Xerocrassa mesostena on Crete inferred from AFLP markers and mitochondrial cox1 sequences can be explained by three mechanisms: gene flow restriction, population expansion and leptokurtic dispersal. Gene flow restriction by geographic barriers caused subdivision of the gene pool into distinct clusters. Population expansion was probably facilitated by deforestation of Crete in the postglacial. Newly available areas were colonized by leptokurtic dispersal, i.e. slow active expansion resulting in isolation by distance within the clusters and occasional long distance dispersal events that resulted in departures from the isolation by distance model. Less than one percent of the AFLP markers show correlations with environmental variables. Random phylogeographic breaks in the distribution of the mitochondrial haplotype groups indicate that single locus markers, especially mitochondrial DNA, might result in a misleading picture of the phylogeographic structure of a species. Restriction of gene flow between metapopulations caused by geographical barriers can interact with sexual selection resulting in the differentiation of these metapopulations into separate species without noticeable ecological differentiation. Evidence for gene flow between parapatrically distributed evolutionary units representing different stages of the speciation process suggests that the ongoing process of fragmentation of the X. mesostena complex might be an example for parapatric speciation. The lack of ecological differentiation between these units confirms theoretical predictions that divergent selection for local adaptation is not required for rapid speciation.The phylogeographic structure of the land snail  Geographical isolation, selection and genetic drift may result in geographic differentiation of populations and finally in speciation. The relative importance of these processes and the conditions which determine which of these processes becomes the pacemaker of speciation are still unclear Littorina saxatilis. In response to selection by crabs in the lower littoral zone and to wave action in the upper littoral zone, differently adapted clusters characterized by different shell forms evolved convergently along several European coasts Cepaea nemoralisPartula taeniataCharpentieria italaSnails often show distinct patterns of morphological and genetic differentiation of metapopulations and are classical study objects for investigating the roles of history and selection in the origin of diversity Xerocrassa on Crete has resulted in ten endemic species X. mesostena is the most widespread of these species, and is paraphyletic with respect to several of the other endemic Xerocrassa species in a mitochondrial gene tree X. mesostena by peripatric speciation X. mesostenaX. mesostena that may evolve into separate species.The radiation of the helicoid land snail genus X. mesostena and their role as initial factors triggering speciation in the Cretan Xerocrassa radiation. We studied the population structure of X. mesostena using amplified fragment length polymorphism (AFLP) markers We investigated the importance of geographical isolation and selection for the geographic differentiation of Xerocrassa specimens were sampled in July/August and September/October 2004 and September/October 2005. These snail species are categorized as Least Concern in the Red List of the International Union for Conservation of Nature (IUCN), and thus no specific permission was required for this study. Mitochondrial cox1 sequences and AFLP data were determined from 95 specimens from 84 localities across Crete covering all known morphotypes. The locality, voucher and AFLP data for the Xerocrassa specimens used in this study can be found in cox1) gene were amplified using Polymerase Chain Reaction (PCR) and sequenced as described by Sauer and Hausdorf Total genomic DNA was extracted from snail foot tissue samples preserved in 100% isopropanol following the protocol proposed by Sokolov http://www.treebase.org, accession number S13717).Forward and reverse sequences were assembled using ChromasPro version 1.33 (Technelysium). The sequences were aligned with the CLUSTAL W algorithm The AFLP data were generated as described by Sauer and Hausdorf cox1 sequences were chosen using ModelTest version 3.7 Xerocrassa subvariegata and X. grabusana were used as outgroups for the phylogenetic reconstruction. Confidence values for the edges of the maximum-likelihood tree were computed by bootstrapping ) and the statistic ΔK  =  m(|L(K+1)-2L(K)+L(K-1)|)/s[L(K)] proposed by Evanno et al. K. For the K determined in that way, a run with a burn-in of 30,000 iterations followed by 100,000 iterations was performed to infer the admixture.We used the Bayesian program STRUCTURE As alternative to STRUCTURE, we investigated the phylogeographic structure based on the AFLP data using Gaussian clustering X. mesostena individuals.Furthermore, we constructed a neighbour-net with SplitsTree4 version 10 cox1 haplotype groups was assessed using one-way analysis of similarities (ANOSIM) as implemented in PAST v.1.95 R (up to 1) signify dissimilarity between groups. The significance was computed by 10,000 permutations of group membership. The p values were Bonferroni corrected for multiple comparisons.The geographical separation of the AFLP based clusters and cox1 sequences. We determined the pairs of individuals that belong to both, the quartile of the highest genetic distances and the quartile of the lowest geographic distances. Barriers to gene flow were visualized by plotting lines between these specimen pairs on a map.Barriers to gene flow are typically indicated by high genetic distances across short geographical distances. We calculated geographic distances with ArcGIS v.9.3 Long distance dispersal events will result in low genetic distances across large geographical distances. Thus, we determined the pairs of individuals that belong to both, the quartile of the lowest genetic distances and the quartile of the highest geographic distances and visualized potential long distance dispersal events by plotting lines between these specimen pairs on a map.X. mesostena can be better described as isolation by distance cox1 sequences) and latitude and longitude as spatial variables to infer how much of the genetic variability can be explained by isolation by distance. p values were estimated from 9,999 permutations of the spatial variables. Then a set of n-1 dummy variables (n  =  number of AFLP based clusters or cox1 haplotype clusters) indicating the genetic cluster affiliation of each individual (a 0/1 table) was constructed. The cluster affiliation of the individuals belonging to the last (nth) cluster would be redundant information. The genetic distance matrices were analysed using dbRDA with the set of variables indicating the genetic cluster affiliation of each individual as predictor variable set and with the spatial variables set as covariates to determine the importance of the subdivision into clusters. We used 9,999 permutations of the cluster affiliations to estimate p values. Additionally, the within cluster variation was analysed separately for the clusters either based on the AFLP data or the cox1 sequences.We used distance-based redundancy analysis dbRDA,  as impleG) as well as with Wald tests http://www.cru.uea.ac.uk). The data are divided in raster cells with a resolution of 10 minutes (34 raster cells on Crete) that were derived from interpolations based on a data set of weather station means for the period from 1961 to 1990 Xerocrassa mesostena individuals were sampled the environmental parameters were extracted. In addition to the monthly values (n = 105), we calculated for each parameter either annual sums or averages. Four individuals had to be excluded because their sampling localities could not be assigned to a raster cell. All processing was done using SAGA-GIS version 2.0.8 In order to identify selection pressures potentially influencing the population genetic structure and AFLP loci that are linked to adaptations to these selection pressures, we used the spatial analysis method implemented in the program MatSAM X. mesostena and one individual of X. subvariegata and of X. grabusana as outgroups . The pairwise comparisons confirmed that all five clusters were significantly separated , because the resulting tree had a lower AIC value than the tree based on a uniform model for the complete dataset (634 bp). Based on the maximum-likelihood tree  from all other haplotypes of X. mesostena. The ‘Psiloritis’ group is so deeply separated from the rest of the X. mesostena haplotypes (average GTR+G distance of ‘Psiloritis’ haplotypes to other X. mesostena haplotypes was 18.8%) that it might be taken for a cryptic species, if only mitochondrial sequences were available. The cox1 haplotype groups are restricted to subregions of Crete, but several of them have overlapping ranges , but that not all eight groups can be geographically separated . Therefore, results of the McDonald-Kreitman test were consistent with neutral evolution of the cox1 gene. A highly significantly negative Fu's Fs statistic  indicated population expansion.The ratio of nonsynonymous to synonymous polymorphisms within R value for the cox1 clades than for the ALFP based STRUCTURE clusters and a lack of significant differentiation between some cox1 clades . A dbRDA with the cluster affiliation of the individuals as predictor variable set and the spatial variables set as covariates showed that the subdivision into clusters explained a higher proportion of genetic variation . A dbRDA with the genetic distance matrices as response variables and the geographic coordinates of the sample sites as predictor variables restricted to the individual AFLP based clusters or cox1 haplotype groups showed that isolation by distance explains an additional proportion of the genetic variation within most