the East versus the rest of Cyprus (one of the Philia submodels) were found significant . However, the best clustering with a district was found for one of the two submodels concerning the arrival of the Philia phase, namely the Pottery Neolithic to Early Bronze versus Late Bronze Age occupation site model, when Kyreneia is considered separately .To assess the degree of possible local patterns of genetic diversity shaped by recent demographic forces, we conducted a SAMOVA using the distributions of 574 haplotypes within a uniform grid across the island. From R2 Pearson’s coefficient of correlation with latitude and longitude above 0.092, p < 0.05). On axis 1, Bulgarian, Czech, Balkan, Hungarian, and Greek groups stand apart from Caucasus and Near Eastern populations, while axis 2 separates the Italian and Near Eastern groups. Notably, Cyprus and Crete occupy a central position. Caucasus, southern Italian, Crete, and neighbor Arab-speaking Egyptian and Iraqi populations show closer genetic relationships with our Cypriot sample set. A recent autosomal survey also revealed genetic affinity between Cypriot and Caucasus individuals, probably dating back from the early Bronze Age [PCA shown in Fig. onze Age . Noticeaonze Age  in earlymY = 66 %), followed by the Levant (mY = 24 %) then the Balkan regions . A closer look at specific proxy lineages permitted us to dissect these results further , subsequently followed later by the expansion of E-V13, I2-M423, and J2b-M12  , Epicardial skeletons from Iberia (7000 y BP), South of France Late Neolithic (5000 y BP), and a Tyrol specimen (5300 y BP) [The Neolithic transition has diffused a wide array of culture, economic strategies, and social changes spanning the Levant, the Caucasus, and Europe –72 inclu00 y BP) –80.In Cyprus, the G2a-U5 assemblage has an overall frequency of 12.9 %. Notably, PF3147 and related lineages occur mostly in the insular Mediterranean and display frequencies consistent with a relic distribution . HaplogIt has been hypothesized that J2b-M12 may have been associated with the Neolithic immigration of farmers to Greece . HaplogrR2 = 0.282, p value = 0.011). The J2b-M205 distribution coincides with the Neolithic crop package dissemination found in common between the Levant and Cyprus [p < .00084). This suggests the possibility that Cyprus experienced a later (Pottery Neolithic) immigration from the Southern Levant.Both genetic data from the literature and Syria  show a frequency peak of J2b-M205 in the Southern Levant in which the frequency decreases northwards with latitude  linked with the distinctive six repeat allele at the DYS445 short tandem repeat locus proposed  to repre % linkedp < .032, one-tailed). J2a-Z489, present in Pafos and Northwest/Central Anatolia, may reflect Bronze Age immigration from Western Anatolia, the Philia phase, or mirror the Jewish population on the island from the Hellenistic/Roman Eras.Recent insights from ancient DNA studies suggest the spread of genes during the Bronze Age involved J2a-M67 individuals who appear in the Central European plains during the Late Bronze Age . J2a-M67R2 coefficient of correlation of E- M34 to longitude: 0.164, p = 0.003).E-V13 is common in the Balkans and may mark some of the Greek demographic input to Cyprus from the Late Bronze Age through the Iron Age . Networkca. 5000 years ago [p < .0256). The Late Bronze Age cities of Enkomi, Kition, and Hala Sultan Tekke, found in the Larnaka and Ammochostos districts, may have received immigrants from Hittite/Luwian Anatolia involved in the trade of the Late Bronze Age Eastern Mediterranean.Haplogroup R1b-L23 membership on Cyprus is predominantly R1b-Z2105 and parallels the M412 lineage that is prolific in central and west Europe . Whole Years ago . In CyprUpdating previous R1b-L23* data pertinent to Cyprus , 28, 56 We report a comprehensive granular Y-chromosome portrait of modern-day Cyprus. Some structural elements are consistent with Neolithic settlements in the central and eastern Mediterranean. Ancient DNA surveys from continental Europe have revealed a subsequent major population replacement within the last five millennia, masking a main part of the pre-Bronze Age genetic substratum , 80, 87.(i)Regionally speaking, Cyprus occupies an intermediate position, both geographically and in terms of its Y-chromosome patterning, between Levant, Crete, Italian, and Anatolian/Caucasus populations. Notably, Greek populations show genetic similarities with groups from Balkans. The Greek influence while culturally and linguistically profound only represents a small number of Y-chromosomes common in the Balkans and Carpathian areas.(ii)The pre-Greek influence is most plausibly encapsulated by the following G2a haplogroups: U5*, PF3147*, L91, L293, P303*, and CTS342. Notably, most of these lineages occur in Anatolian ancient DNA specimens over 8200 years old . In addiSupport for the perspective that present-day male genetic picture of Cyprus is consistent with an early arrival of pre-historical lineages covered by layers of Y-chromosomes in more recent times is summarized by the following:Taking a broad perspective, our results are consistent with the model that Cyprus was an early recipient of Levantine-based origin. Interestingly, it apparently remained relatively isolated until experiencing immigration during the Early Bronze Age from Anatolia by early copper metallurgists. This period of isolation and genetic stasis is reminiscent of islands like Sardinia  that seeAlthough our results report just contemporary patterns and more recent migrations can potentially overwhelm signals of earlier genetic strata, our data approximates perspectives from the archeological record and provides important contextual reference for future ancient DNA studies. The ongoing revolution in ancient genomics  heralds The Cyprus National Bioethics Committee approved the research program and the informed consent process. All donors gave their signed consent for analyzing their DNA anonymously, according to the procedures approved by the Cyprus National Bioethics Committee.


