Due to their strategic geographic location between three different continents, Sicily and Southern Italy have long represented a major Mediterranean crossroad where different peoples and cultures came together over time. However, its multi-layered history of migration pathways and cultural exchanges, has made the reconstruction of its genetic history and population structure extremely controversial and widely debated. To address this debate, we surveyed the genetic variability of 326 accurately selected individuals from 8 different provinces of Sicily and Southern Italy, through a comprehensive evaluation of both Y-chromosome and mtDNA genomes. The main goal was to investigate the structuring of maternal and paternal genetic pools within Sicily and Southern Italy, and to examine their degrees of interaction with other Mediterranean populations. Our findings show high levels of within-population variability, coupled with the lack of significant genetic sub-structures both within Sicily, as well as between Sicily and Southern Italy. When Sicilian and Southern Italian populations were contextualized within the Euro-Mediterranean genetic space, we observed different historical dynamics for maternal and paternal inheritances. Y-chromosome results highlight a significant genetic differentiation between the North-Western and South-Eastern part of the Mediterranean, the Italian Peninsula occupying an intermediate position therein. In particular, Sicily and Southern Italy reveal a shared paternal genetic background with the Balkan Peninsula and the time estimates of main Y-chromosome lineages signal paternal genetic traces of Neolithic and post-Neolithic migration events. On the contrary, despite showing some correspondence with its paternal counterpart, mtDNA reveals a substantially homogeneous genetic landscape, which may reflect older population events or different demographic dynamics between males and females. Overall, both uniparental genetic structures and TMRCA estimates confirm the role of Sicily and Southern Italy as an ancient Mediterranean melting pot for genes and cultures. Due to their central geographic location in the Mediterranean domain, Sicily and Southern Italy hosted various human groups in both prehistoric and historic times imprinted ceramics stentinelliane) have been dated roughly to 5800–5400 BCE imprinted ceramics prestentinelliane) uncovered in western Sicily (Uzzo and Kronio) are coeval (6000–5750 BCE) with the earliest occurrence of Neolithic materials in the more South-Eastern portion of the Italian Peninsula, thus suggesting potentially parallel and culturally independent processes of colonization between the eastern and western parts of the island The first unquestioned colonization of Sicily has been linked to the Palaeolithic, and in particular to Epigravettian human groups coming from the mainland and entering Sicily through the present-day Strait of Messina Magna-Graecia”) started in the 8th century BC from the Southern Balkans; ii) the Phoenician and Carthaginian colonization of the western part of Sicily occurred since the first millennium BC from the Levant through North Africa; iii) the Roman and post-Roman (Germanic) invasions from continental Italy and Central-Western Europe between the 300 BC and 500 AD; and iv) the more recent Muslim and Norman conquests of Sicily and Southern Italy in 8th–9th and 11th–12th centuries AD respectively. If on one hand the Greek colonisation of the south-eastern regions vs. the Phoenician occupation of western Sicily could have caused internal east-west cultural differentiation, on the other hand the later conquests  may have contributed to reshape at different levels the genetic landscape of one of the largest Mediterranean islands, albeit their relative impacts remain still questioned.In addition to Upper-Palaeolithic and Neolithic material cultures, historical and archaeological data offer a detailed and reliable understanding of the more recent population influences on Sicily and Southern Italy. Among the well-documented historical events, at least four main migration processes could potentially have affected the current genetic variability of the area: i) the massive occupation of Greeks  Is the genetic diversity of Sicily structured along its east-west axis and how is it patterned compared to Southern Italy? ii) Are the observed genetic patterns stratified temporally or geographically in terms of more ancient or recent peopling events, and are there any differences between maternal and paternal perspectives? iii) How is the genetic variability of Sicily and Southern Italy related to the wider Euro-Mediterranean genetic space and what are the main contributes to the current genetic pool? Since Sicily and Southern Italy have long played an important key role in the history of demic and cultural transitions occurred in Southern Europe and the Mediterranean, the clarification of these points will be of great relevance for the understanding of the different population, cultural and linguistic dynamics occurred within the whole Mediterranean area.All donors provided a written informed consent to this study according to the ethical standards of the institutions involved. The Ethics Committee at the Azienda Ospedaliero-Universitaria Policlinico S.Orsola-Malpighi of Bologna  approved all procedures.The genetic structure of Sicily and Southern Italy (SSI) was investigated by means of a high resolution analysis of 326 Y-chromosomes and 313 mtDNAs representing eight different SSI provinces . Five ofBlood samples (3–5 cc) were processed to extract the whole genome DNA by using a Salting Out modified protocol HpyCH4IVDdeIPCR amplification of 17 Y-STR loci  was carried out by using the AmpFlSTR Yfiler PCR Amplification Kit  following the manufacturer's recommendations MtDNA genetic markers were successfully typed for 313 out of the 326 total samples. Variation at the mtDNA HVS-I and HVS-II regions was investigated by sequencing a total of 750 base pairs (bp) encompassing nucleotide positions from 15975 to 155. Polymerase chain reaction (PCR) of the HVSI/II regions was carried out in a T-Gradient Thermocycler  with the following amplification profile: initial denaturation 95°C for 5 min, 35 cycles of 95°C for 30 sec, 58°C for 30 sec, 72°C for 5 min and final extension at 72°C for 15 min.http://www.fluxusengineering.com/align.htm).PCR products were purified by ExoSap-IT1  and sequenced on an ABI Prism 3730 Genetic Analyzer by using a Big-Dye Terminator v1.1 Cycle Sequencing Kit  according to the manufacturer's instructions. To reduce ambiguities in sequence determination the forward and reverse primers were used to sequence both strands of HVS-I and HVS-II regions. The CHROMAS 2.33 software was used to read the obtained electropherograms. Sequences were finally aligned to both the Revised Cambridge reference sequence - rCRS http://www.phylotree.org/) and confirmed with the analysis of 22 SNPs in the mtDNA-coding region by means of two PCR and one SNaPshot minisequencing reactions MtDNA haplogroups were determined on the basis of diagnostic sites in the D-loop region following Phylotree mtDNA phylogeny  based on haplogroup frequencies, was evaluated by means of a Mantel test . To investigate the distribution of genetic variability within the Mediterranean Basin, Principal Component Analysis (PCA) and Spatial Principal Component Analysis (sPCA) were performed on HGs frequencies, by using the R software package Fisher exact tests were performed on haplogroup frequencies among Mediterranean population groups, in order to determine significantly over- or under-represented HGs in any of the geographic areas considered. These tests were first performed against a background of all the Mediterranean populations by using the reduced common level of HGs resolution, and then by comparing single haplogroup frequencies of Sicily and Southern Italy with those of each comparison Mediterranean group, this time exploiting the deepest HG level available for each pairwise comparison.The age of haplogroups (TMRCA) was estimated for those lineages found to be significantly differentiated between pairs of Mediterranean population groups, as well as focusing on the most frequent haplogroups of our dataset, due to their peculiar relevance in the genetic composition of the studied area. As for Y-chromosome time estimates, the standard deviation (SD) estimator from Sengupta et al. (2006) mclust R package, adegenet R package, The maternal and paternal genetic relationships of Sicily and Southern Italy with the other Mediterranean populations, were further addressed and compared by means of admixture-like plots based on Fst (HVS-I) and Rst (STRs) genetic distances among Mediterranean groups. Population groups were first clustered by using a non-hierarchical algorithm based on Gaussian mixture models  genetic variation, admixture analysis was carried out by using the mY estimator implemented in the software Admix 2.0 CT) has been detected by regional AMOVAs .High levels of within-population variability have been observed for all the 8 populations analysed, as well as for the whole dataset , thus suIn order to more deeply explore the genetic relationships among Mediterranean groups, our samples were then compared with the 29 Euro-Mediterranean, Levantine and North-African populations extracted from the literature , by usinCT) results however two times higher when grouping according to the sPC1  than sPC2 . The sPCA-suggested pattern of genetic relationships among the different Mediterranean populations, has been confirmed in the classical PCA plots reported in Interestingly, the second sPC , despiteThe two high-structured Mediterranean clusters identified with sPC1, were further tested by means of DAPC analysis. Membership probabilities, represented with a structure-like plot , highligFisher exact tests were carried out among groups of populations in order to identify significantly over- or under-represented HGs in any of the geographic areas analysed, against a background of all the other Mediterranean populations . HaplogrAs for