of the clusters  MatSAM detected correlations between 4 (0.38%) of the 1307 AFLP loci with at least one of the 105 investigated environmental variables: two with mean monthly percentage of maximum possible sunshine (September and October), one with the coefficient of precipitation of the month August, and one with mean monthly diurnal temperature range of August.With a significance threshold set to 99%  was probably facilitated by partial deforestation of Crete in the postglacial. Large parts of Crete were repeatedly forested during more humid periods over the last million years Xerocrassa species that occur only in open environments were at least temporarily restricted to woodless patches during these periods. Forest areas may also have formed barriers to gene flow that contributed to the formation of the AFLP clusters and the corresponding cox1 haplotype clades. Newly available areas were colonized by leptokurtic dispersal X. mesostena the Lefka Mountains, the Psiloriti Mountains, the Dikti Mountains and the Messara plain caused a subdivision of the genepool of X. mesostena into five clusters as revealed by STRUCTURE and Gaussian clustering analyses based on the AFLP data  might act as a trigger for the transition from aestivation to the activity and reproduction period. The results of the spatial analysis method indicate that populations of X. mesostena adapted to differences in the onset of favourable conditions for activity and reproduction in different regions of Crete.The spatial analysis method implemented in MatSAM X. mesostena. Another mechanism that might result in geographical differentiation is regionally different selection by predators. Such selection resulted in geographic differentiation of shell shape and sculpture in some land snail taxa X. mesostena ranging from high dome-shaped to flat keeled shells The few loci affected by adaptation to environmental parameters cannot explain the geographical subdivision of Cepaea nemoralisPartula taeniataTheba on Fuerteventura and Lanzarote Charpentieria italaX. mesostena are still limiting gene flow so that the differentiation of separated metapopulations is an ongoing process.These results are in agreement with other studies of differentiation processes in land snails indicating that large scale geographical variation in land snails is predominantly caused by geographical separation Xerocrassa radiation was initiated by sexual selection X. mesostena clusters, the beginning of differentiation can be observed. For example, there are individuals with unusual proportions of the spermatophore forming organs, i.e. a high epiphallus: flagellum ratio , in the cluster occurring east of the Dikti mountains X. mesostena suggests that the reduction of gene flow between metapopulations by geographical barriers interacts with sexual selection resulting in the differentiation of these metapopulations into separate species without noticeable ecological differentiation.We previously suggested that the Cretan X. mesostena, but also at the next stage of differentiation between closely related species. Xerocrassa franciscoi and the Xerocrassa amphiconus/siderensis group originated recently from X. mesostena as indicated by the non-monophyly of their mitochondrial haplotypes with respect to mitochondrial lineages of X. mesostenaX. mesostena. Both differ from X. mesostena mainly in the proportions of the spermatophore forming organs, and they are geographically restricted to small areas at the fringe of the range of X. mesostenaX. franciscoi and the neighbouring X. mesostena populations X. mesostena specimens occurring close to the hybrid zone shown by AFLP markers might indicate that there is still some gene flow between the diverging species There is evidence for gene flow not only between diverging metapopulations within Xerocrassa units representing different stages of the speciation process, namely between metapopulations within X. mesostena as well as between recently diverged species, indicates that there was always some gene flow during the speciation process. Thus, the ongoing fragmentation of the X. mesostena complex might represent an example for parapatric speciation Xerocrassa metapopulations and species is also in agreement with the theoretical prediction that divergent selection for local adaptation is not required for rapid speciation Xerocrassa sexual selection has probably facilitated the speciation process.The evidence for gene flow between parapatrically distributed Cretan MastusOrculellaAlbinariaAn alternative model would be speciation by vicariance caused by emerging barriers. Under the vicariance model we would expect that other taxa with similar dispersal capacities were affected by the same vicariance events. Thus, the distribution areas of the originating species should be similar and should form distinct geographic clusters. There are radiations of other land snail groups on Crete, e.g., of cox1 gene in X. mesostena show some agreement with the pattern found with the AFLP markers, but deviate in other respects. The geographical distribution of some mitochondrial haplotype groups agrees closely with that of some AFLP clusters .Locality, voucher, (XLS)Click here for additional data file.Table S2Environmental parameters and AFLP data used for the MatSAM analysis (xls).(XLS)Click here for additional data file.


Bayesian cluster analysis showed that the most likely number of groups into which the sample could be partitioned was nine. Based on the proportion of each individuals genome derived from ancestry, pigs with at least 70% of their genome belonging to one cluster were assigned to that cluster. The cluster size ranged from 7 to 17 (n = 108). Genetic variability in this sub-population was slightly lower than in the whole sample, genetic differentiation among clusters was moderate (FST 0.125) and the FIS value was 0.011. NeighborNet and correspondence analysis revealed two clusters as the most divergent. Molecular coancestry analysis confirmed the good within-breed variability and highlighted the clusters that retained the highest genetic diversity. Nero Siciliano is an autochthonous pig breed that is reared mainly in semi-extensive systems in northeastern Sicily. Despite its economic importance and well-appreciated meat products, this breed is currently endangered. Consequently, an analysis of intra-breed variability is a fundamental step in preserving this genetic resource and its breeding system. In this work, we used 25 microsatellite markers to examine the genetic composition of 147 unrelated Nero Siciliano pigs. The total number of alleles detected (249, 9.96 per  In recent decades, many livestock breeds have experience a severe loss of biodiversity that has markedly affected animal production systems, especially in marginal areas. Attempts to reverse this negative trend have led to research in the preservation and exploitation of local animal breeds, with efforts to identify and reintroduce potentially important genetic traits that have been overlooked by globalized production systems. Autochthonous breeds require careful molecular and morphological characterization that takes into account potential influences of the environment in which they were originally developed and adapted.Nero Siciliano is an ancient black pig breed that originated in Sicily and has always been reared in extensive and semi-extensive systems on this island. Currently, most of the farms that rear autochthonous black pigs  are located mainly in the mountainous area of Nebrodi in northeastern Sicily. Black pigs are rustic animals that thrive on roughage and a limited food supply, in addition to being resistant to diseases in harsh conditions. This breed still retains its distinctiveness thanks to the geographical and orographical characteristics of the island and breeding area, but runs a high risk of losing its original traits because of the lack of a suitable plan to safeguard and exploit its production.Nero Siciliano pigs grow slowly and yield tasty meat and fat . In particular, microsatellite markers have been particularly useful for quantifying genetic variation within and among several European pig breeds, including Nero Siciliano  was selected from 22 farms in 11 communes  in the Nebrodi area; first- and second-degree relatives were avoided. The Nebrodi area is part of a natural Park  located 100-1700 m above sea level, and is where the Nero Siciliano breed originated and is still extensively bred. The sample size ranged from 2 to 15 pigs per farm, depending on the herd size. Only pigs that met the morphological standard for the breed were sampled, and digital photographs of each animal were taken. Farmers were also asked about the breeding strategies that they employed to select animals with an exclusively native Sicilian germplasm: farms in which there had been recent crossbreeding with commercial breeds were excluded from the sampling.