Signal transducers and activators of transcription (STATs) are cytoplasmic transcription factors that have a key role in cell fate. STATs, a protein family comprised ofseven members, are proteins which are latent cytoplasmic transcription factors thatconvey signals from the cell surface to the nucleus through activation by cytokinesand growth factors. The signaling pathways have diverse biological functions thatinclude roles in cell differentiation, proliferation, development, apoptosis, and inflammation which place them at the center of a very active area of research. In this review we explain Janus kinase (JAK)/STAT signaling and focus on STAT3, which istransient from cytoplasm to nucleus after phosphorylation. This procedure controlsfundamental biological processes by regulating nuclear genes controlling cell proliferation, survival, and development. In some hematopoietic disorders and cancers,overexpression and activation of STAT3 result in high proliferation, suppression ofcell differentiation and inhibition of cell maturation. This article focuses on STAT3and its role in malignancy, in addition to the role of microRNAs (miRNAs) on STAT3activation in certain cancers. Signal transducers and activators of transcriptions(STATs), originally discovered as DNA-bindingproteins, mediate interferon-dependent geneexpression -3. STATsIn contrast to other TK families, the JAK familyis small. There are only four known mammalianJAKs-JAK1, JAK2, JAK3, and TYK2 that havebeen identified in the early 1990s by techniquesthat capitalized on homology of their kinase domainsto other TKs , 8.2) end (JH4-JH7) of JAKs is calleda FERM domain ; this domain is also found in the focaladhesion kinase (FAK) family and is involved inassociation of JAKs with cytokine receptors and/or other kinases (JAK1 is a member of a new class of protein-TKs(PTKs) characterized by the presence of a secondphosphotransferase related domain immediatelyN-terminal to the PTK domain. The second phosphotransferasedomain bears all the hallmarks of aprotein kinase, although its structure differs significantlyfrom that of the PTK and threonine/serinekinase family members. JAK1 is a large, widelyexpressed membrane-associated phosphoproteininvolved in the interferon-alpha/beta and -gammasignal transduction pathways. The reciprocal interdependencebetween JAK1 and TYK2 activities inthe interferon-alpha pathway as well as betweenJAK1 and JAK2 in the interferon-gamma pathwaymay reflect a need for these kinases in the correctassembly of interferon receptor complexes. Bindingof cytokines, growth factors and hormonesto specific receptors leads to activation of variousTKs. These kinases include JAKs, receptorTKs, and non-receptor TKs such as Src and ABL,which can directly phosphorylate STAT proteinswithout ligand-induced receptor signaling -11.They kinases .In summary it appears that specific JAK kinases,either alone or in combination with other JAKs,may be preferentially activated depending on thereceptor that is being activated. Subsequentiallydifferent STATs will undergo activation.The seven mammalian STATs bear a conservedtyrosine residue (Y) near the C-terminus that isphosphorylated by JAKs. This phosphotyrosineallows for dimerization of a STAT (STATa) by asecond STAT (STATb) through interaction with aconserved SH2 domain of the second STAT. Phosphotyrosineof the second STAT also interacts withthe SH2 domain of STATa . PhosphoSTATs becomedephosphorylated by tyrosine phosphatases andare thus free for subsequent rounds of stimulation familymembers are STAT target genes that bind to receptorsand block further STAT activation by turningoff the initial signal  22)..22).Protein inhibitors of activated STAT (PIAS) includePIAS1, PIAS2, PIAS3, PIAS4, PIASx andPIASy. These proteins have a Zn-binding ring-fingerdomain in the central portion. The PIAS proteinsbind to activated STAT dimers and preventthem from binding DNA. PIAS1 and PIAS3 bindto STAT1 and STAT3, respectively. They inhibittranscriptional activity of the STATs, but do notaffect phosphorylation. Just how specific they arein terms of regulating cytokine signaling remainsto be determined; no knockouts have yet been reported.Tyrosine phosphatases are the simplest way toreverse JAKs activity. The best characterized ofthese is the SH2 domain that contains protein tyrosinephosphatase-1 (SHP-1). It contains twoSH2 domains and can bind to either phosphorylatedJAKs or phosphorylated receptors to facilitatedephosphorylation of these activated signalingmolecules.SOCS proteins are a family of at least eightmembers that contain an SH2 domain and a SOCSbox at the C-terminus. In addition, a small kinaseinhibitory region located N-terminal to theSH2 domain has been identified for SOCS1 andSOCS3. The SOCS are responsible for a negativefeedback loop in the JAK/STAT circuitry: activatedSTATs stimulate transcription of the SOCSgenes. The resultant SOCS proteins bind phosphorylatedJAKs and their receptors to turn off thepathway. SOCS can affect their negative regulationby three means: binding phosphortyrosines onthe receptors , bindingdirectly to JAKs, or to the receptors to specificallyinhibit JAK kinase activity  24)..24).In addition to SOCS, PIAS and SHIP-1 thathave negative regulatory roles in active STATs,sumoylation  is anothersystem that controls STAT activity, howeverits exact mechanism is not known. Thus, it will beimportant to characterize the physiologic functionof this family of molecules .Activation of STATs and JAKs can mediate therecruitment of other molecules involved in signaltransduction such as the Src-family kinases, proteintyrosine phosphatases, Mitogen-activated proteinkinase (MAP) kinases, and Phosphoinositide3-kinase (PI3K) kinase. These molecules processdownstream signals via the Ras-Raf-MAP kinaseand PI3 kinase pathway which results in the activationof additional transcription factors. Thecombined action of STATs and other transcriptionfactors activated by these pathways dictate thephenotype produced by a given cytokine, interferonstimulation , 26. STAAccording to a PubMed search, until today morethan 17700 STATs papers have been published.Most have discussed the direct and indirect functionsof STATs which show the important role ofSTATs in molecular cell biology. The numbersof publications are as follows: STAT3 (40.5%),STAT1 (25%), STAT5 (18%), STAT6 (8.6%),STAT4 (4.5%), and STAT2 (3.4%). The largenumber of STAT3 publications possibly showcontribution of STAT3 in the JAK/STAT pathwaycompared to other STATs. Here we focus on thebiology of STAT3 and briefly describe the rolesof this STAT on hemostasis and malignancies, includinghematopoietic disorders.The protein encoded by this gene is a memberof the STAT protein family. STAT3 is activatedthrough phosphorylation in response to variouscytokines and growth factors that include IFNs,EGF, IL5, IL6, HGF, LIF, IL-11, Ciliary neurotrophicfactor (CNTF), Macrophage colony-stimulatingfactor 1 (CSF-1), Platelet-derived growthfactor (PDGF), Oncostatin-M (OSM) and Bonemorphogenetic protein 2 (BMP2) Tables.This prAmong the mammalian STAT proteins, STAT3 is the most diverse in cell biology. Embryonic stem 405 (ES) cells can be maintained in an undifferentiated state by the addition of leukemia inhibitory factor (LIF) but expression of a dominant negative form of STAT3 leads to the differentiation of ES cells, even when LIF is present . Numerous cytokines induce expression of members of the anti-apoptotic regulator Bcl-2 family of proteins and STAT3 represses apoptosis in human myeloma cells by stimulating expression of Bcl-XL . STAT3 gene occur in hyperimmunoglobulin E recurrent infection syndrome (HIES or Job). This syndrome is characterized by morphological abnormalities, recurrent infections (particularly with Staphylococcus aureus and Candida sp.) and a deficiency of Th17 cells (T helper 17 (Th17) development from naive precursors is dependent upon signal transduction through STAT3. In mice, RORC is a STAT3 target gene and Th17 differentiation is induced by STAT3 signaling cytokines, notably IL-6, IL-21 and IL-23, which can be abrogated effectively by a deficiency in STAT3 . In huma17 cells ,48. Pati17 cells . Not sur17 cells , while mGranulocyte colony-stimulating factor (G-CSF) stimulates proliferation, survival, and differentiation of myeloid progenitor cells towards neutrophilic granulocytes . The bioin vitro and in vivo, by the introduction of antisense, small interfering RNA, decoy molecules, dominant negative STAT3 constructs, and/or blockade of TKs has been associated with growth arrest, apoptosis, decreased angiogenesis and invasion  and recent studies show that STAT3 phosphorylation in CLL cells is dependent on c-ABL activity (As one of the STAT family members, STAT3 is correlated with positive regulation of cell growth and highly activated in cancer cells ,71. In cof STAT3 ,79,80. Rof STAT3 . In posiof STAT3 . The expof STAT3 ,87. In aof STAT3 ,89. STATof STAT3 ,91. Consof STAT3 . In myelof STAT3 . Mountinrcinomas , HNSCC (rcinomas , as wellrcinomas , as wellrcinomas . STAT3 arcinomas ,98. c-ABactivity . These oactivity . STX-011activity . The obvactivity  and its activity , which wSTAT3 gene deletions. Intravenously injected adenoviruses normally home to the liver with infection of more than 80% of hepatocytes and allowing for expression of encoded proteins. Chan et al. (BIC gene through STAT3 (STAT3 gene (in vitro (STAT3 is a vital transcription factor activated by some ligands and IL-6 . It has n et al.  have demn et al. . Ng et an et al.  report en et al. . As a ren et al. . Up-regun et al. . INF indn et al. . IL-6 aln et al. . It is pn et al. . Overexpn et al. . IL-10 igh STAT3 . miR-21 gh STAT3 . miR-20bgh STAT3 . Also miAT3 gene ,117. Onein vitro . miR-9 din vitro . miR-205in vitro . Studiesin vitro . Given tIn the past several years, compelling evidence has accumulated that highlights the role of STAT proteins in leukemogenesis. Constitutive activation of STATs has now been clearly demonstrated in acute and chronic leukemias. Elevated STAT3 activity has been observed in many spontaneous and experimentally established mammalian cancers, which demonstrates its critical role in tumorigenesis. Assessment of miRNAs expression patterns after the use of anticancer drugs can more precisely identify the molecular mechanisms of cancer cells.