TMRCA estimates, STR variation within the most frequent haplogroups of SSI suggests that most of them (with the exception of haplogroup G2a-P15: 9339±3302 YBP) date back to relatively recent times , in someThe maternal genetic ancestry of SSI population was explored by successfully typing both coding region SNPs and HVSI-HVSII sequences in 313 out of the 326 samples. Overall, the polymorphic sites observed in the D-loop and coding region allowed assignment of subjects to 40 mtDNA HGs (including sub-lineages), whose frequencies for both the whole dataset as well as for each of the 8 sampling points are reported in The observed mtDNA HGs distribution reflects the typical maternal variability pattern documented for Mediterranean Europe. In fact, most of the individuals belong to super-haplogroup H, that on the whole accounts for the 38% of the total mtDNA lineages detected in our dataset. Within H, H1 represents the most frequent sub-lineage (10.9%), followed by H5 (3.2%) and H3 (2.6%). Noteworthy is also haplogroup HV, that has been found at relatively high frequencies (4.8%). Most of the remaining samples belong to haplogroups U5, K1, J1, J2, T1, T2, thus confirming prevalent European and Middle-Eastern genetic ancestries. MtDNA haplotypes of African origin are instead represented by few haplogroups at low frequencies, namely M1 (1.3%), U6a (0.6%) and L3 (0.6%).CT values when population samples were grouped according to geography  shows slightly lower diversity values than Southern Italy . Nevertheography . AnalogoCT) results lower than in the case of Y-chromosome (2.45%) but still significant .The mtDNA HGs geographic distribution within the Mediterranean domain was investigated by comparing our sample with 26 Euro-Mediterranean, Levantine and North-African populations selected from the literature . A ManteCT) explained by sPC2 revealed to be not significant . On the whole, the lack of statistical support for the global structure observed in the mtDNA sPCA , suggests a higher homogeneity in Mediterranean genetic variability for maternal than paternal genetic pools. Nevertheless, both uniparental markers show a similar NW-SE distribution pattern of genetic variation.The second sPC  highlighFisher exact tests were applied to determine if differences in HG frequencies among population groups were statistically significant . As expeDifferently from Y-chromosome results, TMRCA estimates for the most frequent mtDNA haplogroups of Sicily and Southern Italy  date bacThe admixture-like plot represented in From a Y-chromosome point of view, SSI form a fairly coherent group with the Levantine and the Balkan populations (cluster 2), despite showing some minor contribution (black component) also from the North-Western Mediterranean group (cluster 3). From a mtDNA point of view, our results show the differentiation between European and non-European Mediterranean populations, with North Africa and the Levant clustering in separate and different groups (1 and 2). However – and differently from the other European populations – SSI shows a noteworthy contribution (grey component) from the Levantine cluster. Both genetic systems reveal a negligible contribution from North Africa (white component).The extent of different contributions to the current SSI genetic variation was further assessed by means of an admixture analysis performed (on HG-frequencies) with the coalescent-based mY estimator implemented in the software Admix 2.0 Sicily and Southern Italy have long represented a natural hub for the expansion of human genes and cultures within the Mediterranean Basin As a contribution to the human history of such a key area of the Mediterranean we surveyed, by means of a comprehensive evaluation of both maternal and paternal genetic landscapes, the genetic variability of a wide number of populations settled in a broad transect encompassing Sicily and Southern Italy . Previouth and 20th centuries); on the contrary it has been preserved at least since the initial founding and spreading of surnames in Italy. In addition, and consistently with the complex history of migration pathways and cultural exchanges characterizing the peopling history of the area, high levels of Y-chromosome and mtDNA genetic variability at both SNP and haplotype (STRs or sequence) data, have been observed in all the SSI populations here examined , the Mediterranean Basin underwent different waves of invasion, particularly by the Greeks of the Aegean Sea and, to a lower extent, by Levantine (Phoenicians) groups On the other hand, Y-chromosome lineage E-V13 is thought to have originated in southern Balkans Y-chromosome haplogroup G2a-P15 turn out to be of particular interest in the paternal genetic make-up of Sicily and Southern Italy. Its older age estimate (9339±3302 YBP) – if compared to those of other haplogroups – along with its significantly over-represented frequency in SSI, are consistent with the hypothesis recently suggested by Boattini et al. (2013) Contrarily to Y-chromosome results, age estimates for mtDNA haplogroups suggest that most of the maternal diversity of the current Sicilian and Southern Italian population is composed by lineages present in Europe as early as the LGM . The LatTogether with the Iberian and Balkan peninsulas, also Italy and particularly SSI might have played an important role during the post-glacial re-expansion, as widely attested by several animal and plant species Haplogroups H1 and H5 appeared to represent the most frequent H-sublineages in SSI, and their age estimates  are consInterestingly, mtDNA haplogroup HV confirmed to be the most ancient lineage in Sicily and Southern Italy, predating the LGM (32242±12595 YBP) and thus representing a possible candidate for the Palaeolithic ancestry of Southern Italy, even though possible post-LGM expansions of its major sub-branches should be taken into account as potentially affecting the time estimates here obtained. Further analyses, involving the complete sequencing of mtDNA genomes and the analysis of ancient DNA samples, are therefore needed in order to more deeply address this point and to confirm the relevance of this haplogroup in the first peopling of Sicily by moderns humans, as recently suggested by some Palaeogenetic researches CT statistically significant tests). These results are consistent with our previous study about Italy When comparing SSI with Mediterranean reference populations, Y-chromosome results  revealedY-chromosome results however contrast with the lack of statistical support to the sPCA global structure observed for mtDNA diversity, excepted for a similar NW-SE genetic pattern identified by sPC1 . The comIn summary, Sicilian genetic diversity revealed to be not structured along the east-west axis of the island; on the contrary both maternal and paternal genetic markers suggest an homogeneous genetic composition both within Sicily, as well as between Sicily and Southern Italy. These results are consistent with the largely shared genetic histories of the Southern Italian populations, and reflect their historical and archaeological role as a major Mediterranean ‘melting pot’ where different peoples and cultures came together over time, albeit with different contributes depending from the source area.When Sicilian and Southern Italian population were contextualized within the Mediterranean domain, the observed homogeneous pattern of genetic variation, however revealed different temporal dynamics and spatial genetic contributions to the maternal and paternal inheritances,.Besides a common SE-NW distribution pattern of genetic variation, mtDNA indeed suggests an homogeneous genetic landscape related to older populations events and/or higher female mobility. On the contrary, Y-chromosomal genetic diversity appears significantly differentiated between a Central/South-Eastern and a North-Western Mediterranean group, the Italian Peninsula occupying an intermediate position between them. In particular, and consistently with the most recent syntheses on the Italian genetic structure based on both uniparental markers Figure S1Geographic map showing the location of the eight populations analysed in the present study. The table at the bottom right details the set of provinces (sampling points) and the number of samples successfully typed for both Y-chromosome and mtDNA markers. .(TIF)Click here for additional data file.Figure S2Principal Component Analysis (PCA) based on haplogroup frequencies for Y-chromosome (a) and mtDNA (b). Population codes as in (TIF)Click here for additional data file.Figure S3Estimated admixture contributions (mY estimator) from three parental populations to the current population of Sicily and Southern Italy for Y-chromosome (left) and mtDNA (right). Color codes: South-Western Europe (blue), the Balkans (yellow) and the Levant (green). Error bars represent standard deviations calculated on the basis of 10,000 bootstraps.(TIF)Click here for additional data file.Table S1List of the selected Mediterranean populations used for Y-chromosome and mtDNA comparative analyses.(XLSX)Click here for additional data file.Table S2Y-chromosome and mtDNA haplogroup frequencies for the whole Sicilian and Southern Italian dataset and for each population analyzed. For each Y-chromosome lineage the absolute number of individual and the percentage frequency (between brackets) are reported.(XLSX)Click here for additional data file.Table S3Y-Chromosome STRs haplotypes and SNPs analysis results for the newly-typed samples of the present study (N = 119).(XLSX)Click here for additional data file.Table S4Analyses of the molecular variance (AMOVA) for Y-chromosome and mtDNA based on both haplogroup frequencies (SNPs) and haplotype data (STRs or sequences).(XLSX)Click here for additional data file.Table S5Diversity parameters for uniparental genomes based on haplogroup frequencies (SNPs) and haplotype data (STRs or sequences).(XLSX)Click here for additional data file.Table S6Fisher exact test for Y-chromosome and mtDNA HG frequencies among the Mediterraean population groups.(XLSX)Click here for additional data file.


