Clock, which influences rhythms in both physiology and behavior. Latitudinal variation in this gene may hence reflect local adaptation to seasonal variation. In some bird populations, there is also an among-individual association between Clock poly-Q genotype and clutch initiation date and incubation period. We examined Clock poly-Q allele variation in the Barn Swallow (Hirundo rustica), a species with a cosmopolitan geographic distribution and considerable variation in life-history traits that may be influenced by the circadian clock. We genotyped Barn Swallows from five populations (from three subspecies) and compared variation at the Clock locus to that at microsatellite loci and mitochondrial DNA (mtDNA). We found very low variation in the Clock poly-Q region, as >96% of individuals were homozygous, and the two other alleles at this locus were globally rare. Genetic differentiation based on the Clock poly-Q locus was not correlated with genetic differentiation based on either microsatellite loci or mtDNA sequences. Our results show that high diversity in Clock poly-Q is not general across avian species. The low Clock variation in the background of heterogeneity in microsatellite and mtDNA loci in Barn Swallows may be an outcome of stabilizing selection on the Clock locus.Recent studies of several species have reported a latitudinal cline in the circadian clock gene,  Clock, has been sequenced from hundreds of taxa, and in vertebrates it codes for a protein (CLOCK) that serves as a transcription factor (as a heterodimer with BMAL1) in the core circadian clock oscillator Clock gene is highly conserved among vertebrates throughout most of its sequence; however, its C-terminal region contains a variable poly-glutamine (poly-Q) motif with variability in the number of glutamine repeats both among and within species By providing the opportunity to explore the association between genetic variation and adaptive phenotypic differentiation, candidate genes contribute to our understanding of the genetic basis of traits that are of ecological importance Clock poly-Q variation provides an opportunity to examine its association with life-history traits that may be related to circadian clock. Studies on various taxa have reported an association between Clock genotype and breeding latitude Clock genotypes have been detected in the Blue Tit (Cyanistes caeruleus) but not for the Bluethroat (Luscinia svecica) Tachycineta swallows Clock genotype and breeding phenology (clutch initiation date and incubation duration) in a Blue Tit population Parus major) population, in which Clock poly-Q variation was low Clock variation and life-history traits.Hirundo rustica) is one of the world's most widely distributed passerines, with a broad geographic distribution throughout most of the northern hemisphere. The six described subspecies Clock poly-Q and life-history traits related to the timing of seasonal reproductive behavior.The Barn Swallow . These populations differ in several life-history traits related to breeding latitude, migratory behavior and breeding phenology  and a population from the United Kingdom (H. r. rustica). The Old World and New World clades are divergent in mtDNA with ∼1.6% sequence difference Clock poly-Q region in Barn Swallows, (b) examine genetic variation among pairs of five populations sampled from three subspecies in relation to differences in their life history traits and (c) compare genetic differentiation at the Clock locus to that of presumably neutral loci . Comparisons among the Clock locus and loci that are selectively neutral provide an important background to examine whether the Clock locus is under selection or exhibits patterns of neutral evolution.In this study we examined henology . We sampWe studied five breeding populations of Barn Swallows representing three of the six described subspecies. In each of these breeding populations we collected blood samples from adults during the breeding season and stored them in lysis buffer until DNA was extracted. Population characteristics and sample sizes used in this study are presented in Clock poly-Q region and to verify the genetic sequence, we first amplified this region . All Barn Swallow sequences were identical to each other and differed only in the number of poly-Q repeats  or using the DNAeasy blood Extraction kit . In order to examine variability in Barn Swallow  repeats . The firClock poly-Q region using the genotyping primer set developed by We then screened 49 adults from Israel, 49 from the United Kingdom, 49 from New York, 31 from California and 52 from Argentina for length polymorphism in the Clock marker and the two neutral markers (microsatellites and mtDNA) are not equal due to sample and data availability. However, similar analyses with a subset of individuals that were analyzed for all three markers  generated similar results. Therefore, we report here only results using all available samples.Mitochondrial and microsatellite loci for the Israeli and UK populations were analyzed as part of a population genetics study which focus on the Old World samples 2, 0.25 mM of each nucleotide, 0.25 µM from each primer, and 0.025 U jumpstart Taq polymerase (Sigma-Aldrich). PCR amplification conditions were as follows: initial denaturation at 95°C for 4 min 30 s; 30–35 cycles of denaturating at 95°C for 1 min, annealing at 54°C or 62°C for 1 min, and extension at 72°C for 2 min; and a final extension at 72°C for 4 min 30 s. PCR products were purified using Exonuclease and Shrimp Alkaline Phosphatase enzymatic reactions . Purified reactions were cycle sequenced in both directions using amplification primers and ABI BigDye Terminator. Sequencing products were cleaned using Sephadex columns and processed with an ABI 3730 Automated DNA Analyzer (Applied Biosystems). We aligned forward and reverse strands for each specimen and checked them using Sequencher 4.7 (Gene Codes Corp.). All sequence data were deposited in GenBank (Accession Nos. JN642465-JN642524 and HQ333550-HQ333663).We amplified and sequenced the mitochondrial protein-coding gene nicotinamide adenine dinucleotide dehydrogenase subunit 2 (ND2) for a subset of each population sample . We usedH. rustica or other birds were analyzed for all populations: Escµ6 2, 0.25 mM of each nucleotide, 0.12–0.24 µM from each primer, and 0.025 U jumpstart Taq polymerase (Sigma-Aldrich). PCR amplification conditions were as following: initial denaturation at 95°C for 5 min; 35 cycles of denaturating at 95°C for 30 s, annealing at 50°C or 58°C for 30 s (specific per multiplex PCR mix), and extension at 72°C for 30 s; and a final extension at 72°C for 30 min. PCR products were genotyped on an ABI 3100 Genetic Analyzer (Applied Biosystems) with GeneScan-500 LIZ (Applied Biosystems) as molecular size standard. Allele sizes were estimated using Genemapper v3.7 (Applied Biosystems) and verified and amended by eye to ensure standardized data collection.A total of six nuclear microsatellite loci previously developed for Clock poly-Q region for each population were calculated using ARLEQUIN v3.11 STF values in ARLEQUIN v3.11 Observed and expected heterozygosities for the STF) between populations estimated from the various genetic markers.We used Pearson correlation matrix in order to examine the relationship between pairwise genetic differentiation values . Results are presented as mean ± SE unless stated otherwise.This study was conducted under the permits of Israel Nature Reserve Authority (permit numbers 28234-2007 and 31345-2008), UK Home Office (project license number 30/2740), Dirección de Administración de Areas Protegidas y Conservación de la Biodiversidad, Provincia Buenos Aires, Argentina (collecting permit number 6607) and the U.S. Fish and Wildlife Service .6, 7, 8, corresponding to 6–8 poly-Q repeats coded by CAG codons , whereas all individuals from one population (Argentina) were homozygous to the ClkpolyQ7 allele. Mean observed heterozygosity (H = 0.030) was correspondingly low and ranged from 0.000 for the Argentina population to 0.082 for the Israeli population.We genotyped a total of 230 Barn Swallows from five populations representing three subspecies. Overall, we found only three different length-variant alleles, ClkpolyQG codons . HoweverClock poly-Q alleles, we could test for deviation from HWE only in the Israeli population, which did not show deviation from HWE (P = 0.150).Owing to the low variation in We genotyped Barn Swallows from the five populations at six polymorphic microsatellite loci. Mean number of alleles for all loci was 11.83 (±4.50 SD) and ranged from 5.40 for Ltr6 to for 14.80 for Escu6 . Most of the populations did not deviate from HWE; however we detected deviation from HWE for the Argentina population in Hir11 and for the New York population in Escu6 and Hir19 . We found no evidence for significant LD for any pair of microsatellite loci.We aligned most of the ND2 gene (1023 bp) for 174 individuals from the five populations and detected 45 unique haplotypes: 21 for the Old World populations and 24 for the New World populations, which represented two discrete clades (minimum of 13 nucleotide changes between them). Out of the 21 Old World haplotypes, two common haplotypes were shared by individuals from the Israeli and UK populations. Three New World haplotypes were shared by individuals from all three New World populations, while two haplotypes were shared by individuals from the New York and Argentina populations.Clock poly-Q locus (STF = 0.0095±0.005) was much lower (and not significant) compared to genetic differentiation based on six microsatellite loci  or on mitochondrial haplotypes . Analysis of genetic differentiation for each of the microsatellite loci separately showed that only Hir20 (STF = 0.0082±0.003) had similarly low values to the Clock poly-Q locus and the other five loci had higher STF values (range: 0.021–0.078).We examined genetic differentiation between all pairs of populations based on the different genetic markers. Mean genetic differentiation based on the 2r = 0.858, t = 6.691, P = 0.0001), while similar comparisons between Clock poly-Q locus pairwise STF values and either mitochondrial haplotypes  or microsatellite loci  pairwise STF values were not correlated. Separate analyses for each of the microsatellite loci showed that pairwise STF values based on these loci were positively correlated with mitochondrial haplotype pairwise STF values (range of Pearson correlation coefficient: r = 0.237–0.864), although only correlations for Escu6, Ltr6 and Hir11 were significant. Parallel correlations of pairwise STF values based on these microsatellite loci to Clock poly-Q pairwise STF values were not significant and lower than the correlations between pairwise STF values of these microsatellite loci and mitochondrial haplotypes (range of Pearson correlation coefficient: r = −0.177–0.734), except for Hir19 which showed a significant correlation with the Clock poly-Q pairwise STF values.Pairwise genetic differentiation values based on mitochondrial haplotypes and microsatellite loci were highly correlated  accounting for 98% of the total alleles , and a mean observed heterozygosity of only 0.030 (range: H = 0.000–0.082). This variation is much lower than similar values reported for the Blue Tit , Bluethroat  11-Q15; H = 0.077) Tachycineta swallows  Clock poly-Q in Barn Swallows was also reflected in the genetic differentiation analysis that showed no differentiation among populations based on this locus, while similar analyses based on mtDNA haplotypes or microsatellite loci showed significant and correlated differentiation between these populations  showed contrasting findings. While neutral loci reflected, as expected, greater divergence between the Old World and New World clades and less structure within these clades Clock poly-Q locus precluded us from associating it with any of the populations' life-history traits . Still, the sole non-migratory population (H. r. transitiva from Israel) showed the highest genetic variation . Longer tandem repeats are more likely to be more polymorphic, therefore the lower variation observed for