Varroa destructor. Even though colony density in the study population is the same today as in the past, a major loss of haplotypic diversity occurred, indicative of a drastic mitochondrial bottleneck, caused by massive colony mortality. In contrast, nuclear genetic diversity did not change, though hundreds of genes show signs of selection. The genetic diversity within each bee colony, particularly as a consequence of polyandry by queens, may enable preservation of genetic diversity even during population bottlenecks. These findings suggest that genetically diverse honey bee populations can recover from introduced diseases by evolving rapid tolerance, while maintaining much of the standing genetic variation.Understanding genetic changes caused by novel pathogens and parasites can reveal mechanisms of adaptation and genetic robustness. Using whole-genome sequencing of museum and modern specimens, we describe the genomic changes in a wild population of honey bees in North America following the introduction of the ectoparasitic mite,  Introduction of pathogens can cause colony collapse in honey bees. Here, the authors use museum specimens to show widespread colony mortality but unaffected nuclear genetic diversity in a wild population of honey bees in North America following the introduction of ectoparasitic Varroa mites. Introductions of novel agents of disease can cause population collapses, creating major conservation problems123Apis mellifera, is a prime example of a species that is hugely important to agriculture and has experienced episodes of large-scale mortality caused by new pathogens and parasites. The ectoparasitic mite, Varroa destructor, which was introduced from Asia, has killed millions of honey bee colonies in Europe and North America in recent decades7V. destructor have affected the genetic diversity of honey bees is critical to their conservation and management.The honey bee, V. destructor on a population of wild honey bee colonies in North America. Prior work has shown that populations of wild colonies in North America experienced crashes coincident with the arrival of V. destructor, followed by periods of recovery1011This study investigated the genetic consequences of It is difficult, however, to acquire a full picture of the genetic effects of natural selection on honey bees by studying only present-day samples. Most tools developed thus far for doing so work best at detecting hard selective sweeps1315V. destructor to this region of the world. We do so using a novel PCR-free library preparation process developed specifically for this purpose.In principle, the most powerful means of detecting microevolutionary change is to compare allelic frequencies pre and post selection. Museum collections, which provide, a window into the past, offer special opportunities to examine the genetic effects of selection events17Varroa resistance, namely dopaminergic control of aversive memory. These findings suggest that wild populations of honey bees have an inherent capacity to mount a rapid evolutionary response to novel parasites.We show that while the honey bee population has been hard hit by the introduction of parasitic mites, it most likely did not go extinct. Rather, the bees have rapidly evolved to tolerate the mites' presence, and now exist at the same colony densities today, as they did in the past. This response was most likely polygenic, but possibly involves some of the same pathways previously identified in a priori expectations of population genetics theory. For instance, the difference between old and modern allele frequencies should be distributed as a random variable with mean zero, and should have a variance related to the number of generations between the two samples, and to the effective population sizeMuseum samples were sequenced using a custom library preparation protocol . The oldSelection, drift and migration, all cause microevolutionary changes seen as deviations from zero allele frequency differences in V. destructor. To detect a change in population size during that time, we examined changes in the diversity of mitochondrial genomes. Present in many copies, these genomes could be sequenced at extremely high coverage in every sample, producing multi-variant genotypes without missing data, allowing the circumvention of postmortem changes in DNA content through redundant sequence coveragex and 20,422±7,115x for old and modern populations, respectively). It consisted of 401 SNPs, and had no missing data. The loss of mitochondrial diversity since 1977 is striking—almost all of the old mitochondrial lineages went extinct, suggesting a massive reduction in effective population size . After filtering, this data set contained 181,352 high-quality SNP sites (about one per 1.5 kilobases). In contrast to the mitochondrial genome results, no overall loss of nuclear genetic diversity was found . Likewise, inbreeding coefficients remained constant . The lower loss of nuclear, relative to mitochondrial, genetic diversity may result from the combination of extreme polyandry and high outbreeding of honey beesNuclear genomes were sequenced at intermediate coverage for both old and modern populations . Variance in differences between allele frequencies in old and modern populations should be equal to the number of generations divided by 2 × NeV. destructor in the study area, and that the current population exists because of immigration of colonies from beekeepers' hives in the surrounding region. A recent microsatellite study of wild and managed honey bee colonies living near Ithaca, NY found strong genetic differences between them, suggesting that the wild population is not merely a sink for escaped domestic coloniesIt is important to consider the possibility that the original population of colonies went extinct following the arrival of Two predictions can be made under the scenario that the Ithaca population did not go extinct, but has persisted with some level of immigration. First, because relatedness coefficients decrease exponentially as the degree of relation decreases, museum bees should have more regions identical by descent in common with the present-day Ithaca population than with other US domestic bees with whom they would share more distant ancestry. Second, recent gene flow between modern Ithaca bees and the US domestic bees should create more regions identical by descent between present-day bee populations than between museum bees and US domestic bees. Both these predictions hold true, suggesting that the population has existed continuously .V. destructor infestation. Signatures of selection are widespread throughout the genome, with 634 sites showing significant changes in frequency across the two time points , which was also apparently under selection in the Ithaca bees  in bees for a wild population that evolved resistanceaca bees . This geaca bees . These gaca bees , suggestV. destructor, which resulted in a severe mitochondrial bottleneck involved little loss of nuclear genetic diversity. This suggests that populations of honey bees living in the wild can survive exposure to novel pathogens. The level of genetic diversity that is present in wild colonies of honey bees may be a good target for managed colonies to assure their future adaptability.Loss of genetic