D. rotundata accessions using joint analysis for 23 morphological traits and 136,429 SNP markers from the whole-genome resequencing platform. Various diversity matrices and clustering methods were evaluated for a comprehensive characterization of genetic diversity in white Guinea yam from West Africa at phenotypic and molecular levels. The translation of the different diversity matrices from the phenotypic and genomic information into distinct groups varied with the hierarchal clustering methods used. Gower distance matrix based on phenotypic data and identity by state (IBS) distance matrix based on SNP data with the UPGMA clustering method found the best fit to dissect the genetic relationship in current set materials. However, the grouping pattern was inconsistent (r = − 0.05) between the morphological and molecular distance matrices due to the non-overlapping information between the two data types. Joint analysis for the phenotypic and molecular information maximized a comprehensive estimate of the actual diversity in the evaluated materials. The results from our study provide valuable insights for measuring quantitative genetic variability for breeding and genetic studies in yam and other root and tuber crops.Understanding the diversity and genetic relationships among and within crop germplasm is invaluable for genetic improvement. This study assessed genetic diversity in a panel of 173  Dioscorea spp.) is a widely cultivated crop in the tropics and subtropics for its edible starchy tubers. The crop is, however, most prominent in five countries in West Africa , known as the “yam belt,” an area accounting for 92% of global yam production1. Of the over 600 Dioscorea species2, D. rotundata, native to West Africa, is the most important in terms of volume of production3 and the most preferred in the yam belt due to its suitability for many traditional foods2. Besides the food and economic value, yam is very important in traditional and contemporary medicine5 and has social, cultural, and religious relevance in West Africa6.Yam , and Rogers are applied for molecular markers24. These similarity coefficients are defined differently and so may produce different results for both the qualitative and quantitative relationships among individuals24, hence, the choice of an appropriate similarity index is very crucial for determining actual genetic dissimilarity among individuals. Also, affecting the results of genetic diversity studies is the method used for summarizing the dissimilarity matrices into groups or clusters25. Hierarchical clustering is the most widely used approach in the analysis of crop genetic diversity26. Several hierarchical clustering methods, including single linkage, complete linkage, simple average, median, unweighted paired group method using arithmetic averages (UPGMA), McQuitty, and Ward’s minimum variance have been used26. Each of these approaches has some distinctive features and may generate different results, hence, the choice of an appropriate method to meet the desired objectives is very imperative25. Comparative studies of different dissimilarity matrices, as well as hierarchical clustering methods, have been conducted to identify the appropriate approach for genetic diversity assessment in many crops, including sweetpotato18, switchgrass21, and maize27, but not yet for white Guinea yam. The objectives of this study were to (1) compare different dissimilarity matrices and hierarchical clustering methods for evaluating genetic diversity in white Guinea yam, (2) assess the genetic diversity and differentiation in a population of white Guinea yam using morphological, molecular and combined data.A standard approach applied to study genetic diversity is the comparison of individual genotypes within and between populations using a genetic dissimilarity matrix of all potential pairwise combinations of individuals for characterizing population structure based on relative affinity of everyone to all other individuals evaluatedResults of the principal component analysis (PCA) indicated that the first ten components with eigenvalues ranging from 1.01 to 6.26 were important in explaining the variation among the 173 accessions studied and cumulatively accounted for 72.32% of the total phenotypic variation  and TDr0900055 (a breeding line from the hybridization of TDr9700973 and TDr9501932), while it was lowest between TDr4180A (landrace from Guinea) and TDr2674A (landrace from Nigeria).Using the 136,429 SNP markers, the 173 accessions were grouped into three major clusters  was composed of 141 accessions, including 80 breeding lines, 51 genebank accessions, and ten farmers’ varieties  were clustered into the same groups by the three methods Fig. . The genMinor allele frequency, as well as the observed and expected heterozygosity, showed very low variation across the three genetic groups identified by the combined analysis Table . In contThe Mantel correlation assay between the phenotypic and genotypic dissimilarity matrices was negligible (r = − 0.048) Fig. . HoweverD. rotundata accessions using 23 most discriminant morphological traits and 136,429 SNP markers from the whole-genome resequencing genotyping platform. The dissimilarity coefficient, as well as clustering method used for genetic diversity analysis, have implications on the results25, hence the choice of an appropriate coefficient and hierarchical clustering method is critical for determining the accuracy of the genetic variability among individuals.Assessment of genetic diversity is an integral aspect of all crop breeding and plant genetic resources management and utilization undertakings; hence, many approaches have been developed to evaluate and quantify the extent of genetic variability in plant populations. This study assessed the variation in a panel of 173 28 and provides estimates of how precisely a dendrogram preserves the pairwise distances between the original data points29. In consonance with the findings of the present study, the UPGMA method of clustering was reported to give high cophenetic correlation coefficients for genetic diversity studies in sweet potato18, soyabean30, and maize31 indicating the viability of using dendrograms from the UPGMA to summarize the information of dissimilarity matrices in genetic diversity studies. Furthermore, Padilla et al.25 and Krzanowski32 observed high internal affinity within clusters and large variability among clusters generated by the UPGMA compared to the other methods.High cophenetic correlation coefficients were observed for most of the hierarchical clusters constructed using the different dissimilarity matrices and clustering methods with a few exceptions for both morphological and molecular data. The UPGMA method was observed to give high cophenetic correlation coefficients for most of the dissimilarity matrices across the molecular, morphological, and combined data, demonstrating that there is a good representation of the dissimilarity matrices and distances in the form of dendrograms. The cophenetic correlation coefficient has been widely employed as a measure for evaluating the efficiency of various clustering techniques since its introduction by Sokal and Rohlf36. Nevertheless, phenotypic characterization is instrumental in defining the plant population and forms the basis for selecting accessions with desirable traits for crop improvement.Using the 23 morphological traits identified by the first ten principal components as most discriminatory, the UPGMA clustering based on the Gower dissimilarity matrix grouped the 173 white yam accessions into three clusters irrespective of their countries of geographical origin (genebank accessions) and pedigree (breeding lines). Many authors assert that genetic diversity assessment using morphological markers is less reliable due to the strong influence of the environment and plant growth stage on their expressionD. rotundata accessions into three groups. The low genetic variability observed among the accessions using the morphological markers could be because variation in phenotypic traits may result from one or few mutations in the genome and epigenetic origin. In contrast, the SNP markers consider variations across the entire genome. The SNP markers, therefore, revealed valuable information about the genetic relationships among the D. rotundata accessions enabling the identification of genetically divergent parents helpful for the yam breeding program. Our results suggest the reliability of SNP markers in dissecting the depth of genetic diversity among white Guinea yam accessions, as also reported by Girma et al.16 and Scarcelli et al.37. Plants showing similar morphological characteristics could be very divergent at the molecular level and vice versa38. This phenomenon, in addition to the negligible correlation observed between the phenotypic and genotypic dissimilarity matrices in this study, could explain the changing and regrouping observed in comparing the membership of the hierarchical cluster dendrograms emanating from the morphological and molecular characterization. The inconsistency between the clusters identified by the genotypic and phenotypic information could also be attributed to the enormous genotype-by-environment interaction effects generally observed for quantitatively inherited morphological and agronomic traits. The lack of correlation between the molecular and the morphological diversity matrices further emphasizes the non-overlapping and complementarity between the genotypic and the phenotypic information to dissect the nature and extent of genetic diversity in crops40. Several studies have also reported inconsistencies between phenotypic and genotypic