Salmo salar (“småblank”) has been isolated from other Atlantic salmon populations for 9,500 years in upper River Namsen, Norway. This is the only European Atlantic salmon population accomplishing its entire life cycle in a river. Hydropower development during the last six decades has introduced movement barriers and changed more than 50% of the river habitat to lentic conditions. Based on microsatellites and SNPs, genetic variation within småblank was only about 50% of that in the anadromous Atlantic salmon within the same river. The genetic differentiation (FST) between småblank and the anadromous population was 0.24. This is similar to the differentiation between anadromous Atlantic salmon in Europe and North America. Microsatellite analyses identified three genetic subpopulations within småblank, each with an effective population size Ne of a few hundred individuals. There was no evidence of reduced heterozygosity and allelic richness in contemporary samples (2005–2008) compared with historical samples (1955–56 and 1978–79). However, there was a reduction in genetic differentiation between sampling localities over time. SNP data supported the differentiation of småblank into subpopulations and revealed downstream asymmetric gene flow between subpopulations. In spite of this, genetic variation was not higher in the lower than in the upper areas. The meta-population structure of småblank probably maintains genetic variation better than one panmictic population would do, as long as gene flow among subpopulations is maintained. Småblank is a unique endemic island population of Atlantic salmon. It is in a precarious situation due to a variety of anthropogenic impacts on its restricted habitat area. Thus, maintaining population size and avoiding further habitat fragmentation are important.The river-resident  The understanding of genetic and structural processes in isolated populations is a central topic in conservation biology and genetics  and brown trout  readily established freshwater-resident populations, while Atlantic salmon  rarely did so. Atlantic salmon normally maintain an anadromous life cycle, as all females smoltify  and migrate from the nursery stream to a more productive feeding environment in the sea, before returning to their nursery stream to spawn as adults. Some males remain in the river and mature sexually at a small body size  occurs in the lower areas  has been introduced to the upper parts of the river system and is spreading downstream, but has not yet reached the småblank areas (own unpublished data).River Namsen runs for 210 km in Nord-Trøndelag County, central Norway, from Lake Namsvatnet (455 m a.s.l.) to the outlet in the sea at the town of Namsos  , while the smallest mature females were 175 mm  in the main river.The original distribution of småblank included the main river from above the waterfall Nedre Fiskumfoss (elevation top 58 m a.s.l.) up to Namskroken (286 m a.s.l.), a distance of about 85 km Berg ; Fig. 2.−1  varies between 0.8 and 12 m kmThe fish material was assayed for genetic variation at eight microsatellite loci, and a subsample of 88 individuals of småblank from three sampling localities was also assayed for genetic variation at 4414 SNP loci Table . The SNPμ20.19 . The following PCR program was run on a Quattro Cycler (VWR): denaturation for 15 min at 95°C: six cycles touchdown PCR of denaturation at 94°C, annealing temperature from 58°C to 52 °C, and extension at 72°C for 60 s. The last 24 cycles were run with denaturation at 94°C for 30 s, annealing at 52°C for 30 s, and extension at 72°C for 60 s; a final step of extension at 72°C for 10 min. Fragments from each multiplex were separated and visualized separately on an ABI 3130xl DNA analyser (Applied Biosystems) and sizing using genemapper ver. 3.7 .PCR was carried out in two different multiplexes containing the following microsatellite loci: Ssa289, Ssa14 . Possible differences in proportion of polymorphic loci (SNPs) were tested by a random resampling approach, using POP tools (an add-in program in excel available at: http://www.poptools.org/). Random sampling was performed 1,000 times, and for each sampling, the proportion of polymorphic loci was estimated. The average proportion of polymorphic loci from each random sampling and their upper (0.975) and lower (0.025) percentiles were recorded and compared with the observed proportion of polymorphic loci in the sampling locality for which there were as many individuals as were resampled in the locality with a larger sample size.Possible differences in allelic richness, expected heterozygosity, and effective number of alleles between sampling localities were tested using Wilcoxon signed-ranked test as implemented in SPSS Statistics 18  implemented in arlequin ver. 3.5.1.2 , effective population size was estimated using the linkage disequilibrium method (LD) by Hill (smm)  than the anadromous population (Table μ20.19) were monomorphic in all småblank samples, but polymorphic in the anadromous population, with two and three alleles, respectively (Appendix FST) between the anadromous population and the småblank samples ranged from 0.22 to 0.26 and were significantly larger than zero (P ≈ 0). There were no signatures of genetic introgression from anadromous salmon in the småblank samples as none of the småblank specimens assigned with a relative assignment score higher than 0.02% to the anadromous sample, whereas all specimens had an assignment score ∼100% to the småblank samples.Expected heterozygosity and allelic richness within småblank sampling locations were much lower . In agreement with the results from microsatellite loci, no signatures of genetic introgression from anadromous salmon were found in the småblank samples, as all småblank specimens had a relative assignment score of zero to the anadromous sample, but 100% to the småblank samples.Pairwise Average expected heterozygosity (range: 0.305–0.377) and allelic richness (range: 2.1–2.6) did not differ significantly between pairs of småblank sample localities (Wilcoxon signed-rank test).FST estimates and homogeneity tests between pairs of sampling locations  was obtained when the E55/E78 samples were placed in Group II and the E sample placed in a separate group . When the E55/E78 samples and the E sample were placed in separate groups, the among-group variance component was 0.073 (P∼0). When the E55/E78 samples and the E sample were both placed in Group II, the variance among groups was relatively high , but the variance among populations within group was larger and significant (P = 0.042). From these analyses, there appears to be at least three genetic clusters: one in the lower region of the distribution area, represented by the samples from A, B, C, and D, one in the uppermost region, represented by the samples from F and G, and one in between the lower and upper region, represented by the E samples.From pairwise Appendix , two maiThe population structure of the småblank samples was further explored by individual genetic assignment to a predefined number of populations (K) using STRUCTURE. The most likely number of populations was two according to the Delta-K value (Delta-K = 25.42) and the Ln probability of the data (Mean LnP(K) = −2805.40), represented by the A, B, C, and D samples in one cluster, the F and G samples in the second cluster, and the E samples positioned in between the two clusters Fig. .55), clustered with the samples from the upper region, while the samples collected in 1978 (E78) clustered somewhat in between the two main clusters.The genetic differences among the småblank sampling localities are summarized in a neighbor-joining dendrogram Fig. . The twoThere were no significant differences in the effective number of alleles, expected heterozygosity (Wilcoxon signed-rank test) or in the proportion of polymorphic loci (random resampling) between pairs of sampling localities Table .FST of 0.046 (P∼0) between the E and B samples, and 0.053 (P∼0) between the E and C samples. In agreement with the microsatellite data, the SNP analysis showed no significant difference in allele frequencies between B and C , and the most likely number of populations detected by STRUCTURE was two according to the Delta-K value (Delta-K = 166.52) but three according to the Ln probability of the data (Mean LnP(K) = −68850). From a closer examination of the results from the STRUCTURE analyses, assuming three populations, , nor between samples from 1956, 1978, and 2008 at the G locality . However, between the E samples from 1955 and 1978, a significant temporal instability in allele frequencies was observed .Historical samples from the A, E, and G sampling localities were analyzed for genetic variation at the eight microsatellite loci Appendix . AverageP = 0.000) in 1979/1978 and significant (P = 0.020) in 2005/2008, and the estimated FST was higher in 1978/1979 (FST = 0.084) compared with the 2005/2008 samples (FST = 0.018). The genetic difference between the A and G localities was highly significant (P = 0.000) in 1979/1978 and also in 2005/2008, but the FST estimate was somewhat higher in 1978/1979 (FST = 0.112) than in 2005/2008 (FST = 0.077). The genetic difference between the E and G localities was higher and significant in the 1955/1956 samples , compared with the nonsignificant 1978 samples . The 2005/2008 samples from the E and G localities were significantly different (P = 0.021), but with an FST estimate (FST = 0.02) not significantly different from 1978. Jack-knifing over five polymorphic loci demonstrated that the latter test had low power .The impression of a general trend of decreasing genetic differentiation among localities over time emerging from Fig. e) ranged from 29  to 302 , with large confidence intervals for some estimates  and because only five loci were examined. Furthermore, the number and origin of founders, the long-term effective population size (Ne), and possible fluctuations in Ne are not known, which makes it difficult to deduce whether the M-values are due to recent or past demographic events , compared with other localities . The second population inhabits the river section from the weir at Namsskogan up to the Bjørnstadfoss waterfall . The third population occupies the main river from the Bjørnstadfoss waterfall up to and including the tributary River Mellingselva . The subdivision of småblank in several populations based on samples from 2005–2008 was also indicated in the samples from the 1950s and 1970s. Similarly, enzyme electrophoresis performed in the 1980s indicated a subdivision of the småblank in two different genetic clusters corresponding to the upper and lower regions of the distribution  large enough to prevent loss of genetic variation in a short period of time (since the 1950s), and larger than the effective sink population size. It should be noted that the area represented by the G samples (the River Mellingselva tributary) is in a more or less pristine condition in terms of human encroachment. The time since the 1950s, corresponding to 8–12 småblank generations, may also have been too short for detecting loss of genetic variation. Moreover, we cannot exclude the possibility that småblank had already been impacted by hydropower development when the first samples were collected in 1955–1956 and 1978–1979. The first hydropower development to influence a major part of River Namsen was the damming of the Namsvatn reservoir (the source of River Namsen) in 1952. Both during the construction and the operational phase, this would have impacted on a major part of the småblank habitat. The weirs in the småblank section of River Namsen were constructed in 1965/1966, 1978, and 1998  than in the lower (A–D) subpopulations was not confirmed. Neither had the relative level of genetic variation in the upper and lower subpopulations changed over the last six decades, since the 1950s. The genetic variation in the source population seems not to be declining, and it is not lower than the sink populations, in spite of the asymmetric downstream gene flow. This may indicate an effective source population size , and genetic divergence . However, we observed a reduced differentiation among the subpopulations within the distribution area.A major reason for the apparent genetic stability of småblank is likely that its metapopulation structure is still relatively intact. To reduce the risk of extinction and ensure the viability of this endemic form of Atlantic salmon, measures causing further fragmentation of the population should be avoided.


Pygocentrus nattereri) are widely caught with different intensities throughout the region of Solimões‐Amazonas River by local fishermen. Thus, the management of this resource is performed in the absence of any information on its genetic stock. P. nattereri is a voracious predator and widely distributed in the Neotropical region, and it is found in other regions of American continent. However, information about genetic variability and structure of wild populations of red‐bellied piranha is unavailable. Here, we describe the levels of genetic diversity and genetic structure of red‐bellied piranha populations collected at different locations of Solimões‐Amazonas River system. We collected 234 red‐bellied piranhas and analyzed throughout eight microsatellite markers. We identified high genetic diversity within populations, although the populations of lakes ANA, ARA, and MAR have shown some decrease in their genetic variability, indicating overfishing at these communities. Was identified the existence of two biological populations when the analysis was taken altogether at the lakes of Solimões‐Amazonas River system, with significant genetic differentiation between them. The red‐bellied piranha populations presented limited gene flow between two groups of populations, which were explained by geographical distance between these lakes. However, high level of gene flow was observed between the lakes within of the biological populations. We have identified high divergence between the Catalão subpopulation and all other subpopulations. We suggest the creation of sustainable reserve for lakes near the city of Manaus to better manage and protect this species, whose populations suffer from both extractive and sport fishing.Red‐bellied piranhas ( In general, fish species are not genetically homogeneous, but structured in groups of individuals who are relatively isolated. The identification of genetically homogenous groups is the basic unit for fish management, conservation, and sustainable use of genetic resources  of the Solimões‐Amazonas river system , warm and hypoxic waters , RNase A , and 1,4‐dithiothreitol . After the digestion, we washed the samples with phenol‐chloroform‐isoamyl alcohol  and isopropanol on ice. The pellet was resuspended in 50 μL DNase‐free H2O. We verified the quality of the extracted DNA by electrophoresis in a 1.5% agarose gel using GelRed®  and visualized it using an L‐PIX Molecular Image transilluminator . We quantified the DNA using a NanoDrop® 2000 spectrophotometer .PCR amplification and data analysis were performed according to Nascimento et al.  and SantP. nattereri samples were genotyped using the eight microsatellite loci  developed by Nascimento et al.  in a final reaction volume of 10 μL. Each PCR contained 1.5 μL of genomic DNA (50 ng/μL), 1.0 μL of forward primer (0.4 mM), 1.5 μL of reverse primer (0.4 mM), 1.0 μL 6‐FAM fluorescent M13 primer , which contains Taq DNA polymerase (0.05 U), MgCl2 (1.5 mM), and dNTPs (0.4 mM). The PCR cycling conditions were as follows: 1 cycle at 94°C for 3 min; 20 cycles at 94°C for 20 sec, 55–58°C for 20 sec , and we visualized it using an L‐PIX Molecular Image transilluminator . The amplified products were genotyped on an ABI 3130xl Genetic Analyzer  using the GeneScan Liz‐500 (‐250) standard size  to determine fragment length. The alleles were scored based on the consistent pattern of their stutter peaks, and on the peak intensity corresponding to each individual at each locus using GeneMapper v4.0 .The o et al. . We perfSchuelke  (0.4 mM)We tested microsatellite data from red‐bellied piranha populations for linkage disequilibrium (LD) and Hardy–Weinberg equilibrium (HWE) using Fstat v2.9.3.2 software Goudet . We usedHE). We estimated the genetic diversity by the observed (HO) and expected heterozygosity (HE) in Hardy–Weinberg equilibrium, fixation index (FIS) , mean number alleles (MNA), number of private alleles (NPA)  estimates on distance for populations, as suggested by Rousset (Isolation by distance (IBD) over the distribution area was assessed by testing the correlation between genetic and geographical distances considering all population pairs in using the regression of  Rousset . This mo Rousset . For geo Rousset , K = 2 w Rousset .K  using GDA v1.1 software  ancestor, which correlates with the gene frequency among the studied populations, using Structure v2.3.1 software   Table . In addiHO: 0.504 ‐ 0.843), and (HE: 0.514–0.719)  to 0.881 (PN7) (Table FIS) ranged from −0.176 (lake PRA) to 0.183 (lake MAR). Only the subpopulation of lake MAR presented FIS values above 0.1. We also identified that the subpopulations of the lakes ANA, ARA, and MAR showed lack of heterozygotes, with a trend for loss of genetic variability in these subpopulations.Populations of red‐bellied piranhas showed high genetic variability (9) Table . ConsideP < 0.05) for microsatellite loci  shows that the geographical distance between the lakes located at the Solimões‐Amazonas River systems has strong influence on the red‐bellied populations Fig. . HoweverThe structure was used for clustering of individuals in 2 ≤ K ≤ 8 software; however, the lower value of K (K = 2) was the better explanation for the subpopulations of red‐bellied piranha, joining fish from the lakes PRA, CAM, and REI, which formed an independent cluster. In K = 3 to K = 8, the subpopulation of the lake CAT resulted in a separated subpopulation, forming an isolated cluster data set in the cluster diagram of K = 3. In fact, for this population, the great the K, the great was the isolation however, considering all subpopulations. The higher ΔK was K = 2 see Fig.  and 6. TConsidering the data obtained herein, we can affirm that the genetic variation in red‐bellied piranha populations is strongly related to a subdivision into two biological populations Table . FirstlyPiaractus brachypomus  populations inhabiting the Orinoco basin and the Amazon basin had similar allelic diversity; however, the heterozygosity levels were reduced in populations of the Orinoco. Moreover, Santos et al. (Colossoma macropomum (Characiforms) populations living in the Rio Amazonas channel, but no genetic differentiation between them, suggesting that this species, which also belongs to Serralmidae family, compose a single panmictic population. Gomes et al.  populations in Paranapanema River basin, observed high levels of genetic variability using RAPD markers, and high levels of genetic structure in its populations. These results suggest that the species belonging to the Order Characiformes have high genetic variability; however, a decrease in gene flow can induce differentiation between populations. According to Solé‐Cava  have low genetic differentiation within populations of rivers or artificial dams, but present high genetic differentiation between these rivers and the artificial dams. Santos et al.  with the Rio Negro (black water) and may have induced local adaptations in the fish living in that kind of watercourses.Instead, the subpopulations gathered in geographical clusters around Manaus live in a mixed water environment (Rio Amazonas) while the other clusters live in a solely water environment (Rio Solimões), which totally differs from the patterns found in the lake CAT subpopulation from white water clusters. Another interesting fact in this study is that the CAT specimens do not show any phenotypic trait that could distinguish this subpopulation from the other, showing strong correlation with the results described by Sultan and Spencer  for locaStructure data, associated with the K‐Delta value , suggest the existence of two red‐bellied piranha biological populations in the Solimões‐Amazonas River   and similarity (M = (1/FST − 1)/4) by Stalkin's (1993)  for red‐bellied piranha populations based on eight microsatellite loci. Rousset  distanceClick here for additional data file.