diversity can render animal and plant populations vulnerable following large-scale collapses caused by new diseases. The genetic changes experienced by honey bees living in the wild outside Ithaca, NY following the introduction of Varroa, since there are likely many selective forces acting in the 33-year period. It seems likely that Varroa has caused major genetic changes in the population based on mitochondrial data . Workers from 32 colonies were collected in 1977 and stored as pinned specimens in the Cornell University Insect CollectionWe used worker honey bees collected from wild colonies living in the forests in Tompkins County near Ithaca, New York, USA .The overall protocol was similar to that of Tin et al.DNA from the museum specimens was extracted as described by Tin −1 FastAP thermosensitive alkaline phosphatase (Thermo Scientific) and 200 ng gDNA in EB buffer. The reaction was adjusted with water to reach the reaction volume. The reaction was incubated at 37 °C for 1 h and then heat inactivated at 75 °C for 10 min. We found that there was a significant increase in library yield compared with the same samples without the phosphatase treatment (data not shown).The ribo-tailing reaction requires the presence of an intact 3′-hydroxyl group, which is lost during DNA strand breaks after the formation of abasic sites during DNA degradationgDNA was heat denatured into single-stranded DNA at 95 °C for 5 min and then quickly chilled on ice before the ribo-tailing reaction with terminal transferase . The 7-μl TdT reaction consisted of 2.5 μl water, 0.7 μl of 10 × buffer 4, 2 μl of 25 mM cobalt chloride (New England Biolabs), 0.8 μl of 100 mM GTP (Takara), 20 U of TdT and denatured DNA. The 20-μl reaction was incubated at 37 °C for 30 min and then TdT was heat inactivated at 70 °C for 10 min.Top strand: 5′-phosphate-AGATCGGAAGAGCGGTTCAGCAGGAATGCddC, ddC=2′,3′-dideoxycytidine-5′-triphosphateBottom strand: 5′-CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT*C*C*CCC, *=phosphorothioate linkageWe ligated the following modified Illumina paired-end (PE) 2 adaptor to the riboG-tailed DNA:The 10-μl ligation reaction contained 2.8 μl water, 1 μl of 10 × buffer 4, 3 μl of 10 mM ATP (Sigma-Aldrich), 1.2 μl of 10 μM PE 2 adaptor and 2 μl of 400,000 cohesive end unit per ml T4 DNA ligase (New England Biolabs), which was added to the TdT reaction mixture. The ligation reaction was carried out at room temperature for 90 min, and then heat inactivated at 65 °C for 10 min.−1 phusion high-fidelity DNA polymerase (Thermo Scientific), which was added to the ligation reaction. The reaction was incubated at 72 °C for 45 min . Dephosphorylation was performed to remove the phosphate groups of excess PE 2 adaptors in the reaction by the addition of 20 U calf-intestinal alkaline phosphatase (Invitrogen). The reaction was carried out at 37 °C for 1 h. 20 U was exactly what we used, 20 U of calf-intestinal alkaline phosphatase were added to the reaction and incubated at 50 °C for 1 h to maximize the phosphatase activity at the 5′ recessed end of the adaptors. The reactions were then purified with MinElute reaction cleanup kit (Qiagen) and eluted in 10 μl EB buffer.The 10-μl reaction consisted of 0.68 μl water, 8 μl of 5 × HF buffer (Thermo Scientific), 0.32 μl of 25 mM dNTP (Promega) and 1 μl of 2 U μlTop strand: 5′-CGACGCTCTTCCGATCTxxxxxxddCBottom strand: 5′-phosphate-GxxxxxxAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT, xxxxxx=barcodeThe library was constructed by ligation of double-stranded DNA fragments with barcoded Illumina PE 1 adaptors:The 11-μl reaction consisted of 5 μl of 2 × Quick ligation buffer (New England Biolabs), 0.5 μl of 50 μM adaptor, 1 μl of 2,000,000 cohesive end units per ml T4 DNA ligase (New England Biolabs) and 4.5 μl of purified DNA. Non-specific ligation was greatly reduced with the use of 3′-ddC adaptor. The ligation reaction was carried out at 16 °C overnight.et al.Ligation products were purified by solid-phase reversible immobilization after . Libraries made from modern bees were sequenced in 2 × 100 bp mode on a single flow cell in an Illumina Hiseq 2000. DNA sequencing was performed at core facility of the Okinawa Institute of Science Technology.http://dx.doi.org/10.5061/dryad.vn607). We briefly summarize the pipeline below. We used the Amel_4.5 genome assembly and the OGSv3.2 gene set for the analysis−3). Raw reads from modern data were mapped using bowtie2 in end-to-end modecall.sh), and estimation of allele frequencies was carried out using ANGSDangsd.sh code repository for details). Inbreeding coefficients were estimated as in Vieira et al.angsd_inbreeding.sh and angsd_ngsFst.sh in the code repository). The file pca_all.sh contains details of the principal component analysisK hypothetical populations, using the same model as STRUCTUREngsAdmix.sh). In this analysis, we used our own data and previously published data sets25K=5, which was a level of granularity sufficient to capture major patterns in worldwide honey bee population structure47The entire bioinformatic pipeline starting from genotype calling and intermediate analysis files can be found on DataDryad , we also removed sites with <10% minor allele frequency. These filters resulted in an increased correlation between allele frequencies in old and modern data. The caption to 7 generations with convergence and adequate estimated sample size (>100 for important parameters) using Tracer (v 1.6)Reads were mapped to the mitochondrial referenceWorker body size was assessed by measuring head width and intertegular span, both reliable methods for determining overall body sizeBoth forewings of each bee were dissected and mounted between microscope glass slides. Subsequently, they were photographed using a Nikon SMZ1500 at × 10 magnification. Coordinates of 19 landmarks at junctions of wing veins were digitized using a pen and tablet system (Wacom Bamboo). Raw coordinates were subjected to Procrustes superimposition. Both data digitization and Procrustes analysis were conducted by using the CLICS software packageP values, assuming a χ2 distribution with one degree of freedom. P values were adjusted for multiple comparisons using Benjamini and HochbergTo identify alleles that differ significantly between the old and modern population, we used a maximum likelihood estimator that does not infer genotypes for any one individualWe focused on alleles that were apparently absent from the old population, many of which were present at low frequencies and were filtered from the main analysis. These alleles were probably introduced into the Ithaca population by migration between 1977 and 2010, and have increased in frequency either by selection or in most cases by drift. We then estimated the 95% confidence interval for the frequency change in alleles absent from the old populations using non-parametric bootstrap. This is a conservative estimate, since some alleles were absent from the old population may be in the regions where degraded DNA did not map well; including such regions would inflate the apparent effect of drift.We used the GOstats package to conduct hypergeometric tests of GO term enrichmentAccession codes: DNA sequencing data generated in this study have been deposited in GenBank/EMBL/DDBJ as BioProject PRJDB3198.How to cite this article: Mikheyev, A. S. et al. Museum samples reveal rapid evolution by wild honey bees exposed to a novel parasite. Nat. Commun. 6:7991 doi: 10.1038/ncomms8991 (2015).Supplementary Figures 1-5, Supplementary Tables 1-3 and Supplementary References


