FAM213A and a related enhancer; the product of this gene acts as an antioxidant to lower oxidative stress and may help to maintain bone mass. The results suggest that positive selection on the enhancer might increase the expression of this antioxidant, and thereby prevent oxidative damage. In addition, the most significant signal in a relative extended haplotype homozygosity analysis was localized to the SFTPD gene, which encodes a surfactant pulmonary-associated protein involved in normal respiration and innate host defense. Our study thus identifies two novel candidate genes and associated pathways that may be involved in high-altitude adaptation in Andean populations.Humans living at high altitude  have acquired unique abilities to survive the associated extreme environmental conditions, including hypoxia, cold temperature, limited food availability and high levels of free radicals and oxidants. Long-term inhabitants of the most elevated regions of the world have undergone extensive physiological and/or genetic changes, particularly in the regulation of respiration and circulation, when compared to lowland populations. Genome scans have identified candidate genes involved in altitude adaption in the Tibetan Plateau and the Ethiopian highlands, in contrast to populations from the Andes, which have not been as intensively investigated. In the present study, we focused on three indigenous populations from Bolivia: two groups of Andean natives, Aymara and Quechua, and the low-altitude control group of Guarani from the Gran Chaco lowlands. Using pooled samples, we identified a number of SNPs exhibiting large allele frequency differences over 900,000 genotyped SNPs. A region in chromosome 10 (within the cytogenetic bands q22.3 and q23.1) was significantly differentiated between highland and lowland groups. We resequenced ~1.5 Mb surrounding the candidate region and identified strong signals of positive selection in the highland populations. A composite of multiple signals like test localized the signal to  It is generally accepted that anatomically modern humans emerged in Africa and radiated from there to colonize most of the world's land masses . During The main environmental stresses of living in elevated plateaus and mountainous regions are the decrease of temperature and humidity, the increase in solar electromagnetic radiation, and hypobaric hypoxia (defined as the decrease in oxygen intake for metabolic processes due to reduced barometric pressure) ,3. AlthoThree main high-altitude regions of the world have supported relatively large human populations for millennia: the Ethiopian highlands of the Semien Mountains ,5, the TEGLN1 and EPAS1), and represent strong targets of selection at high altitude, especially for the Tibetan population [VEGFB and ELTD1) have also been implicated in the elevated hematocrit characteristic of high-altitude populations in the Andes [The characteristic morphology and physiology of high-altitude natives, in particular Tibetans and Andeans, has been studied in detail , enablinpulation –32. Besihe Andes , while nhe Andes –36.In the present study, we focused on three indigenous populations from Bolivia, namely two groups of native inhabitants living at high altitude (≥3600 meters above sea level) in the Andes, Aymara and Quechua, and as a control group the Guarani from the Gran Chaco lowlands. Using a genome-scan approach and pooled population samples, we identified a candidate region in chromosome 10 that exhibited significant allele frequency differences between the high- and lowland populations. Targeted resequencing of approximately 1.5 Mb of the region of interest revealed strong signals of positive selection in both Andean groups, suggesting that this genomic region harbors genes for high-altitude adaptation in these populations.We collected saliva samples for the extraction of genomic DNA from 55  Aymara (AYM) and 21  Quechua (QUE) from locations above 3,600 meters, and 23  Guarani (GUA) from the lowlands see  in BolivTo search for highly-differentiated genic regions among the high- and lowland groups, and therefore putatively involved in altitude adaptation, we pooled DNA samples from each population independently in triplicate see , genotypWe used frequency estimates for every called SNP obtained from the microarray experiments to perform pairwise comparisons among the populations, and we identified candidate regions that contained highly differentiated SNPs see . The tesBased on the HL-LL comparison, we detected 9 candidate regions containing SNPs exhibiting large allele frequency differences (≥ 0.3); these regions were also supported by a t-test . In part–8, assuming 1 million random markers and a single test level of 0.05. With this threshold, five out of the six SNPs from the region on chr10:81.7~82.2 Mb stood out as highly significant in the HL-LL as well as the Aymara-Guarani comparisons ; the individually genotyped SNPs are listed in parisons . It is wparisons  for furtAs the region on chromosome 10 (spanning approximately 500 kb from 81.7 to 82.2 Mb) contained several SNPs exhibiting significant allele-frequency differences between high- and lowland populations , we inveFST values [FST values generally below 0.1 in this region  and maximum |iHS| is 3.144  test, which is based on the principle of long extended haplotypes . We scanNowadays, it is estimated that worldwide some 140 million  people rIn the present study, we focused on three populations from Bolivia, namely two groups of native inhabitants of the Andes: Aymara and Quechua, and Guarani from the Gran Chaco lowlands as a neighboring control group. Special care was taken to obtain samples from members of long-term high-altitude residence families, avoiding the collection of recent immigrants. Moreover, we collected samples only from healthy individuals, especially with no known medical record or indication of CMSWe performed a genome-wide scan of over 900,000 SNPs using microarray technology on pooled DNA samples see , thus exThe region on chromosome 10 is a novel candidate region for high-altitude adaptation, which has not been detected in previous studies. We further verified that the signal of high differentiation between HL and LL groups for the chromosome 10 region was unlikely to arise by demographic events alone after carrying out simulations under various demographic models. In order to investigate in more detail the potential signal of selection in chromosome 10, we performed targeted resequencing of ~1.5 Mb surrounding the region of interest. ANXA11, MAT1A, DYDC1, DYDC2, FAM213A, TSPAN14 and SH2D4B) are included in or near the borders of this region  [ANXA11 [-10).The strongest evidence of positive selection was in the region of 82.0~82.3 Mb in chromosome 10, where several genes  –62. It i [ANXA11 . The risThe FAM213A gene was localized by the CMSL test under all models of population history . Also knIn addition to the FAM213A gene itself, the target region of positive selection includes an enhancer of FAM213A. Two SNPs are located in this enhancer; one (rs77999529) exhibits a low minor allele frequency in various human populations, while the second (rs150230265) exhibits significant allele frequency differences between HL and LL  and the T allele of rs7078012  [–5). However, the derived allele frequency is higher in LL than in HL. Another SNP (rs721917) involves a methionine to threonine substitution and exhibits significant differentiation between HL and LL  with a higher frequency of the methionine-encoding allele in HL. This mutation has been investigated intensively and influences oligomerization, function, and the concentration of SP-D in serum [Although FAM213A was detected as a candidate gene in the CMSL analysis, it was not identified by the REHH analysis. Instead, a different candidate gene in the resequenced region, the SFTPD gene, was identified by this analysis. These different results are not surprising, given that different methods have different power to detect selection, especially in the case of partial selective sweeps and/or selection on standing variation . SFTPD e x 10–9) . Severalin serum . The Thrin serum –78. The in serum . The thiIn addition to SFTPD, there are two other genes coding for surfactant pulmonary-associated proteins (SFTPA1 and SFTPA2) which are within the genomic region resequenced, but outside the region showing the highest signals in the CMSL and REHH tests. Mutations in SFTPA1 and SFTPA2 are associated with idiopathic pulmonary fibrosis , and  in high-altitude adaptation.In summary, we identified a novel candidate region for high-altitude adaptation in Andeans, with several genes and/or enhancers potentially under positive selection. In particular, multiple tests localized the signal to We collected in total 99 saliva samples from South American indigenous individuals from Bolivia. The participants were informed about our study objectives and provided written consent for the anonymous use of the biological material for academic research. This research was approved by the Ethics Committee of the University of Leipzig Medical Faculty. All sampled individuals were unrelated and of self-identified ancestry as either Aymara, Quechua or Guarani. They were members of long-term residence families from the places where samples were gathered; sample collection from recent immigrants was avoided. Furthermore, special care was taken to obtain samples only from healthy individuals, with no known medical record or indication of CMS or other altitude-related illness. The Aymara individuals were sampled in El Alto , Tiwanaku , and La Paz . The Quechua individuals were sampled in Oruro-Soracachi , and the Guarani individuals were sampled in Santa Cruz-Gran Chaco . Genomic DNA was extracted from the saliva samples following the protocol published elsewhere , and theA total of 56 males from our Bolivian collection of samples were genotyped for 24 SNPs  defining the major branches of the Y chromosome tree . The 24 To search for candidate genomic regions of high differentiation between high and low-altitude Bolivian populations, we genotyped pooled samples on microarrays; this approach has been used successfully in other studies –41. A toj is the technical replicate and k is the probe set. The allele frequency difference was taken as p1 and p2 are the allele frequencies in two different groups. We calculated the allele frequency differences between groups in a pairwise fashion, and we also compared the Guarani against Aymara and Quechua individuals combined together into a single highland group.The allele frequency per called SNP and population was estimated from the raw probe intensity data of each microarray as previously described –40; the T should follow a Vs+Vp, where Vs represents the sampling variance, and Vp represents the component from the pooling process. Vs is given by Vp is given by n is the sample size.We applied a t-test to formally evaluate the statistical significance of the calculated allele frequency differences. Therefore, We applied a multi-locus approach to search for highly differentiated genic regions. SNPs were ranked according to either the allele frequency difference or P value significance. The top 0.1% SNPs were connected if they were within 100 kb distance, and a differentiated region was called if there were more than 10 top SNPs connected.http://genome.csdb.cn/cgi-bin/hgPcr/). Primer interactions within the multiplex were evaluated and minimized using the NetPrimer online software (http://www.premierbiosoft.com/). Briefly, single PCRs amplified the target region surrounding the SNP of interest for each individual contained in the full collection set. The amplicons were then assembled into four separate multiplexes and analyzed on an ABI 3130xl Genetic Analyzer. The SNP calling was performed using the ABI GeneMapper ID v3.2 software. As a positive control for the SNaPshot experiments, the sample HapMap #NA06985 CEPH/UTAH Pedigree 1341 was assayed along with the Bolivian samples. The called genotypes for the control were compared with the consensus genotypes for the same 14 SNP loci obtained from the HapMap website; the concordance was 100%. Additionally, one Bolivian sample was assayed in single primer extension reactions for each of the 14 SNPs, and the called genotypes were compared to the genotypes obtained from the multiplex approach; the concordance was 100%. For the 14 SNPs re-genotyped individually, we performed a Fisher’s exact test to validate the results obtained from the DNA pooling approach. The Fisher’s exact test was performed using R (http://www.r-project.org/).Confirmation of the allele frequencies estimated from the RAS scores for the 14 SNPs with the largest HL-LL allele frequency differences was performed using the ABI PRISM SNaPshot Multiplex System (Applied Biosystems by Life Technologies) according to the manufacturer's protocol. Primers for the single PCRs and for the subsequent extension reactions were designed using the UCSC In-Silico PCR tool (We used Agilent custom 1M capture arrays in order to resequence the target region of interest. We designed overlapping microarray probes of 60 bases targeting over 1.5 Mb of the region of interest in chromosome 10 (chr10:81113000–82664000). Probes were tiled every 3 bases across the target region. Probes containing repetitive elements were discarded . We usedIllumina GAIIx libraries were prepared following Meyer and Kircher , with soIndividual-specific indexes were used to multiplex the libraries prior to hybridization enrichment. These were attached by performing a PCR amplification using the Phusion Mastermix . After indexing, samples were pooled in equimolar ratios and hybridized. After hybridization, quantitative PCRs were performed on the sample pools with the DyNAmo qPCR kit (NEB). Based on the resulting qPCR amplification plots, the sample pools were amplified using the Phusion Mastermix so that they did not reach plateau. Each sample pool was sequenced on a single lane of an Illumina GAIIx run by single-end sequencing using 36 cycles.The raw sequencing reads were aligned to the human reference genome sequence GRCh37 by BWA v0.70  with defGenotypes were called by the GATK UnifiedGenotyper v1.4  with theTo analyze the population differences and detect signals of natural selection in either high or low-altitude populations, we employed several methods with both empirical polymorphism data and simulated data. These methods were based on population differentiation, the allele frequency spectrum, properties of haplotypes, and composite signals:FST is a measurement of population differentiation. We calculated it in pairwise manner by using the unbiased estimator of Weir and Cockerham [ockerham .We calculated ΔDAF  between XP-CLR test is a likelihood method for detecting selective sweeps based on multilocus allele frequency differentiation between a putative selected population and a non-selected population . We set Tajima’s D  was perfFay & Wu’s H  was alsoThis method is based on the length of the haplotype associated with ancestral vs. derived alleles; derived alleles subject to positive selection tend to have unusually long haplotypes, as such alleles have risen to high frequency too quickly for recombination and/or new mutations to break down the length of the associated haplotype. The iHS test partitions haplotypes into an ancestral group and a derived group according to the allele states of core SNPs; iHS is defined as the log ratio of the integrated EHH (extended haplotype homozygosity) for these two groups .The REHH is another test based on haplotype length and structure, and was calculated with the Sweep software . We set k is the number of SNPs in one region and Pi is the empirical P value of one test for the SNP i. In Luisi’s study, FST, ΔDAF and iHS statistics are calculated, and regions with high ZF scores indicate positive selection. Following this approach, we used a CMSL method by combining FST, ΔDAF, Tajima’s D, Fay & Wu’s H, XP-CLR and the iHS test. The ZF statistic is computed as above, where k is the number of tests and Pi is the P value of the SNP in test i. We obtained the P value from empirical distributions by simulations.Numerous methods have been developed to detect positive selection based on various patterns of genetic variation, and hundreds of candidate regions have been identified. But usually these regions are typically large and the causal variants remain unknown. A composite of multiple signals method narrows down the candidate regions and aids in identifying the causal variant . RecentlNe trajectory. We therefore used the Ne estimated from the Mexican (MXL) population in the 1000 Genomes Project [Ne in recent history instead of a sharp expansion, as our standard demographic model. We set the divergence time between high and low-altitude populations at 10,000 years ago, and the divergence of the two high-altitude populations at 5,000 years ago. We also used two other models, one with a more intense bottleneck  and one with a constant Ne of 7,000 for the entire history.Simulations were used to calculate the P value of the scores in the empirical data. To account for the impact of demography on the detection of selection, we did simulations under a wide range of demographic scenarios inferred by pairwise sequentially Markovian coalescent (PSMC) model, which is a method to infer the history of population size change based on a single genome sequence . In this Project Click here for additional data file.S2 FigFST in all comparisons. (B) Derived allele frequencies. (C) Tajima’s D. (D) Fay & Wu’s H. Black dashed lines are the 5% threshold of corresponding tests from the standard simulations.(A) (TIF)Click here for additional data file.S3 FigNe by half beginning 10,000 years ago. The purple line is the constant model with a constant Ne of 7,000.The red line is the population size trajectory of MXL as estimated by PSMC. The green line is from the standard model modified from the MXL trajectory by assuming a constant population size in recent history. The blue line is the extreme bottleneck model modified from the standard model by reducing (TIF)Click here for additional data file.S4 FigFST between HL and LL. (B) Derived allele frequencies of LL. (C) Tajima’s D of LL. (D) Fay & Wu’s H of LL. (E) XP-CLR of LL against HL. (F) Absolute iHS score of LL. (G) CMSL score of LL. Black dashed lines are the 5% threshold of each test in standard simulations, green lines are the 1% threshold, and red lines are the 0.1% threshold.(A) (TIF)Click here for additional data file.S5 FigCMSL in highland populations under (A) the extreme bottleneck model and (B) the constant population size model.(TIF)Click here for additional data file.S6 FigData are from this study and 1000 Genomes.(TIF)Click here for additional data file.S7 FigFAM213A and SFTPD  and Denisovan genome sequence in ollowing ). Green (TIF)Click here for additional data file.S1 Tablehttp://enhancer.binf.ku.dk/Pre-defined_tracks.html).The ‘robust set’ enhancers and the ‘enhancer_tss_association’ were downloaded from the FANTOM5 project  (http://(XLSX)Click here for additional data file.


Merino and Merino-derived sheep breeds have been widely distributed across the world, both as purebred and admixed populations. They represent an economically and historically important genetic resource which over time has been used as the basis for the development of new breeds. In order to examine the genetic influence of Merino in the context of a global collection of domestic sheep breeds, we analyzed genotype data that were obtained with the OvineSNP50 BeadChip (Illumina) for 671 individuals from 37 populations, including a subset of breeds from the Sheep HapMap dataset.Based on a multi-dimensional scaling analysis, we highlighted four main clusters in this dataset, which corresponded to wild sheep, mouflon, primitive North European breeds and modern sheep (including Merino), respectively. The neighbor-network analysis further differentiated North-European and Mediterranean domestic breeds, with subclusters of Merino and Merino-derived breeds, other Spanish breeds and other Italian breeds. Model-based clustering, migration analysis and haplotype sharing indicated that genetic exchange occurred between archaic populations and also that a more recent Merino-mediated gene flow to several Merino-derived populations around the world took place. The close relationship between Spanish Merino and other Spanish breeds was consistent with an Iberian origin for the Merino breed, with possible earlier contributions from other Mediterranean stocks. The Merino populations from Australia, New Zealand and China were clearly separated from their European ancestors. We observed a genetic substructuring in the Spanish Merino population, which reflects recent herd management practices.Our data suggest that intensive gene flow, founder effects and geographic isolation are the main factors that determined the genetic makeup of current Merino and Merino-derived breeds. To explain how the current Merino and Merino-derived breeds were obtained, we propose a scenario that includes several consecutive migrations of sheep populations that may serve as working hypotheses for subsequent studies.The online version of this article (doi:10.1186/s12711-015-0139-z) contains supplementary material, which is available to authorized users. Ovis orientalis) occurred more than 11 000 years B.C. in the Fertile Crescent (south west Asia) [Sheep domestication from its wild ancestor (st Asia) . Sheep gst Asia) . In Eurost Asia) . Columelst Asia)   to Southern Italy (at that time ruled by an Aragon dynasty), and mated to native ewes, giving rise to the contemporary Gentile di Puglia breed [th century, purebred Merino rams were exported to Saxony (Germany) and France where they founded the Merinolandschaf and Rambouillet breeds, respectively. The Rambouillet breed was also exported to Central Italy where it gave rise first to the Sopravissana breed [th century. In Hungary, Merino sheep breeding began in 1774 when Empress Maria Theresa bought 300 Merino individuals from Spain [th century, the Merino breed was also exported to Australia and New Zealand through successive introductions of sheep from Germany and France. On the Arapawa island , the Merino population has reverted to the feral status , argali (O. ammon), and urial (O. vignei)) sheep populations belonging to the genus Ovis [See Additional file O. orientalis, O. ammon and O. vignei. Genotype data originated from different sources. The BiOvIta project [We used genotypes that were obtained with the OvineSNP50 BeadChip  for a total of 37 breeds or populations ; genetic diversity (He); inbreeding coefficient (F). The distribution of the number of SNPs over frequency bins for each population was obtained using PLINK and plotted on a graph using Microsoft Excel. Pair-wise IBS  distances among the sheep samples were calculated using PLINK [Within-breed genetic diversity was estimated with the following parameters that were obtained using PLINK : proportng PLINK  and grapK value (number of assumed clusters), prediction errors. The value of K that minimizes this estimated prediction error is assumed to be the most probable. Individual coefficients of membership to each K cluster produced by ADMIXTURE were visualized using the program DISTRUCT [Genetic relationships among breeds and levels of gene flow and admixture were evaluated through the model-based clustering algorithm implemented in the software ADMIXTURE v. 1.22  by applyDISTRUCT .et al. [Relationships among breeds were also explored by neighbor-network analysis of the (1-IBS) distance  and the et al.  calculatet al. . Neighboet al.  implemenet al. .M) ranging from 0 to 50 was tested, and the value of M that had the highest log-likelihood was selected as the most predictive model. Then, the 37 breeds or populations were grouped into six arbitrary groups  and the analyses were repeated as previously described. The tests f3 and f4 that are implemented in the TREEMIX computer package [f3-statistics  to determine if A was derived from the admixture of populations B and C, and the f4-statistics  ) to test the validity of a hierarchical topology in four-population trees. Significant deviations of the f4-statistics from 0 for the three possible topologies of four-population trees are evidence of gene flow in the tree, i.e., that the phylogeny of the four groups is not completely tree-like. A significantly positive Z-score indicates gene flow between populations related to either A and C or B and D and a significantly negative Z-score indicates gene flow between populations related to A and D or B and C.In order to reconstruct historical relationships between the analyzed populations and to test for the presence of gene flow, we adopted the tree-based approach implemented in TREEMIX . First,  package  were alsr for the same pair of SNPs in two different breeds as measures of linkage disequilibrium (LD) [et al., [r coefficients increase for SNPs at longer distances are expected to share a more recent common ancestor. To avoid data flattening, out-groups  were removed from the dataset for this analysis. The second method is based on the direct inference of population haplotypes using fastPHASE [To detect pairs of populations that share common ancestry and/or have experienced gene flow, we investigated the extent of pair-wise haplotype sharing with two approaches. The first approach is based on the calculation of correlation coefficients ium (LD) . First, [et al., , r coeffastPHASE , a softwastPHASE . Then, fpl and He [See Additional file Ppl = 0.617 and He = 0.217). Bias due to sample size was excluded by re-sampling analysis i.e. rarefying each population sample to a maximum of 10 animals (the size of the Macarthur Merino sample) and re-calculating Ppl, He and F [See Additional file After data editing, the dataset included 671 samples from 37 breeds or populations that were genotyped for 45 847 SNPs. Within-breed genetic diversity parameters are in Table S1 [See Additional file x-axis. Interestingly, wild and feral individuals were more scattered than their domestic counterparts. Spanish Merino sheep were also more scattered over the plot than other domestic breeds (data not shown).The MDS plot of the pair-wise IBS distances is in Fig. Regarding the modern breeds Fig. , they shK (number of clusters assumed in the whole sample) ranging from 2 to 37. At K = 2, the first populations to be differentiated are the Sardinian and the European mouflon, which display a similar pattern of genetic differentiation that confirms the hypothesis of mutual introgression between the Sarda breed and the Sardinian mouflon [K = 4, four distinct genetic components are clearly observed: the three wild sheep , the mouflon, the primitive Soay and Boreray breeds, and the modern breeds. At K = 10, a Merino genetic component is detected that is shared by the three Australian Merino populations, most of the Merino-derived breeds , Spanish Merino and, partially, by the four Spanish non-Merino breeds  and the Italian Merino-derived breeds Gentile di Puglia and Sopravissana. Another major component is shared by the Italian breeds Massese, Appenninica, Laticauda, Leccese, Comisana, and Sardinian White , and, partially, by the Gentile di Puglia, Sopravissana, the four Spanish non-Merino breeds, Merinizzata Italiana, Spanish Merino and Merinolandschaf breeds. At K values higher than 10, the three Australian Merino populations form a separate cluster and more and more breeds are differentiated.Model-based clustering was performed to search for admixture and genetic distinctiveness between breeds and breed groups. The plot obtained by ADMIXTURE analysis is in Fig.  mouflon . At K = K value for which the ADMIXTURE cross-validation error was lowest  [See Additional file He values i.e. 0.362) [See Additional file F value close to 0 (−0.033) [See Additional file At the ative −0.51 [See ANeighbor-network graphs account for gene flow among breeds (reticulation) and, thus provide a more plausible reconstruction than linear representations. The Neighbor-network clearly separated the wild, feral and primitive North European sheep from modern domestic sheep Fig. . Among tM = 0, 1, 2, 3, 4, 5, 10, 20, 30, 40, 50) revealed that the most predictive model assumed four migration events [see Additional file Analysis of the TREEMIX log-likelihood values for 0 to 50 migrations revealed that the most predictive model (i.e. that had the highest log-likelihood) assumed the presence of 40 migration events [See Additional file f3 test on the aggregated dataset did not produce any significant Z-score (data not shown). The f4 test highlighted highly significant Z-scores (>3) for three four-population trees [See Additional file The r values across breeds or by direct count of shared blocks reconstructed in silico, are in Figures S5, S6, S7 and S8 [See Additional file Heat maps of pair-wise haplotype sharing, which were estimated either by correlating He values found for wild, feral and primitive sheep reflect the fact that these breeds were not used to identify polymorphic SNPs for the OvineSNP50 BeadChip (Illumina)  rather ent bias ). This vK = 4 groups. Overall, haplotype sharing showed more cross-flow and relatedness between Merino and Merino-derived breeds than between other sheep populations. Our evidence points to admixture events during different time periods. A breed-based TREEMIX analysis detected two basal migrations that involved large assemblages of breeds, which is consistent with the gene flow that occurred between archaic domestic sheep stocks. Signals of gene-flow among feral, primitive and Merino sheep populations were also observed when the TREEMIX analysis was performed on the dataset arranged into the six arbitrary groups. In addition, the four-population test produced strong evidence to support migration between Spanish populations and both Merino and Italian populations. Since this cannot be readily explained by recent gene flow, these signals may indicate remote admixture, which may be the combined effect of documented export of wool sheep from Roman Italy to Spain and late-Medieval export of sheep from Spain to Southern Italy under the Aragonese rule [Our data indicate a clear and consistent subdivision of the breeds considered in this study. The MDS plot highlighted four groups corresponding to modern, primitive, feral and wild sheep. This agreed with the four main branches in the neighbor-network and the model-based clustering at ese rule . As discth century from England .For many years, several authors have addressed the question of when and how the Merino breed developed . Some hyK = 10 in the ADMIXTURE analysis, which provides evidence for an extensive circulation of Merino genetic material as previously reported by Kijas et al. [To obtain an unambiguous picture of the ancient genetic relationships between the breeds in our dataset and, thus gain insight into the origin of the Merino breed is not straightforward. A clear Merino genetic component was detected in several breeds at s et al. , but conF values observed in our sample [In our study, a marked differentiation between the Spanish Merino populations from Estremadura and from Andalusia was observed with both the ADMIXTURE and the neighbor-network analyses. This likely reflects the isolation of animals reared at the governmental selection center of Hinojosa del Duque , where, in 1971, animals from the five main traditional Spanish Merino genetic lines were collected to ensure the conservation of purebred animals . The ADMr sample . It is iK = 7 and K = 10, clear signals of merinization in European and Australian breeds. This was also clearly confirmed by the TREEMIX analysis. A strong merinization signal was detected by ADMIXTURE analysis in the Rambouillet breed, which was developed in France directly from pure Spanish Merino imported in the Rambouillet region during the second half of the 18th century. Remarkably, a less intense Merino signal was detected in the Australian Merino. Although different Merino stocks from Cape Colony, England, Saxony, France and America are known to have contributed to the early development of this breed and although different Merino strains are currently bred in Australia, the three Australian Merino populations that were tested here have homogeneous patterns in the ADMIXTURE analysis, low pair-wise IBS and Reynolds distances and share many haplotypes. This reflects a common distinctiveness from all the other Merino populations considered in this study, which originates from a shared history after their arrival in Australia. The ADMIXTURE patterns at various K values confirm that the Rambouillet breed contributed to the development of Chinese Merino. Breeds, such as Sopravissana, Merinizzata Italiana, Merinolandschaf and Gentile di Puglia, which are known or believed to have been cross-bred to Merino sires still have traces of their autochthonous origin, as suggested by ADMIXTURE and TREEMIX analyses. Macarthur Merino has a low He  and septh century [ADMIXTURE patterns confirm the clear subdivision of the Arapawa island breed . In agre century .This study represents an in-depth genome-wide analysis of Merino and Merino-derived breeds. It provides a general picture of the relationships between Merino and Merino-derived breeds on the one hand, and wild, feral, primitive and other modern domestic sheep, on the other. It contributes to the reconstruction of the history of merinization, which involved many sheep breeds world-wide during the last three centuries. We showed that sub-structuring has affected the current Spanish Merino genetic makeup, which may be relevant for breed management and conservation. Our results support the hypothesis that the Spanish Merino breed has an Iberian origin without excluding ancient contributions from other Mediterranean stocks. Together our results fit with the proposed scenario that describes how this breed may have originated, which is also partially supported by ancient and more recent documentation: (i) admixture among the early domesticated sheep, which explains the shared ancestry of modern Mediterranean breeds; (ii) export of Italian wool sheep during the Roman era into Iberia and other parts or the Empire, later possibly followed by African imports into Iberia; and (iii) a relatively recent circulation of Merino material around the world, which contributed to shape a new, genetically distinct, and world-wide disseminated sheep group that combines both Merino and local ancestry. Each of the steps of this scenario can provide working hypotheses for future studies on modern as well as ancient sheep DNA using advanced genomic methodologies.


Rhinella marina), one of the world’s most successful invasive species, in this complex Amazonian system provides an intriguing model for the investigation of the effects of isolation or the combined effects of isolation and habitat dynamic changes on patterns of genetic variability and population differentiation. We used nine fast-evolving microsatellite loci to contrast patterns of genetic variability in six coastal (three mainlands and three islands) populations of the cane toad near the mouth of the Amazon River. Results from Bayesian multilocus clustering approach and Discriminant Analyses of Principal Component were congruent in showing that each island population was genetically differentiated from the mainland populations. All FST values obtained from all pairwise comparisons were significant, ranging from 0.048 to 0.186. Estimates of both recent and historical gene flow were not significantly different from zero across all population pairs, except the two mainland populations inhabiting continuous habitats. Patterns of population differentiation, with a high level of population substructure and absence/restricted gene flow, suggested that island populations of R. marina are likely isolated since the Holocene sea-level rise. However, considering the similar levels of genetic variability found in both island and mainland populations, it is reliable to assume that they were also isolated for longer periods. Given the genetic uniqueness of each cane toad population, together with the high natural vulnerability of the coastal regions and intense human pressures, we suggest that these populations should be treated as discrete units for conservation management purposes.The Amazonian coast has several unique geological characteristics resulting from the interaction between drainage pattern of the Amazon River and the Atlantic Ocean. It is one of the most extensive and sedimentologically dynamic regions of the world, with a large number of continental islands mostly formed less than 10,000 years ago. The natural distribution of the cane toad ( The Amazonian coastline is a highly dynamic natural system that changes continuously in response to a variety of phenomena that differ considerably in timing and duration, such as Holocene sea level rises, tectonic activities and hydrologic dynamics, including drainage, sedimentation and erosion patterns , 2. IndeIn small populations, genetic drift can lead to a random loss of alleles, and inbreeding may then occur with consequent survival and fitness reductions, an increase of the frequency of deleterious mutations and susceptibility to emerging pathogens , 10. ForRhinella marina) across the complex system of Amazonian coastal islands is an interesting case study for the investigation of the effects of isolation and/or combined effects of isolation and habitat dynamic changes on patterns of genetic variability and population differentiation. The cane toad is a widely distributed species throughout Central and South America [The islands in the Amazon River vary considerably in the level of man-induced habitat loss and/or degradation. While in some islands the main cause of environmental disturbance is pasture and agriculture, others suffered from the direct or indirect influence of harsh chemicals and mineral ores exploitation –23. In t America , 25, and America , 27. As  America , rather R. marina near the mouth of the Amazon River. Specifically, we aimed to assess the potential role of isolation and genetic drift as mechanisms of differentiation in island populations and deepen the knowledge of the complex interaction of historical and current factors that shape the present-day genetic structure of R. marina. Finally, we examine levels of malformations and discuss the potential role of island isolation and/or synergetic effects of anthropogenic threats for their occurrence.In this study, nine fast-evolving microsatellite loci were used to contrast patterns of genetic variability and population genetic structure along the coast and island populations of FIS >0), although it was statistically significant only for the populations from Marajó Island and Bragança, once the p-value was adjusted for multiple comparisons  were found in the largest island population of Marajó, while slightly smaller, but similar values were obtained from the other five populations  to 19 (RM2) with a mean of 11.8 alleles per locus. No evidence of null alleles, significant allele dropouts or stuttering was identified at the 99% confidence level across all loci. Observations of all pairs of loci in all populations showed no linkage disequilibrium in any case. There was evidence of significant deviations from expected Hardy–Weinberg equilibrium (excess of homozygosity) in four of the nine microsatellite loci , but no locus showed consistent deviations across all populations. A general tendency for an excess of homozygotes was detected in all populations  (K), we found that the likelihood values reached a plateau at K values of between 4 and 7  . These t 4 and 7 . The cluulations . For K >ulations . This leulations ; S1 Fig.on River . The onlFST estimates were highly significant, corroborating the differentiation of each population from each other inferred by the STRUCTURE and DAPC analyses (FST = 0.048) while the most differentiated populations were the Islands of Bailique and Viseu (FST = 0.19).All pairwise analyses . The lowBragança_Viseu = 1.458 (1.020–1.784)).Values of theta (ϴ) were similar across all populations, ranging from 0.48 in Algodoal to 0.94 in Viseu . EstimatMorphological abnormalities were detected only on Bailique Island. Twenty-six specimens (30%) had one or more deformities, including Hemimelia , brachydactyly (shortened digits), as well as other malformations of the digits .FST values obtained from all pairwise comparisons. Further, our estimates of both recent and historical gene flow were not significantly different from zero across all population pairs, except the two coastal populations inhabiting continuous habitats (Viseu and Bragança). Given the fundamental pattern of absence/reduced gene flow, the high population differentiation of R. marina likely reflects the effects of genetic drift resulting from the isolation of island populations. Indeed, geological evidence suggests that those island populations became isolated from the current mainland with the Holocene sea-level rise at least 5,000 to 7,000 years ago [The cane toad in the Amazon coastal islands system has a complex evolutionary history characterized by substantial population substructure. The Discriminant of Principal Component analyses (DAPC) and the Bayesian multilocus clustering assignment method (STRUCTURE) were congruent in showing that each population was genetically distinct from the others, which are in accordance with the significant ears ago , 4, 7. Tears ago . While sears ago –33, it iR. marina populations in Australia suggest that this species has a considerable capacity for dispersal [R. marina [3/s) functioned as barrier to dispersal of R. marina. This hypothesis seems to be supported by the genetic differentiation found here between populations separated by the Amazon River, as revealed by FST and STRUCTURE analysis for K = 2. Further population substructure found in the species’ natural range on a regional scale also emphasise the importance of historical factors, which are obviously absent in the case of the Australian populations. For example, the occurrence of individuals on the Marajó Island with mixed ancestry derived from the coastal population of Sucuriju (located on the left bank of the Amazon) is likely to be a remnant of historical contacts between the two divergent groups currently separated by the Amazon River.There is a general assumption that most amphibians have low dispersion capacity due to their physiological constraints , and rivispersal , studiesispersal , 44. Eve. marina  based on2 from 1997 to 2000 [2 from 1972 to 2008 [Many examples of continental island populations investigated to date support the expected pattern of reduced genetic diversity in comparison with mainland populations , 45, 46. to 2000 , as well to 2008 , 53.R. schneideri or other Neotropical amphibians [R. marina seemingly did not suffer a significant reduction in genetic diversity.Furthermore, the evolution of the Amazonian coast under sea-level rise, during the Holocene as well as at different episodes during the Pleistocene, also changed substantially the ocean tide propagation over the gradually drowned continental shelf. It would result in a gradual increase of the tidal range as demonstrated in other regions . With thphibians , 33, sugR. marina.A third factor contributing to explain the high degree of population structure and differentiation may be associated with the isolation and genetic differentiation by genetic drift before the formation of the islands –58. WhilR. marina [Several studies have already reported the occurrence of phenotypic malformations in . marina –62. Howe. marina . These a. marina –66, the FIS values. However, significant FIS values were only found in the Marajó Island and Bragança populations, which not presented any evidence of malformations. In amphibians, one of the world’s most threatened vertebrates group, malformations are mainly associated with abiotic  and biotic factors  or by a synergistically interactions of these factors [Ipomea fistulosa [210Pb and high mercury concentrations [Malformations in natural populations can be an important indicator of inbreeding  or stres factors , 70. Allistulosa . Furtheristulosa , 73, hastrations , 22 on ttrations , 74, inctrations –62. In tThe cane toad is one of the world’s most successful invader species, and is also one of the most intensively studied invasive taxa . HoweverFinally, considering the genetic uniqueness of each cane toad population together with their high natural vulnerability and the intense human pressures affecting of this complex system of continental islands, we recommend that these populations should be treated as discrete units for conservation management purposes.The samples analyzed in the present study were obtained in accordance by the requirements of Brazilian environmental legislation, being approved by the federal Chico Mendes Institute for Biodiversity Conservation (ICMBio), through license number 38047–3. The individuals were euthanized  using an anesthetic application (5% Lidocaine) over the skin to minimize animal suffering, as recommendations of the Herpetological Animal Care and Use Committee (HACC) of the American Society of Ichthyologists and Herpetologists. The Marajó Island population (city of Soure) is located in the Marajó Archipelago Environmental Protection Area, and the collection and transportation of specimens from this reserve was authorized by the same license, which also covered the collection and transportation of specimens from the other sites, located on privately-owned or unprotected public lands.2) is relatively uniform, and its distance from the mainland is approximately 2 km. The Marajó Island, with an area of approximately 40,100 km2, is the biggest island of the region, and it is isolated from the continent by long distances on its seaward side (76 km). However, its riverward side (Amazon estuary) is separated from the mainland by only short distances (0.64 km), through a complex network of tidal channels, creeks and other smaller water bodies. The Algodoal Island (19 km2) is located on the continental shelf and is isolated from the mainland of only ca. 0.20 km. In contrast with both Bailique and Marajó Islands, Algodoal is essentially under the influence of the marine environment. Maps were created in ArcGIS 10.2 ESRI .Cane toad samples were collected from populations of three mainland areas  and three continental islands  , Table 1In small and isolated populations, such as those of islands, genetic drift can lead to a random loss of alleles with consequent survival and fitness reductions and appearance of malformations. Accordingly, we performed a detailed external morphological examination of all specimens both in the field and in the laboratory, using both photographs and a magnifying glass to investigate the presence of malformations. The number of individuals analyzed per population was similar for both morphological and genetic analysis, except Bailique Island, where only 20 of 88 collected toads were genetically characterized. Tissue samples (obtained from the abductor muscle) were preserved in 92% ethanol for subsequent extraction of DNA.Total genomic DNA was isolated using a PROMEGA extraction kit following manufacturer’s protocol and diluted to a final concentration of 40 ng/μl. Nine microsatellite loci were amplified with polymerase chain reaction (PCR), following the conditions described by . MicrosaMicro-Checker 2.2.3 software  was usedStandard genetic variability measures, including observed (Ho) and expected (He) heterozygosity, and the mean number of alleles (Na) were obtained in Genodive 2.0b27. The allelic richness and private alleles richness were estimated in HP-Rare , which uFST [K ranging from 1 to 10, using 107 MCMC (Monte Carlo Markov Chain) repetitions and discarding the first 5x105 iterations as burn-in. The most probable value of K (clusters) that captures the most population structure in the data was identified monitoring the estimated log posterior probability of the data (ln Pr (X/K) [K) [K, post processing STRUCTURE runs were done by the software STRUCTURE Harvester v0.6.93 [K) and ΔK of [K and repeated run. The Clumpp 1.1.2 program [K-value based on the LargeKGreedy algorithm. Graphical representations were performed in Distruct 1.1.2 [Genetic differentiation among populations was estimated as FST , using AFST . We firsPr (X/K) , and estX/K) [K) . To assi v0.6.93 . This prnd ΔK of  per K an program  was usedct 1.1.2 .K (varying from 1 to 14), based on the relationship between the number of clusters (K) and the smallest BIC value.The analysis of population structure was also based on a multivariate approach, DAPC , implemented in Adegenet package  for R 2.6 iterations of the MCMC and delta values of 0.15 for p , m (migration rate), and f (inbreeding), which generated acceptance rates within the limits recommended by the authors (40–60%). The final analyses were based on 3 x 107 MCMC iterations, sampling at every 2000th increment, and 1 x 107 of burn-in. Long-term migration rates and the population genetic parameter theta (θ) were investigated using Migrate-N [FST and an UPGMA tree were used as the initial parameters for this analysis, with an island model being used to determine the migration rate between populations. The estimates of M were obtained based on ten short chains and five long chains with 10,000 and 100,000 recorded genealogies, respectively, and burn-in of 10,000 for each chain. The analyses were run three times to evaluate the consistency of the results.Estimates of recent migrations between populations were obtained in the BayesAss 1.3 software . This Baigrate-N , 90, whiS1 FigK = 2–6) (A); best number of clusters (L(K)) according to STRUCTURE (B); the highest ln-probability and the ΔK optimality criteria (C); and results of BIC (D).Results of STRUCTURE analysis for microsatellites ((TIF)Click here for additional data file.S1 File(TXT)Click here for additional data file.S1 TableThe values are presented by the means and 95% CI of posterior distributions. Bold means significant values. The values in the diagonal of B are the proportion of non-immigrants samples in each generation.(DOCX)Click here for additional data file.


Gammarus fossarum is a key invertebrate in freshwater ecosystems and contains many cryptic species. One of these species is the broadly distributed G. fossarum clade 11 (type B). In this study, we tested for factors driving the genetic structure of G. fossarum clade 11 in a human-impacted landscape at local and regional scales. To determine population structure, we analyzed the mitochondrial cytochrome c oxidase 1 (CO1) gene of 2,086 specimens from 54 sampling sites and microsatellite loci of 420 of these specimens from ten sites.The actual connectivity between populations of freshwater organisms is largely determined by species biology, but is also influenced by many area- and site-specific factors, such as water pollution and habitat fragmentation. Therefore, the prediction of effective gene flow, even for well-studied organisms, is difficult. The amphipod crustacean We detected strong overall genetic differentiation between populations at regional and local scales with both independent marker systems, often even within few kilometers. Interestingly, we observed only a weak correlation of genetic distances with geographic distances or catchment boundaries. Testing for factors explaining the observed population structure revealed, that it was mostly the colonization history, which has influenced the structure rather than any of the chosen environmental factors. Whereas the number of in-stream barriers did not explain population differentiation, the few large water reservoirs in the catchment likely act as dispersal barriers.Gammarus fossarum clade 11 are strongly isolated even at local scales in the human-impacted region. The observed genetic structure was best explained by the effects of random genetic drift acting independently on isolated populations after historical colonization events. Genetic drift in isolated populations was probably further enhanced by anthropogenic impacts, as G. fossarum is sensitive to many anthropogenic stressors. These findings highlight the importance of small-scale genetic studies to determine barriers restricting gene flow to prevent further loss of genetic diversity and maintain intact freshwater ecosystems.We showed that populations of The online version of this article (doi:10.1186/s12862-016-0723-z) contains supplementary material, which is available to authorized users. Biogeographic studies revealed wide ranges for many freshwater invertebrate species . This hoGammarus fossarum KOCH, 1836. Several recent studies revealed an almost exponentially increasing number of overlooked species within the G. fossarum species complex with enhanced geographic sampling and improved sensitivity of molecular detection methods [G. fossarum species complex was found in the southeastern part of the range, where most of the newly discovered species were local endemics with narrow ranges [One ecologically important freshwater taxon, which was formerly thought to be widely distributed in central and southeastern European streams, is the amphipod crustacean  methods –11. The w ranges .G. fossarum is mainly found in the upper reaches of streams and is sensitive to organic pollution [G. fossarum is often used in ecotoxicological studies . How. HowG. fs et al. ) reveales et al. –21. In fs et al. , 22, occs et al.  and was s et al. . Additios et al. , but stis et al. ). Even trs e.g. –27). How. HowG. frs ..G. fossaG. fossarum clade 11 in a human-impacted landscape at local and regional scales. To determine the population structure, we used two different genetic markers. For the main analyses, we used the barcoding fragment of the mitochondrial cytochrome c oxidase 1 (CO1) gene. We also examined nuclear microsatellite markers [CO1 results. The study area was the Sauerland region, a low mountain range in North Rhine-Westphalia, Germany, which contains several small nature reserves, but is also used for agriculture, industry, forestry, and tourism. The hydrological structure of streams in the Sauerland region is strongly influenced by anthropogenic factors, such as in-stream barriers occurring approximately every 1,000 m [Gammarus fossarum clade 11 are genetically differentiated at the regional scale when considering the whole study area. In contrast differentiation is low at the local scale of few kilometers with evidence of gene flow between populations. These expectations are based on previous genetic analyses of G. fossarum species .–36.GammaG. fossarum is confined to aquatic habitats throughout its life cycle. Further, populations show patterns of isolation by distance (IBD) within catchments, especially when considering the waterway distance as this pattern was also found in other population genetic studies of G. fossarum clade 11 .In this study, we tested for factors driving the genetic structure of  markers , 32 for  1,000 m . TherefoAfter testing the two hypotheses, we discuss the findings in order to identify possible drivers of population structure.G. fossarum were collected via kick-sampling at 54 sampling sites in 45 different streams in the Sauerland region (Germany). Animals were preserved in 96 % ethanol. The sampling sites were located in three major catchments . For the analyses, the Ruhr catchment was further divided into four sub-catchments, Lenne (12), Möhne (5), Volme (2), and Ruhr (29), where Ruhr means that the streams flow directly into the Ruhr and not via another main river, such as the Lenne (see Table http://www.lanuv.nrw.de). Chemical measurements were obtained from the ELWAS-web portal. Most of the official measuring points were not directly located at the sampling sites in this study, but 41 of these measuring points were located within a 5 km reach. The frequency with which the chemical values were measured differed among sites, varying from one to four years and one to seven times a year, but not all parameters were measured on all dates. To correct for these differences and variation within sampling sites, the average value for each chemical was calculated. The following chemical parameters were used for the analyses: calcium, iron, oxygen, chloride, ammonium, total organic carbon (TOC), total nitrogen, and pH , the samples were merged into a single sampling site for this study.CO1 was amplified with the standard primers HCO2198 and LCO1490 [2O. The PCR settings for CO1 amplification were as follows: initial denaturation at 94 °C for 2 min; 34 cycles of denaturation at 94 °C for 20 s, annealing at 46 °C for 30 s, and extension at 65 °C for 60 s; final extension at 65 °C for 5 min. The PCR products (9 μl) were purified using 0.5 μl of ExoI (20 U/μl) and 1 μl of FastAP . The reaction was incubated for 25 min at 37 °C followed by an inactivation step at 85 °C for 15 min. Purified PCR products were bidirectionally sequenced by GATC-Biotech .DNA was extracted from the pereopods of 2,086 specimens using a modified salt-extraction protocol  . For Gam 2, the alleles could not be scored because too many stutter bands occurred over a range of 90 base pairs.Allele sizes were determined using polyacrylamide gel electrophoresis on a LI-COR® 4300 DNA Analyzer with the software Sagahttp://www.geneious.com, [FST values between populations from different sampling sites were calculated using Arlequin. Negative values were set to zero. The significance levels for this and all subsequent FST calculations were adjusted to account for multiple testing using the false discovery rate control , and a ous.com,  as impleous.com, . A minimous.com, . To showous.com,  . AdditiUsing the program MICRO-CHECKER 2.2.3 , our datFST values in Arlequin. To correct for null alleles, FreeNA [FST values were also calculated between populations originating from the same stream, but sampled in different years.Genetic distances between populations were calculated as pairwise , FreeNA  was usedFST values were used as a measure of genetic distance, and either straight-line or waterway distances were used for geographic distance. Straight-line distances were calculated for all population pairs and waterway distances for populations within the Ruhr catchment (n = 48) using QGIS v. 2.8.2 [FST values and the number of in-stream barriers between population pairs per km waterway distance, first for barriers >0.5 m and then for barriers >1 m. The number of in-stream barriers was calculated from the QUIS database [X-axis were set to 300 and along the Y-axis to 150 according to the ratio of the sampling area. A second AMOVA implemented in Arlequin using the groups detected in GENELAND was used to analyze if this clustering better reflects the population structure than the sub-catchments.Different landscape genetic approaches were used to identify drivers of genetic differentiation in the study area. First, to test if the distribution of genetic variance can be explained by the partitioning of populations into the six sub-catchments, AMOVA  was impv. 2.8.2  and the database . Barrierdatabase , 59, R vdatabase ). The nuTo test which factors determine the association of the individual populations to the GENELAND groups, a discriminant analysis was performed in IBM SPSS v. 23. For this analysis, the environmental variables obtained to characterize sampling sites were independent variables and the main GENELAND groups (consisting of >3 sites) were dependent variables. Additional to the environmental variables, also the geographic position of the sampling sites, represented by their coordinates, was used as a variable to include a proxy for the possible influence of colonization history on genetic structure. To standardize the predictor variables, z-scores were calculated in SPSS prior to the discriminant analysis. Further, multicollinearity between the 25 environmental variables was tested by calculating the variance inflation factors (VIFs) for each variable using the R package “usdm” . CollineCO1 sequences for 2,086 G. fossarum clade 11 specimens from 54 sampling sites. In the 557 bp alignment, we detected 52 variable sites, of which 12 were non-synonymous substitutions. Specimens clustered into 40 distinct haplotypes (H1–H40), of which ten had a frequency of over 1 %  and were found in 97.2 % of all specimens  and KL2 (n = 57), populations consisted exclusively of one private haplotype each. Overall, we detected between one and six haplotypes per population, with an average of two haplotypes, and a haplotype diversity of between 0.00 and 0.68, with an average of 0.16  and we observed private haplotypes in 20 populations. At two sampling sites, i.e., KL15 . Three of the ten populations were also monomorphic for the same allele at Gamfos 13. We observed between 4 and 23 alleles in all populations at the different loci. We did not detect significant linkage between loci considering all populations. We found evidence for null alleles for three of the loci in different populations. The null allele frequencies, number of alleles, and observed and expected heterozygosity (HO and HE) are given in Additional file FST values.Of the seven microsatellites analyzed to complement the CO1 haplotype composition at the sampling sites differed strongly in many cases on a regional as well as on a local scale , and 81 % of all pairwise FST values were significant  and geographic distance. Here, the fit was better using straight-line distances  than waterway distances .The AMOVA using six sub-catchments as groups revealed significant population differentiation between sub-catchments, with 19.6 % of the variation partitioned between groups. However, 59.8 % of the variation was between populations within sub-catchments Table . Mantel CO1 sequence analyses.Other major barriers located between some of the sampling sites were water reservoirs see Fig. . One sucTo infer the population structure more directly, without manually assigned assumptions regarding affiliations to catchment areas, we performed a clustering analysis with GENELAND. In this analysis, we observed seven distinct groups, termed A–G  is likely the result of independent historic isolation in eastern and western refugia, as suggested by Elbrecht et al. (2014) for the stonefly species. Even though G. fossarum populations belonging to the two divergent groups seem to be isolated, it is likely that they can still interbreed when in contact. This is indicated by the low divergence (between 2.51 and 3.95 %) in comparison to previous estimates of divergence between cryptic species of G. fossarum [. Further, Lagrue et al. [G. fossarum clades only when CO1 sequence divergence was greater than 4 %.Our first hypothesis was that populations are genetically differentiated when considering the regional scale of the whole study area, whereas the differentiation is low at the scale of few kilometers. In agreement with the first expectation of this hypothesis we detected strong regional differentiation in the Sauerland area, especially between the eastern and westernmost populations. The westernmost populations (GENELAND group D) contained only haplotypes (H33–H40) separated by at least 14 substitutions from the other haplotypes (H1–H32), but we did not detect this high degree of differentiation using microsatellites. A similar pattern of east-west differentiation was observed in the stonefly udy area . Specifit et al.  found hafossarum , also vie et al.  found reCO1 FST values were significant), sometimes even within 2 km in the same stream. The strong isolation is further supported by the pattern that many populations contained private haplotypes, which mostly differed by only a single mutation from the main haplotype of the respective population. This indicates long-term isolation because independent mutations were able to accumulate in the populations. In contrast to the overall strong isolation, we found little or no differentiation between some of the populations separated by over 40 km straight-line distances and even higher river distances  for the CO1 gene. However, when analyzing a subset of these populations based on the more rapidly evolving microsatellites, we detected strong differentiation between all populations. Therefore, the subtle differentiation found between populations of group A is most likely not caused by ongoing gene flow; instead, low genetic variation is likely due to bottlenecks and the effects of genetic drift [We did, however, not only find strong differentiation between populations at a regional scale, but contrary to our hypothesis also at a local scale. Actually, most of the populations were highly isolated from each other , an, anG. foly  or s or sG. fly .ly (e.g. –81. The um (e.g. ); accordum e.g. , 83). To. ToG. foCO1 clusters. This clearly suggests that the observed population structure primarily reflects colonization history. If colonization events occur rarely and are initiated by relatively few individuals, this can lead to strong founder effects; genetic drift in small populations can lead to a strong loss of genetic diversity [G. fossarum is sensitive to many anthropogenic stressors, like organic pollution, a lack of oxygen, acidification [In view of the tested parameters that cannot explain population structure sufficiently the question arises, which other factors ultimately underlie the apparent population structure. To determine if the structure was more influenced by colonization history (represented by the geographic position of the population) or by environmental factors that may differ between population groups, we conducted a discriminant analysis for the four main groups including variables for both possible drivers. The best predictor for the assignment of populations to the four GENELAND groups was longitude, indicating that the location of each site from east to west determines the affiliation to the iversity , 84. Thefication , and higfication , isolateG. fossarum clade 11 in the human-impacted Sauerland area than was expected based on previous genetic analyses. The strong isolation was supported by two independent molecular marker systems, indicating that the realized dispersal was low in the study area. Also contrary to our hypotheses we found only a slight isolation by distance (IBD) pattern and structuring of populations according to river catchments but rather a strong geographic pattern (east-west differentiation). In view of published data, the dispersal ability of G. fossarum clade 11 specimens should be sufficient enough to create an IBD pattern. In the absence of this we conclude that there are barriers preventing gene flow partly, even between neighboring populations. Despite the likely effect of larger reservoirs on connectivity, we could not determine specific anthropogenic factors that directly influence the population structure. In fact, it was best predicted by the independent action of genetic drift at local sites after initial colonization. These effects are likely enhanced by the multitude of anthropogenic stressors, because G. fossarum is sensitive to many anthropogenic stressors. As G. fossarum clade 11 is widely distributed and is frequently found in the Sauerland area we conclude that its colonization ability over long time scales is good. The same holds likely true for its ability to establish new populations based on few colonizers, since long distance dispersal by animal vectors is unlikely to occur frequently [G. fossarum clades. These findings highlight the importance to take regional factors into account when predicting the dispersal ability of species. Further more research is needed to determine the most important barriers restricting gene flow between populations to prevent further losses of genetic diversity and maintain an intact ecosystem.In this study we found a considerably higher differentiation between populations of equently . Even thequently  and the CO1, cytochrome c oxidase 1 gene; FDR, False discovery rate; FST, Fixation index; HWE, Hardy-Weinberg equilibrium; IBD, isolation by distance; SHM, Stream Hierarchy Model; VIF, variance inflation factorAMOVA, Analysis of molecular variance;


The Wellcome Trust Case Control Consortium 3 anorexia nervosa genome-wide association scan includes 2907 cases from 15 different populations of European origin genotyped on the Illumina 670K chip. We compared methods for identifying population stratification, and suggest list of markers that may help to counter this problem. It is usual to identify population structure in such studies using only common variants with minor allele frequency (MAF) >5% we find that this may result in highly informative SNPs being discarded, and suggest that instead all SNPs with MAF >1% may be used. We established informative axes of variation identified via principal component analysis and highlight important features of the genetic structure of diverse European-descent populations, some studied for the first time at this scale. Finally, we investigated the substructure within each of these 15 populations and identified SNPs that help capture hidden stratification. This work can provide information regarding the designing and interpretation of association results in the International Consortia. The GC2 approach uses genomic features of the samples to correct for stratification, and thus avoids inflation in the test statistic.1 Population stratification may lead to ‘overdispersion' of the statistics used to test for association; by measuring several polymorphisms across the genome, the degree of this overdispersion may be estimated and taken into account. However, GC may not perform well with too few loci, or may overcorrect and lead to a substantial loss in power.1 Menozzi et al3 described the use of PC analysis (PCA) in human genetics in 1978. PCA summarizes high-dimensionality data by capturing the latent variables that best describe a data set, allowing simple visualization of allele frequency differences among populations. It is possible to correlate PCs of the data with meaningful geographic axes. For example, genetic variation in the first two PCs is closely associated with geographic alignment across Europe.6 As with GC, PCA may also be used to correct for population stratification when working with a very large number of markers, ideally genome-wide data sets. However, population stratification is much of a concern in replication studies or studies focusing on a smaller number of variants, in which GC or PCs cannot be readily calculated. To circumvent this problem, adjustment for the genotypes of ancestry-informative markers (AIMs) has been proposed as an alternative approach.Population stratification can be a major cause of concern in genetic association studies. Specifically, imperfect matching between cases and controls can lead to spurious associations, or failure to detect true associations.et al7 proposed that certain markers with distinct frequency differences across populations may be highly informative for assigning ancestry. These markers are referred to as AIMs. A small number of these AIMs may be used to perform population clustering; between 40 and 80 loci, Rosenberg et al8 demonstrates convergence to five broad continental clusters. Kidd et al9 used 128 AIMs to characterize samples from 119 populations into 8 broad clusters, which agree with continental boundaries. Precalculated lists of AIMs are available, although these are mostly applicable only to cross-continental studies,11 or require a relatively large set of SNPs.12Shriver et al.13 SNPs that contribute heavily to the underlying axes of variation will be instrumental in clustering samples along population lines; it follows that these SNPs may be used to assign ancestry. A recent study has identified these PCAIMs for samples of North-Central European and Mediterranean origin, and has shown that they may be used to assign sample ancestry.14A different way to derive AIMs is to identify SNPs that contribute highly to the significant PCs (PCAIMs), as first discussed by Paschou In this work, we investigated the structure across closely related European populations. We discuss evidence for stratification using PCA and Fst, a measure of genetic distance among samples. Further, we identified lists of AIMs and PCAIMs, which are able to correct for stratification by using a small number of markers.We investigated population stratification using data taken from the Wellcome Trust Case Control Consortium 3 anorexia nervosa (AN) genome-wide association scan, which includes 2907 cases from 15 different populations of European origin (unpublished data). Thirteen of these are European, and are divided between Scandinavian , North-Central European  and Mediterranean populations . Two further populations of European origin included in this study are United States and Canada. Sample sizes range from 39 (Swedish samples) to 475 (Germany); numbers of samples are shown in We discuss the fine structure within these populations, and identify a set of informative SNPs. We compare different methods of calculating these, and assess their usefulness in assigning samples to populations.We used samples that had been collected for an AN GWAS. The samples comprise 15 discovery data sets of European origin. All samples used were female. All samples met the DSM-IV diagnostic criteria for lifetime AN or lifetime ‘eating disorder not otherwise specified', with the exception of the requirement for amenorrhoea. Samples with a lifetime history of bulimia nervosa were also included in the data set.All cases were genotyped using the Illumina 660W-Quad arrays  at the Wellcome Trust Sanger Institute. Quality control was performed individually on each of the 15 case–control subgroups .15 We identified the top PCs by selecting those components that explained the greatest variance.We calculated PCs using the smartpca software .P<10−86).We used the Tracy–Widom (TW) statistic to assess the significance of each PC. The TW statistic tests whether the average eigenvector coordinates across all samples within each population differ significantly across components. We found that the first six PCs differ significantly .We applied a Mantel test. This test calculates the correlation between the two distance matrices, and then computes an empirical 18 We performed the Procrustes test using the ‘vegan' package in R19 with 1000 permutations (as recommended).We applied a Procrustes test. This works in the same way as the Mantel test, but is likely to be more sensitive.We then performed the following correlation tests:et al20 assign a threshold of Fst=0.001, below which populations may not be said to be genetically distinct.Tian 15Fst values were computed using the smartpca software.To test the correlation between Fst (genetic distance) and geographic distance between population centres, we applied a Mantel test, as for the PCA data.AIMs are defined as markers that provide information as to the ancestry of a sample. Informativeness describes the amount of information that is imparted by the marker. We use a harmonized data set of 70 samples per population to calculate informativeness. We selected 70 samples per population to avoid any sample-size associated bias in the Informativeness calculation.Samples were selected at random from all populations; note that Sweden (39 samples) and Canada (54 samples) were omitted owing to small population sizes. The remaining samples were designated as a testing set, to validate AIMs. The Swedish population was set aside to test the ability of AIMs (and PCAIMs) to assign ancestry of samples from a new population.22 A threshold of 0.8 was used.AIMs were thinned for LD using PLINK.et al,8 using the formula below:Informativeness was calculated according to Rosenberg pj is the mean frequency of allele j over all populations, pj is the relative frequency of allele j in population i and K is the total number of populations.where et al.23PCAIMs were selected using a weighting system as outlined by Raaum SNP contributions to each PC were calculated using smartpca.Contributions of each SNP to each PC were normalized to the maximum weight, so that the SNPs that contributed most to a PC was given a weight of 1. These weights were multiplied by the corresponding eigenvector. To get a rank for each SNP, weights were summed across all PCs.22 A threshold of 0.8 was used.AIMs were thinned for LD using PLINK.K=5.). The KNN algorithm identifies the K-nearest genetic neighbours by computing Euclidean distances between samples. We used PLINK to find each sample's K-nearest genetic neighbours, based on only a given number of AIMs. Clustering samples that are ‘closest' together according to a genetic similarity measure, derived by AIMs or PCAIMs, implies that the nearest neighbours share common ancestry with the sample in question. The ancestry of the nearest neighbours was used as a ‘majority vote' to determine the ancestry of the sample.K-nearest-neighbour assignments were used to assess how well AIMs and PCAIMs were able to assign a sample to a certain population. . After rotation, PC1 aligns north-northwest/south-southeast . This is remarkably similar to the −16° angle cited by Novembre et al.4 We see no significant correlation between PC3 and PC4 and geographical axes. We tested for significance between PC locations and geographic centres, and found that this was significant for the first and second PCs .We tested the geographic relevance of the PCs by calculating the correlation between PC magnitude and latitude and longitude, obtained using the geographic centre of each nation, shown in USA samples cluster loosely across North-Central European and Scandinavian populations, with some samples clustering with the Mediterranean population. As expected, we see little overlap between Finnish and USA samples. Canadian samples tend to cluster with North-Central European and Scandinavian populations. We performed a PCA using only USA, Canadian, North-Central and Scandinavian populations (therefore removing Mediterranean and Finnish samples), to illustrate this more clearly, as shown in P<1e−300).We calculated genetic distance among populations by means of the Fst statistic . Fst corIt is clear from 8 for a harmonized data set of 70 samples per population . We used 70 samples per population to avoid over-representing populations with larger sample sizes. Populations with fewer than 70 samples were not used to calculate AIMs.We extracted a list of AIMs using Rosenberg's informativeness calculation,We calculated AIMs using all SNPs with average minor allele frequency (MAF) across all populations >1%. Although it is usual to take 5% as a lower boundary, we find that this risks removing highly informative markers. For example, consider the ‘perfect' marker, which appears in every sample of one population, and not at all in others. For the harmonized set of 13 populations, this marker would have an average MAF of 3.8% across all populations, and would be dismissed under a 5% threshold. We show the top 25 most informative markers in ρ=0.97, although it may be noted that the correlation is slightly lower (ρ=0.907) for the set excluding Finland. The high correlations indicate that no single population is over-represented. The lower correlation when excluding the Finnish samples is owing to the greater genetic distance between Finland and other populations.One caveat when using AIMs is that populations might not contribute evenly to the choice of markers. A large number of our samples originated from central Europe; although these are classified into distinct populations, we have already shown that some of these populations are very closely related ; meanwhile, there were a smaller number of samples from an outlying population (Finland). To ensure that AIMs were chosen evenly to represent all populations, we computed the AIMs using only 12 of 13 populations. We repeated this 13 times, leaving a different population out each time. For each new set of AIMs, we computed the Spearman's rank correlation coefficient with the original list . We founet al23 to select PCAIMs; the top 25 are shown in We use a weighting system as discussed by Raaum We validated the top AIMs and PCAIMs by testing their ability to assign ancestry to new samples. We used the samples not included in the 70-sample per population harmonized data set; any population with more than 10 samples remaining was included in the validation set.We used K-nearest-neighbour algorithms to identify possible ancestry of the samples  .ρ=0.98). PCAIMs, on the other hand, predict Finnish samples better as they take into account the underlying variation of the data, rather than just the entropy of allelic frequency across samples.It may be noted that PCAIMS predict outlying populations better than AIMS. A key example of this is the performance of both sets of markers when predicting Finnish samples ; AIMs prFinally, we considered the assignment of Swedish samples . This poWe investigated within-population substructure by performing PCA on each population individually. K-means clustering was then used to assign samples to separate subclusters . We founWe further investigated substructure within the USA samples by testing nearest-neighbour assignments for the USA samples, using all markers  and two eastern European populations (Czech and Polish), which are usually clustered into one population. We saw a geographical alignment of our first three PCs. Further, populations cluster along meaningful geographic and cultural lines. We see three broad clusters consisting of Finland, North-Central Europe and Scandinavia, and Mediterranean populations. USA samples cluster largely with North-Central European and Scandinavian samples, with a few clustering with Italian samples, consistent with migratory patterns from Europe to North America.We investigated evidence of population stratification across 15 populations of European origin using genome-wide methods such as PCA and Fst. This represents one of the largest studies of this kind, and includes some populations that have not previously been used to assign AIMs (such as Canada and the Czech Republic). Further, these populations are more closely related than those used previously and span a wider geographic range than those seen in recent studies.It appears that Canadian samples cluster closely with French samples; we investigated this in more detail and found that Canadian samples fell into two groups: a tight cluster, which corresponded with the French samples, and a loose cluster, which did not lie close to French samples. This is consistent with some of our samples being of French-Canadian heritage, rather than simply of central European backgrounds.We also found evidence for substructure within the USA population. We found three broad clusters when performing a PCA plot. We found that most samples cluster with the North-Central European populations (likely to correspond to the largest cluster on our PCA plot), but that there is also a distinct group stemming from Mediterranean populations. This is likely to be due to immigration patterns to the United States. Our third and smallest cluster on the PCA plot is likely to represent a mix of Finnish samples and samples with joint Scandinavian and North-Central European heritage.We found a correlation between genetic distance, Fst, and the geographic distance between populations. This fits well with the clusters obtained using PCA, and is likely due to admixture between neighbouring populations. In addition, we see very low Fst values between certain pairs of populations, for example, France, Germany and the Netherlands. It is likely that this is due to a lack of significant geographical boundaries in these regions, for example, the Pyrenees or the Alps, and due to shared territories and shifting empire boundaries.HERC2 and OCA2. These genes are classic examples of positively selected genes in European populations, indicating that some of our PCAIMs are picking up high levels of differentiation due to geographically restricted positive selection, rather than due to neutral genetic drift.We obtained two lists of AIMs: one list was calculated using Rosenberg's informativeness calculation, and the other using Raaum's PCAIMs. Our initial list of 25 PCAIMs shows that SNPs cluster around three loci, corresponding to lactase and pigmentation-associated loci, Using only a small number of markers, both AIMs and PCAIMs were able to predict sample origin accurately. A key difference between the two sets is the ability to predict ancestry of outlying populations; in this case, PCAIMs outperform AIMs. This is likely to be due to how AIMs and PCAIMs are identified. For example, PCAIMs are chosen to represent the underlying variance of all samples; for our data set, a large part of this variance exists between central European populations and outlying populations . As PCAIMs are chosen to explain this variance, even a small number of markers are able to predict outlying populations well.AIMs, on the other hand, are chosen from markers with a high variance across populations. In this instance, we treat individual populations as independent, and select markers, which explain equally well the difference between all these populations. This is obviously a problem with closely related populations; we can see from PCA graphs that central European populations are in fact not independent; further, we have a much larger number of central European populations than outlying populations, causing a skew towards markers that predict central European populations well.This difference between the two sets becomes more pronounced when looking at larger numbers of markers. For example, using 500 or 1000 AIMs performs better than PCAIMs in predicting central European nations , but lag significantly in predicting the ancestry of outlying populations.We used our lists of markers to assign ancestry to samples from a new population (Sweden), and assessed the ability of our markers to assign ancestry to these samples. Both sets of markers performed well, although PCAIMs perform better than AIMs.A small proportion of Swedish samples are unassigned using AIMs, whereas all are assigned using PCAIMs. This is likely to be due to the fact that AIMs have been chosen to explain specific differences between a certain set of populations – they may be thought of as discrete measures of differences between populations. PCAIMs, on the other hand, are chosen to represent the continuum of variation. In this respect, we conclude that PCAIMs are better able to explain the ancestry of a new population, as long as it lies on the same continuum.It is worth bearing in mind the intrinsic limitations of our data set, which consists of clinical samples, obtained by the WTCCC3 for an AN GWAS. Although we have a large number of samples, these have been collected for clinical purposes, rather than for use in population genetics. For this reason, detailed information on ancestry is not always available. Further, samples have been accepted, or excluded, based on clinical relevance and guidelines, rather than based on information about their ancestry. For these reasons, our data may not be as evenly distributed or as well defined as that used in previous population differentiation studies, in which it is usually required that all four grandparents of the sample are also from the region. Further, many anthropological studies focus on rural samples, whereas our samples are statistically more likely to be urban rather than rural. This can also be considered a strength of the study, showing the power of the method to assign ancestry even in a clinically based sample series, which perhaps would not be expected to display the population structure seen in grandparental sampling schemes.In summary, we derive a set of 25 PCAIMs that can be used to adjust for population stratification within European samples. By genotyping these markers in replication experiments of large-scale genetic association studies, spurious associations arising owing to ancestry differences can be identified and corrected.