Bene Israel members consider themselves as descendants of Jews, yet the identity of Jewish ancestors and their arrival time to India are unknown, with speculations on arrival time varying between the 8th century BCE and the 6th century CE. Here, we characterize the genetic history of Bene Israel by collecting and genotyping 18 Bene Israel individuals. Combining with 486 individuals from 41 other Jewish, Indian and Pakistani populations, and additional individuals from worldwide populations, we conducted comprehensive genome-wide analyses based on FST, principal component analysis, ADMIXTURE, identity-by-descent sharing, admixture linkage disequilibrium decay, haplotype sharing and allele sharing autocorrelation decay, as well as contrasted patterns between the X chromosome and the autosomes. The genetics of Bene Israel individuals resemble local Indian populations, while at the same time constituting a clearly separated and unique population in India. They are unique among Indian and Pakistani populations we analyzed in sharing considerable genetic ancestry with other Jewish populations. Putting together the results from all analyses point to Bene Israel being an admixed population with both Jewish and Indian ancestry, with the genetic contribution of each of these ancestral populations being substantial. The admixture took place in the last millennium, about 19–33 generations ago. It involved Middle-Eastern Jews and was sex-biased, with more male Jewish and local female contribution. It was followed by a population bottleneck and high endogamy, which can lead to increased prevalence of recessive diseases in this population. This study provides an example of how genetic analysis advances our knowledge of human history in cases where other disciplines lack the relevant data to do so.The Bene Israel Jewish community from West India is a unique population whose history before the 18 After 1948, most of the community immigrated to Israel. At the beginning of the 21st century, approximately 50,000 members lived in Israel, whereas about 5,000 remained in India, mainly in Mumbai [th century BCE–or later–from Yemen, during the first millennium CE or from Southern Arabia or Persia in the 5th or 6th century CE [How well does the oral history of a group reflect its origins? The Bene Israel community in West India is a unique community whose historical background before the 18 unknown . The Jew unknown . In the n Mumbai ,5. The en Mumbai . Others In the last decades, genetic information has become an important source for the study of human history and has been applied numerous times for various Jewish populations, first based on uniparental Y chromosomal and mitochondrial DNA (mtDNA) markers –11 and lThe complex genetic structure of Indian populations imposes a great challenge for genetic analysis of Bene Israel. Previous studies showed that most contemporary Indian populations are a result of ancient admixture (64–144 generations ago) of two genetically divergent populations: Ancestral North Indians (ANI), who are related to west Eurasians, and Ancestral South Indians (ASI), who are not closely related to populations outside India and related to indigenous Andaman Island people ,18. DiffTo study the genetic history of Bene Israel, while addressing this challenge, we present here the largest collection of Bene Israel individuals that has been assayed genome-wide to date , and we use the collection in conjunction with genotype data of 486 individuals from 41 other Jewish, Indian and Pakistani populations, as well as samples from various worldwide populations. We apply an array of genome-wide population genetics tools to characterize the origins of Bene Israel and their relations to both Indian and Jewish populations, uncovering the genetic history of this unique population.We genotyped Bene Israel individuals and combined the data with 14 other Jewish populations from worldwide Diaspora previously genotyped using the same array ,14. We aPCA  of the merged dataset, including four HapMap populations  showed that Jewish populations cluster together with Europeans and Middle-Eastern populations, while Indian and Pakistani populations form their own cluster, between East-Asians and Jews/Europeans . The BenST statistic, which measures genetic drift between populations based on differences in allele frequencies [ST between pairs of Jewish populations was 0.011, the average FST between Bene Israel and other Jewish populations was significantly higher . Similarly, while the average FST between different Indian populations was 0.011, the mean FST between Bene Israel and other Indian populations was significantly higher .We also examined the relation between Bene Israel and other Indian and Jewish populations using the Fquencies ,21  sharing of their individuals. IBD segments shared by two individuals represent a segment inherited from a common ancestor. Higher IBD sharing, and specifically of long segments, suggests a more recent common ancestor without intervening recombination . FollowiNext, we used the merged dataset with Middle-Eastern and Pakistani populations for a lower-resolution IBD analysis. Still, Bene Israel showed higher IBD sharing with Middle-Eastern Jewish populations in this dataset as well . ImportaMotivated by the above results, we next examined whether the Bene Israel community was an admixture of Indian and Jewish ancestral populations, using two different approaches as implemented in the ALDER  and GLOBf4-based analysis [In addition, we performed analysis  to test In addition, we also applied GLOBETROTTER on our dataset. GLOBETROTTER assigns haplotype segments of the admixed populations to different populations and uses the co-distribution of such segments from different populations to detect and infer admixture. In comparison to ALDER, which focuses on a pair of putative ancestral populations, it considers all populations and assigns ancestry component to all of them simultaneously. Importantly, GLOBETROTTER found evidence for admixture in the similar time range suggested by ALDER –27.7±0.43 generations ago. Admixture proportion estimation was 55% Indian and 45% Jewish. The main contribution from the Jewish cluster was from Greek, Yemenite and Middle-Eastern Jewish populations while the Indian cluster was mainly composed of populations with a high ASI component . This maWe note here that GLOBETROTTER's original study analyzed, among 95 worldwide populations, "Indian Jews" . HoweverWe now turn to examine the post-admixture population structure of Bene Israel. Both Jewish populations ,23, as wST values between Bene Israel and other Indian and Jewish populations  or long stretches of markers IBD that are shaped by more recent history, we observed clear evidence for a uniquely Jewish component in Bene Israel that is not shared by any other Indian or Pakistani population.The unique Jewish component was observed by Bene Israel showing higher IBD sharing with Jewish populations as compared to all other Indian or Pakistani populations. In addition, while ALDER reported significant evidence for Bene Israel having Jewish and Indian ancestry, no other Indian or Pakistani population produced similar evidence for Jewish ancestry. Therefore, while the similarity between Pakistani and Jewish populations is likely a result of being both descendants of an ancestral Middle-Eastern population, we showed that the similarity between Jewish populations and Bene Israel is due to Bene Israel being direct descendants of Jews. If the link between Bene Israel and Jewish populations is more direct as compared to Pakistani population, why did some Pakistani populations seem to have more Jewish-related ancestry in PCA and ADMIXTURE? This is likely because Bene Israel is an admixed population, with only part of their ancestry being Jewish, while the other part is related to Indian ancestry. Indian populations, in turn, are more genetically distant from Jewish and Middle-Eastern populations as compared to the Pakistani populations under question.By contrasting patterns on the X chromosome with the autosomes, we find that the admixture we detected was sex-biased, with relatively more Jewish males contributing to Bene Israel’s gene pool. This is in line with suggestive evidence based on mtDNA and Y chromosome . SimilarThe admixture we detected point to Bene Israel being admixed of both Indian and Middle-Eastern, likely Jewish, ancestry, with each of the two ancestries making a substantial contribution to its gene pool. The fact that the same analyses did not detect a similar trend to any of the other Indian or Pakistani population examined here, even those with relatively large ANI component, suggests that the admixture is not related to the ANI-ASI admixture. The uniqueness of the admixture event is further supported by its timing, which two distinct methods estimated consistently to be between 19 to 33 generations ago (~650–1050 years) ago . Hence, this admixture event is much more recent than the ANI-ASI estimated admixture time (64–144 generations ago ).th century BCE to 6th century CE), yet none of these dates has any independent support [Maimonides' letter describing a Jewish population in India, which may be Bene Israel , was wri support . Importa support .f4-based analysis suggested that Middle-Eastern Jewish populations are closer to Bene Israel as