Wild soybean, the direct progenitor of cultivated soybean, inhabits a wide distribution range across the mainland of East Asia and the Japanese archipelago. A multidisciplinary approach combining analyses of population genetics based on 20 nuclear microsatellites and one plastid locus were applied to reveal the genetic variation of wild soybean, and the contributions of geographical, environmental factors and historic climatic change on its patterns of genetic differentiation. High genetic diversity and significant genetic differentiation were revealed in wild soybean. Wild soybean was inferred to be limited to southern and central China during the Last Glacial Maximum (LGM) and experienced large-scale post-LGM range expansion into northern East Asia. A substantial northward range shift has been predicted to occur by the 2080s. A stronger effect of isolation by environment (IBE) versus isolation by geographical distance (IBD) was found for genetic differentiation in wild soybean, which suggested that environmental factors were responsible for the adaptive eco-geographical differentiation. This study indicated that IBE and historical climatic change together shaped patterns of genetic variation and differentiation of wild soybean. Different conservation measures should be implemented on different populations according to their adaptive potential to future changes in climate and human-induced environmental changes. Genetic diversity is key for a species to survive and adapt to changing environments346789Two processes are widely acknowledged to be major drivers of genetic differentiation: isolation by geographical distance (IBD) and isolation by environment (IBE)11121311181521222324Glycine max (Linnaeus) Merrill), which is widely distributed in East Asia, including major parts of China, the Japanese archipelago, the Korean peninsula and the Russian Far EastEast Asia exhibits high topographic complexity and climate variability and harbours high levels of diversity of temperate plant speciesVarious molecular markers, such as RAPD, SSRs and gene sequences, have been applied to address the population structure of wild soybean3132333434363738403133trnQ-rps16) and a multidisciplinary approach combining population genetic analyses, ecological niche modelling, a Bayesian skyline plot, a Mantel test and a principle components analysis, the major aims of this study were (i) to detect the genetic variation of wild soybean; (ii) to elucidate the relative contribution of geographical, environmental and historical effects on the distribution and genetic differentiation of wild soybean; and (iii) to predict the fate of wild soybean as it is confronted with rapid environmental and climate changes and to provide information to design effective conservation and management strategies for wild soybean.Applying 20 nuclear Simple Sequence Repeat markers (nSSRs) and one cpDNA locus (HE) is 0.426 over all loci for each population ranging from 0.018 to 0.797. High differentiation was revealed by the global FST value (0.509), which indicated significant population genetic structure existed in wild soybean populations. The AMOVA analysis of nSSRs revealed that 6.0% of the genetic variation was due to the genetic distance between the two clusters, 46.7% was due to populations within clusters and 47.3% was due to individuals within populations . All 20 loci were polymorphic . The polulations .K = 2 and 29 when Evanno’s ad hoc estimator of the actual number of groups was used  and H2 (46.1%) were two most common and widespread haplotypes, which were found in most populations of wild soybean. However, each of H5, H6, H7 and H10 was an endemic haplotype, which was found in only one population  were identified based on 9 polymorphism sites detected from pulation . The ancThe Bayesian Skyline plots indicated that the population size of wild soybean has experienced a rapid increase following a long period of relative stability. This rapid increase was inferred to occur after the last glacial maximum and at the beginning of the warming period in the early Holocene , but no significant correlation exists between genetic distance and geographical distance . When geographical factors were controlled, a partial Mantel test also revealed isolation by environmental distance . Where as when environmental factors were controlled, we could not detect significant correlations between genetic differentiation and geographical distance . The MMRR analysis suggested that the environment factors had a higher regression coefficient, whereas the effects of geographic distance were not significant  suggesting a high fit of the modeltrnQ-rps16 failed to detect any deep subdivisions within wild soybean, two commonly haplotypes (H1 and H2) were widely distributed across the range of wild soybean  using a primer pair of trnQ (GCGTGGCCAAGYGGTAAGGC) and rps16 (GTTGCTTTYTACCACATCGTTT)TrnQ-rps16 was amplified and sequenced following the methods of Shaw, et al.The plastid A), the observed heterozygosity (HO) and expected heterozygosity (HE) were calculated using GENEALEX v6.4et al.The number of alleles 74K) in the model was systematically varied from 1 to 43. To decrease the margin of error, an average value of 20 simulations performed for each K was used. We used the ΔK method, representing the highest median likelihood values, to assign wild soybean accessions using the online tool Structure HarvesterK value, the run that had the highest likelihood estimate was adopted to assign individuals to clusters. The results were visualized using DISTRUCT v1.1Genetic differentiation between populations was assessed by the calculation of pairwise Glycine tabacina as an outgroup. A Bayesian Skyline Plot (BSP) in Beast was employed to reconstruct demographic history−9 substitutions per neutral site per year (s/s/y)−7 generations and were sampled every 1,000 generations, with the first 10% being discarded as burn-in.Gaps (indels) detected in the cpDNA dataset were treated as single mutation events and coded as substitutions (A or T). The haplotype distribution map was constructed using ArcMap v9.3 . A haplotype network was conducted in NETWORK v4.6http://www.worldclim.org/) interpolated to 30-arcsec (ca. 1 km) resolutionFirst, the 19 climatic variables of the studied sites were extracted from the WorldClim data set  were included in this study To model the suitability of wild soybean in future climates, we applied one commonly used general circulation model, the Model for Interdisciplinary Research on Climate (MIRIC). The ecological niche modelling predicted with present climatic variables was projected on the global circulation model for the year 2080. The performance of the model prediction was evaluated using the area under the (receiver operation characteristic) curve (AUC) calculated by MAXENT.How to cite this article: He, S. et al. Environmental and Historical Determinants of Patterns of Genetic Differentiation in Wild Soybean (Glycine soja Sieb. et Zucc). Sci. Rep.6, 22795; doi: 10.1038/srep22795 (2016).