Hakea oldfieldii, a fire‐sensitive shrub with naturally disjunct, isolated populations prone to reduction in size and extinction following fires. We mapped and genotyped a sample of 102 plants from a large population, and all plants from two smaller populations , and genotyped 158–210 progeny from each population. We found high outcrossing despite the possibility of geitonogamous pollination, small amounts of biparental inbreeding, a limited number of successful pollen parents within populations, and significant correlated paternity. The number of pollen parents for each seed parent was moderate. There was low but significant spatial genetic structure up to 10 m around plants, but the majority of successful pollen came from outside this area including substantial proportions from distant plants within populations. Seed production varied among seven populations investigated but was not correlated with census population size. We suggest there may be a mechanism to prevent self‐pollination in H. oldfieldii and that high outcrossing and pollen dispersal within populations would promote genetic diversity among the relatively small amount of seed stored in the canopy. These features of the mating system would contribute to the persistence of genetically isolated populations prone to fluctuations in size.Pollen movements and mating patterns are key features that influence population genetic structure. When gene flow is low, small populations are prone to increased genetic drift and inbreeding, but naturally disjunct species may have features that reduce inbreeding and contribute to their persistence despite genetic isolation. Using microsatellite loci, we investigated outcrossing levels, family mating parameters, pollen dispersal, and spatial genetic structure in three populations of  Some plant species occur as disjunct populations separated from one another by unsuitable habitat over which migration and gene flow can be limited  is a dense multistemmed woody shrub that grows to 5 m and is found in naturally disjunct and isolated populations associated with the soils of uncommon isolated ironstone formations in the southwestern Australian biodiversity hot spot. The species is monecious and produces numerous axillary racemes with 8–20 hermaphrodite flowers generally between September and October. Low levels of fruit and seed production per flower are common within Proteaceae . Fruit generally open in the second year and contain up to two large, heavy, winged seeds that are unlikely to disperse more than a few meters from the seed parent. The specific pollinators are not known, but field observations and floral morphology suggest pollination by insects rather than birds or mammals.Nm < 1.0), below the level required to reduce the harmful effects of local inbreeding  among populations that show moderate levels of diversity and significant nuclear (microsatellite) and plastid divergence. Little is known of the mating system of H. oldfieldii. In the study of Sampson et al. (F = 0.036–0.198) but did not indicate that inbreeding was an important component of the breeding system in 10 of 14 populations, and were not positively correlated with long‐term effective population size.Populations are prone to fluctuations in size and extirpation because, although the species can be long‐lived (>50 years), plants are killed by fire that occurs frequently in the shrublands of southwestern Australia. A recent study  in seven populations that encompassed a range of population sizes and varying vegetation conditions Table . Ten repReproductive output was estimated as the mean number of fruit and seed produced per unit branch weight for each plant in each population in each year. In addition, mean seed:fruit ratios were also calculated. We used generalized linear models with randomized complete block designs to examine the effects of population on the fruit and seed production per branch weight and mean seed:fruit ratios. Population was treated as a fixed factor and year as a random blocking factor. Prior to performing statistical analysis, data were tested for normality and homogeneity of variances. The data generally needed square root transformations to stabilize variance, and the results reported in text are back‐transformed. The proportions of sampled plants that produced fruit were averaged for each of the three years. We used Spearman rank correlation coefficients to calculate correlations between population means of (1) fruit production, (2) seed production, and (3) seed:fruit ratios, and census population sizes.N = 28) that has been reduced in size by removal of native vegetation, but the local understory vegetation remains intact. PRI is a disturbed population with degraded understory vegetation. These populations were selected within the sampling limitations imposed for threatened species to provide examples of a range of extant populations.Mating system and paternity analyses were carried out in three populations  and all plants in KOL (N = 28) and PRI (N = 20) were mapped, and leaf samples were taken to genotype adult plants. Open‐pollinated fruits were collected from 10 seed parents in GAL, 11 seed parents in KOL, and nine seed parents in PRI. Fruit from seed parents were bulked and desiccated in a drying oven (27°C) to release the seeds, which were germinated and grown in a glasshouse for 1 month. Leaf tissue was harvested from 17 to 21 seedlings per seed parent for DNA extraction and genotyping to give the following totals of genotyped progeny: GAL, 199; KOL, 210; and PRI, 159.All plants within a 50 m × 70 m sampling area in GAL  using a LIZ 500 (‐250) size standard. Bins were set and genotypes were scored using GENEMAPPER version 4.0 (Applied Biosystems) and checked manually. Samples were repeated for loci that failed to amplify or where seed genotypes were inconsistent with maternal genotypes. In addition, approximately 5% of samples were reamplified and rescored to check scoring accuracy.To validate the microsatellite data, genotypes were tested for large allele dropout and stuttering artifacts, and the occurrence of null alleles using MICROCHECKER V. 2.2.3  of Loiselle et al. , the proportion of selfing due to biparental inbreeding (1 − rs), and correlated paternity (rp), for 10, 11, and nine families in GAL, KOL, and PRI, respectively (Table We used the maximum‐likelihood sibling‐pair method and the correlated mating model implemented in the program MLTR 3.4 Ritland  to estimly Table . EstimatF) and family mating system parameters based on the paternity of seed: the number of outcross pollen parents (Np), the proportion of outcrossed full‐siblings (FSoutcross), and the correlation of outcross paternal diversity (cpdiv). Correlated outcross paternal diversity (cpdiv) was calculated as the proportion of outcrossed full‐siblings divided by the number of pollen parents identified among the full‐siblings , or habitat quality (intact vs. degraded understory vegetation), and mean family parameters .Comparisons of To estimate pollen movements within sampled populations, we used the paternities inferred from full‐pedigree analyses. We also used the full‐pedigree likelihood method to estimate pollen immigration into the sampled area in GAL, and into the KOL and PRI populations. Pollen immigration rates were estimated as the proportion of the total seeds within each population for which no pollen parent was identified from within the population.H. oldfieldii plants do not flower every year, and we were not able to identify which plants flowered in the years in which the sampled fruit were set. Therefore, to estimate the distance each seed parent was from all potential pollen sources (plant isolation), we calculated the mean distance to all other plants in the population. We tested for correlations between Np, FSoutcross, and cpdiv and (1) plant isolation and (2) mean pollination distances of each seed parent. To compare the pollination success of each pollen parent , we tested whether numbers of assigned pollinations were correlated with mean pollination distances, and with mean distances to all other plants in the population.Observations indicate rij) was estimated following Smouse and Robledo‐Arnuncio  and the shape of the dispersal kernel tail (b). We excluded loci with genotyping error rates above 0.1 (HoB105 in KOL) and set precision at 0.001.To provide an alternative estimate of pollen dispersal and immigration for comparison, we used the P < 0.05. Paternity analyses in COLONY are robust to the linkage of markers, and therefore, no loci were excluded on the basis of the observed linkage disequilibrium.No evidence of stutter or large allele dropout was found. Two instances of significant linkage disequilibrium were detected among 63 pairwise comparisons in three populations. These findings are unlikely to indicate chromosomal linkage as 3.15 instances were expected by chance from type I error at The seven microsatellite markers contained 78 alleles over all populations with from two to 17 alleles per locus (see Table S2). The locus HoA116 was monomorphic in the PRI population and was excluded from analyses in PRI. The mean PIC values for GAL, KOL, and PRI were high to moderate . General genotyping errors estimated with Nm + 1.1 were relatively low (<0.1), except for locus HoB105 in KOL (see Table S1). This locus was excluded from full‐pedigree analyses for KOL.F1,6 = 0.60, P = 0.73), but there was a significant difference between years , and in mean seed production between populations . Both fruit production and seed production were significantly lower in HAI (N = 4), PAY (N > 300), and IRO (N > 2000), but the two smallest populations  showed both the lowest and highest fruit and seed production. The proportion of sampled plants setting fecund fruit varied between populations, but there was also variation between years  in the 0 to 10 m distance class in all populations sampled  , indicating plants within this distance class were genetically similar. But the kinship value was low in GAL, and only in KOL and PRI did Fij values approach those expected for half‐siblings , and only slightly so in PRI , indicating predominant outcrossing in H. oldfieldii . Inbreeding coefficients  estimates were zero in all populations, suggesting any selfing detected was probably due to biparental mating rather than self‐pollination. This is based on Ritland's (s < 0.2), rs directly approximates the fraction of inbreeding due to self‐pollination. This estimate does not suffer from the bias that affects estimates of biparental inbreeding based on comparison of single‐ and multilocus estimates of t. Correlated paternity . Estimates of rp were lowest in the GAL population where there were more potential mates but differences were not significant. Estimates of the overlap of pollen source between seed parents (mean rij) were low in all populations , moderate numbers of pollen donors , and moderate correlation of paternal diversity . When the mating parameters of 30 families were compared by ANOVA, there were no significant differences in the proportions of full‐siblings, but the number of pollen donors  and the correlation of paternal diversity  did vary significantly. Families from GAL had significantly more outcross pollen parents than families from KOL and PRI . This is not unexpected because there were many more potential mates in the larger GAL population.Overall, families were characterized by large proportions of full‐siblings  than from PRI. GAL and KOL populations differed greatly in size  but have intact understory vegetation. The distribution of paternity was more uneven with fewer pollen parents siring more of the full‐siblings in PRI which has a degraded understory vegetation.When P < 0.05), indicating pollen movements were not random  despite differences in plant density; the density of plants in GAL was four times that of KOL or PRI. However, the majority of pollen came from plants more than 10 m away in all populations Table ; Fig. 3BHigh proportions of pollen came from sources within the population in KOL 85.2%) and PRI (100%), and the maximum pollination distances extended over