compared to non-Jewish Middle-Eastern populations. Finally, ALDER exhibited more significant results with Jewish populations as compared to non-Jewish Middle-Eastern populations. Regarding the ancestors from the Indian side, initial results based on PCA with both Indian and Jewish populations  when using proxy populations that did not directly descend from the true ancestral populations. Similarly, it is difficult to distinguish between Middle-Eastern and Jewish specific origin because of the Middle-Eastern origin of Jewish populations. However, our results suggest that the non-Indian component of Bene Israel ancestry is more likely to be Jewish-specific rather than Middle-Eastern more broadly, and that the Jewish forefathers of Bene Israel came to India from geographically close Middle-Eastern communities, perhaps through the Silk Road, and not from farther communities. Thus, Middle-Eastern Jewish populations showed larger IBD sharing with Bene Israel as compared to both other Jewish populations as well as other Middle-Eastern populations. Similarly, ulations  positionf3 values, likely due to the post-admixture genetic drift [Further analysis of the genetic history of Bene Israel post-admixture reveals isolation and high endogamy in this population. While both Jewish ,15 and Iic drift ,36, whilic drift , are ablic drift . Our anaRevealing the high endogamy and founder event(s) in Bene Israel is important not only from historical but also from medical perspective, as it predicts higher rates of recessive diseases within this population . Indeed,A more complete history of Jewish Indian populations requires characterizing the history of Cochin Jews as well, which we pursued over the last few years in a separate study see also . The resIn conclusion, our results, based on an ensemble of different approaches, combine to support some versions of the oral history of the Bene Israel community as having both Jewish (likely from the Middle-East) and Indian origin, though the timing of this admixture is more recent as compared to their oral history. This study serves as an example of a genetic history study that does not only confirm known historical events but also reveal novel historical insights.Recruitment of Bene Israel members occurred at Sheba Medical Center in Tel Hashomer, Israel (20 subjects) and at the Indian Bene Israel Community in Ramla, Israel (8 subjects) following approval of the study protocol and consent form by the Sheba Medical Center Helsinki Ethics Committee and the Director General of the Israeli Ministry of Health. All subjects provided written informed consent. The 20 samples collected at Sheba Medical Center were taken from individuals  identifying themselves as Indian Jews, rather than necessarily Bene Israel and therefore were either Cochin Jews or Bene Israel, the two large Indian Jewish communities in Israel. Although all individuals were recruited in Israel, where the vast majority of Indian Jews now live, they did not mix with non-Indian Jewish populations: all individuals recruited reported that their four grandparents belonged to the same Jewish community, similar to other Jewish populations analyzed in the current and in previous works ,14.In order to distinguish between Bene Israel and Cochin Jews in the samples collected in Sheba Medical Center, we used the SMARTPCA tool from the EIGENSOFT software  and ADMIFollowing this procedure, eleven Indian Jews from Sheba Medical Center were labeled as Bene Israel. One of the Bene Israel samples was later removed in the quality control (QC) steps performed on the dataset, as described below, resulting in a total 18 Bene Israel samples used in the current analysis.In addition to that, we repeated PCA  and ADMIFurther support to our Bene Israel and Cochin Jews labeling was obtained from another independent source. Previously, Behar et al.  genotypeCochin Jews have a different history , as alsoWe included in this study a Jewish dataset containing samples from additional 14 Jewish populations, which were collected as described previously ,14 S1 T. All samWe also incorporated a dataset of Indian populations based on the study of Reich et al.  where thFor some of the analyses we also included data for non-Jewish Middle-Eastern populations , and nine different Pakistani populations . Middle-Eastern populations were used to distinguish between Middle-Eastern and Jewish specific genetic attributes while Pakistani populations were taken as populations with high ANI component that reside geographically between India and the Middle-East. We incorporated these samples by merging our dataset with data from the Human Genome Diversity Project (HGDP)  genotypeAfter removing SNPs with low call rate (<95%) from the two datasets (Jewish and Indian), we merged them together (including the HapMap samples in the Indian dataset ) and remRelatives. Following Campbell et al. , which aGenetic outliers. We used the SMARTPCA program  to detecThe merged dataset following these QC steps included 513,581 and 25,379 autosomal and X chromosome  single nucleotide polymorphisms (SNPs), respectively, for 461 individuals from 33 Jewish and Indian groups. Additional 844 samples from 11 HapMap3 populations were also available in this dataset, resulting in total 1305 samples. Further merging with the HGDP dataset  consists of 1363 samples with 304,973 shared autosomal SNPs.The number of samples from each population is shown in ST, ADMIXTURE, runs-of-homozygosity and heterozygosity. For each pair of SNPs showing LD of r2>0.5 we considered only one representative (using SMARTPCA's [A set of filtered SNPs based on linkage disequilibrium (LD) was used in the following analyses: PCA, FARTPCA's  r2threshftp://ftp.ncbi.nlm.nih.gov/hapmap/recombination/2011-01_phaseII_B37/).We phased the data with the BEAGLE software (version 3.3.2)  and extrFor each pair of unrelated individuals we calculated the total length of autosomal IBD sharing. Given two populations, the average IBD sharing of these two populations was defined as the average IBD sharing between all pairs of individuals from these populations. Similarly, the average IBD sharing within a population was defined as the average IBD sharing between all pairs from this population. In addition, we also calculated the average IBD sharing between a population and the group of all Jewish populations, by averaging the IBD sharing between this population and each of the other Jewish populations. We repeated a similar procedure to calculate average IBD sharing between a population and the group of all Indian populations. As this analysis was done to compare other populations to Bene Israel, the Bene Israel population was not considered to be either Jewish or Indian in this analysis.We obtained empirical estimations for the distribution of average IBD sharing between populations by sampling 10,000 times ten Bene Israel individuals and measuring the average IBD between these individuals and members of other populations. These distributions were used to obtain standard error estimates for the average IBD sharing between Bene Israel and these populations. In addition, we used these distributions to examine whether Middle-Eastern Jewish populations show higher IBD sharing with Bene Israel as compared to other Jewish populations. This was done by comparing the average IBD sharing distributions of two Jewish populations  using Wilcoxon rank sum test.Principal component analysis (PCA) was performed using the SMARTPCA program  on the JST statistic, following the definitions described previously in Reich et al. [pi be the frequency of a variant in a biallelic SNP in two populations  and define qi = 1-pi. We defined FST asWe calculated population differentiation based on differences in allele frequencies between each pair of populations using the Fh et al. . SpecifiN and D were averaged separately and only then their ratio was taken. Thus, let Ni, Di be the above definitions for SNP i, then for a set of SNPs S, FST was defined as:To generalize this measure to more than a single SNP, we followed the "ratio of averages" approach , where NN and D:Similar to Reich et al. , the folai and bi are the allele counts of the two alleles, and ni= ai+bi. We calculated FST separately for the autosomes and for the X chromosome.Where ADMIXTURE , a STRUCWe applied two tools to examine the hypothesis that Bene Israel population was an admixed population and to infer admixture proportions and time: ALDER (version 1.03)  (version(1) ALDER: Given a putative admixed population and two surrogate populations taken as a proxy for the presumed ancestral populations, ALDER uses admixture LD statistic to look for evidence for admixture (assuming a single admixture event). For each pair of SNPs ALDER calculates a statistic being the covariance of these two SNPs in the admixed population, weighted by the allele frequency differences between the two reference populations. Exploring the behavior of this admixture LD statistic as a function of the genetic distance between the two SNPs can imply whether the population is admixed or not.n is the number of generations since admixture and d is the genetic distance (in Morgans). In addition to the test of admixture using two reference populations, ALDER examines evidence for admixture using only one surrogate population as a reference, with the admixed population serving as a proxy for the second population.ALDER fits the statistics curve to an exponential functionWe considered a pair of populations as a candidate for being the ancestral populations for a certain population if all three ALDER results  were significant and the estimated time of decay was consistent between the three.min as a lower bound for the admixture proportion of the Jewish population in each significant pair. To determine α from the output of ALDER two-reference population test, f2 values  as well as in the two-reference version. As the populations examined here are taken as a proxy for the true mixing populations, the admixture proportions suggested are lower bounds . A caveaulations ) are neeulations  and thesulations .