® 377 sequencer equipped with Genescan Analysis® v.3.1.2 and Genotyper® v. 2.5 softwares .For each pig, 10 mL of peripheral blood was collected in K3-EDTA tubes. DNA was extracted from blood using the commercial Illustra blood genomic Prep Mini Spin kits . Genetic characterization was done with a set of 25 microsatellite markers (Table S1) chosen from a list maintained by the Pig Biodiversity project and USDA MARC database. The markers were chosen based on preliminary data about their degree of heterozygosity and polymorphism obtained from small samples of Nero Siciliano and other local Italian pig populations. Six PCR multiplex reactions and two single PCR reactions were done to amplify the microsatellites according to standard protocols. The amplicons (2 μL) were mixed with 4 μL of loading buffer containing formamide and 350 TAMRA as an internal size standard . Individual genotypes were determined with an ABI PRISMet al., 2005) to calculate the main parameters of genetic variability. For each locus and group of pigs, the allele frequencies, private alleles (Ap), effective number of alleles (Ae), and observed (Ho) and expected (He) heterozygosities were calculated. Molecular coancestry coefficients and kinship distances (Dk) weighted by polymorphic information content (PIC) were also assessed. In addition, the contribution of the different groups of pigs to the overall or total diversity was also inferred, according to Individual multilocus genotypes were analyzed by Molkin v. 3.0 software  tested was from 1 to 15, and 10 runs were done for each K. CLUMPP software  to 19 (locusS0005) alleles per locus and an average number per locus (9.96) that was fairly high. The effective number of alleles (Ae), which takes into account the expected heterozygosity (He), showed that S0005 was the most polymorphic locus and SW951 the least polymorphic. The expected heterozygosity was higher than that observed at each locus and ranged from 0.245 to 0.907 (mean: 0.708). The estimated polymorphic information content (PIC) ranged from 0.236 at SW951 to 0.901 at S0005 (data not shown). Overall, Nero Siciliano pigs showed Hardy-Weinberg disequilibrium, with significant deviations from equilibrium being observed for 10 microsatellites , could explain the excess of homozygotes and the deviation from genetic equilibrium at these loci. The heterozygote deficiency within populations (FIS) was significant for nine loci. The overall FIS coefficient for the loci was 0.109, indicating a significant (p < 0.001) excess of homozygotes in the whole sample.Nero Siciliano pigs showed high genetic variability . Two huntellites . The preK = 9 and then decreased. As indicated by K that captures the major structure in the dataset. Based on the method proposed by K = 9 and K = 2 (the latter being particularly indicative of a very low likelihood variance) were observed in the ΔK distribution. The highest values of the similarity coefficient G' (> 95%) were detected at K = 2, K = 7 and K = 9. Based on these three approaches, we assumed K = 9 to be the most likely number of clusters.The level of population structuring was quite high. Model-based clustering of the microsatellite genotypes revealed that the likelihood variance of the observed data initially increased by the predefined number of clusters to reach a peak value at FST 0.125; p = 0.001). The genetic variability of this subsample was still high with the overall FIS value (0.011) not significantly different from zero, indicating frequent random mating. The observed and expected heterozygosities were slightly lower than in the whole sample of 147 pigs.Based on the average matrix of membership (data not shown), animals with at least 70% of their genome belonging to one cluster were assigned to that cluster. The cluster size ranged from 7 to 17 . K7, K5 and K3) consisted of individuals from single farms, whereas two (K9 and K4) consisted of pigs from different herds reared in the same area.Among the 22 farms sampled, nine were highly homogenous, with > 70% of the genome belonging to a cluster. In contrast, animals from three farms that were particularly active in fattening pigs for meat production and pork products showed a wide genome distribution among clusters. Some clusters (K7) to 4.40 (K1). Thirty-three private alleles were detected in the model-inferred group of pigs. Each cluster was characterized by at least one unique allele: high frequencies were observed for alleles SW1695 (167 bp), SW1928 (85 bp) and S0005 (258 bp) in K3 (0.39), K7 (0.36) and K2 (0.25), respectively.The rarefacted number of alleles (Ar), which measures the contribution of alleles weighted by the sample size, ranged from 3.01 .NeighborNet built on Reynolds' distances revealed a close linkage among most of the clusters: S0017 (155 bp), SW1928 (85 bp), SW240 (122 bp) and S0017 (155 bp) were the most important alleles, each with a contribution > 5.7%.Correspondence analysis provided an alternative spatial representation of individuals and clusters scattered in the metric space . The firSW1928 (85 bp), which was found only in K7 at a high frequency (35.7%), contributed significantly to dispersion in the first and second axes ; an additional three alleles contributed more than 5% to dispersion in the first two axes and occurred at very high frequencies, i.e., 50% for SW240 (122 bp) in K2, 56% for S0017 (155 bp) in K2 and 82% for SW1873 (136 bp) in K7.The spatial dispersion of clusters and related individuals was determined mainly by four alleles that had very different frequencies among clusters, and by high frequency alleles detected exclusively in a given cluster. In particular, allele K1 to 0.389 in K7. Self-coancestry and the inbreeding coefficient showed the same trend: the lowest values were detected in K6, the highest in K3. Kinship distances ranged from 0.277 to 0.361. The contribution to the diversity of the whole sample is shown in K8 the least, when assessed according to K1 and K7 provided the highest and lowest contributions (CAr), respectively (Molecular coancestry in each cluster ranged from 0.287 in locus (NA) and expected heterozygosity (He): the values for these parameters were higher than for several European breeds , including Iberian breeds  (e = 0.49-0.69) (e = 0.67) studied by e = 0.49) (e = 0.65) (e = 0.57-0.77)   was higher than in European breeds (2.74 on average) and Nero Siciliano pigs (4.03) studied by loci may reflect the presence of null alleles at some loci and the Wahlund effect, i.e., a reduction in the observed heterozygosity that occurs in subdivided populations. Genetic substructures in Nero Siciliano pigs apparently resulted from different management strategies across herds and inbreeding within herds. In the Nebrodi area, farmers use their own boars for a year or more, with only a limited exchange of animals among farms and herds. The within-group inbreeding values (FIS) support this hypothesis  locus responsible for the Porcine Stress Syndrome (PSS) has been reported  defect from the population gene pool. Individuals belonging to different clusters could be used in planned matings to maintain a good level of genetic variability and rusticity (stress-resistance) and avoid excessive inbreeding. On the other hand, pigs sharing the same clusters and chosen based on their individual multilocus genotypes may be used in planned matings to preserve the most typical traits in this autochthonous population. Nero Siciliano pigs belonging to the most divergent cluster (K2) and pigs not included in the most homogenous subpopulation of 108 animals need to be incorporated into selection schemes in order to counterbalance any increase in inbreeding. Such approaches should help to preserve the Nero Siciliano breed and ensure the production of high quality products for local and national markets.As shown here, the genetic profile established by using neutral markers and the analysis of intra-population structure provided a general outline of the breeding systems applied to Nero Siciliano pigs in the Nebrodi area. The model-based approach identified homogenous groups in the sample, some of which coincided with specific farms and others with breeding areas. Our results indicate that molecular data can be helpful in the selection of parental stocks and planned matings as part of strategies to preserve and restore the rational breeding of black pigs. Boars belonging to cluster The following material is available online for this article:Multiple (M) and single (S) PCR, locus name and chromosomal location, and primer sequences and dyes used in PCRThis material is available as part of the online article from http://www.scielo.br/gmb.


Bolitotherus cornutus) that exploits three sympatric host fungi. We collected B. cornutus from plots containing the three host fungi in different proportions and quantified population genetic structure in each plot using a panel of microsatellite loci. We found no relationship between host community diversity and population differentiation in this species; however, we also found no evidence of resource-associated differentiation, suggesting that host fungi are not substantial barriers to gene flow. Moreover, we detected no genetic differentiation among B. cornutus populations separated by several kilometers, even though a previous study demonstrated moderate genetic structure on the scale of a few hundred meters. Although we found no effect of community diversity on population genetic structure in this study, the role of host communities in the structuring of genetic variation in heterogeneous landscapes should be further explored in a species that exhibits resource-associated population genetic structure.In heterogeneous environments, landscape features directly affect the structure of genetic variation among populations by functioning as barriers to gene flow. Resource-associated population genetic structure, in which populations that use different resources  are genetically distinct, is a well-studied example of how environmental heterogeneity structures populations. However, the pattern that emerges in a given landscape should depend on its particular combination of resources. If resources constitute barriers to gene flow, population differentiation should be lowest in homogeneous landscapes, and highest where resources exist in equal proportions. In this study, we tested whether host community diversity affects population genetic structure in a beetle ( Most natural populations exist in spatially heterogeneous environments. Because environmental heterogeneity impacts two forces of evolution – selection and gene flow – it often shapes patterns of genetic variation at the landscape level  that are sympatric over much of their range, and are often found growing within a few meters of each other (Gilbertson and Ryvarden applanatum), found a moderate level of genetic differentiation . The average adult lifespan