FST, TreeMix, identity by decedent (IBD), principal component analysis (PCA), and qualitative f-statistics . We found that Liaoning Manchus have a close genetic relationship and significant admixture signal with northern Han Chinese, which is in line with the cluster patterns in the haplotype-based results. Additionally, the qpAdm-based admixture models showed that modern Manchu people were formed as major ancestry related to Yellow River farmers and minor ancestry linked to ancient populations from Amur River Bain, or others. In summary, the northeastern Chinese Manchu people in Liaoning were an exception to the coherent genetic structure of Tungusic-speaking populations, probably due to the large-scale population migrations and genetic admixtures in the past few hundred years.Manchu is the third-largest ethnic minority in China and has the largest population size among the Tungusic-speaking groups. However, the genetic origin and admixture history of the Manchu people are far from clear due to the sparse sampling and a limited number of markers genotyped. Here, we provided the first batch of genome-wide data of genotyping approximate 700,000 single-nucleotide polymorphisms (SNPs) in 93 Manchu individuals collected from northeast China. We merged the newly generated data with data of publicly available modern and ancient East Asians to comprehensively characterize the genetic diversity and fine-scale population structure, as well as explore the genetic origin and admixture history of northern Chinese Manchus. We applied both descriptive methods of ADMIXTURE, fineSTRUCTURE,  After the Jin Dynasty was annihilated by the Mongols in 1234 AD, the surviving Jurchen people gradually developed into Manchu. When Manchu regained control of Manchuria, they moved further south and gradually controlled all sections of China, and they were suggested to have left detectable genetic imprints on the modern north and northeast Chinese, especially in the Qing Dynasty  and single-nucleotide polymorphisms (SNPs) on the autosome , Y-chromManchu language belongs to the Tungusic group of the Altaic language family, but previous genetic studies indicated that Manchus were genetically different from other Tungusic-speaking groups, which is probably due to the genetic influence from surrounding Han Chinese into Manchus . Chen et® Global Screening Array (GSA) covering 659,509 SNPs to genotype targeted SNPs in Manchus. Genotype calling was carried out following the default parameters. Raw data were initially filtered using PLINK 1.9  in China, South Siberia, and Southeast Asia (We merged our newly genotyped data with previously published modern and ancient population data from the Affymetrix Human Origins (HO) Array dataset  and the ast Asia . Ancientast Asia . We alsoast Asia . Finallysmartpca, part of the EIGENSOFT package  was carried out using  package . The addDMIXTURE  to conduDMIXTURE  with theDMIXTURE .FST genetic distance between Liaoning Manchu and other included modern and ancient reference populations using smartpca of EIGENSOFT package  to examine the shared genetic drift between Liaoning Manchus and non-African reference populations. Central African of Mbuti was used as the outgroup. We also used the f3-test in the form of f3  to formally test whether there was an admixture signature in Liaoning Manchus with different source pairs from modern and ancient eastern Eurasians. Negative values with the absolute Z-score of less than −3 indicated the included two source-related populations may be the plausible ancestral sources. We applied the f4-test in the forms of f4  and f4  to estimate the differentiated allele sharing between Liaoning Manchus and other representative sources compared with focused comparative subjects, where X and Y are the included ancient and present-day populations. The tested form f4  was designed to examine the differentiated genetic ancestry between Manchus and X or Y, in which significantly negative values indicate more shared alleles between Y and Manchus compared with X, significantly positive values indicated more shared alleles between X and Manchus relative to Y, and nonsignificant values (Z-scores ranged from −3 to 3) indicated that X and Y formed one clade compared with the Manchus  to calculate the pairwise shared IBD . ChromoP5, and we used in-house scripts to assign the Y-chromosomal paternal lineage following the basic regulations reaccommodated via the International Society of Genetic Genealogy (2018)6.The mtDNA haplogroups were classified using HaploGrep2  based onTo understand the general pattern of the genetic structure of Liaoning Manchus, we first performed PCA to explore the two-dimensional genetic relationship between Manchus and other reference eastern Eurasians. We found that the observed genetic clusters were consistent with the geographic and linguistic categories within East Asia . We obseK = 5   to detect possible admixture signatures, in which X and Y were modern or ancient populations that might be the candidate sources for modeling the admixture of Liaoning Manchus. We observed significant signals of admixture (Z < −5) in the Liaoning Manchus when using CHS or Dai as the southern source and present-day Oroqen, Yakut, Buryat, and Hezhen or ancient Mongolia populations as the northern ancestral source. We listed the Z < −5 in the To further investigate the genetic origin and admixture of Liaoning Manchus, we performed outgroup-; Mbuti) , we founf4-statistics to explore genetic substructure between studied groups and other modern/ancient populations in the forms of f4  and f4  ; and we found significant negative values when X was the Tungusic and Mongolic people, suggesting that Liaoning Manchus harbored more Tungusic-related ancestry than did Han Chinese , suggesting that Manchu people shared more alleles with southern East Asians compared with Tungusic and Mongolic people in the Amur River Region. We also observed significant negative values in f4  when X represented Han, Dai, Tujia, or other southern populations, suggesting that there was gene flow from the southern part of China into Manchu after the Xianbei period. When X was ancient Eurasians, Liaoning Manchus shared more derived alleles with populations from Yellow River Basin and West Liao River Basin  . When co Chinese . We furter Basin .FST, and f-statistics; Liaoning Manchus and Guizhou Manchus had experienced genetic influence from surrounding Han Chinese after they were separated from northern Tungusic- and Mongolic-speaking ancestors.In the TreeMix analysis , we founqpWave and qpAdm methods to further infer the possible ancestral populations and estimate the admixture proportions. We used the related available ancient northern populations  as the northern sources, used all available ancient populations  as the source of Yellow River sources, and used Iron Age Hanben (Hanben_IA) and Gongguan samples from Taiwan and Neolithic southern populations  as the southern sources. We observed that Manchus can be modeled as deriving 32.4% ancestry from Mohe people and the remaining ancestry from the farming-related ancient populations in the Yellow River Basin  in the Southern and Northern Dynasties period.We successfully identified 34 uniparental Y-chromosome lineages and 93 mtDNA lineages in Liaoning Manchus as shown in the FST genetic distances. We found that Manchus shared the most IBD with Han Chinese but had the smallest FST genetic distance with northern East Asians  project. We performed the IBD-based clustering and calculated the pairwise Previous genetic studies have found that northern Han Chinese from Liaoning and Jilin Provinces had genetic admixture with Liaoning Manchus from the analysis of Y-chromosome and autosomal STRs . In this8 . Northeastern China was the hometown of Manchus, and the first emperor of the Qing Dynasty was born there. With the establishment of the Qing Dynasty (see text footnote 2), Manchuria did not allow people who were non-Eight Banner to enter. Manchuria was vast and rich in material but sparsely populated. Therefore, many Han Chinese who lived in Hebei and Shandong Provinces left their hometown and went to Manchuria for survival because of various reasons such as multi-year natural disasters and shortage of food  (420–589 AD). It was a turbulent period of war and also a period of large-scale population admixture. At that time period, the Xianbei population controlled the Amur River Basin, Mongolian Plateau, and southern Siberia, and they became the largest ruling power in the North. In this study, we can also model Manchus deriving ancestry from the Xianbei people.Previous studies have found strong associations between population genetic structure and linguistic similarity in Asia, and the populations from the same language group have a closer genetic affinity . MongoliqpAdm, qpWave, f-statistic, FST, ALDER, IBD, fineSTRUCTURE, and TreeMix to investigate the complex genetic history and dynamic admixture process of northern Chinese populations. Our previous study documented a long-term genetic continuity from Neolithic hunter-gatherers to present-day Tungusic-speaking people in northeast Asia. It is therefore believed that the Manchu people, being members of an old branch of the Tungusic, should have a consistent genetic profile with other Tungusic groups. However, we found that Liaoning Manchus have a close genetic relationship and significant admixture signal with Han Chinese. The Manchu population was an exception to the coherent genetic structure of Tungusic-speaking people, probably due to the large-scale population migrations and genetic admixtures in the past hundred years.We reported the first research-based genome-wide SNP data of the Manchus from Liaoning Province. We used comprehensive population genetic analyses of PCA, ADMIXTURE, The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by Our study and sample collection were reviewed and approved by the Ethics Committee of Jinzhou Medical University (JZMULL2021010) and followed the recommendations provided by the revised Helsinki Declaration of 2000. The patients/participants provided their written informed consent to participate in this study.YoW and C-CW designed the study. XZ and GH analyzed the data and wrote the manuscript. WL, YuW, YiW, and HX carried out the sample collection. XL, YC, and QQ conducted the experiment. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mediterranean trout is one of the most threatened freshwater fish at risk of extinction. A complex pattern of climatic and anthropogenic pressures has dramatically compromised its biodiversity. In particular, the introduction of non-native trout represents one of the most important threats with negative impact on intraspecific diversity of native populations. The introgressive hybridization between native and alien trout reduces the fitness of native trout and creates hybrid swarms, resulting in native genome extinction. Recently, several conservation projects have been proposed to restore the genetic integrity status of native Mediterranean trout. In this study, we report the first use of the Affymetrix 57 K rainbow-trout-derived SNP array in research on Italian Mediterranean trout populations. The results provide insight into the genetic relationships and spatial distribution of two trout populations inhabiting the Volturno and Biferno rivers  and provide useful information for the identification of a fine-scale genetic structure, as well as the determination of subpopulations and their related habitats. These data are crucial to undertake effective conservation and management strategies with the aim to preserve native trout and recover autochthonous genetic heritage in such rivers. Overall, our outcomes support the use of the rainbow-trout-derived SNP array to identify SNPs that are informative in relation to the Mediterranean trout genome.ST and overall F-statistic estimated by locus-by-locus analysis of molecular variance. Furthermore, an SNP data set was processed through principal coordinates analysis, discriminant analysis of principal components and admixture Bayesian clustering analysis. Firstly, our results demonstrated that rainbow trout SNP array can be successfully used for Mediterranean trout genotyping. In fact, despite an overwhelming number of loci that resulted as monomorphic in our populations, it must be emphasized that the resulted number of polymorphic loci  has been sufficient to reveal a fine-scale genetic structure in the investigated populations, which is useful in supporting conservation and management actions. In particular, our findings allowed us to select candidate sites for the collection of adults, needed for the production of genetically pure juvenile trout, and sites to carry out the eradication of alien trout and successive re-introduction of native trout.Mediterranean trout is a freshwater fish of particular interest with economic significance for fishery management, aquaculture and conservation biology. Unfortunately, native trout populations’ abundance is significantly threatened by anthropogenic disturbance. The introduction of commercial hatchery strains for recreation activities has compromised the genetic integrity status of native populations. This work assessed the fine-scale genetic structure of Mediterranean trout in the two main rivers of Molise region  to support conservation actions. In total, 288 specimens were caught in 28 different sites (14 per basins) and genotyped using the Affymetrix 57 K rainbow-trout-derived SNP array. Population differentiation was analyzed using pairwise weighted F Salmo trutta complex) represents one of the five main genetic lineages for European brown trout [Salmo cettii syn. Salmo macrostigma [www.iucn.it, accessed on: 31 March 2021).The brown trout is a wide-ranging species, naturally distributed in Eurasia and North Africa, that exhibits high genetic, ecological and morphological variability . The Medwn trout . In Italrostigma ,5,6,7,8 rostigma ,7,9. Itsrostigma ,11, and In particular, the introduction of non-native trout represents one of the most important threats for Mediterranean trout ,12. The In this general context, it must be emphasized that planning an effective and evidence-based conservation project is a quite complex process  that notThe selection of hybrids based only on morphomeristic characteristics has been long recognized as insufficient to provide a clear taxonomic information/classification, and several biochemical or genetic methods have been traditionally adopted ,23. WithNevertheless, due to improvements in the speed, cost and accuracy of next generation sequencing, an increasing popularity of single nucleotide polymorphism (SNP) analysis is widely recognizable in many research areas, including evolutionary, ecological and conservation studies ,36. In pIn this work, we investigated the genetic integrity status of Mediterranean trout populations in the Biferno and Volturno rivers, two Central-Southern Italian basins with a different drainage pattern . Although trout inhabiting these two rivers are of particular concern for biodiversity conservation, no relevant conservation practices have been adopted in the past nor are there any previous genetic reports available. Thus, this study aimed to give insight into genetic relationships and spatial distribution of trout population inhabiting these rivers in order to provide useful information to be considered in conservation and management activities. In this research, the use of the 57 K rainbow-trout-derived SNP array (Affymetrix)  for the Sampling was carried out in the Biferno and Volturno rivers, the main basins of Molise . For each of these watercourses, sample sites were chosen based on accessibility, survey records, habitat suitability and river connectivity within the LIFE17 NAT/IT/000547 conservation Project area. In the study area, the main threats for native trout populations are represented by habitat alterations and activities of restocking with domestic trout mainly for recreational fishing purpose. In total, 14 interesting sites were identified for each basin , and 288®  following manufacturers’ protocols and quality checked by agarose gel and BioPhotometer .Individual genomic DNA was isolated from fin tissue fragments preserved in ethanol (~20 mg) that was carefully dissected with a sterile scalpel using DNeasy Blood and Tissue Kithttps://www.thermofisher.com, accessed on: 2 October 2020). A no-call threshold of 0.05 was applied and, to avoid ‘missing data’ problems, the 90% threshold for missing data was adopted. SNPs with a minor allele frequency (MAF) below the 0.01 threshold were discarded in order to prevent errors due to the presence of rare alleles. QC was performed considering all populations together and/or within each population separately to perform genetic analysis considering all or single basin samples.One hundred nanograms of genomic DNA for each sample were sent to the genotyping facility  for marker analysis. Genotyping was performed by the 57 K rainbow trout Axiom SNP array . Axiom Ap-value < 0.05 in Arlequin.The number of polymorphic loci, the mean number of alleles, and the observed and expected heterozygosity (Ho and He) for all loci and for each population were estimated in Arlequin 3.5.2.2 software . DepartuIS, pairwise weighted FST, and overall F-statistic [p-value < 0.05 in Arlequin. To test the role of barriers on the protection of brown trout native genetic diversity and to investigate the genetic structure within each basin, F-statistics were also applied considering each river separately. In particular, FST comparisons involving groups of samples according to the sampling locations were carried out. A sequential Bonferroni-type method was employed to correct for multiple significance tests. The two basins were also tested for patterns of isolation-by-distance (IBD), by using a Mantel test [ST matrices and geographic distances between sampling locations.Population-specific Ftatistic  were esttel test  with 10,With the aim to give insight into the genetic relationships and spatial distribution of the two trout populations, genetic distance among sampling locations was investigated by Principal Coordinates Analysis (PCoA) with GenAlex software v.6.503 ,50 consiBslI. The resulting restriction patterns distinguish homozygote Atlantic (Atl) or Mediterranean (Med) samples from the heterozygote hybrids. A 100 bp band identifies the Med allele, while a 90 bp band recognizes the allele of Atlantic taxa [RsaI endonuclease, which recognizes the Atl sequence and generates two differently sized fragments, whilst the absence of the RsaI restriction site in the Mediterranean lineage is revealed by a single electrophoretic band.All 288 samples were genotyped for 16 S rDNA, and LDH-C1* genes according to McMeel et al.  and Chietic taxa . The 16SIn total, 288 DNA samples were analyzed using a 57 K SNP array . No lociIS obtained by AMOVA was high (0.109) and statistically significant . The average FST over all 920 loci for the two studied populations was 0.121; in turn, pairwise FST was significant and reached 0.123, indicating a high level of differentiation. The highest percentage of variation was detected within individuals 78.3% . PCoA multivariate results showed a clear split between the analyzed populations and clustering of all samples  was detected for the B8 vs. B12 comparison, whereas the lowest (0.025) in B1 vs. B9 comparison . PCoA multivariate results helped to identify sampling sites with distinct genetics  and values ranged from 0.00 to 1.00, whereas the He amounted to 0.235 (±0.185) and ranged from 0.007 to 0.502 . In totamparison . Pairwisgenetics . In thisgenetics , which pgenetics . Multivagenetics  and cleagenetics . This scgenetics . In partgenetics  and it wST over all 828 loci for the Volturno river population was 0.120; in turn, the highest and lowest significant pairwise FST reached 0.437 in V2 vs. V8 comparison and 0.048 in V4 vs. V12 comparison, respectively , suggesting the presence of genetic discontinuity. PCoA multivariate results helped to identify sampling sites with distinct genetics  and 0.200 (±0.165), and ranged from 0.00 to 1.00 and from 0.007 to 0.502. In total, 320 monomorphic loci  resulted, 131 of which in common with the Biferno river . Focusinectively . The IBDgenetics . Multivagenetics . This scgenetics , which agenetics . Taken tThe introduction of non-native cultured fish has been recognized as one of the most important threats to native trout populations in many countries , includiAlong with the strictly related population genetics outcomes, from a general point of view, our results firstly demonstrated that an SNP array derived from rainbow trout genome can be successfully used for Mediterranean trout genotyping. Indeed, ~63% of total loci were successfully amplified and passed quality control when DNA of Mediterranean trout was genotyped. Furthermore, despite a rather small number of polymorphic loci being identified in our populations , it must be recognized that such number has been sufficient to reveal a fine-scale genetic structure and to separate the trout populations under study as shown by ADMIXTURE, PCoA and DAPC analyses . ObtaineST value was significant and reached 0.123 across all 920 polymorphic SNPs. This divergence was also well-represented in PCoA results by a marked split between the two analyzed populations , a more complex pattern was detected in Volturno, where K = 4 was the optimal genetic cluster repartition . The insST and geographic distance matrices for the Volturno river. Although peculiar processes that maintain genetic differentiation in the Volturno when physical barriers are lacking would merit further attention, no eradication strategy was proposed for Volturno tributaries’ locations in order to preserve this peculiar genetic structure.This situation was further complicated in the Volturno river by the presence of a third autochthonous genetics, mainly associated with the V9 and V10 stations . In lighIn summary, the genetic characterization of the trout population in the Biferno and Volturno rivers allowed us to appreciate subpopulation structure and helped to define sites for the collections of pure adult spawners and sites to carry out the eradication of alien trout and successive re-introduction of Mediterranean trout. The complex pattern of naturally isolated and connected subpopulations of Mediterranean trout described in this study demonstrated the importance of considering the fine-scale genetic structure in a conservation management project.This study reported the first use of SNP-array technology in research on Mediterranean trout populations in Italy. In particular, this paper aimed to explore the trout genetic structure of Biferno and Volturno rivers , in order to assist trout conservation and management activities. The expected scenario in such rivers was that anthropogenic translocation of non-native trout has altered the native trout biodiversity.Firstly, our results suggested that rainbow trout 57 K SNP array can be effective in the identification of informative SNPs in the Mediterranean trout genome, which is useful for fine-scale population genetic research. Specifically, our genetic structure outcomes allowed us to appreciate a complex metapopulation pattern notably in the Volturno river, whereas a more homogenous scenario was observed in Biferno, suggesting a high migration rate and gene flow among its subpopulations.The fine-scale genetic characterization of these populations allowed us to select sites for the collection of adults and spawners, as well as sites to carry out the eradication of alien trout. Overall, our data provided


Schistosoma mansoni parasites form genetic clusters that coincide with the communities of their human hosts. We also looked for genetic clustering of parasites at the sub-community level. Our data consists of 14 microsatellite DNA markers, typed from pooled DNA samples from S. mansoni in Brazil juxtaposes local isolation that is occasionally broken by long-range migration. Permanent eradication of schistosomes will require both local efforts and the identification of regional infection reservoirs.Patterns of diversity in pathogen genomes provide a window into the spatiotemporal spread of disease. In this study, we tested the hypothesis that  Schistosoma mansoni is one of six species of parasitic trematode worms that cause the disease in humans. This species infects millions of people throughout sub-Saharan Africa, the Arabian Peninsula, Madagascar, the Caribbean, and South America2. Population genetic analysis of whole genome sequences indicates that S. mansoni emerged in Africa as a distinct species approximately 126.5 thousand years ago when it split from a common ancestor with its congener S. rodhaini. Schistosoma mansoni was dispersed to the Americas within the last five centuries by the trans-Atlantic Slave Trade3.Schistosomiasis is an important cause of mortality globally that may account for up to 200,000 deaths annuallyS. mansoni life cycle requires two hosts, humans, where sexual reproduction takes place, and freshwater snails in the genus Biomphalaria spp., where the parasite reproduces asexually. Since the parasite is long-lived in the human host and the snail life span is only about three months, most of the parasite’s genetic potential resides in the human population. Adult worms live and reproduce in male-female pairs in the mesenteric veins that drain the host’s intestines. Female worms release fertilized eggs into the host’s blood stream until they lodge in tissues or pass through the intestinal wall and are expelled in feces4. Upon reaching fresh water, eggs hatch and release miracidia that infect the snail intermediate host, multiply asexually, and mature into the cercarial stage which leaves the snail. The cercariae penetrate human skin when contact is made with contaminated water4. Schistosomiasis is most common in rural communities. However, migration of people from the countryside to cities has resulted in presence and transmission of schistosomaisis in urban environments6.The 9. Schistosoma mansoni has a multitiered population structure. The convention among schistosome researchers is to refer to the worms inhabiting a single person as an “infrapopulation”10. The number of worms in an infrapopulation is difficult to measure. An autopsy study of 96 people found an average of 45.5 pairs per person with a strong positive skew11. Recent studies employing indirect methods produced similar, albeit somewhat higher, estimates of infrapopulation sizes13. The next level of population structure is the “component population” which is the collection of infrapopulations in a given geographic location10. Gower and colleagues conducted two studies of S. mansoni population genetic structure in Africa14. Both studies document high diversity within and between infrapopulations within component populations. One study on pan-African populations found low differentiation between children in different schools within the same country, and substantial differentiation between infrapopulations in school children living in different countries14. The other study found low differentiation between nearby local schools in Kenya but high differentiation when comparing the same schools before and after mass praziquantel treatment7. A study of S. mansoni in four geographically separated communities in Ethiopia12 found moderately high differentiation among communities. A study of S. mansoni in Brazil found significant differentiation between schistosomes in two rural communities, although these communities are separated by only 8 km and connected by a highway. An analysis of measurable epidemiological variables in the Brazilian study’s participants did not reveal factors that contribute to isolation or connections between the schistosome populations15. In summary, schistosome transmission dynamics are likely to be highly influenced by local factors and to differ by place.Population genetic analyses can provide insight into transmission patterns and responses by schistosome populations to control programsS. mansoni carried by infected residents of three communities located in the State of Bahia, Brazil. The first community is the urban neighborhood of São Bartolomeu, which is located in the City of Salvador. The second and third communities, Jenipapo and Volta do Rio, are rural communities located 200 km southwest from Salvador in the municipality of Ubara. We analyze allele frequency profiles for a panel of 14 microsatellite loci for each infrapopulation. The allele frequency profiles were obtained from assays of pooled DNA samples according to previously published methods16. We begin by computing Nei’s hierarchical gene differentiation statistics for infrapopulations and communities18. However, the genetic structure of S. mansoni in Brazil is complicated, perhaps by repeated founder effects caused by its recent introduction and range expansion. We augment the simple gene differentiation statistics using cluster analyses and principal coordinates.In the present study, we perform a detailed analysis of patterns of genetic differentiation and relatedness in e.g.,The 14 microsatellite loci that we analyze here are highly polymorphic in the three Brazilian communities. The number of alleles per locus ranges from 4 to 15 with a median of 10.14  are surprisingly similar despite the differences among infrapopulations within communities  are quite variable across loci. However, the multiple locus averages show that allele frequencies are significantly different at the level of infrapopulations within the same community G-statistic values are notably higher for the loci that harbor the diagnostic alleles.Nei’s genetic differentiation coefficients . Cluster analysis with To search for further levels of genetic structure we applied K-means cluster analysis with entclass1pt{minimaThe fact that one coordinate (PC2) reveals both Cluster 4 and Cluster 5 is unexpected. The reason for this is that the vast majority of members of Cluster 4 are concentrated on the neighbor joining tree as descendants of a single branch that emerges from São Bartolomeu, while most members of Cluster 5 are concentrated on a single branch that emerges from Jenipapo. Thus, the neighbor joining tree gives the appearance that these two clusters are independently evolving lineages. We take two steps to evaluate the possibility that Cluster 4 and Cluster 5 have a stronger genetic relationship than the neighbor joining tree reveals.The first step in our evaluation is to compare the observed genetic distances with genetic distances derived from the neighbor-joining tree. We have computed the latter distances by taking the sum of all branch lengths connecting a pair of infrapopulations. Random deviations from a tree-like structure will produce both positive and negative differences between the observed genetic distances and the tree distances. It is note worthy here that the tree distances are larger than the observed genetic distances for all 377 pairs of infrapopulations from Cluster 4 with Cluster 5. In other words, the tree over-estimates genetic distances in comparisons between a member of Cluster 4 and a member of Cluster 5  at three levels of population structure. Our G-statistics are based on the multiple locus averages  an infected outsider visited both communities, or (b) members of both communities traveled to the same location, acquired their infections, and then returned home. We find that the first two scenarios are less compatible with the pattern in the gene identity matrix than the third scenario. Our reasoning is as follows, suppose that a randomly chosen subset of infrapopulations in São Bartolomeu were to reproduce in Jenipapo, then we would see increased gene identity between those infrapopulations in São Bartolomeu and their descendants in Jenipapo, but the donor infrapopulations would not stand out as a unique cluster in São Bartolomeu. We would not see Cluster 4 appear as a dark block in the matrix. The second scenario would produce the reciprocal pattern. There would be increased gene identity between the donor lineages in Jenipapo and their descendants in São Bartolomeu, but not a distinct block within Jenipapo. Finally, the third scenario has more potential to create subclusters of infrapopulations with high gene identity within sampled communities and high gene identity between subclusters in different communities. This is the pattern of gene identity displayed by the matrix. Importantly, the geographic separation of São Bartolomeu and Jenipapo (200 km) requires that the humans who transferred the parasites traveled over a great distance. However, our data do not allow us to distinguish between possibilities (a) and (b) noted above.24 and gene flow would have homogenized the frequencies of the diagnostic alleles. We have not yet identified the source of outside migration, but that should be an objective of furture investigations of schistosome populations in Bahia.We wish to emphasize that our reasoning in favor of the scenario involving outside migration is heuristic, as we have not rejected any migration scenario using a formal statistical test. Nevertheless, there are several other hints that unsampled genetically diverse schistosome populations have influenced the allele frequency profiles in the three communities that we have sampled. First, the distribution of diagnostic alleles in Volta do Rio and São Bartolomeu is incompatible with gene flow. Even a low level of mixing between these three populations would have been sufficient to place all allelic types in each community. Second, the distribution of diagnositic alleles is also incompatible with a phyletic process. If the branching pattern of the neighbor-joining tree Fig.  represenIn conclusion, the work presented here demonstrates how population genetic studies can reveal unique aspects of schistosomiasis transmission. We found that parasite infrapopulations are genetically differentiated among human communities in Brazil, including those that are in close geographic proximity. In addition, we discovered genetically differentiated parasite subgroups within communities. Taken together, these two findings indicate that residential areas serve to genetically isolate schistosome populations. However, we have also found strong evidence that the overall pattern of isolation is incomplete and broken by occasional long-range migration. Such long-range migration must be human mediated because the snail hosts are highly sedentary. The migration of schistosomes from one location to another does not require that infected humans change their residences. It is possible that itinerant activities, either professional or recreational, are responsible for the transfer of schistosomes. A surprising finding is the genetic relationship between subgroups in widely separated human communities. This suggests the existence of transmission centers that have wide influence. At the very least, our work shows that permanent eradication of schistosomes in particular communities will require both the identification of regional infection reservoirs and intensive action within communities. While the microsatellite data that we have used supports the present findings and conlclusions, we note that full genome data may be helpful in future analyses that focus on the fine details of Schistosome population structure and transmission dynamics.Schistosma mansoni carried by infected residents of three communities located in the State of Bahia, Brazil. The first community is the urban neighborhood of São Bartolomeu, which is located in the City of Salvador. The population of São Bartolomeu is approximately 6,000 people. Some 1,423 residents of São Bartolmeu over 4 years of age and living closest to the Cobre River were contacted. Each responded to a demographic questionnaire and parasitologic examinations. The second and third communities, Jenipapo and Volta do Rio, are rural communities located 200 km southwest from Salvador in the municipality of Ubara. The populations of Jenipapo and Volta do Rio were 482 and 367, respectively. All inhabitants over 1 year of age in these communities were invited to participate and a parasitologic examination was administered to those who agreed to particiapte. Fecal samples were processed by the Kato-Katz method and examined microscopically to identify ova of S. mansoni. Each stool positive for S. mansoni ova was homogenized, and DNA was extracted using a standard phenol-chloroform extraction procedure followed by treatment with hexadecyltrimethylammonium bromide (CTAB) to remove PCR inhibitors. DNA from fecal samples was analyzed for N = 219 infrapopualtions in São Bartolomeu, N = 80 in Jenipapo, and N = 38 in Volta do Rio.The focus of this study is 16. The 14 loci used in this study were SMMS2, SMMS13, SMDA23, SMU31768, 15J15A, 29E6A, SM13-478, SMMS3, SMMS18, 1F8A, 13TAGA, SMMS16, SMMS21, and SM13-410.Following PCR amplification and capillary electrophoresis, allele frequency profiles for 14 microsatellite loci were constructed for each fecal sample. The microsatellite loci are a subset of a panel containing loci with tri- or tetra-nucleotide repeat motifs. The microsatellite panel and laboratory procedures were described recently15.As described in earlier publications, both data collections were approved by The Committee on Ethics in Research of the Oswaldo Cruz Foundation of Salvador, Bahia, the Brazilian National Committee on Ethics in Research and the Institutional Review Board for Human Investigation of University Hospitals Case Medical Center, Cleveland, Ohio. All subjects provided written informed consent, or in the case of minors, written consent was secured from their guardians. All aspects of the study were conducted according to the principles expressed in the Declaration of HelsinkiEach DNA sample from an infected person represents an infrapopulation of worms. Our primary unit of analysis is the 14 locus allele frequency profile from each infrapopulation. Complete data is required for one of our principal statistical methods (K-Means clustering). Therefore, we analyzed only those infrapopulations for which laboratory assays were successful at all 14 loci. While some of our other methods accommodate missing data, we prefer to perform all statistical analyses using the same data. Thus, the numbers of infrapopulations analyzed were N = 161 for São Bartolomeu, N = 69 for Jenipapo, and N = 24 for Volta do Rio.18. For a single genetic locus with allele frequencies measured for a collection of population subdivisions, the gene identity within the ith subdivision is estimated as kth allele in subdivision i. The summation is taken over all alleles at the locus. We calculated gene identity at three levels of population structure, G-statistics18. We compute J to represent the average of gene identities taken over all 14 loci. We used the bootstrap method to calculate 95% confidence intervals for the multiple locus estimates of gene identity and genetic differentiation coefficients25.We used Nei’s gene identity statistic as the first step to summarize patterns of genetic diversity within and between infrapopulation allele frequency profiles26 and others24 have shown that a matrix of gene identities, with elements We use the gene identity between infrapopulations to reveal fine-scale relationships. For a single locus, the gene identity between the ith and jth subdivisions for a single genetic locus is computed as 27. Principal coordinates calculated from Euclidean distances have been shown to be equivalent to principal components of the original data matrix28.We convert the gene identitfy matrix into a matrix of Nei’s minimum genetic distance to quantify the differentiation between population subdivisions. The minimum genetic distance between the K). We evaluated models with kmeans with the arguments iter.max = 20 and nstart = 50. These values are more stringent than the default values of iter.max = 10 and nstart = 1 and should insure that the numerical solutions are close to the optimum. We repeated analyses using these parameters of each K ten times and checked to see if the cluster partitions matched one-to-one. With K would detect substructure within the three sampling locations. Second, we compute an unrooted neighbor-joining tree from the minimum genetic distance matrix29. Once again, we were interested in whether or not the samples from the three communities would appear as distinct branches on the neighbor-joining tree. K-means and neighbor-joining take opposite approaches to clustering. Whereas, K-means is a divisive method that starts with the entire data set and divides it into K-subgroups, neighbor-joining is an agglomerative method that successively fuses pairs of groups until the entire data set is contained in one cluster. We were interested to see if these opposite approaches would find the same clustering structure within the entire set of 254 infrapopulations.We take two approaches that are independent of sampled communities to find clusters of infrapopulations that might reveal component population structure. First, we use K-means clustering to partition our data into a pre-specified number of groups (30. All other graphs were produced using R’s base graphics. The cmdscale and kmeans functions in the base package were used to compute principal coordinates and cluster analyses, respectively.We performed all data analyses using the R language and statistical computing environment. We wrote original functions for data manipulations and to calculate Nei’s genetic diversity and genetic distance statistics. We used the ’ape’ package to compute and plot the neighbor-joining treeSupplementary Figures.


Dioscorea opposita had the highest genetic diversity, followed by D. alata, D. persimilis, D. fordii, and D. esculenta. Among the 26 phenotypic traits, the Shannon diversity indexes of leaf shape, petiole color, and stem color were high, and the range in the variation of tuber‐related traits in the underground part was higher than that in the aboveground part. All accessions were divided into six groups by phenotypic trait clustering, which was also supported by principal component analysis (PCA). Molecular marker analysis showed that SSR and SRAP markers had good amplification effects and could effectively and accurately evaluate the genetic variation of yam. The unweighted pair‐group method with arithmetic means analysis based on SSR‐SRAP marker data showed that the 112 accessions were also divided into six groups, similar to the phenotypic trait results. The results of PCA and population structure analysis based on SSR‐SRAP data also produced similar results. In addition, the analysis of the origin and genetic relationship of yam indicated that the species D. opposita may have originated from China. These results demonstrate the genetic diversity and distinctness among the widely cultivated species of Chinese yam and provide a theoretical reference for the classification, breeding, germplasm innovation, utilization, and variety protection of Chinese yam resources.Yam is an important edible tuber and root plant worldwide; China as one of the native places of yams has many diverse local resources. The goal of this study was to clarify the genetic diversity of the commonly cultivated yam landraces and the genetic relationship between the main yam species in China. In this study, 26 phenotypic traits of 112 yam accessions from 21 provinces in China were evaluated, and 24 simple sequence repeat (SSR) and 29 sequence‐related amplified polymorphism (SRAP) markers were used for the genetic diversity analysis. Phenotypic traits revealed that  Dioscoreaopposita probably originated from China.Representative yam resources from five species were collected in 21 provinces in China. The genetic diversity of 112 yam accessions in China was evaluated roundly by combining phenotypic traits, SSR, and SRAP markers. Genetic difference between yam resources in China was identified roughly, and  The species of yams widely cultivated worldwide are D. bulbifera, D. panthaica, D. esculenta, D. japonica, D. trifida, D. pentaphylla, and D. rotundata , D. alata (41), D. persimilis (12), D. fordii (4), and D. esculenta (2), which are currently the main cultivated species in China . The DNA samples were quantified using a NanoDrop 2000  spectrophotometer and checked on 1.0% (w/v) agarose gels stained with ethidium bromide. Based on concentration estimations, all samples were diluted to 20 ng/μl and stored at −20°C.2.42O. The following cycling parameters were used in the amplification reaction: first predenaturation at 94°C for 5 min, followed by 40 cycles of 30 s at 94°C, annealing for 30 s at 54°C, and 30 s at 72°C, and a final extension of 10 min at 72°C. The amplified PCR products were detected on an 8% nondenaturing polyacrylamide gel. Silver nitrate staining was employed, and images were captured for analysis.Twenty‐four SSR markers Table  with pol2O. PCR amplification was performed under the following conditions: denaturation at 94°C for 5 min, five cycles of three steps: denaturation at 94°C for 1 min, annealing at 35°C for 1 min, and elongation at 72°C for 1 min. In the following 30 cycles, the annealing temperature was increased to 56°C, with a final extension step of 10 min at 72°C. The amplified products were analyzed through 3% agarose gel electrophoresis prepared in 1× TBE buffer. The gels were then visualized in a UV transilluminator  and photodocumented.Forty‐nine different SRAP primers were obtained from the combination of seven forward primers and seven reverse primers  was calculated in accordance with the distribution frequency as follows:ip represents the relative frequency of the ith phenotypic class of a trait , and coefficient of variation (CV) of six quantitative traits were calculated using SPSS 25.0 software. Then, in accordance with the overall average (x¯) and SD (σ), the quantitative trait data were divided into 10 levels, from the first level [Xi<(x¯‐2σ)] to the 10th level [Xi>(x¯+2σ)], in increments of 0.5 σ. In accordance with the phenotypic trait survey data, a matrix  was constructed, and the registration at the ith level of a trait was 1; otherwise, it was 0.The maximum, minimum, average, standard deviation (Na), effective number of alleles (Ne), allele frequency, Shannon's diversity index (I), observed heterozygosity (Ho), expected heterozygosity (He), and Nei's (1973) gene diversity index (H) of each SSR and SRAP primer were calculated with POPGENE software version 1.32 . Cluster analysis of the unweighted pair‐group method with arithmetic means of the phenotypic traits and molecular markers was performed using MEGA software version 4.1  was estimated to be in the range of 2–10, and the software was run ten times to determine this value. Estimates were obtained with the Markov chain Monte Carlo (MCMC) method with 100,000 iterations followed by a burn‐in period of 500,000 iterations. STRUCTURE HARVESTER  and absent (0) for each primer at each locus, and the binary matrix was constructed and statistically analyzed. The observed number of alleles , while the I values of stem thorn and twining direction were the lowest , and that in D. esculenta was the lowest (4.39%). The CV of leaf width in D. opposita was the highest (21.17%) and that of D. persimilis was the lowest (3.55%). The CV of the ratio of length‐to‐width of leaves was the highest in D. alata and the lowest in D. esculenta. D. persimilis had the highest CV of tuber length, and the highest CVs of tuber diameter and tuber fresh weight were both found in D. opposita. The CVs of tuber length, tuber diameter, and tuber fresh weight in D. esculenta were the lowest , followed by tuber length (89.92 cm), tuber diameter (15.75 mm), leaf length (13.39 cm), and leaf width  was employed to analyze the phenotypic traits Figure . As show3.23.2.1Na values (eight) were found using primers YM06 and YM08, with an average of four alleles per primer. The Ne values ranged from 1 to 6.429, with an average of 2.488. The mean I, observed heterozygosity (Ho), and expected heterozygosity (He) were 0.864, 0.422, and 0.441, respectively. The H values ranged from 0 to 0.845, with an average of 0.439  were D. fordii. In the G6 group, CY‐227 and CY‐23 were D. opposita, and the remaining accessions were D. alata. From the clustering results, D. alata also had high diversity and could be further divided into four subgroups.Based on the polymorphic band data of SSR, SRAP, and SSR‐SRAP, cluster dendrogram analysis was performed, and the clustering results of SSR and SRAP markers were relatively consistent Figure . Based oD. persimilis and D. opposita could be separated separately, while the accessions of D. alata and D. fordii are clustered together.The results of PCA were basically consistent with those of molecular marker cluster analysis Figure . The 1123.2.3K was at a maximum, the optimal K value was 2  and green representing the second subgroup (61 accessions). The first subgroup was a collection of all accessions of D. opposita, and the second subgroup was a collection of accessions of D. alata, D. persimilis, D. fordii, and D. esculenta. When K = 3, the accessions of D. opposita and D. persimilis were clustered into one group separately, whereas the other accessions were clustered into another group. At K = 4, the accessions of D. alata and D. fordii were mixed together to form a group, while the accessions of the other three species were grouped separately , and some phenotypic traits could be used for species identification. For instance, stem wings could be used effectively to identify Dioscorea is characterized by dioecy, and most important yam varieties are cultivated for their edible tubers and do not flower  and are dominant in nature  of 4 per marker locus, with a range of 1 to 7 alleles per marker locus, indicating the presence of haploids, diploids, triploids, tetraploids, pentaploids, and septaploids in the population. Sartie et al. , while Loko et al. (Na = 8.69). Nei's (1973) gene diversity (H = 0.439) in this study was higher than that in Siqueira et al.  but lower than that in Tostain et al. (D. rotundata (H = 0.500). In addition, the observed heterozygosity (0.422) was close to the expected heterozygosity (0.441), indicating that Dioscorea crops had a high level of genetic diversity , Nei's (1973) gene diversity (H = 0.216), the observed number of alleles (Na=1.990), and the effective number of alleles (Ne=1.342) were also similar to the results of Li et al.  and Wu .Molecular markers have been widely used in the genetic diversity analysis of different species , we preliminarily speculated that this group may be a new species or a new variant of D. opposita.Furthermore, the cluster analysis of 112 Chinese yam landraces based on the polymorphic band data of molecular markers was in good agreement with the cluster analysis of phenotypic traits. The difference between the two analyses was that the accessions of 4.2Dioscorea is considered to be a monophyletic group originating from a common ancestor , Hebei (CY‐94), Guizhou (CY‐104), Sichuan (CY‐124), and Jiangxi (CY‐153) provinces. This may be evidence that D. opposita may have originated in China and been domesticated from wild species.In this study, a total of 112 cultivars, landraces, and wild varieties of yam were collected in 21 provinces (cities) in China, and their genetic diversity was comprehensively evaluated. D. alata were collected in eight provinces in Southern China, accounting for 36.61% of the total samples. The most typical features of this species were stem wings, strong growth potential, and a long growth period, and these characteristics were similar to previous results  sequences also showed that D. persimilis and D. opposita were closely related ; Investigation ; Methodology ; Writing‐original draft . Jingyu Sun: Methodology . Nan Shan: Formal analysis . Xin Chen: Formal analysis . Putao Wang: Formal analysis ; Supervision . Qianglong Zhu: Formal analysis . Yao Xiao: Writing‐review & editing (supporting). Hongyu Zhang: Investigation . Qinghong Zhou: Formal analysis ; Project administration ; Writing‐review & editing . Yingjin Huang: Project administration ; Supervision ; Writing‐review & editing .Supplementary MaterialClick here for additional data file.


Salvia miltiorrhiza populations, four intergenic spacer sequences, ETS, psbA-trnH, trnL-trnF, and ycf1-rps15 of the 40 populations collected from China were Polymerase Chain Reaction (PCR) amplified, analyzed both individually and in combination. Haplotype diversity analysis showed that the cultivated S. miltiorrhiza populations had a very rich genetic diversity and an excellent capacity to resist environmental pressure. The best-fit nucleotide substitution models for ETS, psbA-trnH, trnL-trnF, ycf1-rps15, and their combined sequences were HKY+I, T92, T92, T92+G, and T92+G, respectively; the nucleotide conversion frequency in the combined sequences was lower than the transversion, and the relatively high nucleotide substitution frequencies suggests its high genetic variability. Neutral tests showed that the spacer sequences of the populations conform with the neutral evolution model, and there has been no current expansion events occurred. Phylogeny analyses based on both the individual and the combined sequences showed that the 40 populations were clustered in two clades with a very similar topological structure. The discrimination rate of the combined sequence marker is significantly increased to 52.5% (21 populations) over the highest 35% (13 populations) by the single marker of ETS, though still inadequate but a big step forward. Further exploration of more DNA markers is needed. This study for the first time revealed the rich genetic diversity and phylogeny of the currently cultivated S. miltiorrhiza populations in China and provides novel alternative molecular markers for the genetic identification and resources evaluation of the cultivated S. miltiorrhiza populations.For better understanding the genetic diversity and phylogeny of the cultivated  Salvia miltiorrhiza Bunge is a perennial medicinal plant of Salvia in the Labiatae family [S. miltiorrhiza has many other pharmacological effects, such as antioxidant [S. miltiorrhiza is also widely cultivated in other countries such as South Korea, Vietnam and Australia [S. miltiorrhiza mainly include fat-soluble tanshinones and water-soluble phenolic acids [e family . Its rhie family . In the ioxidant , neuroprioxidant , anti-fiioxidant , anti-inioxidant , and antioxidant ; and itsustralia . The phaic acids .S. miltiorrhiza.Genetic diversity, the genetic variability present within species that changes with time and space, is the product of recombination of genetic material by sexual reproduction, and by mutation of genes, genetic drift, and gene flow. It gives different physical attributes to the individual and capacity to adapt to stress, diseases and unfavorable environmental conditions. Low genetic diversity is an important factor affecting species survival, leading to endangerment or even to distinction. The genetic diversity at various levels provides the basis for the collection, preservation, utilization and evaluation. To ensure and maintain good levels of genetic diversity in crop populations is crucial. The genetic diversity at various levels provides the basis for the collection, preservation, utilization and evaluation of S. miltiorrhiza cultivated in different producing areas are very similar and difficult to identify, and the yields and medicinal ingredient contents of different germplasm are also different, which affects the stability of the quality of S. miltiorrhiza medicinal materials and brings multiple difficulties to the standardized production of S. miltiorrhiza [S. miltiorrhiza. They have played important roles in the identification and quality evaluation of Chinese medicinal materials, but also have their own shortcomings. Trait identification is difficult to identify materials with similar morphological traits, the microscopic identification requires the relevant professional knowledge, and the physical and chemical properties are easily affected by many environmental factors. In view of the limitations of traditional identification, new methods as supplements have been emerging to ensure more accurate and reliable identification. In recent years, molecular marker technology that reveals polymorphism at the DNA level has become a powerful tool for studying plant genetic diversity [The external properties of iorrhiza . At presiversity . Both thETS), the non-transcribed spacer (NTS), 18SrDNA, internal transcription spacer 1 (ITS1), 5.8SrDNA, internal transcription spacer 2 (ITS2), and 28SrDNA. The non-coding regions of nrDNA are hypervariable, due to the much less selective pressure they subjected. Sequence differences manifested in these regions have potentials for application in systematics, hybridization and population evolution researches among closely related groups [ITS2 reached the highest discriminating rate of 92.7% at the species level among the 7 fragments [ETS evolves rapidly and has a high degree of polymorphism and variability, and is extensively used in the study of genetic variation, classification and phylogeny. For example, the phylogenetic study on the origin and spread of millet based on ETS detected both intra- and inter-species polymorphisms [ETS of sycamore showed that all the Scandinavian populations composed of well-defined subgroups of the species’ genes and had a spontaneous origin closest with other spontaneous populations from neighboring areas of the Nordic continent [ETS and ITS2) and found that despite its long-term cultivation, the genetic variation was relatively high existing mainly within each province rather than between provinces [The nuclear ribosomal DNAs (nrDNAs) in higher plants are highly tandem repeating sequences consisted of the external transcribed spacer , maturase K (matK), NAD(P)H dehydrogenase F (ndhF), the β-subunit of ATP synthase (atpB), the RNA polymerase B and C subunit (rpoB and rpoC) genes, and intergenic spacers between tRNALeu and tRNAPhe genes (trnL-trnF), between D1 protein of PSII and tRNAHis genes (psbA-trnH), and between CP47 protein and small subunit I genes of PSII (psbK-psbI) in the chloroplast genome were used individually for plant species identification. Later, it was proposed to use different combinations of these fragments. For example, the relatively conserved psbA [ycf1) in the chloroplast genome [rps15 gene encoding a ribosomal protein S15 (rps15), mediating the interaction between the subunits and interacting with mRNA and tRNA [The chloroplasts of plants have higher evolving rates than mitochondria; its uniparental inheritance enables the researchers to target DNA markers in the genome . Initialved psbA  has beenved psbA ; the sect genome  which hat genome  has onlyand tRNA  plays ceand tRNA . It has and tRNA .trnH-psbA spacer and partial coding region of rbcL gene has been combined as two global terrestrial plant molecular genetic markers and provided the necessary versatility and species differentiation [matK + atpF-atpH + psbK-psbI [Lepechina in the Lamiaceae plants have been tested based on separate and combined datasets of cpDNA  and the combined nrDNA (ITS and ETS) datasets [rbcL, rps16, rpl32-trnH, psbA-trnH and trnL-trnF) and two nuclear sequences (ITS and ETS) has been explored to interpret the disjunct distribution of Northern Hemisphere herbaceous plants [ITS, ETS), and the other combined the four cpDNA  [Current researches on plant genetic geography have been under transition from individual sequences in the past to the joint analysis of the sequences from the chloroplast genome or nuclear ribosomal genes. For example, the ntiation . A highesbK-psbI ; the mondatasets . The phys plants . The phynd rbcL) .S. miltiorrhiza has been sequenced and its detailed structure provides an excellent basis for systematic analyses [The chloroplast genome of analyses . Its morS. miltiorrhiza has been playing increasingly important roles in clinical and health-care utilization. However, the random introduction of unidentified source varieties in production and nonstandard management have resulted in a chaotic variety sources and unstable quality; and studies concerning the genetic diversity and molecular identification of the germplasm resources have been also inadequate. So far as we known, no reports on the genetic diversity of the cultivated S. miltiorrhiza populations have been seen. In this study, the nuclear ETS and three chloroplast intergenic spacers of the 40 cultivated S. miltiorrhiza populations in China were for the first time analyzed in order to identify the SNP fingerprints, understand the genetic diversity and phylogeny both individually and in combination. A basis for the protection, introduction, domestication, novel variety selection, and breeding of the cultivated S. miltiorrhiza germplasm resources, was provided.As one of China’s traditional bulk medicinal materials, S. miltiorrhiza populations representing more than 30 regions of China were collected from three major seed industries .Well-documented 4 primer pairs respectively for the 4 intergenic spacers  were adoThe manually checked and confirmed sequence data were subject to BLAST analysis and a corresponding reference sequence with the highest identity percentage for each locus was determined and used for manual finishing and multiple alignment with BioEdit 7.09 and Vector NTI Advance 11.5.3, respectively. The best nucleotide substitution models were identified via the Find Best DNA/Protein Models in MEGA 7.0 , and theETS, psbA-trnH, trnL-trnF and ycf1-rps15 of the 40 cultivated S. miltiorrhiza populations were assorted and submitted to GenBank, and the corresponding accession numbers were obtained , was 1488-1517bp, with a GC content of 35.9–38.8%, a variable rate of 29.4%, and a total of 466 SNP variable sites consisting of 392 parsimony informative sites and 74 singlet variable sites  with a relative identification rate of 35%, the specific SNP fingerprints of the psbA-trnH can distinguish 8 S. miltiorrhiza populations  with a relative identification of 20%, the specific SNP fingerprints of trnL-trnF can distinguish 4 S. miltiorrhiza populations  with a relative identification of 10%, the specific SNP fingerprints of ycf1-rps15 can distinguish 11 S. miltiorrhiza populations  with a relative identification of 27.5%. Specific SNP fingerprints based on the composite DNA marker could discriminate 21 S. miltiorrhiza populations  with a discrimination rate of 52.5%.The specific SNP fingerprints based on the ETS sequence was the highest  is lower than the transversion (59.9%), while for the other three intergenic spacers of the chloroplast genome, conversion frequencies were higher than those of transversion. The conversion and transversion frequencies of the combined sequences were 43.4% and 56.8% respectively, in good accordance with the those of the three non-coding regions of the chloroplast genome.Statistics on the nucleotide substitution frequencies  showed tETS were 0.22815 and -0.37550 respectively, and the Tajima’s D was -1.27644, not significant at the level of P> 0.10, indicating its conformity with the neutral evolution model; the Fu and Li’s D* and F* test statistics for the psbA-trnH were 0.19802 and 0.53273 respectively, and the Tajima’s D was 0.90040, not significant at the level of P> 0.10, conforming also with the neutral evolution model; the Fu and Li’s D* test statistic for the trnL-trnF was 1.44651, with significant difference at the level of P <0.05, Fu and Li’s F* test statistic was 1.89841, showing a significant difference at the level of P <0.02, and the Tajima’s D was 1.90978, not significant at the level of 0.10> P> 0.05, suggesting its inconformity with the neutral evolution model; the Fu and Li’s D* and F* test statistics for the ycf1-rps15 were -0.02679 and -0.19784 respectively, and the Tajima’s D was -0.41630, not significant at the level of P> 0.10, in line with the neutral evolution model; the Fu and Li’s D* and F* test statistics of the combined sequence were 0.34297 and 0.08141respectively, and the Tajima’s D was -0.41550, insignificant at the level of P> 0.10, and conforms with the neutral evolution model , nucleotide diversity (π), variance of haplotype diversity (Vh) and standard deviation of haplotype diversity (Sh) of the four intergenic regions and the combined sequence. The results of neutral analysis showed that the Fu and Li’s D* and F* test statistics for on model .ETS, psbA-trnH, ycf1-rps15, and the combined sequence of the cultivated S. miltiorrhiza populations conform with the neutral evolution at the species level, while the trnL-trnF does not.The results suggested that the three intergenic spacers, ETS, psbA-trnH, trnL-trnF, and ycf1-rps15) of the 40 cultivated Danshen populations were constructed. The phylogenetic trees based on ETS and psbA-trnH of S. miltiorrhiza were similarly shown a two-clade structure: for ETS, 28 populations clustered as one, and the remaining 12 populations clustered in the other clade . However, the number of parsimony informative sites of the combined sequence was even higher (392), reaching a discrimination rate of 52.5%.In this study, the four intergenic spacers of ectively . The resIS rates  were gene plants  indicateS. miltiorrhiza showed that the Hd of ETS, psbA-trnH, trnL-trnF and ycf1-rps15 sequences were all greater than 0.5, indicating that S. miltiorrhiza has very rich genetic diversity at the species level, which is consistent with the result based on EST-SSR markers for S. miltiorrhiza [S. miltiorrhiza populations by multi-sequence combination, and could be important in evaluating its genetic variation and adaptability. The best-fit nucleotide substitution models for ETS, psbA-trnH, trnL-trnF, ycf1-rps15 and the combined sequence are HKY+I, T92, T92, T92+G and T92+G respectively. The lower conversion versus transversion frequency of the joint sequences is consistent with all the three IGSs from the chloroplast genome. The relative high nucleotide substitution rates found in this research might be the reason for the high genetic variation among the cultivated S. miltiorrhiza populations, as suggested [ETS, psbA-trnH and ycf1-rps15 all conform with the neutral evolution model, while trnL-trnF does not. The combined sequence is not significant at the level of P>0.10, and conforms with the neutral evolution model. It is suggested that the cultivated S. miltiorrhiza populations has not recently experienced expansion events.It is generally accepted that for most of the common species, the more widely distributed, the higher its genetic diversity, while the genetic diversity of endemic, rare or narrowly distributed species is low, and the low genetic diversity is an important factor leading to species endangerment or even extinction . In thisiorrhiza . The Hd uggested . ResultsS. miltiorrhiza exhibited topologically very similar two-clade structures. For single sequences, ETS has the highest identification rate of 35.0%, and the trnL-trnF sequence has the lowest identification rate of 10.0%. The combined DNA marker has an identification rate as high as 52.5%. But still, they could not discriminate all the tested populations. Further endeavor to explore more and effective DNA markers for the identification of more reliable composite molecular markers is needed so that convenient, fast, efficient and standard identification at the gene level could be established.It was also found, in this research, that the phylogenetic trees based on both the individual and the combined sequences of the four intergenic spacers of the 40 cultivated populations of S1 Table(DOCX)Click here for additional data file.


Paralichthys adspersus) selected from a hatchery population and genotyped for five microsatellite loci.The advancement of molecular techniques in an era in which high-throughput sequencing has revolutionized biology renders old-fashioned alternatives to high-throughput methods obsolete. Such advanced molecular techniques, however, are not yet accessible to economically disadvantaged region-based laboratories that still obtain DNA profiles using gel-based techniques. To explore whether cost-efficient techniques can produce results that are as robust as those obtained using high-throughput methods, we compared the performance of polyacrylamide gel electrophoresis (PAGE)- and capillary electrophoresis (CE)-derived genomic data in estimating genetic diversity and inferring relatedness using 70 individuals of fine flounder (Here, we show that PAGE- and CE-derived genomic datasets yield comparable genetic diversity levels regarding allelic diversity measures and heterozygosity. However, relatedness inferred from each dataset showed that the categorization of dyads in the different relationship categories strongly differed. This suggests that while scientists can reliably use PAGE-derived genomic data to estimate genetic diversity, they cannot use the same for parentage testing. The findings could help laboratories committed to population research not be discouraged from using the PAGE system if high-throughput technologies are unavailable and the method is adequate to address the biological question.The online version contains supplementary material available at 10.1186/s13104-021-05861-9. Ever since their discovery in 1981 . MicrosaBesides allelic dropouts and null alleles, the most common errors during microsatellite genotyping include contaminant DNA, incorrect data entry, and scoring errors . AlthougHere, we present a direct comparison between PAGE and CE methods by applying them in the genotypification of five microsatellite loci in an economically important fish population. To the best of our knowledge, this study is the first of its kind that compares not only allele frequency data but also the different genetic diversity parameters analyzed in population research. Moreover, this study evaluates the performance of the PAGE method by comparing the results of relatedness analysis to those obtained using the CE method.Paralichthys adspersus  belonging to a hatchery population were genotyped by PAGE and CE methods using the flanking primers of five microsatellite loci developed for P. olivaceus : Poli9TUF, Poli28TUF [Po35, Po91 [KOP45 [2 (25 mM), 0.33 μL dNTPs (2.5 mM), 0.06 μL forward/reverse primer (12.5 μM each), 0.06 μL Maximo Taq DNA Polymerase (5 U/μL) , and 1 μL DNA (25 ng/μL). Thermal cycling conditions for each locus are detailed in Additional file Total 70 adults of oli28TUF , Po35, P35, Po91 , and KOP1 [KOP45 . DNA was1 [KOP45 . The PCR® 1-D Analysis software (Bio-Rad).PCR amplification products were visualized on 12% polyacrylamide gels following the standard Laemmli system for discontinuous gel electrophoresis . Gels coPCR amplification products were also genotyped by means of automated parallel CE using the Fragment Analyzer™ Automated CE System . Separation gel and samples were prepared using the DNF-900 dsDNA Reagent Kit (Agilent) following the manufacturer’s protocol. Allele scoring was performed using PROSize data analysis software (Agilent) by interpolating their position to a 35–500 bp DNA marker and 75–400 bp range DNA ladder (Agilent).e), allelic frequency, observed heterozygosity (Ho), and expected heterozygosity (He) using GenAlEx v.6.5 [P values  < 0.05 were considered to be statistically significant. Further data visualization was performed in R [Typographic errors, i.e., the misinterpretation of allele banding patterns because of stutter bands, and allelic dropout were determined using Micro-Checker v.2.2.3 . The freEx v.6.5 . AllelicEx v.6.5 . Statistmed in R .rxy) was estimated using the method-of-moment estimator developed by Wang (rw) [rw values of any two adjacent distributions, as suggested by Blouin et al. [rw values between 0.5 and 0.375, HF the dyads with rw values between 0.375 and 0.125, and UR the dyads with an rw lower than 0.125. It has to be noted, however, that although the rw was developed from the identical by descent (IBD)‐based concept of relatedness, where rxy can only go from 0 to 1 if neither of the two individuals being compared is inbred, this method-of-moment estimator provide negative values if the average relatedness among sampled individuals becomes close to zero [rxy, the allele frequency data is estimated from the current sample instead of an ancestral population (as assumed when the estimators were developed) [The coefficient of relatedness (ang (rw)  through ang (rw) . The cutn et al. , 38. Fol to zero , 40. Thiveloped) . Negativveloped) , 41 in tveloped) ; they bePoli9TUF, the number of alleles detected by PAGE and CE methods varied depending on the specific locus    Fig. a–f. The rw estimator inferred from the PAGE and CE datasets supported the category unrelated as the most probable type of relationship between the individuals from the captive population of P. adspersus, with average values ranging from − 0.111 to − 0.093. However, strong differences in the categorization of HS and FS were observed - and polyacrylamide gel electrophoresis (PAGE)-derived genomic datasets give comparable genetic diversity measures. Total 40 samples of a wild population of fine flounder  were genotyped for five microsatellite loci using CE and PAGE methods, and the genetic diversity levels obtained from both genomic datasets were compared. a-f Violin plots with included boxplots summarizing the data distribution of different genetic diversity parameters. g Violin plots with included boxplots summarizing the distribution of null allele frequencies. Each box plot shows the median (middle line) and interquartile range (boxes). The bottom and top of each box indicate the 25th and 75th percentiles, while whiskers represent the minimum and maximum. h Allele frequency spectra of the five microsatellite loci used. Statistical analysis was performed using unpaired t tests. PIC, polymorphic information content; A number of alleles; ae effective number of alleles; R allelic richness; Ho observed heterozygosity; He expected heterozygosity. Figure S2. Differences in relatedness estimation between capillary electrophoresis (CE)- and polyacrylamide gel electrophoresis (PAGE)-derived genomic datasets. Total 40 samples of a wild population of fine flounder  were genotyped for five microsatellite loci using CE and PAGE methods, and relatedness among males and females was inferred using the Wang estimator (rw). a Heat maps showing the rw values calculated from the CE-derived genomic dataset for all dyads within each relationship category. b Heat maps showing the rw values calculated from the PAGE-derived genomic dataset for all dyads within each relationship category. Heat maps were made to compare and visualize the difference in rw values and the number of dyads obtained from the CE- and PAGE-derived genomic datasets. Each bar in heat maps represents one dyad, and colors show the variation in rw values. UR unrelated; HS half-sibs; FS full-sibs. Table S1. PCR conditions and size range of the fragments for each microsatellite locus. Table S2. Genetic diversity obtained from capillary electrophoresis (CE)- and polyacrylamide gel electrophoresis (PAGE)-derived genomic data for a captive population of Paralichthys adspersus. Repeat motif, annealing temperature (Ta), frequency of null alleles, polymorphic information content (PIC), number of alleles per locus (A), effective number of alleles (ae), allelic richness (R), and observed and expected heterozygosity (Ho/He) for each microsatellite locus are shown. Table S3. Genetic diversity obtained from capillary electrophoresis (CE)- and polyacrylamide gel electrophoresis (PAGE)-derived genomic data for a wild population of Paralichthys adspersus. Repeat motif, annealing temperature (Ta), frequency of null alleles, polymorphic information content (PIC), number of alleles per locus (A), effective number of alleles (ae), allelic richness (R), and observed and expected heterozygosity (Ho/He) for each microsatellite locus are shown.


Plasmodium falciparum in humans. This study was conducted in an area of high seasonal malaria transmission in Bongo District, Ghana. IRS was implemented during the dry season (November–May) in three consecutive years between 2013 and 2015 to reduce transmission and attempt to bottleneck the parasite population in humans towards lower diversity with greater linkage disequilibrium. The study was done against a background of widespread use of long‐lasting insecticidal nets, typical for contemporary malaria control in West Africa. Microsatellite genotyping with 10 loci was used to construct 392 P. falciparum multilocus infection haplotypes collected from two age‐stratified cross‐sectional surveys at the end of the wet seasons pre‐ and post‐IRS. Three‐rounds of IRS, under operational conditions, led to a >90% reduction in transmission intensity and a 35.7% reduction in the P. falciparum prevalence (p < .001). Despite these declines, population genetic analysis of the infection haplotypes revealed no dramatic changes with only a slight, but significant increase in genetic diversity . Reduced relatedness of the parasite population (p < .001) was observed post‐IRS, probably due to decreased opportunities for outcrossing. Spatiotemporal genetic differentiation between the pre‐ and post‐IRS surveys  was identified. These data provide a genetic explanation for the resilience of P. falciparum to short‐term IRS programmes in high‐transmission settings in sub‐Saharan Africa.Here, we report the first population genetic study to examine the impact of indoor residual spraying (IRS) on  In contrast, low genetic diversity, extensive population differentiation, and strong linkage disequilibrium, are typically seen in low‐transmission regions  or areas under intense control suggesting higher levels of inbreeding in these populations. The latter are “ideal elimination units” that may lead to a population bottleneck and where clonal parasites may be more readily lost to genetic drift events  and troughs in the dry season . Since mosquitoes require pools of water to breed, vector densities along with malaria transmission will increase during the wet season, while during the dry season fewer mosquitoes will propagate, leading to reduced transmission intensity. Previous studies have shown that in these areas characterized by high seasonal malaria transmission a large proportion of the human population across all ages harbour asymptomatic 22.1The study was reviewed and approved by the ethics committees at the Navrongo Health Research Centre, Ghana (NHRC IRB‐131), Noguchi Memorial Institute for Medical Research, Ghana (NMIMR‐IRB CPN 089/11‐12), The University of Melbourne, Australia (HREC 144‐1986 and HREC 195‐5652), and the University of Chicago, United States (IRB 14–1495). Individual informed consent was obtained in the local language from each enrolled participant by signature/thumbprint, accompanied by the signature of an independent witness. For children <18 years of age a parent or guardian provided consent. In addition, all children between the ages of 12 and 17 years provided assent.2.2Plasmodium falciparum reservoir was conducted in Bongo District, located in the Upper East Region of Ghana  , were afebrile (axillary temperature <37.5°C) on the day the survey was conducted, and did not report a history of fever in the 24 h prior to being surveyed were defined as having an “asymptomatic P. falciparum infection” .Using a cross‐sectional study‐design, two age‐stratified surveys of ~2000 participants per survey were undertaken at the end of the wet seasons  pre‐IRS  and post‐IRS  Figure . During Over the last decade IRS has been scaled up across northern Ghana through ongoing support from the United States President's Malaria Initiative (PMI) and the Global Fund in partnership with the AngloGold Ashanti Malaria Control Programme  Figure  Nationa. BetweenSeveral other malaria control interventions have been deployed in Ghana and Bongo District, including integrated vector‐control, management and treatment of uncomplicated malaria with artemisinin‐based combination therapies (ACTs), community mobilization and education, etc. , two 5 x 5 mm sections were cut from each dried blood spot and placed in a 1.5‐ml centrifuge tube, with genomic DNA (gDNA) being extracted using the QIAmp DNA mini kit (Qiagen) as previously described  and post‐IRS (T2) surveys were selected for microsatellite genotyping based on their multiplicity of infection (MOI)  as determined using var genotyping , while isolates with >60 var DBLα types were classified as multiple‐clone infections (MOI > 1). To facilitate a more accurate assignment of the fluorescent peaks during the analysis (described below), only those isolates with a MOI = 1 or 2, were selected for the microsatellite genotyping  selected from the pre‐ and post‐IRS surveys were genotyped using a verified panel of 12 putatively neutral microsatellite markers located across the 14 chromosomes as described by Anderson et al. . Isolates with two or more peaks at ≥1 loci were considered to be multiple‐clone infections (MOI > 1) and the multilocus haplotypes were constructed using the predominant peak at each locus. The combined data set with the single‐clone and multiple‐clone infections was defined as the “all infections” data set Figure . Isolate2.42.4.1poppr v. 2.7.1 , the number of alleles per locus (A), the allele frequency per marker and expected heterozygosity (He) between microsatellite marker pairs.Binned data files were processed manually in Microsoft Excel v. 16.30 to generate input files for the population genetics software packages, as described below. Patterns of genetic diversity were analysed using the R package He was calculated using the formula:p is the allele frequencies at a given locus and n is the number of observed alleles in each locus  was calculated using the R package PopGenReport v. 0.10 : He(x)−He(y).“Dominant” constructed haplotypes were used for these analyses. Allelic richness . For selectively neutral loci, A and He result from an equilibrium between mutation and genetic drift  . If a bottleneck event has occurred, A decreases immediately but He is briefly retained, becoming larger than Heq  , while population expansions typically exhibit a heterozygosity deficiency (where Heq > He)  and stepwise mutation model (SMM) were run as recommended by BOTTLENECK and as reported in the literature    to reduce bias. To uncover whether any patterns of LD were due to a single pair of loci, or if there were any significantly associated pairs of loci masked by an insignificant overall LD, we calculated the r¯d for all microsatellite pairs (pairwise r¯d). The r¯d and pairwise r¯d among loci were estimated using a Monte Carlo simulation method of 9999 samplings, where alleles were reshuffled at random among haplotypes. This analysis tested our r¯d values against the null distribution of no linkage among/between markers, as expected for a randomly mating population  to ensure that the permutation analysis shuffled the alleles per haplotype without bias.The extent of inbreeding within and between populations was estimated using the R package 2.4.4PAS) statistic using only those isolates in the “dominant infections” data set that had complete infection haplotypes. Complete multilocus haplotypes were used to ensure that the denominator would be consistent for all comparisons.To investigate genetic relatedness between isolates we calculated the pairwise allele sharing   to 0 . Any infection haplotype pairs with a PAS ≤ 0.25 would be considered “unrelated”, 0.25 < PAS < 0.5 would be considered “half‐siblings”, and a PAS ≥ 0.5 would be considered “related” . PAS comparisons were calculated between all possible infection haplotype pairs both within  and between  the survey time points investigated.The resulting PAS ≥ 0.70 . This threshold was selected to visualise the genetic similarity between isolates that probably resulted from a recent transmission and/or recombination event. These spatial networks were plotted using the R packages ggraph v. 1.0.2  were calculated using the R package DEMEtics v. 0.8‐7  based on their inferred ancestry as determined by allele frequencies at each locus  with 20 stochastic simulations for each K and 100,000 Markov chain Monte Carlo iterations, after a burnin period of 100,000 using the admixture model and assuming correlated allele frequencies. The STRUCTURE program output contains the log probability of the data, LnP[D], which can be used to determine the optimal K cluster  v. 1.1  were used for univariate analyses of discrete variables to compare proportions; nonparametric Wilcoxon signed rank test (comparing distributions across two paired groups), Mann–Whitney U (comparing distributions across two groups), Kruskal–Wallis (comparing distributions across k groups) and tests were used to compare distributions for continuous variables. A test was deemed statistically significant if the p‐value was <.05.R v. 3.5.0  to 27.0% in the post‐IRS survey   to 15.6% (post‐IRS)) and the oldest children  to 40.8% (T2)) surveyed (p < .001) (Table p = .005)  and post‐IRS (320 parasites/µl) surveys (p < .001) (Table p ≤ .006). It should however be noted that for all age groups surveyed, except those ≥40 years (p < .001), there were no significant changes in parasitemia between the pre‐ and post‐IRS surveys. Finally, median MOI   Table . During 5) Table . However1) Table . These s1) Table . These s3.2P. falciparum infections from the pre‐ and post‐IRS surveys, respectively, were used for the microsatellite genotyping (Table (p > .05)  and parasitaemia post‐IRS (p < .01). For the subset of participants selected, there were no significant differences between the pre‐ and post‐IRS surveys for any of the demographic or parasitological parameters, except for age and reported antimalarial usage   Table , except S Tables  and S4. S Tables , we foun3.3Based on microsatellite genotyping, 53.6% and 54.0% of the isolates in the pre‐ and post‐IRS surveys, respectively, had an MOI > 1, indicating the majority of population in Bongo harboured multiple‐clone infections pre‐ and post‐IRS Figure . In Bongp = .050), ARA2 (p = .040), PFG377 (p < .001) and TA60 (p = .030)  Figure ‐S8.A) and allelic richness estimates (Rs)  Table . There wRS Table . We alsoys Table . In addiys Table  there weys Table . We alsoRS Table .He remained high post‐IRS despite the IRS intervention with a significant, but slight increase in He between in the pre‐ and post‐IRS surveys  was calculated to estimate the seeding parasite populations in Bongo. Ne increased pre‐ to post‐IRS for both the stepwise mutation model (SMM) and infinite alleles model (IAM) (Table Heq > He) when comparing the pre‐ and post‐IRS surveys under the more conservative SMM, indicating that the parasite populations in Bongo had not undergone a bottleneck at either time point comparison (Table A), and therefore He, was maintained at the levels observed before the introduction of the IRS intervention in Bongo. This suggests that there was no bottleneck event between the pre‐ and post‐IRS surveys investigated .Effective population size (M) Table  and S11.on Table  and S11.3.5r¯d, was used to assess multilocus linkage disequilibrium (LD), or nonrandom associations among loci using only the “dominant infections” with complete haplotypes  comparisons between infection haplotypes were next used to examine parasite genetic relatedness. Even though all the multilocus haplotypes were unique , it is possible that these infection haplotypes may only differ at one or two loci and may reflect parasites that are highly related and/or have recently undergone sexual recombination in the mosquito .Using the dominant infections with complete haplotypes, we calculated PAS scores both within  and between  the time point surveys. Using this analysis, we found that both pre‐ and post‐IRS the majority of infection haplotypes were unrelated and only shared ≤0.2 of their alleles  , indicating that parasite clones (as defined by their multilocus microsatellite haplotypes) did not appear to be maintained temporally in Bongo , and just three isolate pairs (0.04%) were “highly related” being identical at ≥7 out of 10 loci   Figure . Multilo3.73.7.1PAS relationships, we constructed spatial networks of the PAS scores between infection haplotypes to investigate if the “highly related” isolates (PAS ≥ 0.7) clustered at different spatial scales  and/or spatiotemporally . We observed no apparent geospatial clustering and very few “highly related” haplotypes within pre‐ and post‐IRS  haplotypes, there was no discernible spatial clustering.To visualize these 3.7.2GST and Jost's pairwise index of differentiation (D) over time  and over the different spatial scales   Figure . Both Gy Figure , where Jy Figure .D: p = .034), with significant differences in allelic fixation (STG: p = .021) .When the pre‐ and post‐IRS surveys were compared, the parasite population in Bongo was genetically differentiated (Jost's 1) Table . In fact1) Table . To dete3.7.3K = 6 and 7, respectively .To further explore the observed spatiotemporal population structure, a Bayesian cluster analysis was performed. This infers the number of ancestral genetic clusters and assigns each infection haplotype to these clusters. The optimal number of genetic clusters in Bongo and after stratifying by catchment area were identified as 4Plasmodium falciparum population diversity and structure. Despite a more than 90% reduction in local transmission and a 35.7% reduction in parasite prevalence pre‐ to post‐IRS, we observed that both genetic diversity and the effective population size of the parasite population increased slightly, rather than reduced. These data highlight the resilience of the parasite population in the human host to vector control. They have relevance for IRS in the many regions of high seasonal transmission typical of sub‐Saharan Africa and a focus of the WHO’s HBHI strategy  in Vea/Gowrie, Figure P. falciparum clones from the surrounding uncontrolled areas may have increased genetic diversity but not LD (see discussion below). This contrasts with the surveillance application of microsatellites to detect changes in LD when going from moderate‐ to low‐transmission as observed in low‐transmission areas, including those with perturbations such as LLINs and ACTs  were still prevalent in the population at the end of the wet season post‐IRS. Therefore, sexual recombination between diverse parasites  still occurred following the IRS intervention, resulting in linkage equilibrium among the 10 microsatellite loci investigated. An alternative explanation for the lack of multilocus LD is that even if outcrossing was reduced following the IRS, continuous gene flow via migration of P. falciparum isolates with MOI ≤ 2, while excluding those with higher MOI. To date, there is no way around this restricted use of data in high‐transmission settings. Microsatellites do, however, provide a greater capacity to detect a higher number of alleles in the population with relatively few markers so can more accurately capture MOI up to three. Thus, for studies of genetic diversity and population structure in high‐transmission settings, they can be preferable to using multiple SNPs that are only biallelic  post‐IRS geospatial differences between Vea/Gowrie and Soe, and (b) temporal pre/post‐IRS differences in Soe. The IRS programme may have restricted vector movement between the catchment areas (~15–20 km apart) since Vea/Gowrie and Soe were genetically differentiated from Vea/Gowrie post‐IRS, with approximately 6.8% of alleles being private  to achieve low‐transmission and bottleneck the parasite population in the human population in high‐transmission areas like Bongo. Further investigation into these trends would provide insight into the threshold needed for perturbations to have a substantial effect on neutral diversity in these high‐transmission settings where the M.P., K.A.K. and K.P.D. conceived and designed the study. A.R.O., S.K.D., M.A.A., V.A. and K.E.T. contributed to the study design and co‐ordinated the field studies. D.C.A., S.R.‐P., S.L.D. and K.E.T. processed the samples. D.C.A., S.R.P. and S.L.D. conducted the microsatellite genotyping. D.C.A., S.R.‐P. and K.E.T. analysed the data. D.C.A. wrote the original draft of the manuscript. S.R.‐P., K.P.D. and K.E.T. critically revised the manuscript. A.R.O., M.P., K.A.K. and K.P.D. acquired funding.Supplementary MaterialClick here for additional data file.


In most languages, the arterial language, i.e. the language that a person has been exposed to from birth or within the critical period of language learning, is referred to as the ‘mother tongue’. Whether or not this phrase is factually correct has been a matter of debate for a long time.One way to tackle this problem is to use genetic markers, and look at their concordance or discordance with linguistic geographical distributions and phylogenetic relationships.To the best of my knowledge, the first study into the relationship between a genetic marker and a linguistic marker was published by C. D. Darlington . He usedet al. [The first modern studies into genetic markers and the evolution of languages are considered to be those conducted in the middle of the 1960s by Luigi Luca Cavalli-Sforza and colleagues . HoweveSo far, the ‘mother tongue’ hypothesis has received extremely limited support in the Austronesian-speaking populations  and the In the late 1990s, Laurent Excoffier and colleagues started looking at linguistic correlations with Y-linked genetic markers versus mitochondrial genetic markers with the aim of distinguishing between paternal and maternal effects on language parentage . In Afriet al. (2018) distinguished two linguistic components and analyzed their relationships to genetic distances separately: lexical and phonemic distances [Given the fact that most children acquire their arterial language from their mothers, the ‘father tongue hypothesis’ raises the problem of mothers teaching their children the language of the father even if they are not native speakers of this language. To resolve this problem, Zhang istances . BrieflyThe words are your father's; the way you say these words are your mother's.