(2) GLOBETROTTER: Given a putative admixed population and a set of populations , GLOBETROTTER examines whether the putative admixed population is an admixed population of some of the populations from that set . As GLOBWe used CHROMOPAINTER (version 2) and ran GLOBETROTTER on Bene Israel and the Jewish and Indian populations, using 100 bootstrap replicates to obtain standard error estimates for the admixture time.n is the number of generations since admixture, we convert it to the year 1950-29(n+1) (CE). Changes in generation lengths estimations will scale the time estimations proportionally.We converted the number of generations into years by assuming 29 years per generation for such recent history ,27 and tWe used PLINK (version 1.07)  to identThe heterozygosity score of an individual was defined as the fraction of the heterozygous SNPs among all autosomal SNPs (after LD-pruning).t represents the number of generations since the founder event and D is the genetic distance (in Morgans) between the two SNPs [We used allele sharing autocorrelation for estimating time of founder event, along the lines suggested by recent studies ,28. Spectwo SNPs ,28.t1 and t2 were the times (in generations) since the two founder events. Fitting was done by non-linear least squares, using the nls function in R [We applied this method for Bene Israel and calculated allele sharing autocorrelation between each pair of SNPs less than 30 cM apart. We partitioned the values into 0.1 cM bins and considered the mean of each bin. To consider two founder events, we fitted the decay to an equation of the formion in R . EvaluatST) between two populations for the autosomes (FSTAUTO) and for the X chromosome (FSTX) to estimate a ratio:To examine sex-biased demography, we calculated a statistic presented by Keinan et al. : It estiQ captures the relative genetic drift between the X chromosome and the autosomes. Under several assumptions [Q is expected to be 3/4, the ratio of effective population size of the X chromosome to the autosomes in this case. A significant deviation from 3/4 may suggest sex-biased demography since population split.umptions , if the mtDNA genotypes were used to assign to each of the Bene Israel samples a mtDNA haplogroup based on HaploGrep classification .f4 where J and ME are Jewish and Middle-Eastern populations, respectively. Assuming YRI is an outgroup for all other populations , If ME is an outgroup in respect to Bene Israel and J, f4 is expected to be negative, while if J is an outgroup in respect to Bene Israel and ME, it is expected to be positive [We performed f4 test ,36 of thpositive ,36. For S1 FigIn this figure, and in order to avoid bias due to the larger number of Jewish samples, we repeated the analysis as in the main text  while li(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigWe colored some of the populations groups: Bene Israel (purple), Jews (red), Indians (blue) and Middle-Eastern (green).(PDF)Click here for additional data file.S4 FigHeat map of IBD sharing between Indian and Jewish populations for (A) Jewish and Indians populations and (B) for Jewish, Indian, Pakistani and Middle-Eastern populations. Entry  in the heat map presents the mean IBD sharing between individuals from populations i and j. Values on the diagonal represent IBD within populations.(PDF)Click here for additional data file.S5 FigStandard errors estimations are based on 10,000 bootstrapping of Bene Israel individuals . Except for Italian Jews in some cases, in all other cases, Georgian, Iraqi, Syrian or Iranian Jews showed significantly  higher IBD sharing with Bene Israel as compared to other Jewish populations.(PDF)Click here for additional data file.S6 FigThe number above each plot represent the predicted time (in generations) since admixture. See (PDF)Click here for additional data file.S7 FigThe number above each plot represent the predicted time (in generations) since admixture. See (PDF)Click here for additional data file.S8 Figf4 tests where J and ME are Jewish and Middle-Eastern population, respectively. Negative Z-scores suggest that ME is an outgroup populations for .We performed (PDF)Click here for additional data file.S9 FigThe larger variance in Het values in some Indian populations is due to smaller sample size.(PDF)Click here for additional data file.S10 FigThis was carried out to divide the Sheba samples into Bene Israel and Cochin Jews .(PDF)Click here for additional data file.S11 FigThis was carried out to assign the Sheba samples into Bene Israel and Cochin Jews .(PNG)Click here for additional data file.S12 FigWe repeated the analysis similar to that in the main text  with Ben(PNG)Click here for additional data file.S13 FigWe repeated the analysis similar to that in the main text  with Ben(PDF)Click here for additional data file.S14 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


Barbus barbus were investigated using 18 populations in England, where it is indigenous to eastern‐flowing rivers and where stocking has been used to enhance these populations. Invasive populations are also present in western‐flowing rivers following introductions of translocated fish. Two genetic clusters were evident in the indigenous range, centered on catchments in northeast and southeast England. However, stocking activities, including the release of hatchery‐reared fish, have significantly reduced the genetic differentiation across the majority of this range. In addition, in smaller indigenous rivers, populations appeared to mainly comprise fish of hatchery origin. In the nonindigenous range, genetic data largely aligned to historical stocking records, corroborating information that one particular river (Kennet) in southeast England was the original source of most invasive B. barbus in England. It is recommended that these genetic outputs inform management measures to either restore or maintain the original genetic diversity of the indigenous rivers, as this should help ensure populations can maintain their ability to adapt to changing environmental conditions. Where stocking is considered necessary, it is recommended that only broodstock from within the catchment is used.Anthropogenic activities, including the intentional releases of fish for enhancing populations (stocking), are recognized as adversely impacting the adaptive potential of wild populations. Here, the genetic characteristics of European barbel  In this era of rapid environmental change, local adaptation processes and the adaptive potential of wild populations are important to conserve as they potentially provide populations with inherent resilience to the disturbed conditions  released into the wild in the USA in 2004, a rate considered as low compared with 1951 to 2000  reconstruct introduction patterns in nonindigenous river basins and identify the genetic source of successful invasive populations, and (3) evaluate the evolutionary significance of these outputs and their potential applications for improving the management of their populations.A species frequently used to create and enhance recreational fisheries in England, and elsewhere in Europe, is European barbel Barbus barbus is the only Barbus species present in England and Wales and thus translocated fish pose no risk of hybridisation with endemic Barbus species as has occurred elsewhere in Europe  and Wales (River Taff), but samples were not available to the study. The species remains absent from Ireland . Although these releases were regulated, there is less detail on the numbers of fish released  and two were in the nonindigenous range , of which seven catchments were in the indigenous range  and growth hormone (Gh‐2) genes were amplified using paralog‐specific primers  and sequenced in both directions with amplification primers on an ABI 3130xl Genetic Analyzer using Big Dye 3.1 terminator (Applied Biosystem). The nucleotide sequences of nuclear alleles and CR haplotypes were deposited in GenBank database under accession numbers  (Table S1).Total genomic DNA was extracted from scales using DNA extraction kit , under manufacturer instructions. Phylogenetic and population genetic analyses were performed on (1) mitochondrial DNA Control Region (CR) gene and 2) two nuclear genes  (Table  two nuclπ %), were calculated using DnaSP v. 5  were added to the dataset, that was rooted with Barbus meridionalis (GenBank acc. No. AJ388417).In phylogenetic analyses of mtDNA CR, four sequences from previously sampled individuals of P < 0.01).Phylogenetic analyses were performed on mtDNA CR using three different optimality criteria: maximum likelihood (ML), neighboring‐joining (NJ) and