of B. cornutus is 69 days  along two drainages, Hunters Branch and Pond Drain , for the three host fungi .The study site was located on Salt Pond Mountain near Mountain Lake Biological Station in Giles County, Virginia. In May–June 2011, we surveyed an area of approximately 0.25 kmst fungi . We georB. cornutus adults. All B. cornutus were collected and brought back to the lab. Each individual received a unique ID, which was painted on its elytra using Testors Gloss Enamel in earth tone colors. We collected 0.2–5 μL of hemolymph from the defensive glands of all beetles using the method described in Donald et al. , we performed all tests using F-statistics.We used hierarchical analysis of molecular variance (AMOVA) to test whether host fungi contribute to population structure in this system. Population differentiation was assessed using  Slatkin . To deteF-statistics: FIS, the correlation between haplotypes within individuals relative to their subpopulation; FSC, the correlation between haplotypes within subpopulations relative to all individuals from the same host fungus; FCT, the correlation between haplotypes within host fungi relative to the entire metapopulation; and FST, the correlation between haplotypes within subpopulations relative to the entire metapopulation. A significant value for any of these F-statistics indicates significant population genetic structure with respect to the relevant group .AMOVA partitions total genetic variance into covariance components that describe the correlation between random haplotypes within the same subgroup, relative to a larger group. For our analysis, individuals were nested within logs (subpopulations), and logs were nested within fungus species. On the basis of these hierarchical groupings, we calculated the following F-statistics were obtained by bootstrapping over loci.All genetic analyses were performed using Arlequin , we included a coinfected category as a fourth level of the fungus factor  in the AMOVA analysis. All but one of the coinfected logs were infected by G. applanatum and F. fomentarius; the remaining coinfected log hosted G. applanatum and G. tsugae. Excluding coinfected logs did not qualitatively alter our results.Because some logs were coinfected by more than one species of fungus (P = 0.91), allowing us to pool individuals across plots to test forest-wide structure. Finally, because sex-biased dispersal may result in sex-specific patterns of population structure, we also ran the above AMOVA separately for females and males. Only populations with at least five females or five males, respectively, were included in this analysis.Before performing the combined analysis, we ran a separate AMOVA to test for a significant effect of plot. Plot did not contribute significantly to total genetic variance . Because the host fungi did not affect population structure in the Salt Pond Mountain data set, these beetles were pooled by site for the isolation-by-distance analysis. Pairwise genetic distances ) were calculated using Arlequin, and pairwise geographic distances were calculated using the Geographic Distance Matrix Generator v 1.2.3. , we tested for isolation-by-distance on a larger geographic scale by augmenting our data set with 88 individuals collected from two sites 6 km  and 9 km  away from our primary study site on Salt Pond Mountain. At each of these sites, beetles were collected from several logs that were infected by either 3. Ersts  and log 3. Ersts . The sig3. Ersts .J; Pielou J′) is given byHost fungus community diversity in each of the five plots was quantified with the Shannon–Wiener equitability index  in order to distinguish between host communities in which all host species are present, but in very different proportions.where J′ for each plot, we counted all logs in the plot that were infected by the three host fungi. Because we intended this index to reflect the community of available host fungi, we excluded logs that did not exhibit any evidence of B. cornutus  or on which all brackets were in an advanced state of decay. Logs that were coinfected by two of the host fungi  were counted twice, once for each fungus species. All three fungi were present in all five plots.To calculate FST in each plot. We assessed the relationship between the Shannon–Wiener equitability index and these FST values using Spearman's rank correlation. Each observation in this data set was one of the five study plots. This analysis was performed in R 2.10.1 , but this value was not significant. However, we did detect a significant inbreeding coefficient  produces brackets on an annual basis, so it tends to be less abundant in the spring and becomes more common as the summer progresses. As it becomes a more abundant resource, it likely absorbs migrants from the other two host fungi, erasing genetic differentiation between populations on different fungi. Interannual variation in the relative abundance of the three host fungi would have a similar effect. Such temporal heterogeneity in the host fungus community could generate selection for ecological generalists who are able to exploit whichever fungus is most abundant during a given month or year , a species in which Wolbachia infection results in parthenogenesis  on the same mountain. It is worth noting that Whitlock . Widespread eastern hemlock (Tsuga canadensis) decline, driven by the invasion of hemlock woolly adelgid, may have led to an increase in the frequency of G. tsugae – a hemlock specialist. Hemlock woolly adelgid is a sap-sucking insect that feeds at the base of hemlock needles, killing the tree (Young et al. An alternative hypothesis for disappearance of microgeographic population structure in G. tsugae brackets. Whether exploitation of this newly abundant resource may have erased population genetic structure in B. cornutus that Whitlock (An invasive from Asia, hemlock woolly adelgid was first recorded in North America in 1951 near Richmond, VA, and since has spread into the Appalachian Mountains south to Georgia and north to Maine (Fitzpatrick et al. Whitlock  observedThis study accords with a growing body of evidence suggesting that population genetic structure can be surprisingly dynamic (Charbonnel et al. QST-FST comparisons: Regions associated with ecologically important quantitative traits may show high levels of divergence not reflected in neutral genetic variation (Scheffer and Hawthorne B. cornutus genome could be a mosaic in which loci that affect habitat use are characterized by host-associated genetic structure, while neutral loci are not. However, all nine microsatellite loci included in this study yielded similar estimates of population structure, providing little evidence for this interpretation.Finally, the degree to which genetic structure reflects the environment may differ across different regions of the genome, particularly in the early stages of local adaptation Via . Under dB. cornutus. Because habitat-associated population structure is common in other systems, the most robust test of our hypothesis would be conducted in a species in which gene flow among environments is known to be restricted (e.g., Hoekstra et al. Although this study did not find a relationship between host community diversity and population differentiation, such a relationship may exist in populations that do exhibit habitat-associated population structure, unlike


Salvia officinalis L. A total of 125 alleles, with 8 to 21 alleles per locus, were detected in a natural population from the east Adriatic coast. The observed heterozygosity, expected heterozygosity, and polymorphic information content ranged from 0.46 to 0.83, 0.73 to 0.93 and 0.70 to 0.92, respectively. New microsatellite markers, as well as previously published markers, were tested for cross-amplification in Salvia brachyodon Vandas, a narrow endemic species known to be present in only two localities on the Balkan Peninsula. Out of 30 microsatellite markers tested on the natural S. brachyodon population, 15 were successfully amplified. To obtain evidence of recent bottleneck events in the populations of both species, observed genetic diversity (HE) was compared to the expected genetic diversity at mutation-drift equilibrium (HEQ) and calculated from the observed number of alleles using a two-phased mutation model (TPM). Recent bottleneck events were detected only in the S. brachyodon population. This result suggests the need to reconsider the current threat category of this endemic species.Nine new microsatellite markers (SSR) were isolated from  Salvia is one of the largest plant genera, with approximately 1000 species [Salvia officinalis group [The genus is group .S. officinalis L.) is economically one of the most prominent species of the Salvia officinalis group [Dalmatian, or common, sage  is one of the most interesting species of the Salvia officinalis group from ecological, biogeographical, conservation and phylogenetic points of view . After removing low quality reads, 235 unique sequences remained in a total length of 107,875 bp. Out of 235 unique sequences, 224 contained GA or GT microsatellite repeats. High quality PCR primer pairs were designed for 15 dinucleotide microsatellite loci. Eleven of these were polymorphic, while four were either monomorphic or did not amplify at all. Finally, nine primer pairs had suitable amplification patterns and signal intensity and were used to screen 25 individuals representing a natural Dalmatian sage population . In accordance with this opinion, we recommend a more detailed sampling in future population genetic studies of Dalmatian sage than was performed in this study. If using this sampling design results in the same loci continuing to show an overall excess of homozygotes and null allele frequency, corrections for null alleles or their exclusion from the study can be used as a last resort [Eight out of nine microsatellites showed a high polymorphic