Barn Swallows may be related to their relatively shorter Clock poly-Q repeats. However, despite long Clock poly-Q repeats in a Great Tit population (Q11–15), polymorphism was relatively low Clock poly-Q14 allele. Clock poly-Q variation in Barn Swallows in based on CAG-repeats as well, which may be also tightly regulated. However, CAG-repeat polymorphism exists also in the much more polymorphic Blue Tit and Bluethroat Why is 7 allele is under positive selection in Barn Swallows . However, Johnsen et al. Clock poly CAG repeat at a single length in several species is at least suggestive that the variation is itself selectively advantageous. Our results demonstrate homogeneity in the locus in a background of heterogeneity of presumably selectively neutral loci. This scenario suggests that the Clock poly-Q variation does reflect the demographic processes among the Barn Swallow population, and is more expected from a selection operating on the Clock locus to maintain the low variation while neutral processes, such as genetic drift, may be contribute to the variation in unrelated microsatellite and mtDNA loci Selection for a specific allele could explain the low variation observed in Barn Swallows (and Great Tits). It is possible that the QClock poly-Q locus in Barn Swallows might represent a holdover from an ancestral population that itself had low variation in the Clock locus. Examination of the Clock poly-Q locus in a few individuals of other swallow species in the genus Hirundo  has shown that they are also homozygotes for the Q7 allele . In addition, the Barn Swallow population from Argentina, which showed no polymorphism in the Clock poly-Q locus, was established only in the 1980's, most likely from North American populations Clock poly-Q may stem from a founder effect. However, even in this newly founded population, presumably selectively neutral loci (mtDNA haplotypes and microsatellite loci) displayed much more variation than the Clock poly-Q locus. We cannot exclude the possibility that these presumably selectively neutral loci were already more variable in the ancestral population. However, it is more likely that the Clock poly-Q locus in Barn Swallows is not entirely neutral and that some type of selection is operating to reduce variation in the poly-Q region.Alternatively, this low variation in the Clock gene is associated with photoperiod, then the nature of selection for this gene  could vary depending on whether individuals in that species or population had increased reproductive success relying on non-photic or photoperiod cues. Social cues (associated with mate choice preferences) are very important in determining reproductive success in Barn Swallows Clock gene variation reflects social cues primacy over photoperiod cues in this species. Examination of this hypothesis warrants future work.The timing of the central clock can be adjusted by several environmental cues. Photoperiod cues have been studied extensively; however the effects of non-photic cues on reproduction have been demonstrated as well


ST and increased allelic richness revealed temporal genetic changes in six of the populations. These changes were highly significant in four of them. For example, 76% and 100% of the fish comprising the contemporary samples for the rivers Vosso and Opo were excluded from their respective historical samples at P = 0.001. Based upon several genetic parameters, including simulations, genetic drift was excluded as the primary cause of the observed genetic changes. In the remaining 15 populations, some of which had also been exposed to high numbers of escapees, clear genetic changes were not detected. Significant population genetic structuring was observed among the 21 populations in the historical  and contemporary data sets , although significantly reduced with time (P = 0.008). This reduction was especially distinct when looking at the six populations displaying temporal changes . We draw two main conclusions: 1. The majority of the historical population genetic structure throughout Norway still appears to be retained, suggesting a low to modest overall success of farmed escapees in the wild; 2. Genetic introgression of farmed escapees in native salmon populations has been strongly population-dependent, and it appears to be linked with the density of the native population.Each year, hundreds of thousands of domesticated farmed Atlantic salmon escape into the wild. In Norway, which is the world’s largest commercial producer, many native Atlantic salmon populations have experienced large numbers of escapees on the spawning grounds for the past 15–30 years. In order to study the potential genetic impact, we conducted a spatio-temporal analysis of 3049 fish from 21 populations throughout Norway, sampled in the period 1970–2010. Based upon the analysis of 22 microsatellites, individual admixture, F Delineation of historical genetic structure can provide an insight into how contemporary evolutionary relationships among populations have been shaped by demographic, environmental and anthropogenic factors. Understanding these processes and their potential interactions will assist in predicting how natural populations are likely to evolve in relation to present and future challenges.Salmo salar) is no exception to these characteristics, and the analysis of molecular genetic markers has revealed highly significant population genetic structuring throughout its entire range Salmonid fishes provide excellent opportunities to study evolutionary relationships among populations in both time and space. They inhabit a variety of habitats and display phenotypic and life-history variation among populations The contemporary population genetic structure of Atlantic salmon can be ascribed to a hierarchical system, whereby the largest genetic differences are observed among fish from different continents and regions Atlantic salmon populations have been heavily exploited and influenced by a wide-range of anthropogenic factors over a long period of time Genetic changes in native Atlantic salmon populations as a result of introgression from farmed escapees have been observed in Ireland Norway is the world’s largest commercial producer of Atlantic salmon, and is the country where the highest numbers of farmed escapees have been recorded on the spawning grounds. Therefore, Norway represents an ideal country in which to examine how genetic structure has changed both within and among native Atlantic salmon populations in response to widespread migration of farmed escapees onto the spawning grounds. Here, we have conducted a spatio-temporal genetic analysis in order to investigate the potential genetic impacts of farmed escapees on population structure throughout an entire country.Atlantic salmon farming in Norway is currently based upon rearing multiple domesticated strains and sub-strains that were initially founded on fish originating from over 40 Norwegian rivers in the 1970’s Increasing the complexity of detecting genetic introgression of farmed escapees in wild Atlantic salmon populations is that the farmed strains  have, and continue to change significantly with time, i.e., some of the populations used at an earlier stage have been terminated or combined with other strains, while new sub-strains  have been established. Consequently, it is not possible to accurately reconstruct the allele frequencies of the farmed escapees in Norway over the 15–30 year period in which this study is conducted. Nevertheless, despite the above challenges, modeling has demonstrated that gene flow from farmed escapees will lead to a reduction in genetic structure among wild populations As a consequence of the situation described above, the methodological approach implemented in this study is to look at both within and among-population genetic structure in the time-period where the numbers of escapees reported in Norwegian rivers has been highest . Have native Norwegian salmon populations displayed temporal genetic changes in this period? And if so, can genetic drift be excluded as the primary driver of these temporal changes? Furthermore, where temporal genetic changes have been observed, have the populations become more similar or more differentiated to each other?Historical and contemporary samples of Atlantic salmon populations were collected from 21 rivers spanning the entire Norwegian coastline which extends over 2500 km , 2. PopuHistorical samples were exclusively represented by fish scales taken from adult spawners captured in their specific rivers by rod and line . IntermeSome of the relevant available information for the populations included in this study, which can be found in Norwegian reports Samples of farmed salmon have been included for the analysis of admixture. These samples were selected from multiple data sets that have been analysed to identify the farms of origin for escapees as a DNA forensic service for the Norwegian ministry of fisheries in the period 2006 - present DNA extraction was performed in 96-well plates using the Qiagen DNeasy®96 Blood & Tissue Kit. Each DNA plate contained two or more negative controls.SSsp3016 (Genbank no. AY372820), SSsp2210, SSspG7, SSsp2201, SSsp1605, SSsp2216Ssa197, Ssa171, Ssa202SsaD157, SsaD486, SsaD144Ssa289, Ssa14SsaF43SsaOsl85MHC IMHC IISsa19NVH (Genbank no. AF256670), CA060208SsalR002TKU and SsalR010TKUThe following twenty two microsatellite loci were used; Microsatellites are known to be prone to genotyping errors For most of the statistical analyses conducted, samples were grouped into historical, intermediate and contemporary data sets. Other sub-sets of the data set were analysed for specific tests . These variations are identified in the results. Bonferroni adjustment of the significance level for multiple testing was not presented. Instead, statistical significance was tested at α 0.05 and a more stringent level of α 0.001.Ho, expected heterozygosity He, allelic richness, FIS and pairwise FST were calculated using MSA 4.05 The genotype distribution of each locus in each population was compared with the expected Hardy-Weinberg distribution using the program GenePop ST among historical populations was significantly larger than the global FST among contemporary populations, a bootstrap test based on 10 000 re-sampled datasets was computed. For each resample, the global FST in historical and contemporary data was calculated based on a random sample of 30% of the individuals from each population and 30% of the markers (7 out of 22). After re-sampling, the distribution of the 10 000 differences between historical and contemporary FST was used to test the alternative hypothesis  against the null hypothesis .In order to test whether the global FThe program Geneclass 2.0 ST) in the historic and contemporary data sets, Mantel tests were conducted with the software PASSaGE In order to investigate the potential relationship between geographic and genetic distance  was computed in each river. This was conducted separately for both the historical and contemporary samples, using the one sample linkage disequilibrium method implemented in the program LDNE Ne influences genetic drift over multiple loci simultaneously, these simulations were conducted 1000 times for each population assuming Ne of 25, 50, 75, 100, 200, 300, 400 and 500, and setting a non-overlapping generation interval to 5 years. The number of generations in which drift was simulated was thereafter a function of the number of years between the historical sample and the corresponding contemporary one, divided by 5, and then rounded up to the nearest whole generation. The genetic distance (FST) between the observed historical genetic profile for that population, and the 1000 simulated contemporary populations at each level of Ne, were then compared to the genetic distance that was actually observed between the historical and contemporary sample. The probability that the observed pair-wise FST was greater than the genetic drift simulated FST was thereafter computed. As in ST values exceeding the genetic-drift simulated FST values for that population. These simulations were also used to look at global FST