Bayesian analysis. The ML analysis was performed through GARLI v1.0 software Zwickl  using TrHE), observed (HO) heterozygosity, and all loci were tested for deviation from Hardy–Weinberg equilibrium) using GenePop , while the interspecies, B. plebejus, distance was over 4%. The geographic distribution of the haplotypes revealed a homogenous pattern  diversity (see Table S1).Sequence analysis of three nuclear loci yielded 1998 sequences, with a whole 2058‐bp‐long alignment . A total of 1813 SNPs were found, mainly concentrated on the Gh‐2 locus, while only one indel was assumed in the S7‐2 locus. Levels of sequence polymorphism for each marker are summarized in Table S1. Among the 1998 nuclear sequences, 94 haplotypes were scored prevalently expressed by Gh‐2 locus, characterized both by the higher haplotype (H) and by nucleotide  to −0.14 (River Medway) and were all significant except for two populations . Pairwise FST values that were not significantly different (P > 0.05) were among nonindigenous (NI) populations , while all values were significant among populations in the indigenous range but where there is conjecture over whether their rivers have natural populations ; Table Among the 171 K = 1 to K = 20. Using the Evanno et al. (K = 2) were identified and the two statistics used to infer the number of clusters, LnP(D) (−2492.79) and ΔK, were consistent for K = 2. The populations were then grouped using their K assignment, with a threshold of K > 0.7   −292.79 andB. barbus in their indigenous range, and introductions into their nonindigenous range, has been occurring in the UK for over 100 years and these activities continue today even though there is no knowledge of the genetic relationships between the source and recipient populations. This is despite it being well established that understanding genetic differentiation among and genetic variation within populations is important for ensuring measures are implemented to safeguard genetic variability across populations  haplotype.The indigenous range of B. barbus populations from different river catchments. For example, across the River Thames catchment, there is significant genetic structuring whereby the Kennet and Lea rivers (both tributary rivers of the River Thames) cluster together but are significantly different from the main River Thames population. Unexpectedly, the main River Thames population is genetically similar to the Yorkshire Ouse and River Trent, possibly due to the introduction of hatchery‐reared fish, with stocking records for the river showing releases in recent years of B. barbus from a hatchery that uses River Trent broodstock . Similarly, the fish from the River Ure (in the Yorkshire Ouse catchment) grouped with the River Kennet and Lee, suggesting that fish were introduced there that originated from these rivers. Losses of population genetic integrity following stocking events have also been detected in populations of salmonid fishes in England when hatchery‐reared fish from other catchments have been released . The outputs here suggested their populations comprised fish of similar genetic origin. While this could be through the rivers having ancestral populations of similar genetic composition, Wheeler and Jordan  had at least one specific haplotype and were significantly genetically differentiated from other catchments, it can be argued that they require protection from this being further disrupted by more stockings that originate from outside the catchment. For all other river catchments now containing B. barbus in the UK, the genetic data can be applied to either ensuring populations are managed to facilitate the maintenance of their existing levels of genetic differentiation  or engage in active management to re‐establish populations to their prestocking genetic variability .The mtDNA data revealed B. barbus across their range, with their IUCN Red Listing being of least concern , then it can be argued that it remains appropriate that UK populations are managed primarily for recreational, catch‐and‐release angling, especially given the considerable socioeconomic benefits their fisheries can generate .Click here for additional data file.


Western corn rootworm (WCR) is one of the most significant insect pests of maize in North America. WCR has dramatically increased its range in the last century, invading key maize production areas in the US and abroad. In addition, this species has a history of evolving traits that allow it to escape various control options. Improved genetic and genomic resources are crucial tools for understanding population history and the genetic basis of trait evolution. Here we produce and analyze a transcriptome assembly for WCR. We also perform whole genome population resequencing, and combine these resources to better understand the evolutionary history of WCR.The WCR transcriptome assembly presented here contains approximately 16,000 unigenes, many of which have high similarity to other insect species. Among these unigenes we found several gene families that have been implicated in insecticide resistance in other species. We also identified over 500,000 unigene based SNPs among 26 WCR populations. We used these SNPs to scan for outliers among the candidate genes, and to understand how population processes have shaped genetic variation in this species.This study highlights the utility of transcriptomic and genomic resources as foundational tools for dealing with highly adaptive pest species. Using these tools we identified candidate gene families for insecticide resistance and reveal aspects of WCR population history in light of the species’ recent range expansion.The online version of this article (doi:10.1186/1471-2164-15-195) contains supplementary material, which is available to authorized users. Diabrotica virgifera virgifera) is a beetle (order Coleoptera) that is native to North America and the dominant maize pest in the US Corn Belt. It is estimated that each year Western Corn Rootworm (WCR) costs US farmers at least 1 billion dollars through yield losses and treatment costs [th century it spread eastward, colonizing the major maize production areas in the north central US (the Corn Belt) [Western corn rootworm (nt costs . WCR is nt costs . Throughrn Belt) . More rern Belt) , 4, makiBacillus thuringiensis (Bt) [The impact of WCR on maize production has been exacerbated by the fact that WCR has proved difficult to control. Over the last few decades WCR has evolved resistance to crop rotation , 5, 6, asis (Bt) . The gensis (Bt) –14. For Developing a genetic toolbox for WCR is an important step toward better understanding the genetic basis of insecticide resistance in WCR. To this end, Siegfried et al.  producedStudies in other insect species have suggested that cytochrome P450s (CYPs) are involved in resistance to several insecticides including methyl parathion, carbaryl, carbamate and organophosphates , with all studies indicating large amounts of genetic diversity and a general lack of population structure across the Corn Belt , 21–25. 454 Roche pyrosequencing. We then mined this data to identify candidate genes in pathways predicted to be involved in insecticide resistance. Our second objective was to develop an expansive set of SNPs that can be used for more robust inferences of population structure, construction of linkage maps, and genomic scans of selection. To produce these SNPs we generated genomic sequences from multiple laboratory and field populations, including five populations that showed reduced response to Cry3Bb1 in lab-based single plant bioassays. Furthermore, our ability to combine both transcriptomic and genomic data provide important information regarding genetic variation and level of selection among candidate resistance genes.As the amount gene flow among populations largely dictates the spread of resistance alleles, a better understanding of the genomic variation across populations will lead to a more informed assessment of resistance monitoring and mitigation, and prolong the life of various control options. Modern DNA sequencing technologies have increased our ability to undertake large-scale sequencing projects for almost any organism, including those with complicated genomes such as WCR . Therefo454 EST sequencing WCR third-instar midgut tissues were isolated from 200 larvae from the Waterman population . In total, 1,271,800 454 EST reads were generated with a mean length of 297.6 bp, together totaling 378,442,184 bp of total sequence. These 454 sequences were supplemented with 17,833 Sanger sequences from NCBI .Prior to assembly all e value < 10-3) were performed between WCR unigene sequences and the NCBI non-redundant (nr) database. Following the mapping step, gene ontology (GO) terms with e value < 10-6, annotation cut-off > 55, and GO weight > 5 were used for annotation. The “Aqua” classification in CateGOrizer [Tribolium casteneum (available at http://www.b2gfar.org/showspecies?species=7070). To find the pathways in which putative proteins of the WCR transcriptome are involved, analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed using Blast2GO [e value < 10-3) between WCR contig sequences and genomes of model insect species  were performed. Results of these BLASTx searches were also used to calculate the ortholog hit ratio ([http://www.oardc.ohiostate.edu/mcic/bioinformatics/bionformatics.html). The