Triticum aestivum L.) in relation to farmers’ practices to decipher their contribution to crop species evolution. Using 19 microsatellites markers, we conducted two complementary graph theory-based methods to analyze population structure and gene flow among 19 sub-populations of a single population-variety [Rouge de Bordeaux (RDB)]. The ethnobotany approach allowed us to determine the RDB history including diffusion and reproduction events. We found that the complex genetic structure among the RDB sub-populations is highly consistent with the structure of the seed diffusion and reproduction network drawn based on the ethnobotanical study. This structure highlighted the key role of the farmer-led seed diffusion through founder effects, selection and genetic drift because of human practices. An important result is that the genetic diversity conserved on farm is complementary to that found in the genebank indicating that both systems are required for a more efficient crop diversity conservation.Since the domestication of crop species, humans have derived specific varieties for particular uses and shaped the genetic diversity of these varieties. Here, using an interdisciplinary approach combining ethnobotany and population genetics, we document the within-variety genetic structure of a population-variety of bread wheat ( Ten thousand years ago, human societies began to domesticate wild species so they could be easily cultivated, more productive, and better adapted to their needs . As the Both farming communities and the scientific literature usually identify different varieties for a given cultivated species. Specific varieties within a species have been selected and used for a particular purpose and are distinct from other varieties of the same species by morphological traits and their particular use or quality characteristics. In contrast, diversity among individual plants within the variety so defined, (within-variety diversity), has rarely been characterized . HoweverIn traditional farming, human and natural processes still strongly interact to determine the rate of change in population-varieties . Two levIn industrialized countries, although landraces and folk varieties are no longer cultivated by the majority of farmers, seed saving and seed exchange networks have recently emerged in the context of organic agriculture [reviewed by Thomas et al. (2011)]. Organic farmers, faced with a shortage of varieties meeting their needs in terms of agronomic and quality traits, have begun cultivating varieties obtained from genebanks or from elders. Farmers within these associations generally exchange small quantities of seed which are then multiplied on farm for their own use. While these seed exchanges share characteristics with the informal seed systems of traditional agricultures, they also have specificities as they are situated in the context of modern organic agriculture in developed countries  .The role of this type of seed exchange network in the conservation of genetic diversity in an industrialized context can be important but is not yet well characterized. In this paper, we develop an interdisciplinary approach by combining genetics and ethnobotany to assess for the first time the level of genetic diversity and the population structure at the variety level, from the example of Rouge de Bordeaux (RDB), a folk variety of bread wheat distributed among a network of actors in France. Our goal was to assess to what extent seed diffusion and farming practices influence the genetic diversity of this variety and its population structure. Outcomes from this research could contribute to the proposition of recommendations in terms of management strategies of crop diversity.Initially, a socio-anthropological study focused on the dynamics of seed circulation within the social network composed of farmers from the national Réseau Semences Paysannes organization, an organization created in 2003 to revive on-farm management of seeds and linking concerned farmers’ associations  and with the curator of the French National Genebank at Clermont-Ferrand (CLM). A snowball approach was used to trace back seed circulation of bread wheat varieties among the different actors. This study revealed that RDB was one of the most popular varieties among farmers in the RSP .Historical archives revealed that RDB appeared probably around 1865 in Lectoure, in the south-western France, then started moving toward Bordeaux (still in south-western France) and toward the central France during the years 1870–1871 . RDB wasRelying on this information, we asked the genebank curator and some farmers cultivating RDB to provide us one or more seed samples from their populations. The nomenclature used to identify each sample was as follows: the first three-first characters for the name of the seed lot provider and two characters for the year of the last harvest. One optional letter was added if two samples came from different seed management practices on the same farm in the same year. We obtained 19 seed samples from 11 actors distributed among the French territory  , Fig. 1.Interviews focusing specifically on sampled populations of RDB were performed to obtain more detailed information about seed circulation and cultural practices. Applying the snowball approach to trace back the seed circulation of RDB, new actors mentioned during the interviews were contacted and interviewed. For each dissemination event, we recorded the actors involved, the date, and when this information was available, the quantity of seed diffused.Although farmers involved in seed systems have received increasing attention as potential partners for participatory plant breeding and development programs , only feIn the spring of 2007, leaf samples were taken from 13 to 44 plants per population (mean number of plants: 31), sown on November 8, 2006 at Le Moulon experimental station. For each plant, total DNA was extracted from 50 mg of fresh material following a protocol derived from the Dneasy 96 Plant Kit . Sixteen microsatellite markers developed by Flowering time is a major adaptive trait in plants and in particular in the case of wheat because it determines the environmental conditions of reproduction with respect to climate and pathogen pressures . The VRNPopulation structure was assessed at two levels, among and within populations.amova . Then, a community corresponds to the union of all k-cliques that can be reached from one to the other through a set of adjacent k-cliques (where adjacent means share k−1 nodes). The inverse of the distance matrix was used as a weighted matrix for the community detection. Communities can then be defined using an algorithm adapted for the weighted networks (I) and the size of the clique (k) need to be chosen to have the lowest possible values while avoiding the detection of a single giant network. No giant network appeared when k is equal to 3 and without a fixed threshold for I. The algorithm was therefore used with these parameters.To understand the general organization of the Population Graph, it was necessary to detect whether structural sub-units (communities) were associated with more highly interconnected parts of the network. A deterministic approach that detects potentially overlapping communities based on the Clique Percolation Method with weight (CPMw) was performed using Palla’s algorithm implemented in CFinder software . In thisnetworks . The intRS), unbiased Nei’s estimate of genetic diversity (He) (Ho), and the deviation from Hardy–Weinberg genotypic proportions (FIS) were calculated with Genetix software (Ne) using the temporal method proposed by Fc): Sx is the number of individuals sampled at the tx generation .Genetic diversity was studied for both the 19 neutral markers and the four loci  located in three orthologous genes . Mean number of alleles (ity (He) , mean obsoftware . GenotypThe fine population structure was studied considering each genotype as two haplotypes. Haplotype reconstruction and inference of missing data were performed using PHASE software . Based oHd), which accounts for small population sizes, computed as: n is the number of gene combinations analyzed in a population and p is the frequency of the ith haplotype in a population  was drawn to track haplotypes represented at low frequencies among populations. Two populations were considered connected if they shared at least one haplotype. A threshold of haplotype occurrence in the whole dataset was set to 50 to represent only rare haplotypes. The Clique Percolation Method (CPM) was performed on the SHN using Palla’s algorithm implemented in Cfinder software , to deteStudent’s tests were performed using R software  to test The interviews with the different actors allowed us to trace the circulation of RDB populations to almost 30 years back. Thirty-five populations of RDB were documented with 28 seed diffusion events identified between 17 actors in addition to the 11 who provided seed samples. Populations were grown from 1 to 14 generations on the same farm. Based on this information, an oriented SDRN was drawn (this information was summarized in 2), medium (10–100 m2), or large (>100 m2) plots. These different areas corresponded to different functions: small plots were used for collections of several varieties ; medium plots are also used for collections of a few varieties or multiplication of seed lots to increase the seed quantity as preliminary step before production ; and large plots corresponded to production in fields . Practice diversity is observed among farms but also within farms. For example, ALB used three different practices on his farm. ALB03A corresponded to a population maintained in collection . ALB03B and ALB06B are temporal samples of the same population maintained following conservation practices , with seed samples grown on 10 m2 (medium). ALB06C has been grown in isolation within a field of another species (medium plot size). We also learned that JOP06 applied spike mass selection when he received the RDB in mixture with other varieties. JFB made a selection within his RDB population in 2001 based on an ear type with awns. This population was sampled in 2005 after four generations cultivated independently to his RDB population (JFB05). Another sample of this selection was obtained by CLM and was provided for this study after one cycle of reproduction using the conservation practices of CLM (CLM04).The interviews with the different actors indicated that three main cultural practices were observed: populations grown on small (1–10 mNe) was possible for the only temporal samples we had: the JFB and BER populations between 2003 and 2006 (respectively JFB03–JFB06 and BER03–BER06). Genetic effective population size was estimated as 104.5 individuals for the JFB population. Ne tended toward infinite for the BER population because allele frequencies varied only very little leading to a very low Fc value compared with the sample size effect.The level of genetic diversity estimated in each population with the unbiased Nei’s index showed a large range of values  and the other 11 populations (denoted group2)  where populations from the same group were more connected than populations from different groups. This observation was confirmed by a community detection using CPMw algorithm. Two nonoverlapping communities were detected for a size VRN1 loci revealed a similar structure (data not shown). Eighteen among the 19 studied populations fell into the same groups regardless of the kind of marker. Only JOP06 was in the green group for the SSR markers but in the blue group for the VRN1 genes. This result was confirmed by a strong correlation between pairwise FST computed for SSR markers and VRN1 genes, respectively  included 119 distinct nodes, where each node was a distinct haplotype. The haplotype distribution among individuals  showed tUsing the previous haplotype clustering, we plotted the frequency of each haplotype group in the sampled populations, using pie charts on the Population Graph presented in A SHN was drawn to track haplotypes that were present in different populations at low frequencies . A 6-cliP-value < 2.2 × 10−16) with cGD averaging 5.8 for populations belonging