Isoodon obesulus) in a heavily modified peri-urban landscape in South Australia, Australia. We used 14 microsatellite markers to genotype 254 individuals which were sampled from 15 sites. Analyses revealed significant genetic structure. Our analyses also indicated that dispersal was mostly limited to neighbouring sites. Comparisons of these results with analyses of a different population of the same species revealed that gene flow/dispersal was more limited in this peri-urban landscape than in a pine plantation landscape approximately 400 km to the south-east. These findings increase our understanding of how the nature of fragmentation can lead to profound differences in levels of genetic connectivity among populations of the same species.Genetic connectivity is a key factor for maintaining the persistence of populations in fragmented landscapes. In highly modified landscapes such us peri-urban areas, organisms’ dispersal among fragmented habitat patches can be reduced due to the surrounding matrix, leading to subsequent decreased gene flow and increased potential extinction risk in isolated sub-populations. However, few studies have compared within species how dispersal/gene flow varies between regions and among different forms of matrix that might be encountered. In the current study, we investigated gene flow and dispersal in an endangered marsupial, the southern brown bandicoot ( Habitat loss and fragmentation are the leading threats to biological diversity worldwide , 2, and The importance of different types of matrices in fragmented landscapes has been recognised and their influences on biodiversity  have been investigated in numerous studies (see review by Prevedello & Vieira ). In higIn practice, the effects of matrices on organisms’ movements between habitat patches are species-specific , 12, andIsoodon obesulus) in South Australia (SA) and their habitat represent an ideal system to explore this issue. I. obesulus is a rabbit-sized ground-dwelling marsupial, which has dramatically declined in number over the last 220 years, with studies providing evidence for a contracted distribution and local population extinctions [I. obesulus is the only surviving member of the family Peramelidae. This family originally included eleven Australian species before European settlement, but now only eight species survive in the whole country. The subspecies I. o. obesulus, distributed in eastern and southern regions of Australia (including SA), is listed as Nationally Endangered , and, therefore, further understanding of the impact of habitat fragmentation on population connectivity has important implications for conservation management.Populations of the southern brown bandicoot species , one of Australia’s major softwood plantation regions currently managed by the state government-based organisation, ForestrySA. Here, numerous small fragments of native forest are managed as reserves embedded in matrices of pine (Pinus radiata) plantations or agricultural land. Investigation of genetic connectivity of I. obesulus showed significant population genetic structuring and restricted gene flow and dispersal to neighbouring patches [Three current strongholds of t region . Within  patches .I. obesulus. With its rich biodiversity, the area was identified as one of 15 Australian biodiversity hotspots by the Commonwealth Government in 2003 . However, the region has experienced extensive native vegetation clearance and only 13% of the original vegetation remains [The Mount Lofty Ranges are another stronghold of  remains . It is aI. obesulus in the central Mount Lofty Ranges  and vegustralis . Within ustralis . A smallI. obesulus: five  by Zenger & Johnston [DNA was isolated using the Gentra Puregene extraction kit, following the manufacturer’s instructions (Gentra Systems Inc.). Individuals were genotyped at 14 microsatellite loci developed for Johnston , and ninJohnston . PCR ampJohnston . ApproxiJohnston . Repeat Variation in allele frequencies within sites sampled across multiple years was examined via ANOVA  in SPSS For this study, we did not have complete information on the juvenile or adult status of each individual. Previous analyses of the Mount Burr population  indicateConformation with Hardy-Weinberg equilibrium (HWE) across loci and sites was assessed in Genepop 4.1.0  and a teA), observed heterozygosity (HO) and expected heterozygosity (HE) were calculated in Arlequin 3.11 [FIS) and allelic richness  were estimated in FSTAT 2.9.3.2 [IR) [Number of alleles per locus   in an R 3.2 [IR) .FST in Arlequin 3.11 [DEST as a measurement of actual differentiation in the package DEMEtics [DEST takes account of the effective number of alleles and may perform better than FST in the case of highly polymorphic markers such as microsatellites [To assess the degree of genetic differentiation of bandicoots across sites, we measured pairwise uin 3.11  and calcDEMEtics  implementellites .K (K is the number of likely clusters) was set from 1 to 15 with ten replicates of each K to verify the convergence of the Markov chain. The method described in [K. For TESS, we ran the analysis under an admixture model using K ranging from 2 to 15 (10 replicates per K), with 10 000 burn-in and 50 000 sweeps. The value of the interaction parameter ψ  was set to the default value, 0.6. The optimal K for TESS was chosen as the one with the stabilized value of the Deviance Information Criterion (DIC). For both analyses, CLUMPP 1.1.2 [K and DISTRUCT 1.1 [We implemented Bayesian clustering analysis in STRUCTURE 2.3.3  and TESSribed in  was usedRUCT 1.1  was usedFST / (1-FST) and DEST / (1- DEST)) and the logarithm of geographical distance using the subprogram Isolde of Genepop 4.1.0 [To investigate the effect of isolation by distance (IBD), we ran Mantel tests  between linearised genetic distance  to explore the relationship between genetic structure and explanatory variables (see below). RDA is an analogue of multivariate linear regression and is reported to have greater power than Mantel tests in cases where there are multivariate species-environment relationships . We usedn = 140) and females (n = 105) to check for sex-biased dispersal (nine samples of unknown gender were excluded). Statistical testing was based on the 95% confidence interval defined by 1 000 random permutations.Spatial autocorrelation analyses were performed in GenAlEx 6.41  to furthWe explored for recent migration rates among both sites and STRUCTURE clusters with the program BayesAss3.0.3 . BayesAsMigrants and individuals with mixed ancestry were detected in both STRUCTURE 2.3.3  and GENEThe protocol of sample collections in this study was performed under the University of Adelaide Animal Ethics Committee (project number S-2011-041) and Department of the Environment, Water and Natural Resources (DEWNR) permit to undertake scientific research (permit number G23771-13).R) was 0.08, ranging from 0.04 in SC to 0.19 in SCC. Site SC had the fewest  and BNPS had the most highly related individuals  . We repeated the analyses with the related individuals excluded. Since their exclusion did not alter results of the population analyses (data not shown) we retained the entire data set for analyses.Results from COANCESTRY indicated that sampling was not biased towards highly related individuals. Mean relatedness (Significant linkage disequilibrium was detected in 18 of the 1365 (1.3%) pairwise locus combinations and none of these were consistent across sites. Close physical linkage between any of the 14 loci was therefore considered unlikely. Twenty-one of the 210 locus × sites tests departed significantly from HWE after sequential Bonferroni correction, involving six loci . Three loci  deviated from HWE at more than two sites. Micro-checker detected that these three loci might contain null alleles. However, the presence of null alleles at these loci was not consistent across sites. In addition, deviations from HWE can result from inbreeding or the Wahlund effect (the reduction of heterozygosity due to population subdivision). To be cautious, we ran all the analyses without the three loci and the results showed a similar pattern to that of the full 14 locus data set. For this reason, we did not apply a correction for null alleles and retained the three loci for the analyses presented here.FIS values ranged from -0.354 in SCC to 0.352 in SC (A) was lowest in BNPS (2.29) and highest in QUD (4.14), with a mean of 3.23  = 1.616, 1.796, and 1.464, P = 0.077, 0.071 and 0.128 respectively). Internal relatedness (IR) was