ortholog hit ratio was calculated by dividing the number of non-gap characters in the query (WCR unigene) by the length of the subject (model insect ortholog) [The WCR transcriptome was annotated using Blast2GO . BLASTx eGOrizer  was usedBlast2GO . For comt ratio  .Population genomic diversity was estimated by sampling 26 WCR populations Figure ; Table 1For each population we randomly selected 5 individuals (effectively 10 haploid chromosome sets) from mixed sex populations for Illumina sequencing . All individuals within a population were combined prior to DNA extraction . DNA was extracted using the E.Z.N.A. Insect DNA isolation kit (Omega Bio-Tek). Illumina sequencing libraries were prepared using the Nextera kit (Illumina) and sequenced on an Illumina HiSeq 2000 genome sequencer in a 2 × 100 bp paired-end configuration. After sequencing, barcode adapters were removed and all reads were quality trimmed from both ends to a minimum PHRED score of 20. All raw Illumina sequences used in this study can be found on the NCBI Sequence Read Archive (Accession SRP035322).mpileup program from the SAMtools package , with d 0.1.18; ), at allin silico by screening alternate SNP haplotypes against the commercially available restriction enzyme database found in Biopython . Primer sequences can be found in Additional file Taq DNA polymerase in PCR reaction buffer pH 8.5) and 0.5 mM each primer. Amplified products for each sample were digested with 1 μl (10 u/μl) RsaI at 37°C for 2 hrs. The resulting digest was visualized after electrophoresing in a 2% agarose gel stained with ethidium bromide.To validate SNPs, we used the Cleaved Amplified Polymorphic Sequences (CAPS) technique . Potention 1.60; ). We selπ throughout this manuscript) derived for pooled populations of individuals [b) for the inclusion of a particular SNP variant in the calculation. Here we used b = 2 as our minimum. We also disregarded any sites with greater than > 40X coverage to minimize the inclusion of paralogous sequences or undetected copy number variants. Within each population mean π was approximately 0.006, however in each population we observed 16 to 30 outlier unigenes (π > 0.15). These outliers were removed prior to the calculation of summary statistics, as these loci are likely impacted by alignment issues given their extreme departure from the mean. These calculations were done using a custom Python script (available on request).We estimated nucleotide diversity within populations for each unigene using a bias-corrected estimator , again FST is a measure of genetic differentiation relative to the net genetic diversity of the populations sampled, which makes FST sensitive to fluctuations in net genetic diversity. To complement FST, several authors have concluded that absolute metrics of genetic differentiation should also be employed [πxy) to estimate absolute genetic differentiation between populations using pooled sequence data. This statistic is an estimator of Nei’s dXY statistic [dXY it uses estimated allele frequencies rather than allele counts, making it suitable for pooled sequence data. To estimate πxy we first recorded a four element sequence profile b1, b2, b3, b4, for every nucleotide site within each pooled population, that contains the frequency of A, C, G, and T bases in the sample. Then for each unigene we calculated the average per-site expected heterozygosity among gametic unions between populations x and y (i.e. πxy), using the equation below.employed , 39. Fortatistic , though n is the number of nucleotide sites available for a unigene, bj at nucleotide site i in populations x and y, respectively. πxy requires only estimates of within population allele frequencies and not estimates of within population nucleotide diversity, unlike π and FST, and thus πxy does not require bias-correction for pooled populations.Where πxy metric to calculate genetic distances between populations for principal coordinates analysis . We also applied the πxy metric to characterize the genetic divergence between combined populations with low or high responsiveness to Cry3Bb1 or diapause and non-diapause populations  should be quite accurate, despite the small within population sample size. Due to the small within population sample size (n = 5), we expect our single locus estimates of divergence (i.e. the πxy and FST outlier scans described above) to have relatively modest statistical power. These scans should be viewed as exploratory.Our sampling procedure involved estimating population parameters from five individuals per population across approx. 10,000 loci. Theory suggests that multi-locus estimates of population genetic parameters can be robust even when estimated from just five individuals , 42. Thode novo assembly of the WCR transcriptome yielded 16,130 high quality unigenes totaling 20,643,715 bp, in addition to 95,335 singleton reads. The length of unigenes varied from 99-14,348 bp with an average of 1,280 bp  had an ortholog hit ratio > 0.7 and 54-58% were > 0.5  may represent novel genes with functions not yet been identified, or they could be a product of assembly errors. Interestingly, the percentage of unmatched unigenes obtained in our study is considerably lower than those obtained for other insect transcriptomes [About 64% 10,359/16,130) of the WCR unigenes had one or more hits to protein sequences in the non-redundant (nr) database at GenBank while the remaining (36%) had no match , followed by searches to N. vitripennis , B. mori , and D. melanogaster  databases , N. vitripennis (n = 288) and B. mori (n = 130). Analyzing the pairwise comparisons, the highest level of agreement was observed between WCR and T. casteneum, likely reflecting the evolutionary relatedness between these two insect species and the completeness of the Tribolium genome [Using pairwise BLASTx searches to protein databases for four model insects, significant matches for a majority of WCR unigenes 10,356/16,130) were obtained. The BLASTx search to the /16,130 wm genome .T. casteneum revealed a highly similar distribution for both insect species. The majority of contigs assigned to the ‘biological process’ domain were involved in cellular, regulatory, and developmental activities, while most of the contigs under the ‘molecular function’ domain were predicted to have catalytic, binding and transporter functions. Through the KEGG-based pathway analysis using Blast2GO, a total of 2,376 WCR unigenes were assigned to one or more of 117 pathways  WCR unigenes were annotated. The observed gene ontology (GO) terms for each domain  were widely distributed into different categories . These totals mark a considerable expansion beyond what was previously known for WCR candidate resistance gene targets. We further characterized two of the more important groups, the CYPs and cathepsins, because of their known relevance to resistance.We further characterized potential genes that may be involved in WCR Bt resistance, specifically to Cry3Bb1 , focusinTribolium CYP database [Cytochrome P450s (CYPs) are a superfamily of enzymes which are ubiquitous among organisms and play an important role in metabolizing endogenous and exogenous substances. In insects, CYP genes are classified into four clades: CYP2, CYP3, CYP4, and mitochondrial P450s . Despitedatabase . The maj454 sequencing combined with previous resources show a considerable diversity in the number of cathepsins in WCR. We found a total of 98 unigenes that matched known cathepsins, including some with high similarity to those already described in WCR [The expanded d in WCR  . We identified 39 πxy outliers and 26 FST outliers . Among all 64 πxy and FST outlier unigenes we found two on our lists of candidates for genes potentially involved in reduced response to Cry3Bb1. These include one esterase (unigene: DIAVI-09JAN12-CLUS09401_1) and one CYP , both of which were FST outliers. All outliers should be considered preliminary and exploratory as our tests are low-powered given the relatively small within population sample sizes, and because our strategy assumes that populations with a low response to Cry3Bb1 share resistance alleles, though there is no evidence supporting this assumption. Nonetheless, these loci may represent a promising pool of potential candidates for Cry3Bb1 resistance.For all unigenes, including those that may be involved in WCR resistance to Cry3Bb1, we pooled SNP data from different populations with high or low response to Cry3Bb1 . For 24 of the 27 SNPs the CAPs genotypes showed evidence of both allelic versions of the predicted SNP  we used a bias-corrected estimator [πxy), which is an estimator of the dXY statistic [π within each population can be found in Table π, and the highest mean π values were found among field collected populations.Using the SNPs extracted from pooled population sequencing we estimated genetic diversity within and between WCR populations. To estimate within population nucleotide diversity  compared to the randomly mated Brookings population (mean π = 0.0055) from which it was derived. This suggests that full-sib mating has been fairly ineffective at removing variation. Second, there appears to be slightly less nucleotide diversity within the 2012 collections, when compared to the 2011 collections . This could reflect annual variation in population sizes across