to the same SDRN and 22.8 for populations that did not belong to the same SDRN. This result was consistent with the high level of differentiation observed between the two genetic groups detected in (FST = 0.697). Comparison between mean diversity indexes for SDRN1 and SDRN2 shown significant Student’s tests for He , RS , Hd , and Polyclonality . Only GS diversity, Ho and FIS were not significant  was built to describe whether two populations belong to the same connected component or not. To quantify genetic differentiation among sampled populations, averaged cGD were computed within each group and between the two groups on the Population Graph. We tested for a significant difference in cGD values within and between groups using a Student’s test. The difference was highly significant  involved in conservation and use of crop diversity. The goal of these analyses was to provide insights into the history of the populations to assess the impact of human practices on genetic diversity at the molecular level, to guide decisions on the conservation of genetic resources. In this study, we did not analyze quantitative genetic variation of adaptive or economical significance.FST-based or distance-based methods developed within the theoretical framework of population genetics (Lophocereus schottii L.) submitted to an historical vicariance   .Poseidonia oceanica in the Mediterranean basin, the authors highlighted the key role of a few populations as hubs for relaying gene flow (In a study on a metapopulation of the seagrass ene flow . In the ene flow . Except Manihot esculenta Crantz) landraces (FIS < −0.37). This was because of a complex system of agricultural management: volunteer plants recruited from soil seed banks often resulted from outcrosses. The most productive volunteer plants, in general largely heterozygous, are propagated by clonal reproduction. For this reason, heterozygotes occured at a high frequency. In bread wheat, rare spontaneous cross-pollination can also occur, which could increase the heterozygosity. However, after successive generations of self-pollination, heterozygosity decreases. Thus, self-pollination in heterogeneous populations can lead to the maintenance of polyclonal or composite populations with a low level of heterozygotes, as has been shown in natural population of Medicago truncatula  have been divided into two distinct processes, one acting at the overall scale of the system, that is, seed diffusions, and the other acting locally, at the farm level, that is, reproduction of the seed lot, which is largely dependent on agronomic practices.Manihot esculenta Crantz) was also recently described in Gabon . Furthermore, the fact that all the populations have been diffused suggested that populations might not yet have achieved equilibrium.Seed diffusion can be considered as a colonization event in the metapopulation model with two basic mechanisms: the ‘migrant pool’ model and the ‘propagule pool’ model . In the Thus, the genetic analysis provided new insights into the seed diffusion history and by extension into the associated social processes. Relying on information collected through the interviews, it was initially not possible to connect three populations  to any SDRN although we collected seed circulation information back to the 1990s. With the molecular analyses of the population structure, it was possible to assign these three populations to the SDRN2, because they showed a pattern similar to that of SDRN2 populations. In addition, because two of them also presented a composite structure, we thought that the property of composite population was relatively old in the history of the RDB population-variety. Because JEF06 was not a composite population and showed no trace of alleles from haplotype class II while showing several satellite haplotypes from class III, JEF probably received a seed lot from a RDB population before the composite pattern occurred in SDRN2. We also showed that haplotypes at low frequency were shared by different populations of the SDRN2 . This rein situ collection was also highlighted in several papers , in contrast to others who follow multiplication, isolation, or production practices . Genetic drift, particularly in diversified populations with a small demographic size, might reduce the genetic diversity and increase the genetic load. This situation could account for some patterns observed in SDRN1, because five populations out of seven were grown in small plots. However, as mentioned in the previous paragraph, the overall low level of genetic diversity found in SDRN1 could be explained by the historical conservative practices of the formal system. Using the temporal variation of allele frequencies between the two samples available at the farm BER resulted in an infinite estimate of effective size, Ne, because allelic frequency variation was too low. This was associated with a low variation in terms of haplotype composition of the population between 2003 and 2006 which is consistent with the conservative practices used by BER. Except for JFB05 and JOP06, which followed cultural practices best described as selection, populations in SDRN2 seemed to have larger size than populations from SDRN1. Estimated Ne based on the JFB03 and JFB06 populations, within SDRN2, was of the same order of magnitude of bread wheat populations grown under dynamic management experiment [104.5 in this study compared with 123.0 after 10 generations of evolution in Demographic size of crop populations is generally highly variable . In thisMigration is one of the evolutionary forces that could significantly influence the differentiation within the system. In the case of an open-pollinated species such as maize, pollen-mediated gene flow is important and generally leads to a low level of genetic differentiation, though farmers’ selection on ear type induces stronger phenotypic differentiation among landraces . BecauseFST) measured in neutral regions was highly correlated with genetic differentiation measured in VRN-1 genes involved in flowering time (adaptive trait)  with only one class of haplotype detected (class II). Finally, there was another case of positive selection when in 2001, one farmer (JFB) made a selection of a new derived ear type (red awned) which appeared spontaneously in his RDB population. He further grew the progeny as a separate population, which he named ‘Rouge du Roc’. This process corresponds to the creation of a new population-variety related to RDB. In 2003, he gave a sample to CLM.Genetic differentiation (pairwise e trait) . Diverge markers . Thus, tThis article investigated how human activities shape genetic diversity of crops at the variety level. We showed that the network of actors involved in the RDB cultivation or conservation strongly influenced the population-variety structure and maintained it under a nonequilibrium state. Using a metapopulation genetic framework helped us to identify two processes that led to coexistence of two cryptic genetic groups: (i) at the global scale, the combined analysis between the seed diffusion dynamics and the genotyping of RDB populations highlighted two distinct seed diffusion pathways which appeared to be strongly consistent with the genetic structure of this population-variety, (ii) cultural practice diversity affected the local scale , leading to the maintenance of contrasting populations with a large range of diversity from fixed populations to composite populations.ex situ and in situ conservation to conserve crop genetic diversity at the different levels.From a genetic resources perspective, these results give convincing arguments to the stakeholders involved in genetic resource management for collecting critical information about seed circulation and cultural practices in the context of on-farm conservation of cultivated diversity. Here, we showed that on-farm conservation has the particular characteristic of maintaining intra-varietal genetic diversity. This leads us to emphasize the need to foster collaboration among partners from


Pygospio elegans, a poecilogonous polychaete with within-species variation in developmental mode. P. elegans produces either planktonic, benthic, or intermediate larvae, varying both among and within populations, providing a within-species test of the generality of a relationship between temporal genetic variation and larval developmental mode.Temporal variation in the genetic structure of populations can be caused by multiple factors, including natural selection, stochastic environmental variation, migration, or genetic drift. In benthic marine species, the developmental mode of larvae may indicate a possibility for temporal genetic variation: species with dispersive planktonic larvae are expected to be more likely to show temporal genetic variation than species with benthic or brooded non-dispersive larvae, due to differences in larval mortality and dispersal ability. We examined temporal genetic structure in populations of P. elegans revealed temporal genetic stability in the UK population with planktonic larvae, whereas there was variation indicative of drift in temporal samples of the populations from the Baltic Sea, which have predominantly benthic and intermediate larvae. We also detected temporal variation in relatedness within these populations. A large temporal shift in genetic structure was detected in a population from the Netherlands, having multiple developmental modes. This shift could have been caused by local extiction due to extreme environmental conditions and (re)colonization by planktonic larvae from neighboring populations.In contrast to our expectations, our microsatellite analyses of P. elegans, temporal genetic variation appears to be due to not only larval developmental mode, but also the stochastic environment of adults. Large temporal genetic shifts may be more likely in marine intertidal habitats (e.g. North Sea and Wadden Sea) which are more prone to environmental stochasticity than the sub-tidal Baltic habitats. Sub-tidal and/or brackish  habitats may support smaller P. elegans populations and these may be more susceptible to the effects of random genetic drift. Moreover, higher frequencies of asexual reproduction and the benthic larval developmental mode in these populations leads to higher relatedness and contributes to drift. Our results indicate that a general relationship between larval developmental mode and temporal genetic variation may not exist.In our study of  Several factors can affect the temporal stability of population genetic structure in the unpredictable marine environment. Unstable habitat can make a population more vulnerable to local extinctions and recolonizations ,2. StochLittorina gastropod species with different larval developmental modes. They found the expected pattern of significant temporal genetic variation in two species with planktotrophic (pelagic) larvae, whereas the species with direct-developing offspring were temporally stable.In many cases, the developmental mode of larvae can be used to generalize the dispersal potential of marine species , poecilasonally ,27,28,33Pygospio elegans, is a poecilogonous spionid polychaete [P. elegans has been described as opportunistic [P. elegans worms inhabit sand tubes and are relatively sedentary. After fertilization via direct transfer of spermatophores from males to females, the females lay their embryos inside egg capsules within the maternal tube . ,46. P. eea grass . At locaea grass ,47-49). ea grass ,50,51.P. elegans differ in the number of larvae produced per female, the duration of brooding time within capsules and the duration of the planktonic period, as well as in the size of larvae at release from the capsules and the presence or absence of larval swimming setae, the larvae developing via these different modes are expected to differ in their dispersal potential and their susceptibility to predation. As a result, developmental mode of larvae may affect the population genetic structure of P. elegans both spatially [P. elegans populations with planktonic larvae, overall a pattern of significant spatial population genetic structure and low connectivity has been found among European P. elegans