highest in BNPS (0.472) and lowest in SCC (-0.116)  to 10  with an average of 6.1. At each site, mean observed heterozygosity across loci ranged from 0.381 (site BNPS) to 0.709 (site SCC) and expected heterozygosity ranged from 0.400 (BNPS) to 0.627 (SCD) (see 52 in SC . Allelic of 3.23 . Allelic of 3.23 . Statist(-0.116) . Post hoer sites .FST values were significant for 96 of the 105 comparisons , and the lowest was between SCC and SCD . Pairwise DEST values were generally higher than FST, with the highest value between BNPS and MHS  and the lowest between SCC and SCD . Mantel tests showed a significant association between genetic distance and geographical distance ) at both individual and site level .Pairwise parisons . The higK = 4) were identified  were identified, with four clusters being identical to that found using STRUCTURE  and the southern cluster   .P = 0.010, P = 0.005, RDA analysis with the full model showed a significant association between genetic variance and geographic location , spatial autocorrelation analysis revealed a significant and positive correlation for individuals up to 1 km and the genetic similarities (r) then stabilized at a value around zero . When anBayesian estimation of migration rates from BayesAss indicated that total migration among STRUCTURE clusters was low and approximately equal in each direction, averaging 0.011 ± 0.007 . We founq-values less than 0.8 and were thus classified as individuals with potentially mixed ancestry  fragmented forest system [I. obesulus, the northern brown bandicoot (I. macrourus), in urban habitat fragments in Brisbane [Ochotona princeps [Gorilla gorilla diehli [Glis glis [Bayesian clustering analyses revealed that the 15 Mount Lofty Ranges sites formed four distinct genetic clusters or populations. Analyses of migration rates also indicated an absence of recent migration between clusters and between most sites. In agreement with this, the eight first generation migrants detected were restricted to moving between neighbouring sites of the same population cluster, with one putative longer-distance dispersal which may have occurred via intermediate sites in a stepping-stone manner. Evidence for short distance dispersal and a tendency of dispersing only between proximate sites was also observed for t system  and for t system ,64. ShorBrisbane . It is aprinceps , cross ra diehli , the edilis glis ) and verlis glis  species lis glis .FST , lower individual relatedness, evidence for more migration events and directional migration to adjacent sites. These findings may reflect a larger population of bandicoots at each site, compared to other sites that we studied, hence genetic drift would occur more slowly, or it may suggest more extensive gene flow among each of the sites. Although we did not consider that Scott Creek CP was a continuous forest, because it contains areas of relatively open land, the park does contain extensive areas of thick vegetation that may have aided dispersal among sites and provided protection against predators. Large patches are considered important and critical in fragmented landscapes because they can reduce extinction proneness of populations of individual species, and increase species richness, vegetation diversity and immigration rates [I. obesulus evident from other sites in the Mount Lofty Ranges is most likely to have resulted from habitat fragmentation and the poor permeability of the matrix, rather than a natural feature of I. obesulus such as a poor dispersal ability. Further comparison of I. obesulus between isolated patches and relatively larger continuous forest would be beneficial and may be possible in other regions of Australia where continuous forest systems still occur  is one of the three national/conservation parks in our study area, but much larger than Mark Oliphant . Compared to other sites, the three within Scott Creek Conservation Park showed non-significant pairwise on rates . Our reson rates , 70–71).ictoria, ).2) than the south-east populations (~520 km2). We had predicted that the scale of genetic differentiation at Mount Lofty Ranges would be similar or even lower than that of Mount Burr since the former system is seemingly less fragmented. Yet, we observed that Mount Lofty Ranges sites appear to be genetically differentiated to a greater degree than the Mount Burr population at a similar spatial scale. This finding is consistent with the hypothesis that gene flow was limited to a higher degree in the Mount Lofty Ranges compared to Mount Burr, although we cannot rule out the possibility that the patterns resulted from differences in the effective population sizes of each study area. The landscape of the Mount Lofty Ranges has been heavily modified, with a matrix mixture of urban constructs and agricultural land and heterogeneous native vegetation with various level of degradation. The habitat within native forest fragments at Mount Burr is generally less disturbed and relatively more homogenous, with Pinus radiata plantations being the dominant matrix surrounding the fragments. The Mount Burr study suggested that bandicoot movement between proximate patches through pine plantations of a couple of km distance was possible. Hence, it is possible that bandicoots can utilise the pine forest better to move among native forest fragments, compared to the heterogeneous matrix mixture of the Mount Lofty Ranges. There are four potential reasons for this difference. First, pine plantations can be used as habitat by a range of invertebrate taxa, since they provide shelter and moist microhabitats due to plantation practices such as windrowing, mound ploughing, pruning and thinning [I. obesulus [Antechinus agilis, [I. obesulus when dispersing in pine plantations, compared to open agricultural land. Third, the central Mount Lofty Ranges have greater vehicle traffic volumes than that in the Mount Burr Range , potentially leading to higher mortalities of I. obesulus. Deceased bandicoots along roads in the Mount Lofty Ranges have often been observed by local residents and staff of Department of Environment, Water, and Natural Resources (DEWNR). Another possible factor that could affect the dispersal of bandicoots in the two landscapes is predation by introduced animals . Studies have shown that the introduction of feral cats caused substantial wildlife mortality [I. obesulus populations.Comparison of our results to those from south-east South Australia 400 km away , showed thinning , 74. In obesulus –77, haveobesulus . Hence,  agilis, ). Secondortality  and can ortality . To dateortality ) are neeFST and DEST and the highest internal relatedness, suggesting that inbreeding may be occurring. Major roads to the south and north with high traffic volumes are adjacent to BNPS and may also impact dispersal. Road effects on increased genetic differentiation and decreased genetic connectivity have been reported in an increasing array of species, including mammals, amphibians, reptiles and invertebrates . Not. NotFST Rubus fruticosus agg), along creek lines may also assist dispersal of bandicoots, and its removal, without replacement of a dense understory vegetation is likely to further fragment bandicoot populations [Comparisons among populations of the same species can help assess the levels of permeability of different matrices e.g. , 21, 93), 9321, 9ulations .In order to reduce the effects of inbreeding and increase long-term persistence of the numerous genetically distinct populations, the management of these populations may benefit from augmentation of gene flow between populations. Such genetic rescue and/or genetic restoration could be accomplished by moving individuals between disparate populations . If translocations were considered, we suggest managers should first evaluate the risks associated with translocations and consider potential mitigation strategies, as recommended by Weeks et al. . If bandS1 Fig(DOCX)Click here for additional data file.S1 TableP < 0.05).Significant values were denoted in bold ((DOCX)Click here for additional data file.S2 TableMigration rates greater than 2% are shown in bold, and self-migration rates shown in italics. Standard deviation of migration rates averaged 0.010 and did not exceed 0.022 (BNPS cluster-BNPS cluster).(DOCX)Click here for additional data file.S3 TableMigration rates greater than 2% are shown in bold, and self-migration rates shown in italics. Standard deviation of migration rates averaged 0.014 and did not exceed 0.053 (SCC-SCS).(DOCX)Click here for additional data file.


Boulengerochromis microlepis, a predominantly piscivorous endemic of Lake Tanganyika and the world’s largest cichlid. While the lineage diverged from its closest relatives at the onset of the Lake Tanganyika radiation >8 MYA, mitochondrial control region sequences collected in this study dated the most recent common ancestor of B. microlepis to ~60–110 KYA. There was no evidence of phylogeographic structure in the lake-wide sample. Patterns of genetic diversity and demographic analyses were consistent with slow and steady population growth throughout the reconstructed timescale. Additionally, the shallow divergence within the species may be related to a possibly large variance in reproductive success in this highly fecund species. Trophic niche space restriction by sympatric piscivores, lack of geographic structure, low potential for sexual selection arising from the monogamous mating system and extinction may have contributed to keeping the lineage monotypic.The largely endemic cichlid species flocks of the East African Great Lakes are among the prime examples for explosive speciation and adaptive radiation. Speciation rates differ among cichlid lineages, and the propensity to radiate has been linked to intrinsic and extrinsic factors such as sexual selection and ecological opportunity. Remarkably, only one cichlid tribe—the Boulengerochromini—comprises just a single species,  Boulengerochromis microlepis, one of the few benthopelagic top-predators of the lake.The largely endemic cichlid species flocks of the East African Great Lakes represent one of the most outstanding examples of explosive speciation and adaptive radiation . It is oPrevious studies have shown that environmental influences, mainly climatically driven lake level fluctuations , have syB. microlepis, the only member of the tribe Boulengerochromini  fry  fry . Occasioal yield . As beacB. microlepis or any other large predatory and highly mobile Lake Tanganyika cichlid fish species. Such studies may not only increase our understanding about factors and processes shaping intraspecific diversity, but might also help to identify potentially segregated fish stocks and thus provide important insights for conservation and fisheries management. Here, we characterize the genetic diversity of B. microlepis and reconstruct its phylogeographic structure and past population size trajectories based on DNA sequences of the most variable part of the mitochondrial control region. The findings are discussed in the light of the hydrologic history of Lake Tanganyika and the biological characteristics of the species.To date there have been no studies of spatial or temporal population genetic structure of B. microlepis collected with gill net or harpoon from 13 localities in Lake Tanganyika, or obtained at local fish markets in Bujumbura and Mpulungu or from artisanal fishermen on the lake, during several field trips between 2001 and 2013 . Haplotype (Hd) and nucleotide diversity (p) were calculated in DnaSP 5.10  was retained as the best spatially genetic clustering. To test for signals of past population expansion, we calculated a mismatch distribution as well as Fu’s Fs (D (SSD) and the raggedness index (rg). Furthermore, past population size trajectories were inferred by means of a Bayesian coalescent approach [Gaussian Markov random field (GMRF) skyride tree prior; Phylogenetic relationships among haplotypes were visualized by a full median-joining (MJ) network , by settaSP 5.10 . SpatialMOVA 1.0 , which d1.0 FCT;  among K  Fu’s Fs  and Taji’s Fs (D  in Arleq’s Fs (D . The fitHd) diversity (Hd = 0.871) contrasts with low nucleotide diversity (π) and little genetic divergence between haplotypes . The MJ network did not indicate geographic structure. Haplotypes are shared between geographically distant localities, sometimes even from opposite ends of the lake   (K ranging from 2 to 7) achieved similar FCT values, and all solutions consisted of one large group spanning a lakewide distribution, and variable numbers of small and geographically restricted groups. Importantly, the scenario of K = 1, i.e., one single panmictic population, cannot be tested in the SAMOVA framework. Based on the structure of the haplotype network and the failure of SAMOVA to group individuals geographically, we conclude that B. microlepis is connected by high levels of gene flow across Lake Tanganyika. This does not preclude the possibility of population genetic differentiation, which might be detectable with appropriate sampling.In total 29 haplotypes were detected in 88 individuals. High haplotype ( Zambia) . SAMOVA < 0.001) . HoweverSSD and rg values  are consistent with recent populationgrowth. Tajima’s D was negative, as expected for recent population growth, but not significantly different from zero  maximum divergence in Variabilichromis moorii, Eretmodus cyanostictus, Ophthalmotilapia ventralis, Neolamprologus caudopunctatus, and Perissodus microlepis, all sampled within southern Zambia   . The fryLatidae) . Large cB. microlepis, judging by its monogamous mating system and lack of sexual dimorphism. Moreover, the community structure in Lake Tanganyika may also have played a significant role in preventing a radiation of the Boulengerochromini. In Lake Tanganyika, numerous piscivorous fish species from other (cichlid and non-cichlid) lineages coexist with B. microlepis, including the smaller piscivores in the genera Lepidiolamprologus and Bathybates as well as Hemibates stenosoma, and several endemic species of clariid catfish and lates perches that grow considerably larger than B. microlepis. Hence, available niche space of B. microlepis, as determined by body size, is probably restricted by heterospecific trophic competition. The extant piscivorous cichlid lineages colonized Lake Tanganyika roughly simultaneously, shortly after the establishment of deep-water conditions (Another puzzle, given the age of the lineage, is its failure to seed more than a single species. The other lineages of predatory benthopelagic cichlids in Lakes Malawi and Tanganyika are typically species poor as well, but nevertheless radiated into at few distinct taxa . A meta-B. microlepis is very shallow despite the old age of the lineage and that there is no indication of phylogeographic structuring. The observed patterns of genetic diversity and divergence may be the product of recent population growth and large variance in reproductive success. Low potential for sexual selection and limited trophic niche space may have hindered speciation of the Boulengerochromini. Despite their old age, the Boulengerochromini are the only known cichlid tribe comprising only a single species, the giant cichlid from Lake Tanganyika.To conclude, we show that the mitochondrial genealogy of Supplementary Material


A growing multitude of known genetic diagnoses can result in presentation to child psychiatry. For numerous reasons, it is important to identify a genetic etiology in child psychiatry patients when it is present. Genetic diagnoses can guide treatment and enable access to specialized clinics and appropriate screening measures. They can also allow for genetic counseling for the patient and family. A better understanding of etiology with a named diagnosis can itself be of great value to many patients and families; prognostic information can be empowering. Since patients with genetic conditions may present to psychiatric care in diverse ways, child psychiatrists must decide who to refer for genetic evaluation. Here we create a table to provide a framework of concerning/notable history and exam features that a practicing child psychiatrist may encounter that should prompt one to consider whether a larger, unifying genetic diagnosis is at hand. We hope this framework will facilitate referral of child psychiatry patients to genetics so that more patients can benefit from an appropriate diagnosis. As genetic understanding continues to progress, the overlap with child psychiatry grows and it becomes increasingly important for child psychiatrists to recognize signs of a possible underlying genetic diagnoses  . There iiagnoses .In some instances, a genetic condition may have a typical psychiatric presentation and psychiatric symptoms may appear isolated. In such cases, presentation may be indistinguishable from that of a typical psychiatric patient whose disease is polygenic and multifactorial and genetic diagnosis is more difficult . HoweverThis article addresses two questions: Why is it important to diagnose genetic conditions in child psychiatry patients? What should prompt a child psychiatrist to request a genetic consult for a patient?Identifying a genetic etiology in child psychiatry patients has many benefits . DiagnosAdditionally, some genetic conditions are associated with known medical complications and may require routine screening or further medical work-up. For example, a patient presenting with autism spectrum disorder and macrocephaly who is found to have a PTEN mutation can then be enrolled in a cancer screening protocol. Further, diagnosis can allow for appropriate genetic counseling for the patient and family , 13.Finally, a better understanding of etiology with a named diagnosis may itself be of great value to many. Most individuals would rather know they have a serious disease than continue without a diagnosis . DiagnosIt is not practical to conduct a genetic workup on all patients, so child psychiatrists must decide whom to refer for genetic evaluation . Child pPatients with genetic conditions may present to psychiatric care in diverse ways. Table Some unusual psychiatric presentations or behaviors may suggest a particular genetic etiology. Skin picking may occur in Prader-Willi syndrome  while seThe purpose of Table If one or more of these red flags are present, a child psychiatrist should consider requesting a genetic consultation. Whenever possible, genetic tests should be ordered in conjunction with a genetic team. On a practical level, determining which genetic tests to order can be confusing and outside the usual practice of most child psychiatrists. Even more important, many child psychiatrists are not comfortable explaining the intricacies of genetic testing and lack the infrastructure to deal with ramifications of positive results . InformeIt is important to identify a genetic etiology in child psychiatry patients when it is present, as it has implications for treatment and counseling. Patients with genetic conditions may have unusual psychiatric symptoms or other abnormal history and physical findings. Here we present a table of red flags that a practicing child psychiatrist may identify, which could indicate an underlying genetic diagnosis. We hope this table will inspire child psychiatrists to think about genetic possibilities in their patient populations and make referrals for genetic evaluation when appropriate. Future work will be needed to validate this table in practice. However, being aware of these the flags has immediate utility in the clinical practice of child psychiatrists. To highlight how simple practice changes may have a big impact for diagnosing conditions, we provide a short list of tips and resources for making a genetic diagnosis in child psychiatry Table . We hope