the maximum possible distance between plants  and significantly negatively correlated with cpdiv . This suggests that in GAL, seed parents with longer mean pollination distances had more pollen parents and more even paternal diversity.Overall, mean pollination distances of seed parents were not significantly correlated with r = −0.736, P < 0.05). Male success was not significantly correlated with the mean distances to all other plants in the population (plant isolation). This suggests successful pollination of more distant plants was less likely for pollen parents in this population where the understory vegetation was degraded.Overall, there were also no significant correlations between male success (the number of seeds sired by a pollen parent) and mean pollination distances for families or mean distance to other plants. But in PRI, male success was significantly negatively correlated with the pollen parent's mean pollination distances  in GAL with the highest likelihood found with the Weibull dispersal kernel, although it was only slightly higher than that of the exponential power model. Pollen immigration from outside the sample area was estimated as 46.8%, which was similar to the estimate based on full‐pedigree analyses (42.2%). The estimated shapes of the dispersal curves in KOL and PRI were thin‐tailed indicating less pollen from more distant sources but 19.5% and 14.3% pollen immigration, respectively. These results are higher than those based on full‐pedigree analyses but may be biased because of the linear shape of the populations. Immigration rates estimated using the neighborhood model are a function of the dispersal kernel and are based on the assumption that the neighborhood is circular , relatively unrelated sources within populations. High levels of outcrossing have been reported previously in many members of the Proteaceae  around plants in all populations and the significant number of pollinations from within this area would lead to the biparental inbreeding observed. This spatial genetic structure may have developed because seeds are large and heavy with have no adaptations for dispersal and are unlikely to disperse more than a few meters from the maternal plant.In H. oldfieldii  was comparable or higher than those reported for other animal‐pollinated plants: Grevillea iaspicula  system in H. oldfieldii. Formal confirmation of SI would require controlled crossing experiments that were not possible in this study. Gametophytic SI (controlled by the gametophyte) is the only form of SI that has been reported in the Proteaceae and, as incompatibility systems tend to be conserved within families  sources. This pattern of pollen dispersal would promote paternal diversity among seeds because significant spatial structure was low and only found up to 10 m around plants. We propose that the diversity of paternity observed within families in H. oldfieldii is promoted by a combination of short and distant pollen movements to the limited number of mates spread throughout the population, although the direct relationship between these factors was not able to be determined by this study. The lack of seed production of individual plants in some years, the high proportions of pollen parents that were also seed parents, the low overlap in the pollen sources of seed parents, and the lack of pollination success of some individuals could be the result of a number of factors, including mate limitation due to SI, lack of floral initiation in some plants, or a mismatch between flowering and the availability of pollinators.The pollen dispersal patterns found in s Ashley , and ours Ashley . But in H. oldfieldii plants, it is unlikely the pollen came from an undetected local plant. The nearest population was over 1 km away and therefore this provides evidence for some pollen dispersal between populations across different habitat at a level similar to those found in other woody species with disjunct populations (Robledo‐Arnuncio and Gill neighborhood model were higher (14.3–19.5%) than direct estimates based on paternity. But estimates are of a similar magnitude when the bias introduced by the model assumption of circular neighborhoods around plants was taken into account. However, pollen immigration rates do not measure effective gene flow, that is, where genes become established in a population and subject to the impact of evolutionary forces (Ellstrand Nm < 1 Sampson et al. H. oldfieldii populations using indirect methods. If pollen immigration is low, we suggest that high outcrossing rates and pollen dispersal within populations would be important contributors to paternal and genetic diversity among seeds in H. oldfieldii.As Ashley  noted whH. oldfieldii may be affected by the condition of the understory vegetation but our study included only one population with degraded understory vegetation. Inferences about mating patterns and their association with ecological parameters such as distance, density, flowering and fragmentation are often reported in the literature based on single populations or multiple populations of different size, condition and level of genetic isolation (Goto et al. We found some indications that mating patterns in With these limitations in mind, we found families with degraded understory vegetation showed higher correlation of paternal diversity when compared to families with an intact understory, regardless of whether they were from small or large populations. In addition, significantly more male success was associated with shorter pollination distances when the understory vegetation was degraded. Previous studies have shown that the behavior of pollinators within degraded remnants is likely to differ from that in vegetation remnants that are in good condition (Yates et al. The importance of habitat quality in determining mating patterns has been observed in other plant species where increased inbreeding and reduced paternal diversity have been associated previously with degraded habitat (Coates et al. H. oldfieldii despite the possibility of geitonogamous pollination within inflorescences, suggesting there may be a mechanism to avoid self‐pollination as found in other Proteaceae. Although correlated matings were significant, and the number of successful pollen parents were low within populations, moderate numbers and diversity of pollen parents were maintained for seed parents by a combination of low spatial genetic structure and substantial pollen dispersal. High outcrossing with moderate paternal diversity would help maintain genetic diversity among the relatively small store of seeds in the canopy even when populations become very small following fires that are common in the landscape. These features of the mating system would contribute to the persistence of H. oldfieldii as isolated populations.We found high outcrossing with some biparental inbreeding in None declared.Figure S1. Spatial autocorrelation coefficients in study populations.Click here for additional data file.Table S1. Estimates of genotyping error calculated in NM + 1.1 (Chybicki and Burczyk Hakea oldfieldii populations for which mating system parameters and pollen‐mediated gene dispersal and immigration were estimated.Click here for additional data file.Table S2. Allele frequencies for microsatellite markers in Hakea oldfieldii populations for which mating system parameters and pollen‐mediated gene dispersal and immigration were estimated.Click here for additional data file.


Anthropological and genetic data agree in indicating the African continent as the main place of origin for anatomically modern humans. However, it is unclear whether early modern humans left Africa through a single, major process, dispersing simultaneously over Asia and Europe, or in two main waves, first through the Arab Peninsula into southern Asia and Oceania, and later through a northern route crossing the Levant.Here, we show that accurate genomic estimates of the divergence times between European and African populations are more recent than those between Australo-Melanesia and Africa and incompatible with the effects of a single dispersal. This difference cannot possibly be accounted for by the effects of either hybridization with archaic human forms in Australo-Melanesia or back migration from Europe into Africa. Furthermore, in several populations of Asia we found evidence for relatively recent genetic admixture events, which could have obscured the signatures of the earliest processes.We conclude that the hypothesis of a single major human dispersal from Africa appears hardly compatible with the observed historical and geographical patterns of genome diversity and that Australo-Melanesian populations seem still to retain a genomic signature of a more ancient divergence from AfricaThe online version of this article (doi:10.1186/s13323-015-0030-2) contains supplementary material, which is available to authorized users. Anatomically modern humans (AMH), defined by a lightly built skeleton, large brain, reduced face, and prominent chin, first appear in the East African fossil record around 200,000 years ago , 2. TherAlthough the general picture is getting clearer, many aspects of these processes are still poorly understood, starting from the timing and the modes of AMH dispersal. The main exit from Africa, through the Levant, has been dated around 56,000 years ago , 11. HowRegardless of whether there was a single major expansion or two, several DNA studies clearly showed that genetic diversity tends to decrease , 22 and One preliminary problem, however, is how to select the appropriate populations for informative comparisons. The details of the dispersal routes, and the relationships between fossils and contemporary populations, are all but established. Whereas Europeans are consistently regarded as largely derived from the most recent African exit in all relevant studies, opinions differ as for many aspects of the peopling of Asia –19, withThis way, we could tackle two questions, related, respectively, with the historical and geographical context of the dispersal process, namely (1) are separation times between non-African and African populations the same (as expected under SD), or is there evidence of a longer separation between Far Eastern and Africans than between Europeans and Africans (as expected under MD)? And (2) which geographical migration routes were followed by first humans outside Africa?n = 40 samples from 10 populations genotyped on Affymetrix GeneChip Human Mapping 500K Array Set), Pugach et al. (2013) [n = 117 samples from 12 populations genotyped on an Affymetrix 6.0 array), Reich et al. (2009) [n = 56 samples from 11 populations genotyped on an Affymetrix 6.0 array), Reich et al. (2011) [n = 509 samples from 13 populations genotyped on an Affymetrix 6.0 array), Xing et al. (2009) [n = 243 samples from 17 populations genotyped on one array (version NspI) from the Affymetrix GeneChip Human Mapping 500K Array Set), and Xing et al. (2010) [n = 165 samples from 8 populations genotyped on an Affymetrix 6.0 array)   (n = 117. (2009)  (n = 56 . (2009)  (n = 243. (2010)  .When merging data from different SNP chip versions, strand identification can be ambiguous, possibly leading to mistakes in identifying the right alleles for A/T and G/C SNPs . Thus, Z-score. Z-score distributions were examined from the first to the fifth neighbor. Outliers were identified by an extremely negative Z-score produced by low allele sharing with their nearest neighbor and were then dropped from the population. We grouped populations according to ethnological and linguistic information; the final dataset is shown in Fig. Using the same software, we selected only the autosomal SNPs with genotyping success rate >98 % and minor allele frequency (MAF) >0.01. We identified cryptic relatedness among samples computing identity by descent (IBD) statistic for all pairs of individuals, as unmodeled excess of genetic sharing would violate sample independence assumption of downstream analyses. When pairs of individuals showed a Pi-Hat value >0.3, we removed the individual with the lowest genotyping rate. We did not apply this screening procedure for the Southeast Asia and Oceania samples, since they come from populations with extremely low effective sizes, where a certain degree of random inbreeding is inevitable . To deteTo visualize the genetic relationships between such populations, we performed a principal component analysis using the R  SNPRelatk hypothetical ancestral populations. Because this model assumes linkage equilibrium among markers, we checked with the PLINK v1.07 tool [r2 = 0.3; this way, in the pruned dataset, 54,978 markers were retained. The optimal value of k was evaluated through a cross-validation procedure, testing values from k = 2 to k = 14, thus identifying the number of ancestral populations for which the model had the best predictive accuracy. We then ran an unsupervised analysis, assuming a number of ancestral admixing populations from k = 2 to k = 7. The proportion of the individuals’ genome belonging to each ancestral population was calculated for each k value from five independent runs, then combined by the software CLUMPP [Distruct [Individual genotypes were clustered, and admixture proportions were inferred, by the algorithm embedded in the software ADMIXTURE, based on the principle of maximum likelihood . This me.07 tool  that thee CLUMPP  and plotDistruct .In addition to ADMIXTURE, to identify and describe clusters of genetically related individuals, we used a Discriminant Analysis of Principal Components (DAPC)  implemenfind.clusters, we determined the most likely number of genetic clusters in our dataset, using all principal components (PCs) calculated on the data. The method uses a K-means clustering of PCs [By the function g of PCs  and a BaThen, we determined the optimal number of PCs to retain to perform a discriminant analysis avoiding unstable (and improper) assignment of individuals to clusters. It is worth noting that, unlike K-means, DAPC can benefit from not using too many PCs: retaining too many components with respect to the number of individuals can lead to over-fitting and instability in the membership probabilities returned by the method.T) were estimated from the population differentiation index (FST) and the effective population size (Ne). FST is the proportion of the total variance in allele frequencies that is found between groups, and it was calculated between pairs of populations for each SNP individually under the random population model for diploid loci, as described by Weir and Cockerham [Ne) and directly proportional to the time passed since separation of the two populations (T).The divergence times between populations  also depend on Ne and on the recombination rate between the SNPs considered [Ne, whereas LD over short recombination distances depending on relatively ancient Ne [Therefore, to estimate nsidered , with LDcient Ne . Thus, wcient Ne . This apcient Ne  to estimcient Ne .r2LD [σ2LD [ρ). All the observed ρ values were then binned into one of the 250 overlapping recombination distance classes. Pairs of SNPs separated by less than 0.005 cM were not considered in the analysis, since at these very short distances, gene conversion may mimic the effects of recombination [ρ value for the sample size using c is the recombination distance between loci, in Morgans [Ne for each population was calculated as the harmonic mean of Ne over all recombination distance classes up to 0.25 cM. The confidence intervals of these Ne values were inferred from the observed variation in the estimates across chromosomes.We assigned to each SNP a genetic map position based on HapMap2 (Release #22) recombination data, and for each pair of SNPs separated by less than 0.25 cM, we quantified LD as r2LD  or as σ2LD [σ2LD   and the scaled population recombination rate (ρ) were fixed at 400. For a sequence length of 1 Mb and an effective population size of Ne = 10,000, these parameters correspond to a mutation rate of 10−8 and a recombination rate of 1 cM/Mb.To understand whether the divergence times estimated were compatible with a SD model, we used a neutral coalescent approach to simulate genetic polymorphism data under the infinite sites model of mutation. We simulated data representing 1-Mb chromosome segments in two populations according to the demographic scenario shown in Additional file ms . We assuT)  and 6 estimates of the actual effective size for population_2b . For each of the 24 simulation conditions, 1000 independent datasets were simulated and then analyzed according to the following procedure:A sample of 50 individuals  was randomly selected from each population. The simulated genetic data were single nucleotide polymorphisms (SNPs) segregating within the two populations.We converted the ms  output fAny SNPs with a MAF less than 0.05 were removed from the datasets.We estimated the population differentiation index, effective population size, and divergence times between the two simulated populations following the same procedures used for the observed data and detailed above.Estimators were calculated for each 1000 independent replications.To account for the uncertainty in the estimates of both the timing of the process and the effective population sizes, according to –59, thisFor a subset of combinations of parameters (effective population size of population_2b = 3000 and the whole range of possible divergence times), we considered a more complicated  model that includes several founder effects after the dispersal from Africa. We simulated a founder effect every 500 generations , each time generating a reduction of the 50 % of the populations, followed by an exponential growth , to define the ancestral state, we used the East African individuals selecting the SNPs where East Africans were homozygous and considering those as ancestral.FST [Once we had thus identified a subset of sites putatively introgressed from Denisova, we removed them from the dataset. The remaining 80,619 SNPs were used to compute the pairwise FST  values aTreeMix, we inferred from genomic data a tree in which populations may exchange migrants after they have split from their common ancestor, thus violating the assumptions upon which simple bifurcating trees are built [Using re built . This mek option).Allele frequencies for the TreeMix analysis were calculated by PLINK tool , after pf3 statistic [We also ran a three-population test, calculating an tatistic  for all tatistic .We developed explicit geographic models of demographic expansion and looked for the model giving the closest association between genomic and geographical distances. In all cases, migration routes were constrained by five obligatory waypoints, identified in  and acceTo obtain a realistic representation of migrational distances between populations, we did not simply estimate the shortest (great circle) distances between sampling localities. Rather, we modeled resistance to gene flow, based on the landscape features known to influence human dispersal. We used a resistance method from the circuit theory implemented in the software Circuitscape v.3.5.2 , startinUnder the SD model, we hampered movement from Arabia to India (rv = 100), hence preventing the dispersal along the Southern route; under the MD models, we created a buffer of low resistance value (rv = 1) along the Southern route. For all models, we then estimated least-resistance distances between the populations analyzed, when applicable going through Addis Ababa, chosen as a starting point for the African expansion . The finFST) and geographic distances, while holding divergence times constant. This way, we could control for the drift effects, due to the fact that populations separated at distinct points in time and space.We evaluated by partial Mantel tests  the corrWe assembled genome-wide SNP data from the literature obtaining information on 71 population samples sharing, after cleaning and integration, 96,156 autosomal SNPs. By merging samples from adjacent geographical regions and with similar linguistic affiliations, we organized the data in 24 metapopulations; the final dataset comprised 1130 individuals  and non-African (yellow) populations; k = 3 further distinguishes Europeans from Asians (orange); k = 4 identifies an Australo-Melanesian component (green) within the Asian cluster; at k = 5, the additional component is mainly associated with the Indian subcontinent (red); the same is the case at k = 6 for Polynesia and Fiji (pink) and at k = 7 for many island communities of Southeast Asia and Oceania (purple). Remarkably, some populations show more than 99 % contribution from the same ancestral population along different k values , whereas other populations include several individuals with an apparently admixed genomic background, possibly resulting from successive gene flow , and one by other populations from Asia.Then, to further investigate the worldwide genomic structure, we applied the unsupervised ancestry inference algorithm of the ADMIXTURE software . After iers Fig. . At k = t Africa ). A DAPCt Africa  led to eNe from LD which allowed us to tell apart the effects of population sizes and separation times in the observed FST values.There is a clear geographical structure in the data, which in principle allows one to test for the relative goodness of fit of the two models. The SD model implies that the separation time from Africa of all samples should be the same, whereas significantly larger times of separation are expected under the MD model for the eastern most than for the European populations. To answer, we calculated the divergence times between each out-of-Africa population and East, South, and West African populations as described in the “Ne values) and a constant declining trend through time, whereas Eurasian populations (and more markedly the Asian ones) tend to increase in size, especially in the last 10,000 years. Australians and New Guineans  generally maintain a constant size until present times, whereas the Negrito populations show low and declining sizes. In general, these results were not surprising, but the fact we obtained them suggests that the procedure followed is by and large accurate and therefore that the estimated average Nes are plausible. The values obtained using the two estimators of LD  and Australia having a lower confidence limit 88K/80K years ago. The harmonic means of the effective population sizes we estimated were not correlated with the divergence times, meaning that this difference cannot possibly be accounted for by the different impact of genetic drift upon these populations. Therefore, this result supports a rather complex “out-of-Africa” scenario, suggesting at least two main phenomena of AMH dispersal from Africa. The Australo-Melanesian populations, i.e., Australians and New Guineans, with their early separation times from East Africa, may be regarded as the putative descendants of an early dispersal process, whereas the status of most Asian populations would seem, at this stage of the analysis, unclear.From the pairwise ns Table . The avens Table , are disNe and T estimates obtained using the unweighted r2 statistic). To answer, we needed a null distribution of T values under the SD model, which we constructed by simulation, using the software ms [T estimates. Whereas the value estimated in the European sample falls perfectly within the range of times predicted by the classical SD model, that is not the case for the New Guinean and the Australian values, falling in the right tail of this distribution at P < 0.05 level  and other African samples and one non-African population as the sources of admixture. The results  if there has been any bias in the estimated divergence times between East Africa and Europe and between East Africa and Caucasus due to the back-to-Africa process, this bias should also affect in the same way all other divergence times estimate; (b) this bias, being shared among all samples analyzed, would affect the punctual estimated value of the divergence times and not the differences between estimates; and (c) the differences in the divergence times estimated for West Africa and South Africa, which are not showing any admixture signals with out-of-Africa populations, are credible.Another realistic explanation for the observed pattern could also involve a simple isolation-by-distance model, in which geographically close populations are expected to be genetically close due to gene flow. Under this model, Europeans are expected to exchange more migrants with Africa than more remote populations of East and Southeast Asia, and Australo/Melanesia, which would bias downwards their divergence time estimates. To test for this possibility, we performed two different analyses, both estimating the extent of the European genetic contribution to the African gene pool considered in the present work. First, we ran a TreeMix  analysisk = 4) that is widely present in Australia and New Guinea and much less so in neighboring populations. To understand whether this component can actually be considered an ancestral marker of the first migration of modern humans out of Africa rather than the results of other population genetic phenomena , we calculated the f3 statistic considering Australia or New Guinea as the target population  supports the Southern route hypothesis for populations of Southeast Asia and Oceania, but the difference between models 3 and 1 is not significant by Fisher’s criterion [Z = −1.26, P = 0.08).To conclude, we tried to better define some details of the AMH dispersal out of the African continent by evaluating which geographical migration route can better account for the current patterns of genome diversity. To minimize the effects of recent gene flow unrelated with the first human dispersals, which was clearly not negligible (see the previous section), we selected populations with at least 80 % of a single ancestral component in the ADMIXTURE results . All the Mantel correlations thus calculated were positive and significant Table , suggestriterion   through the classical northern route . The mof3 statistics did not show any detectable impact of back migration to Africa upon the set of markers we considered. To exclude the possibility that a higher level of African ancestry in the European samples we considered (perhaps due to back migration into Africa from Southern Europe) is responsible for the observed pattern, we ran additional comparisons of divergence time distributions between sub-Saharan Africans and Europeans with different geographic origins. To do this, we analyzed Basque and English populations from the POPRES dataset [Conversely, two processes may actually have a potentially more serious confounding effect on these calculations; Denisovan admixture may inflate the estimated divergence times in the easternmost populations, and back migration from Europe into Africa may have the opposite effect upon the European populations. Removal of the SNPs identified as a potential Denisovan contribution to the modern genomes caused no substantial change in the results, and the analysis of  dataset  and the  dataset . The div dataset  or gene As for admixture among modern populations after the split from Africa, which may certainly affect estimates of their divergence time , the demNe values should converge to a value representing the size of the common ancestral African population. However, a similar result was also obtained in the only previous study based on the same method [Ne in all non-Africans. However, the resulting distortion, if any, should have affected the absolute values of T, but not the relative timing of the Europeans’ and Asians’ separation from Africans, which is what this study is concerned with. Another possibility is that 100,000 or so years ago, the ancestors of current Eurasians were already genetically distinct from the ancestors of modern Africans . Ify et al.  estimatey et al. .Two independent analyses (by ADMIXTURE and TreeMix) suggest that the genotypes of most Central Asians reflect variable degrees of gene flow between populations which may have left Africa in different waves. As a result, the distribution of divergence times is essentially continuous, and hence, it would make no sense to try to classify Central Asian populations as derived from either the first or the second African exit under the model of multiple dispersals.When we modeled population dispersal in space, the correlation between genetic and geographic distances was higher under the MD than under the SD model, but this difference was statistically insignificant Table . This seNe and hence the separation time, through LD, is expected to be relatively unaffected by this kind of bias [closer to Africans than Australo-Melanesians, a result which therefore cannot be due to that kind of ascertainment bias. Moreover, our FST estimates are comparable with those obtained by the last survey of the 1000 Genomes Project; when rare variants are excluded (MAF > 0.05) and for the populations of this study, the average FSTs differ by less than 1 %. This means that we are not losing power considering a subset of SNPs and that the ascertainment bias did not substantially affect our estimates.The data we analyzed are probably affected, to an unknown but not negligible extent, by a bias due to the fact that most SNPs in the genotyping platforms were discovered in European populations; however, the approach we used to calculate  of bias , 76. At Can selection account, at least in part, for these findings? In principle, we have no way to rule this out. However, in practice, even though positive selection may have extensively affected the human genome, large allele frequency shifts at individual loci are surprisingly rare , so muchAnalyses of genomic data based on different sets of assumptions and different methods agree in indicating that (i) a model with a single early dispersal from Africa fails to account for one crucial aspect of human genome diversity, the distribution of divergence times from Africa, and (ii) within the model of multiple dispersal, geographical patterns of genome diversity are more accurately predicted assuming that not all Asian and New Guinean/Australian populations have had the same evolutionary history (question 1 of the “In the light of these results, we propose that at least two major dispersal phenomena from Africa led to the peopling of Eurasia and Australo-Melanesia. These phenomena seem clearly distinct both in their timing and in their geographical scope.The view whereby only part of the ancestors of current non-African populations dispersed through the Levant has some non-trivial consequences upon the possible interactions between AMH and archaic forms, traces of whose genomes have been identified in many non-African populations, including New Guineans , 81. The


Populus szechuanica var. tibetica Schneid), which is distributed at altitudes of 2,000-4,500 m above sea level, is an ecologically important species of the Qinghai-Tibet Plateau and adjacent areas. However, the genetic adaptations responsible for its ability to cope with the harsh environment remain unknown.The Tibetan poplar (HE) value ranged from 0.475 to 0.488 with the highest value found in low-altitude populations and the lowest in high-altitude populations. Genetic variation was low among populations, indicating a limited influence of altitude on microsatellite variation. Low genetic differentiation and high levels of gene flow were detected both between and within the populations along the altitude gradient. An analysis of molecular variance (AMOVA) showed that 6.38% of the total molecular variance was attributed to diversity between populations, while 93.62% variance was associated with differences within populations. There was no clear correlation between genetic variation and altitude, and a Mantel test between genetic distance and altitude resulted in a coefficient of association of r = 0.001, indicating virtually no correlation.In this study, a total of 24 expressed sequence tag microsatellite (EST-SSR) markers were used to evaluate the genetic diversity and population structure of Tibetan poplars along an altitude gradient. The 172 individuals were of genotypes from low-, medium- and high-altitude populations, and 126 alleles were identified. The expected heterozygosity (Microsatellite genotyping results showing genetic diversity and low differentiation suggest that extensive gene flow may have counteracted local adaptations imposed by differences in altitude. The genetic analyses carried out in this study provide new insight for conservation and optimization of future arboriculture. Altitude gradients represent one of the most useful natural environments to investigate ecological and evolutionary responses of biota to geophysical influences . For spe6 km2. In recent years, the QTP has become a hotspot for plant phylogeographical studies [et al.) [The Qinghai-Tibetan Plateau (QTP) is the highest and largest plateau in the world, with a mean altitude of 4 000 m above sea level, and an area of 2.5 × 10 studies ,16, focu[et al.) . HoweverPopulus sect. Tacamahaca in the genus Populus and is an ecologically important species, mainly distributed in Sichuan and Tibet at altitudes from 2 000 to 4 500 m [The Tibetan poplar belongs to  4 500 m . Recent  4 500 m . HoweverPopulus euphratica transcriptome [In this study, a total of 24 EST- SSR loci based on criptome  were useWe collected leaves from 64, 34 and 74 individuals from high-, medium- and low-altitude populations, respectively Figure , Table 1Populus euphratica transcriptome [113 EST-SSR primer pairs based on the criptome  were devTaq buffer, 0.2 µM dNTPs, 10-20 ng template DNA, 1.6 pmol reverse primer, 1.6 pmol fluorescently labeled M13 primer, 0.4 pmol forward primer and 1 U Taq polymerase (BioMed). PCR amplification was performed using a Biometra thermocycler  under the following conditions: 94°C for 5 min; 30 cycles of 94°C for 30 s, annealing at 56°C for 45 s and elongation at 72°C for 45 s; 8 cycles of 94°C for 30 s, annealing at 53°C for 45 s, elongation at 72°C for 45 s; and a final extension at 72°C for 10 min. The PCR products were separated by capillary electrophoresis using an ABI 3730xl DNA Analyzer  after confirmation of amplification on a 1.5% agarose gel. Approximately 0.5 μL of the PCR products obtained using each of the four fluorescently labeled primers was then combined. The products were separated using an ABI 3730xl DNA Analyzer with GeneScan-500 LIZ as an internal marker (Applied Biosystems). The amplicon fragments were sized using GeneMarker version 1.75 .After screening, 24 primer pairs were selected for the PCR analysis. The forward primer of each pair was tagged with a section of the universal M13 sequence (5′-TGTAAAACGACGGCCAGT-3′) during synthesis. Each 10-μL PCR mixture contained 1× FST outlier test using LOSITAN [Na); observed heterozygosity (HO); expected heterozygosity (HE) within a subpopulation; Wright's fixation indices for within-subpopulation (FIS) and in the total population (FIT); and pair-wise differentiation among subpopulations (FST), according to Weir & Cockerham [IS Fmeasures the deviation from the Hardy-Weinberg equilibrium (HWE) of genotype frequencies in sub-populations, whereas FIT measures the deviation from HWE in the total population. The values of FIT and FIS can be negative, whereas FST is always a positive value. The Shannon's diversity index was conducted using Nei's model, along with the expected heterozygosity [Nm) was calculated to ascertain the conditions of gene communication among populations, and was estimated as follows: ST)/4 FST Nm = /4 FST . SummaryT)/4 FST . Inter- T)/4 FST . ClusterT)/4 FST ,32. STRUT)/4 FST . In this [et al. . The BayA Mantel test, performed with GenAlEx version 6.41 , was usePopulus euphratica genome, which were transferable to the Tibetan poplar. Sequencing results were uploaded to GenBank,  [Populus trichocarpa. This implied that this SSR locus could be under selection pressure.SSRs are generally used in genetic diversity studies as evolutionary neutral markers. In this study, 24 SSR primer pairs were developed using the HO and HE are important parameters for assessing genetic diversity of populations, and they ranged from 0.40-0.42 and 0.48-0.49, respectively. In the three populations, as the results were consistent with the Na, it indicated that genetic variation was not significant, and the populations were similar in all parameters. FIS presented a similar pattern in that no significant difference was detected among populations; it ranged from -0.85 to 0.56 , with only a moderate difference for loci from trees at low altitude. The FST was estimated at each locus for all individuals, and ranged from 0.0004 to 0.16 . In populations sampled from high, medium, and low altitudes, all genetic diversity parameters were similar, indicating no local adaptation or population differentiation in the study area.FST value showed little differentiation between the populations. Since STRUCTURE could not perform an analysis of K = 1 on populations with no difference, we did not accept the results of K = 2, based on the low FST and the genetic parameters pattern among the three populations. To compare the structure of new clusters (cluster 1 and cluster 2), further STRUCTURE analyses were performed in cluster 1, which contained individuals from a high altitude, and cluster 2 from low altitude. It shows no clear structure despite peaks in K = 3 for cluster 1 and K = 2 for cluster 2 , suggesting that altitude was not the principal factor influencing genetic differentiation in the Tibetan poplar.The SSR data was sorted in order of altitude. Population structure analysis was processed according to the known order of individuals, yielding an optimal of K = 2 . Estimat2 Figure . The anc2 Figure  showed tPopulus euphratica genome, to analyze genetic diversity within three populations of Tibetan poplar living at different altitudes in Linzhi, Tibet. As the loci were transferable between the two species, this indicates that they may be sited in a conserved region. However, based on the FST using LOSITAN, one locus appeared to be an outlier. SSR loci mutations occasionally occur as a result of the stress of adapting to a change of environment [Populus trichocarpa. We conclude that the microsatellite may be linked to expressed genes, and therefore, neutrality should not be assumed, but tested in all of the markers before genetic diversity and structure analysis. This type of marker, however, could be useful for phylogenetic studies of closely related species [In this study, we used GeneMarker version 1.75 to identify the fluorescently labeled PCR products. We selected 24 SSR primer pairs based on the ironment  or an exironment . Furtherironment ,39, and ironment , and ind species ,41.Populus species [Populus genetic diversity [Na of 3.73 in our study is lower than the Na in P. tremuloides (4.9) as described previously [FST value showed little differentiation between the populations. As STRUCTURE could not provide data for K = 1, we rejected the result showing that the population was divided into two groups. There was no peak in the estimate of the log-likelihood of the cluster number (L(k)) since the lowest likelihood was for K = 1, and L(k) either consistently increased or showed an erratic pattern with increasing variance, with all individuals admixed and the proportion of any individual assigned to each subpopulation remaining roughly similar. The Evanno criterion, ΔK [As expected from perennial and woody species ranging across most areas of the Qinghai-Tibet plateau, the study population contained a high level of genetic diversity, but we did not identify any significant differences among the three populations from different altitudes. The number of alleles per locus in our study was less than in other related  species . A mean iversity . The Na eviously . The difrion, ΔK , was notThe population structure and Mantel test results suggest that the relationship between genetic diversity and altitude is not significant, and hence it is possible to hypothesize that the species has not had sufficient time for evolutionary differentiation to occur along an altitude gradient.FST was low for all loci, except for SSR 18. There was no noticeable differentiation among populations at three different altitudes. This may contribute to the local geographic structure and strong gene flow among individuals. The STRUCTURE results showed that the medium-altitude group was an admixture of the low- and high-altitude groups, clearly indicating that the mountain harbored two groups (clusters) of poplars, and that they separated into these clusters at an altitude of ~2700 m. Because the study area altitude ranged from 2 000 to 4 000 m, and the Tibetan poplar is distributed from 2000 to 3096 m, the tree line represented a limiting factor for tree distribution, but it appeared to have had limited impact on gene exchange between individuals and did not hinder pollen or seed dispersal. In this study, gene flow occurred among the populations. Gene flow is a vital element in local adaptation studies, because it can instruct the establishment of the local genetic structure or influence it indirectly. Gene flow among populations can also lead to combining of gene pools, reducing genetic variation among groups [Populus is of long duration; for example, flowering in P. × canadensis and P. nigra [Populus trees live adjacent to rivers and roads, and some in the river channel itself. Therefore, rivers cannot be ignored as an important factor in seed dispersal. Poplar populations are evolutionarily homogeneous. The germplasm and genetic diversity of the Tibetan poplar could be protected by random selection in the future work, which couldprovid all of the genetic diversity to date. An unpublished experiment comparing poplars at two sites showed some differences in the growth rate, leaf characteristics, and branch numbers, etc. of individual clones sampled at different altitudes, indicating that natural selection conserved some fitness types. Genes linked to adaptation mechanisms could contribute to phenotypic variation without genetic structure differentiation which has been proved in this study. Consequently, this makes the population ideal for identifying functional genes and mechanisms of adaptation to high altitudes.g groups . Therefog groups , by recog groups ,47. Howeg groups . Howeverg groups ,50. For g groups . In summP. nigra  lasts foFST, as was observed. We consider the Sejila mountain population to be appropriate for investigation of the mechanisms of adaptation to high altitudes, despite the low level of genetic structure differentiation among populations at different altitudes.To our knowledge, this is the first genetic analysis of the Tibetan poplar. The results indicate that the Tibetan poplar populations living at different altitudes on the Sejila mountain have a low level of differentiation. They have an excellent ability to adapt to different altitudes; however, local adaptation is not observed due to the lack of a geographic barrier. The high levels of gene flow lead to a low The authors declare that they have no competing interests.Rongling Wu designed the study. Dengfeng Shen, Wenhao Bo and Fang Xu, contributed extensively to the samples collection. Dengfeng Shen performed PCR experiments and genetic diversity analysis. Dengfeng Shen and Rongling Wu wrote the manuscript. Wenhao Bo and Fang Xu prepared and revised the manuscript. All authors read and approved the final manuscript.Publication charges for this article came from the Special Fund for Forest Scientific Research in the Public Welfare (201404102), NSF/IOS-0923975, Changjiang Scholars Award and "Thousand-person Plan" Award.BMC Genetics Volume 15 Supplement 1, 2014: Selected articles from the International Symposium on Quantitative Genetics and Genomics of Woody Plants. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenet/supplements/15/S1.This article has been published as part of


The HUGO Pan-Asian SNP Consortium (PASNP) has generated a genetic resource of almost 55,000 autosomal single nucleotide polymorphisms (SNPs) across more than 1,800 individuals from 73 urban and indigenous populations in Asia. This has offered valuable insights into the correlation between the genetic ancestry of these populations with major linguistic systems and geography. Here, we attempt to understand whether adaptation to local climate, diet and environment partly explains the genetic variation present in these populations by investigating the genomic signatures of positive selection.To evaluate the impact to the selection analyses due to the considerably lower SNP density as compared to other population genetics resources such as the International HapMap Project (HapMap) or the Singapore Genome Variation Project, we evaluated the extent of haplotype phasing switch errors and the consistency of selection signals from three haplotype-based approaches  when the HapMap data is thinned to a similar density as PASNP. We subsequently applied haploPS to detect and characterize positive selection in the PASNP populations, identifying 59 genomics regions that were selected in at least one PASNP populations. A cluster analysis on the basis of these 59 signals showed that indigenous populations such as the Negrito from Malaysia and Philippines, the China Hmong, and the Taiwan Ami and Atayal shared more of these signals. We also reported evidence of a positive selection signal encompassing the beta globin gene in the Taiwan Ami and Atayal that was distinct from the signal in the HapMap Africans, suggesting the possibility of convergent evolution at this locus due to malarial selection.We established that the lower SNP content of the PASNP data conferred weaker ability to detect signatures of positive selection, but the availability of the new approach haploPS retained modest power. Out of all the populations in PASNP, we identified only 59 signals, suggesting a strong need for high-density population-level genotyping data or sequencing data in order to achieve a comprehensive survey of positive selection in Asian populations.The online version of this article (doi:10.1186/1471-2164-15-332) contains supplementary material, which is available to authorized users. Asia is the largest continent on Earth, covering 30% of the available land area and playing host to more than 60% of the human populations in the world. With a latitudinal range of 11.6°S to 81.9°N and a longitudinal range of 27.3°E to 169.0°W, Asia possesses extremely diverse climates and geographical conditions, with temperatures ranging from arctic in northern Asia to tropical along the Equator and humidity ranging between below 10% in the interior of the continent to in excess of 90% along the coast and in Southeast Asia. Comprising 49 countries, many of which contain a wide variety of ethnicities and subpopulations, Asia also hosts a myriad of unrelated language families such as the Sino-Tibetan languages predominantly spoken in East Asia; the Indo-European and Dravidian languages predominantly spoken in south and central Asia; the Altaic languages predominantly found in Korea, Japan, and central and northern Asia; and the Austronesian and Tai-Kadai languages commonly spoken in Southeast Asia.The distinct languages in Asia have limited the extent of historical interactions between different population groups, leading to a greater degree of genetic homogeneity between populations sharing the same linguistic system while extending the genetic differences between populations with different linguistic systems. This situation is similar to that present in the Africa continent . The divThe availability of genetic datasets for global populations from the International HapMap Project (HapMap) –5, the HST index that highlights striking differences in allele frequencies across a region [The HUGO Pan-Asian SNP Consortium (PASNP) was a collaborative agreement established to perform an unprecedented genetic survey of Asian populations, and has provided a valuable resource of around 54,974 autosomal single nucleotide polymorphisms (SNPs) for 1,928 individuals from 73 Asian and two non-Asian HapMap populations  . The inda region , insteada region  that conThe introduction of a new haplotype-based approach haploPS  presentsHere, we aim to discover and characterize the origin and segregation of positive selection signals that are present in the Asian populations in PASNP. As the ability to detect the extended haplotypes using haplotype-based approaches relies on the accuracy and fidelity of the haplotype phasing, we first compared the extent of switch errors found in phasing the haplotypes for the full set of SNPs from the three population panels in Phase 2 of the HapMap (HapMap2), to the extent of the errors present when the SNP density for the HapMap samples has been reduced to a similar content as the PASNP data. In addition, we evaluated the power of haploPS via simulations to locate true signals of positive selection for haplotype data with a sparse set of SNPs, and also performed an empirical comparison of the degree of overlap in the selection signals found for the HapMap populations using the full and sparse sets of SNPs. The latter two exercises will allow an evaluation of the degree of power loss as a result of the sparser SNP density. By clustering the 73 PASNP populations into 31 groups according to shared linguistic systems and geographical proximity of the populations, we run haploPS to discover evidence of local adaptations in these 31 groups. The results provide the first systematic and large-scale survey of local adaptation in Asia, particularly in mapping the genomic features for the indigenous populations that are attributed to evolutionary pressures.The PASNP dataset consists of genotypes at 54,974 autosomal SNPs for 1,928 individuals from 73 Asian and two non-Asian HapMap populations  were generated. We calculated the power of haploPS to detect the positively selected regions using the original datasets and when the datasets were thinned to a SNP density that was similar to the PASNP (around 1 SNP per 10 kb). We observed that the power of haploPS to locate the selection signals was much reduced for the thinned datasets as compared to the original datasets, with almost no power to locate a signal that was at fixation Figure . For exaWe also performed an empirical comparison of the consistency in the positive selection signals that have been detected by haploPS, iHS, XP-EHH and Fst for HapMap2 populations with the original and thinned datasets. The purpose of this was to investigate the extent of the positive selection signals that were present in the analyses on the reduced-density data with around 55,000 SNPs, compared to what were originally detected in the full HapMap2 dataset with 2.6 million SNPs. More importantly, it was used to benchmark the consistency in the signals that were present in both datasets. All four methods managed to identify signals in the reduced dataset, although haploPS only managed to identify three regions from the HapMap2 populations, while 57 and 9 regions were deemed to exhibit significant evidence of positive selection by iHS and XP-EHH respectively; and Fst discovered 123 selection regions, which is similar to the number of regions (159) detected in the original dataset , Philippines (Negrito), China Hmong and Taiwan Ami and Atayal  that contains three nonsynonymous mutations  that impair red blood cell functions and cause anemia. The frequency of this extended haplotype was inferred to be between 10% and 15% in the Taiwan indigenous populations, and both characteristics of the haplotype length and frequency were similar to that of the malaria-driven selection signal in the HapMap2 Africans  in the Taiwan indigenous populations of Ami and Atayal, encompassing numerous hemoglobin and olfactory receptor genes including the beta globin gene . ThHBB in the Philippines Negrito and two groups of Thailand indigenous populations  in HapMap and SGVP. Our analyses similarly identified this locus in the Japan Okinawans and Koreans, but failed to locate a signal in other East Asian populations.Haplotype-based approaches to locate selection signals require: (i) accurate haplotype phasing across the SNPs to preserve the extended haplotype structure; and (ii) sufficient number of SNPs to be present on the extended haplotype to anchor the signal. In this paper, we have illustrated that the sparse SNP density provided a double whammy to the statistical power to locate selection signals, since the lower density also affected the accuracy of haplotype phasing by introducing more switch errors that can break up the structure of extended haplotypes. In light of this, we have focused on reporting only what have been observed to be positively selected, and the absence of classical signals such as the pigmentation-linked Of the 59 regions identified in this analysis, 34 of them overlapped with positive selection signals previously reported in East and Southeast Asian populations from HapMap and SGVP, and where 24 of the signals were present in at least three of these populations . HoweverHBB) in the Taiwan Ami and Atayal may present the first evidence of genetic resistance against malaria. The incidence of thalassemia is higher in the Taiwan indigenous populations than the urban populations, and there are at least two possible hypotheses: (i) malaria is endemic in Malaysia and the Philippines and based on existing evidence that suggests the Taiwan aborigines are related to the indigenous people of Malaysia and/or the Philippines, the advantageous mutations may have arisen prior to the divergence of the Taiwan and these Southeast Asian indigenous people; (ii) malaria is present in Taiwan and that has driven the emergence of genetic factors providing host resistance to malaria, in a similar situation as in African populations such as the Gambia, Nigeria and Kenya. The PASNP data included samples from Malaysian and Philippines Negrito, and our survey did not present any conclusive evidence of extended haplotypes in these populations, or any indication that the haplotypes from these Southeast Asian populations were similar to the selected haplotype form in the Ami and Atayal. There have however been historical reports of migrant Chinese being more susceptible to malaria than the Taiwan aborigines [The discovery of a positive selection signal that extends for almost 5 Mb around the cluster of hemoglobin genes  and genes related to skin pigmentation . However, genetics studies involving Asian ethnic minorities and indigenous populations will require careful engagement of local communities, as the challenges of educating and obtaining informed consent from these communities are comparable to the situation in Africa. This may require the cooperation and knowledge transfer between genetic scientists across Asia, to share experiences and leadership in research ethics, data analyses and the principles of data sharing and ownership in order to develop a genomics research network in Asia.This study has established that the lower SNP content of the PASNP data conferred weaker ability to detect signatures of positive selection, but the availability of the new approach haploPS retained modest power. Despite the analysis of 73 Asian populations, we have only identified 59 signals, highlighting the need for a comprehensive survey of Asian genomics with either microarray data of higher SNP density or population-level whole-genome sequencing such as that by the 1000 Genomes Project, in order to understand the subtle variations in local adaption between Asian populations due to climate, diet and environmental differences. Indeed in the study of population genetics, both the presence and absence of adaptation can be insightful, evident in classical signals at the lactase gene  that have been genotyped on the Affymetrix GeneChip HumanMapping 50 K Xba Array. A total of 54,794 SNPs passed quality control and were present in all the populations. The 73 Asian populations represent major, ethnic minority and indigenous populations from East Asia, Southeast Asia and South Asia. The sample size of each population ranges from 5 in Melanesian to 90 in the Koreans.The 73 Asian populations were partitioned into 31 groups according to the maximum likelihood phylogenetic tree for the populations as described in the original PASNP publication . BrieflyHaplotypes for each individual were estimated from the genotype data with SHAPEIT  using thhttp://www.statgen.nus.edu.sg/~haplops and http://hgdp.uchicago.edu/Software/.Three different haplotype-based methods  and one allele frequency based method (Fst) were used to detect genomic signatures of positive selection on the reduced set of SNPs from the populations in HapMap2. However, only haploPS was used in the analysis of the PASNP data. Population-average recombination rates were used by all three methods. We used the C++ software for haploPS, iHS and XP-EHH that were publicly available at HaploPS performs an explicit search for uncharacteristically long haplotypes in the genome that are found at a particular frequency . By perfThe integrated haplotype score (iHS) was calculated by estimating the extended haplotype homozygosity (EHH) score. The EHH is the probability of identity-by-descent for two haplotypes that carry a core haplotype within a distance to a pre-defined focal SNP , and theThe cross-population extended haplotype homozygosity (XP-EHH) contrasts evidence of positive selection between a target population and a reference population at a focal SNP . At eachFst analysis was performed following description of Qian et al. . For eachttp://www.statgen.nus.edu.sg/~haplops/ and thinned the available dataset to 1/20th of the original SNP density. Briefly, the dataset has been simulated using SelSim[e was assumed to be 17,469, and the mutation rate was set to 3 × 10−8 per base per generation. The recombination rate was generated by cosi[th of the original density (or around 10 SNPs per 100 kb) to reflect the SNP density that is present in the PASNP data. Null simulations were performed with cosi to generate regions without positive selection. A total of 2,000 positively selected regions and 2,000 neutral regions were generated. HaploPS was applied to both the original simulated dataset and the reduced-density dataset to evaluate statistical power. The power is quantified as the fraction of the 2,000 positive selection iterations where the haploPS score obtained is less than the 1st percentile of the distribution of haploPS scores obtained from the 2,000 iterations under the null model.In order to assess the degree of power loss of haploPS at a reduced SNP density similar to that of the PASNP data, we utilized the simulation data for haploPS that have been made publicly available at ng SelSim which prd by cosi with a bi, j)th element of the matrix takes value 1 if the jth region is found to be positively selected in population i, and is 0 otherwise. This is used to calculate a 31 × 31 correlation matrix, which indicates the degree of sharing of the 59 selection signals across the 31 population groupings. This correlation matrix is used to perform a hierarchical clustering using the Ward’s minimum variance method implemented in hclust in R.For the 59 genomic regions that have been identified to be positively selected in at least one of the 31 population groupings, we constructed a 31 × 59 indicator matrix, where the th entry of the matrix corresponds to the scaled Manhattan distance between the selected haplotype forms for population i and population j defined as M = 1–l/L with l represent the number of sites out of L where the two haplotypes carry different alleles. An eigen-decomposition is performed on the matrix M, and the haplotype similarity index (HSI) is defined as the amount of variance explained by the first principal component. We infer a shared signal as a single mutation event if the HSI > 0.98, and as convergent evolution if HSI < 0.9.To infer whether positive selection events shared by multiple populations originated from the same mutation event or from separate mutation events, we assessed the degree of similarity between the identified haplotype forms in the different populations using the haplotype similarity index (HSI) . This asSupplementary material. Detecting evidence of positive selection across Asia with sparse genotype data from the HUGO Pan-Asian SNP Consortium. (DOCX 338 KB)Additional file 1: