E) ranged from 0.371 in the SA population to 0.397 in the AR population. The SA goats were the only population with a positive average inbreeding coefficient value of 0.009. After merging the three datasets, standard QC and LD-pruning, 15 105 SNPs remained for further analyses. Principal component and clustering analyses were used to visualize individual relationships within and between populations. All SA Angora goats were separated from the others and formed a well-defined, unique cluster, while outliers were identified in the FR and AR breeds. Apparent admixture between the AR and FR populations was observed, while both these populations showed signs of having some common ancestry with the SA goats. LD averaged over adjacent loci within the three populations per chromosome were calculated. The highest LD values estimated across populations were observed in the shorter intervals across populations. The Ne for the Angora breed was estimated to be 149 animals ten generations ago indicating a declining trend. Results confirmed that geographic isolation and different selection strategies caused genetic distinctiveness between the populations.The Angora goat populations in Argentina (AR), France (FR) and South Africa (SA) have been kept geographically and genetically distinct. Due to country-specific selection and breeding strategies, there is a need to characterize the populations on a genetic level. In this study we analysed genetic variability of Angora goats from three distinct geographical regions using the standardized 50k Goat SNP Chip. A total of 104 goats  were genotyped. Heterozygosity values as well as inbreeding coefficients across all autosomes per population were calculated. Diversity, as measured by expected heterozygosity (H The use of animal fibres from sheep and goats date back to the Biblical age, where wool was often used to make clothing, and goat hair for decoration in the temples . The ancth century in Spain and France and then later in the mid-19th century, when a small number of goats were exported to both South Africa and the United States of America (USA). Similar to many other goat breeds, Angora goats have since spread throughout Europe, the Americas and Australasia via human-mediated distribution [The Angora goat originates from the district of Ankara in Turkey . The Sulribution . Other cribution .During 1838 the first Angora goats were imported from Turkey to South Africa, with more than 3000 animals following these between 1856 and 1896. The harsh, semi-arid Eastern Cape region proved to be well-suited to the Angora goats, and a flourishing industry evolved from this beginning. Today the mohair industry in South Africa consist of approximately 700 000 Angora goats, producing in excess of 2 million kg mohair yearly [France started their mohair industry in the 1980’s, close to the time when mohair production approached a global decline. They maintained a small, but resilient niche market, and by 2006 had approximately 7500 pure-bred Angoras producing 30 tons of mohair annually . Unlike th century, but the actual Angora population began with imported animals in 1962 from the USA. The goats were firstly located in the Northern part of the country but has since spread to the southern parts such as Patagonia. Additional animals were also imported from Australia and New Zealand [In Argentina the Federal government imported some Angora and Tibetian goats in the early 19 Zealand . Today mThe Federal government implemented a Mohair project in the mid-1990s with the main focus to establish a dispersed open nucleus scheme, connecting commercial flocks with the INTA experimental flock. Furthermore a strategy of dissemination by controlled mating and artificial insemination was applied. The selection goal was to increase mohair production (fleece weight) and quality, mainly relative to fibre diameter (fineness) and medullated fibre proportion. It is expected that the program will provide organizational and operational structure to maintain the provision of males to commercial flocks .The genetic improvement of Angora goats until recently was based on quantitative research and application of microsatellite markers for mainly population genetics . The devDue to country-specific selection and breeding strategies, a collaborative project was established to characterize the Angora goat populations of South Africa, France and Argentina. In this study we analysed genetic variability of Angora goats from three distinct geographical regions to assess the influence of genetic and geographical isolation as well as divergent selection patterns.Angora goat populations from SA, France and Argentina were included in the study. A first population of forty eight goats’ samples were obtained in South Africa (SA) from the Small-stock Biobank (31°29’38”S and 25°1’2”E) at Grootfontein Agricultural Development Institute (GADI). These goats are all farmed in the Karoo region of South Africa with an average altitude of 1279 meters (m) above sea level. The area is known for its dry climate with approximately 350 mm rain per year and temperatures ranging from 6°C (average minimum) to 22°C (average maximum) .A second population of 26 goats was obtained in France (FR) from seven different farm members of the French Angora selection scheme. These farms were located in the Western part (“Bretagne” and “Pays de Loire” regions) of France and in the South close to Pyrenean Mountains. Goats were raised under semi-extensive conditions (pasture with supplementary feeding during pregnancy). Climate conditions are temperate with approximately 800–1000 mm rain per year and temperature ranging from 1–3°C (average minimum) to 21–25° (average maximum).The third population of 30 goats was obtained in Argentina (AR) from the Angora goat experimental flock of INTA National Institute of Agricultural Technology (41°8´S and 71°8´E), located in the Pilcaniyeu, Rıo Negro province. Goats were raised extensively in a rangelands area of approximately 920 m altitude. The climate is semiarid with dry summers and cold winters. Annual precipitation is between 250 and 400 mm and the annual average temperature is 7.58°C .All animals were selected within these populations to minimize relatedness between individuals. Sampling was done in each country with the required ethical approval. Ethics approval for the South African part of this study was obtained from the Ethics Committee of the Faculty of Natural and Agricultural Sciences at the University of Pretoria (EC130618-060 & EC104-13). The whole blood samples were collected from the DNA biobank at GADI. French DNA samples included in this study are stored at LABOGENA DNA. Sperm collection was performed on bucks by Capgenes, which obtained the authorization from DGAL  FR CC 860. Sample collection was not performed specifically for this project in either South Africa or France–blood or DNA samples that were already available were used. Blood sample collection from Argentinian goats were approved by the Institutional Committee for Care and Use of Experimental Animals of the National Institute of Agricultural Technology (CICUAE-INTA) under protocol number 35/2010 and followed the guidelines described in the institutional manual. Blood samples were collected into 20ml EDTA tubes from the jugular vein under the supervision of qualified veterinarians. This study did not involve animals from any endangered or protected species/breeds.DNA was extracted at the various institutes using the Qiagen DNeasy Blood and Tissue Kit® and AxyPrep Blood Genomic DNA Miniprep Kit® kits following manufacturers’ protocols. Genotyping was conducted at the ARC-Biotechnology Platform  in South Africa (SA population), Labogena DNA platform  in France (FR population) and GeneSeek  in the USA (AR population) using the Illumina goat SNP50 Bead chip . All genotype calls were extracted from the raw data using GenomeStudio (Illumina).p-value<0.001). For the calculation of heterozygosity and individual inbreeding, SNPs in linkage disequilibrium (LD) equating to an r2 value of more than 0.2 were also removed using PLINK’s—indep-pairwise command . In PLINK, an EM algorithm [r2 values between all SNP pairs across all autosomes.The three data sets were subjected to quality control (QC) separately to assess both sample quality and differences between SNP genotyping, followed by merging of the three datasets. Sample- and marker-based QC was performed using PLINK  softwarelgorithm  is used E) and observed (HO) heterozygosity, as well as relatedness and average individual inbreeding coefficients (FIS), which were calculated for LD-filtered mapped, autosomal SNPs within and across the different sub-population. Relatedness was calculated as the proportion identity-by-decent (IBD) between individual pairs, as indicated by PLINK’s PI_HAT value. Arlequin version 3.5.2 [ST, was also calculated using Arlequin [PLINK was used for the estimation of mean expected  were removed from further analysis. After QC, 46 510 SNPs remained, of which 45 244 were autosomal. LD-based pruning was performed to remove SNPs that were in linkage disequilibrium with one another using PLINK’s simple pairwise threshold model . This removed 30 139 SNPs that exceeded an PGDSpider version 2.0.8.2  was usedr2, was calculated for post-QC SNPs with known genomic location of autosomal chromosomes, using PLINK software. The r2 parameter refers to the squared correlation coefficient (r) between two variables—in this case, between the alleles at two separate SNP loci [r2 is defined by the following formula;ab represents the frequency of haplotypes consisting of allele a at the first locus and allele b at the second locus [D’ refers to the normalized measure of D, which is a parameter that quantitatively describes allelic association [D’ are sensitive to allele frequency, especially when one allele is rare, and is inflated for small sample sizes. The r2 measure was therefore focused on for further characterization of LD.LD, measured as SNP loci . Here r2nd locus . Lewontiociation . Estimatr2 values were calculated for each chromosome in each sub-population, as well as across sub-populations, with the—r2 command, using for the most part default settings. The—ld-window and—ld-window-r2 commands’ values were both set so that correlations between all possible SNPs were tested and that there was no r2 threshold, in order to encapsulate all possible linkage interaction between SNPs per chromosome. For each chromosome, r2 values were then sorted by inter-SNP distance, binned into different inter-SNP intervals , and averaged across the afore-mentioned intervals to observe possible r2 patterns for increasing inter-SNP distances.Pairwise e) was estimated using the recently available software, SNeP version 1.1 [e from genome-wide linkage disequilibrium (LD), using the following formula suggested by Corbin and co-authors [NT (t) represents the past effective population size estimated t generations ago, tc represents the recombination rate t generations ago in the past, r2adj represents the linkage disequilibrium estimation adjusted for sampling bias and α represents a constant. The recombination rate was calculated using the following equation suggested by Sved [Effective population size =1 by Sved ,f(c)=c[e calculation. Minimum and maximum inter-SNP distances of 0 and 1000Mb, respectively, were used. The data sets for each sub-population, as well as the merged dataset, were grouped into 20 distance bins of 50kb each. Ne estimates were subsequently calculated from the r2 values obtained for the average distance of each distance bin.PLINK input files for quality filtered, autosomal SNP data sets were used for NA total of 101 samples remained in the merged data set following quality control. Three individuals were removed from the Argentinian population due to poor call rate (<95%). The call rates were high for all three populations genotyped varying from 0.981 for the Argentinian population to 0.996 for the South African goats. After marker-based quality control  15.7%, 1The average relatedness, based on the proportion IBD, between individual pairs was calculated as 7%, 8% and 8% for AR, FR and SA populations, respectively, and was lower than relatedness between third-degree relatives (12.5%). Relatedness between individual pairs across populations was insignificantly small and average PI-HAT values range from 0% between FR and SA populations, to 0.05% between AR and FR populations.E ranged from 0.371 in the SA population to 0.397 in the AR population. These values decreased slightly when the LD-pruned datasets were used, as opposed to when all available SNPs were used. Inbreeding coefficients (f) calculated for each individual based on the observed and expected heterozygosity values showed limited loss of heterozygosity. The animal with the highest individual value of f (0.202) was found in the SA population. Within breeds, the SA population was the only population with a positive average f value of 0.009  value of 0.12 was estimated.AMOVA indicated that almost 12% of the variation was accounted for by variation among the populations, while 87.9% of the variation was due to within individual variation . Across Principal component analysis (PCA) was used to visualize individual relationships within and between populations. The first, second and third principal components accounted for 47.3%, 35.3% and 17.4%, respectively, of the variability. All SA Angora goats formed a tight cluster, as shown in Likelihood scores for runs of various K-values in Admixture showed a decrease in cross-validation error values with an inflection point at K = 3 . At K = At K = 3  is comparable to the 0.44 estimate reported by [This study reports the results of three geographically-distinct Angora goat populations based on SNP technology. Marker-based quality control indicated that the SA population had the highest number of SNPs (1757) that did not adhere to HWE. This could be the result of strong directional selection in the SA population. The most polymorphic loci were typed in the AR population that also showed the highest Horted by  for AngoST) of 0.12 confirms the differentiation of the three sub-populations within the Angora goats studied. Most diversity studies include a number of different breeds and these intra-breed diversity values are comparable to the current study, e.g. 12% variation among West African cattle breeds [The variation among the populations in this study was almost 12%, which was unexpectedly high as these animals are all from one breed. The fixation index . This, in combination with strict phenotypic selection has resulted in a well-defined cluster. Compared to the other two populations, the FR goats are relatively more dispersed within their cluster. The French Angora goat population has been subjected to less intensive selection with a relatively young improvement scheme of less than 30 years. In the FR and AR populations, clear outliers were observed. The French outlier was identified as the only animal of the French sample without full pedigree information, and that was not the offspring of a Texan (via Canada) import. It is assumed that this animal could be the descendant of an Australian import (known to be connected to SA through Zimbabwean exports). This would also explain the high level of admixture in this individual. The four outliers in the AR population were also of Australian origin (at least three of them with one generation of pedigree information and most likely with SA links) as opposed to the other animals that were descendants from American imports.The Structure analyses indicates a higher level of admixture between the AR and FR populations, most likely due to their original importations from the USA and Australia. It is believed that some Angora goats in Australia originated from SA via bordering African countries. This explains the relatedness between the SA population and the other two groups. These results indicate that although the Angora goat as a breed has migrated across continents, the dispersion is relatively recent and different populations still show common ancestry.r2 value was used as a more informative parameter due to the small population size, rather than D’ [e compared to them. The average r2 across chromosomes for all the populations was 0.11, which was similar to values of 0.14–0.17 reported by [LD is a useful tool as it describes the non-random association of alleles and loci due to selection, population history and breeding strategies , 43. In  than D’ , 45. Theorted by , but loworted by . These re for the Angora breed, across the subpopulations sampled in this study, was estimated to be 149 animals ten generations ago and, considering the declining trend observed, is expected to be even lower today. This trend could pose a threat to the genetic diversity of the Angora goat population, and should be monitored routinely to ensure a continuous sustainable mohair industry.The NIt is important to note that the high diversity between populations could be used to exchange genetic material between populations. In this way outcrossing can contribute to improving certain unfavourable characteristics of specific populations (e.g. poor reproductive efficiency or growth rates) while maintaining superior mohair qualities.S1 Fig(TIF)Click here for additional data file.


A. halleri in Central Europe. 1,281 accessions sampled from 52 populations within the investigated area were used in the study of genetic variation based on chloroplast DNA. Over 500 high-quality species occurrence records were used in ecological niche modelling experiments. We evidenced the presence of a clear phylogeographic structure within A. halleri in Central Europe. Our results showed that two genetically different groups of populations are present in western and eastern part of the Carpathians. The hypothesis of the existence of a glacial refugium in the Western Carpathians adn the Bohemian Forest cannot be rejected from our data. It seems, however, that the evidence collected during the present study is not conclusive. The area of Sudetes was colonised after LGM probably by migrants from the Bohemian Forest.The present study aimed to investigate phylogeographical patterns present within  A phylogeographical approach has been used in numerous studies addressing the Quaternary history of the flora of Europe, shaped by repeated range contractions during cold periods and subsequent extension of available habitats during warmer periods . These rAreas of relative ecological stability that provided suitable habitats for species survival during periods of glaciation are termed refugia . NumerouIn Europe the potential for phylogeographical research has been exploited intensively in the Alps, where the abundance and complexity of the available phylogeographic studies has already resulted in synthetic and comparative analyses . The sitArabidopsis halleri with its pattern of occurrence covering nearly all mountain regions of Central Europe  remains, with some notable exceptions  O’Kane & Al-Shehbaz is a perennial, self-incompatible and highly outcrossing . A recent study has shown that reticulation and hybridization among lineages that might have transferred cpDNA types from one lineage into the other is unlikely , and PCR amplifications were performed on 1:20 dilutions.Collected leaf tissue was immediately dried in silica gel prior to molecular analysis. DNA was extracted from 10 to 15 mg of dry material using NucleoSpintrnK intron and two intergenic regions . The observed polymorphisms are briefly characterized in During the genotyping process we screened 12 polymorphic sites previously detected by PCR-RFLP in three cpDNA regions: ® SNaPshot® Multiplex Kit (Applied Biosystems) and followed the protocol given by the manufacturer.After PCR amplification of the three cpDNA regions of interest, we used SNaPshot assay for simultaneous detection of seven single nucleotide polymorphisms SNP, cf. . In the 2, 1.2 µl of 2.5 mM solution of dNTP, 0.3 µl of BSA solution (10 mg/ml), 0.3 µl of 10 mM solution of each primer and 0.075 µl of AmpliTaq®DNA Polymerase (5U/µl). The reaction was carried out in Mastercycler® ep gradient S thermal cycler using one cycle of 5 min at 95 °C, and 36 cycles of 45 s at 92 °C, 45 s at 58 °C–62 °C ; and 2 min 30 s at 72 °C, followed by one cycle of 10 min at 72 °C. The primer sequences for specific PCR amplifications are given in ® SNaPshot® Multiplex Kit). The primer sequences used in the SNaPshot reaction are given in ® 3,130 xl Genetic Analyzer (Applied Biosystems) using a capillary of 36 cm, POP 4 Migration Buffer and Dye Set E5. The data were collected using Foundation Data Collection 4.0 software and analyzed with GeneMapper 4.0.The following PCR conditions were utilized: a total volume of 15 µl consisting of 3 µl of template DNA (20–100 ng), 6.225 µl of water, 1.5 µl of 10X PCR Buffer, 2.1 µl of 25 mM solution of MgCltrnC-trnD region by specifically amplifying the genomic region containing the SNP polymorphisms mentioned in AcsI restriction enzyme. The PCR primer sequences were given in ® ep gradient S (Eppendorf) thermal cyclers. Fragments were separated by electrophoresis on 2% agarose gels stained by ethidium bromide and photographed with BioImage system (Bioprobe) under UV light.The CAPS assay was used for genotyping three SNP polymorphisms in the trnK and trnC-trnD regions (cf. ® ep gradient S (Eppendorf) thermal cyclers using one cycle of 5 min at 95 °C, and 36 cycles of 30 s at 94 °C, 30 s at 51 °C or 56 °C (depending of the primers sequences); and 30 s at 72 °C, followed by one cycle of 10 min at 72 °C. Fragments were separated on 8% polyacrylamide gels using a Li-Cor 4200 Global IR2 DNA Sequencer.Two indel polymorphisms in the ions cf.  were genSince cpDNA is a non-recombining molecule, alleles observed at all twelve loci were combined into cpDNA haplotypes  was constructed on the basis of a distance matrix reflecting molecular differences between each pair of chlorotypes using a modification of the algorithm described by ASc) was calculated for each population according to the rarefaction method . Chlorotypic diversity (HSd) and its sampling variance were calculated according to the methodology given by Allelic richness (n method  using Fsn method . Estimatk-means clustering (SKMC). The computations were based on a matrix of chlorotype frequencies in analysed populations. The statistically optimal number of clusters was assessed using the quasi-Akaike information criterion  and Last Glacial Maximum . Paleoclimatic data were obtained from simulations in the following Global Climate Models: CCSM4, MIROC-ESM and MPI-ESM-P. Bioclimatic variables calculated on the basis of these models and downscaled to 5 arc-minute resolution were downloaded from the WorldClim dataset (http://www.worldclim.org), together with present-day climate data at the same resolution. We tested all the variables for multi-collinearity by examining the cross-correlations among them (Pearson’s r) based on the 544 species occurrence records. Highly correlated variables (r > 0.7) were excluded from the models  as well as from our own field research carried out in France, Germany, Poland, Austria, Czech Republic, Slovakia and Ukraine. After initial screening for duplicates and data aggregation into a 5 min resolution raster we obtained 544 unique records that were used to calibrate and validate the models (http://grass.osgeo.org).Distribution data were obtained from GBIF database  we performed ensemble forecasting . This prA total of 12 cpDNA haplotypes were found in the investigated populations . All neiChlorotypes did not show equal frequencies in overall sampling . The mosChlorotypes E and J were the most widespread geographically , with thSc) and chlorotype diversity index (HSd) were calculated for each investigated population. They varied broadly from 1 to 4.983 for ASc and from 0 to 0.893 for HSd  enabled us to study the structure present in our dataset on several levels. We examined a broad spectrum of different k values from k = 2 to k = 25. The results of SKMC from k = 2 to k = 10 were plotted on the map (k levels), that differed from all the other populations. A subdivision of the populations into 6 clusters , as much as 65.8% of the total variation was observed between groups of populations, whereas 15.82% was found among populations within groups  and Area under Receiver Operating Characteristic (ROC). All models performed well and had TSS >0.7 and ROC >0.88. The performance of two models CTA and FDA was weaker than the performance of the remaining methods, but still of acceptable quality.A. halleri  out of the three climatic models suggest that the species survived the LGM (ca. 22 kyr BP) only in the southernmost part of the Carpathians . The Din halleri .A. halleri during the Holocene climatic optimum (ca. 6 kyr BP). Our models showed that recolonization must have advanced very slowly in the Carpathians, when compared with areas north of the Alps. All the models showed that conditions facilitating the spread of A. halleri occurred much earlier in between the Western Carpathians, the Sudetes and areas north from the Alps, while suitable areas in the Eastern Carpathians were at first much more restricted  that a glacial refugium existed in the Eastern Carpathians , was also documented in charcoal for Moravany in Slovakia with a radiocarbon date of ca. 18 kyr BP  from the Eastern Carpathians. The presence of a genetic admixture in Western Carpathian populations was also revealed by A. halleri in western and eastern part of Carpathians form two different genetic groups. It might be hypothesized that these groups could be derived from two different refugia. Populations in the Eastern Carpathians might belong to the group that survived in a southern refugium  as suggested by In the SKMC analysis, populations from the Western Carpathians were clustered together with populations from upland regions of southern Poland. This fact supports the hypothesis of a Western Carpathian origin of populations located north of the Western Carpathians. However, for the reasons stated above, it is very difficult to say when these populations were founded. We have shown that populations of The situation in the areas north and north-east of the Alps is more complex than in the Carpathians. SKMC analysis showed that at least two groups of populations can be recognized regardless of the level of k: populations from the Harz Mountains and from the Alps. Populations from the Bohemian Forest were usually assigned to different clusters, forming very heterogeneous group. This heterogeneity was also evidenced in AMOVA.Differentiation is also apparent between the regions of the Harz Mts. and the Bohemian Forest, with the first harbouring haplotype D, which is not present in the Bohemian Forest. In the latter region the occurrence of haplotype C can be observed, which, in turn, is present neither in the Harz, nor in the Alps or the Šumava. A relatively recent  origin of this differentiation could be hypothesized as both haplotypes occupy external nodes of the MST tree.A. halleri might have also survived in a vast area, and that its Pleistocene distribution covered not only the region mentioned above, but also extended northwards and westwards to the Ardennes and Hautes Fagnes (High Fens) in Belgium. So far one natural population from this area has been tested by A. halleri distribution in Western Europe. The vast extent of possible refugial areas north of the Alps was also clearly evidenced by our modelling experiments. Other studies based on molecular methods also suggest the presence of a glacial refugium in the Central Europe  showed high within-population diversity throughout central Europe, accompanied by low regional differentiation and geographically widespread polymorphism. The authors hypothesized that (given the unlikeliness of gene flow) a common gene pool must have existed for central European populations  had a devastating effect on the regional flora, mainly due to the close proximity of the continental ice sheet  origin. This is supported by the very low genetic variation found in Sudetes as well as the fact that despite dense sampling, we have not found any chlorotypes specific to this area. On the basis of the evidence from cpDNA variation, it could be hypothesized that populations from the Bohemian Forest could be a source of migrants that established new populations of the species in this region after LGM.We have shown that ce sheet . Geologice sheet . Therefo Sudetes . Quite a Sudetes . It is n Sudetes  . It seeA. halleri in the Sudetes. The occurrence of this uncommon haplotype has also been recorded in high-mountain populations in the Western Carpathians as well as for one population in the Bohemian Forest. A similar pattern have been also recognized in Pulsatilla vernalis  Deyl and Thymus carpathicus Čelak. that are present in the Carpathians, occur also in high-mountain environments of the Sudetes  Kolník, and subsp. dacica (Heuff.) Kolník (Taxonomy of ) Kolník . This di) Kolník . We have1.A. halleri is strongly geographically structured within the investigated area and at least 6 clusters of populations can be identified on the basis of cpDNA variation.Genetic variation in 2.There is a clear genetic differentiation within the Carpathian populations. Two distinct clusters of populations were identified in western and eastern part of the mountain range. It is clear that the two groups originated from two different glacial refugia. There are traces of gene flow between the two groups.3.The possibility of the existence of a glacial refugium in the Western Carpathians and the Bohemian Forest cannot be ruled out on the basis of the present research but the evidence is not conclusive.4.A. halleri populations in the area.It seems that the area of Sudetes was colonised after LGM. Populations from the Bohemian Forest can be hypothesised as a source of the migrants which established 10.7717/peerj.1645/supp-1Figure S1Click here for additional data file.10.7717/peerj.1645/supp-2Table S1Click here for additional data file.10.7717/peerj.1645/supp-3Table S2Click here for additional data file.10.7717/peerj.1645/supp-4File S1Click here for additional data file.10.7717/peerj.1645/supp-5Figure S2k = 2 (two groups) to k = 10 (ten groups of populations). Population differentiation was inferred from a data matrix of chlorotype frequencies.SKMC of investigated populations carried out using Shperikm . DiffereClick here for additional data file.


In the departments of the Vaupés and Guaviare, in southeastern Colombia, in a transitional area between Amazonia and the eastern plains, inhabit indigenous groups belonging to the Tukanoan (East) and Guahiban linguistic families. Although some studies have dealt with the culture and the cosmology description of these groups, little research has been done on the biological diversity and genetic relationships of such groups. To estimate the diversity, the structure, and the genetic relationships of one Guahiban and two Tukanoan groups of the Colombian Amazonian region. Samples were collected (n = 106) from unrelated individuals belonging to the Vaupés native indigenous communities. The DNA was extracted and nine autosomal microsatellites were typed. Several measures of diversity, FST, pairwise FST, and population differentiation between groups were calculated. Finally, it was estimated the genetic distances of the groups studied in relation with other Amazonian, Andean and Central American indigenous people.  1. The genetic diversity found stands within the range of other Amazonian populations, whereas compared to the mestizo and afro-descendant Colombian populations, such diversity showed to be lower. 2. The structure and population differentiation tests showed two clusters; one consisting of the Vaupés Tukanoan and Guaviare Tukanoan groups, and a second one formed by the Guayabero. 3. Tukanoan groups are found to be closer related to the Brazilian Amazonian populations than to the Guayabero. The results of this study suggest that the Guayabero group from Guaviare, are genetically differentiated from those Tukanoan groups of the Vaupés and Guaviare. The Vaupés and Guaviare regions in southeastern Colombia are inhabited by several indigenous groups belonging to different linguistic families. These groups have an extensive history of demographic expansions, wars, alliances, migrations and trades. With the arrival of European populations into the region, a new chapter was opened, leading to the disruption of some cultural and social dynamics of the existing indigenous groups. To a large extent, it can be said that such disruptions caused a major reduction on the size of the indigenous populations, displacements from original territories, or even a total extinction of some groupsCurrently, 24 ethnic groups live in this area representing about 30% of the national linguistic diversity. These groups have been classified-,Great efforts have been made towards an understanding of the social and cultural dynamics of the groups from this regionFor this study were collected biological samples of peripheral blood (4 mL) of 106 individuals belonging to indigenous groups in the departments of the Guaviare and the Vaupés, in southeastern Colombia . The parThe sample from Mitú (n= 55), named here Vaupés Tukanoans, groups TukanoanThe DNA was extracted by the salting out procedurei2), where n is the sample size and pi is the frequency of allele i. The total gene diversity was calculated as the average diversity over all loci . The calculations were made with the software Arlequin 3.11The allele frequencies were obtained by counting; the intralocus independence (Hardy-Weinberg equilibrium) was tested. The observed heterozygosity (Ho) of each locus was calculated as the proportion of heterozygotes in each population. The observed heterozygosity of each population is the average heterozygosity across all loci. The gene diversity for each locus was calculated as n / (n-1) 1-Σpi2, where ST. Gene flow was measured as Nem = (1/FST -1)/4. The calculations were performed with Arlequin 3.11.Through an analysis of molecular variance, the degree of population genetic structure in two hierarchical levels was estimated: among groups  and among individuals within groups. This analysis divides the total variance into covariance components due to the intraindividual, interindividual and/or interpopulation differences. Covariance components were used to compute fixation indices, FTo determine the degree of differentiation among the three populations studied, exact test was performed based on the distribution of the genotypic frequencies, the calculations were made in GENEPOP version Web-A were calculated and graphically depicted with a Neighbor-joining tree (NJ) using the program POPTREE2The allelic frequencies of four loci  were compared with data sets from six Amazonian populations, seven Andean and two Panamanian populations previously reportedThe distribution of allelic frequencies of the Vaupés Tukanoans, Guaviare Tukanoans and Guaviare Guayaberos are shown in The observed heterozygosity of Tukanoans and Guayaberos was found in the range 0.665-0.544, the gene diversity in the range 0.622-0.466 and the average number of alleles in the range 5.444-4.444 . The GuaST estimated by AMOVA for the three groups was 0.06691 (p <0.00001). The FST estimated across all loci was 0.07477 (IC 95% 0.02897 - 0.13481). With respect to the pairwise FST (p <0.01) regarding the two Tukanoans groups, while the FST between the two Tukanoan populations showed no significant differentiation (p> 0.05).The Fwise FST , the Guap <0.01). However, it was not possible to detect any significant differences between the Vaupés Tukanoans and Guaviare Tukanoans, showing that genotypic frequencies of each locus have a similar distribution in the two populations. Similarly, the test of spatial differentiation in Geneland showed in all cases (ten separate runs to test the consistency of the results), the separation between Guayaberos and Eastern Tukanoan groups, with the two Tukanoan populations forming a single undifferentiated population.The exact test of population differentiation showed highly significant differences between Guayaberos and the two Tukanoans groups  stands within the range reported for other Amerindian South American populationsAdditionally to the reduction of population sizes, another important factor in the structuring of the genetic variation in Guayaberos is the inbreeding marriage practicesST, support the biological separation between the Guayaberos and Eastern Tukanoan groups. Culturally, Guayaberos have not participated, nor they currently do, in the exogamous system of the Vaupés. This lack of exogamous marital net is reflected in the results presented here. On the other hand, geographical proximity is not synonymous of a common biological history. The Guaviare Eastern Tukanoans are located near the Guayaberos (~ 7 km) ever since approximately five decades ago.The Exact Test of population differentiation, the spatial structure test, and pairwise FNonetheless no evidence of marriages or a high gene flow between these communities were reported during the field work period carry out for this study. The estimated number of migrants between the Tukanoan and Guayabero groups was low compared to the number of migrants among the Vaupés Tukanoans and the Guaviare Tukanoans. This can potentially be a good evidence that in human populations socio-linguistic practices are crucial in the structuring of genetic variation; this can contribute or limit the gene flow between populations.Comparison with other indigenous populations through the estimation of genetic distances group Tukanoans in the cluster of Amazonian populations. This grouping should not be interpreted as a common origin; nonetheless it could represent an ancient relationship among Tukanoans groups and other Brazilian Amazonian populations; these relationships are reflected in both oral tradition and cultural practices. The Tukanoan groups, for example, place their mythological origin in the ''milk lake'', an unspecified location downstream the Amazon RiverThe case of Guayabero and Andean populations is similar to Tukanoan and the Amazonian populations. It is not possible to trace with certainty an origin of the Guayaberos, but the evidence here presented suggests that these populations may possibly have an Andean-Eastern plains origin more rather than an Amazon origin, notwithstanding some of the Guayabero's ancient practices- such as consumption of manioc-which presents some resemblances to the Tukanoan and Arawakan practices, in the Amazon Basin. The Guayabero location in ancient times  and the community of El Barrancón (Guaviare Guayaberos), culture and language would be acting as a barrier to gene flow, maintaining a level of genetic differentiation between these two groups.5. The Tukanoans are located closer to the populations of the Brazilian Amazon than the Andean populations. Opposite happens with the Guayabero, showing more proximity to the Andean populations than the Amazon.


Cycas guizhouensis Lan & R.F. Zou is an endemic and endangered species in Southwest China that is primarily distributed along the valleys of the Nanpan River. In this study, we used four chloroplast DNAs (cpDNA), three nuclear genes (nDNA) and 13 microsatellite (SSR) loci to investigate the genetic structure, divergence time and demographic history of 11 populations of C. guizhouensis. High genetic diversity and high levels of genetic differentiation among the populations were observed. Two evolutionary units were revealed based on network and Structure analysis. The divergence time estimations suggested that haplotypes of C. guizhouensis were diverged during the Middle-Upper Pleistocene. Additionally, the demographic histories deduced from different DNA sequences were discordant, but overall indicated that C. guizhouensis had experienced a recent population expansion during the post-glacial period. Microsatellite data revealed that there was a contraction in effective population size in the past. These genetic features allow conservation measures to be taken to ensure the protection of this endangered species from extinction.Climatic oscillations in the Pleistocene have had profound effects on the demography and genetic diversity of many extant species.  Different regions have experienced different scales and frequencies of glaciations in the Pleistocene4566910Cycas, belonging to CycadaceaeCycas species are given ‘First Grade’ conservation status in ChinaCycads are generally considered to be the oldest group of living seed plants. Cycads reached their greatest levels of diversity during the Jurassic-Cretaceous . Their diversity dwindled to the present number of species (approximately 300 species) as a result of the emergence and dominance of flowering plants12131415Cycas was in South China, with the deepest divergence occurring via a vicariant barrier: the Red River FaultCycas species generally grow on low-altitude slopes of ridges and cliffs along river valleys. Their fertile seeds are large, heavy and sink in water, which precludes water dispersal over long distances. In addition, cycad seeds contain virulent cycasin, preventing their dispersal by animals for long distances. Thus, in this region, seed dispersal was likely limited to short distances2122Cycas species are dioecious and allogamous. In plant species, both the mating system and dispersal mechanism have important effects on genetic differentiation among populations24Recent research which has combined ancestral area reconstructions with fossil evidence proposed that the origin of Cycas guizhouensis Lan & R.F. Zou was described in 1983 and can be identified by its morphological characteristics of a cylindrical trunk up to one meter in height, fusiform male cone, loose and open female cone, densely hairy sporophylls, nearly round apical lobe (the margins with numerous tapered lobes) and yellow with reddish brown mucro of seeds (nearly globose)Cycas guizhouensis is endemic to Southwest China, occurring primarily in the valleys of the Nanpan River basin of southwestern Guizhou, northwestern Guangxi and the eastern Yunnan provinces. The species mainly prefers sand or limestone habitats where the climate is characterized by mild with cool, moist summers and moist, frost-free wintersC. guizhouensis has been severely threatened by rapid habitat destruction for the cultivation of economic plants and over-collection because of its edible stem and ornamental attributes. This species has dramatically decreased in numbers and is becoming endangered. Field surveys have shown that there are two populations with fewer than 20 individuals. Thus, the need to develop effective protection measures for this species is urgent.C. guizhouensis were by Yang and Meerow who used allozymes to investigate three populations of C. guizhouensis, and by Xiao who studied 12 populations of C. guizhouensis using ISSR markers19C. guizhouensis and compare these results with previous studies on this species, we used four cpDNAs ) and 13 microsatellite markers363738394041C. guizhouensis in an further effort to investigate the causes of its endangerment and provide basic guidelines for its conservation.Understanding the genetic variation, genetic structure and demographic history in a rare species is essential for the establishment of effective and efficient conservation practices. Protection measures can be proposed by relying on detailed population genetic studies. Several population genetic studies on cycads based on different molecular markers have been recently published910111927C. guizhouensis identified 12 haplotypes (guiH1-guiH12) in total. Of these, eight haplotypes were specific to one population, whereas haplotypes guiH1, guiH2, guiH4 and guiH6 were shared by five, four, two and two populations, respectively. Detection of recombination in nuclear genes showed that no recombination occurred in GTP and PHYP. In contrast, eight recombination events were occurred in F3H. Most software used in our analysis didn’t need the data to remove recombination. Thus, we did not remove recombination from gene F3H because some individuals with recombination were also removed. A total of seven haplotypes (guiG1-guiG7) were derived from the nuclear gene GTP, of which haplotype guiG1 was the most abundant and predominant in all 11 populations. The remaining six haplotypes were private. Ten haplotypes (guiP1-guiP10) were derived from the nuclear gene PHYP. Of these, haplotypes guiP1 and guiP2 were most frequent and were widely shared by all 11 and ten populations, respectively. Haplotype guiP4 was distributed in three populations and the remaining haplotypes were specific to single populations. The nuclear gene F3H identified 32 haplotypes (guiF1-guiF32) in total. guiF1 and guiF4 were the most widely distributed haplotypes. Information on cpDNA and three nDNA haplotypes and their distribution in populations are shown in The four cpDNA regions surveyed across the 110 individuals . NST was not significantly greater than GST (P > 0.05), suggesting that there was no correspondence between haplotype similarities and their geographic distribution in C. guizhouensis.Genetic diversity indices rsity Hs , which mGTP, PHYP, and F3H, respectively. FST for these genes thus ranged from 0.192–0.698C. guizhouensis populations  partitioned among populations rather than within (30.18%) populations based on cpDNA data, whereas there was less variation  partitioned among populations than variation  within populations based on nuclear genes ulations .C. guizhouensis with Cycas edentata and Cycas rumphii as outgroups. The 12 cpDNA haplotypes were grouped into two clades, showing that haplotype guiH7 was the firstly diverged haplotype, and the others were grouped into a clade. Among this clade, haplotypes guiH2, guiH3, guiH4, guiH5, guiH8, guiH9 and guiH10 were clustered into a subclade, indicating that they were more closely related than others, although the phylogenetic relationship within the subclade was not resolved  where 26 haplotypes were clustered in, although some genetic relationships of some F3H haplotypes were basically resolved such as haplotypes guiF1, guiF5 and guiF10, parallel relationships were also observed in most haplotypes , 0.181 MYA (PHYP) and 1.334 MYA (F3H)  . CoalescD, Fu and Li’s D* and F*, Fu’s Fs tests as well as SSD and raggedness statistics were listed in C. guizhouensis had not undergone a recent population expansion. For the nuclear genes, all values were non-significant except for significant negative values of Fu’s Fs for all three genes, which suggested the presence of population growth or recombinationF3H, respectively  and a subsequent slight decline (approximately 2 Ka-present)  . During gene GTP , while sTP (PHYP . In cont present .F) were positive for all populations, with a mean value F = 0.204, indicating that there was a high level of inbreeding within each population. This inbreeding resulted in a deficiency of heterozygotes and significant deviations from Hardy-Weinberg equilibrium at most loci and in most populations (FST = 0.138 (Nm) between pairs of populations were shown in Diversity estimates from 13 microsatellites varied among populations , with thulations . Estimat = 0.138 . The cor = 0.138 . EstimatThe clustering analyses resulted in population groupings that were generally consistent with the above haplotype aggregation analyses. The UPGMA clustering dendrogram, Structure analysis, and principal coordinate analysis (PCoA), all indicated the same general grouping: individuals belonging to populations AL, YP, LW and LL clustered into one group (Clade I), while the remaining individuals clustered into a second group (Clade II) ; Fig. 5.C. guizhouensis had not experienced a recent severe bottleneck. However, C. guizhouensis apparently experienced a population decline (bottleneck) in history as inferred from the calculation of Garza-Williamson indices which were lower than the critical Mc value of 0.68  were also all greater than zero  (The observation of isolation by distance (IBD) was generally consistent with the previous studyhouensis . In addi and II) . Populat and II) , PCoA (F and II)  and hapl and II) . The popC. guizhouensis haplotypes from the four phylogenies was strongly supported by Bayesian inference, the phylogenetic relationships of haplotypes were not well resolved (F3H) all suggested that there are two star-like evolutionary units separately dominating two lineages. The assumed two ancestral haplotypes were distributed in the inner nodes of the reticulate evolutionary diagrams at a high frequency. The derived haplotypes were distributed around the ancestral haplotypes with 1–3 mutational steps, showing a star-like topology, which indicated a recent rapid divergence. The topology of the haplotypes from the nuclear gene F3H was highly reticulate because eight recombination events had occurred . As a result, Pleistocene climatic oscillations had no noticeable effect on the demography of C. guizhouensis. Alternatively, the warmer climate during the post-glacial period (MIS1) promoted the expansion of this species.Marine Isotope Stages (MIS), based on oscillations in oxygen-18 values of benthic foraminifera, are timescales that are now widely used to express warm and cool periods in the Quaternarygene F3H . Cycas gC. guizhouensis than some other Cycas species. However, massive illegal digging of C. guizhouensis for trading or ornaments has jeopardized the existing populations of this species and its effective population size. For example, some populations that were sampled in Xiao’s study, such as LP, have since been extirpatedC. guizhouensis was less than 100 , except for population LL . Moreover, ex situ conservation is a viable auxiliary conservation approach for protecting this species by collecting seeds or seedling individuals from different populations which possess key sources of genetic diversity to remote adaptive areas. In addition, we suggest that more efforts should be made to raise awareness of conservation to local farmers and that a prohibition on deforestation in Cycas distribution areas be implemented. Taken together, these measures could protect C. guizhouensis from extinction.Information on the genetic diversity, genetic structure, and demographic history of rare and endangered species can aid the development of appropriate management strategies for their conservation. One goal for the conservation of threatened plants is to preserve their genetic diversityation LL . TherefoC. guizhouensis in southwestern Guizhou, northwestern Guangxi and eastern Yunnan province, China. Young and healthy leaves were dried in silica gel immediately after collection. All samples were stored in Kunming Institute of Botany, Chinese Academy of Sciences, China. Within the 209 samples, 10 individuals from each population were randomly selected for chloroplast and nuclear DNA sequencing, while all of the 209 individuals were used for the microsatellite study. Information on each sampling location and the number of individuals from each population that were used in the DNA sequence and microsatellite analyses are presented in C. guizhouensis that were used in this study .A total of 209 individuals were collected from 11 populations of psbA-trnHtrnL-trnFpsbM-trnDtrnL-trnTGTPPHYP and F3H for complete analysis after preliminary screening from universal chloroplast and nuclear primers. The seven pairs of fragments were amplified for the most polymorphic sites of the 110 individuals. PCR amplification procedures were the same as those used in C. simplicipinnaGenomic DNA was extracted from dried leaves using the modified CTAB methodCycas363738394041C. simplicipinnaC. guizhouensis, the selected microsatellite loci were stained with fluorescent dye at the 5′ end, their PCR products were separated and visualized using an ABI 3770 automated sequencer, and their profiles were read with GeneMapper software. An individual was declared null (nonamplifying) at a locus and was treated as missing data after two or more amplification failures. Finally, we chose 13 polymorphic microsatellite loci .Sequences were edited and assembled using SeqMan. Multiple alignments of DNA sequences were performed manually and subsequently adjusted in Bioedit, version 7.0.4.161Nei’s nucleotide diversity (Pi) and haplotype diversity (Hd) indices using DnaSP, version 5.0HS), gene diversity in total populations (HT)GST and NST, according to the methods described by Pons & Petithttp://www.pierroton.inra.fr/genetics/labo/Software/Permut). We used the program Arlequin, version 3.11We first used DnaSP, version 5.0C. guizhouensis were inferred using Bayesian methods implemented in MrBayes, version 3.1.2Cycas edentata and Cycas rumphii as outgroups. The degree of relatedness among cpDNA and nDNA haplotypes was also estimated using Network, version 4.2.0.1Phylogenetic relationships among cpDNA and nDNA haplotypes of −9 and 5.1−7.0 × 10−9  F3H. The BEAST program was also used to create a Bayesian Skyline Plot to infer the historical demography of C. guizhouensis. Posterior estimates of the mutation rate and time of divergence were obtained by Markov Chain Monte Carlo (MCMC) analysis. The analysis was run for 108 iterations with a burn-in of 104 under a strict clock and the HKY model or HKY + I model. Log parameters were sampled every 10,000 iterations. The analysis ran three times under the condition that all parameters were unchanged. The ESS parameter was found to exceed 200 by TRACER, version 1.5D, Fu and Li’s D* and F* and Fu’s FSP-values were calculated with the software Arlequin, version 3.11We used the evolutionary rates that had previously been estimated for seed plants to be 1.01 × 10NA), private alleles (AP), effective number of alleles (AE), expected heterozygosity (HE), observed heterozygosity (HO), information index (I), fixation index (F) and percentage of polymorphic loci (PPB), were calculated using GenAlEx, version 6.3AR) was estimated with FSTAT, version 1.2FST between pairs of populations was computed with Arlequin, version 3.11ST/(1 − FST) with geographic distance for all pairs of populations. FST/(1 − FST) was calculated with Genepop, version 4.1.4Nm = (1 − FST)/4FSTDataset editing and formatting were performed in GenAlEx, version 6.35 iterations and 1 × 105 subsequent MCMC steps. The best-fit number of groupings was evaluated using ΔK and the log-likelihood value by STRUCTURE HARVESTER, version 0.6.8The genetic structure of sampled populations was estimated by unweighted pair group mean analysis (UPGMA) using TEPGA, version 1.3Mc value of 0.68, a value obtained by simulations based on the empirical data in bottlenecked populations, which suggests a reduction in population size66The effective population sizes of each population were estimated in the program LDNe at three levels of the lowest allele frequency  with a 95% confidence intervalHow to cite this article: Feng, X. et al. Middle-Upper Pleistocene climate changes shaped the divergence and demography of Cycas guizhouensis (Cycadaceae): Evidence from DNA sequences and microsatellite markers. Sci. Rep.6, 27368; doi: 10.1038/srep27368 (2016).


Trichuris suis and T. trichiura are two different whipworm species that infect pigs and humans, respectively. T. suis is found in pigs worldwide while T. trichiura is responsible for nearly 460 million infections in people, mainly in areas of poor sanitation in tropical and subtropical areas. The evolutionary relationship and the historical factors responsible for this worldwide distribution are poorly understood. In this study, we aimed to reconstruct the demographic history of Trichuris in humans and pigs, the evolutionary origin of Trichuris in these hosts and factors responsible for parasite dispersal globally.nad1 and rrnL genes were sequenced followed by population genetic and phylogenetic analyses. Populations of Trichuris examined were recovered from humans (n = 31), pigs (n = 58) and non-human primates (n = 49) in different countries on different continents, namely Denmark, USA, Uganda, Ecuador, China and St. Kitts (Caribbean). Additional sequences available from GenBank were incorporated into the analyses.Parts of the mitochondrial Trichuris in Uganda and the majority of the Trichuris samples from non-human primates suggesting a common African origin of the parasite, which then was transmitted to Asia and further to South America. On the other hand, there was no differentiation between pig-derived Trichuris from Europe and the New World suggesting dispersal relates to human activities by transporting pigs and their parasites through colonisation and trade. Evidence for recent pig transport from China to Ecuador and from Europe to Uganda was also observed from their parasites. In contrast, there was high genetic differentiation between the pig Trichuris in Denmark and China in concordance with the host genetics.We found no differentiation between human-derived T. trichiura which were then transmitted with human ancestors to Asia and further to South America. A host shift to pigs may have occurred in Asia from where T. suis seems to have been transmitted globally by a combination of natural host dispersal and anthropogenic factors.We found evidence for an African origin of The online version of this article (doi:10.1186/s13071-016-1325-8) contains supplementary material, which is available to authorized users. Trichuris. Around 460 million humans are infected with T. trichiura mainly in developing countries in South and South-East Asia, Sub-Saharan Africa and Latin America [T. trichuris [T. suis infection in pigs is globally widespread with the highest prevalence in young pigs reared in outdoor production systems [Trichuris but their evolutionary relationship is still poorly understood and in particular the anthropogenic and environmental factors responsible for their global distribution.A range of mammalian hosts is infected with parasitic whipworms belonging to the genus  America . The prerichuris . T. suis systems , 4. A nuAscaris suum and A. lumbricoides between pigs and humans across the globe [Wuchereriabancrofti suggests that this parasite was introduced to India 60,000-70,000 years ago through human migration out of Africa. In contrast, the introduction of W. bancrofti to Papua New Guinea cannot be explained by ancient human migration but must have been introduced through more recent human migration [Population genetic tools provide a valuable opportunity to investigate the epidemiological history and transmission of parasites and have been adopted to study many parasitic nematodes , 6. For he globe . Moreovehe globe . Reconsthe globe . For insigration .Homo nearly 4 million years ago, there has been continuous contact with many parasites. Those infecting humans today may have different evolutionary origin and can be categorized into two groups: 1) Parasites transmitted to humans through primate common ancestors and are referred to as “heirloom” and 2) parasites acquired more recently through contact with animals  and are referred to as “souvenirs” [Trichuris species can be found in non-human primates such as the Trichuris sp. in Francois leaf monkey in China and Trichuris spp. infecting a number of African non-human primates in Uganda [Trichuris from humans and non-human primates is poorly understood.During the evolution of genus uvenirs” . The humuvenirs” . Howevern Uganda , 14. HenTrichuris spp. isolates from different host speices. For instance, several studies have used the nuclear ribosomal internal transcribed spacers (ITS) to investigate the genetic relationship among Trichuris from humans, non-human primates and pigs [cox1 gene [Trichuris spp. but includes only a limited number of worm samples [Different molecular markers have been used to resolve the genetic and evolutionary relationship between and pigs , 15, 16 ox1 gene , 18. Ful samples , 19, 20.Trichuris parasites derived from humans, non-human primates and pigs from different continents. The mitochondrial nad1 and rrnL genes of 138 worms were sequenced and coalescent simulations applied to infer the demographic history of the different human and pig Trichuris populations and their evolutionary origin.Herein, we investigated the genetic and evolutionary relationships between populations of Trichuris from Uganda and Ecuador were recovered from the faeces of children and adults after anthelmintic treatment. Permission was obtained from the Ministry of Health and the National Council of Science and Technology in Uganda and by the Ethical Committees of Liverpool School of Tropical Medicine and Pontificia Universidad Catolica del Ecuador in Ecuador. The Danish Central Medical Ethics Committee approved the study. Informed written consent was obtained from all participants or their guardians (for children) after being informed about the study in both English/Spanish and the local language.The human Worms from baboons at Southwest National Primate Research Center, Texas, USA, Copenhagen Zoo, Denmark, Knuthenborg Park, Denmark and African Green Monkeys at Saint Kitts were recovered during post mortem examination which is performed routinely at these places on culled animals.T. suis was obtained from experimentally infected pigs in Denmark and US. The Animal Experiments Inspectorate, Ministry of Justice, DK approved the animal study protocol and was carried out according to their guidelines (Licence no. 2005/561-1060). In US the animals were handled and managed according to a protocol (Protocol #07-011) specifically approved by the USDA-ARS Beltsville Area Animal Care and Use Committee and followed the Institutional Animal Care and Use Committees (IACUC) guidelines. In Uganda T. suis was obtained from naturally infected pigs raised on private farms, slaughtered and used for local consumption. Permission to recover worms was obtained from the owners. In Ecuador, worms were collected after anthelmintic treatment of a naturally infected pig raised on a private farm. Written consent to recover worms was obtained from the owner. T. suis were obtained from natural infected pigs in China when slaughtered at local abattoirs. Collection was approved by the Animal Ethics Committee of the Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences.Trichuris from baboons and humans showed banding pattern characteristic of Trichuris from primates, while all T. suis worms showed the banding pattern characteristic of T. suis.A total of 138 worms were collected from humans, pigs and non-human primates from different regions  and reverse SuiND1_R (5′-CGTTGTAGCCTCTTACTAATTCTCTTT-3′) primers while 422 bp of the rrnL gene was amplified using primers, forward TrirrnL_F (5′-TGTAAWTCTCCTGCCCAATGA) and reverse TrirrnL_R (5′-CGGTTTAAACTCAAATCACGTA). The PCR conditions were identical for both markers and were conducted in a total volume of 20 μl using 1 μl DNA as template. PCR ingredients were: 1X PCR buffer, 0.2 mM of each dNTP, 0.4 mM of each primer pair, 2.0 mM MgCl2, and 1 U of Hot Start DNA-polymerase (Ampliqon). PCR conditions were initial denaturation at 95 °C for 15 min followed by 35 cycles consisting of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 1 min and a final extension at 72 °C for 10 min. Agarose gel electrophoresis (1.5 %) was used to verify amplification of a single fragment of the expected size. PCR products were enzymatically cleaned prior to sequencing using 10 μl of PCR product, 1 μl Exonuclease I and 2 μl Fast AP Thermosensitive Alkaline Phosphotase (1 U/μl) (Fermentas). The samples were incubated for 15 min at 37 °C followed by 15 min at 85 °C. Finally, cleaned amplicons were sequenced in both directions using same primers used for PCR by Macrogen Inc. in Seoul, South Korea.Partial sequences of the two mitochondrial genes nad1 and rrnL genes, respectively. Two sequences of the nad1 and rrnL from the Chinese human and pig Trichuris mitochondrial genomes  were included in the dataset. Also, T. trichiurarrnL gene sequences from humans in China were included in the dataset (Accession no. AM993017-AM993021). Phylogenetic relationship was inferred using NJ and ML phylogenetic trees in MEGA v6.1 [Trichinellaspiralis was used as outgroup (Accession No: AF293969). The most parsimonious network was inferred by the Neighbor-net method using SplitsTree v.4.13.1 [Forward and reverse sequences from each sample were checked, edited manually and assembled using vector NTI  and thenEGA v6.1 . The besEGA v6.1  under AkEGA v6.1 . Trichinv.4.13.1 . Neighbost between Trichuris populations using Arlequin v.3.5.1.2 [st for the human and pig derived worms were calculated in separate analysis using the geographical origin of the worms to define the different populations. As the origin of Trichuris collected from baboons was unknown due to previous transport between zoological gardens, we used the phylogenetic analysis to define two populations for which Fst subsequently was estimated for. Lastly, Fst was estimated between the human and baboon worms that were found to cluster together. 10,000 permutations were used to test for differentiation between pairs of populations. The p-distance between the distinct clades identified in the phylogenetic analysis was calculated using MEGA v.6.1. [Analysis of Molecular Variance (AMOVA) was used to estimate the Fixation index, F.3.5.1.2 . The FstA v.6.1. .T. trichiura and T. suis were calculated using the formula Θ = 2Neffμ where Θ (theta) is the genetic diversity of a population, μ is the mutation rate per gene and Neff is the effective population size. Theta (Θ) and the ancestral history were estimated from Genetree [Caenorhabditiselegans was used which is 1.6 X 10−7 per site per generation [−4 per gene per generation.For uniparentally inherited DNA, the time to the most recent common ancestor (TMRCA) in generations is equal to the population size (N). The effective population sizes for the populations of Genetree  using a Genetree  to collaGenetree . The simneration  and founneration . To obtaTrichuris from pigs and humans as monophyletic groups. Different mutation models were used and the final analysis was done using strict molecular clock with a normal distributed substitution rate of 1.6 X 10−7(±0.3 X 10−7) based on the value of C. elegans [BEAST v.1.6.1  was used elegans . The sub elegans  in jMode elegans . Yule pr elegans . Tree An elegans  was used elegans  was usedTrichuris populations using IMa2 based on an isolation and migration model [T. trichiura, t (the upper bound of splitting time) = 295, q (upper bound of population size) = 750, while for T. suis t = 40, q = 100. For both data sets, HKY substitution model was used, no migration between populations after splitting was assumed (m = 0), 20 Markov chains with geometric heating scheme  and 106 burn-in steps with 105 sampling genealogies were used. Three independent runs were conducted with different seed numbers to assess the convergence.Divergence time was also estimated for the human and pig on model  using thTrichuris populations obtained from humans, non-human primates and pigs from various geographical regions (Table rrnL) and NADH dehydrogenase subunit 1 (nad1) were generated for all worms except for Trichuris from African green monkey, for which only the rrnL gene was sequenced (GenBank accession numbers: KU524489-KU524606 and KU524607-KU524730). The phylogenetic relationship was found to be identical when inferred using neighbor joining (NJ) and maximum likelihood (ML) clustering methods and for both genes, and hence only the NJ tree for the rrnL gene is shown in Fig. nad1 gene and Tamura-Nei with gamma distribution (TrN + G) model for the rrnL gene as the best-to-fit substitution models. It is noteworthy that two samples  showed double peaks in the chromatogram of the nad1 gene and gave conflicting signals for the rrnL gene (clustered in the pig clades). This may indicate co-amplification of nuclear mitochondrial pseudogenes (numts) or a heteroplasmy. Hence, these samples were excluded from further analyses.We used two mitochondrial markers to infer the genetic relationships and the evolutionary history between different ns Table . PartialT. suis populations was observed as worms from Uganda and from China were found in separate clades whereas T. suis from Denmark and USA clustered together. The Ecuadorian T. suis were found in two clades, namely the Denmark & USA clade (4 samples) and the China clade (3 samples). In addition, two pig worms from Uganda clustered with T. suis from Denmark & USA. Phylogeographic structure was also observed for worms recovered from humans as T. trichiura from Uganda, China and Ecuador were found in separate clades. The majority of the baboon and green monkey worms clustered with the human T. trichiura from Uganda. Additionally, a few baboon samples (n = 7) and a single green monkey worm grouped together in a different clade (Trichuris non-human primates). The Neighbor-net network identified splits that correspond to the clades in the NJ tree  between Trichuris populations and is summarized in Tables T. suis from Ecuador as they cluster both with worms from China and Denmark. In general all the populations were highly differentiated as Fst values are above 0.25. Trichuris from baboons and green monkey in the two different clades (Trichuris African non-human primates and Trichuris African Primates) were highly differentiated with Fst values of 0.363 for rrnL (P < 0.01) and 0.471 for nad1 (P < 0.01). In contrast, human, baboon and green monkey Trichuris in the T. trichiura Afican clade represented undifferentiated populations . The genetic distances (p-distance) between and within each clade are given in Additional file nad1 and rrnL genes, respectively.AMOVA was used to analyse the degree of genetic differentiation  and 560,000 generations for T. trichiura . The time of divergence between the different populations as estimated by BEAST is given at each node in number of generations  algorithm for the coalescent simulations while BEAST and IMa2 each rely on correlated sampling (CS). As the IS algorithm is more suitable for data of low polymorphism [T. suis and T. trichiura populations. For T. suis there was an ancient split between the USA/Denmark populations and the China/Uganda populations  and a more recent split between the populations from China and Uganda . For T. trichiura, the first split is between the Uganda population and China/Ecuador populations  and a more recent split between the China and Ecuador populations .The estimated Θ was 20.7 ± 6.68 for morphism , its estTrichuris from primates and pigs from different geographical areas in order to infer the evolutionary and demographic history of the parasites, and to identify possible environmental and anthropological factors driving their spread across the globe. In this study Trichuris from humans and pigs were genetically very distinct with independent demographic histories as summarised in Fig. Herein, we investigated the evolutionary and genetic relationships between populations of T. suis populations. First an ancient split between the DK/USA and the Chinese/Ugandan populations around 80,000 generations ago with a second more recent divergence between the China and Uganda populations 32,000 generations ago. Interestingly, this latter split between T. suis from China and Uganda is in line with that of their host [T. suis populations of China and Uganda and assuming one to three generations for T. suis per year, the divergence between Chinese and Uganda T. suis population happened 32,000-10,700 years ago. Hence, it is unlikely that T. suis was introduced by an European intermediary only a few hundred years ago but may be traced back to the introduction of domesticated pigs from the Far East thousands of years ago. However, this does not exclude other waves of recent introduction of T. suis in domesticated pigs from the Far East. A recent study found that A. lumbricoides in humans on Zanzibar were closely related to worms from Bangladesh [The coalescent analysis identified two divergence events in eir host . Common eir host , 38. Fireir host . Given tT. suis population in Uganda was found to be monomorphic for both markers. This may relate to either a founder effect  or a selective sweep  or a combination of both factors. Such a selective sweep might have occurred due to new adaptations to host physiology in the new environment or, more likely, due to a recent bottleneck in the pig populations: e.g. African swine fever outbreaks in Uganda [Intriguingly, the n Uganda  resultedn Uganda . HoweverT. suis from Denmark and China. However, the divergence time between the pigs in China and Europe has been estimated to be roughly a million years ago [T. suis populations in our study . In principle, when populations suffer from many bottlenecks, the coalescent will date back to the most recent bottleneck rather than the most recent common ancestor [T. suis can survive in the environment for at least 11 years [Several studies have reported high genetic distinctiveness between European and Chinese pigs , 42 in lancestor . Conside11 years  the numbT. suis from Denmark and USA were found to cluster together  divergence between Uganda and China and a more recent one between China and Ecuador . Assuming the highest and lowest number of generations to be 1 and 3, respectively the divergence times were 500,000-160,000 generations and 120,000-40,000 generations for the Ugandan/Chinese and Chinese/Ecuadorian populations, respectively. The split between the China and Uganda populations preceded the modern human (Homo sapiens) migration out of Africa to South East Asia around 60,000-100,000 years ago [H. erectus) may have transmitted T. trichuris when migrating out of Africa [C. elegans) may not be applicable to parasitic nematodes as the mutation rate normally is higher for the latter [T. trichiura eggs in Brazil dating back to 6000–7000 BP [As for pig worms two divergence events were also observed for human derived ears ago , 46 and ears ago . There af Africa  but thise latter . T. tric–7000 BP , which iTrichuris African non-human primate), suggesting that different populations are circulating among these hosts’ species although they were sampled from the same habitat. Since many of these worms were sampled from unnatural habitats (zoos), the causes for this differentiation could not be investigated. However, in a sympatric natural transmission area in Uganda some Trichuris genotypes were found among all seven investigated non-human primates, whereas other genotypes seem to be more host specific [Trichuris in primates in Africa may have been isolated either by geography or by host species leading to population differentiation followed by a more recent secondary sympatry [Trichuris population between humans from Uganda and the non-human primates are undifferentiated this suggests continuous or recent gene flow between the host species and suggests Africa as the origin of Trichuris in primates.Green monkeys, olive, yellow and hamadryas baboons are indigenous species in central and western Africa and the introduction of the green monkey into Saint Kitts by the French in the late Seventeenth century  may explspecific . Hence, sympatry . As the Ascaris in humans and pigs took place during animal domestication in the Neolithic period 10,000 years ago [Ascaris among non-human primates is observed rarely [Trichuris and Ascaris share several parasitic traits such as mode of transmission and infection and these two parasite species therefore usually co-occur today [Unlike whipworms, it is expected that host shift of the giant roundworm ears ago , 12. Thiears ago , 54 and d rarely , suggestd rarely . Their dur today .Trichuris from humans and pigs and their potential evolutionary epicenter, namely in primates in Africa. We suggest that Trichuris was dispersed to Asia with human ancestors and that host switching to pigs occurred in China where pigs evolved [T. suis was then spread across the globe mainly by anthropogenic factors. We found that T. trichiura in humans in Africa is genetically similar to Trichuris in non-human primates of African origin suggesting that Trichuris in humans represents a heirloom parasite. Further studies should investigate the genetic relationships of whipworms from different primates and other host species living in natural habitats in order to explore their demographic history and evolutionary origins and to identify the possible switching events between host species. Moreover, since we relied only on mitochondrial markers to resolve the evolutionary history of the parasite which in certain cases might be problematic due to the presence of the mitochondrial pseudogenes (numts), heteroplasmy or incomplete lineage sorting [We inferred the possible demographic history of  evolved . T. suis sorting  further


CYP19A1 facilitates the bioconversion of estrogens from androgens. CYP19A1 intron single nucleotide polymorphisms (SNPs) may alter mRNA splicing, resulting in altered CYP19A1 activity, and potentially influencing disease susceptibility. Genetic studies of CYP19A1 SNPs have been well documented in populations of European ancestry; however, studies in populations of African ancestry are limited. In the present study, ten ‘candidate’ intronic SNPs in CYP19A1 from 125 African Americans (AA) and 277 European Americans (EA) were genotyped and their frequencies compared. Allele frequencies were also compared with HapMap and ASW 1000 Genomes populations. We observed significant differences in the minor allele frequencies between AA and EA in six of the ten SNPs including rs10459592 (p<0.0001), rs12908960 (p<0.0001), rs1902584 (p = 0.016), rs2470144 (p<0.0001), rs1961177 (p<0.0001), and rs6493497 (p = 0.003). While there were no significant differences in allele frequencies between EA and CEU in the HapMap population, a 1.2- to 19-fold difference in allele frequency for rs10459592 (p = 0.004), rs12908960 (p = 0.0006), rs1902584 (p<0.0001), rs2470144 (p = 0.0006), rs1961177 (p<0.0001), and rs6493497 (p = 0.0092) was observed between AA and the Yoruba (YRI) population. Linkage disequilibrium (LD) blocks and haplotype clusters that is unique to the EA population but not AA was also observed. In summary, we demonstrate that differences in the allele frequencies of CYP19A1 intron SNPs are not consistent between populations of African and European ancestry. Thus, investigations into whether CYP19A1 intron SNPs contribute to variations in cancer incidence, outcomes and pharmacological response seen in populations of different ancestry may prove beneficial. CYP19A1) encodes the enzyme aromatase, which catalyzes the conversion of the C19 androgens, androstenedione and testosterone, to estrone and estradiol, respectively   . Thus, wal women ,24, predal women , and sigal women  and endoal women ,24. Due CYP19A1 SNPs, rs10459592, rs12908960, rs1902584, rs2470144, rs1961177, and rs6493497, were significantly different between populations of European and African ancestry from Arkansas and when compared to international HapMap populations. Similar findings have also been reported in populations of South Indian (SI) and Korean (KOR) origin, respectively [et al., [et al. [CYP19A1 intronic SNPs were significantly different in SI and KOR populations, respectively, compared with HapMap populations of similar ethnicity. Using Taqman SNP genotyping assays on 163 healthy subjects of South Indian origin, Umamaheswaran et al observed significant differences in the minor allele frequencies for rs10459592, rs749292, and rs6493497 when compared to HapMap populations [CYP19A1 between ethnic groups stress the importance for considering ancestral differences when determining causal SNPs for disease association studies.The allele frequencies of six of the ten et al demonstrated that the rs749292 CYP19A1 SNP was a predictor of circulating estrogen levels in white women of primarily European descent [For instance, Haiman  descent . In the  descent . Another descent . In our  descent , which fStratification of AA and EA populations by ethnicity and region also revealed significant differences in haplotype frequencies and LD patterns that were unique to EA and also those common to both populations. Two large LD blocks of 58Kb and 19Kb were observed among EA while a smaller 9Kb LD block was observed in AA. The haplotype blocks clustered in smaller blocks among AA population compared to the EA population, which showed evidence of haplotypes clustering in larger blocks. This feature is most likely attributed to populations of African ancestry that have higher effective population size and genetic diversity. Furthermore, similar LD patterns in the human genome across populations have also been reported previously ,28. TherCYP19A1 SNPs between populations of African and European ancestry from Arkansas. CYP19A1 is critical for estrogen biosynthesis, thus, identifying genetic variants in CYP19A1 is necessary for assessing cancer risk and predicting response to aromatase inhibitor drugs across ethnically diverse and disparate populations. Furthermore, because CYP19A1 contains thousands of intronic SNPs some of which may lie in regulatory regions that may independently alter the function of the enzyme, analysis of low-frequency SNPs and identifying rare haplotypes among ethnic groups that have higher disease risk is critical for understanding the influence of SNPs which define an individual’s genetic background within or adjacent to functional domains that may influence drug response and disease risk.In summary, our results provide evidence of ethnic differences in the frequency of