distances in different crops43.Our analysis of the genetic diversity using the Identity by state (IBS) dissimilarity matrix generated from 136,429 SNP markers partitioned the 173 44. The application of joint analysis for phenotypic and molecular information identified three genetic groups in the current set of materials highly valuable for genetic improvement in white Guinea yam. This has significant implications for yam improvement in that new breeding populations can be developed by hybridization among these three divergent genetic groups, thereby broadening the genetic base of the breeding program. Superior accessions with desirable end-user attributes from these diverse genetic groups are a source of rare alleles for incorporation into elite breeding lines thereby maximizing heterosis in the progenies. The high and moderate correlations observed between the combined matrix and the genotypic and phenotypic dissimilarity matrices, respectively agree with the findings of Alves et al.22 who dissected genetic diversity in their studies using phenotypic, genotypic and combined distances. These correlations suggest that genetic diversity assessment employing the combined matrix could be an essential tool to capture phenotypic and genotypic information cumulatively. Adequate knowledge about the genetic relationships among accessions is essential to preserve genetic diversity besides identifying superior parental combinations to create segregating populations in a breeding program45. Superior clones from the three distinct clusters identified by the combined distances could serve as trait progenitors for hybridization to maximize genetic variability and heterosis in the D. rotundata breeding program.An approach that combines the phenotypic and genotypic dissimilarity matrices into a single matrix for genetic diversity assessment was suggested to capture the entire genetic variability in plant populationsD. rotundata accessions in this study has provided valuable insights to inform breeding strategies and to identify promising parents for the development of improved white Guinea yam varieties with acceptable end-user qualities. High genetic variability was revealed among the white Guinea yam accessions by the SNP makers than the morphological markers, whereas the combined distance showed a high and moderate correlation with the genotypic and phenotypic distances, respectively. Hence quantification of genetic diversity using the combined matrix of phenotypic and genotypic distances explores the synergy of the two approaches, thereby cumulatively capturing the phenotypic and genotypic information to provide a comprehensive outlook of the entire diversity in the population. Clustering of accessions by different dissimilarity coefficients as well as hierarchical clustering methods without careful consideration of these approaches could affect the results. The Gower and IBS dissimilarity matrices presented relatively high cophenetic correlation coefficients using different hierarchical clustering methods; hence, they are more appropriate for genetic diversity studies using phenotypic and genotypic data, respectively.Genetic diversity analysis of the D. rotundata accessions, including 86 breeding lines, 77 genebank accessions, and ten farmers’ varieties, were used for the study. Details of these accessions, including countries of origin and pedigree information, are provided in Supplementary Information 2. While, the trial in 2018–2019 was established using a single plant plot arranged in a randomized block design replicated three times. Weeds were controlled manually to keep the experimental field free of weeds all through the growth period of the plants. The accessions were evaluated using thirty agro-morphological traits following the yam crop ontology46. The list of traits recorded, period of evaluation and data collection method is summarized in Supplementary Table One hundred and seventy-three Lyophilized leaves were sent to Iwate Biotechnology Research Center (IBRC-Japan) for DNA extraction, library construction and whole-genome resequencing. For the whole genome sequencing, total genomic DNA was extracted from the leaf samples using a NucleoSpin Plant II Kit according to the manufacturer’s protocol (MachereyNagel GmbH & Co) with slight modifications.D. rotundata reference genome version 2 (https://drive.google.com/drive/folders/1H5T4xjKAEl9LliR-4qK_IR6TypCDe8nj) with Hisat247. The SAM files were converted to BAM format and sorted by name using SAMtools48. In cases where multiple sequencing samples were generated from the same biological clone, the corresponding sorted BAM files for each clone were merged using SAMtools. Duplicates were marked and read groups added with the Picard package (https://broadinstitute.github.io/picard/) (v2.17.5). GATK (v3.8-0)49 was used to perform indel realignment, variant calling , and joint genotyping (using GenotypeGVCFs). The VCF file developed was filtered for MAF > 0.1, no missing data both at genotypes and SNP markers level. Only bi-allelic SNP markers with genotype quality > 20, read depth > 5 were retained after using vcftools50 and plink51 for filtering. The resulting SNPs were subjected to linkage disequilibrium (LD) pruning using the following parameters: 50 bp as window size in SNPs, 5 as step to shift the window and 0.5 as R square and a total of 136,429 SNP markers were retained for all subsequent analysis.Paired-end sequencing reads generated as fastq files were mapped to the 52 according to the model:Y is the trait, µ is the grand mean, E is the environment effect (year), B(E) is the Block effect in environment (year), G is the genotype effect, GE is the genotype by environment interaction, e is the error.Analysis of variance was performed to determine differences among the accessions for the various traits across the two years using the statistical analysis system software version 9.453. The optimal number of factors to be retained was determined using dimdesc function in R52. The selected traits from the above analysis were used in generating four different dissimilarity matrices .The LSmeans from the genotype by year analysis was used for principal component analysis in the FactorMiner and missMDA R packagesGower dissimilarity matrix was constructed using daisy function in cluster and graphics R packages54. Based on this, the dissimilarity matrix was estimated using the following formula:Euclidean dissimilarity matrix Euclidean distance was estimated using the Cluster R package54 and defined as:i and j are observations and p is the number of variables.Manhattan dissimilarity matrix Manhattan distance, a special case of the Minkowski distance was defined as:i and yi are two vectors in n-dimensional space.Mahalanobis dissimilarity matrix Mahalanobis distance was estimated according to the formula of Mahalanobis55, implemented using the “mahanalobis.dist” function in StatMatch R package56. For each variable, the mean and covariance were generated and used as cofactors:SNP marker data of the 173 white yam accessions were used to generate four dissimilarity matrices as follows:Identity by State dissimilarity matrix was generated according to the formula of Wessel and Schork57 using tassel software58 and converted into a matrix using as.matrix function in R.L is the number of loci considered; i and j, respectively, at the j lth locus ; and i and j at locus l.Nei dissimilarity matrix was determined by the formula of Nei59 using the nei.dist function implemented in poppr R package version 2.8.360Ss = 2 is the frequency of one of the two alleles at the sth diallelic locus.Jaccard dissimilarity matrix The raw vcf file with the total number of SNPs was converted to the dosage numeric format using plink51 and submitted to philentropy R package61 to estimate the Jaccard dissimilarity matrix through the following formula:i in Pi and Qi.Modified Rogers dissimilarity matrix was estimated in the cluster R package according to the relation of Rogers62:ij and qij are allele frequencies of the jth allele at the ith locus in the two taxonomic units under consideration, ni is the number of alleles at the ith locus, and m is the number of loci.28 for the different hierarchical clustering methods including ward.D2, single, average (UPGMA), median, McQuitty and complete. Dissimilarity matrix and the hierarchical clustering method with the highest cophenetic correlation coefficient value was retained to plot the final hierarchical cluster dendrogram. Using the method of Alves et al.22, graphic representations of the dissimilarity matrices (phenotypic and genotypic) were generated based on color gradients for the expression of dissimilarity among the accessions. A Venn diagram was constructed to assess the agreement of cluster memberships assigned by the phenotypic, genotypic and the combined data. To assess the resemblance between the genotypic and phenotypic matrices and between the genetic dissimilarity matrices and joint dissimilarity matrix, the correlations and their significances were tested with the Mantel Z test with 9,999 permutations using the ade4 R package63. Additionally, the Shannon Wiener Index (H′), Inverse Simpson’s (HB), Simpson’s Index (λ) and Pilou evenness (J) were assessed using library vegan64, while the fixation index (Fst) was assessed using Weir and Cockerham Fst estimates function implemented in vcftools50.To estimate the correlation between the underlying distance matrix and the distance between instances in the dendrogram using the different dissimilarity matrix, the cophenetic correlation coefficient was estimatedSupplementary Information.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.