values, and evaluate allelic richness in the presence of genetic drift.Genetic drift may be considered as a random evolutionary process whereby a population’s allele frequency at one or more loci can change through time. This process is especially influential in small populations D157, to 99.4% for the marker SsaF43. When genotyping success was broken down into the historical and contemporary data sets, coverage was 94.8% and 97.9% respectively.The final data set consisted of 3049 salmon displaying a mean genotyping coverage of 96.1%. Coverage ranged from 87.1% for the marker SsaFrom 9314 alleles scored independently on two occasions, a mean genotyping error rate (defined here as inconsistent scoring between two independent runs of the same sample) of 0.1% was computed. The absolute number of alleles scored twice/errors observed = 7506/7, 806/1, and 1002/2 for the historical, intermediate and contemporary samples respectively. This is consistent with previous estimates for these Analysis of HWE and LD can identify technical issues (marker robustness and genetic linkage between loci) and biological processes (mixing of populations and population disturbance through introgression). At the significance level of α 0.05, a total of 32 (7.1%), 5 (2.9%) and 32 (7.2%) loci by sample combinations displayed significant deviations from HWE in the historical, intermediate, and contemporary samples respectively .When computed for all combinations of pairs of loci, within each population separately, LD was detected 309 (6.4%) and 35 (0.7%) times among the historical samples, 122 (6.6%) and 12 (0.6%) times in the intermediate samples, and 422 (8.7%) and 25 (0.5%) times in the contemporary samples at α 0.05, and α 0.001, respectively. Deviations were distributed evenly among the different combinations of pairs of loci, but unevenly distributed among the samples . For exaST estimates in the historical and contemporary data sets  and five under possible stabilizing selection , whereas the contemporary data set showed the same loci under possible directional selection but only two of the former ones under possible stabilizing selection   . SubsequR. Looking specifically at temporal variation of AR within populations, most showed a very slight increase with time, however, the populations Vosso, Opo and Loneelva increased by 18–27 . The change in AR from the historical to the contemporary samples was significantly higher  in the six populations showing temporal genetic changes (mean increase per population  = 15.8), than in the six ones displaying the strongest temporal stability (mean increase per population = 2.6).When considering data from the set of 22 loci, and the 14 neutral ones separately, statistically significant temporal genetic change, as measured by Fulations . PopulatST between the historical and contemporary samples for the same population     . When us<0.0001) . However = 0.20) . The lac = 0.20) , Fig. 2.F-statistics. Thus, populations such as Opo, Vosso, Loneelva and Vestre Jakobselv, which showed temporal genetic changes in FST, also showed evident signs of admixture  and the contemporary samples both for the total and neutral sets of microsatellites. The assessment of ▵K in single-population assignment analyses revealed that the most likely number of clusters ranged between two and three ; Fig. S1dmixture .ST values (R2 = 0.86 P<0.0001). For example, the populations Opo, Vosso and Loneelva displayed the highest pair-wise FST values between historical and contemporary samples  in addition to the highest exclusion rates . While other assignment methods implemented in the program Geneclass gave different absolute exclusion percentages, the above trend remained.The percentage of fish from each contemporary sample that was excluded from its historical population sample when conducting genetic assignment ranged from 0–100% when using all 22 loci, and 0–90% when using the reduced set of neutral loci . There wST among the 21 historical samples was significantly larger than among the 21 contemporary ones  and contemporary (0.027) data sets. Inspection of the pair-wise FST values among the six populations displaying within-population changes showed that all of them contributed to the distinct temporal decrease in global FST  and in the contemporary samples . When looking specifically at the six populations not displaying temporal genetic change, a strong relationship was found in the historical , and contemporary data sets . When examining the six populations displaying temporal genetic change, the relationship between genetic and geographic distance was not statistically significant in either the historical  nor the contemporary data sets .Using data from all 22 markers, a significant relationship between geographic and genetic distance was observed for the total set of populations both in the historical ] and the ad hoc statistic ΔK, for historical and contemporary samples when using the 22 loci  and the analysis . Turninganalysis ; Fig. S1In order to investigate whether the inclusion of farmed salmon would improve the power to detect temporal genetic changes in population genetic structure (either within or among populations), samples from nine genetically distinct farm sources were included in the admixture analyses. Runs were conducted for K = 12 and K = 13 as the analyses included salmon from 9 distinct farm samples, and, that K for the northern and southern clusters had already been estimated at 3 or 4. Both sets of analyses were conducted with and without a prior for the farm samples (which made no difference to the result). As expected, samples from the farms were confirmed to be highly distinct to each other, whereas wild populations were strongly admixed in both the historical and contemporary samples . Thus, iNe) was larger than 200  In most of the historical and contemporary samples representing each population, the computed effective population size . In these cases the pair-wise FST that was observed between the historical and contemporary sample was greater than the pair-wise FST between the historical sample and the simulated population in all the replicates . In the river Figgjo, a value of Ne of 300 or more would be required to achieve the same level of significance (P<0.001). Comparing these genetic drift simulations with the computed Ne values . This is at the level of Ne where the potential for genetic drift to contribute to temporal genetic changes on the time-scale studied can be excluded at modest levels of statistical significance   , Table 7ST value between the six populations displaying statistically significant temporal genetic changes after having simulated genetic drift independently within each  when it is stronger than the influence of gene-flow hin each . The “glith time .P = 0.001 and when using data from all 22 loci. At the same time, genetic drift was excluded as the primary contributing factor. These changes have occurred during 15–30 years, equivalent to approximately 3–6 generations in native populations. Thus, these data demonstrate that farmed Atlantic salmon have successfully introgressed and caused genetic changes in some wild Norwegian populations.This study represents one of the largest temporal analyses of population genetic structure conducted thus far. Samples covering an entire country, and spanning up to four decades, have permitted the identification of genetic changes occurring both within and among 21 populations, through time. Two main conclusions can be drawn from these analyses. First, despite the fact that farmed escapees have been recorded on the spawning grounds for all of the populations studied, outnumbering wild conspecifics in some years in some of the populations, only weak to moderate changes in among-population genetic structure have been observed in the time-period studied, and in most rivers, statistically significant temporal genetic changes were not observed. This demonstrates that generally, farmed escaped salmon have had poor to moderate success in the wild. Second, not all populations were equally resilient. Genetic changes were observed in six of the populations (29% of those studied), and in four of them, the changes were highly significant. For example, 100%, 76% and 52% of the fish comprising the contemporary samples for Opo, Vosso and Loneelva were excluded from their respective historical baseline samples at Pinctada margaritifera cumingii) throughout French Polynesia Salvelinus fontinalis) populations in Canadian lakes A weak to moderate but statistically significant reduction in population genetic structure was observed among the 21 populations with time. This is consistent with an increase in gene flow, and has been previously reported in response to extensive supplementation and translocations of brown trout in Denmark ST nearly halved between the historical and contemporary data sets. From population genetics theory Aquila adalberti) Panther onca) Ne estimates, and based upon simulations, genetic drift was conclusively excluded as the primary driver of the observed temporal genetic changes within most of these rivers. Furthermore, genetic drift was demonstrated to be incompatible with the observed drop in differentiation among these populations with time, and not least, cannot explain the increase in the number of alleles observed in all of these populations. Therefore, in consideration of the genetic data and simulations presented, characteristics of these populations, the high numbers of escapees observed on the spawning grounds  For several technical reasons, it is possible that the estimated level of within-population temporal genetic changes, as estimated by the 22 microsatellites implemented here, is lower than the ilibrium . FurtherNe) represents an important parameter in conservation genetics as it provides information about the potential for genetic drift, inbreeding and natural selection to act upon populations. A range of methods for computing Ne are available, and may be broadly split into temporal Ne for both the historical and contemporary samples separately, in order to estimate whether these were small populations likely to be under the influence of genetic drift. All methods of computing Ne include underlying assumptions that are rarely fulfilled in the populations in which they are implemented. For example, linkage disequilibrium, which is the primary parameter used to estimate Ne in single-sample methods, can be caused by several factors not related to Ne, such as immigration and overlapping generations. Both of these two underlying assumptions were violated by the populations in the present study, although the LD method implemented by Ne estimations presented here should be treated with some caution, they nevertheless provide indications regarding each population’s effective size, and thus potential for genetic drift.The effective population size , intermediate (I) and contemporary (C) samples for 21 Atlantic salmon rivers separately.(DOC)Click here for additional data file.Figure S2Hierarchical Bayesian clustering of the 21 rivers in the historical and contemporary data sets.(DOC)Click here for additional data file.Figure S3Bayesian clustering of the 21 rivers in the historical and contemporary data sets when combined together with data from 9 distinct farm sources.(DOC)Click here for additional data file.Table S1Years in which samples were taken for the historical, intermediate, and contemporary data sets.(XLS)Click here for additional data file.Table S2Allele frequencies observed in the historical and contemporary data sets for 22 microsatellite markers.(XLS)Click here for additional data file.Table S3Locus by sample summary statistics for the historical, intermediate and contemporary samples collected from 21 Norwegian rivers.(XLS)Click here for additional data file.Table S4Effective population size for samples in the historical and contemporary data sets as computed by the LD method as implemented in LDNE .(XLS)Click here for additional data file.Text S1Description of methods and results for identification of markers putatively under selection.(DOC)Click here for additional data file.


Adaptation to seasonal changes in the northern hemisphere includes an ability to predict the forthcoming cold season from gradual changes in environmental cues early enough to prepare for the harsh winter conditions. The magnitude and speed of changes in these cues vary between the latitudes, which induces strong selection pressures for local adaptation.Drosophila montana, a northern maltfly, by defining the photoperiodic conditions leading to adult reproductive diapause along a latitudinal cline in Finland and by measuring genetic differentiation and the amount of gene flow between the sampling sites with microsatellites. Our data revealed a clear correlation between the latitude and the critical day length (CDL), in which half of the females of different cline populations enter photoperiodic reproductive diapause. There was no sign of limited gene flow between the cline populations, even though these populations showed isolation by distance. Our results show that local adaptation may occur even in the presence of high gene flow, when selection for locally adaptive life-history traits is strong. A wide range of variation in the CDLs of the fly strains within and between the cline populations may be partly due to gene flow and partly due to the opposing selection pressures for fly reproduction and overwinter survival. This variation in the timing of diapause will enhance populations’ survival over the years that differ in the severity of the winter and in the length of the warm period and may also help them respond to long-term changes in environmental conditions.We studied adaptation to seasonal changes in  Local adaptation is a crucial source of biodiversity and one of the key processes that may lead to speciation. Several scenarios for sympatric and parapatric speciation suggest that this kind of adaptation plays an important role especially in the early stages of population divergence , while the phenomenon was not so clear on the island of Honshu (41°30′–34°52′N) located south of Hokkaido. Moreover, there was a clear disruption in the cline between the islands  and studied whether the population samples from different parts of the cline show any sign of genetic differentiation. Drosophila montana females show clear photoperiodic responses and enter adult reproductive diapause when the day length decreases below the CDL  in autumn 2008. In spring–summer 2009, we determined the CDLs for D. montana females from these populations  by studying the diapause incidence of females held in four light:dark (LD) cycles: 16:8, 17.5:6.5, 19:5, and 20.5:3.5. However, at the end of summer 2009, we managed to get flies from a sixth, more southern locality  and, to achieve a longer cline, we also determined CDLs for this population . In Lahti, the longest day in the summer lasts only about 19 h and so we omitted from this experiment LD 20.5:3.5 and added there two shorter day lengths 14:10 and 15:9. As the diapause percentages of females in Pudasjärvi, Paltamo, and Jyväskylä had not reached 100% in LD 16:8 in the first set of experiments, we studied the diapause responses of isofemale lines from these populations also in LD cycles 14:10 and 15:9.2 anesthesia within one day after their emergence before the sensitive period for diapause  to obtain 80–150 females per line for each photoperiod. Females were put in vials  containing 7 mL of yeast-sucrose-agar medium . However, while this classification is straightforward in short and long day lengths, the ovaries show more variation in their shape, transparency, and nutrient accumulation when the day length approaches the CDL. As a result, near the CDL, our classification of females as “reproducing” was slightly relaxed to include those where at least one well-formed ovariole filament was found. For each locality of the cline, the CDL for female reproductive diapause was determined by calculating the percentages of diapausing females from four isofemale lines tested in each LD cycle.The flies were sexed under light COr medium  with somTo check how well our data fit the hypothesis of correlation between the locality/latitude and CDL, we used nonlinear regression analysis in the drc package in R . With thGenomic DNA was extracted from wild-caught individuals of both sexes  using thFST) of D. montana cline populations was estimated using data from the 12 microsatellite loci. To test genotypic linkage disequilibrium and Hardy–Weinberg equilibrium (HWE), we used GENEPOP 4.0.10 software package  test in GENEPOP 4.0.10. To get another estimate of differentiation, we also calculated DEST differentiation statistics  with a burn-in period of 30,000 generations and 1,000,000 Markov Chain Monte Carlo (MCMC) iterations. Isolation by distance was tested using a Mantel test in GENEPOP version 4.0.10.Population structure  revealed a significant relationship between these parameters . Variation in CDLs within populations was 1–1.7 h  showed strong deviations from HWE due to null alleles in all populations (data not shown) and were discarded from further analyses. Pairwise FST and DST comparison, calculated between pairs of cline populations,  and expected (HE) heterozygosity values are given in R2= 0.464; P= 0.038) indicating limited gene flow between the most distant populations.Testing for population differentiation did not reveal any structure among the lations, , were quD. montana cline was approximately 2 h. This corresponds well with Danilevsky's rule that the CDL in genetic clines lengthens 1– 1 h and 30 min per 5° of latitude northwards , the flies of the D. virilis group species, including D. montana, have a gap between the adult generations in July, when the overwintered flies have already died and the first summer generation has not yet emerged  has found that the CDLs of 14 D. montana isofemale lines from one of our study populations (Oulanka) vary by about 2 h, which suggests that our data using four lines per location already give a fairly good estimate of the amount of within population variation. All of our study strains had been collected in autumn and kept in the laboratory under diapause-preventing conditions for about 6 months before using the flies in the experiments. In these conditions , the diapause behavior of the flies remains invariable for years  finding patterns of local adaptation in stress-related traits. Both of these studies included microsatellite analyses and showed that gene flow between populations was high. However, the fact that microsatellite markers do not indicate any population structure does not rule out the possibility that the cline populations could differ from each other at specific chrosomosomal areas or genes that are under selection. Another, more powerful, approach for tracing the differences between the cline populations would be to perform genome scans for the most northern and southern populations of the cline. This method might also help identify “genomic islands,” that is those parts of genome that have diverged between populations due to different selection pressures, or to detect “regions” of local adaptation at the genetic level (e.g., The finding that local adaptation can lead to strong latitudinal clines in phenotypic characters in spite of gene flow is not unique. el e.g., . It woul


Triticum aestivum) from 17 populations across the United States and Mexico.Single nucleotide polymorphisms (SNPs) are ideally suited for the construction of high-resolution genetic maps, studying population evolutionary history and performing genome-wide association mapping experiments. Here, we used a genome-wide set of 1536 SNPs to study linkage disequilibrium (LD) and population structure in a panel of 478 spring and winter wheat cultivars (ST) at individual loci showed a high level of heterogeneity across the genome. In addition, seven genomic regions with elevated FST were detected between the spring and winter wheat populations. Some of these regions overlapped with previously mapped flowering time QTL. Across all populations, the highest extent of significant LD was observed in the wheat D-genome, followed by lower LD in the A- and B-genomes. The differences in the extent of LD among populations and genomes were mostly driven by differences in long-range LD ( > 10 cM).Most of the wheat oligo pool assay (OPA) SNPs that were polymorphic within the complete set of 478 cultivars were also polymorphic in all subpopulations. Higher levels of genetic differentiation were observed among wheat lines within populations than among populations. A total of nine genetically distinct clusters were identified, suggesting that some of the pre-defined populations shared significant proportion of genetic ancestry. Estimates of population structure (FGenome- and population-specific patterns of genetic differentiation and LD were discovered in the populations of wheat cultivars from different geographic regions. Our study demonstrated that the estimates of population structure between spring and winter wheat lines can identify genomic regions harboring candidate genes involved in the regulation of growth habit. Variation in LD suggests that breeding and selection had a different impact on each wheat genome both within and among populations. The higher extent of LD in the wheat D-genome versus the A- and B-genomes likely reflects the episodes of recent introgression and population bottleneck accompanying the origin of hexaploid wheat. The assessment of LD and population structure in this assembled panel of diverse lines provides critical information for the development of genetic resources for genome-wide association mapping of agronomically important traits in wheat. Aegilops tauschii for the D-genome [In crops, the level of genetic diversity and linkage disequilibrium (LD) can be affected by various factors including demography and inbreeding -6, selecD-genome ,21 resulD-genome . While dD-genome ,19. The D-genome .Interpreting patterns of genetic diversity in modern crop cultivars is further complicated by strong selection and interbreeding with landraces and genetically distant wild relatives. Regions of the genome subjected to recent selection or introgressions from landraces or wild relatives were shown to have elevated LD and low genetic diversity ,12,23. FT. turgidum) retained more than 50% of their initial LD value at distances up to 20 cM [On the practical level, the distribution of genetic diversity in modern cultivars plays an important role in the choice of specific mapping and crop improvement strategies. In recent years, association mapping was shown to be a powerful method for complementing the traditional gene mapping studies based on controlled crosses ,28-30. Tto 20 cM . In theoRecent advances in DNA sequencing and genotyping have enabled genome-wide studies capable of characterizing genetic variation and the extent of LD in natural and breeding populations. Single nucleotide polymorphism (SNP) has become the most frequently used type of molecular marker for these analyses in many species because of their high abundance across the genome and the availability of cost-effective high-throughput genotyping assays -34. One The wheat lines included in our study represent diverse cultivars utilized in 17 wheat breeding programs including 9 winter and 8 spring wheat populations. All cultivars were selected to represent the current genetic and phenotypic diversity of a specific state's breeding program. The phenotypic traits targeted by breeding programs include: disease resistance , winter survival for winter wheat breeding programs in the Midwest, end-use quality, terminal heat tolerance, resistance to drought stress, yield potential, early maturity, resistance to the wheat stem sawfly and herbicide tolerance. The complete list of lines with their pedigrees is provided in the Additional File T. aestivum cv. Chinese Spring and Jagger. Repetitive elements were detected and masked by comparing sequences with the TREP [® Assay Design Tool (ADT). The ADT generates designability rank scores for each SNP that can vary from 0 to 1. The SNPs with scores above 0.6 have a high probability of being converted into a successful genotyping assay. A total of 1536 SNPs were selected for developing the wheat OPA  for theh allele . Analyse5 and 106, respectively, were selected based on the convergence of summary statistics (log probability of data) among several independent runs. Results of independent runs for the same value of K were summarized using the CLUMPP program [For analysis of population structure, the SNP dataset was divided into the three genome-specific datasets and one combined dataset. To reduce the effect of frequency correlation between linked alleles, we selected SNP loci located approximately 4 cM or farther apart from each other. The A-genome dataset included 91 SNP loci while the B-genome and D-genome dataset included 89 and 39 SNP loci, respectively  present in the dataset was estimated by plotting the probability of data ln Pr(X|K) for each value of K. The variation of ln Pr (X|K) among independent simulation runs with the same value of K and the rate of ln Pr (X|K) change from K-1 to K was used to select the optimal number of populations in the sample  implemented in the software EIGENSTRAT . Data frST for individual loci and the components of variance for two levels of population hierarchy using methods described by Weir and Cockerham [ST provides a measure of population differentiation by estimating the correlation of alleles within the same sub-population relative to that found in the entire population. The overall distribution of genetic variation in the wheat cultivars was estimated for two levels of population hierarchy: growth habit (spring and winter wheat) and origin (breeding populations). The winter wheat population from Kansas was excluded from the analysis due to the insufficient number of lines. The mean FST values in a sliding window of 5 consecutive linked SNPs were calculated to identify genomic regions genetically differentiated between spring and winter wheat lines. A 95% confidence interval (CI) for mean FST values was estimated by sampling 1,000 times with replacement the sets of 5 SNPs randomly selected from the 849-SNP dataset and taking the 95th percentile of the distribution of means.The differentiation of populations was further investigated by estimating Fockerham  and Weirockerham  as impleockerham . FST proST levels were compared with the positions of previously mapped or cloned flowering time QTL. The sequences of genes containing SNPs included in the wheat OPA were compared with the sequences of gene-derived flanking molecular markers  used in QTL mapping studies. The syntenic relationship between wheat, rice and Brachypodium genomes was used to compare and validate map positions.Regions of the wheat genome showing elevated FAe. tauschii genetic map reported by Luo et al. [r2 , according to Weir [r2 for pairs of SNP loci and plotting them against genetic distance. The relationship between LD decay and genetic distance was summarized by fitting a locally-weighted linear regression (loess) line to r2 data. The statistical significance of individual r2 estimates was calculated by the exact test following Weir [r2 values were plotted using the R package LDheatmap [th-percentile of the distribution of r2 values for unlinked SNP loci [For measuring LD, the locations of gene loci harboring SNPs on the o et al.  were use to Weir . In ordeing Weir . The faling Weir . ChromosDheatmap . Blocks Dheatmap  and implDheatmap . BackgroSNP loci .The genotyping of 478 spring and winter wheat lines with multiplexed 1,536 Illumina Golden Gate SNP assay generated 734,208 genotyping data points , B- (89 loosely linked SNPs), and D- (39 loosely linked SNPs) genomes and a combined set of 219 SNPs covering the entire wheat genome  [K values. These observations were consistent with previously reported analyses of population structure in barley and maize breeding populations using multi-locus SNP data [K in this study was based on the analysis of relationship between Pr (X|K), value of K and the variation of Pr (X|K) among multiple independent runs of Gibb's sampler.To identify the optimal number of Pr (K|X)  and ΔK [Pr (K|X)  for eachPr (K|X)  showed aSNP data ,57. TherK from 2 to 5 for the A-genome SNP dataset was consistent among multiple independent runs of Structure  for values of K = 4 or higher  of population structure revealed that the first two principal components can separate the wheat populations into 3 clusters Figure . The eigAnalysis of molecular variance components based on the 849 SNPs showed that wheat lines within breeding populations are more highly genetically differentiated than wheat lines among different populations, which is reflected by a higher proportion of variance within breeding populations than among breeding populations and growth habit groups Table . A higheST estimates for individual loci between spring and winter wheat populations inferred a high level of heterogeneity across chromosomes  relative to the A- (mean FST = 0.1) and B-genomes (mean FST = 0.11). Up to 50% reduction in variation of single-locus FST estimates can be achieved by calculating the group means of adjacent SNP markers  for window-based FST values. The confidence interval was used as a threshold to identify regions showing FST values higher than the genome-wide mean. Seven regions with FST values higher than 95% CI were detected on chromosomes 2A, 2B, 5A, 6B and 7B , followed by lower LD in the A- (mean 16.7 - 19.8 cM) and B-genomes (mean 20.4 - 21.5 cM)  decaying to 50% of its value at about 10.5 cM. In the remaining spring wheat populations LD decayed to half of its initial value within 6-9 cM range  was only slightly lower than the proportion of variation among subpopulations within the growth habit groups (12.9%). Historical gene flow between the spring and winter wheat groups during crop improvement and breeding can potentially be responsible for the low level of genetic differentiation between these populations. The proportion of variance between growth habit and among populations was significantly lower than the within-population genetic variance, indicating that each of the breeding programs included in our study employs genetically diverse lines. These results also indicate that the polymorphic SNPs included in the wheat OPA are represented in most populations and, therefore, will be useful for genotyping diverse collections of wheat cultivars.K using the A- and D- genome SNP sets, the proportion of genetic ancestry of cultivars in these clusters was variable for different SNP sets, implying that the three wheat genomes have different degrees of genetic differentiation among breeding programs. This outcome may be a consequence of inadequate representation of SNP alleles within a particular genome in different populations, or alternatively, can reflect the different impact that demography, population and breeding history had on genomes of wheat lines. Strong selection for adaptation to diverse environmental conditions, together with different founders and introgression histories can modulate the differentiation of allelic frequencies among breeding populations and genomes and result in the slightly different clustering patterns obtained here using the three genome-specific SNP sets. A similar trend was documented for US, Australian and UK varieties using DArT markers [In spite of the high proportion of shared SNPs among populations as well as small among-population genetic variance components, the model-based clustering approach was able to successfully assign cultivars to clusters. The clustering analysis performed using the whole genome SNP set produced more genetically distinct clusters than clustering obtained with smaller sets of SNPs from the A-, B- or D- genomes. Although cultivars can be optimally clustered at the same value of  markers , which s markers .ST obtained for the D-genome.Even when the full SNP data set was used, wheat cultivars in the 17 wheat populations rarely shared the same membership coefficient in the inferred clusters reflecting the complexity of the breeding histories of lines included in this analysis. Most wheat lines showed evidence of admixture with the portions of their genomes assigned to 2 - 4 different inferred clusters, which is an expected result of the frequent crosses used in wheat breeding programs between adapted germplasm and the donors of different traits. When the whole population was forced to divide into two groups (K = 2), the clusters aligned mainly by growth habit, with most spring and winter wheat cultivars being assigned to separate clusters. The same grouping by growth habit was also apparent when the analyses were performed separately for the A- and B-genome SNPs. However, the informativeness of D-genome SNPs for the separation of spring and winter varieties was low, likely a consequence of low allelic frequency differentiation between these two wheat groups as evidenced by the low inter-population FClustering using the combined SNP set showed that the inferred number of clusters in our population is smaller than the number of pre-defined breeding populations, largely due to the fact that breeding programs from the same region tend to use cultivars of common ancestry. The spring wheat population included cultivars from breeding programs targeting more geographically separated areas that were also more genetically differentiated that the winter wheat population. A high level of genetic differentiation was observed between the populations originating from two major geographical locations one including northern states SD, MN and MT and the other including Mexico and western states WA, ID and CA. In contrary, the winter wheat populations largely originating from the central states showed higher levels of admixture and a lower extent of genetic differentiation.ST across chromosomes provided additional insights into the structure of genetic variation between the spring and winter wheat populations. Assuming the same evolutionary processes affect neutral loci, identifying genomic regions showing elevated FST between spring and winter wheat populations should make it possible to localize the targets of selection controlling growth habit phenotype. However, the substantial heterogeneity of FST estimates for SNP loci across the wheat genome make it impossible to use single-locus FST values for detecting past selection events. This problem was circumvented by calculating FST for a group of sequential SNP loci which was shown to be an efficient strategy to reduce variation in FST estimates relative to estimates based on individual loci [Vrn1 gene locus, the major gene involved in regulation of flowering time in wheat [ST were co-localized with previously mapped genes known to be involved in flowering time regulation. Some wheat chromosome 6B substitution lines are known to affect flowering time in the absence of vernalization [ST region identified on the chromosome 6B in this study. Although the distribution of empirical FST estimates cannot serve as a formal test for selection, this finding suggests that high FST genomic regions can harbor genes subject to diversifying selection providing good targets for further studies.The characterization of Fual loci . The higin wheat . This loin wheat ,66. Addilization , but sinST detected on the chromosome 6B. This structural rearrangement can potentially impact the frequency of allele exchange between the spring and winter wheat populations and contribute to the genetic differentiation of this genomic region.The genetic differentiation of some of the genomic regions can also be due to structural rearrangements abundant in one of the populations. For example, chromosomal inversions are known to be a major barrier for gene flow between populations due to limited recombination near the affected genomic regions and also one of the mechanisms facilitating reproductive isolation and species formation . PreviouUsing genome-wide SNP data we demonstrated the extensive amount of LD in the populations of wheat cultivars. The variation in the patterns of LD among the populations and wheat genomes reflects the complexity of evolutionary and breeding history of wheat . The extAe. tauschii (D-genome) accessions followed by chromosome duplication using colchicine. The synthetic wheats and their derivatives have greatly increased genetic diversity in hexaploid wheat, particularly in the D-genome [Analyses of LD decay by breeding population showed similar profiles among populations except for the CIMMYT population, which had the lowest LD among completely linked loci and the slowest rate of LD decay. A possible explanation for this observation is the intensive usage of synthetic wheat lines in the CIMMYT program. Synthetic wheats are generated by hybridization of diverse tetraploid (A- and B-genomes) and D-genome -73. It iD-genome .Vrn-A1 gene has a stronger effect than the Vrn-B1 gene, it has higher number of widely distributed haplotypes [Depending on the genomic location of genes controlling important adaptive traits, these broad crosses can have a differential impact on LD in different genomes. For example, because the plotypes  and is tA genetic bottleneck may also increase the level of LD ,74. The Rates of LD decay varied among populations, but as expected, individual populations showed higher overall levels of LD than the combined datasets. These higher LD levels were also reflected in elevated levels of long-range LD extending above 10 cM. Interestingly, across all populations, LD decayed to 50% of its initial value within relatively narrow genetic intervals ranging from 6 to 9 cM. This rate of LD decay is probably associated with the high level of genetic diversity used in the individual breeding program. The cultivars in all of these programs captured comparable number of recombination events resulting in fast erosion of LD. However, each population showed variation in the extent of long range LD which was highest in the SD winter wheat population and WB spring wheat population. As pointed out earlier, these differences are probably the consequence of breeding history and selection specific to each breeding program.The comparison of the LD levels obtained in our study with results obtained in other studies dealing with wheat and other inbreeding crops was complicated by the differences in the type of markers used for genotyping, and by sample size variation in the different studies. Both factors can impact LD estimates. Previously reported LD estimates in wheat were obtained using more polymorphic SSR markers ,24,25. IVariation in the extent of LD along the chromosome affect the number of tagSNPs  requireThe evolutionary history of an allele also has a strong impact on the probability of detecting marker-trait associations. Alleles of loci that are involved in local adaptation and subjected to recent selection can be more readily detected using an even more sparsely distributed set of markers. For example, marker-trait associations of alleles involved in the regulation of flowering time in Arabidopsis  and cultOur study demonstrated a high level of genetic diversity and relatively fast decay of LD within each wheat breeding program. Extensive exchange of genetic material between breeding programs resulted in a low level of genetic differentiation between populations of spring and winter wheat. The regions of the wheat genome harboring flowering time QTL demonstrated the highest levels of genetic differentiation between the spring and winter wheat populations. Breeding, selection and founder effect had a different impact on the wheat genomes in distinct populations, highlighting the significance of allopolyploidy for the development of cultivars adapted to a broad range of environmental conditions. Assessment of the extent of LD and population structure in the assembled panel provided valuable information for the design of GWAM experiments in wheat.EDA and JDu designed research; EDA coordinated research activity; EDA performed bioinformatical analysis of data and the design of wheat OPA; JDv, MCL, and EDA generated the genetic map; SC performed genotyping; EDA and SC analyzed genotyping data; JDu, SPB, RM, DRC, LET, JAA, SD, KG, JC, KC, PLB, JCR, SH, BFC, SP and MES performed selection and assembly of the panels of spring and winter wheat lines; EDA drafted the manuscript; JDu, JDv, SC, JC, JAA, MES, SH, SPB, SD, LET worked on the preparation of the final version of the manuscript; all authors read and approved the final version of the manuscript.Complete list of wheat cultivars used in the study. The file contains the list of spring and winter wheat cultivars selected from 17 breeding programs in US and CIMMYT. Pedigree (when available/known) of each cultivar is also provided.Click here for fileSNPs and their flanking sequences used for the design of wheat OPA. The file contains the list of SNPs and their flanking sequences used for the design of wheat Illumina OPA. The Illumina® Assay Design Tool was used to generate designability rank scores for each SNP.Click here for fileList of 219 SNPs used for population structure analysis. The file contains the list of 219 SNPs and their genetic map locations. The analysis of population structure was performed using all SNPs and SNPs separated into genome-specific sets .Click here for fileRelationship between the log probability of data and the number of clusters K. The log probability of data (Ln Pr(X|K)) was plotted as a function of the number of clusters K for different SNP datasets and structure models assuming correlated (top three graphs) and independent (bottom three graphs) alleles frequencies. Means (black bars) and 95% confidence intervals (grey bars) of log probability of data Ln Pr(X|K) for each value of K were calculated from 10 independent runs of Structure with 100,000 burn-in steps and 106 simulation steps.Click here for fileMembership coefficients of 17 pre-defined wheat populations in 2 clusters (K = 2). Clustering was estimated using SNPs mapped to the A-, B- and D-genomes. Membership coefficients were calculated from 10 independent runs of Structure with 100,000 burn-in steps and 106 simulation steps.Click here for fileMembership coefficients of 17 pre-defined wheat populations in 9 clusters (K = 9). Membership coefficients (Q) were estimated for 17 wheat populations assuming 9 clusters in data (K = 9). Clustering was estimated using combined set of 219 SNPs from 10 independent runs of Structure with 100,000 burn-in steps and 106 simulation steps.Click here for fileST estimates for individual SNP loci and windows of 5 SNPsDistribution of F. A) The distribution of single-locus FST values between spring and winter wheat populations. B) The distribution of FST values in a sliding window of 5 consecutively located SNP loci.Click here for fileSummary of significant LD in the spring, winter and combined populations. The file contains mean and median estimates of statistically significant LD in the A- B- and D-genomes of spring, winter and combined populations. The pair-wise LD was measured using the squared allele-frequency correlations r2 according to Weir [r2 estimates was calculated by the exact test following the procedure described by Weir [ to Weir . The sta by Weir . The fal by Weir .Click here for fileSummary of LD estimates. The file contains the exact test for LD, genetic distances between pairs of SNP markers and minor allele frequencies (MAF) of alleles used for LD calculation. The pair-wise LD was measured using the squared allele-frequency correlations r2 according to Weir [r2 estimates was calculated by the exact test following the procedure described by Weir [ to Weir . The sta by Weir . The fal by Weir .Click here for file