the Corn Belt, or perhaps some unnoticed deviation in our sampling procedures between these two years. Finally, there is little evidence for a significant reduction of genetic diversity among lab populations, suggesting that maintenance in artificial environments has not caused an erosion of genetic diversity. This finding mirrors observations by Kim et al. [Our extensive SNP dataset reveals several interesting aspects of genetic variation in WCR. First, the inbred population had experienced 5 generations of full-sib mating, which is theoretically expected to fix approximately 41% of the sites segregating among the gametes of the founding sib-mated pair , yet weπxy ranged between 0.0079 and 0.0097 with a mean of 0.0088. From this we observed that the levels of within population nucleotide diversity (mean π = 0.006) are of a similar magnitude as between population divergence (mean πxy = 0.0088). This indicates that each population maintains considerable genetic variation, and that there is little evidence for substantial genetic differentiation between populations (see below).Among the 325 pairwise comparisons between populations, πxy values were also used to generate a principal coordinates analysis among all populations. Diapause status appears to be the most important factor in population differentiation , and only 5.6% of all unigenes had FST values > 0.2 . This observation is expected, as inbreeding impacts allele frequencies and will tend to increase FST. Interestingly, the smallest mean pairwise FST estimate was between the Onslow, IA and Trent, SD populations . Despite being 508 km apart, these two field collected populations have both demonstrated reduced response to Cry3B1 in a lab-based single plant bioassays . Among ears ago ). These r = 0.207; Mantel test p-value = 0.002). Thus, there may be a subtle signature of the range expansion in the form of genetic isolation by distance. Moreover, the significance of the isolation by distance is strongly influenced by the inclusion of the most easterly population . If we remove this population, the correlation between genetic and physical distance falls to 0.106 and is no longer significant . In contrast, if we remove all 5 populations from eastern Colorado and western Kansas (the most westerly populations) the correlation between genetic and physical distance is still significant . The importance of this eastern population in detecting isolation by distance is consistent with incomplete homogenization following range expansion, however, because this result is driven by a single population we cannot exclude the hypothesis this population is in some way anomalous. Moreover, because recently formed Corn Belt WCR populations may not be at migration-drift equilibrium we cannot determine if the weak pattern of genetic isolation by distance is a product of genetic drift overcoming gene flow or if it is a hold-over from population founder effects during the species eastward invasion. Based on theory and these preliminary results we predict that sampling populations further east will tend to increase the extent of detectable of isolation by distance in WCR, and may reveal important estimates of genetic diversity in recently colonized areas.We do observe a modest – albeit significant – trend of increasing genetic isolation as a function of geographic distance among field collected populations  between 10-8 or 10-9 bp-1 generation-1 (a range that encompasses estimates from D. melanogaster and Caenorhabditis elegans[π imply that WCR field populations maintains approximate effective populations sizes (Ne) on the order of 0.2 to 2 million individuals (i.e. Ne ≈ π/4 μ). This level of Ne gives little indication of recurrent population bottlenecks in WCR, which gives WCR populations the ability to generate and maintain many individuals with novel mutations. This creates a large pool of standing genetic variation that may in turn increase the probability of evolving resistance to control strategies. Moreover, we show very little genetic differentiation between field populations, and that genetic variation has spread quickly eastward following range expansion, both of which are consistent with high rates of gene flow between populations. Thus, if resistance does evolve, there are few barriers preventing it from spreading between populations. Together these two features of WCR population genetics are hypothesized to lead to a high evolutionary potential to evade controls [In a review article, McDonald and Linde  posit ths elegans, 61), thcontrols , and maycontrols , 10.WCR is one of the most significant agricultural pests in North American, yet at the time of publication WCR has a modest number of nucleotide sequences are available in GenBank. Improved genetic and genomic resources for WCR are crucial tools in the ongoing effort to control this pest. Here we produce a large transcriptome assembly for WCR and complement these unigenes with diversity data obtained by whole genome population resequencing. Our WCR transcriptome assembly contains approximately 16,000 unigenes, including several gene families that have been implicated in insecticide resistance in other species. We identify a large pool of SNPs among 26 WCR populations, and show that these SNPs can be readily adapted for use as genotyping markers. We also use these SNPs to scan for outliers among candidate Bt resistance genes and to understand how population processes have shaped genetic variation in this species. We find two potential candidate Bt resistance genes that show strong patterns of molecular differentiation between high and low response to Cry3Bb1 populations. We also expand on past population genetic studies in WCR and show that this species has significant genetic diversity, little population structure, and a high evolutionary potential to resist control strategies. This study will provide a solid foundation for future research on the molecular genetics of WCR.454 ESTs and Illumina pooled population sequences used in this study can be found on the NCBI Sequence Read Archive under the accessions SRS528965 and SRP035322, respectively.GenBank IDs for public WCR EST sequences. This spreadsheet contains the GenBank IDs for the public WCR sequences incorporated our transcriptome assembly. (XLSX 188 KB)Additional file 1: Population level WCR SNP genotypes. A comma separated values file (csv) containing the genotypic status of all 26 populations sampled at 532,396 SNP sites. Missing genotypes are denoted by a dash. The sequence of each unigenes is listed on first usage. This file has been compressed with the bzip2 compression software. (ZIP 9 MB)Additional file 2: SNP genotyping validation study. This spreadsheet contains the CAPs SNP validation information, including primer sequences, and observed restriction patterns and inferred genotypes. (XLSX 96 KB)Additional file 3: Power analysis of population genetic sampling procedure.(DOCX 21 KB)Additional file 4:Blast2GO annotations. This spreadsheet contains annotations for 10,359 WCR unigenes as identified by Blast2GO. (XLSX 1 MB)Additional file 5: Comparative genomics of WCR. A Venn diagram showing the number of WCR contigs with significant matches (unique and common) to genomes of T. casteneum, D. melanogaster, N. vitripennis, and B. mori. The significant matches  were calculated after pairwise comparisons (BLASTx) to each individual genome. (PNG 77 KB)Additional file 6: Distribution and comparison of GO categories. Vertical bars indicate the distribution of WCR (Diabrotica) and red flower beetle (Tribolium) GO term mappings that belong to each of the three top-level GO categories . (PDF 47 KB)Additional file 7: KEGG pathway annotations. This file contains two spreadsheets. The “Detailed” spreadsheet contains putative KEGG pathways assignments to unigenes in the WCR transcriptome, while the “Summary” spreadsheet lists a summary of the counts of all pathways identified. (XLSX 53 KB)Additional file 8: Identification of putative candidate genes for Cry3Bb1 resistance. This file contains a spreadsheet for each of several putative gene families that may be candidates for resistance to Bt toxins, including the unigenes name and descriptions, and results from πxyand FST outlier scans between Cry3Bb1 resistant and susceptible populations. The last two spreadsheets list the results of πxyand FST outlier scans between populations with high or low response to Cry3Bb1 for all available unigenes. (XLSX 4 MB)Additional file 9: Characterization of WCR-CYP genes. BLASTp result comparing WCR-CYP unigenes to a Tribolium CYP data set [Additional file 10: data set . Additional file 11: Plot of calculatedπxyvalues between diapause and non-diapause populations and permuted 99thpercentile values. Each point represents a unigenes calculated πxy value and the 99th percentile value from 1,000 permutations of the diapause and non-diapause population labels. Red dots indicate calculated πxy > 99th percentile permutation value (outliers), while black dots represent calculated πxy < 99th percentile permutation value. The blue line represents the boundary between outliers and non-outliers. (PDF 1 MB)Additional file 12: A histogram of pairwiseFSTbetween WCR populations. This histogram represents FST estimates for all genes among all pairwise comparisons of the 26 WCR populations. (PDF 76 KB)Additional file 13: