The Hemagglutination assay (HA) is widely used in plague diagnosis, however, it has a subjective interpretation and demands high amounts of antigen and other immunobiological supplies. On the other hand, the conventional Anti-IgG ELISA is limited by the need of specific conjugates for multiple plague hosts, which leaves a gap for new diagnostic methods able to cover both the diagnosis of human cases and the epidemiological surveillance of multiple sentinel species.We developed an ELISA Protein A-peroxidase method to detect anti-F1 antibodies across several species, including humans. To determine the cut-off and performance rates, HA results from 288 samples  were used as reference. Next, we evaluated the agreement between Protein A-ELISA and Anti-IgG ELISA in an expanded sample set (n = 487).Optimal conditions were found with 250ng/well of F1 and 1:500 serum dilution. Protein A-ELISA showed high repeatability and reproducibility. We observed good correlation rates between the Protein A and IgG ELISAs optical densities and a higher positive/negative OD ratio for the Protein A-ELISA method. The overall sensitivity, specificity and area under the curve for Protein A-ELISA were 94%, 99% and 0.99, respectively. Similar results were observed for each species separately. In the analysis of the expanded sample set, there was a strong agreement between Protein A and IgG assays (kappa = 0.97). Furthermore, there was no cross-reaction with other common infectious diseases, such as dengue, Zika, Chagas disease, tuberculosis (humans) and ehrlichiosis, anaplasmosis and leishmaniasis (dogs).Altogether, the Protein A-ELISA showed high performance when compared both to HA and Anti-IgG ELISA, with a polyvalent single protocol that requires reduced amounts of antigen and can be employed to any plague hosts. Yersinia pestis, the causative agent of plague, have a long list of animal reservoirs across distinct ecosystems. Briefly, we first evaluated the best reaction parameters, such as antigen concentration, serum and protein A-conjugate dilutions. Next, we used serum samples from humans, dogs, rodents and rabbits (n = 288) with known results for plague serology by a conventional method, to evaluate the performance of the new Protein A-ELISA test. We observed a good performance of the novel Protein A-ELISA test, with high sensitivity and specificity rates. Evaluation of the coefficient of variation revealed that the test measurements suffer little variation, and therefore, has high repeatability and reproducibility. Next, by evaluating 487 samples, we observed a high degree of concordance between the Protein A-ELISA with a conventional IgG-based ELISA. Furthermore, this test showed no significant cross-reaction with other common infectious diseases. Altogether, the Protein A-ELISA showed high performance when compared both to HA and Anti-IgG ELISA, with a single protocol that requires reduced amounts of antigen and can be employed to several plague hosts.Here, we developed and evaluated an ELISA diagnostic test based on the Protein A-peroxidase conjugate that allows the test to be used for plague laboratorial diagnosis not only in humans, but also in a wide range of mammalian species. This particularity is specifically important for plague epidemiological surveillance, given that  Yersinia pestis and was responsible for at least three pandemics in the past [Plague is a flea-transmitted disease caused by the gram-negative bacterium the past . Althougthe past . Human cthe past . Despitethe past ,4.Y. pestis and may take part in the dynamics of the infection [Y. pestis, and highly immunogenic for humans and other mammals [Although the rodents are the main plague reservoir, practically any mammal can be infected by nfection . An intenfection ,7,8. Duenfection . Most se mammals ,11.Given its polyvalence for sera from all taxonomic family groups, hemagglutination (HA) has been widely used for plague serological diagnosis for several decades ,11,12. HStaphylococcus aureus protein A has been proposed for diagnosis of other multi-host diseases due to its universal affinity for immunoglobulins from various species of domestic and wild mammals [Alternatively to immunoglobulin (anti-IgG) conjugates, the  mammals –21. To tThe production of immune sera for positive controls was approved by the Animal Ethics Committee of the Aggeu Magalhães Institute , following the local Animal Ethics Committees (CEUA/IAM) guidelines and it supplies the laboratorial diagnosis of plague by the SRP. The use of the retrospectively collected human serum samples in this research was approved by Ethics Committee of the Aggeu Magalhães Institute . Sera from rodents and dogs were obtained during the routine operations of the Brazilian plague surveillance program and are maintained in the serum collection of the SRP-IAM.Y. pestis strain (pCD1-), according to the protocol described by Chu [The F1 antigen was extracted from the attenuated A1122 d by Chu , in a BiInitially, we retrospectively accessed 288 sera (98 positives and 190 negatives) including 81 control rabbits (37 positives and 44 negatives), 64 humans (21 positives and 43 negatives), 66 rodents (20 positives and 46 negatives) and 77 dogs (20 positives and 57 negatives) with well-characterized results in HA for cut-off determination and Protein A-ELISA validation. Next, 265 additional samples with unknown HA results nor used in cut-off determination were included to evaluate the agreement between the Protein A and IgG methods in ELISA. Sera were provided by the Brazilian Plague Reference Service (SRP) from the Aggeu Magalhães Institute (IAM) and originated mainly from the routine surveillance of the Brazilian plague areas including human cases, several rodent species and domestic carnivores (stray dogs) that prey on rodents ,23. MoreThe set of 21 HA-positive samples originated from human cases that occurred in the Brazilian states of Ceará, Pernambuco, Paraiba, Bahia and Minas Gerais between 1978 and 1995. These patients inhabited rural areas and underwent initial clinical and epidemiological investigation with suspected diagnosis of plague and had their diagnosis confirmed by either positive serology HA,  alone orCalomys callosus (n = 18), Cerradomys langguthi (n = 1), Mus musculus (n = 20), Necromys lasiurus (n = 10), Thrichomys laurentius (n = 4) and Rattus rattus (n = 13). From these, 30 samples were animals captured in plague foci areas in the Brazilian state of Pernambuco during surveillance routine in 2019 and 2020 . The collection of the rodents was performed as described [M. musculus Swiss Webster mice (18 positives and 2 negatives) and 16 C. callosus (2 positives and 14 negatives) from colonies raised in the IAM facilities and submitted to Y. pestis inoculation for control on development and evaluation of diagnosis tests.The rodents’ samples comprised the species escribed . The othY. pestis and other pathogenic Yersinia strains (whole-cells immunization) or with the purified F1 antigen, produced as previously described [Y. pestis strains in independent experiments, 18 were from rabbits exposed to diverse Brazilian Y. pestis strains from the Fiocruz-CYP (http://cyp.fiocruz.br) bacterial cultures collection and five were from rabbits exposed to the purified F1 antigen  [Yersinia pseudotuberculosis and two with Yersinia enterocolitica were included. The other 37 negative control rabbit sera were obtained from animals from the IAM facilities that did not underwent any experimental intervention.Sera from rabbits immunized with formol-killed escribed  for posiE. coli) . AdditioStaphylococcus aureus/horseradish  or goat anti-human, rabbit or dog-peroxidase  diluted in 10% milk/PBS-T solution  were added and incubated at room temperature for one hour and washed twice with 500 μl of PBS-T. Finally, 150 μl of 2 mg/mL OPD  and 1:103 H2O2 diluted in citrate-phosphate buffer (pH = 5.0) was incubated in each well for 30 minutes at room temperature in a dark environment. The reaction was stopped by the addition of 100 μl 2.5 M sulfuric acid (H2SO4) per well and plates were read at the optical density of 490nm . In the ELISA Protein A and IgG standardization experiments, previously characterized positive sera with intermediate titers in HA were selected as a representative sample for each species.The ELISA tests were adapted from previously established protocols ,14. BrieAll samples were measured in triplicates and the background (blank) optical density (OD) from each plate was subtracted from the average sample OD. Distinct concentrations of F1 antigen, peroxidase conjugates and sera dilution were tested to determine optimal conditions. The cut-offs were determined according to the best specificity/sensitivity (Youden’s index) from the Receiver Operating Characteristic (ROC) curve. A distinct cut-off was calculated for each peroxidase conjugate. Since the rodent samples included a rather heterogeneous range of wild species , we coulThe hemagglutination (HA) assay was performed as described previously . In shorhttps://www.medcalc.org platform. Receiver Operating Characteristic (ROC) curves, area under the curve (AUC), scatterplots and correlations were calculated to measure the ELISA test performance [Y. pestis strain and one negative rabbit serum. Samples were tested in eight replicates within runs and across six experiments in non-consecutive days.The HA test, which is routinely used in the SRP, was used as the gold standard to calculate Protein-A ELISA and IgG ELISA performance rates. Sensitivity, specificity, accuracy and confidence intervals ,29 were formance  using thhttps://www.graphpad.com/quickcalcs/kappa2). Statistical tests were applied with a 95% confidence interval.The Kappa test was initially applied to determine the agreement rate between the ELISA and HA tests (n = 288) and next, in an expanded sample set to measure the agreement rate between Protein A-ELISA and Anti-IgG ELISA (n = 487). The index was calculated using the Quickcalc GraphPad tool . The ratios between the OD means from positive and negative samples were considerably higher for the protein A conjugate , when compared to anti-rabbit (22.4), anti-human (11.8) and anti-dog (4.9) IgG conjugates. OD ratios and averages are shown in Overall, the average ODs from positive samples were significantly higher than the ODs from negative samples both in protein A and in IgG tests  or Y. enterocolitica (two) strains were tested. Although the average ODs from these sera were slightly higher than other negative samples (protein A: 0.076 versus 0.014 and IgG: 0.133 versus 0.031), only one from the seven tested samples (Y. enterocolitica) presented a false-positive result for Protein A-ELISA . Excellent agreement rates were observed in samples from all species . Next, wAdditionally, we investigated whether the Protein A-ELISA assay for plague would produce cross-reactivity with other endemic pathogens, by testing human and dog samples with confirmed infections and observed no cross-reaction with these conditions .Y. pestis in bacteriological cultures from the clinical specimen. However, as proper diagnosis is often not feasible due to the acute progression of the disease and geographic isolation of cases, patients frequently receive treatment without laboratory results [The gold standard for plague diagnosis is the identification and isolation of the  results ,12. Ther results ,10. In t results ,22,23.Y. pestis culturing in biosafety level 3 (BSL3) laboratories [Here, we describe a Protein A-based approach designed to overcome some limitations faced by routine laboratories when using other serological methods, such as HA  and conventional ELISA . While the protocol here established requires 750 ng of F1 antigen per tested sample (using triplicates), HA-HI spends up to 20,000 ng of F1 per tested sample (considering the standard eight dilutions according to Chu) , resultiratories .Throughout a broad range of host species hereby tested, the Protein A-ELISA method showed high sensitivity, specificity and reproducibility rates using a single cut-off value for all species. The analysis of the ROC curve showed AUCs above 0.990 in all groups tested in protein A-ELISA, while AUCs from Anti-IgG ELISA ranged from 0.930 to 0.982. The Cohen’s Kappa test revealed high agreement rates for this protocol when compared to HA (n = 288) and Anti-IgG ELISA (n = 487). Because human cases of plague have not been reported in Brazil since 2005, we were not able to estimate positive/negative predictive values . It is iRemarkably, we observed a good correlation between ODs from Protein A and anti-IgG, with higher positive/negative OD ratios in the Protein A-ELISA test, which allows a safer window of opportunity for cut-off determination between positive and negative samples. Of note, little cross-reaction was observed in sera from rabbits immunized with other pathogenic yersiniae. Additionally, we observed no cross-reaction in the Protein A test with human and dog sera from individuals positive for other common infectious diseases. Interestingly, whilst negative samples showed low background signals in Protein A, anti-rabbit and anti-humans IgG conjugates, a rather marked background in anti-dog IgG conjugate was observed. This could be associated with the non-specific agglutination routinely observed in sera from dogs in diagnosis by HA.Y. pestis usually can be detected from the fifth day of infection by HA and from the eighth day by Anti-IgG ELISA [Although remaining detectable in humans for several years after infection, antibodies against gG ELISA ,32,33. OgG ELISA ,35.M. musculus (Muridae) and C. callosus (Cricetidae). This underrepresentation of the diversity within the Rodentia order is an important limitation of this study, as the Protein A has variable affinity to antibodies across the rodent’s species. This particularity can potentially impact the sensitivity of the test for species with low protein A binding capacity, such as R. rattus [The Protein A-ELISA had good sensitivity/specificity for the rodents’ group, however, we were able to access positive samples from only two species: . rattus . TherefoAltogether, we evaluated a Protein A-based indirect ELISA test that is sensitive, specific and reproducible, with a single protocol that can be used for both diagnosis of plague in humans and epidemiological surveillance in animal reservoirs and sentinels from active foci.S1 FigFour distinct titers were tested for each anti-IgG conjugates in triplicate for one positive and one negative serum from each species. Antigen concentration and sample titer were the same from the previously established in the Protein A-ELISA assay.(TIF)Click here for additional data file.S2 FigThe majority of the samples tested for Protein A-ELISA (n = 553) had triplicates with low CVs. Higher variation was found in negative samples, with the ODs close to the lower detection limit, where small numeric variations imply in high CVs.(TIF)Click here for additional data file.S3 FigStratified ROC curves and area under the curve (AUC) for protein A-ELISA according to each evaluated species.(TIF)Click here for additional data file.S4 FigThe right vertical axis shows the distribution of the 21 positive human sera according to their HA titers (bars in gray) and the left vertical axis indicate the OD values obtained in the ELISA Protein A for each group of samples with a specific HA titer (data points). The error bars show the range of the ODs for each group.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Centaurea (Asteraceae), both mechanisms are frequent and lead to reticulate evolutions. However, in the Western Mediterranean section, Seridia studies are scarce. In this section, Centaurea aspera forms a complex including four European diploid and one Moroccan autotetraploid subspecies, an allopolyploid, and hybrids among them. Here, we aimed to delimit the different taxa, identify any introgressions, and discuss their evolutionary history. Samples of all taxa were analysed using 1688 SNPs obtained through GBS and were morphologically characterized. Three genetically well-differentiated clusters were observed, corresponding to the allopolyploid C. seridis, the diploid C. aspera and the cryptic autotetraploid C. aspera ssp. gentilii, which is proposed to be considered as a species. Centaurea seridis showed a high isolation by distance, a greater morphological variability, and a lack of interspecific gene flow. Diploid and autotetraploid C. aspera individuals were morphologically similar, and some introgressions were detected in Southern Spain, where new forms may promote diversification. This gene flow might have taken place during the Messinian and before autopolyploidization occurred in Morocco. In the C. aspera complex, current interspecific barriers are strong, while polyploidization may provide a better adaptation to drier environments.Hybridization and polyploidy are major drivers of plant evolution. In  Interspecific hybridization is considered to be an important mechanism in angiosperm speciation and evolution with several consequences, such as introgressions , extinctIn nature, both hybridization and polyploidization act in complex and frequently long processes , which Centaurea L. includes nearly 250 species and is particularly diversified in the Mediterranean Basin and the Irano-Turanian region . Each value of K was evaluated using ten independent Markov chain Monte Carlo replicates. The number of clusters was inferred following Evanno et al. [K for each value of K (except for K = 1 and the maximum K tested).The joint analysis of taxa of different ploidy can be problematic, and part of the available software is only suited for diploid or tetraploid data sets. For the analysis of genetic structure in mixed-ploidy populations, it has been shown that STRUCTURE  is more o et al. , based oSubsequent population structure analyses were performed using the R package StaMPP , which wST [ST [ST was calculated following Hedrick [ST, which has been shown to be the better choice for calculating the degree of differentiation between cytotypes [ST values were estimated along with confidence intervals (5% and 95%) and p values between populations by performing 100 bootstrappings across loci and according to the method proposed by Wright [C. x subdecurrens were removed from the analysis, and each remaining individual was assigned to a single cluster according to its highest Kq. To test for genetic isolation by distance, the matrix of the previously mentioned genetic distances [For each cluster obtained using STRUCTURE, SNP variants were used to calculate some measures of population diversity and differentiation using vcfR package. Population mean heterozygosities, GST ,77, and ST [ST  were est Hedrick  with the Hedrick . We alsoytotypes . FST valy Wright  and upday Wright . A hieray Wright  among inistances  among inistances  using adistances  in R forCentaurea section Seridia as can be found in determination keys and floras. A total of 38 quantitative variables were evaluated: nine corresponded to reproductive traits and 29 to vegetative traits.A morphological characterization of the sampled individuals was performed to reveal morphological patterns among the clusters obtained in STRUCTURE. In spring, plants have caulinar leaves, stems, and capitula but lack basal leaves that are already dry. Morphological evaluation was accomplished in the field during the field trips. p < 0.01) among clusters were selected (Statistical analysis of morphological data was carried out in R using the packages mass  and agriselected . Three p


The use of genetic and genomic technology to infer ancestry is commonplace in a variety of contexts, particularly in biomedical research and for direct-to-consumer genetic testing. In 2013 and 2015, two roundtables engaged a diverse group of stakeholders toward the development of guidelines for inferring genetic ancestry in academia and industry. This report shares the stakeholder groups’ work and provides an analysis of, commentary on, and views from the groundbreaking and sustained dialogue. We describe the engagement processes and the stakeholder groups’ resulting statements and proposed guidelines. The guidelines focus on five key areas: application of genetic ancestry inference, assumptions and confidence/laboratory and statistical methods, terminology and population identifiers, impact on individuals and groups, and communication or translation of genetic ancestry inferences. We delineate the terms and limitations of the guidelines and discuss their critical role in advancing the development and implementation of best practices for inferring genetic ancestry and reporting the results. These efforts should inform both governmental regulation and self-regulation. Two roundtables engaging a diverse group of stakeholders enabled the development of guidelines for inferring genetic ancestry in academia and industry. This report describes the engagement process and the resulting guidelines, which can inform continued improvement of practices and assist ongoing scholarly, industry, and policy discussions. Both research findings and individual DTC results are often featured in the print and broadcast media. Genetic ancestry inference is thus used across a wide variety of settings, with different goals, assumptions, methods, and claims of accuracy and validity. It is therefore not surprising that there has been considerable disagreement among diverse stakeholders about the merits, limitations, utility, and impact of genetic ancestry inference. Consequently, efforts by any individual stakeholder group to advocate for standards or regulation have met with little success. The need for standards and other guidance has never been greater, as the popularity of DTC genetic ancestry testing continues to expand,https://www.ashg.org/wp-content/uploads/2008/11/Statement-20081311-ASHGAncestryTesting.pdf) and approved an ASHG White Paper that provided recommendations aimed at addressing these issues.In 2008 the American Society of Human Genetics (ASHG) convened a task force to study issues emerging from genetic ancestry inference in academia and industry and to explore a constructive response to the need for more transparency and discussion about what genetic ancestry inference entails, as well as how it works and what it can and cannot do. The ASHG Board of Directors approved an ASHG position statement developed by the task force and the Society’s Social Issues Committee on ancestry inference that called attention to motivations, technical accuracy and reliability of results, health implications, societal and personal implications, proprietary databases, communication of limitations and potential impacts, public and personal education, interdisciplinary collaboration, and mechanisms for accountability , representatives of human population geneticists, the DTC ancestry testing industry, scholars in ethical and social implications of human genetics, and consumers of ancestry testing , and it organized a “roundtable” to develop consensus statements and guidelines for genetic ancestry inference. A first meeting  was held in Washington, DC in September 2013. There were 65 participants representing seven stakeholder groups: population geneticists, DTC ancestry testing companies, social and behavioral scientists, humanists, clinicians, community representatives, and consumer groups including genealogical associations/societies. The participants generated 22 “consensus” statements—defined as statements accepted by a majority but not all participants—about the uses, methods, and outcomes of genetic ancestry inference . CriticaHere we report on these efforts before discussing emergent issues and offering suggestions for next steps and insights that could inform both governmental regulation and self-regulation.The planning committee produced a set of eight draft consensus statements and related questions focused on eight key areas: (1) the application of genetic ancestry inference; (2) assumptions underlying genetic ancestry inference; (3) laboratory and statistical methods; (4) estimating confidence and communicating uncertainty; (5) terminology and population identifiers; (6) assessing and addressing individual and group benefits; (7) assessing and addressing individual and group harms; and (8) translating genetic ancestry findings for the media and public. These statements were distributed to attendees of Roundtable I prior to the meeting and served as starting points for discussion. Roundtable I was structured into eight sessions of small group discussions followed by negotiation and adjudication, and each attendee was allotted one vote in a general session of all attendees. Each small group consisted of representatives from each stakeholder category. Their discussions revolved around questions provided to them about each draft consensus statement. They were instructed to accept the statement as is, edit the statement, or develop a new consensus statement.Roundtable I resulted in an expansion of the eight draft consensus statements to 22 , reflectA reflective critique of Roundtable I revealed to the planning committee that the design might have unintentionally constrained the meeting’s potential for achieving its goal: the development of guidelines through consensus. Had the initial small group discussions been organized as an opportunity to first reach consensus within stakeholder groups and later work toward consensus between and among stakeholder groups in the full discussion, the process might have more readily identified the most serious gaps in understanding of the issues and underlying bases for divergent opinions. Instead, some participants made compromises to their positions within their small group discussion only to see those points rehashed in the full group discussion. Thus, this process was not as efficient, or satisfying to participants, as it otherwise might have been, and there is a possibility that the votes tallied on the consensus statements at the end of a protracted discussion reflected an indifference or disengagement of some participants rather than genuine support for any particular statement. Accordingly, the approach to Roundtable II was changed to improve the process and maximize the utility of the progress that was made in Roundtable I.Based on the consensus statements, minority reports, and feedback from and self-critique of Roundtable I, the planning committee produced a set of draft guidelines in six areas: (1) application of genetic ancestry inference; (2) assumptions and confidence underlying genetic ancestry inference; (3) laboratory and statistical methods; (4) terminology and population identifiers; (5) assessing and addressing impact on individuals and groups; and (6) communication/translation of genetic ancestry inferences. These draft guidelines were distributed to attendees of Roundtable II prior to the meeting and served as starting points for the discussion.Roundtable II was structured differently from Roundtable I to facilitate conversation both within and between the stakeholder groups . Specifically, in Roundtable II there were four sessions of stakeholder group discussions followed by four sessions of mixed-stakeholder groups and then a review in a plenary session with all attendees. As with Roundtable I, groups were instructed to accept each guideline as is, edit the guideline, or develop a new guideline. Roundtable II resulted in reduction of the six draft guidelines to five. More than 80% of attendees agreed with four guidelines, and no guideline received less than 76% of positive votes . To captGenetic ancestry testing can be used in myriad ways, for example, to estimate individual ancestry proportions, infer genealogical relationships among individuals, characterize population history, and adjust for population stratification in biomedical research. Researchers, study participants, ancestry testing companies, and consumers should be aware that many aspects of ancestry inference continue to change because of changing reference populations, type and number of marker sets, methods of analysis, and understandings of human history. As the process of ancestry inference evolves, interpretations may change. Genetic ancestry information is only one line of evidence for establishing or denying an individual’s or group’s eligibility for particular entitlements . Where they exist, other lines of evidence should also be considered.This guideline is important for several reasons. While genetic ancestry inference is a moving target, consumers often think that “a test is a test” and that it offers definitive, static answers. This guideline recognizes the role that genetic ancestry inference does or might play in how individuals think about their racial or ethnic background and in informing individuals’ quests for entitlements as well as underscoring the many variables that contribute to why ancestry testing is capable of offering only partial information due, for example, to the limited type and number of markers tested, as well as the populations and time frame sampled. Genetic ancestry is not synonymous with genealogical ancestry, as an individual does not inherit genetic material from all of one’s distant ancestors, and the inheritance of genetic material from a particular distant ancestor need not be a prerequisite to, or preclude, a social affinity with that distant ancestor’s culture. The guideline explains why the nature of genetic ancestry information might be subject to redefinition, reclassification, or other changes. In this way, this guideline aims to prevent overstating results and, in the case of DTC companies, overselling information. The guideline further helps a broad readership understand the distinction between acting on information that is an “inference” versus treating the information as a “fact.” Genetic ancestry information (or “results”) is subject to change, but many people might not, for a variety of reasons, understand how, why, or what that means. Rather than indicating a problem, changes in an individual’s “results” over time, and even differences across DTC services, could reflect uncertainty in science, which at its best is a self-correcting way of knowing the world. For example, changes in an individual’s results could be attributed to analytical methods having improved or discoveries having occurred after the information was initially generated and/or communicated to the consumer or user. That the “result” is not a definitive or permanent answer is important when thinking about the appropriateness of decision-making based upon that information. Such decisions include, for example, setting a threshold proportional ancestry that must be met for group membership or acquiring citizenship based on “results” suggesting affiliation with one country that later does or could turn out to be less likely than affiliation with another country.spillover occurs when an individual’s experience with one domain of genetic analysis informs his or her understanding of other forms of it or authorizes its use in another domain.”This guideline is included to provide context to the changing nature of the field to consumers and researchers alike; to acknowledge the potential benefits and harms to individuals and groups; to emphasize the wide-ranging applications of genetic ancestry testing; and to stress that caution should be taken when any entity communicates results. Genetic ancestry inference affects the public’s confidence in genetic testing in general and vice versa, even if the methods, analysis, and resulting information gleaned for the analysis and application are quite different. As noted by Nelson , “DNA Further explanation is needed for this guideline to clarify why test interpretations continue to change, and should be expected to continue to change, to provide examples of when ancestry inference has been used for determining eligibility for entitlements, and to elucidate points of tension across stakeholder groups regarding whether and when genetic ancestry information is ever appropriate without corroborating non-genetic evidence.There are limitations to this guideline. Some might view it as a prelude to guidelines rather than a guideline itself. Additionally, it might not be clear or obvious to consumers how changes in testing or reference samples would impact interpretation. Current interpretations—particularly interpretations indicating admixture proportions—are not static and will change in the future in foreseeable and unforeseeable ways. Perhaps the most glaring limitation of this guideline is that it calls out four groups  and, in so doing, apparently overlooks how users , bystanders, and others actively make decisions based on the genetic ancestry information, are passively influenced by the information, or unknowingly are affected—directly and indirectly—by what the four identified groups know, believe, and do.This guideline lends itself to a few practical next steps for those engaged in genetic ancestry inference (which were not themselves addressed as part of the roundtable discussions). First, it is important to monitor DTC testing websites for updated information and compile that information for easy reference. While a particular actor—whether a specific individual or entity—to perform this task is not inherently obvious, an independent, impartial, and trustworthy actor  would be appropriate. Second, we encourage the creation and use of a model disclosure form throughout the DTC genetic ancestry industry  that includes the material information necessary to understand the genetic ancestry testing. Third, it is preferred that the research and commercial providers make educational materials available and discuss this information prior to or as an integral part of the informed consent and purchasing processes.The assumptions, type and number of genetic markers, and reference populations used for ancestry inference should be explicitly described. An easily interpretable assessment of confidence, accuracy, and limitations should accompany quantitative claims about genetic ancestry inference. Steps should be taken to adequately convey the amount and sources of uncertainty in the inferences about genetic ancestry, whether in a research or commercial setting. As a means of proficiency testing, providers of genetic ancestry inference should analyze samples against a standard reference population panel  that represents human global diversity to the extent possible and should communicate the results. Scientifically acceptable standards should be established for validation of results. Individuals should be informed of the availability of their raw data or lack thereof.The goal of this guideline is to offer transparency about how results are determined and to establish standards so that results can be replicated or validated. For example, there are many different ways to look at data as rich as human genetic variation, and a model or approach appropriate for one purpose and context could be misguided for another purpose or context depending upon the assumptions and constraints of that particular model or approach. This guideline is important to enable researchers as well as consumers to make informed decisions about genetic ancestry tests and to enable consumers and other users to interpret the test results within the proper context of the test’s limitations. Methods are not uniform from one researcher or DTC provider to another or from one type of test to another. Without disclosure of these material details, it is difficult for a person (whether study participant or consumer) to obtain a second opinion (that is truly an apples-to-apples comparison) or make sense of the information. This guideline, by encouraging the disclosure of this information, will aid in the identification of the “snake oil salesmen” among those offering genetic ancestry analyses to the public. This guideline is included to avoid misinterpretation and overinterpretation of genetic ancestry test or inference results.Box 1•○How do genetic ancestry inferences for each test company compare to one another?○How do genetic ancestry inferences for each test company compare to a “gold standard”  or benchmark ?How is validation being defined and, relatedly, what specific measures are expected?•○If the former, the numbers need to be limited to avoid overburdening small companies (which would disproportionately feel the associated costs).○For proficiency testing to be effective, the process needs to have support and participation across the industry .○It seems feasible for those engaged in genetic ancestry inference to select a subset of 1000 Genomes Project samples that include diverse representation of samples with substantial admixture and samples from different geographic areas.○Companies would need to be blinded to the DNA IDs of the benchmark sample set to avoid foreseeable gaming of the proficiency testing system .Does the proficiency testing process require companies to take a proficiency test from DNA to interpretation, or can the companies interpret existing genotypes?Further explanation is needed for this guidance, as it does not define validation or what specific measures are expected or steps to be taken. First, the guideline does not articulate how proficiency testing would best or most reasonably be achieved. Establishing an accepted proficiency testing approach would require substantial work . AdditioMoreover, this guideline needs further explanation as to what is meant by “should communicate the results” and what is intended by “an easily interpretable assessment.” Without stating the perspective by which this should be judged , there might be widely varying ways of complying. One could envision an assessment being easily interpretable from one geneticist to another as satisfying the underlying purpose of this guideline; however, without more explanation, some might interpret this guideline as requiring a type of communication that goes beyond typical consumer protection requirements for formatting and text to address complex matters of both data literacy and scientific literacy in addition to basic literacy.Limitations of this guideline are (1) that thresholds for acceptability, validity, and the like are not stated or given but would be needed for interpretation by consumers and (2) that there are many different types and sources of uncertainty, which are difficult to quantify. This guideline lends itself to at least two genetic ancestry inference practice suggestions. Establishing and sustaining a centralized resource where regular updates of these measures could be compiled and displayed in one trusted location for comparisons by consumers, study participants, and others would be meritorious. Additionally, interoperability practices should be developed and encouraged. Establishing a standardized file format for returning data to participants would enable another DTC provider or researcher to examine genetic ancestry inferences using different methods and offer “second opinions.”Those inferring genetic ancestry should clearly explain how they are using the terms pertaining to race, ethnicity, nationality, and other population identifiers. Those inferring ancestry should provide clear information on what reference samples are used to represent populations and what criteria are used for inclusion in a reference sample . Reference populations used to draw ancestral inferences should be labeled using specific geographic, cultural, and temporal identifiers.This guideline is important because genetic ancestry inference has the simultaneous potential (1) to disconnect, alienate, and estrange individuals from groups and identities with which they are affiliated based on other types of evidence as well as (2) to connect and reconnect individuals, families, and communities with groups and identities to which they have always felt themselves to belong, or, on the other hand, to provide a bridge to peoples with whom they have no current affiliation. These disconnections, new connections, and reconnections could have real consequences in terms of how individuals and institutions understand cultural identities and define social rights and obligations as well as political responsibilities. This guideline underscores that inaccurate or even sometimes colloquial association of various terms with “ancestry” has the potential to lead consumers and the broader public to incorrect conclusions about socially ascribed populations, especially traditionally marginalized populations. In the absence of consistent terminology, and an acknowledgment that populations have complex histories of mixing and migration that should also be noted alongside their present-day appellations, consumers are sometimes confused about differences in tests that produce varying results. Increasing popularity of DTC genetic testing offers an opportunity to engage and educate the public, and common terminology and identifiers would be useful. This guideline emphasizes that accurate population labeling should include geographic, cultural, and temporal identifiers to acknowledge that these aspects of human identity are also important and that they are not static or fixed but, rather, have changed and will continue to change over time.,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,Identifiers are not uniform across geopolitical boundaries and do not remain constant over time. The instability of identifiers in the United States Census is well known, and common practices to aggregate and disaggregate distinct concepts of ethnicity, country of origin, and nationality into one or more Office of Management and Budget (OMB) race categories or to synthesize global data further complicate the matter  took steps toward developing and recommending best practices for use of population descriptors not only in genetic ancestry inferences but also in human genetics and genomics research more broadly. In 2021, NASEM established an ad hoc committee tasked with examining population descriptors  in genomics research and offering “best practices” including recommendations for “culturally responsive methods and common data elements” that could enable the harmonization of population descriptors used in genomics research within and beyond the United States.Individuals pursuing ancestry inference should be informed of potential impacts on themselves, family members, and communities. They should also be informed of the potential for planned and unplanned access to their genetic ancestry information by law enforcement, government agencies, commercial entities, and other third parties. Genetic ancestry inference is an international activity; thus, people should be aware of the different regulations regarding privacy and data security across countries. Genetic ancestry inference can lead to the discovery of unexpected results that conflict with existing beliefs or knowledge. Potential positive and negative impacts should be laid out prior to testing or participation in research. Individuals should consider discussions with family members prior to undertaking genetic ancestry inference because it may have implications for relatives. Those inferring genetic ancestry should facilitate the study of potential individual- or group-level impacts to inform the development and implementation of best practices for communicating potential impacts of ancestry inference. The potential outcomes should be broadly disseminated.,,This guideline’s importance is to protect consumers and study participants from the complex web of third-party interests and varying regulations that intersect with genetic ancestry testing and information. It is intended to enable consumers to make informed decisions about genetic ancestry tests and sharing their data directly with others, whether within an ancestry testing company’s website or more broadly through online publication elsewhere. The guideline also is important in its recognition that there are potential benefits and risks of harm that can occur at the individual level and at familial, community, or broader social levels. For example, for groups in the United States that have been oppressed and had their “roots,” cultures, and actual ancestors purposefully eradicated, actively erased, or historically concealed from them, the acquisition of genetic ancestry information could affect not only the connections that individuals make with the past but also the viability of connections that individuals pursue in the present and future. Unexpected outcomes from DTC genetic testing are not infrequent, and, in most cases, these outcomes involve kinship rather than ancestral origins  of variation in perceived and potential benefits and risks associated with genetic ancestry testing that might impact either individuals or socially-ascribed groups and (2) that it is an ethical responsibility to communicate these possibilities to the potential test-takers prior to the DNA collection and analysis. The issue of third-party uses of ancestry data and the variation in regulations governing data security and privacy increase the potential risk of harm to consumers and the need for protections. Related to this is the pervasive commercial interest in the digital age that requires consumer confidence in mechanisms for privacy and consensual use of data perceived as personal information. Following the roundtables of 2013 and 2015, considerable privacy law scholarship and data protection policy developments have materialized in the genetics communityNotably, this guideline is an aggregate of several related but distinct components that the roundtable participants conceptualized under the rubric of potential risks and benefits. The broad scope or apparent randomness of this guideline’s aggregated components might limit its effectiveness. A reordering of the topics or offering of subcategories might be helpful to clarify the meaning. Additionally, the impacts  are emerging continuously and might not be immediately apparent, obvious, or noticed by those inferring genetic ancestry or seeking genetic ancestry inferences. Another potential limitation of this guideline is that privacy and third-party data use issues are broader than the ancestry-specific context, so there is potential for these recommendations to be subsumed within existing best practices regarding genetic information more generally.There are several potential next steps prompted by this guideline. Firstly, the research community, the DTC ancestry testing companies, and/or another entity should compile and provide analyses of applicable laws and regulations regarding access and use of genetic ancestry information from the state and federal levels in the United States and international jurisdictions. Secondly, the potential benefits and risks for groups of people should be presented to prospective study participants and consumers in addition to potential benefits and risks for the individual. Thirdly, the final two sentences of this guideline impose a directive that geneticists and social scientists work collaboratively and, moreover, suggest that when genetic ancestry inference is being conducted as research it would be good practice to integrate pre- and post-assessment of outcomes and enable ongoing communications with participants . Another good practice is to identify conspicuously  the major issues for those having their genetic ancestry inferred and also to promote an informed consent or informed purchase process that prompts the individual (whether study participant or consumer) (1) to review the aforementioned personal, familial, social, data, and oversight considerations and (2) to communicate with family members about their intention prior to obtaining the genetic ancestry information.Genetic ancestry inference should be communicated in ways that are as accurate and understandable as possible for the end user and public at large. To avoid misunderstanding there should be transparency about genetic ancestry inference assumptions and techniques. Claims must be supported by evidence that is clearly communicated. Effective translation of genetic ancestry inferences for the public requires acknowledgment of historical, social and cultural contexts and constructs of ancestry, race, and ethnicity. The public should be educated on the topic of genetic ancestry inference. Those engaging in genetic ancestry inference should promote education and inform the public.This guideline is relevant to advertising, research publications, press releases, and other communication of genetic ancestry inference. Reasonable expectations of “end users” should include not only test-takers or roots-seekers and their immediate family members but also, for example, policymakers who might consider genetic ancestry inferences and the use of the results in litigation and administrative proceedings; the press and social media users whose understandings or misunderstandings have the potential for easy amplification and influence; members of the legal profession (such as attorneys and judges) who might proffer or encounter results in novel contexts; and educators. The guideline is important to minimize misrepresentations of genetic ancestry testing and inference and to protect consumers of genetic ancestry testing from bogus claims. Genetic ancestry information is often presented as exciting, revealing, and/or a challenge to existing knowledge, and this guideline is important by underscoring the need to not oversell or overstate results to the public. This guideline urges researchers, companies, and users to avoid embellishing claims through hypothetical storytelling used for dramatic effect, including such claims in press releases, news media, film, television, and other media.The recommendations regarding communication and translation in this guideline stem from a fundamental difference of opinion regarding the impact of genetic ancestry inference/estimation. One area of agreement among the roundtables’ diverse stakeholders was the need for greater transparency and education, which hopefully will enable consumers and prospective study participants (whom researchers might offer opportunities to receive genetic ancestry information as a study benefit) to make informed decisions about whether to obtain an ancestry test. Communication and education are shared responsibilities that can be fulfilled in several ways but are dependent upon available resources. This guideline underscores the importance of ongoing efforts to improve communication and education while acknowledging the tensions commonly observed between those in marketing departments seeking to puff products and services with colorful commentary to increase sales and interest and the perceived “dull” or black-and-white scientific state of knowledge. Further explanation is needed for this guideline to emphasize that this is a rational recommendation based on disagreement among stakeholders about specific details. This guideline is vulnerable to subjective interpretation of what constitutes transparent communication and education. Subjective interpretation allows for flexibility necessary to garner broad support for the guideline and multiple ways with which to fulfill the responsibilities set by the guideline. However, a third party and/or non-partisan or bipartisan review of content and form of communications over time could alleviate the limitation.This guideline lends itself to four potential next steps. Firstly, the next step toward establishing a set of best practices would be to define standards . Secondly, it would be prudent to take steps to avoid hype when reporting research findings and to take corrective actions when research findings or inferences reported by others are inflated or otherwise misinterpreted. Thirdly, it is appropriate for those engaged in genetic ancestry inference to treat every transaction and study participation as a “teachable moment.” Additionally, a roundtable discussion with communication scholars and science journalists might be useful to address the aforementioned limitation of the guideline.By way of a process of consensus building through two roundtables involving dozens of stakeholders, we have developed a set of broadly agreed upon guidelines that form the basis for more specific recommendations about the use of genetic ancestry inference. Development and operationalization of such recommendations will require substantially more discussion by stakeholders and beyond. These two roundtables stemmed from an earlier working group and identify a wide range of implications involving heterogeneous stakeholders. It is only through continued engagement that the details  can even be established. At present, we hope that those conducting genetic ancestry inference, be they researchers or DTC testing companies, consider adopting and continuing to contribute to the refinement and implementation of these guidelines. Most important will be their leadership in operationalizing these guidelines with the support of scholars and policymakers. As with all guidelines, we expect them—like genetic ancestry inference itself—to change over time via stakeholder-engaged processes. Most specifically, several major governmental bodies and regulators have important roles to play in supporting and promulgating a number of key practices and shared resources.As an attempt at full transparency, we have attested to the imprecision of both our processes to develop these guidelines and the guidelines themselves. Because genetic ancestry inference tests and practices are moving targets, we must balance precision of our guidelines with the need for those guidelines to be both responsive and durable. Again, only through empirical study and expanding to new issues from new stakeholders will we have a chance at maintaining or increasing any sense of precision in these and future guidelines.We have highlighted several limits to our processes and have acknowledged both flaws in the approach taken and caveats with the resulting deliverables. For example, the structure of Roundtable I  provoked noticeable frustrations among participants, as compromises reached in small group discussions were reopened and issues with particular statements were rehashed and renegotiated among the full group discussions. This inefficiency likely influenced the tally of votes on “consensus” statements. Also illustrative is our admission of ways in which the guidelines from Roundtable II require further explanation and the details to be operationalized. Yet the fact that we anchor these guidelines with their limitations and dissents provides the important opportunity to both enhance future processes and most transparently continue to modify and hopefully improve these guidelines. For example, the evolution from Roundtable I to Roundtable II involved moving from a long list of fragmented statements with varying degrees of support to a smaller set of thematically coherent guidelines representing a process of consensus.,,,,,,,,,,,,,,,https://isogg.org/wiki/List_of_DNA_testing_companies); and some genetic genealogy standards have emerged.,,,Since the roundtable discussions, scholarly work examining genetic ancestry inference, descriptions of human variation, and impacts of genetic ancestry testing have continued;While this report reflects efforts undertaken several years ago, the issues remain salient. Moreover, the deliverables  from the impassioned roundtable discussions represent an ongoing need to address current scientific and societal challenges with genetic ancestry inference. These guidelines were never intended to serve as an endpoint but, rather, as a crucial step toward the development, implementation, and continued improvement of best practices. It is our hope that this report will assist in ongoing scholarly, industry, and policy discussions for the advancement of science and broad human benefit.In addition to the authors in the writing group, the participants in the roundtable discussions included Thomas W. Athey, Mathew Barber, Jessica Bardill, Terry Barton, Blaine Bettinger, Deborah Bolnick, Vence Bonham, Katherine Borges, Joann Boughman, Esteban Burchard, Carlos Bustamante, Jake Byrnes, Shannon Stewart Christmas, Mike Dougherty, Carla Easter, Alice M. Fairhurst, Peter Förster, Elise Friedman, Malia Fullerton, Jonathan Gitlin, Bennett Greenspan, Tim Janzen, Kathryn Johnston, Rick Kittles, Sandra Soo-Jin Lee, Sally Lehrman, Jeffrey C. Long, Rita Anne Lopez, Joe McInerney, Yolanda T. Moses, Joanna Mountain, Alondra Nelson, Jean-Chretien Norreel, John Novembre, Pilar N. Ossorio, Harry Ostrer, David Pike, Terry Powell, Jenny Reardon, Casey Ross-Pethernick, Laura Lyman Rodriguez, Charles Rotimi, Ali M. Salih, Pamela Sankar, Theodore Schurr, Alexandra Shields, Mark Shriver, Eugene J. Silva, Lindsay Smith, Megan Smolenyak, Bryan Sykes, Kim Tallbear, Noah Tamarkin, Ann Turner, Marilyn Vann, James Larry Vick, Spencer Wells, Ron Whitener, Jun J. Yang, and Laurie Zoloth.This effort did not generate or analyze datasets or code except for the tally of votes during the roundtables, which are included in the tables of this publication.


This study aimed to identify the genetic diversity and population structure of Mongolian horse populations according to the province of residence  using 14 microsatellite (MS) markers.A total of 269 whole blood samples were obtained from the four populations  geographically distinct provinces. Multiplex polymerase chain reaction (PCR) was conducted using 14 MS markers , as recommended by the International Society for Animal Genetics. Capillary electrophoresis was conducted using the amplified PCR products, alleles were determined. Alleles were used for statistical analysis of genetic variability, Nei’s DA genetic distance, principal coordinate analysis (PCoA), factorial corresponding analysis (FCA), and population structure.Exp), observed heterozygosity (HObs), and polymorphic information content among all populations were 11.43, 0.772, 0.757, and 0.737, respectively. In the PCoA and FCA, GOP, and KGP were genetically distinct from other populations, and the KTP and USP showed a close relationship. The two clusters identified using Nei’s DA genetic distance analysis and population structure highlighted the presence of structurally clear genetic separation.On average, the number of alleles, expected heterozygosity (HOverall, the results of this study suggest that genetic diversity between KTP and USP was low, and that between GOP and KGP was high. It is thought that these results will help in the effective preservation and improvement of Mongolian horses through genetic diversity analysis and phylogenetic relationships. Equus caballus) have been domesticated in steppes and have played an important role in the development of several civilizations. In Mongolia, horses are one of the five livestock species, and according to [Horses  are used as molecular markers for genetic diversity analysis and breeding programs ,5. TheseIn Mongolia, various breeds of horses inhabit the climate and topographical characteristics of each province. According to Wright  and GanbAs such, Mongolian horses have different traits depend ing on the province where they live, but the genetic diversity and correlations using MS markers have not yet been verified. Therefore, this study aimed to identify the genetic characteristics of Mongolian horses according to the province of residence using 14 MS markers published by the International Society of Animal Genetics (ISAG). We analyzed the genetic relationships and genetic diversity of horses raised in each of the five provinces  in Mongolia, which were divided into four populations. Genetic diversity analysis is expected to help in the effective protection and management of Mongolian horse genetic resources. In addition, the genetic structure and phylogenetic relationships of the Mongolian horse populations were also identified, and can be used as basic data for research on genetic resources of not only horses but also other livestock in Mongolia.The experimental methods were approved by the Hankyong National University Animal Ethics Committee, Anseong, Republic of Korea (No.2021-1). A total of 269 whole blood samples were obtained from the four populations of the five geographically distinct provinces . SamplinThe genetic diversity of Mongolian horse populations was identified using 14 MS markers , as recommended by the ISAG.Multiplex polymerase chain reaction (PCR) was conducted using the Equine Genotypes Panel 1.1 Kit  for genotyping of the 14 MS markers. Following the manufacturer’s instructions, the reaction mixture was prepared using 2 μL genomic DNA (1.0 ng/μL), 9 μL master mix, and 9 μL primer mix, obtaining a total volume of 20 μL. The PCR was conducted using a GeneAmp PCR system 9700 . The PCR amplification was conducted using the following conditions: pre-denaturation at 98°C for 3 min followed by 30 cycles of 15 s at 98°C, 75 s at 60°C, and 1 cycle of 30 s at 72°C. The final extension step was performed at 72°C for 5 min and the mixture was cooled to 4°C.Using Hi-Di formamide, amplified PCR products were diluted 50 to 100 times, and the diluted PCR products (1 μL) were further diluted using a mixture of 9 μL Hi-Di formamide and GeneScan 500LIZ size standard . After denaturation at 95°C for 3 min, capillary electrophoresis was conducted using an ABI 3730xl Genetic Analyzer . The size of each MS marker was determined using GeneMapper ver 5.0 . The determined alleles were collated using Microsoft Excel  and used for statistical analysis.Exp and HObs, respectively), and polymorphism information content (PIC) values. To identify genetic correlations between populations, allele frequencies for each marker were confirmed using GenAlEx 6.4 [The MS Toolkit software  was usedAlEx 6.4  and GeneAlEx 6.4 . For thiAlEx 6.4  using POAlEx 6.4 . A phyloAlEx 6.4  with theAlEx 6.4 . PopulatAlEx 6.4 ,19 was uAlEx 6.4  using thAlEx 6.4 .Exp, HObs, and PIC values for the Mongolian horse populations are summarized (Exp and HObs ranged from 0.572 (HTG4) to 0.885 (ASB17) and 0.561 (HTG4) to 0.868 (ASB17), with mean values of 0.772 and 0.757, respectively. PIC values ranged from 0.535 (HTG4) to 0.866 (ASB17), with a mean value of 0.737. Except for the HTG4 marker  used in this study, all other MS markers were considered to be highly useful in analyzing polymorphism of Mongolian horse. MS markers were previously used to assess the genotypic diversity of heterozygosity and PIC value during animal breed selection. According to Botstein et al [Exp is ≥0.6 and PIC is ≥0.5, then the marker is determined to be highly polymorphic. The heterozygosity (HExp and HObs) and PIC values with respect to the four Mongolian horse populations are summarized  and lowest for KGP populations .The number of alleles, Hmmarized . A totalin et al , polymormmarized . Among tThe genetic divergences among the four Mongolian horse populations based on allele frequencies were calculated according to Nei’s DA genetic distance . The phyAs the phylogenetic tree may not take into account the effects of admixture among the four populations, we conducted PCoA and FCA, using allele frequencies of the 14 MS markers, as an alternative approach to understand the genetic relationships among populations. The PCoA contributed to 100% of the variation, including the third ingredient. The first three principal coordinates represented 60.13%, 29.75%, and 10.12% of the total variation, respectively. The KGP and GOP populations were distinct from the other populations . In contA Bayesian clustering method and population structure were used for clustering algorithms of multif-locus genotypes to identify the population structure and pattern of admixture within the populations. Population structure was used to estimate the number of groups that were classified by the group surveyed. The Bayes analysis set K values from 2 to 4 and examined the formation of colonies by group . The barThe purpose of this study was to verify the genetic diversity and correlation of Mongolian horses raised in five provinces using 14 MS markers. MS markers have been used to study the genetic diversity and population structure of horse breeds  and are Obs values ranged from 0.561 (HTG4) to 0.868 (ASB17). ASB17 showed the highest HExp and PIC values of 0.885 and 0.866, respectively, whereas HTG4 showed the lowest HExp and PIC values of 0.572 and 0.535, respectively.The results of genetic variability, Nei’s DA genetic dis tance, PCoA, FCA, and population structure provided genetic evidence for the differentiation of the population, and polymorphism was observed in all provinces. A total of 160 alleles were detected in the 14 MS markers, the allele numbers ranged from 6 (HTG7) to 22 (ASB2), and the HExp = 0.772, HObs = 0.757, and PIC = 0.737) in the Mongolian horse populations was found to be similar to that reported by Cho [Exp = 0.773, HObs = 0.696, and PIC = 0.742). Tozaki et al [Exp = 0.809, HObs = 0.833, and PIC = 0.761) and Dorji et al [Exp = 0.78, HObs = 0.79, and PIC = 0.77). Lower values were reported by Giacomoni et al [Exp = 0.74, HObs = 0.628, and PIC = 0.706). The average number of alleles in our study was 11.43 in the 14 MS loci. This is higher than that reported by Cho [Our estimate of genetic diversity , and the largest difference was calculated for the GOP and KGP populations (DA = 0.1797). The same results were obtained for PCoA, FCA, and the population structure. The KTP and USP populations had the closest genetic distances. This is possibly because they were each intercrossed with each other. In contrast, KGP and GOP populations were genetically differentiated from other provinces and suggest that both populations do not lose specific alleles unique. The population structure results also confirmed that the KGP and GOP populations were separated into different clusters in all cases.Exp≥0.6 and PIC≥0.5. Therefore, the results of this study suggest that MS markers, except the HTG4 marker, can be used to aid the conservation, traceability, and improved abilities of the horse populations in Mongolia. Consequently, the Mongolian horses in this study showed a high level of genetic diversity. It is essential to secure genetic resources to analyze the genetic diversity of livestock and to identify their genetic relationships. While horses are economically important animals in Mongolia, only a few molecular genetic studies have been conducted on the genetic resources of Mongolian horses. Therefore, individual management of Mongolian horses based on genetic differences between them seems to be necessary. Finally, it is expected that this study can be used as basic data for the preservation and improvement of Mongolian horses and the establishment of breeds in the future.According to Seo et al  and Dier


P < 0.05, P < 0.01). The high expression of STAT3 was significantly associated with the pTNM stage (P < 0.05), and the increased expression of p-STAT3 was significantly associated with depth of invasion (pT), lymph node metastasis (pN), and pTNM stage . The 5-year survival rate for AEG patients was 41.0% and was significantly associated with tumor differentiation, pN, pTNM, and p-STAT3 . Cox regression analysis confirmed that tumor differentiation, pN, and high expression of p-STAT3 were independent risk factors for the 5-year survival rate in patients with AEG . Our study showed that STAT3 and p-STAT3 play a critical role in AEG development.Adenocarcinoma of the esophagogastric junction (AEG) has increased rapidly worldwide during the last few decades. The purpose of this study is to investigate the clinical and prognostic characteristics of signal transduction and activator of transcription factor 3(STAT3) and phosphorylated STAT3 (p-STAT3) expression in AEG patients. We retrospectively analyzed the immunohistochemical results of 61 AEG patients and followed up for 5 years, while Western blot was performed on tissues from another 30 AEG patients. The results showed that STAT3 and p-STAT3 were overexpressed in AEG tissues ( Adenocarcinoma of the esophagogastric junction (AEG) was first described by Siewert and Stein in 1998, defined as tumors which have their center within five centimeters of the anatomical cardia –4. ObesiAs a member of the signal transduction and activator of transcription factors (STAT) family, STAT3 is widely recognized as an oncogene. STAT3 can be activated by the Janus kinase after responding to cytokines like IL-6 , produciIn this study, we determined the expressions of STAT3 and p-STAT3 proteins in AEG tissues and analyzed their correlations with clinicopathological parameters, including gender, age, and pTNM. Additionally, we assessed the influence of STAT3 and p-STAT3 expression on the overall survival of patients with AEG.All patients enrolled in this study underwent AEG radical surgery at the Department of Thoracic Surgery and General Surgery, Jinan Central Hospital. The specimens for immunohistochemistry staining were obtained from 61 AEG patients between January 2010 and December 2012 , and speImmunohistochemistry staining for STAT3, p-STAT3 protein was detected by the streptavidin peroxidase method (SP method). AEG specimens and 10 paracancer normal tissues were fixed in 10% neutral buffered formalin and cut into 4 mm-thick slices. The analysis was applied using rabbit antibody against human STAT3  and rabbit antibody against human p-STAT3  and visualized by the Envision System . The immunohistochemistry process was described previously , 17. TheWe use HE staining to confirm the pathological type and measure the degree of differentiation of tumor tissues. AEG tissues and paracancer normal tissues were fixed in 10% neutral buffered formalin and cut into 4 mm-thick slices. HE staining were prepared by using the standard method.The fresh AEG tissues were lysed by RIPA  with protease inhibitors and centrifuged at 12,000 rpm for 10 min at 4°C. The concentrations of protein were analyzed by BCA protein assay kit . Equal amounts of protein were separated by 10% SDS-PAGE and transferred to PVDF membranes . The membranes were blocked with 5% skim milk and incubated with primary antibodies against STAT3  or p-STAT3 , at 4°C overnight, followed by incubation with HRP-conjugated secondary antibodies  for 1 h at room temperature. We used ECL reagents  to visualize immunoblotted proteins, and the signals were detected by Alphamager 2200 imaging system  and Image J analysis software .We ensured all 61 patients underwent AEG radical surgery between January 2010 and December 2012 came to the hospital regularly for comprehensive examination and received a complete follow-up. Overall, 32 cases received postsurgical chemotherapy, 3 cases received postsurgical radiotherapy, and 19 received both postsurgical radiotherapy and chemotherapy. Patients who died of tumors were enrolled in the prognostic analysis, and the reason for death was confirmed by the detailed preoperative evaluation and postoperative follow-up.χ2 test, Fisher's exact probability test, or t′ test. Univariate analysis was applied by modeling Kaplan–Meier survival curves. The log-rank test was performed to calculate the survival rate. Multivariate analysis was carried using the Cox proportional hazard model. All statistical data were analyzed using SPSS . P < 0.05 was considered to indicate a statistically significant difference.Enumeration data were analyzed by P < 0.05, P < 0.01). The results of Western blot were analyzed by t′ test. The expressions of STAT3 and p-STAT3 in AEG tissues were significantly higher than those in normal paracancer gastric/esophageal tissues , lymph node metastasis , and pTNM stage , and multivariate analysis shows the same results . The best way to apply postoperative treatment to improve the prognosis of AEG patients reminds to be explored. Secondly, the sample size enrolled in this study is relatively small. Besides, whether the level of STAT3 and p-STAT3 would change in the different AEG subtypes is worthy for further research, and a randomized controlled prospective study with a large sample size will be considered.This is the first study on the clinical and prognostic features of STAT3 in AEG. We ensured that all patients successfully underwent radical surgery and regional lymph node dissection. These tumors did not invade other organs, and routine histological examination confirmed no residual cancer cells on both sides of the cutting edge to ensure complete resection. However, there are still some limitations in this study. First of all, patients' willingness and economic status may lead patients to give up postoperative radiotherapy and chemotherapy, thus affecting the prognosis of patients. To find out whether different postsurgery therapy affects the prognosis of patients, we applied univariate analysis and multivariate analysis for the relevant data. The univariate analysis shows that neither chemotherapy nor radiotherapy has a significant correlation with the 5-year survival of patients (STAT3 and p-STAT3 are highly expressed in AEG tissue. The level of STAT3 was significantly correlated with the pTNM stage, while the expression of p-STAT3 was significantly associated with the pT, pN, and pTNM stages. Tumor differentiation, pN, and high expression of p-STAT3 are independent risk factors for 5-year survival in patients with AEG. Collectively, our findings suggest that STAT3 and p-STAT3 might serve as essential biomarkers for the prognosis of AEG patients.


Genetic design automationtools are necessary to expand the scaleand complexity of possible synthetic genetic networks. These toolsare enabled by abstraction of a hierarchy of standardized componentsand devices. Abstracted elements must be parametrized from data derivedfrom relevant experiments, and these experiments must be related tothe part composition of the abstract components. Here we present LogicalOperators for Integrated Cell Algorithms (LOICA), a Python packagefor designing, modeling, and characterizing genetic networks basedon a simple object-oriented design abstraction. LOICA uses classesto represent different biological and experimental components, whichgenerate models through their interactions. These models can be parametrizedby direct connection to data contained in Flapjack so that abstractedcomponents of designs can characterize themselves. Models can be simulatedusing continuous or stochastic methods and the data published andmanaged using Flapjack. LOICA also outputs SBOL3 descriptions andgenerates graph representations of genetic network designs. From this perspective, living systems areobjects to engineer, and a rational way to design them is by modifyingtheir genetic code. This can be done by introducing synthetic DNAthat encodes a synthetic regulatory network, also known as a geneticnetwork or genetic circuit. The design–build–test–learn(DBTL) cycle is central to engineering disciplines, and each phaserequires appropriate tools, standards, and workflows, which are stillin development. Synthetic Biology Open Language (SBOL) is an openstandard for the representation of Modeling is key to the DBTL cycle and is essential to thedesignand learn stages since a model states a well-defined hypothesis aboutthe system operation. Abstraction enables the construction and analysisof models based on components, devices, and systems that can be usedto compose genetic networks and derive their DNA sequences. It isthe basis for genetic design automation (GDA), which can accelerateand automate the genetic network design process by compiling modelsinto DNA sequences. In order for GDA to proceed in a rational way,the abstract elements of genetic networks must be accessible to characterization,allowing parametrization of models of their operation and interactions.7 However, for large-scale genetic network design,higher-level abstractions are required, as provided by the logic formalism.8 In this approach, network compositions are abstractedinto genetic logic gates that transition between discrete low andhigh steady-state gene expression levels according to input signals,either external or internal to the network.9 These genetic logic networks can be designed automatically usingCello,8 in an analogous way to electroniccircuits, on the basis of the required discrete logical truth table,but this specification requires knowledge of the domain-specific programminglanguage Verilog.10Functional abstraction of DNA sequences as parts such as transcriptionalpromoters, ribosome binding sites (RBSs), coding sequences (CDSs),terminators, and other elements has enabled the assembly of relativelysmall genetic networks.6 Furthermore, typical operatingconditions for engineered networks such as colonies, bioreactors,and microbiomes are time-varying, which can lead to complex behaviorsfrom even simple genetic networks.11Despite the discretelogical design formalism, these genetic networksare dynamical systems and can have autonomous, continuous, non-steady-statedynamics, displaying complex and rich behaviors from bistability tooscillations and even chaos.via common exchange standards in a user-friendly and accessiblefashion.To design genetic networks, we therefore require kinetic gene expressiondata generated at the test phase. These data must be integrated withmodels to enable characterization of abstracted parts, devices, andsystems as well as metadata, including the DNA part composition andsequence, to enable automated design. Thus, there is a need for softwaredesign tools that integrate abstract network designs, dynamical models,kinetic gene expression data, DNA part composition, and sequence 4 rather than composing individualgenetic parts, LOICA provides a high-level design abstraction thatsimplifies the design process by representing networks as combinationsof components accessible to parametrization. This parametrizationof genetic network models is enabled by direct connection to experimentaldata via Flapjack, which also provides a platformfor publishing and sharing simulation results. Furthermore, whileLOICA abstracts genetic networks at a higher level, designs can berepresented using the latest SBOL3 standard. LOICA is a Python packageallowing programmatic design, simulation, parametrization, and analysisof genetic networks. While perhaps not as accessible as a graphicaluser interface, this approach is more flexible, extensible, and amenableto automation. It can be easily combined with the large ecosystemof biological Python projects15 and uses simple programming concepts that are commonlyunderstood by researchers from a range of disciplines.Logical Operators for Integrated Cell Algorithms (LOICA)is a toolfor the design, modeling, and parametrization of synthetic geneticnetworks. In contrast to existing genetic network design and modelingtools,via two-way communication with Flapjack, a data management and analysistool for genetic network characterization.13 LOICA objects can be represented using SBOL3, enabling direct translationto part composition and DNA sequence as well as connection to repositoriessuch as SynBioHub16 and the ecosystem ofSBOL tools.4 All of the code, examples, and documentation are publicly availableat https://github.com/RudgeLab/LOICA.LOICA provides a high-level genetic design abstraction using asimple and flexible object-oriented programming approach in Python.LOICA integrates models with experimental data Operator and GeneProduct, which may be either a Regulator or Reporter , and terminators.These objects can be represented by SBOL Components containing features that describe individual parts and their DNAsequences. The interactions between the Operators and the Regulators encode models for genetic networktemporal dynamics, which can be simulated with ordinary differentialequations (ODEs) or the stochastic simulation algorithm (SSA). Thesystem of ODEs is thus:p = T is the vectorof GeneProducts, which includes different Regulators T) and Reporters T). The nonlinear operator Ψ maps Regulator concentrations to GeneProduct synthesis rates. Γ is a diagonalmatrix of GeneProduct degradation rates γi, and μ(t) is theinstantaneous growth rate of the cells. Ψ encodes the whole network and consists of a sum of individual LOICA OperatorsΦk (The basic objectsin LOICA are Reporter 1A. A RegratorsΦk 2.Operators encode the GeneProduct production reactions (*→ pi) with propensities ai given by the sum of Operator synthesis rates:Operators that synthesize GeneProducti.The degradation rate γi and growthrate μ(t) determine the propensities bi of the GeneProduct extinction reactions (pi → *):In the stochastic simulationapproach, these Operators currently implemented in LOICA. Inthe first,the output expression rate is a simple Hill function of the input Regulator concentration  oraBuffer (α0 < α1). The Operator is the set of genetic parts that regulate geneexpression . The Reporters are linked to the Flapjack Signal model and together with the Operators and Regulators are incorporated into a GeneticNetwork, linked to the Flapjack Vector, which with the Metabolism drives the dynamicsof the Sample  or web interface (http://flapjack.rudge-lab.org).The designis made programmatically 2A, B ande Sample 1A. The Sterparts 1A. The cwn in 20 3B—contaiGeneticNetwork has a method to generate an SBOL document representation of itself. GeneticNetwork maps to a Component that contains representationsof Operators and GeneProducts andtheir interactions. The combination of an Operator and its GeneProducts is represented by SubComponentsof a transcriptional unit Component of type DNA with role EngineeredRegion. Each GeneProduct has a genetic productionInteraction that generates a molecular species (protein or RNA). Ifthe molecular species is a Regulator, then it hasan inhibition or stimulation Interaction with one or more Operators. Whether it is an inhibition or stimulation dependson the parameters of the Operator. A Model is addedto the SBOL document and includes the source, language, and framework.To enable the synthesis or assembly of the design, the Operator and GeneProduct Components should include a Sequence.Constraints are added to ensure correct part order.GeneticNetworks, which can be used for further analysis of their structure and forvisual inspection A description of howto use LOICA to generate and analyze simulated data from models hasbeen provided. However, another workflow scenario goes from data tomodel parametrization. We demonstrate this process for a two-input Operator, three auxiliary genetic networks are required. Each genetic networkneeds a Reporter as a measurable output. In thisexample, we used LOICA to generate simulated kinetic time-course data.The outputs must be quantified with respect to model parameters, andtherefore, in an experimental setup the measurements should be properlycalibrated.21 Two genetic networks composedof an input Supplement , a Receiver Operator, anda Reporter must be characterized. The Receiver Operator transforms the input Supplement concentrations into output expression rates of the Reporter, modeled as a Hill function. Each of these genetic networks wassimulated over a range of input concentrations, and the data wereuploaded to Flapjack. This allowed parametrization of the Receiver Operators by fitting of their dynamic models to the simulateddata.In order to characterize the two-input Operators werecomposedinto a genetic network to drive the Regulator inputsof the two-input Operator, which in turn drives theoutput Reporter. This genetic network was then simulatedover a range of concentrations of input signals, and the data wereuploaded to Flapjack. The two-input Operator model,combined with the parameters of the two Receivers, was then fittedto the simulated data.Next, the two Receiver Operator class providesa single functionthat performs this parametrization process, connecting to Flapjack via identifiers of the appropriate genetic networks, automaticallycombining all of the available data.The TheDBTL cycle is fundamental in synthetic biology, and thus, varioustools have been developed to optimize the different stages. Withinthis cycle, modeling and characterization are essential for the designand learn stages, for which we have designed LOICA, a tool that connectsthese processes in an automated fashion. LOICA integrates the design,modeling, and characterization of genetic networks and their componentsinto Python workspaces, providing a powerful and easy-to-understandhigh-level design abstraction for GDA that is implemented using simpleobject-oriented programming principles. Importantly, this programminginterface does not require specialist or domain-specific knowledgebut instead leverages common programming skills, making it accessiblebut also providing customization capabilities for advanced users.via links to specific experimental data in Flapjack.13 In this way, designs and their DNA instantiationsare associated with online repositories of experimental data, whichenable upload and sharing by multiple users or laboratories. Thisallows division of labor and reuse of experimental data that couldbe integrated into the workflow of distributed biofoundries. GeneticNetworks can automatically build SBOL3 representationsof their structure that encapsulate SBOL3 Components defining LOICAobjects and incorporating their part composition and DNA sequence.As well as enabling GDA, this provides an easy way to construct SBOL3documents promoting the use of the standard and providing the capacityto export SBOL3 files, which then can be loaded to different SBOL-basedtools. In this way, LOICA not only links models to part compositionand DNA sequence, allowing automated assembly22 or synthesis, but also connects designs to DNA provenance and othermetadata contained in repositories such as SynBioHub.16LOICA genetic network designs are composed of objects that correspondto DNA sequences and are capable of characterizing themselves Operator–Regulator abstractionassumes a one-step Regulator synthesis model. IfmRNA dynamics is important to network function, this is clearly notappropriate. Furthermore, since Regulators interactonly with Operators, protein–protein or RNA–RNAinteractions are not possible. These limitations may be overcome byimplementing more complex models of Operators thattrack the dynamics of internal events such as mRNA production andinput Regulator–Regulator interactions. Also, all GeneProducts synthesizedby an Operator are currently assumed to be expressedat the same rate, which may be overcome by specifying more complex Operator models.Therefore, LOICA provides simple, easy-to-use, high-leveldesignabstraction for modeling and characterization of genetic networksand their components, which connects to existing synthetic biologystandards, tools, and repositories of experimental data to enableGDA. As with any abstraction, simplification comes at the cost ofsome limitations, which will be addressed in future revisions. TheLOICA 21 However, since many datasets are not calibrated,future versions of LOICA will incorporate explicit specification ofunits, allowing for conversion of experimental measurements to valuesdirectly comparable to model simulations.Another issue is calibration of experimentalmeasurement data withrespect to model parameters and the use of different units. Followingbest practice, we propose the use of Molecules of Equivalent Fluorescein(MEFL) as units for outputs using GFP or its derivatives.Metabolism as a modelcomponent but currently includes limited interaction between thisand the GeneticNetwork. It has previously been shownthat gene expression is modulated by resource competition becauseof metabolic limitations and in turn has an effect on metabolism,including growth rates.26 These interactions are not currently included inLOICA models, but the necessary interactions between classes are present,meaning that given suitable models, the interactions between Metabolism and GeneticNetwork and theircomponents could be encoded in a straightforward manner. Furthermore,the characterization method implemented in LOICA assumes that geneticparts are not affected by their compositional context. Various methodshave been developed to reduce such effects to a minimum,28 but they cannot be discounted completely. It may be appropriateto develop a constraint-based specification of such interactions betweenspecific parts, similar to the approach of Cello.8LOICA explicitly hasthe 29 which will extend the existingcharacterizationof continuous models. A major improvement will be the implementationof spatiotemporal dynamics of gene expression in multicellular populations,including a connection to CellModeller30 for individual-based modeling. SBOL integration will continue tobe improved to leverage more features and to allow model consistencychecking based on known interactions between Operators and Regulators, such that for example a known repressorcannot be encoded in a model as an activator. Ultimately, we aim tocomplete and automate the DBTL cycle through an open-source workflowthat incorporates LOICA, Flapjack,13 SynBioHub,16 and tools powered by SBOL.1Future work also includes parametrization of stochasticmodels,