populations [Since the planktonic, benthic and intermediate larval developmental modes of patially  and tempulations . Moreoveulations ,52,53.P. elegans populations and its relationship to developmental mode. Following previous observations [P. elegans[e and mostly unrelated individuals expected in those populations with primarily the planktonic developmental mode and lacking asexual reproduction. Our analysis of a poecilogonous species aims to clarify the connection between developmental mode and temporal population genetic variation and could shed light on the role of stochastic environmental variation on the evolution of marine invertebrate larvae.In this study, we examined temporal genetic structure in rvations , we expe. elegans and high. elegans. We alsoP. elegans adults were collected from seven sites in Europe during a period of four years , worm density and in the larval developmental modes that we observed , whereas 11 000 ind. m2 is common at the UK site [Marine intertidal sand and mud flats in the Wadden Sea (NET) and North Sea (UK) were also sampled. In both, sediment sampling was done during low tide when the sediment and worm tubes were partly exposed. In the Netherlands, we sampled a mudflat from the mainland side of Schiermonnikoog Island (site NET) and in the UK, the Drum Sands sand flat  was sampled Table . Even th UK site ,41.P. elegans at this spatial scale  and was not frequent. These observations imply that developmental mode is associated with salinity, but because they are based on a limited number of samples taken primarily in spring, we cannot be sure that other developmental modes at these sites have not been overlooked. For example, we have visited the Finnish sites at different times during spring and summer, but have not observed gametes or larvae in the FIF individuals.For all populations, collecting was done at the same location each year by the same person/s. At each location, approximately 15 sediment samples were collected, with a minimum distance of 0.4 m and maximum distance of 20 m between any two samples. Fine scale genetic patterns may be likely in species with restricted larval dispersal (e.g. ), howeveale (see ). The seale , after which the samples were genotyped using eight highly polymorphic microsatellite markers following protocols described in .IS values for each sample and their statistical significance as well as to test for linkage disequilibrium between loci within the populations. Frequencies of null alleles were estimated using FreeNA [Genetic variation within each sample was estimated by calculating expected and observed heterozygosity and gene diversity with Arlequin v.3.5.1.2 . Also, ag FreeNA .ST values (using Arlequin and 10 000 permutations) and Jost’s [ST/Dest). Pair-wise comparisons of FST were also calculated among populations collected in the same year . Linearized pair-wise FST values were analyzed with PCA in GenAlEx v. 6.5 [To examine proportions of genetic variation explained by both spatial and temporal variation, analysis of molecular variance (AMOVA) was performed with two different sample sets using Arlequin (20 000 permutations). First, an analysis was carried out grouping the samples according to sample year . Second, the samples were grouped according to population . To further investigate temporal genetic variation within the different populations, pair-wise Fd Jost’s  pairwised Jost’s ) among tx v. 6.5  to visuaAfter these analyses, we noticed a large temporal genetic shift in our samples from NET, as described in the results. To further investigate this shift, we used STRUCTURE v.2.3.4,  and alsoP. elegans are not expected to disperse after emerging from the maternal sand tube, we expected that populations with predominately benthic developmental modes would also have a large number of related individuals. To test this hypothesis, we did a sibship analysis using COLONY [P. elegans is not expected to live longer than one year in nature [O, HE and FIS) and FST calculations were repeated to investigate the effect of related individuals on population genetic patterns. We also estimated mean relatedness (r) values for each sample using the maximum likelihood method in ML-Relate [Since the benthic larvae of g COLONY  to identg COLONY , the fuln nature , its abiL-Relate . ML-Relae) can be calculated using temporal methods. These methods are based on changes in allele frequencies between the temporal samples [e assuming closed populations (no migration), but then we also estimated Ne assuming open populations . In these analyses, we defined potential source populations as all the sites reported in this study, except when NET was the focal population. In this case, samples from other locations in the Netherlands and France, reported previously [e values were tested with no change in the results, and Ne max = 10000 was used in the final analyses.If the same site is sampled at two or more time points, a short term effective population size  implemeod , the adE from 0.614 to 0.655) and highest in the UK (HE from 0.797 to 0.799). There were fluctuations in allelic richness and heterozygosity among samples within most populations, with UK, DKR and FIF showing the most consistent values among samples  was lower in 2010 compared to the previous years, but in DKH diversity was higher in 2010 than in previous years. Most fluctuations in genetic diversity within populations were not of high magnitude, except in DKV, where there was a decline in variation from 2008 to 2010, and in NET, where there was a noticeable increase in variation in the 2011 sample. The fluctuations among samples within a population are indicated best by the numbers of private alleles  which were calculated for each population separately and, unlike allelic richness, are not comparable among populations but only among temporal samples within a population. Most extreme increases in the number of private alleles were seen in DKH from 2008 to 2010 (3.5 to 5.3), and in NET from the 2009 and 2010 samples  to the 2011 sample  were similar when calculated with (ENA) and without estimating a null allele correction , we feel confident that the presence of null alleles has not affected our calculations of genetic structure. Regardless, Pe12 was eliminated from the data set when estimating the number of full-sib families with COLONY and when estimating the effective population size. Significant linkage disequilibrium was observed in only a few comparisons within some of the Danish populations , occurring within some years, but not others. Although asexual reproduction is possible in P. elegans, we observed only two instances of identical multilocus genotypes in our samples: one pair of identical individuals in the DKV2008 sample and one pair of individuals in the DKR2010 sample. One individual from each of these pairs was removed from the COLONY, STRUCTURE and PCA analyses.Heterozygote deficiencies were seen in most samples and significant deviations from Hardy-Weinberg equilibrium were observed in some loci in most populations as reflected by significant positive Fes Table . There wanalyses ,54, nullST values in different years were not significantly different . Genetic structure between populations was also evident in our analysis of (within year) population pair-wise FST but there was temporal variation in structure among the Baltic Sea populations , and significant in 2008 and 2010, but not in 2009  as well as between samples from longer time periods (2008 vs. 2010 in FIA). Analysis of either FST or Dest indicated the same patterns of differentiation, except in DKR (Table ST and Dest). Likewise, PCA analysis of linearized FST showed that in most cases, temporal samples from the same population clustered together  that had been sampled only in 2010 and were not the focus of this analysis (data from [ST and Dest between NET2011 and the 2010 samples collected from Denmark and UK (this study). Comparisons with the Danish samples indicated significant genetic differentiation, but with UK, the results were inconsistent  . We fout) Table . STRUCTUFull-sibs were found within almost all temporal samples, but not in FIF and UK Table . In mostU test P = 0.031). In NET, the 2011 sample relatedness is significantly lower than in the previous years , while 2009 and 2010 values were similar (Mann–Whitney U-test P = 0.432). In other sites, the relatedness values among temporal samples (pair-wise tests) were not significantly different.Mean relatedness values for each sample are shown in Figure e produced somewhat different results (Table open) gave the lowest estimates. Overall, the values were low in comparison to estimations of population census sizes based on the densities of P. elegans in our sampling sites. Also, the 95% confidence intervals were wide and overlapping estimates for the different populations. With MLNE (without migration) the Ne estimations were similar in most sites (ranging from 142.1 to 355.5) except in UK, where a higher Ne was estimated (1117.8). With the Moment based temporal method (MBT), there was more variation among the estimates for the Baltic Sea populations, but the confidence intervals were overlapping, so the differences in estimated Ne are not significant. However, when comparing NET and the UK, the UK site has significantly higher Ne when estimated by MBT and MNLEopen methods. The overall trend seen in data is that the Ne estimates were highest in the strictly planktonic population, UK, and lowest in NET.The different methods used for estimating contemporary Nts Table : MLNE ese estimations. The largest change in allele frequencies was seen between the NET2010 and 2011 samples (Fs’ = 0.0635), and this incidence of strong genetic drift is likely the cause of the low mean Ne values estimated for the NET site over the whole sampling period. In contrast, the genetic drift estimate for NET2009 and NET2010 was an order of magnitude lower (Fs’ = 0.0077).The estimated temporal shifts in allele frequencies (i.e. drift) between the samples were quite high in the Baltic Sea populations . However in DKR and DKV, the significant FST comparisons observed with full data set were not observed when full-sibs were removed. In the Netherlands, the NET2011 still remained significantly differentiated from the earlier temporal samples, indicating a strong genetic shift.To reveal the effect of full-sibs within a sample on our analyses, one individual of each full-sib pair was randomly removed  and some of the analyses were repeated with the reduced dataset (see Table U test P = 0.639). In NET, however, relatedness in the 2011 sample still remained significantly lower than relatedness in previous years .As expected, the removal of full-sib individuals also decreased the mean relatedness values calculated for each sample  had more temporal stability in genetic variation, due to their lower fecundity and lower offspring mortality, whereas species with planktotrophic (pelagic) larvae showed temporal fluctuations in genetic structure, possibly due to sweepstakes reproductive success [P. elegans, a poecilogonous species in which the developmental mode of larvae is known to vary among and within populations [Life history traits associated with the planktonic development of marine invertebrate larvae, including high fecundity, high larval mortality, and, for broadcast spawning species, low fertilization success, have been hypothesized to contribute to temporal dynamics in populations and their genetic structure ,19,21,33 success . We examulations . Our stuP. elegans populations in which planktonic larvae are predominant: UK, having only planktonic larvae; and NET, where planktonic larvae predominate, even though other developmental modes are also observed in this population. Both of these sites are intertidal habitats with high worm density. Planktonic P. elegans larvae are known to suffer very high mortality [Strepblospio benedicti, [; and Boccardia proboscidea, [P. elegans in populations from the Baltic Sea (Finland and Denmark), where benthic and intermediate larvae predominate. Although mortality rates are not known for benthic larvae of P. elegans, we assume that they are significantly lower than those for planktonic larvae. In contrast to our expectations, we found temporal genetic variation in populations from the Baltic Sea, temporal genetic stability in the UK population, and evidence of a genetic turnover in NET. Temporal variation in mean relatedness within populations was also found. Several possible explanations for our findings are discussed below.We expected to find temporal variation in the genetic structure of ortality , which cortality , pers. onedicti, ; and Bocoscidea, ). On theP. elegans is known to colonize new or defaunated areas relatively quickly [P. elegans population after a crash in population density. The NET sampling site was muddier in 2011, noticeably different than in the previous years, and the P. elegans distribution at this site was also patchier and less dense than previously . It is possible that the harsh winter of 2010/2011 decreased P. elegans density at this site, which subsequently increased with immigration of planktonic larvae from nearby populations. Indeed, we saw that the NET2011 sample is genetically differentiated from temporal samples collected earlier at the same site (NET2010 & NET2009), but undifferentiated from samples from e.g. other Dutch populations collected in 2010 , but that is not always the case , covering two generations of ons (see ). MetapoST and Dest values in the significant pair-wise comparisons among temporal samples in these populations were not of high magnitude, however, and were lower than seen in NET . We . We ST pal sites ,90 and wP. elegans, the prevalence of planktonic larvae reduces the likelihood of finding full-sibs in a sample. Although we did not examine larval cohorts to specifically test for sweepstakes reproductive success, our results also suggest that in P. elegans there is low variation in the reproductive success among individuals, since larger full-sibs families are more likely to be seen when variation in reproductive success is high [Recently, Iacchei and colleagues  discusse is high ,96. SampP. elegans observed in the Baltic Sea populations may also have a role in explaining the observed patterns of relatedness, and can also explain why cross-year full-sib families were found in Finland and Denmark. Asexual reproduction likely allows certain genotypes of P. elegans to persist in a population over multiple years. There are no reported cases of asexual reproduction at the UK site . Fo. FoP. eless (see ), howevee from temporal allele frequency changes can be biased and result in low estimates if there are overlapping generations, small sample sizes, or short time intervals between samples [P. elegans, [open) may be biased if all potential source populations are not sampled [e estimates obtained with this method were lower than those using MLNE and assuming closed populations, a pattern which has also been reported for other species [e, all have limitations, and our study system may not be ideally suited to these analyses. The wide confidence intervals indicate that these estimates of Ne should be considered with caution. Although it is suggestive, we cannot say with certainty whether the low Ne estimated for the Baltic Sea populations is due to the occurrence of the benthic larval developmental mode, since other unexplored population demographic factors may play a role.It should be noted that the three methods used to estimate contemporary N samples ,104. Alselegans, , pers. o sampled . The Ne  species . AlthougP. elegans populations with planktonic larvae to greater temporal genetic variation. Theoretically, high mortality of larvae via predation, variance in successful larval dispersal and recruitment , or variance in reproductive success can lead to fluctuating genetic patterns . Howeverns . In P. elegans, and potentially other benthic invertebrates, temporal genetic stability of a population is likely described by a complex interplay of larval developmental mode and environmental characteristics of the focal habitat.In the marine environment, larval developmental mode can affect population dynamics and stability via its effects on fecundity, population growth rate and survivorship of larvae and juveniles. It has been suggested that species with planktonic larval developmental modes would show more fluctuations in abundance over time ,24,33 anhttp://doi.org/10.5061/dryad.r90j0.The data set supporting the results of this article is available in the Dryad Digital Repository, DOI: The authors declare that they have no competing interests.JEK and KEK conceived the study. JEK, CB, BWH and KEK collected samples used for the study. JEK and MM collected all genetic data. JEK, MM and KEK analyzed the genetic data. JEK and KEK wrote the manuscript, incorporating contributions from all authors. All authors read and approved the final manuscript.Pair-wise FST values for among populations comparisons within years. These results show significant genetic structure among the populations: the few exceptions are among some geographically close populations (as described in the text).Click here for fileResults of PCA analysis on linearized pair-wise FST. Differences among the temporal samples and populations are visualized.Click here for fileGenetic variation in the temporal samples after the removal of some full-sib individuals. Observed (HO) and expected (HE) heterozygosity and inbreeding coefficients (FIS) are reported.Click here for fileWithin population temporal pair-wise FST values after the removal of some full-sib individuals.Click here for fileMean relatedness (r) in the samples calculated with and without full-sibs (no FS). In DKR 2009, the mean relatedness is significantly lower after the removal of full-sib individuals.Click here for file


P-value<2e–16). Despite this strong correlation, there are significant interactions between SES and skin color that are independent of the relationship between skin color and genetic ancestry. The observed distributions of admixture, genetic variation and skin color and the relationship of skin color with SES relate to historical and social events taking place during the settlement history of Cape Verde, and have implications for the design of association studies using this population.Recently admixed populations offer unique opportunities for studying human history and for elucidating the genetic basis of complex traits that differ in prevalence between human populations. Historical records, classical protein markers, and preliminary genetic data indicate that the Cape Verde islands in West Africa are highly admixed and primarily descended from European males and African females. However, little is known about the variation in admixture levels, admixture dynamics and genetic diversity across the islands, or about the potential of Cape Verde for admixture mapping studies. We have performed a detailed analysis of phenotypic and genetic variation in Cape Verde based on objective skin color measurements, socio-economic status (SES) evaluations and data for 50 autosomal, 34 X-chromosome, and 21 non-recombinant Y-chromosome (NRY) markers in 845 individuals from six islands of the archipelago. We find extensive genetic admixture between European and African ancestral populations , with individual African ancestry proportions varying considerably among the islands. African ancestry proportions calculated with X and Y-chromosome markers confirm that the pattern of admixture has been sex-biased. The high-resolution NRY-STRs reveal additional patterns of variation among the islands that are most consistent with differentiation after admixture. The differences in the autosomal admixture proportions are clearly evident in the skin color distribution across the islands (Pearson r = 0.54,  Populations with peculiar genetic structures offer unique opportunities for studying human population history and for understanding the genetic basis of complex traits. In particular, recently admixed populations that trace their ancestry to multiple continents are especially well suited for identifying genes for traits and diseases that differ in prevalence between parental populations th and the 16th centuries, corresponds to the peopling of Santiago and Fogo islands, both located in the south of the archipelago .The eastern islands of Sal, Boa Vista, and Maio , remainePrevious work on the genetic composition of the Cape Verde islands, based on classic protein markers and autosomal Short Tandem Repeat (STR) loci, detected substantial levels of African-European admixture, with mean proportions of European ancestry ranging from 36 to 54%, depending on the markers and statistical methods used to quantify admixture Despite the potential usefulness of Cape Verde for conducting admixture mapping studies, with the exception of a recent study on iris texture traits Here, we provide a more detailed picture of the patterns of genetic and phenotypic variation in Cape Verde by using objective skin color measurements, and a panel of 50 autosomal, 34 X-chromosome and 21 NRY markers to analyze a total of 845 individuals from six islands that were peopled across different settlement stages. In particular, we explore the following poorly studied aspects of the admixed structure and settlement history of the archipelago: i) the distribution of group and individual ancestry proportions across islands; ii) the relationship between genetic ancestry and skin pigmentation; iii) the interplay between skin color, genetic ancestry and socio-economic status (SES); and iv) the impact of population history on the degree of genetic differentiation between islands.The population sample comprised 845 individuals from the islands of Santiago, Fogo, Santo Antão, São Vicente, São Nicolau, and Boa Vista . With thA total of 646 individuals were characterized for autosomal genetic ancestry. X-chromosome diversity was studied in a subset of 210 males, and NRY diversity was studied in a subset of 232 men augmented with an extra set of 199 male individuals.Information about age, SES indicators, and individual and parent place of birth was collected via questionnaire. To avoid including close relatives in our analysis, we also recorded the individual and both parents’ full name and inquired for acknowledge relationships between donors at each sampling location. With these procedures, we were able to detect pairs of parent/offspring, full siblings, half-siblings and avuncular relatives, of which we removed one individual from the analysis.To minimize missing data we grouped individuals according to their own place of birth . However, the results were not significantly different from analyses based on combined self and parent’s place of birth.All population samples were collected with informed consent according to procedures approved by the IPATIMUP Human Subjects Committee and by the National Ethical Committee for Health Research of Cape Verde.Objective skin color measurements were taken with the handheld narrow-band reflectometer DSMII ColorMeter . The DSMII ColoMeter is an updated version of the DermaSpectrometer  used in previous studies The melanin content was quantified by the melanin index (M index) provided by the instrument, which equals 100×log (1/% reflectance at 655 nm) For each subject, three consecutive measurements were taken on the unexposed upper inner side of each arm and the six measures were averaged to yield a mean M index value per individual.To estimate population and individual admixture proportions, we selected a panel of 50 autosomal and 34 X-chromosome Ancestry Informative Markers (AIMs), showing allele frequency differentials (δ) >0.5 between Europeans and West Africans  Samples were genotyped for 10 NRY unique event polymorphisms  with a hierarchical approach based in the Y-Chromosome Consortium (YCC) phylogeny Group and individual ancestry estimates based in autosomal and X-chromosome AIMS were calculated with the software ADMIXMAP v3.7 for Windows http://www.r-project.org/).Since individual West African ancestry distributions were approximately normal in all islands, we examined differences in the distributions between islands with standard one-way analysis of variance (ANOVA). These analyses were performed in the R statistical computing environment  uncompleted high school; iv) completed high school or uncompleted college; v) completed college, vocational or professional training. For occupation, we created three non-ordered categories: 1) “white collar” professions, combining technical/managerial/administrative activities; 2) “blue collar” occupations, combining craftsman/machine operator/laborer and farming/fishing work; and 3) “other occupations”, including housewife and other occupations. Regarding household amenities, we performed a principal component analysis (PCA) on number-coded answers to 10 questions made in accordance to the census prepared by the Cape Verde National Institute of Statistics . Here we use scores on the two first principal components, which explain 64% of the variation in household amenity features.We measured SES using 3 variables: self-reported education, occupation and household amenities. Education was assessed with a five-level ordinal categorical variable corresponding to: i) up to 2 years in school; ii) 6To evaluate the relationship between skin pigmentation and genetic ancestry, we performed linear regression analysis on the estimated proportion of West African ancestry for the overall sample, including sex and age as covariates. The relationship among SES, skin color, and genetic ancestry, was evaluated through multiple regression analyses on the M index and on the estimated proportion of West African ancestry for the overall sample and for each island, including sex and age as covariates. The analyses were performed in the R statistical computing environment.ST and RST genetic distances for NRY binary marker haplogroups and STR-based haplotype variation, respectively, were calculated with the ARLEQUIN 3.11 software package ST and RST distance matrices was performed using the STATISTICA software package. Haplotype matching analyses among the islands were performed with the full STR set, excepting the DYS385 locus, due to its duplicated status. The relationships between NRY-STR based haplotypes sampled in different islands were assessed in networks constructed with NETWORK 4.5 software (http://www.fluxus-engineering.com). To resolve extensive reticulation, the reduced-median We used the increased resolution of NRY in discriminating between closely related populations to assess the patterns of genetic differentiation within the archipelago. Pairwise FF = 39.59, df = 6, P = 2.2e–16). The highest and lowest mean West African ancestry levels are found in Santiago  and Fogo islands , respectively . In the North . Finally Boa Vista  has an intermediate position, showing significantly higher individual West African ancestry values than the northern cluster and Fogo .The distribution of individual West African (WAfr) ancestry estimated with 50 autosomal AIMs in the six sampled islands from the Cape Verde archipelago is displayed in he North , the islP-value = 1.8e–05), confirming that the admixture pattern was sex biased, mostly involving European man and African women.To formally assess the paternal contribution of different populations, we performed an admixture analysis, using the approach implemented in Admix 2.0 According to the admixture analysis, the majority of male contributions to Cape Verde were derived from the Iberian Peninsula (0.68). The second most important contribution (0.27) came from West Africa, while contributions from Northern Africa and Sephardic Jews seem to have been residual .As with the autosomal and X-chromosome data, NRY-based admixture estimates are not homogeneous across islands . SantiagThe overall distribution of skin pigmentation as measured by the melanin (M) index, ranges from 29.6 to 97.9 with a mean of 53.4 (median of 51.3). Because we measured skin color with an updated version of the reflectometer employed in previous studies of African-Americans populations P = 0.622), and are not correlated with age (P = 0.813), but are clearly different among islands , mimicking the patterns of variation in autosomal West African ancestry levels . The neighboring island of Fogo (mean skin M index = 50.2) harbors the lightest skin M values, together with Santo Antão (mean skin M index = 50.6), São Vicente (mean skin M index = 50.2) and São Nicolau (mean skin M index = 51.6) in the North , but significantly lighter than Santiago .Skin pigmentation levels do not differ between sexes . Although our panel of autosomal AIMs includes five markers located within pigmentation candidate genes .In the total Cape Verde sample, skin pigmentation is significantly correlated with individual ancestry, with a clear trend towards darker pigmentations with increasing levels of West African ancestry , occupation (three non-ordered categories) and household amenities  as dependent variables, and individual West African ancestry based on autosomal data and skin color as independent variables, while controlling for sex and age . In the Because admixture proportions and skin color are not homogeneous across islands, we also tested for associations in each island . For thiST distances and haplogroup frequency data, Santiago is clearly distinguished from the other islands in the first dimension, reflecting its higher levels of West African male ancestry , followed by Boa Vista and São Vicente (51%), Fogo (38%), São Nicolau (35%), and Santo Antão (31%). istances . Howeverth century by the French immigrant Armand Montrond, who is known to have enjoyed a remarkably high reproductive success To better understand the phylogeographic relationships underlying lineage sharing patterns, we further compared the NRY-STR haplotype diversity within the most frequent haplogroups through network analysis . It is cIn this study we present an analysis of admixture and background population structure of the archipelago of Cape Verde using genetic information from autosomal and X-chromosome AIMS, and NRY-specific polymorphisms. The relevance of genotyped sample in terms of size and geographic coverage of the archipelago, as well as the large discriminating power of assayed markers allowed us to add substantial detail to the understanding of the historical factors that have shaped the patterns of genetic diversity within and among local island populations.Population-based admixture proportions estimated with 50 autosomal AIMs confirm our expectations based on historical evidence of extensive genetic admixture between Europeans and Africans in Cape Verde. As far as we know, Cape Verde is presently one of the most highly admixed populations resulting from the mixing of European and African parental contributions The African ancestry proportions estimated with different panels of markers also revealed substantial variation in admixture among the sampled islands, with Santiago showing significantly higher levels of African ancestry than the other islands. This variation is generally consistent with the settlement history of the archipelago, since Santiago was the first island to be peopled and its economy was initially based on a plantation system that largely depended on African slaves There is, however, an apparent discordance to this general pattern: Fogo island displays low African ancestry levels that are similar to the northern islands, even though its settlement history is concurrent with Santiago and based on the same slave labor system To test for the impact of the admixture process on phenotypic variation, we obtained quantitative measurements of skin color, a phenotype that is highly divergent between Cape Verde’s parental populations. There are well-established correlations between skin color and individual ancestry that depend on the admixture dynamics The differences in the admixture proportions among the islands of the archipelago are clearly evident in the skin pigmentation distribution across the islands, since individuals from islands with higher levels of European ancestry tend to have significantly lighter skin colors than individuals from islands where African ancestry predominates . OverallWe investigated the social impact of skin color in Cape Verde by analyzing its relationship with SES and genetic ancestry. Our study may be considered preliminary, since we only used three categorical variables to evaluate SES. A more thorough evaluation of these variables and of other unmeasured socioeconomic differences and how these affect skin color variation is in order. Notwithstanding, the fact that the correlations analyzed were consistent across all three SES measures and that the results were similar when comparing the different islands strengthens our conclusions. We observed significant correlations between SES and skin color, as measured by reflectometry, after adjusting for genetic ancestry, but no correlations between SES and genetic ancestry. This finding suggests that although genetic ancestry is significantly correlated with skin color, it does not fully capture the effect of skin color on the social dimensions of the contemporary population of Cape Verde.The perception of skin color as a basis for social stratification was also observed in other admixed populations Our observation, together with these results, indicate that culturally perceived color, objective measures of skin pigmentation and genetic ancestry may not always be adequate proxies of each other, and their relationship with socioeconomic risk factors needs to be carefully evaluated to completely understand how human biological diversity shapes variation in disease patterns.To investigate the genetic relationship among the Cape Verde Islands, we focused on the patterns of NRY variation, since the higher sensitivity of Y chromosome to genetic drift provides adequate resolution to study microevolutionary events occurring since colonization. Moreover, as the maternal contribution was almost exclusively derived from Africa We found that patterns of NRY-UEP variation essentially reflect differences in admixture proportions between islands, while NRY-STR variation reveals additional patterns of population differentiation . One majTaking into consideration the historical data Consistent with the first hypothesis, we found that Santiago is the most genetically diverse island of the archipelago , and plaAll these results, together with the admixture analysis, are concordant in indicating that the most likely scenario for the colonization of the Cape Verde archipelago may lie in between the two stated hypotheses, suggesting that the various groups of islands have a shared genetic history that results from a common origin in Santiago, followed by differentiation through genetic drift and subsequent input of independent external migrations.In conclusion, our work demonstrates that Cape Verde is a highly admixed population with substantial geographic heterogeneity resulting from different historical and demographic events that have taken place during and since the colonization period.The wide distribution of individual African ancestry anticipates that Cape Verde holds great potential for analyzing the genetic basis of complex phenotypic traits differing between Africans and Europeans, provided that the study has enough resolution to extract ancestry information to control for population stratification and that differences in SES are carefully taken into account.Figure S1Patterns of NRY-STR haplotype sharing between the Cape Verde islands. Only Y-chromosomes found to be shared between at least one pair of islands were included in the calculations.(PDF)Click here for additional data file.Table S1Characteristics of the 50 autosomal AIMS. The table shows the physical and genetic locations, frequencies of the reference sequence allele and allele frequency differences between European and West African parental populations (δ).(DOC)Click here for additional data file.Table S2Characteristics of the 34 X-chromosome AIMS. The table shows the physical and genetic locations, frequencies of the reference sequence allele and allele frequency differences between European and West African parental populations (δ).(DOC)Click here for additional data file.Table S3Populations used for Y-chromosome admixture estimation.(DOC)Click here for additional data file.Note S1Assessment of household amenities.(PDF)Click here for additional data file.


Agelaius phoeniceus) show a mosaic phenotypic distribution, with a “bicolored” form occurring disjunctly in California and Mexico. We analyzed the relationships among these bicolored populations and neighboring typical populations, using ∼600 bp of mitochondrial DNA sequence data and 10 nuclear short tandem repeat loci. We find that bicolored populations, although separated by ∼3000 km, are genetically more similar to one other than they are to typical populations separated by ∼400 km. We also find evidence of ongoing gene flow among populations, including some evidence of asymmetric gene flow. We conclude that the current distribution of bicolored forms represents incomplete speciation, where recent asymmetric hybridization with typical A. phoeniceus is dividing the range of a formerly widespread bicolored form. This hypothesis predicts that bicolored forms may suffer extinction by hybridization. Future work will use fine-scaled geographical sampling and nuclear sequence data to test for hybrid origins of currently typical populations and to more precisely quantify the directionality of gene flow.Geographic distributions of genetic and phenotypic characters can illuminate historical evolutionary processes. In particular, mosaic distributions of phenotypically similar populations can arise from parallel evolution or from irregular patterns of dispersal and colonization by divergent forms. Two phenotypically divergent forms of the red-winged blackbird ( Speciation appears to take place most often in allopatry , behavioral characters , or sexual competitiveness . Such asymmetries imply that differentiation is not always a neutral process and that differences acquired in allopatry – along with the selective forces that favor them – have important implications for the success of new lineages. Moreover, these asymmetries may be an underappreciated factor in community assembly and stability  were synonymized with A. phoeniceus in 1931  and central Mexico  indicates that admixture of bicolored and typical blackbirds is much more widespread than reported in the 1960s . Cycling conditions consisted of 30 cycles of 20 s denaturation at 95°C, 20 s annealing at 55°C, and 45 s extension at 72°C, followed by a 3 min extension at 72°C. For toe pad extractions, reactions were identical to those above, but used 35 cycles instead of 30, and the time for each step was doubled . Most PCR products from toe pads were then reamplified with 25 additional cycles prior to sequencing, in order to increase concentration of PCR product. Because we were not always able to amplify CR in one fragment from toe pad extractions, we designed the following internal primers to amplify and sequence CR in two fragments: BBCR1_intF (5′-CACCTTTGAATTCCCCTAGTCT-3′) and BBCR1_intR (5′-GCTTGGGTGYTCYTGAAGGCT-3′).DNA was extracted with DNEasy kits  according to the manufacturer's protocols. For toe pad samples, we added 20 μmol of dithiothreitol (DTT) in aqueous solution to the digestion stage to aid in keratin proteolysis and concentrated final elutions by ethanol precipitation and resuspension of DNA in 1/5 the normal volume of buffer. We sequenced 595 bp of the mitochondrial DNA control region (CR) from each sample. The sequenced region is highly variable and information dense .All products were purified with Exonuclease and shrimp alkaline phosphatase  and sequenced using ABI BigDye, version 3 chemistry, and an ABI 3730XL capillary sequencer . All fragments were sequenced in both directions. Reads were assembled, trimmed, and edited in Sequencher, version 4.1 . Sequences were aligned by hand; alignment of A. phoeniceus, which were previously tested for optimal annealing, consistent amplification, homology, and variability in both bicolored and typical populations . For toe pad extractions, primer quantities were adjusted to increase the quantity of fluorescently labeled PCR product. These reactions were as above, but with 1.25 pmol forward primer, 5 pmol reverse primer, and 5 pmol dye-labeled M13 primer. Cycling consisted of 25 cycles of 15 s denaturation at 95°C, 15 s annealing at primer-specific temperatures, and 20 s extension at 72°C, followed by 10 similar cycles with 53°C annealing temperature, and finally a 7 min extension at 72°C.We genotyped each individual at five short tandem repeat (STR or microsatellite) loci  developed specifically for GeneMarker, version 1.91 . Raw size data were binned using the method of Idury and Cardon  and reference fragments to assess variation across runs and dyes. Size scores were estimated by the analysis of electropherograms in d Cardon  implemenBEAST, version 1.7.2 . We then assigned individuals to clusters using discriminant functions. We determined the appropriate number of discriminant functions to retain by estimating the optimal α score, using randomization of alleles to ensure that models were not overfit. Finally, we plotted individuals on the discriminant function axes in order to visualize variation within and among the sampled populations.In addition to population-level analyses, we clustered individuals using two complementary analyses of the STR data. First, we inferred genetic clusters using discriminant analysis of principal components  values, we estimated the posterior probability for each value of k and selected the value of k with the highest probability for subsequent analyses. We then performed 100 runs for this value of k, aggregated runs in clumpp  to nearly 0.16 (between Lerma and Veracruz), and similar variability was shown by other loci and measures of differentiation  to the exclusion of the two typical populations . The concordance between the DAPC and Structure results is notable, as it indicates that the inferred number of genetic clusters is insensitive to the different assumptions of these two methods. In addition, the assignment of individuals to clusters was strongly correlated between the two methods .As with the DAPC analysis, s 2 Fig. , with a s 2 Fig. . In runsP < 0.01). This model implies dispersal from California to Mexico, with little or no dispersal in the other direction (Table P = 0.15), in this case implying higher dispersal rates from Shasta Valley to Black Point than the reverse.We found strong evidence of gene flow between populations. First, in all cases, the model with minimum AIC included migration Table . In addion Table . The secm1→2 + m2→1]/2) between Black Point and Lerma estimated by coalescent analyses of all the data were higher than between any other pair, although this difference is not statistically significant , to abrupt transitions . The presence of multiple abrupt transitions between phenotypes yields a “mosaic” pattern, where isolated populations may all be different from one another, or where some populations (adjacent or otherwise) share phenotypic characteristics. Mosaic patterns of variation, with geographically disjunct but phenotypically similar forms – as seen in the red-winged blackbird – are particularly interesting, as there are relatively few evolutionary processes that can produce them, and these processes are of intrinsic interest. On the one hand, adaptation in a heterogeneous landscape can drive population differentiation, and similar phenotypes may arise if disjunct regions share similar selective environments . Alternatively, differentiation in allopatry, followed by partial displacement of one postisolation form by another, can yield similar disjunct patterns. Examples of both these patterns have been reported previously, but are relatively rare.Gasterosteus aculeatus) has revealed repeated, independent evolution of multiple locally adapted freshwater forms from an oceanic ancestor. These disjunct forms show consistent phenotypic changes related to locomotion and feeding in different habitats (Schluter and McPhail Chaetodipus intermedius) has evolved dark coat coloration on multiple disjunct lava flows in the desert southwest. Although quite similar in pelage, these populations are not distinct from one another or surrounding populations based on mtDNA  suggests that this pattern is an historical species difference rather than local adaptation, most likely due to contraction of P. shermani and expansion of P. teyahalee distributions since the last deglaciation , which are behaviorally dominant to red wings and currently sympatric with them in California  rather than local selection. Notably, there is nothing apparent in the environments of central Mexico and California that might select for the observed plumage differences, particularly parallel changes in both male and female plumage. We are currently conducting analyses of environmental similarities between these regions using niche modeling  as one of a growing list of examples of asymmetric hybridization and suggest that the biological determinants of this phenomenon in blackbirds merit additional study.Cases of asymmetric hybridization between closely related but incompletely isolated forms offer insight into the processes influencing species differentiation at its earliest stages. Recognition and analysis of such cases contributes significantly to the study of speciation. In particular, the fact of asymmetric range expansion reveals the existence of biologically meaningful differentiation – be it demographic, behavioral, or otherwise – between hybridizing forms. Comparative analysis of multiple lineages showing such characteristics may reveal generalities regarding the selective factors that drive such differences  or the lack of such . Here, we offer red-winged blackbirds (


Sus scrofa has not been compared yet. Herewith, we have explored the genetic relationships among 43 wild boar from Europe (N = 21), Near East (N = 19) and Korea (N = 3), and 40 Iberian (N = 16), Canarian (N = 4) and Mangalitza (N = 20) pigs by using a high throughput SNP genotyping platform. After data filtering, 37,167 autosomal SNPs were used to perform population genetics analyses. A multidimensional scaling plot based on genome-wide identity-by-state pairwise distances inferred with PLINK showed that Near Eastern and European wild boar populations are genetically differentiated. Maximum likelihood trees built with TreeMix supported this conclusion i.e. an early population split between Near Eastern and European Sus scrofa was observed. Moreover, analysis of the data with Structure evidenced that the sampled Iberian, Canarian and Mangalitza pigs did not carry any autosomal signature compatible with a Near Eastern ancestry, a finding that agrees well with previous mitochondrial studies.The lack of a Near Eastern genetic signature in modern European porcine breeds indicates that, although domestic pigs from the Fertile Crescent entered Europe during the Neolithic, they were completely replaced by their European counterparts in a short window of time. Whilst the absence of such genetic signature has been convincingly demonstrated at the mitochondrial level, variation at the autosomal genomes of European and Near Eastern  A fundamental contribution to understand how pigs were domesticated was provided through the analysis of mitochondrial sequences from a worldwide sample of pigs and wild boar Sus scrofa with a combination of mitochondrial, Y-chromosome and autosomal microsatellite markers et al.One important drawback of mitochondrial analyses is that they just give a partial view of the total diversity of a given species. As stated by Bruford Sus scrofa diverged about 0.6–1.6 MYR Table S1). One possible reason for this result would be ascertainment bias i.e. the Illumina Porcine SNP60 BeadChip was built on the basis of 19 reduced representation libraries derived from four swine breeds  and a single wild boar population Sus scrofa populations  can be severely underestimated. Of course, reduced diversity could be also the consequence of genetic drift combined with past demographic events such as founder effects and bottlenecks. In the case of wild boar, it is well known that excessive hunting and progressive loss of habitat have caused a sustained demographic decline that, in certain cases (e.g. United Kingdom), ended with the local extinction of this species By using a whole genome SNPs typing technique, we have analysed the autosomal diversity of wild boar from Europe  and the Near East  as well as of domestic pigs from Spain (Iberian and Canarian breeds) and Romania . Remarkably, the Romanian and Spanish populations are located at the two Eastern and Western extremes of the geographic distribution of European pig breeds, respectively. Besides, Iberian and Mangalitza pigs have not been significantly introgressed with Far Eastern blood , Figure S1) showed that wild boar from Turkey, Iran and Armenia cluster together and independently from those of Russia, Belgium and Spain. In the study of Ramírez et al.Figure S1 illustrate that pig and wild boar specimens clustered in strict accordance with their geographic origin, a feature that evidences the existence of a significant level of population structure . This conclusion is supported by the highly significant pairwise FST values we have found, that range from 0.129 to 0.247 , might explain in part this lack of consistency. At m = 1 we found evidence of gene flow between European Sus scrofa and Canarian pigs, but its direction was opposite to what was expected (Canarian→European Sus scrofa). Indeed, the Canarian breed has a very limited geographic distribution and census (it faced extinction thirty years ago) and there is no reason to believe that it has participated in the foundation of other European breeds. More likely, this result can be explained because of the artificial parental status  of Canarian pigs. We also detected evidence of gene flow between Far Eastern wild boar and Mangalitza pigs when we modeled 3 migration events . Besides, when we made a second analysis of population structure with the software Admixture  we found that the most likely K-value was 5, since Iberian pigs and European wild boar, despite their genetic affinity, were distinguished as belonging to two separate populations. As mentioned by Goedbloed et al.i.e (1) Near Eastern wild boar, (2) Mangalitza pig, (3) Iberian pig, (4) European wild boar and (5) Korean wild boar, whilst Canarian pigs  had a mixed origin, as previously discussed.Although the method of Evanno pointed to K = 4 as the most significant K-value, a plot of the log likelihood of K indicated that the true K-value is 5–6 , although at K = 6–10 the Near Eastern cluster was split into two Iranian and Armenian vs Turkish subclusters evidencing the existence of population substructure. As a whole, we must conclude that, in contrast with our previous observation Interestingly, in the Structure analysis Figure 3Figure S3) analyses. This finding was very consistent although its significance is limited by the reduced number of sampled individuals. The TreeMix analysis showed evidence of gene flow from Near Eastern to the European main cluster at m = 2 and 3, but not at other m-values  was slightly higher than that between Iberian pigs and European wild boar (FST = 0.129). Similar results were obtained by Ramírez et al.Sus scrofa is bidirectional et al.Finally, we would like to discuss the close genetic relationship we have found between Iberian pigs and European wild boar. The multidimensional scaling plot shown in T values Table 2 e.g. peas, beans, dairy waste etc.), exploitation of heterosis through crossbreeding and implementation of selection schemes, did not begin until the end of the 17th century, as a consequence of increasing human population densities and progressive deforestation i.e. pannage) that in the past were broadly widespread across Europe. Historical data suggest that in the last 9,000 years European pigs and wild boar shared a similar ecological environment Sus scrofa specimens, the Porcine HapMap project is expected to reveal the magnitude, direction and timing of this gene flow as well as to unveil the genetic ancestry of a wide array of European breeds.According to Vilà Sus scrofa DNA samples were provided by people listed in the Acknowledgments section. These samples have been referenced in diverse publications Table S1. Nine Turkish and one Iranian samples with low genomic DNA concentration were amplified using the REPLI-g UltraFast Mini Kit (Qiagen) for whole genome amplification. Genotypes were inferred with the Illumina Porcine SNP60 Beadchip following manufacturer instructions. SNP data were filtered by imposing thresholds of 0.95, 0.05, and a P-value of 0.00001 for the call rate, minor allele frequency (MAF), and Hardy-Weinberg equilibrium test, respectively. X-chromosome SNP were excluded from the population genetics analyses.Pig and wild boar genomic DNA obtained in previous studies ST calculations were carried out with the same software, using 1,000 permutations to infer statistical significance. Average observed and expected heterozygosities were estimated with PLINK v. 1.07 The multidimensional scaling plot was based on the calculation of genome-wide identity-by-state pairwise distances with the PLINK whole genome association analysis toolset Sus scrofa populations were inferred with TreeMix v. 1.04 i.e. the fraction of alleles in the descendant population that originated in each parental population . We tested for a range of migration events , using the Korean population as an outgroup.The patterns of population splits and mixtures in Table S1Sample sizes of the Sus scrofa populations analysed in the current study with the Illumina Porcine SNP60 BeadChip.(DOC)Click here for additional data file.Figure S1Multidimensional scaling plot of wild boar and pig populations based on genome-wide identity-by-state pairwise distances. Korean wild boar was excluded from this analysis to facilitate the visualization of the genetic relationships amongst the remaining populations.(TIF)Click here for additional data file.Figure S2Estimates of the most likely number of clusters in the Structure analysis derived from the log likelihood associated with each K-value, i.e. L(K) mean and standard deviation (blue points), and the second order rate of change of the likelihood, i.e. delta K (red points). When K is approaching to its true value, L(K) reaches stability or decreases moderately.(TIF)Click here for additional data file.Figure S3Bar-plot of Admixture results and cross-validation error for each K- value. The lowest CV-error indicates the most likely K-value (K = 5).(PPT)Click here for additional data file.


KIR gene content polymorphism has been studied worldwide, only a few isolated or Amerindian populations have been analyzed. This extremely diverse gene family codifies receptors that are expressed mainly in NK cells and bind HLA class I molecules. KIR-HLA combinations have been associated to several diseases and population studies are important to comprehend their evolution and their role in immunity. Here we analyzed, by PCR-SSP (specific sequencing priming), 327 individuals from four isolated groups of two of the most important Brazilian Amerindian populations: Kaingang and Guarani. The pattern of KIR diversity among these and other ten Amerindian populations disclosed a wide range of variation for both KIR haplotypes and gene frequencies, indicating that demographic factors, such as bottleneck and founder effects, were the most important evolutionary factors in shaping the KIR polymorphism in these populations.Although the  Killer cell immunoglobulin-like receptors (KIR) are expressed on natural killer (NK) cells and subsets of T cells playing an important role in innate immunity and influencing also the course of adaptive immune responses. These cells are regulated by many activating and inhibitory signals, including signals delivered by the interaction between KIR and their ligands The known ligand of KIR2DL1 is HLA-C2 while KIR2DL2 and KIR2DL3 interact with HLA-C1 and some C2 allotypes KIR genes and alleles is not always clear. Seemingly 3DL1 and 3DS1  are alleles of the same locus 2DL2 and 2DL3 are 3DL1 and 3DS1 or lack both 2DL5 genes, named 2DL5A and 2DL5B and these same haplotypes may contain one or two 2DS3 genes KIR genes and pseudogenes became blurred since it has been recognized that the 3DP1 pseudogene has one functional allele 2DL4 and 2DS4 genes have common non-functional alleles KIR genomic region at which the evolutionary recent expansions and contractions by unequal crossover played an important role The distinction between the 3DL3, 2DL3, 2DP1, 2DL1, 3DP1, 2DL4, 3DL1, 2DS4, 3DL2. However, a few A haplotypes missing one or more of these genes above have been described . In other Amerindians, the frequency of 2DS3 varies from zero in Wichi, Yucpa, Bari and Tarahuama to 20% in Warao 2DS3 gene at considerable frequencies (>20%), the highest frequency being reported for Australian Aborigines (81%) 2DS3, while most Eastern Asian including Japanese, Korean and Chinese populations present frequencies lower than 20% The framework genes quencies , with th2DS4 is the only short tailed activating gene present in the haplotype A, the most frequent haplotype worldwide KIR1D2DS4*003, *004, *006, *007 and the uncommon *008, *009, *010, *012 and *013 alleles. Alleles that do not present the 22pb deletion are the common 2DS4*001 alleles and the rare *011, *014, *015 alleles  to 24% in the Guarani-Ñandeva  into a set of values of linearly uncorrelated variables called principal components. The first principal component has the largest possible variance, accounting for as much of the variability in the data as possible, and each succeeding component has the largest variance under the constraint that it be uncorrelated with the preceding components. Similar populations will be plotted nearby in a XY graph. Both approaches are powerful to compare populations and can corroborate the results of each other. All Amerindian populations grouped together in the dendrogram (We used KIR gene frequencies to measure differences between the populations and to compare them to other previously described groups. We used two different approaches: (1) estimating genetic distances and drawing a dendrogram and (2) performing a principal component analysis. The first method is a quantitative approach that estimates genetic distances between populations based on their gene frequencies and show these distances in a dendrogram that groups populations presenting similar frequencies. The second method converts a set of observations of possibly correlated variables , so no evidence of natural selection on the KIR-HLA combinations was detected by this approach. However, the number of individuals having none of the known functional KIR-HLA combinations was zero in the population samples analyzed, an indication that natural selection could be maintaining at least one KIR-HLA functional pair per individual.Next, we analyzed the frequencies of KIR receptors combined with the frequencies of their known HLA ligands in every population. As KIR profiles, the set of KIR genes present in one individual, also named phenotypes or genotypes throughout the literature, can be used as markers of certain ancestral groups, being more informative than frequencies of individual genes. We found 10 to 16 different profiles in the study populations  profile (up to 41.5%) were seen in Amerindian populations from Mexico and Venezuela 2DS3 gene and its frequency varies from 12.5% to 37.7% in Amerindian populations, which present the highest frequencies of this profile worldwide. It is seemingly absent in Africa and presents low to intermediate frequencies  in other continents. Considering the haplotypes inferred, this profile is compatible with genotype Hap1/Hap3 or Hap2/Hap4. Taking into account the haplotypic frequencies and the frequency of the profile, probably most of the ascertained individuals have genotype Hap1/Hap3. The expected proportion of these two genotypes in the total population sample is 0.88 versus 0.12, respectively.The A/B #2 (ID 3) profile was the most frequent in the Kaingang group (27%) and is amongst the three most common profiles also in the other study populations (13% to 22%). It is characterized by the absence of The profiles #4 and #5  presented frequencies of 10% and 11% in the Kaingang population, occurring at lower frequencies in the three Guarani groups. Both are shared with other Amerindian populations, but only the Warao in Venezuela present a similarly high frequency (10%) of profile #5 and only in the Yucpa profile #4 is commoner than in the Kaingang . In non-Amerindians, profile #4 is virtually absent, while profile #5 was observed at low frequencies (1%–2%) in some populations of other continents excepting Africa  to estimate the frequencies of haplogroups A and B, because it was not possible to differentiate between the A/B and B/B genotypes. Starting with the observed frequency of A/A homozygotes, we estimated that the haplogroup B frequencies in Guarani varied from 37% in the Guarani-Ñandeva to 58% in Guarani M’byá. In the Kaingang, the haplogroup B frequency was 52% . We also2DS3 differ between the study populations and generally among the Amerindians in South America. The frequency of 2DS3 differs significantly (P<0.001) between the Kaingang and the Guarani, varying from zero in the Kaingang to 8% in the Guarani-M’byá. Furthermore, the frequency of the group of alleles which share the 22 bp deletion in 2DS4 differs among the study populations  and frequencies of alleles may have arisen because of natural selection in response to pathogens KIR and so of haplogroup B would be higher It is suspected that the variation of the presence/absence of The impact of demographic factors is further corroborated by the frequencies of profiles and inferred haplotypes. Profiles #2 and #3 vary from 13% to 22% and 15% to 36% respectively in the Guarani groups  and haplThe frequencies of the genes, haplotypes, haplogroups and profiles discussed above do not follow clines along the continent or geographic regions, an observation that also supports the hypothesis of major founder and bottleneck effects and random genetic drift.KIR/HLA genotypes and variation of susceptibility to multifactorial diseases have been reported KIR3DL1/S1 polymorphism KIR genes and their HLA ligands in the present study was close to zero indicates that the selection pressure at least at this level is low. We reported a similar conclusion when analyzing a Brazilian urban population In order to test the hypothesis of HLA/KIR co-evolution we looked for correlations between the frequencies of KIR and their respective HLA ligands in the four Amerindian populations. For none of the three analyzed receptor-ligand pairs the observed frequencies differed from those expected under equilibrium (P>0.05). In addition, the observed frequencies of individual KIR and their known or putative HLA ligands were not correlated. These results are compatible with independent evolution and the hypothesis of neutral evolution of receptor-ligand combinations was not rejected. The combinations of KIR variants and specific variants of the cognate HLA ligands should be analyzed in future studies to refine the analysis in search of signatures of natural selection. Several examples of associations between Internal nodes of a dendrogram based on genetic distances as performed in this study do not necessarily represent common ancestry. The similarities and differences between extant populations can be consequences of genetic drift, bottleneck, founder effect and other demographic factors and of natural selection, besides common ancestry. The limitations of the different typing methods also have to be considered, as they could be the reason of some unexpected grouping. Altogether, our results of both genetic distances  and prinKIR gene frequencies in Amerindians, the absence of individuals who lack functional KIR recognition provides evidence that natural selection may also be involved in evolution of the KIR polymorphism in these populations. Our results are not suited to reject the hypothesis that natural selection may be acting on KIR evolution. However, based on our data, we conclude that demographic factors have been more important in shaping KIR gene frequencies in Amerindians than other evolutionary factors. Notwithstanding, analyzing isolated populations worldwide for both KIR and HLA diversity using different approaches would be a good step to bring more light to this discussion.Although our data support the hypothesis of a major influence of demography on The coordinator of the project (MLPE) contacted the FUNAI  and visited the indigenous areas to explain the purposes of the project and to obtain the consent from the authorities of the population. After permission, the research team went to the indigenous areas. Persons who were willing to participate came spontaneously to the health facility and were informed about the procedures and the purposes of the work. Participants provided verbal informed consent. Written consent was not required in 1988, when the samples were obtained. The ethics committee approved this procedure. The study was part of a protocol of population genetics of Amerindian groups approved by the Brazilian National Commission on Research Ethics (CONEP) under number 2046/2001.The study included 327 individuals from four different Amerindian populations from Brazil: Guarani M’byá , Kaingang , Guarani Kaiowá  and Guarani Ñandeva . The GRC and KRC are from the indigenous area Rio das Cobras, municipality of Nova Laranjeiras, Paraná state , living in different villages. The GKW are from the reservations areas of Amambai  and Limão Verde . GND are from Amambai and Porto Lindo  in southern Mato Grosso do Sul state, central Brazil. More information about these populations can be found in Petzl-Erler et al. (1993) KIR genes was performed by PCR-SSP with the primers sets that have been previously described 2, 1× buffer and 0.25U of Taq Platinum DNA Polymerase (Invitrogen). Cycling conditions were: 2 min at 94°C; five cycles of 94°C for 5 sec, 65°C for 15 sec and 72°C for 30 sec; 21 cycles of 94°C for 5 sec, 60°C for 15 sec and 72°C for 30 sec; 4 cycles of 94°C for 5 sec, 55°C for 15 sec and 72°C for 30 sec. Gel electrophoresis on 3% agarose and ethidium bromide staining were used to visualize the presence or absence of the amplicons corresponding to each of the KIR genes and also to detect the group of non-functional alleles of 2DS4 characterized by a 22 pb deletion.The typing of all HLA typing was previously performed by PCR-SSOP  and the phenotypic frequencies for the whole set of genes analyzed (KIR gene profile) were obtained by direct counting. Gene frequencies (GF) were estimated using Bernstein’s formula GF = 1−√(1−PF). The frequencies of haplogroups A and B were obtained by two approaches: (1) applying Bernstein’s formula; (2) after haplotype inference, by direct counting of haplotypes belonging to groups A and B.The frequency of individuals carrying each KIR profile frequency data assuming that: (1) linkage disequilibrium patterns in these populations would be the same as previously described worldwide; (2) haplogroups or haplotypes widespread among populations, as haplogroup A, would exist also in the Amerindian populations analyzed. The haplotypes were assigned using the Haplo-IHP software that employs a greedy-EM hybrid algorithm. The expectation-maximization (EM) algorithm is an iterative procedure that uses unphased multilocus genotype frequencies along with the assumption of Hardy-Weinberg proportions to converge on final haplotype frequencies estimates. Additionally the Haplo-IHP software requires an a priori list of known/possible haplotypes as input. We used the list of haplotypes described previously a priori haplotype list. As constraints for the estimation, we considered 3DL3 and 3DL2 present in all individuals and 2DL2/2DL3 and 3DL1/3DS1 as alleles of same locus. Haplotypes were also inferred manually, based on linkage disequilibrium and the segregation in some Amerindian families.Haplotype inference was performed using the software Haplo-IHP KIR gene frequencies of the study populations and those of 57 worldwide distributed populations with distinct ancestries were used to calculate Ney’s http://forrest.psych.unc.edu/research/), to visualize populations genetic relationships by means of a two dimensional plot. The non-informative pseudogenes and framework genes were excluded from these analyses.The The expected frequency of co-occurrence of KIR and their HLA class I ligands was estimated and compared to the observed frequency using the chi-square test. The receptor/ligand pairs tested were KIR3DL1/S1+HLA Bw4, KIR 2DL2/3+HLA-C group 1, and KIR2DL1+HLA-C group 2. In addition, we counted in each individual the number of functional KIR–HLA combinations to estimate their average number and to determine the proportion of individuals in the population lacking functional receptor/ligand combinations.