Schistosoma haematobium/Schistosoma bovis viable hybrids were found to be abundant, often occurring in significantly higher proportions than that of single-species S. haematobium infections. By examining parasite population genetic structures across hosts, treatment regimens, and the spatial landscape, our results to date illustrate key transmission processes over and above that which could be achieved through standard parasitological monitoring of prevalence and intensity alone, as well as adding to our understanding of Schistosoma spp. life history strategies in general.Analyses of the population genetic structure of schistosomes under the “Schistosomiasis Consortium for Operational Research and Evaluation” (SCORE) contrasting treatment pressure scenarios in Tanzania, Niger, and Zanzibar were performed to provide supplementary critical information with which to evaluate the impact of these large-scale control activities and guide how activities could be adjusted. We predicted that population genetic analyses would reveal information on a range of important parameters including, but not exclusive to, recruitment and transmission of genotypes, occurrence of hybridization events, differences in reproductive mode, and degrees of inbreeding, and hence, the evolutionary potential, and responses of parasite populations under contrasting treatment pressures. Key findings revealed that naturally high levels of gene flow and mixing of the parasite populations between neighboring sites were likely to dilute any effects imposed by the SCORE treatment arms. Furthermore, significant inherent differences in parasite fecundity were observed, independent of current treatment arm, but potentially of major impact in terms of maintaining high levels of ongoing transmission in persistent “biological hotspot” sites. Within Niger, naturally occurring  Schistosoma, estimated to currently infect more than 220 million people.1 The disease burden is greatest (at least 90%) within sub-Saharan Africa (SSA), where the main species causing human schistosomiasis are Schistosoma mansoni  and Schistosoma haematobium , transmitted via eggs excreted in feces or urine, respectively. After 60 years of major multidisciplinary control efforts, great success has been achieved against Schistosoma japonicum  within China, and actual elimination/interruption of transmission has been achieved in Japan. However, although efforts to control S. mansoni and S. haematobium through large-scale preventive chemotherapy (PC)/mass drug administration (MDA) with praziquantel (PZQ) across SSA have also had a substantial impact on preventing or relieving morbidity,2 considerable work is still needed to achieve elimination.Schistosomiasis is a neglected tropical disease, caused by dioecious blood flukes of the genus 3 especially among preschool-aged children and infants.4 There is also evidence of maintenance and reemergence of schistosomiasis in previously controlled regions, exacerbated, at least in part, through wildlife and/or domestic animal reservoirs, not only in Asia7 but also it is becoming increasingly apparent within Africa too.9 Linked to this, it is also now known that hybrid/introgressed Schistosoma species are common across several high-endemicity regions of SSA,10 especially West Africa,13 and have recently spread into Europe.14 Perhaps, most disconcerting of all in terms of achieving the World Health Organization (WHO) goals of sustainable control, elimination of schistosomiasis as a public health problem, and ultimately interruption of transmission is the potential of reduced drug efficacy among populations under high chemotherapeutic pressure.15 Yet accurate interpretation of prevalence and intensity data is often hampered because of the poor sensitivity of traditional parasitological diagnostics, particularly at low intensities of infection.16 Finally, there remain many critical unresolved questions on the schistosomes’ resilience to the various interventions imposed on them.Contributing to this challenge, a recent work serves to highlight that schistosomes are highly complex organisms, and numerous essential characteristics of their biology and epidemiology remain unknown. Many of these unknowns potentially pose major challenges for sustainable disease control and certainly for the interruption of transmission. For instance, with more sensitive diagnostic surveillance tools, there are reports that prevalence levels are substantially greater than previously thought,https://score.uga.edu/) was designed to conduct large-scale operational health research into morbidity control and elimination.19 The overall aim within the SCORE gaining and sustaining control studies was to evaluate different schedules for providing PC through MDA either delivered through community-wide treatment (CWT) or through school-based treatment (SBT)  . AnalyseS. mansoni in Tanzania, with samples collected from school-aged children (age [predominantly] 9–12 years) before MDA from areas with intestinal schistosomiasis  close to Lake Victoria  on each island, to assess the differential impact of the following: 1) biannual MDA alone, 2) biannual MDA + snail control, and 3) biannual MDA + behavioral interventions . This raises fundamental and unresolved questions on the population biology of schistosomes that could play a key role in shaping the transmission dynamics of the parasite and its potential response or resilience to the treatment.32The two most commonly used indicators to determine the impact of schistosome control programs are prevalence and intensity of infection . Intensities of infection are assumed to relate indirectly to the degree of host morbidity and directly to the hosts’ potential contributions to transmission. Prevalence and intensity data can also be used to evaluate the efficacy of treatment through the cure rate and the egg reduction rate (ERR), the latter recommended by the WHO.34 Additional refinements developed through SCORE studies and beyond now allow repeat analyses on unamplified genomic DNA, cost-effectively and without genome amplification bias, thereby improving data quality and giving the potential for greater analytical depth.36 Such new methodologies and refinements allow an enormous reduction in the logistical effort required in assaying parasite populations. Furthermore, these methodologies can be applied to natural schistosome populations across continents, allowing wide-scale genetic and genomic analyses of schistosome populations over space and time, and encompasses all life stages from adult worms to free-living miracidia and cercariae , these logistical, ethical, and biological caveats have been circumvented.Schistosoma species–specific microsatellite markers have thus been developed and evaluated—from those for S. japonicum in Asia6 to S. mansoni42 and, more recently, S. haematobium.44 For the SCORE Tanzanian S. mansoni samples, two multiplex microsatellite assays were developed from 17 previously published microsatellite loci, which spanned the S. mansoni genome.42 Following data cleaning and quality assessment, 13 of the 17 microsatellites loci were successfully amplified. Similarly, for the SCORE samples from Niger and Zanzibar, we developed two novel multiplex microsatellite assays to enable cost-effective simultaneous amplification of 18 informative microsatellite loci of S. haematobium populations.45 However, the cross-reactivity of these S. haematobium microsatellite loci with other medically and veterinary important schistosomes across the closely related S. haematobium group species required additional consideration, particularly given the ability of species within this group to hybridize.12 The advances in the DNA preparation of schistosome larval stages stored on Whatman FTA cards further enabled both the replication within technique and also the use of additional tools and techniques to be performed on the same individual Schistosoma larval samples. In particular, we were thus able to include species diagnostic PCR’s and DNA sequencing through analysis of mitochondrial cox1 and nuclear ITS regions on the Nigerian samples.11Microsatellites remain one of the most powerful Mendelian markers currently available, although the mutation rate varies across loci, and are ideal for population genetic studies to identify clusters of genetically related individuals. This is potentially indicative of population-level differences over space and time, as well as bottleneck effects, and of both historical and contemporary population-level changes. A number of S. mansoni, with no significant clustering or genetic differentiation by treatment or by year. Instead, it appeared that a large refugia of parasites from untreated individuals in the community and/or in neighboring communities undergoing different treatment schedules allowed for high levels of gene flow and, hence, a mixing of the parasite population between years and between sites. Any observed genetic differentiation at the village or school level appeared most likely to be explicable by the eco-epidemiological location itself (such as distance to water or human population movement), rather than the SCORE treatment arm. This is consistent with the previous work that observed that most of the genetic diversity in S. mansoni occurs at the human host level, rather than at the village or district level.47 Infrapopulation genetic diversity is usually interpreted as the combined outcome of the genetic diversity of parasites circulating in the environment, differences in exposure due to variability in host behaviors, particularly those related to water contact; that is, location, duration, and time of day, as well as parasite establishment, often dependent on host gender and immune response. These factors could well be predicted to dilute any effect imposed by the SCORE-contrasting treatment arms, particularly under conditions of the high gene flow that was apparent across these lacustrine Tanzanian populations.Key results to date of the SCORE population genetic datasets indicate that, within the Tanzanian studies, there was little or no differential impact of annual CWT or SBT on the population genetic structure of S. haematobium, within many of the Niger sites was apparent.Within the SCORE Niger data sets, there were also no significant differences observed between the original SBT and CWT in terms of genetic diversity and/or bottlenecking effects. However, although there appeared to be no significant clustering or genetic differentiation by CWT × 1 and CWT × 2 treatment arms, the greatest reduction in overall genetic diversity levels was observed by the end of the study between the SBT × 2 relative to SBT × 1, potentially consistent with the greater drug pressures (SBT × 2) imposed on these groups. Furthermore, an extensive proportion of hybrid schistosomes, rather than 18 Population genetic analyses also revealed a number of important issues that could not have been detected by classical parasitological prevalence and intensity measures alone. For instance, the key outcome of the main SCORE study was that, although MDA substantially reduced the S. haematobium prevalence and infection intensity, and, hence, “elimination as a public health problem,” it was insufficient to interrupt transmission either alone or when combined with either snail control or behavioral change activities.22 Yet, ongoing population genetic analyses indicate the strongest selective pressures placed on these S. haematobium populations, as suggested by both genetic diversity and outbreeding estimates, together with reductions in the estimated number of adult worms present within each child/adult host, were observed in the MDA + snail control arms. Furthermore, on each island, it appeared that many of the shehias with the highest fecundity levels went on to become persistent hotspots (PHS) relative to those where control was successful. These findings thus highlight the additional contributions that population genetic analyses can make in elucidating biological drivers, and potential gene–environmental interactions, that can lead to areas remaining PHS49 despite ongoing control measures.In the SCORE Zanzibar elimination study within ZEST, the greatest differences overall, both in terms of genetic sub-structuring and parasite fecundity (see in the following text), were observed between the islands, consistent with Pemba’s higher mean infection intensities than Unguja.Ne) (or the effective number of breeders contributing to the next generation [Nb]) of the remaining schistosomes. Analyses of both Ne/Nb within both our SCORE S. haematobium datasets from Zanzibar and Niger (unpublished observations) using two available distinct software packages produced notably different patterns, resulting in too much uncertainty to allow conclusions to be drawn. It was evident that further tools and analytical development would be required for the Schistosoma system to enable robust estimates of Ne/Nb. Indeed, a previous study that used parentage analysis to quantify family structure (including Nb and the number of full-sib families) showed that the reproductive success of breeding schistosomes was likely skewed, resulting in differential representation of each family in the offspring pool.32 It is thus likely that different reproductive outputs could affect the results of statistical models used to estimate female worm burdens from miracidial offspring data using parentage analysis.31 Similarly, sibship reconstruction is a form of parentage analysis that can be used to identify the number of helminth parental genotypes infecting individual hosts using genetic data on only their offspring. However, methods for inferring worm burdens from sibship reconstruction data on numbers of unique parental genotypes were lacking, limiting the method’s scope of application. Therefore, to proceed with our SCORE population genetic analyses, further statistical methods for estimating female worm burdens from data on the unique number of distinct female parental genotypes derived from sibship reconstruction were developed31 and applied to both the S. haematobium and S. mansoni SCORE datasets to produce more robust measures on the within-host parasite populations present.However, even with such sensitive and specific population genetic tools, interrogation of these SCORE datasets also highlighted that many challenges remain for analyses and interpretation. For instance, one of the original aims of SCORE population genetic analyses was to examine the differential impact of drug pressure on the potential for bottlenecking events as well as effective population sizes , and can enhance resilience to interventions. Density dependence can be either positive (density-dependent facilitation) or negative (density-dependent inhibition). For example, in dioecious organisms such as schistosomes, the probability of a female worm encountering a male worm and forming a mating pair may be so low at low parasite densities that reproduction is restricted, whereas at higher parasite densities, the probability of mating pair forming and successful reproduction occurring increases—hence positive density-dependent facilitation. By contrast, in negative density-dependent inhibition, parasite population growth is inhibited at higher parasite densities because of a reduction in per capita egg contribution by each female worm (= adult worm pair) as a function of within-host infection intensity . CompetiSchistosoma egg counts and worm burdens (at least within human hosts). Furthermore, there appears to be no simple worm–egg/larva relationship because of either the trapping of eggs in host tissues and/or to the occurrence of density-dependent mechanisms. The strongest direct evidence for density-dependent fecundity inhibition in schistosomes was generated over 40 years ago, from two human autopsy studies providing data on S. mansoni and S. haematobium worm burdens and associated egg counts.53 However, further statistical analyses of these datasets have provided controversial and contradictory conclusions.56 More recent studies have taken a different approach, by relating S. mansoni in egg counts to circulating anodic (CAA) and cathodic (CCA) antigens, glycoconjugates secreted from the gut of schistosomes, and found the slopes less than unity, suggestive of density dependence.58 Still, the existence of a direct relationship between CAA or CCA antigens and worm burdens has not been proven in humans because it is not possible to exclude other mechanisms affecting antigen production and clearance.60Whether and to what extent density-dependent processes regulate the fecundity of schistosomes is an unresolved, longstanding question because, as stated earlier, it is not possible to directly quantify the relationship between S. mansoni in Tanzanian children using parentage analysis and sibship reconstruction from microsatellite analyses of miracidia.47 Analyses using our newly developed statistical tools indicated that, despite the (minimum) number of adult worm pairs present in school-aged children having reduced over time in response to PZQ treatment, there was little or no evidence of any corresponding reduction in parasite infection intensities, as measured by EPG. This indicated there was a relaxation of the density-dependent inhibition on schistosome fecundity following treatment with PZQ among these S. mansoni populations.31 Furthermore, it highlighted how the continued high EPG infection intensity levels observed after PZQ treatment could have, without the added value of population genetic analyses, been erroneously otherwise interpreted as evidence of, for instance, either lack of treatment coverage and/or reduced drug efficacies if measured only through standard monitoring and evaluation parasitological protocols. Nevertheless, it must also be emphasized that where interventions, through reductions in size of the parasite population, relax density-dependent inhibition in parasite fecundity, as observed here, this can also subsequently lead to increased reproduction rates in drug-resistant or reduced-susceptibility parasites that survive treatment.61One recent population genetic study, however, estimated the number of adult worm genotypes of S. haematobium populations from Zanzibar appear, however, to indicate that such density dependence in response to PZQ/control pressures may not be universal because there was no obvious evidence of relaxed density-dependent inhibition in parasite fecundity in relation to intervention arm. Rather, it appeared that both the number of estimated adult worms and the estimated egg output per female worm significantly reduced across all arms on both islands. Although this could potentially reflect species differences between S. haematobium and S. mansoni, consistent with their contrasting mean daily egg output per adult worm, it is important to acknowledge that the aforementioned Tanzanian S. mansoni populations represented a true pretreatment baseline population,44 whereas these Zanzibar S. haematobium populations had been subjected to many previous treatments, over several years, with PZQ before the start of the SCORE/ZEST program.62 Indeed, within these Zanzibarian S. haematobium populations, there was evidence of density dependence within individuals in relation to host age and gender , consistent with that previously documented through population genetic analyses for both S. mansoni in Tanzania47 and S. haematobium in Mali.44 Furthermore, population genetic and statistical analyses did indicate inherent differences in schistosome fecundity levels by site across both islands, frequently consistent with those shehias that went on to be classed as nonresponder hotspot sites. Thus, although where and under what conditions density dependence regulates schistosome transmission dynamics remains complex, and certainly further work is required in terms of its measurement , we have demonstrated its existence and potential for the interpretation of, and resilience within, public health interventions. Likewise, our results to date do emphasize that potential variability in density-dependent process must now be incorporated into those mathematical transmission models which are being increasingly used to inform intervention policy and design decisions.Our ongoing analyses of SCORE Schistosoma combinations observed, specifically that of S. haematobium with Schistosoma bovis hybrids, in comparison with the pure S. haematobium originally targeted for investigation. Furthermore, most of the infected Bulinus snails across all sites were found to be transmitting the livestock schistosome S. bovis alone, with the remainder, from only five villages north of Niamey, shedding hybrids and only very few shedding S. haematobium. Hybridization of parasites is an emerging public health concern at the interface of infectious disease biology and evolution. Increasing economic development, human migration, global trade, and climate change are all modifying the geographic distribution of existing human, livestock, companion animal, and wildlife parasites.10 As a result, human populations encounter new infections more frequently, and coinfection by multiple parasites from different lineages or species within individual hosts can occur. When closely related species, such as within the S. haematobium group here, coinfect a host, introgression can result , and whole genome admixture can occur through hybridization. Such hybridization can have major implications in light of the current global push for human disease control programs to shift from controlling morbidity to interrupting transmission.9 To what proportion, and where, such hybrids are ancient or ongoing,65 how such introgression may alter host range or host morbidity, and what this implies regarding our concept of species within the Schistosoma system and beyond are critical areas to explore in future research. The challenge of hybridization of schistosomes for routine MDA monitoring and evaluation of parasitological-based diagnostics, however, versus molecular typing diagnostics, is illustrated in One of the most notable results to date with the SCORE population genetic analyses from Niger was the consistently high proportions, often around 80%, of hybridized Last, but by no means least, is the role population genetic (and genomic) analyses can play in elucidating the sustainability of any drug essential for PC/MDA. Drug efficacy, or drug resistance, could not be directly examined within the overall SCORE framework because of the inability to interfere with the timings of the treatment schedule and ethical requirements regarding the need to treat those found to be infected. Nevertheless, the potential importance of drug resistance for operational research and effective disease control, and the need to predict and/or detect as it emerges, cannot be overstated. Drug resistance has been the bane of veterinary anthelmintic treatments, and for human schistosomiasis, this is a particularly pertinent issue, given that PZQ is currently the only drug available for PC. Although the genetics of anthelminthic resistance in general are poorly understood, this is particularly true in regard to the genetics of PZQ resistance in schistosomes. Indeed, even the potential mechanism of action of PZQ remains uncertain.67Resistance generally arises through repeated and extensive exposure of the parasite to the selective pressure of a chemotherapeutic agent. Parasites that survive multiple exposures to a given drug may pass on genetic variants that make the offspring more resistant to the drug. The rate and likelihood of resistance developing is influenced by a broad range of factors including, but not exclusive to, baseline parasite genetic structure and underlying standing variation in drug tolerance on which selection can act, effective population size, refugia, and gene flow in parallel to treatment frequency, dosage, and whether single or combination drug regimens.S. mansoni collected 4–6 weeks after treatment in Brazil has been reported to show reduced genetic diversity,68 with a similar reduction in S. mansoni genetic diversity 6 months after a single PZQ treatment in two schools in Tanzania.70 However, genetic diversity of S. mansoni from these same regions of Tanzania over a 5-year period of MDA was not found to be reduced.47 Similarly, one longitudinal study examining the genetic diversity and population structure of S. mansoni at the beginning of a national control program in Uganda in 2003, within a highly endemic region on Lake Victoria, found that although egg output and genetic diversity significantly decreased immediately after drug administration, both recovered within 6 months.67 Both these Tanzanian47 and Ugandan67 studies also observed low genetic differentiation between children from different schools, indicative of high rates of parasite gene flow at this geographic scale. This is likely to reduce the likelihood of local adaptation, so even if repeated drug treatments select resistant genotypes, these may be diluted and outcompeted by new genotypes migrating in. These findings also suggested that resistance to PZQ may be unlikely to establish or spread in these populations at the beginning of the national control program, given the current selection of targeting school-aged children alone.69 Nevertheless, these studies on S. mansoni susceptibility to PZQ (at least within Uganda), as recent findings have shown for oxamniquine resistance,66 have identified standing variation in drug efficacies and clearance rates on which selection can act. This might indeed be expected to accentuate as drug pressures over time increase. It is possible that evidence of such may be apparent already within Uganda, at least, where those populations under the longest and strongest past treatment pressures have been associated with significant reductions in drug efficacy.15Given this inherent range of factors influencing the rate and likelihood of PZQ resistance emerging, as may be predicted, population genetic data gathered to date can be contradictory, particularly in relation to the time of assessment. For example, 25 In practice, this guidance is seldom adopted and the efficacy of PZQ is not monitored on a systematic basis. The fact that some schistosomes do survive exposure to PZQ as it is currently dosed and that PZQ is being distributed on an unprecedented scale should be a strong stimulus to monitor for the possible development of drug resistance on a more routine basis. Indeed, mathematical predictive models already suggest that the increased chemotherapeutic treatment pressures now being dispensed in SSA might allow both parasites with drug resistance/intermediate susceptibility to successfully invade and coexist with susceptible and resistant parasite strains.71Therefore, even without current molecular markers of PZQ resistance, we can demonstrate that quantifying population structure can help predict the likelihood of genotype spread when local populations are under selection, such as by PZQ MDA. The WHO recommends that efficacy be assessed either when there is a suspicion that PZQ is not performing as expected despite satisfactory coverage and adherence or when the drug has been distributed by MDA for 4 years or more.Gene flow, effective population sizes, reproductive and mating systems, host transmission and host specificity, and the standing variation in terms of potential resistance alleles, to name but a few, will all affect the epidemiology of schistosomiasis by influencing the evolutionary potential of these parasites in response to the control pressures imposed on them. Understanding these parameters will help predict the likelihood of success while mitigating against the evolution and establishment of drug resistance and increase the longevity and sustainability of interventions. When designing disease control programs, and monitoring and evaluating their impact, it is essential that we can accurately understand and predict parasite populations. Indeed, knowing the population boundaries is required to characterize the units relevant for infection control and the geographical scale at which control measures must be applied. Because of their complex indirectly transmitted life cycle, size, and location within the (human) definitive host, deciphering the patterns of transmission and mating/reproductive patterns of schistosomes requires indirect methods such as population genetic analyses using tools such as microsatellite markers.S. mansoni on mainland Tanzania and S. haematobium in Niger, where we have seen extremely high levels of gene flow, the migration of parasites between treatment arms and villages, and between treated and untreated people, occurs and mitigates against definitive demarcation of treatment pressure impact. By contrast, the potential limited gene flow of S. haematobium on the Zanzibar islands may result in contrasting population genetic structures between even geographically close areas, overwhelming any potential treatment arm effects imposed.Within those SCORE countries and treatment arms encompassing population genetic analyses, further insights have been revealed that could not have been achieved by classical prevalence and intensity parasitological tools alone. We have also observed a basic challenge to genetic studies based on the cluster randomization design in these operational research studies. For example, with both 72 However, population genetic analyses on these SCORE datasets to date indicate that, with regard to Tanzanian S. mansoni at least, persistence may relate to localized high-transmission water contact sites, whereas in Zanzibar, it may relate more to inherent differences in the fecundity and, hence, egg output of the infecting S. haematobium across small geographical scales, potentially independent of the treatment arm. In regard to SCORE studies from Niger, the major factor relating to ongoing transmission sites appeared to be the geographical variation in the occurrence of introgressed hybridized schistosomes between S. haematobium with S. bovis. Although such hybridization events in Niger may well be independent of the SCORE treatment arms, they do highlight the issue of animal reservoirs of schistosome infection in SSA, and the need for a One Health approach to achieve WHO control and elimination targets.73Furthermore, there is no general consensus on how to define biological hotspots, based on parasite genetics/responsiveness, as distinct from operational hotspots, even if a broad definition that accounts for any failure to decrease prevalence and/or intensity over time is favored.74 This, coupled with the increased availability of genome sequences and the recent advances and affordability of DNA sequencing technologies, will greatly facilitate comparative studies of species/populations from contrasting situations (such as between those in PHS from responder sites). This will further facilitate the identification of loci of particular interest, and population genetic and genomic studies of schistosomes may become even more helpful in determining the impact of differential treatment regimens on schistosomes. In turn, such findings will shed light on what strategies and counterstrategies schistosomes may possess to help maintain and maximize their ongoing transmission. Population genetic and genomic analyses of these complex and resourceful parasites under intervention pressure should help further elucidate fundamental principles of ecological and evolutionary theory from that of host specialization, reproductive mode, transmission dynamics, speciation, and even perhaps the evolution of parasitism itself, as well as providing much needed information for control and elimination efforts.It should be noted that all the SCORE parasite samples are archived within the Schistosomiasis collection at Natural History Museum (SCAN) repository.


Danio rerio) is an excellent model to study a wide diversity of human cancers. In this review, we provide an overview of the genetic and reverse genetic toolbox allowing the generation of zebrafish lines that develop tumors. The large spectrum of genetic tools enables the engineering of zebrafish lines harboring precise genetic alterations found in human patients, the generation of zebrafish carrying somatic or germline inheritable mutations or zebrafish showing conditional expression of the oncogenic mutations. Comparative transcriptomics demonstrate that many of the zebrafish tumors share molecular signatures similar to those found in human cancers. Thus, zebrafish cancer models provide a unique in vivo platform to investigate cancer initiation and progression at the molecular and cellular levels, to identify novel genes involved in tumorigenesis as well as to contemplate new therapeutic strategies.Zebrafish ( Xiphophorus maculatus and the green swordtail Xiphophorus helleri spontaneously develop malignant melanomas . . 233]. OTHOR .Oncogene expression through transgenesis via the injection of transgenes into one-cell stage embryos implies that these oncogenes will be expressed from early embryogenesis, with potentially deleterious effects during development. To bypass this possible limitation, Richard White and colleagues  implemented a method called TEAZ (Transgene Electroporation in Adult Zebrafish) to deliver transgenes driving oncogene as well as CRISPR/Cas9 components expression, directly into somatic tissues of adult zebrafish using electroporation . One intThen, the continuous progress in the improvement of conditional transgenesis approaches in zebrafish has generated an immense toolbox for controlling oncogene and tumor-suppressor gene activities in specific cells and a given time. This set of tools offers a variety of modelling possibilities in the field of cancer research.Over the past two decades, the progresses accomplished in the implementation of sophisticated genetic and reverse genetic tools allowed the emergence of a remarkable diversity of genetically engineered zebrafish cancer models. The precision of the reverse genetic toolbox enables the generation of zebrafish lines carrying genetic modifications that are identical to the alterations found in patients. Transcriptomic and oncogenomic comparisons between human and zebrafish tumors reveal common molecular signatures and potentially lead to the discovery of novel genes involved in tumorigenesis ,240,241.


Alouatta caraya) is a neotropical primate threatened by habitat loss and capture for illegal trade in Argentina. Using multilocus microsatellite genotypes from 178 A. caraya individuals sampled from 15 localities in Argentina, we built a genotype reference database (GRDB). Bayesian assignment methods applied to the GRDB allowed us to correctly re-assign 73% of individuals to their true location of origin and 93.3% to their cluster of origin. We used the GRDB to assign 22 confiscated individuals (17 of which were reintroduced), and 3 corpses to both localities and clusters of origin. We assigned with a probability >70% the locality of origin of 14 individuals and the cluster of origin of 21. We found that most of the confiscated individuals were assigned to one cluster (F-Ch-C) and two localities included in the GRDB, suggesting that trafficked A. caraya primarily originated in this area. Our results reveal that only 4 of 17 reintroduced individuals were released in sites corresponding to their cluster of origin. Our findings illustrate the applicability of genotype databases for inferring hotspots of illegal capture and for guiding future reintroduction efforts, both of which are essential elements of species protection and recovery programs.The black and gold howler monkey ( For example, when significant genetic structure exists within the species in question, translocations may inadvertently lead to admixture of distinct evolutionary lineages and act to homogenize existing diversity and biogeographic patterns instead of protect them5. However, the genetic consequences of translocations have seldom been studied6, and in Latin America, the development of biodiversity management and conservation plans as part of public policy have not yet taken advantage of newly developed genetic techniques to inform translocation policy and decisions7. Conservation genetics can help strengthen the links between scientists and decision makers and improve reintroduction and translocation policy.Similar to other countries, wildlife illegal hunting and trade are threats to Argentinian wildlife. Confiscated and surrendered animals from trafficking are transported to rehabilitation centres, and the return of these confiscated animals to the wild receives strong support from the public. Ideally, trafficked animals would be reintroduced into the population that they were extracted from or translocated to another suitable site within the species’ original range. Although translocations are considered a good option for conservation and a solution to trafficked animals10. Based on this, the populations studied may be grouped into clusters based on genetic similarity. Once such an assessment of differentiation between populations/localities or clusters is obtained, genetic assignment analysis of individuals of unknown provenience can be performed to identify the likely locality or cluster to which they belong – i.e., to infer their geographical sites of origin. This identification, made in advance of any translocation attempt, enables researchers to estimate the genetic diversity introduced into the environment when translocations and/or reintroductions occur. Genetic assignment to a geographic area of origin is possible only when a suitable genotype reference database (GRDB) is available to compare results. The construction of a GRDB represents a requirement and, ideally, should include the largest possible sample across a species’ distribution range. Moreover, the data quality must be extremely reliable, because future determinations will rely on these data.Molecular genetic studies using individuals of known origin allow researchers to calculate levels of differentiation among populations and to assess population structure within a species in the wild11, tortoises12, African elephants for the ivory trade14, mouflons15, bears16, salmon17, timber18, palm trees19, and macaws10. These studies allowed researchers to: (1) accurately discriminate among ivory trafficked from elephants from the four major regions of Africa14, (2) identify poaching of the protected Sardinian mouflon, where a suspected poacher found in possession of a carcass initially claimed that it was from a sheep from his flock15, (3) identify farmed Atlantic salmon escapes representing a threat to the genetic integrity of natural populations17, (4) develop a genetic reference database characterising the populations of origin for illegally logged timber, which creates market disadvantages for products from sustainable forestry18, (5) determine that illegally-traded palm species in Brazil were obtained from several sites showing that there is no single target locality used by poachers19, and (6) differentiate among macaws from 3 geographic regions of Brazil and identify the most probable regions of origin of 24 confiscated individuals10. These approaches underscore the importance and potential of molecular analyses for wildlife management and for identification of species to prevent illegal trafficking.Geographic origin determination using simple tandem repeats markers  has already been applied to several species, such as bobcatsAlouatta caraya is the most commonly trafficked primate species found in the illegal pet trade in Argentina. Trade in this species appears to be internal as there are no records of cross-national trafficking. This trafficking is reflected by the number of individuals confiscated during control and inspection operations22. Official records from the National Wildlife Surveillance and Control Directorate show that 10 individuals of A. caraya were confiscated and/or voluntarily surrendered in the last 4 years, although the number of unofficial cases is higher. Alouatta caraya is globally categorized as “Near Threatened” by the IUCN23, but is considered “Vulnerable” in the red list of Argentina24 due to population reduction, decreased area of occupancy and/or quality of habitat, increased exploitation due to hunting or illegal traffic (pet trade), and the effects of pathogens and parasites. Currently, Argentina’s native forests are rapidly being replaced as a result of growing urbanization, industrialization, and large-scale clearing for agricultural purposes22. This process of transforming native subtropical forests is clearly evident in northern Argentina where five species of primates are found . Of these five, both Alouatta species are of great epidemiological importance since they are highly sensitive to the Yellow Fever virus (YFV). Alouatta caraya show high mortality when infected by the virus. Thus, this species serves as an early epidemiological sentinel, allowing the establishment of control and prevention measures27.Alouatta caraya has been the focus of several population genetic studies of dispersal patterns, kinship, reproductive success, and phylogeography, among other topics31. In a recent study, ten nuclear microsatellites were used to generate a DNA genetic reference database characterising the southernmost populations of this species9 . As part of the monitoring program for the epidemiological surveillance of YFV and other arboviruses in non-human primates (NHP), the number of genotypes included in the reference database for this area was increased with samples collected during 2017 and 2018.A. caraya confiscated from the illegal trade and subsequently housed at the Güirá-Oga rescue centre (Güirá-Oga) in Puerto Iguazú, Misiones and the rescue centre Estación Zoológica Experimental Granja La Esmeralda  in Santa Fe, Argentina, as well as 3 individuals found dead in cities in northern Argentina. We aimed to infer the geographic site of origin of these animals by comparing the specimens’ genetic profiles with the profiles included in a regional GRDB for A. caraya.Here, we present the results of genetic analyses designed to estimate the origin of 22 This study underscores the use of STR-based genetic databases as a tool for identifying the origin of individual animals, an outcome with application to understanding patterns of illegal wildlife trade, for assessing whether prior translocations in northern Argentina had indeed occurred into likely areas of origin, and to provide a reliable tool for future translocation/reintroduction program protocols.28. To these, we added newly derived and complete genotypes at 10 microsatellite loci obtained from fecal samples of 39 individuals from 5 new localities. Two fecal samples were collected from each of these individuals, and the genotype assignment for each individual at each locus was replicated twice (in the case of heterozygotes) or four times (in case of homozygotes) in order to minimize possible genotyping errors due to allelic dropout. We likewise constructed complete 10-locus genotypes for all 25 additional individual samples of unknown provenience for genetic assignment. The complete Genotype database obtained from all localities and clusters of A. caraya studied in this investigation is available at: 10.5281/zenodo.3660723, and the genotypes of all the assigned individuals are presented as Supplementary Table Genotypes of 143 individuals from 10 localities (Locs) were available from previous studies32.We did not observe evidence of scoring errors due to stuttering, large allele dropout, or null alleles for any locus in any population in our screening with the program Micro-Checker v2.2.333, we did not observe evidence of linkage between any pair of loci (P > 0.05). Significant deviation from Hardy-Weinberg equilibrium was only detected by GenAlEx v 6.534 and Arlequin v 3.533 softwares for the marker D8S165 in Loc 9 . Significant evidence of inbreeding (inbreeding coefficient: FIS = 0.29) was already found for this population in a previous study9. The numbers of different alleles, effective and private alleles, observed heterozygosity (Ho), expected heterozygosity (He) and unbiased expected heterozygosity (uHe) are presented in Table Using Arlequin v 3.535 and applying the method described by Evanno36 to a dataset comprising genotypes already collected for A. caraya in Oklander et al.9 plus the new localities sampled here showed that the most likely number of different genetic clusters was three  when separated according to the 15 localities and 93.3%  when separated according to the three clusters. We then used GeneClass2 to assign the genotypes of each of the 22 confiscated individuals and 3 corpses of unknown origin to both localities and clusters in the GRDB  most likely came from Loc 3, two from Loc 2, two from Loc 5, two from Loc 6, one from Loc 1 and one from Loc 13. Additionally, of the 3 corpses found in cities, we could only assign one them, found in Loc 13, to Loc 5, therefore showing a different likely site of origin from the place where was found  to 1 of the 3 clusters (F-Ch-C). Four were assigned to the P-PR cluster and 3 to the M-RU cluster.A. caraya , while 11 belonged to the cluster F-Ch-C and other 4 to the P-RP clusters Table  were conFinally, of the 17 individuals reintroduced (12 in Misiones and 5 in Santa Fe) only four were reintroduced into a site in their cluster of origin , allowing a reliable assessment of the effects of exploitation and the conservation needs of species that would be impossible otherwise. This is the case of the genetic assignment of 28 ivory samples from different elephant populations in Africa12, where researchers also developed a GRDB and correctly assigned 90% of the individuals in that database to their population of origin, the lack of assignment of confiscated individuals was attributed to the fact that they came from different locations than the sampling sites included in the GRDB. Nevertheless, the researchers were able to determine that all the confiscated individuals came from the same population. This other approach of genetic assignment of living animals (mostly confiscated or in captivity) shows how genetic tools can be used by wildlife managers to identify the most probable regions of origin of individuals as well as to determine the genetic appropriateness of potential recipient populations when designing reintroduction projects. Translocations have been used to mitigate population decline and restore locally extinct populations6. In these cases, genetic data are necessary to guide the selection of populations of origin to which translocated individuals should be released and subsequently evaluate the success of the restoration43.In a study on tortoisesA. caraya originated in this area.In this study, the first application of the GRDB of howler monkeys, our results indicate that the most likely origins of most of the confiscated and surrendered individuals were from the areas around Locs 2 and 3, close to the Argentina-Paraguay border Fig. . TherefoA. caraya has been reported at several locations along this highway22. This information supports a possible animal trafficking route that begins in northeastern Argentina and ends in Buenos Aires, where the majority of confiscations occur . Importantly, most of the confiscations and surrenders occurred in cities outside the normal distribution of the species  and 39 new individuals. Table The GRDB for Twenty-five samples were collected for individual genetic assignment. We received hair samples from 17 howler monkeys arriving at Güirá-Oga in 2017. Twelve of these individuals were later reintroduced into a protected area in Isla Palacio at 25°53′32″S, 54°24′38″W . DNA was extracted from faeces using the QIAamp DNA Stool Mini Kit , according to the manufacturer’s protocols with slight modifications. DNA was extracted from tissue and hair samples using standard SDS/Proteinase K digestion followed by phenol: chloroform (1 to 1 volume ratio) organic extraction and Microcon P-100 counter-dialysis filters45.Two separate faecal samples per individual were stored at room temperature in 50 ml screw-top tubes containing solid NaClA. caraya or other primates and previously used in studies of A. caraya population genetics, were amplified from each sample: AC14, AC17, AC45, TGMS1, TGMS2, D8S165, D17S804, LL1118, LL157 and AB0749. Genotyping PCRs were performed in a final volume of 25 µl using 5–10 ng of DNA template for tissue samples or 5 µl of the extraction pool from stool samples and included 20 mM Tris–HCl, 50 mM KCl, 1.5 mM MgCl2, 0.2 mM each dNTP, 1 U of GoTaq DNA polymerase , 1 pmol of each forward primer bearing an M13 tail, 4 pmol of each reverse primer, and 4 pmol of M13 labelled with a fluorescent dye (6‐FAM) on its 5′ end following recommendations from previous studies50. All amplifications were performed in a Gen Amp ABI 9700 machine . PCR products labelled with different fluorochromes were combined and the amplicons separated by electrophoresis on an ABI PRISM 310 Genetic Analyzer . Alleles were manually scored by performing a visual inspection of electropherograms after developing the bin panel for each locus in GeneMapper ID-X v. 1.2  using HD400-ROX as internal size standard. For DNA extracted from stool samples, PCR and sizing was repeated twice  or four times  to minimize possible genotyping errors due to allelic dropout52. We recorded an allele only if it was observed at least twice in different amplifications from the same DNA extract. All amplification assays included negative controls.Ten microsatellites, developed for https://www.asp.org/society/resolutions/EthicalTreatmentOfNonHumanPrimates.cfm). We received specific approval to conduct this study by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) of Argentina (no. 11420110100322CO). Sampling permits for all the locations of the original database previously complied were already published7. Additional specific sampling permits for the new samples presented in this study were obtained from the Ministry of Ecology, Misiones province, Argentina (Permit number: 9910-00086/17) and from the Ministry of Production Santa Fe province, Argentina (Permit Number: GT 13605). Faecal collection was conducted without capturing animals and therefore does not cause any harm to the studied species.This study was carried out in strict accordance with Argentinean laws for research on NHP and following the recommendations of ‘Principles for the Ethical Treatment of Primates’ of the American Society of Primatologists , expected heterozygosity (He), unbiased expected heterozygosity (uHe) and inbreeding coefficient were computed with the software GenAlEx v6.534 for each locus and population. Deviations from Hardy-Weinberg equilibrium (HWE) were assessed by employing an exact test and FIS inbreeding coefficient using Arlequin v 3.5 software33. Allelic richness was calculated for each locus in a population using the equation: equation:Ni represents the number of alleles of type i among the 2N genes, and n is sample size, using the software, Fstat v2.9.453.Genotypes were screened for null-alleles and to discriminate between errors in allele frequency estimates caused by null-alleles, allele dropout or stutter bands using Micro-Checker v2.2.335 program. A series of 20 independent runs per K (ranging from 2 to 6) was conducted using the admixture model with correlated allele frequencies, sampling locations as a prior (LOCPRIOR), and 500,000 Monte Carlo-Markov iterations after a burn-in of 50,000 replicates. The data analysis procedure was further refined using Clump software54 and a bar plot was constructed with the Disrupt software55. The most likely number of K was identified using the method described by Evanno36.The new complete set of samples collected was analysed using non-spatial Bayesian clustering with the Structure v.2.3.456.Assignment tests give the probability of an individual’s multilocus genotype of belonging to reference populations/locations or clusters37 to assign the origin of confiscated individuals of unknown origin into the 15 potential locations sampled and into the different three clusters described here using a leave one-out procedure, excluding self-assignment and being 0.05 the assignment threshold score . After testing all the combinations of approaches presented by the program, we chose the Bayesian criteria described by Rannala & Mountain57, resulting in a higher quality index and the highest number of correctly assigned individuals when tested against the database. This program is suitable for our study because it does not assume that all populations of origin have been sampled.We used Bayesian methods computed by the software GeneClass2i, in a population, T, is computed as follows:Li, T the likelihood value of the individual i in the population T.For each individual, the best matching location and cluster were sorted and a score in percentage was obtained. The score of an individual, 39.Once this probability of assignment of each individual to a certain location or cluster was obtained, we established a threshold value above which assignment was considered reliable. The criteria were that the scoring for a given population was superior to 70%. This threshold was based on our correctly obtained re-assignments of 73% of the database individuals to their locality and in previous works on assignments for fishes and birdsSupplementary Tables.


Oral mucosal diseases (OMDs) encompass a variety of different types of diseases. Our aim was to evaluate the prevalence and related risk factors of OMDs among residents in the Baoshan District of Shanghai, China, and provide a scientific basis for prevention and control strategies.2 test was used to compare rates of risk factors and logistic regression analysis was used to detect the correlation between disease and risk factors.A sample of 653 residents aged 17 to 92 years from the Baoshan community was investigated in 2014. Each resident was surveyed by questionnaire to evaluate their oral mucosa and oral mucosa examinations were conducted. We followed up with 607 residents in 2018. All data were statistically analyzed using the SPSS 25.0 software package  at the general population, gender and age levels. A XThe prevalence rate of OMDs was found to be 9.19%–9.56% (2014–2018). The most common OMDs were atrophic glossitis (1.84%), recurrent aphthous ulcer , burning mouth syndrome , oral lichen planus  and traumatic ulcers (1.23%). The prevalence of RAU and BMS in different age groups was significantly different. Tobacco and alcohol use and psychological factors in the OMDs group were higher than the no-OMDs group. Systemic diseases including diabetes mellitus (DM) was significantly relevant to OLP.Age, tobacco and alcohol use, and psychological factor correlated strongly with the occurrence and development of OMDs, and they should be the focus of primary prevention. General epidemiological studies suggested that OLP was closely related to DM. Oral mucosal diseases (OMDs) occur in the oral mucosa and most of them are associated with systemic diseases . The varMost of the causes of OPMDs are complex, and their pathogenesis and cancerous mechanism are unclear. Generally, there are no specific treatments and no effective chemical prophylaxis drugs for cancer . OPMDs hShanghai, with a population of more than 24 million, was chosen as the location of the study because work stress, mental stress, environmental changes, dietary changes and accelerated life rhythms are thought to increase the prevalence of OMDs. Therefore, it is of great interest to understand the epidemiological characteristics of OMDs in this city and analyze the risk factors associated with those diseases. Early prevention and treatment of OMDs and early detection of high-risk groups of oral cancer will improve people’s quality of life.The prevalence of OMDs was studied in a cross-sectional study using general epidemiology. Our subjects were either selected from the general population in the Shanghai region or were oral outpatients. There is currently little available data on the OMDs in the Shanghai population. In the last 30 years, epidemiological investigations of OMDs have been mostly based on analysis of clinical data, or case-controlled studies .Little work has been done in the epidemiology of OMDs, especially descriptive epidemiology; most previous studies are investigations of specific age groups or special diseases . In ordeIn this article, we report on the epidemiological characteristics of OMDs in the Baoshan District of Shanghai, analyze the risk factors of the diseases to better understand prevalence and epidemic characteristics, and establish a scientific basis for the prevention and treatment of OMDs.n = t2∗p∕d(1 − P) 2, p = 14.93%, d = 0.1 * p, take α = 0.05, t = 1.96] yielded a theoretical figure of 2,189 people. Unfortunately, this study was only able to sample 653 people. We conducted follow up with patients 4 years later to assess the long-term effects of persistent risk factors on the course of the disease. The procedures were approved by the Ethical Committee of the Stomatological Hospital Affiliated to Nanjing Medical University (PJ2014-132) and the ethical committee of Shanghai First People’s Hospital (2019KY063).We used multistage stratified random sampling and field surveys to investigate the population of the Baoshan District of Shanghai in 2014. Four neighborhood committees were selected by a random cluster method, and entire families were selected for oral examination according to house number. We calculated maximum sample size using the OMD prevalence rate of 14.93% . Two people were responsible for data entry and analysis. A XA total of 653 permanent residents were surveyed in 2014, including 337 males and 316 females. We followed up with approximately 93% of those residents in the 2018 survey: 607 residents (311 men and 296 women). The remaining 7% were lost to follow up.The education levels of the test subjects were mainly middle school (and below) level, mostly retired people. The average age of the respondents in 2014 was 66.05 years old (17–92), and the average age in 2018 was 67.38 years old (18–91).In the 2014 survey, a total of 60 people (9.19%) had OMDs, which included 12 cases of atrophic glossitis (1.84%), 11 cases of RAU (1.68%), nine cases of burning mouth syndrome (BMS) (1.38%), eight cases of OLP (1.23%), eight cases of traumatic ulcer (1.23%), six cases of furrowed tongue (0.92%), three cases of geographic tongue (0.46%), one case of cheilitis (0.15%), one case of angular cheilitis (0.15%) and one case of leukoplakia (0.15%). The results showed that the OMDs were mostly localized at the back of tongue, buccal mucosa, labial mucosa and gingival (gums).In 2018, 58 people (9.56%) had OMDs . Compared to 2014, there were ten cases of decreased OMDs and nine cases of new OMDs as follows: 11 cases of atrophic glossitis , nine cases of RAU , seven cases with furrowed tongue , nine cases of OLP , seven cases of traumatic ulcer , one case of geographic tongue . One case each of cheilitis, angular cheilitis and leukoplakia .p < 0.05; p < 0.05). The cases of RAU and BMS were statistically significant in different age groups. The prevalence of RAU in 15–29 year-olds and 30–39 year-olds was significantly higher than that in the older age groups  (p < 0.01), but there was no significant difference between the two groups (p > 0.05). We found BMS more frequently in people over 40 years old, prevalence increased with age, and was significantly higher in the 70–79 age group than in the 40–49 group (p < 0.01). OLP was detected in the population over 40 years old, and there was no significant difference among age groups  and alcohol consumption (p = 0.00003) in persons with OMDs was higher compared to those with no OMDs (p = 0. 0002). However, there was no statistically significant difference in stress between persons with OMDs and no OMDs (p = 0. 903)  smokers (people who had smoked for over three years) and 93 (14.24%) who were drinkers (millet wine and beer). The rate of smoking ( no OMDs . Our sur 0. 903) . The samThis study found that there was no significant difference in the prevalence of OMDs between men and women in the population of Baoshan, Shanghai. It also found that the occurrence and prevalence of OMDs, particularly atrophic glossitis, RAU, BMS and OLP, were positively correlated with increasing age among residents.p = 0.025) and metabolic diseases or diabetes (p = 0.028), were closely related to OMDs. Systemic diseases such as cardiovascular and cerebrovascular hypertension (p = 0.025), and metabolic diseases diabetes (p = 0.028) were closely related to OMDs. The investigation group theorized that the high number of elderly patients would have proportionally higher incidence of dental cavities and removable dentures and presumed that incidence of traumatic ulcers would also increase proportionally  (OLP was more common in the elderly age group (40–60 years) and occurred in more females than males. In this study, OLPs mostly occurred in the buccal region. The cause of OLP is complex and still unknown. The WHO defines OLP as a potentially malignant disease of the oral mucosa. Currently, there is no specific treatment for OLP in clinical practice. As a result, the course of the disease is prolonged and the condition often recurs. OLP may develop into cancer, which has a vital impact on patients’ physical and mental health. The results of the general epidemiological investigation showed that there were eight community residents (1.23%) with OLP: two males and six females . OLP was significantly associated with age, smoking and diabetes mellitus ( < 0.01) .In this survey, we found that the prevalence rate of OMDs in the Shanghai region varied from 9.19% (2014) to 9.56% (2018). The most prevalent OMDs were incidence atrophic glossitis, RAU, BMS and OLP. Other surveys in Shanghai have found higher OMD prevalence rates: Work, psychological stress, environmental changes, changes in diet structure and the accelerated pace of life are all thought to lead to an increased prevalence of the OMD condition.In our study, OMDs were positively correlated with age, which supports the findings of OLP and BMS also occurred in the older age group (40–60), and more women than men were ill.In 2014, eight residents with traumatic ulcers were recommended treatment at the time of the first examination. During the 2018 follow-up, all eight patients dealt with trauma factors and their ulcers had disappeared. It indicated that community epidemiological surveys have, in addition to a diagnostic function, a preventative function that may contribute to the early treatment of residents, reduce cancer risk, and limit the number of untreated ulcers. The follow-up indicated that early prevention and control of mucosal diseases was of great significance. Seven new cases of traumatic ulcers found in the current investigation were treated and the patients were informed of the risks. Many of the interviewees were elderly people with high rates of dental cavities and periodontitis. The elderly lacked the awareness to treat cavities and maintain dentures. As a result of the study, detection rates of traumatic ulcers increased, in particular, those occurring at the denture base covering the gums and palate areas. Glossy, traumatic ulcers were associated with residual roots and crowns. With continuous improvement in living standards, the rate of visits to medical centers to treat traumatic ulcers has gradually increased.Our study showed that the prevalence of OMDs in patients who smoke, drink and experience anxiety was higher than in those who were healthy. Many studies have confirmed that tobacco and alcohol are closely related to OMDs and are risk factors for OMDs . In pastIt is noteworthy that the 4-year follow-up of community residents with OMDs suggested that systemic metabolic diseases, such as DM, were closely related to OPMDs, such as OLP. This result is consistent with the results of a large-scale oral health epidemiological investigation, in which we participated, in 2012 in Shanghai . The OMDEarly prevention and control of mucosal disease is of great importance. It is necessary to better understand the prevalence of OMDs among Shanghai residents, and to carry out effective intervention activities through better health education, policy formulation and the creation of a supportive environment. Further, there is a need to reduce related risk factors, to promote the health of oral mucosa, and to improve the quality of life of the population. Finally, we consider it imperative to elevate the importance of primary prevention and treatment of OMDs.10.7717/peerj.8644/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.8644/supp-2Supplemental Information 2Click here for additional data file.