Sarcocheilichthys (Cyprinidae), which inhabits contrasting habitats in the littoral zones (rocky vs. pebbly habitats) in Lake Biwa, a representative ancient lake in East Asia. Eco-morphological analyses revealed that Sarcocheilichthys variegatus microoculus from rocky and pebbly zones differed in morphology and diet, and that populations from rocky environments had longer heads and deeper bodies, which are expected to be advantageous for capturing cryptic and/or attached prey in structurally complex, rocky habitats. Sarcocheilichthys biwaensis, a rock-dwelling specialist, exhibited similar morphologies to the sympatric congener, S. v. microoculus, except for body/fin coloration. Genetic analyses based on mitochondrial and nuclear microsatellite DNA data revealed no clear genetic differentiation among local populations within/between the gudgeon species. Although the morphogenetic factors that contribute to morphological divergence remain unclear, our results suggest that the gudgeon populations in Lake Biwa show a state of resource polymorphism associated with differences in the bottom environment. This is a novel example of resource polymorphism in fish within an Asian ancient lake, emphasizing the importance and generality of feeding adaptation as an evolutionary mechanism that generates morphological diversification.Divergent natural selection rooted in differential resource use can generate and maintain intraspecific eco-morphological divergence , ultimately leading to population splitting and speciation. Differing bottom environments create lake habitats with different benthos communities, which may cause selection in benthivorous fishes. Here, we document the nature of eco-morphological and genetic divergence among local populations of the Japanese gudgeon  Resource polymorphism, the occurrence of intraspecific morphs within a single population exhibiting different niche use, is widespread over several taxa including fish, amphibians, and birds Differences in prey utilization between structurally contrasting habitats often drive selection pressure for resource polymorphisms. Fish exhibit a variety of examples of resource polymorphisms, the majority of which include open-water habitats and the littoral zones of lakes, in which planktivorous limnetic and benthivorous benthic pairs often occur . Since it first formed, the geological and limnological features of the lake have changed due to tectonic movements. The variety of habitats in Lake Biwa, which are characterized by a large and deep pelagic zone as well as complicated bottom environments in the littoral area, including sandy, pebbly, and rocky zones, were established in the mid-Pleistocene , provides an ideal opportunity for testing the hypothesis that bottom environmental differences lead to the evolution of resource polymorphism. Sarcocheilichthys usually feed on benthic prey from substrates by suction, swimming biased to the subbenthic column. These fish occur almost entirely in the littoral area of Lake Biwa. Thus, they utilize several types of bottom environments. Interestingly, previous studies noted that the gudgeons have a remarkable, continuous variation in head shape, exhibiting a short, intermediate, or long head Sarcocheilichthys in Lake Biwa could provide another novel example of resource polymorphism.One group of such fishes, the gudgeon Sarcocheilichthys in Lake Biwa. First, based on mitochondrial DNA sequence and microsatellite data, we evaluated the degree of genetic differentiation among local populations. Second, using a geometric morphometrics technique, we tested the morphological divergence among local populations with respect to head and body shape, which are crucial for prey capture and locomotion in fish. Third, we analyzed the stomach contents of fishes from rocky and pebbly habitats. If divergent selection arising from prey use of different bottom environments is as strong as that displayed by the limnetic–benthic divergence, it would be reasonable to suppose eco-morphological divergence between the habitat types . In such a case, information regarding the genetic differentiation/structure at the local population level will help to elucidate the origin of the polymorphism and speciation process of this fish group.Focusing on differences in bottom environments (rocky versus pebbly habitats), we assessed the genetic population structure and eco-morphological divergence among The study complies with the Fisheries Act in Japan, and was conducted under permission for fish sampling in Lake Biwa (#14-36 and #15-14) from the local government (Shiga Prefecture).Sarcocheilichthys endemically inhabit Lake Biwa, with different body colors and distributions Sarcocheilichthys variegatus microoculus is an endemic form of S. variegatus that is distributed widely in western Japan and the Korean Peninsula. It has a grayish body and occurs throughout the littoral zone, including sandy, pebbly, and rocky zones. Sarcocheilichthys biwaensis, with a brownish body, strictly inhabits areas in and around rocky zones in the lake. The variation in head morphology shows a contrasting pattern between the two; the former exhibits large continuous variation, having a short, intermediate, or long head, whereas the latter has a long head Two species/subspecies of S. biwaensis and S. v. microoculus from the entire shoreline of Lake Biwa between 2002 and 2007  and eastern (R3 and R5) rocky zones were pooled under the codes BN and BE, respectively. We judged whether each site represented a rocky or pebbly environment according to quantitative data on substratum constitutions reported by Nishino For the morphometric and molecular analyses, we collected samples of and 2007 ; Table 1b gene) and 14 microsatellite loci were used to assess the genetic divergence among the local populations of Sarcocheilichthys in Lake Biwa. Genomic DNA was extracted from fin clips preserved in 100% ethanol using an Aqua Pure Genomic DNA Isolation Kit  according to the instructions provided by the manufacturer. For the mtDNA analysis, a partial sequence of the mtDNA cytochrome b (cytb) region was amplified by polymerase chain reaction (PCR) using the primer pair L14724 (5′-TGACTTGAARAACCAYCGYYG-3′) 5′-ACCTCCGATCTYCGGATTACAAGAC-3′) b sequences (620 bp) were determined and deposited in the DDBJ, EMBL, and GenBank (accession numbers AB601449–601470). The haplotype frequency of each local population was deposited in GEDIMAP , haplotype diversity (h) and nucleotide diversity (π) for mtDNA; mean number of alleles per locus (na), allelic richness, mean observed (HO), and mean expected (HE) heterozygosities for microsatellites. These analyses were carried out using ARLEQUIN version 3.11 K  were evaluated, and ad hoc statistics (ΔK: second-order rate of change with respect to K) were calculated to determine the best estimation of KGenetic divergence and structure among local populations were examined based on mtDNA and microsatellite data. To test the partitioning of genetic variation, we performed an analysis of molecular variance (AMOVA). AMOVA tests were conducted separately for species or habitat grouping to investigate inter- and intraspecific divergence. We further calculated the pairwise We hypothesized that natural selection might favor morphological divergence of multiple features (body units). We therefore focused on the relationships between locomotion behavior and body units that are often highlighted for successful feeding , as well as the relationship between prey-capture ability and head shape. To quantify shape variation among individuals, we conducted landmark-based geometric morphometric analyses GM; . GM is aS. biwaensis and S. v. microoculus local populations were included in the analysis. To control for differences in body size between populations, we used centroid size, which is defined as the square root of the sum of squared distances of all landmarks from their centroid, as a covariate. The above procedure returned canonical axes associated with habitat effect of each MANCOVA. To examine the nature of morphological divergence between habitats, we tested whether those canonical axes were correlated with superimposed landmark coordinates and also compared thin-plate spline transformation grids. Statistical analyses were conducted using JMP software version 5.01 . Discriminant function analyses (DFA) were performed using shape variables as the dependent factors and habitat type as the grouping factor. We examined how correctly individuals can be classified into their habitat type based on morphology. To enhance the reliability and generalizability of the classification, a cross-validation technique was included in our DFAs. DFAs were performed using SPSS version 17 .We conducted a nested MANCOVA using shape variables  as a multivariate measure of shapes to test the morphological divergence between rocky and pebbly zones  and among local populations nested within habitat type. Both S. biwaensis captured from the eastern rocky zone (BE) were excluded from the analysis due to poor measurement conditions.A two-block partial least-squares analysis PLS;  was perfS. v. microoculus from two rocky (R4 and R5) and two pebbly (P2 and P8) sites were analyzed. Prey items were categorized under a stereoscope into six groups: chironomid and trichopteran larvae, snails, shrimps, zooplankton, and others. The relative contribution of each food group to the diet of an individual was estimated using the points method Stomach contents of local populations of Sarcocheilichthys in Lake Biwa, which were largely divided into three clades (A–C) in the network (Sarcocheilichthys biwaensis shared haplotypes (clades A and B) largely with S. v. microoculus, with only two haplotypes being unique to the former.A summary of the genetic diversity indices of mtDNA and microsatellites of each local population is presented in  network . Clade Ap = 0.047). The pairwise FST tests among local populations showed non-significant values for all the comparisons in mtDNA, and all but two out of 78 microsatellite comparisons, for which significantly higher than zero (p<0.05) but quite low values (FST = 0.0447 and 0.0584) were represented , but results showed no clear assignment bias among local populations  and low or no variation at the higher hierarchies  . There wresented . In the p<0.001, and for body shape, Wilks' lambda = 0.634, p<0.001). The DFAs correctly assigned a majority of individuals to their respective habitats: 83.9% and 74.3% for head and body analysis, respectively . The first dimension accounted for 79.8% of the covariance. The correlation between the blocks was 0.52, which was also significant , of which only the first dimension was significantly greater than expected by chance , whereas this was not the case for fishes from pebbly zones (P2 and P8). Although the proportions of the other prey groups were found in a mosaic fashion among the four local populations, the diet of the pebbly fish showed relatively similar composition, comprising mainly snails, chironomid, and trichopteran larvae. Fish from both the rocky (R5) and pebbly populations (P2 and P8) fed on trichopteran larvae, but used different ecological/taxonomic groups; i.e., fishes from R5 used mainly the larvae of the Polycentropodidae family, which makes cryptic scaffolding nets on the rock surface for filtering, whereas fishes from P2 and P8 fed mainly on larvae of the Leptoceridae, Hydroptilidae, and Sericostomatidae families, which are in portable organic or mineral cases and crawl around for filtering or gathering.The non-parametric MANOVA revealed that the stomach contents of gudgeons were significantly different between habitat types and local populations , Table 4Sarcocheilichthys usually swim close to the bottom (subbenthic habitat), searching for prey and picking it up from the substrates by suction . We hypothesized that the fish forms responsible for prey capture  would vary between populations inhabiting different bottom environments, i.e., rocky vs. pebbly zones. Indeed, the observed pattern of their morphological divergence largely matched the general view concerning the relationship between fish form and habitat complexity. Namely, in pebbly zones, where structurally simpler environments are widespread, gudgeon individuals were found to have a streamlined body with a short, round head, which would be optimal for minimizing water resistance (hence energy loss) during fast and extensive cruising while searching for widely dispersed prey Sarcocheilichthys in Lake Biwa; these