information content (PIC) of more than 0.75. It should be noted that the PIC value of the remaining locus SoUZ021) was rather high (0.70), indicating that all nine loci could be very useful in assessing the genetic diversity and population structure of Dalmatian sage. Three out of nine newly developed microsatellite markers  showed significant deviations from Hardy-Weinberg expectations (HWE) after application of the sequential Bonferroni corrections. These three loci also exhibited an overall excess of homozygotes and null allele frequencies using Brookfield’s formula . They va was ratht resort .p < 0.01) after applying sequential Bonferroni corrections . Deviations from HWE and linkage disequilibrium are the result of primer-site mismatch , which are common in cross-amplified species, or as a consequence of specific population structures . It is noteworthy that during fieldwork, dense patches of S. brachyodon individuals and stolons at the soil surface or below ground were observed  is compared to the expected genetic diversity at mutation-drift equilibrium (HEQ) in the same way regardless of the actual number of alleles at a locus and there is no advantage in using highly variable markers, as shown by the power analysis [This study is among those that have shown that rare species have less genetic variation than widespread species ,29. Howeanalysis .p = 0.02), suggesting that this population underwent a recent bottleneck that reduced its genetic diversity. The results of the same test, when applied to Dalmatian sage and based on 29  or 15 (only those loci that were amplified in short-tooth sage) microsatellite loci, were not significant in either case . This result was expected for Dalmatian sage population because this is a large population with an extended distribution range.Tests for evidence of a recent bottleneck based on microsatellite loci and assuming the two-phased mutation model (TPM) were applied to both Dalmatian and short-tooth sage populations. A statistically significant departure from mutation-drift equilibrium was detected in short-tooth sage  species of Croatian flora . AccordiS. officinalis L. were developed from genomic DNA libraries enriched for GA and GT repeats as described earlier [This study was carried out on 25 Dalmatian sage and 25 short-tooth sage plants from the natural populations originating on the Pelješac peninsula (Croatia). New microsatellites for  earlier , but witHaeIII, MseI, Sau3AI, RsaI, AluI, HinfI, EcoRV, BglII and EcoRI) . Single-stranded overhangs of restriction fragments were removed using mung bean nuclease and dephosphorylated using calf intestinal alkaline phosphatase (CIP) . Phosphorylated linkers were prepared from SNXfor and SNXrev primers using T4 polynucleotide kinase  [XmnI restriction enzyme and T4 DNA ligase . Long (GA)n and (GT)n probes were constructed in a PCR extension reaction, followed by their attachment to small (5 × 5 mm) pieces of nylon membrane  and overnight hybridization of DNA fragments containing microsatellite regions. After the nylon membranes were rinsed, the microsatellite fragments were ligated into the pGEM-T Easy Vector , and heat-shock transformation into XL10-Gold Ultracomponent Cells  was performed. The resulting culture was spread on LB-agar plates containing ampicillin, IPTG and X-gal. After overnight incubation, white bacterial colonies were transferred by toothpick into 384-well plates containing Luria-Bertani (LB) freezing media 2SO4, 4.4% v/v glycerol) for long-term storage. Libraries were transferred onto nylon membranes and screened by Southern analysis using Cy5- and Cy3-labeled 30 bp oligonucleotides with microsatellite core repeats. Positives were detected after stringent washing by scanning the blots using an Ettan DIGE Imager .Genomic DNA samples were extracted from dried leaves using the GenElute Plant Genomic DNA Miniprep Kit . Nine restriction enzymes were used for genomic DNA digestion  . Ligatio® PCR System 9700  using a two-step protocol with an initial touchdown cycle. The cycling condition were as follows: 94 °C for 5 min; five cycles of 45 s at 94 °C, 30 s at 60 °C for the first cycle and 1 °C less in each subsequent cycle, and 90 s at 72 °C; 25 cycles of 45 s at 94 °C, 30 s at 55 °C, and 90 s at 72 °C; and an 8 min extension step at 72 °C. Three different lebeled PCR products  and 500-LIZ size standard were collected and run as a single sample on an ABI 3730XL . The results were analyzed using GeneMapper 4.0 software .Positive clones were randomly selected from the libraries and used for plasmid isolation . Sequencing of plasmid isolates was performed by means of T7 and SP6 universal primers using Big Dye chemistry and an ABI 3730XL analyzer . Sequences were edited and assembled using CodonCode Aligner software version 2.0.6 . Microsatellite repeats in sequences were localized by MISA PERL SCRIPT . PCR priNa), the observed heterozygosity (HO), the expected heterozygosity or genetic diversity (HE), and the polymorphism information content (PIC) [HE) was compared to the expected genetic diversity at mutation-drift equilibrium (HEQ) and calculated from the observed number of alleles under the intermediate Two-Phase Model (TPM), assuming 30% multistep changes. The Two-Phase Model was applied because it has been shown to be the most appropriate for microsatellite DNA data [et al. [For each microsatellite locus, the average number of alleles per locus (nt (PIC)  were calnt (PIC) . GENEPOPnt (PIC)  was usednt (PIC)  were appnt (PIC) . The pront (PIC)  was usednt (PIC) . The obsDNA data . Based oDNA data  was chos [et al. .Salvia officinalis L.). The observed parameters indicate that all loci may be very useful for assessing genetic diversity and population structures of Dalmatian sage. New microsatellite markers, as well as previously published markers, were tested for cross-amplification in short-tooth sage . The amplification rate was 52%. The greatest benefit of this study was the opportunity to compare genetic variation in rare and widespread congeners. This study is among those that have shown that rare species have less genetic variation than widespread species. Recent bottleneck events detected in the short-tooth sage population using a two-phased mutation model (TPM) highlight the need to reconsider the current threat category of this endemic species.Nine new microsatellite markers (SSR) were isolated from Dalmatian sage (


Theobroma cacao L cultivation, and specifically to the native fine flavour Nacional cocoa variety. The original Nacional cocoa trees are presently in danger of extinction due to foreign germplasm introductions. In a previous work, a few non-introgressed Nacional types were identified as potential founders of the modern Ecuadorian cocoa population, but so far their origin could not be formally identified. In order to determine the putative centre of origin of Nacional and trace its domestication history, we used 80 simple sequence repeat (SSR) markers to analyse the relationships between these potential Nacional founders and 169 wild and cultivated cocoa accessions from South and Central America. The highest genetic similarity was observed between the Nacional pool and some wild genotypes from the southern Amazonian region of Ecuador, sampled along the Yacuambi, Nangaritza and Zamora rivers in Zamora Chinchipe province. This result was confirmed by a parentage analysis. Based on our results and on data about pre-Columbian civilization and Spanish colonization history of Ecuador, we determined, for the first time, the possible centre of origin and migration events of the Nacional variety from the Amazonian area until its arrival in the coastal provinces. As large unexplored forest areas still exist in the southern part of the Ecuadorian Amazonian region, our findings could provide clues as to where precious new genetic resources could be collected, and subsequently used to improve the flavour and disease resistance of modern Ecuadorian cocoa varieties.Ecuador’s economic history has been closely linked to  Theobroma cacao L. is the most economically important species of the Theobroma genus which is a member of the Malvaceae family. It is a diploid perennial species (2n = 2× = 20) with a small genome ranging in size from 411 to 494 Mb T. cacao accessions are self incompatible due to a gameto-sporophytic incompatibility system . Trinitario, a third group, corresponding to hybrids between Criollo and Forastero, was also recognised. However, the Forastero group also includes many other populations from all the Amazonian and Orinoco regions and presents a high diversity, as revealed by many genetic studies Traditionnaly, two main genetic groups have been defined to classify cocoa accessions, based on morphological traits and geographical origins th century, was very narrow.The domestication of Criollo, originally cultivated by the Mayas in Central America was previously studied T. cacao and its products played any part in the lives of Ecuadorian coastal inhabitants during the Chorrera phase of the pre-Columbian Valdivia culture (2000 BC), contrary to the extensive amount of information available on this phenomenon with respect to Mesoamerican people, most notably the Mayas and Aztecs. In Ecuador, available information related to the history of Nacional cocoa cultivation is linked with the Spanish colonization history in this country.Cocoa cultivation in Ecuador is very ancient, dating back to the pre-Columbian age before the Spanish colonization of this territory. Pizarro, during his first voyage in 1526 along the South American coasts (nowadays Ecuador), found evidence of small plantations of an apparently native cocoa tree Since the time when the first Spanish colonists began deforestation of the Ecuadorian coastal region, a large number of native cocoa trees have been reported Moniliophthora perniciosa) and frosty pod (Moniliophthora roreri), which together devastated the native plantations.The native Nacional variety was the only one planted in Ecuador until the early 1890s, when foreign germplasm was first introduced in this country. In 1890, due to the quality traits of Nacional cocoa beans, Ecuador had a privileged position in the markets of Hamburg and London A large genetic admixture between the native variety and foreign germplasm is currently found in modern Ecuadorian cocoa plantations In a previous work, a few non-introgressed Nacional types were identified within this hybrid population as potential representatives of the native Nacional variety A total of 176 individuals from different geographical origins were used for simple sequence repeat (SSR) analyses in this study :Seven putative ancient Nacional variety individuals cultivated along the Ecuadorian coastal region Sixty-five wild genotypes collected in the northern region A (LCT-EENa), central region B (LCT-EENb) and southern region C (LCT-EENc) of the upper Amazonian region of Ecuador  [21].Sixty-six wild genotypes collected in the upper Amazonian region of Peru  [22], [2Eleven wild cocoa samples collected in French Guiana (Guy) Three cultivated genotypes from the lower Amazonian region of Brazil (BA).Twenty-four Criollo (Cr) variety samples collected in Central America, from Venezuela to Mexico The germplasm collection and country of origin is reported for each accession in Eighty SSR markers were chosen for these analyses . SeventyDNA samples were isolated from each individual as described previously Amplified SSR fragments were scored as alleles. The following genetic parameters were calculated for each population using GENETIX software We also estimated allelic richness (Rs) for each population, using a rarefaction approach to correct for unequal sample sizes F-statistics were estimated according to Weir and Cockerham FIS, an estimate of heterozygote deficiency or excess, calculated for the whole population sample and for each population, and FST, the proportion of variance due to population differenciation, calculated for the whole population sample and between pairs of populations; their significance were assessed using a 1000-fold permutation test.Wright’s RSTRhoST, an unbiased version of RST in which allele sizes are transformed to standardize variances, was estimated for pairs of populations using GENEPOP software, WEB version 4.0.10 As most mutations in SSR involve the addition or subtraction of a small number of repeat units, according to the stepwise mutation model population, genetic differentiation was also estimated by Analyses of genetic distance among pairs of populations In addition, a dendrogram was constructed using a dissimilarity index (simple matching) and the neighbour-joining (NJ) method with 500 bootstrap replicates. Neighbour-joining cluster analyses were carried out using DARWIN software 5.0 Finally, a paternity analysis was performed using the software CERVUS, version 2.0 A total of 463 alleles were detected at the 80 analysed SSR loci across all 176 individuals sampled . A wide FIS values were observed in most of the populations with positive values showing an excess of homozygotes and negative values showing an excess of heterozygotes  was estimated between pairs of populations , and the FST between the Pound and the Nanay populations.Genetic differentiation .Cluster 3 included the Nacional cocoa accessions, the wild cocoa genotypes from the southern part of Ecuadorian Amazonia (region C) and the wild cocoa accessions from Peru collected along the Morona river.Cluster 4 included all Criollo accessions.The NJ tree  highlighTo refine the previous analysis, coefficients of genetic distance were calculated for pair-wise comparisons of the 14 cocoa groups Subsequent analyses of genetic distance were carried out to identify the genetic distances between the Nacional pool and wild individuals collected along the neighbouring rivers within each Amazonian region of Ecuador , including all accessions collected in the upper Amazonian region of Peru (data not shown). This new approach allowed us to identify a few LCT-EENc samples , collected along the adjacent rivers Yacuambi and Nangaritza, as being wild cocoa trees genetically closest to the Nacional pool originally cultivated in the coast region. A key feature of some of the wild cocoa trees from these adjacent rivers is their low heterozygosity level (e.g. 16% for LCT-EEN 91). Note also that the Amazonian region C is geographically close to the Guayas basin  where thA paternity analysis was performed to identify the most likely representatives of the ancestral population from which the Nacional variety originated. All putative tested parents were from the south Amazonian region C of Ecuador, closest to the Nacional genotypes.Our results  indicateT. cacao accessions was undertaken by Lerceteau et al. Throughout the history of cocoa cultivation in Ecuador, substantial genetic changes, including reproductive behaviour, have occurred as a consequence of foreign germplasm introductions and genetic admixture associated with natural and human selection. The first study of genetic diversity of modern Ecuadorian In this study, we used 80 SSR markers to analyse relationships between representatives of the putative founders of the Nacional variety and wild populations from Amazonia in order to identify its wild genetic origin and elucidate the domestication events that occurred before the first Nacional plantations were set up in Ecuador in the Guayas Basin.T. cacao accessions, except for the Upper Amazonian Brazilian accessions, which were not very represented in this study, but for which no specific cluster was identified in the last proposed classification The accessions studied here cover most of the geographic origins of known FST of 0.0081 and RhoST of −0.0018) between these 2 groups.The first group (cluster 1) included mostly accessions from the northern and central regions of Ecuadorian Amazonia (regions A and B), but without any structuring between them. This is in agreement with the very low genetic distance (0.039) and low genetic differentiation (The second group (cluster 2) included most of the Peruvian accessions, i.e. mainly those collected in the northern region of Peru, associated with wild accessions collected in French Guiana and in the Orinoco region of Venezuela, and with some accessions from Ecuador. The cultivated genotypes from Lower Amazonia in Brazil were also included in this group.T. cacao L. species T. cacao evolution the extension of cocoa populations towards the eastern part of South America and until the French Guyana region, could have started from this Parinari Upper Amazonian region.The Upper Amazonian region has always been considered as the primary centre of origin and diversity of The third group (cluster 3) included most of the accessions from the southern Amazonian region of Ecuador, the accessions collected in Peru along the Morona river, close to this southern Ecuadorian region, and the Nacional accessions. In this group, a close genetic relationship was found among some wild accessions from the southern part of Ecuadorian Amazonia (region C), and the Nacional pool from the coastal region. These results were supported by the genetic distance assessment, as well as by a parentage analysis. Our results suggest that the Amazonian region located in the Province of Zamora Chinchipe could be the centre of domestication of the Nacional variety at the origin of the first Nacional cocoa plantation that was set up on the Guayas riverbanks. Later, from this latter plantation, cocoa cultivation could have expanded along the Guayas tributary rivers (upper waters), which is now the ‘Arriba’ cocoa production region in the provinces of Guayas, Los Ríos and Manabí .The fourth group (cluster 4) included all Criollo accessions collected from Mexico to Venezuela, reflecting its narrow genetic base. In a previous study, some accessions from Colombia (EBC) were identified as being the closest to Criollo This migration and domestication hypothesis is supported by the geographical proximity between the first cocoa plantation set up along the Guayas riverbanks FIS) demonstrate a clear and significant excess of homozygotes in some groups. It is the case for the old domesticated and self compatible varieties Criollo and BA varieties, but also for some wild genotypes such as those of the VEN, GUY and LCT-EEN groups. Highly homozygous wild genotypes were identified in each region sampled. This latter result suggests that self compatible alleles already existed in these wild populations and that natural selection could have already eliminated part of the genetic burden due to inbreeding, thus facilitating the process of domestication of some wild populations.Inbreeding coefficients , which is evidence that commercial exchanges with coastal people occured in this region Recently, a ceremonial formative site was discovered on the eastern slopes of the Andes in the southern part of Ecuadorian Amazonia, and dating back to the 3rd millennium BC. The cultural remains showed a high degree of development of these societies, with ceramics and marine shells ; CIC: Centro de Investigación Caribia (Colombia); CIRAD/Mpl: Centre de Coopération internationale en recherche agronomique pour le développement (France)/Montpellier Centre; CNRA: Centre National de Recherches Agronomiques (Côte d’Ivoire); CRU: Cocoa research Unit – University of the West Indies (Trinidad and Tobago); EET-P: Estación Experimental Tropical-Pichilingue (Ecuador); FONAIAP: Fondo Nacional de Investigaciones Agropecuarias (Venezuela); INTA: Instituto Nicaraguense de Tecnologìa Agropecuario (Nicaragua); INIFAP: Instituto Nacional de Investigaciones Forestales. Agrícolas y Pecuarias (Mexico). The % of heterozygosity was evaluated by 80 SSR (as reported in (DOCX)Click here for additional data file.


Tursiops sp.) and short-beaked common dolphins (Delphinus delphis). We compare these results with those previously obtained from biopsy sampled free-ranging dolphins in the same area to test whether carcass samples yield similar patterns of genetic variability and population structure. Data from dolphin carcasses were gathered using seven microsatellite markers and a fragment of the mitochondrial DNA control region. Analyses based on carcass samples alone failed to detect genetic structure in Tursiops sp., a species previously shown to exhibit restricted dispersal and moderate genetic differentiation across a small spatial scale in this region. However, genetic structure was correctly inferred in D. delphis, a species previously shown to have reduced genetic structure over a similar geographic area. We propose that in the absence of corroborating data, and when population structure is assessed over relatively small spatial scales, the sole use of carcasses may lead to an underestimate of genetic differentiation. This can lead to a failure in identifying management units for conservation. Therefore, this risk should be carefully assessed when planning population genetic studies of cetaceans.Advances in molecular techniques have enabled the study of genetic diversity and population structure in many different contexts. Studies that assess the genetic structure of cetacean populations often use biopsy samples from free-ranging individuals and tissue samples from stranded animals or individuals that became entangled in fishery or aquaculture equipment. This leads to the question of how representative the location of a stranded or entangled animal is with respect to its natural range, and whether similar results would be obtained when comparing carcass samples with samples from free-ranging individuals in studies of population structure. Here we use tissue samples from carcasses of dolphins that stranded or died as a result of bycatch in South Australia to investigate spatial population structure in two species: coastal bottlenose ( Tissue samples obtained from stranded cetaceans and from entanglements of fisheries or aquaculture interactions have been used for many genetic studies Cetaceans die or live strand for many reasons, including disease, neonatal death, pollutants, entanglement in fishing and aquaculture gear, boat strikes and intentional killing Studies that assess genetic variability and population structure using stranded animals or individuals from entanglements are either entirely based on these samples Assessing population genetic structure on a small to medium spatial scale with samples from cetacean carcasses leads to the question of how representative the location of a stranded animal is with respect to its natural range Tursiops sp.) and short-beaked common (Delphinus delphis) dolphins have been collected over the past two decades by the South Australian Museum (SAM) and associates. These samples were mainly obtained from strandings , fatal entanglements in fishing and finfish aquaculture operations, and illegally killed dolphins Tursiops sp., a species previously shown to exhibit restricted dispersal and moderate genetic differentiation across a small spatial scale in this region.In the Australian state of South Australia (SA), a large number of tissue samples from carcasses of coastal bottlenose (Our datasets contained carcass samples from 51 bottlenose and 54 common dolphins . There wn = 51)  = 0.59, H(biopsied) = 0.74; Spencer Gulf: H(carcass) = 0,68, H(biopsied) = 0.86).Haplotypic diversity (h) and nucleotide diversity (π) of dolphin carcasses differed little between the two regions . The sam(carcass) = 3.3, AR(biopsied) = 5.8), and for SG the value was less than half (AR(carcass) = 3.3, AR(biopsied) = 7.9).For microsatellites we detected a high null allele frequency (21%) at locus KW2, and therefore we excluded this locus from the analysis, consistent with our free-ranging dataset (carcass) = 0.95, H(biopsied) = 0.93). This is in contrast to the differences we detected for the two types of bottlenose dolphin datasets.For the mtDNA dataset of common dolphins (n = 51), amplification of control region sequences was unsuccessful for three individuals. Datasets of carcasses and free-ranging individuals were compared for the regions W coastal and SG. Sequences of carcasses from W coastal areas and SG yielded 21 haplotypes and 38 polymorphic sites, with relatively high levels of h and π.. Values For microsatellites, we found no evidence for the presence of null alleles, and therefore the analysis for common dolphins was based on the original seven loci. Two individuals were excluded from the analysis due to unsuccessful amplification. Significant deviations from Hardy-Weinberg equilibrium were not observed in the regions or for any of the loci. We found no evidence for linkage disequilibrium between any locus pair. AR was similar for the two regions . ObserveST and ΦST, significant differentiation was detected between W coastal areas and SG. Since values for mtDNA FST and ΦST were similar, we only report here the value for ΦST . For microsatellites, pairwise comparison of FST showed highly significant differentiation . POWSIM suggested a 100% likelihood of estimating FST correctly when FST = 0.025 or above, implying that our dataset has sufficient power for accurate FST estimates.For both mtDNA FP(X/K) to be highest at K = 1 when using either version of the program (standard version 2.0 and version 2.3.3 for weak population structure) and when applying either the independent or the correlated allele frequency model, suggesting the presence of only one population in W coastal areas and SG of SA when using the carcass dataset. Estimates of P(X/K) and the prior α showed consistency among multiple runs, indicating that the burn-in length and the length of the runs were appropriate. Additionally, we ran the STRUCTURE analysis by fixing the number of populations to K = 2 and including population location information to identify potential migrants. Three individuals from SG were identified as potential migrants from W coastal. All three carcasses were relatively fresh. One of the three individuals was found near the population boundary to the W coastal area, and the other two individuals were found far away indicating that they were unlikely to have drifted over the population boundary.STRUCTURE did not detect genetic partitioning for bottlenose dolphin between W coastal areas and SG when using carcass samples, which is in contrast to results based on biopsied free-ranging individuals P(X/K) at K = 2 approximating one. The assignment probabilities for free-ranging dolphins to a particular population A separate STRUCTURE analysis that combined dolphin carcasses and biopsied free-ranging individuals in one dataset led to the identification of genetic differentiation between dolphins from the two areas . In the Assignment tests with GENECLASS revealed that although the majority of dolphin carcasses were assigned with higher probability to the area in which they were found, ie W coastal or SG, there was no clear separation of dolphins into these two populations. A large proportion of individuals were assigned to both areas and therefore not rejected from the population in which they showed lower assignment probability. A total of 82.5% of individuals were correctly assigned to their population of origin (based on higher probability), however, the quality index was only 68.52%. As a result of this, the log likelihood plot shows no clustering of dolphins from similar geographic locations into these two areas .ST = 0.059, FST = −0.014; P>0.05). The test for statistical power of microsatellite FST values in POWSIM suggested a 99% likelihood of estimating FST correctly when FST = 0.01, and a likelihood of 100% when FST = 0.02 and above. Our carcass dataset therefore has sufficient statistical power to detect relatively low levels of genetic differentiation. Thus the lack of genetic differentiation found for common dolphins from SA is unlikely to be a function of insufficient statistical power.For both mtDNA and microsatellites, we detected no significant genetic differentiation for dolphin carcasses from W coastal areas and SG .The lack of genetic structure detected with FWe constructed a haplotype network using mtDNA control region sequences for bottlenose dolphin carcasses . The netDistribution and frequency of carcass haplotypes differed between the regions , a patteThe haplotype network of samples from common dolphin carcasses showed a large number of haplotypes (NH = 21) and a relatively high sequence divergence, with 38 polymorphic sites. The haplotype network is not shown here because it is very similar to that obtained using samples from free-ranging common dolphins see .We compared patterns of population genetic structure obtained using carcass samples of South Australian bottlenose and common dolphins with those obtained using samples from biopsied free-ranging individuals in the same geographic area. This comparison is of particular interest because we used two different sample types (carcasses versus biopsy samples) from the same region, and species that differ in their ecology and that were expected to differ in their levels of population genetic structuring. Our study shows that the ability to detect population structure along the South Australian coast was reduced when using samples from bottlenose dolphin carcasses compared to biopsied free-ranging individuals. On the other hand, for common dolphins no genetic differentiation was detected using either dataset.Tursiops sp., T. aduncus, Orcinus orca, T. truncatus, The previous genetic analysis based on samples from biopsied free-ranging bottlenose dolphins in SA For South Australian bottlenose dolphins we detected lower levels of mtDNA and nuclear genetic variability in carcass samples. Free-ranging bottlenose dolphins showed levels 20% higher for mtDNA variability in both regions, and for nuclear genetic variability twice the level in Spencer Gulf and more than twice in western coastal areas Genetic studies based on carcass samples may result in other difficulties of interpretation. The location of a stranded individual does not necessarily represent the natural range of the animal Our study shows that underestimating population genetic structure can occur when a study is solely based on dolphin carcasses, but does not necessarily apply in all situation and/or species. In many studies samples from dolphin carcasses can be useful and may be the only samples available. However, when planning a study to elucidate population genetic structure that includes samples from dolphin carcasses, in particular when dealing with small to medium spatial scales, we recommend carefully assessing the risk of underestimating genetic differentiation. Our findings may help develop a greater sensitivity towards the use of different types of samples for studies of genetic structure in cetaceans. Here we have shown samples from free-ranging bottlenose dolphins in central SA led to a much more realistic estimate of genetic differentiation, and identification of two Management Units for the area Accurate identification of Management Units may be critical for the conservation of cetaceans, in particular when populations are under threat due to anthropogenic impacts. This is likely to be the case for populations of coastal bottlenose and short-beaked common dolphins in SA, which are impacted by interactions with fisheries and aquaculture for finfish Our study shows that for bottlenose dolphins in SA but not for common dolphins, samples from dolphin carcasses were not suitable for studies of population genetic structure over small to medium spatial scales, and identification of Management Units in SA was only possible for bottlenose dolphins due to the availability of samples from biopsied free-ranging individuals.The collection of biopsy samples from free-ranging dolphins was conducted following the protocols in the ethics and research permits. Animal ethics approval was provided by the Department of Environment and Heritage (42/2003) and by the Animal Ethics Committee at Macquarie University (2003/23-2). A research permit (A24720) for the collection of samples from free-ranging dolphins was provided by Department of Environment and Heritage, South Australia.Tursiops sp.) and short-beaked common (Delphinus delphis) dolphins were collected by SAM in the Great Australian Bight and Spencer Gulf  control region was amplified using primers reported in We amplified seven polymorphic cetacean microsatellite loci for each individual dolphin: KW2, KW12 Tursiops sp., see Delphinus delphis), mtDNA control region sequences were compared with sequences from GenBank, and with a large dataset of southern and eastern Australian bottlenose dolphin sequences T. aduncus), common bottlenose dolphins (T. truncatus), southern Australian bottlenose dolphins (Tursiops sp.), short-beaked common dolphins (D. delphis), and long-beaked common dolphins (D. capensis).In order to confirm that all dolphin carcasses were either from coastal bottlenose dolphins  control region sequences was performed in SEQUENCHER 4.1 . Haplotypic and nucleotide diversities were estimated for each species and sampling region using ARLEQUIN v. 2.001 E) and observed (HO) heterozygosities for each sampling region. Allelic richness (AR), which takes sample size into account, was determined in FSTAT v. 2.9.3 (carcass) and AR(biopsied)) for significance. Comparisons of AR are therefore reported as percentages only, and the results should be interpreted with this in mind.Deviations from Hardy-Weinberg equilibrium and linkage disequilibrium were tested in GENEPOP v. 3.4 ST using ARLEQUIN ST estimates for each dataset was tested in POWSIM K), we used the program STRUCTURE v. 2.0 5 iterations and a simulation length of 106 Markov chain Monte Carlo (MCMC) repetitions. For the new model in version 2.3.3., we included information on sampling locations. A series of five independent runs for each value of K (1–5) was performed for each dataset to test for consistency in results, convergence of the priors and appropriateness of the burn-in and simulation length. Estimates of the posterior probabilities P(X/K) of each run were then assessed to determine the most likely number of K for each dataset. The program was applied to a dataset (1) including only carcasses, and (2) including both carcasses and free-ranging individuals, to investigate if the ability to detect population structure increases with combined datasets. Samples solely from free-ranging individuals from SA were analysed prior to this study by employing the same genetic and statistical methods (Tursiops sp., D. delphis, We estimated genetic differentiation between pairs of sampling regions by FFor the bottlenose dolphin dataset, we further assessed genetic differentiation using assignment tests carried out in GENECLASS v. 2.0


Atrial fibrillation (AF) is a common arrhythmia of substantial public health importance. Recent evidence demonstrates a heritable component underlying AF, and genetic discoveries have identified common variants associated with the arrhythmia. Ultimately one hopes that the consideration of genetic variation in clinical practice may enhance care and improve health outcomes. In this review we explore areas of potential clinical utility in AF management including those relating to pharmacogenetics and risk prediction. Atrial fibrillation (AF) is a common cardiac arrhythmia marked by loss of coordinated atrial electrical and mechanical function. Among the potential consequences of AF are symptoms and decreased functional status , as wellKCNE1 were recently described in this journal using a candidate gene approach in patients with early onset AF .-16.12-16Genetic associations with AF have spawned an exciting new era of biological investigation. Integrating recent discoveries with previous insights about atrial remodeling and AF pathophysiology has led to resurgence of interest in exploiting biological pathways with pharmaceuticals for AF management ,18. GiveIn many ways, as with most areas of clinical medicine, the management of patients with AF is already personalized ,20. PresDespite the personalized nature of AF management, care of patients with AF is challenging owing to the complexity of the arrhythmia. Patient management is limited by unpredictable recurrence and progression of AF, differing susceptibilities to stroke and heart failure, and variable treatment responses. These challenges are opportunities for diagnostic and therapeutic innovation, and represent areas where genetic medicine might facilitate clinical management. Two promising areas for the near-term application of genetic information in AF include pharmacological management and risk prediction Table .Many medications used for AF treatment have narrow therapeutic margins. Antiarrhythmic efficacy is only about 50% at 12 months , and proPharmacogenetics refers to how genetic variation accounts for differences in drug responses among individuals. Measurable differences in pharmacodynamics  and pharmacokinetics  are likely to be associated with genetic variation. In a recent proof-of-concept paper, whole genome sequencing in a single individual identified over 60 previously reported pharmacogenetic variants affecting drug response .CYP2C9 [VKORC1 [A high-profile example of a pharmacogenetic effect relevant to AF management relates to warfarin. Warfarin dose requirements have been linked to variation in CYP2C9  and VKOR [VKORC1 , genes e [VKORC1 . Account [VKORC1  who may  [VKORC1  and dosiNevertheless, routine warfarin testing has not been widely adopted for several reasons. Novel anticoagulants  and procRegardless of the outcome of this trial, the striking associations between genetic variants and warfarin doses have revealed the clinical potential for pharmacogenetics. Emerging reports demonstrate other associations between genetic variants and drug response. Genetic variation has been reported to associate with response to β-adrenergic antagonism in patients with AF , and genThe next steps for assessing the clinical utility of pharmacogenetics will involve performing systematic phenotyping, genotyping, and association testing of established and novel pharmacological agents used for AF management. Candidate genes that ought to be prioritized for screening include those encoding products involved in drug uptake or metabolism, and those encoding drug targets.Risk prediction encompasses estimation of AF probability and prediction of AF-related complications such as stroke, heart failure, and death. The goal of prediction algorithms is to identify individuals prior to the onset of AF or AF-related morbidity, and thereby help target prevention efforts. Estimation of risk may also assist with clinical trial development by enabling enrollment of individuals in a particular risk stratum.Clinical prediction algorithms for new-onset AF have been developed in community-based cohorts of European and African American ancestry -46. ThesWe and other investigators from the Framingham Heart Study tested whether accounting for the occurrence of AF in a first-degree relative  improved AF risk prediction beyond clinical risk factors . AF predFor most complex conditions, genetic risk scores have not achieved substantial improvements in discrimination over scores comprised of clinical risk factors. Yet enhancements in the understanding of genetic risk modeling reveal insights that may improve the utility of genetic risk scores in the future. For example, variants included in genetic risk scores to date have small effect sizes . Single risk factors with such modest relative risks are unlikely to have meaningful impacts on discrimination alone . Large-eFine mapping and sequencing offer promise for the discovery of such large-effect variants . For exaAnother potential reason that genetic risk scores have not substantially improved prediction models is that few variants were tested. Accounting for additional genetic variants associated with conditions at far less stringent significance thresholds can substantially increase the proportion of variance explained by genetic factors -53. In sNovel metrics have been developed to assess clinical utility of prediction models, with increased recognition that measures of discrimination alone can be insensitive . The rolFurthermore, although a widespread heritable component underlies AF, the arrhythmia may result from a variety of different pathological processes. Whether genetic profiling will successfully predict incident AF may depend on the extent to which the 'type' of AF studied is influenced by genetic factors. The importance of refining AF classification has been highlighted elsewhere ,57, and The absence of data demonstrating clinical utility of routinely using genotype information to guide clinical management in AF should not deter hope at this stage, but rather should be viewed as an opportunity for systematic testing of the clinical role of genetic information. Nevertheless, there is a risk that hype can obscure the monumental insights gained from the current era of genetic discovery. As an example, companies such as deCODE and 23andMe are performing direct to consumer genetic testing, and purport to calculate one's genetic risk of AF based on one or a few polymorphisms associated with modestly increased relative risks of AF. These calculations are not based on well-defined genetic effect estimates, and do not consider other genetic risk factors, interactions between genes, the predictive utility of genetic risk markers, or competing risks. Moreover, in the absence of data regarding clinical risk factors and environmental exposures, how are these results to be interpreted? Problems with direct to consumer genetic testing are reinforced by recent literature highlighting the facts that patients often have misperceptions about the results genetic testing , and praOwing to the absence of currently available outcome data, there is insufficient evidence to recommend routine testing for genetic variation in the management of patients with AF, as was highlighted by a recent Heart Rhythm Society and European Heart Rhythm Association consensus statement on genetic testing . HoweverAs with any new technology or development, systematic assessment of its utility in clinical medicine is warranted. Such assessment includes identifying suboptimal realms of clinical care, determining whether such areas present opportunities for application of genetic knowledge, testing whether genetic discoveries are superior to standard care for improving health outcomes, and evaluating whether the expected benefits of implementing genetic testing justify the resource utilization that would be necessary for its adoption.Recent AF genetic discoveries herald a sea change in the approach to AF research. Investigation of the pathogenic mechanisms underlying this common and morbid arrhythmia is now informed by unbiased techniques, allowing investigators to branch out from preconceived biological mechanisms and reliance on established animal models of AF. It will take many years to realize the full impact of recent genetic discoveries. In the meantime, questions surrounding the utility of applying genetic discoveries to patient management will only be clarified by rigorous testing of genetic information in clinical practice. Ultimately, it is the opportunity for innovation, discovery, and improvement in health outcomes that makes this era of genetic discovery so exciting.The authors declare that they have no competing interests.SAL and PTE were involved in drafting the manuscript and revising it critically for important intellectual content. Both gave final approval of the manuscript.Steven Lubitz, MD, MPH is a cardiac electrophysiologist at the Massachusetts General Hospital and Instructor in Medicine at Harvard Medical School. Patrick Ellinor, MD, PhD is a cardiac electrophysiologist at Massachusetts General Hospital and Associate Professor at Harvard Medical School.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1741-7015/10/155/prepub