gudgeons are typical suction feeders (not ram or filter feeders), and the degree of specialization to zooplankton seems to be low, as no divergence was observed in the number/density of gill rakers, which function as filters to capture small, plankton-like particles Gambusia) in the southern US and Caribbean islands have a highly developed caudal peduncle region that increases burst swimming speed when they co-occur with predatory fishes S. biwaensis might act as camouflage. These characteristics seem to be adapted to avoiding predation by piscivores such as the rock-dwelling catfish Silurus lithophilus and the cyprinid Opsariichthys uncirostris, which are endemic or semi-endemic species in Lake Biwa. Interestingly, few piscivorous fishes originally inhabited the shallow, pebbly zones of Lake Biwa , suggesting a higher predation threat in the rocky zone compared with the pebbly zone. As the feeding and anti-predation adaptation hypotheses do not contradict one another , the synergetic effects of natural selection might have caused the observed body shape/color differences. Exploring the interaction between feeding adaptation and predation avoidance represents an interesting field for future studies.Other than the trophic traits around the buccal region  that are clearly important for feeding adaptation, an alternative hypothesis for phenotypic differences corresponding to habitat type may involve divergent selection driven by differing predation threats among habitats. Traits that enhance the ability to escape would evolve with strong predation e.g., . For exaSarcocheilichthys in Lake Biwa was obtained from either mitochondrial or nuclear genetic markers. Specifically, no genetic differentiation was detected within species from different habitat types or between S. biwaensis and S. v. microoculus. The panmictic status suggested by presumably neutral genetic markers may be due to recurrent moderate gene flow between populations/species. On the other hand, the typical star-like network shown for the major mtDNA clade (A) strongly implies a recent bottleneck in gudgeon populations Despite eco-morphological divergence, no evidence of clear population divergence among S. v. microoculus and S. biwaensis even in hyper-variable microsatellite markers. They mostly shared the mtDNA haplotypes of the major clade (A) and one of the two minor clades (B). This suggests three possibilities. The first is that S. biwaensis is just a color variant of S. v. microoculus, which is strictly restricted to the major rocky areas, possibly associated with some local adaptation. Sarcocheilichthys biwaensis was somewhat recently described, mainly based on unique body/fin color, and its body and head shape largely overlap with those of the ‘long-headed’ type of S. v. microoculus from rocky areas . The reproductive isolation between S. biwaensis and S. v. microoculus has not been well examined yet, although they do crossbreed in captivity . The second possibility is that they have developed some reproductive isolation mechanism, and S. biwaensis is recently derived from S. variagatus ancestral stock, meaning that it has not yet accumulated genetic differences in any neutral markers.No differentiation signal was found between S. biwaensisS. v. microoculus have been suggested (Japan Ministry of the Environment 2003). This situation implies the possibility of extinction of genetically pure S. biwaensis, even if it was present in the past. In any case, S. biwaensis was revealed to be a genetically indefinable form, at least at present. Further research is needed to examine the reproductive isolation between the two species under natural conditions.The other possible reason for no interspecific genetic difference is historical and/or contemporary introgressive hybridization between two well-differentiated species. In addition to the original smaller population size of Sarcocheilichthys in Lake Biwa could be caused by phenotypic plasticity, genetic differences, or a combination of both. In general, the relative importance of these effects varies among fish species Sarcocheilichthys. However, according to Nakamura's S. biwaensis being recessive. Also noted was that head-length variation was somewhat genetically controlled. The existence of genetic components in these morphologies suggests adaptive genetic divergence between populations utilizing different resources. Although the panmictic status of the Lake Biwa gudgeons was revealed by presumably neutral markers, and extensive gene flow was expected among them, it is possible that only specific genes associated with local adaptation show differentiation between populations/species The phenotypic divergence observed in Figure S1Bayesian clustering analysis of Sarcocheilichthys populations using STRUCTURE  for K = 2. Each vertical bar represents an individual partitioned into the two clusters defined by STRUCTURE. Sample codes correspond to those in (EPS)Click here for additional data file.Table S1Sarcocheilichthys in Lake Biwa: SC, sample codes; n, number of samples; hn, number of haplotypes; h, haplotype diversity; π, nucleotide diversity; na, number of alleles; AR, allelic richness; OH, observed heterozygosity; EH, expected heterozygosity. For microsatellites, no excesses or deficits in heterozygosity were found among any local samples (α = 0.05).Genetic diversity estimates for (DOC)Click here for additional data file.Table S2FST values between local populations : above diagonal, microsatellite; below diagonal, mtDNA.Pairwise (DOC)Click here for additional data file.Table S3p<0.05). The final column shows the relative direction of landmarks found in rocky populations compared to pebbly populations. For example, landmark h1 is located at a relatively anterior and dorsal position in rocky populations.Correlation between superimposed landmark coordinates of head shape and the habitat canonical axis from the MANCOVA. Significant values are shown in bold ((DOC)Click here for additional data file.Table S4p<0.05). The final column shows the relative direction of landmarks found in rocky populations compared to those of pebbly populations. For example, landmark b5 is located at a relatively anterior and dorsal position in rocky populations.Correlation between superimposed landmark coordinates of body shape and the habitat canonical axis from the multivariate analysis of covariance (MANCOVA). Significant values are shown in bold ((DOC)Click here for additional data file.


Partamona is a Neotropical genus of stingless bees that comprises 33 species distributed from Mexico to southern Brazil. These bees are well-adapted to anthropic environments and build their nests in several substrates. In this study, 66 colonies of Partamona helleri from five localities in the Brazilian state of Minas Gerais  were analyzed using nine microsatellite loci in order to assess their genetic variability. Low levels of observed (Ho = 0.099-0.137) and expected (H e = 0.128-0.145) heterozygosity were encountered and revealed discrete genetic differentiation among the populations (F ST = 0.025). AMOVA further showed that most of the total genetic variation (94.24%) in P. helleri was explained by the variability within local populations.  Partamona is a Neotropical genus of stingless bees that comprises 33 species distributed from Mexico to southern Brazil  collected in Viçosa , Rio Vermelho , Teixeiras , Porto Firme  and São Miguel do Anta  in Minas Gerais state , 1X buffer containing 15 mM MgCl2 and 10 ng of DNA. The P. helleri DNA was amplified as reported by The genomic DNA of five adult workers from each colony was extracted according to the protocol recommended by M. bicolor (o) and observed (Ho) and expected (He) heterozygosities.The genetic variability in the populations was estimated from the analysis of nine microsatellite loci  described for F statistic  were polymorphic (P = 22.22%). Locus Mbi254 generated 5-8 alleles in the different populations while locus Mbi278 generated two alleles in all populations. The number of alleles observed for the Mbi254 locus in P. helleri (8) was greater than in M. bicolor (3), whereas five alleles were observed for the Mbi278 locus in M. bicolor  was much lower than that reported for M. bicolor (Ho = 0.40) (Plebeia remota (Ho = 0.24) (M. rufiventris (Ho = 0.07) and M. mondury (Ho = 0.12)   was higher than that of P. helleri. This difference may be explained by the multiple matings of honeybee queens that have an effective mate number of 12.4 . Hence, it is reasonable to assume that, after swarming, new P. helleri colonies will not disperse more than 1700 m from their maternal colony. In addition, during swarming, the parental colony generally provides the new colonies with food and wax  that encountered favourable conditions for expansion. The establishing of new nests by the dispersing colony in the neighborhood of the parental colony probably led to a decrease in the genetic variability of the population. The artificial transport of P. helleri colonies between regions, which could increase the population genetic variability, is not a common practice among beekeepers, which do not rear this species commercially because of its aggressiveness and the low commercial value of its honey.The ability of P. helleri analyzed.Nei's genetic distance calculated pairwise between populations varied from 0.00 to 0.0079. These values were consistent with the geographic proximity of the sampled sites (except for Rio Vermelho), and with the fact that most of the alleles were shared by all of the populations. Indeed, the presence of an exclusive allele  was detected only in the population from Rio Vermelho, which is at least 270 km away from the other populations. All of the other alleles detected in the two polymorphic loci were present in at least two populations. The presence of this exclusive allele is evidence of restricted genetic flow between this and the other populations of IS (ST (FST = 0.025) values. The FST value indicated that the genetic differentiation among the populations was discrete, whereas AMOVA showed that most (94.24%) of the total genetic variation in P. helleri was explained by the genetic diversity within local populations, while 4.76% was distributed among the populations. Based on these findings, we suggest that there are genetic differences among the populations analyzed but that these differences are not great enough to classify the populations as separate subpopulations.The presence of an exclusive allele may also indicate the existence of genetic structure among populations. In the present study, genetic structure was initially detected based on differences in the allelic frequencies among populations (data not shown) and further supported by the FIS  and meanP. helleri analyzed here showed little genetic variability and genetic differentiation. This finding suggests that these populations originated from only a few colonies. However, additional studies are needed to improve our knowledge of the population dynamics and mode of colonization in this stingless bee.In conclusion, the populations of