Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered.Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles.Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups.Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity. The use of population descriptors is currently under the spotlight, both in genetics research specifically and across biomedical research more broadly . Social American Journal of Human Genetics has shown that the term “ancestry” is increasingly used in genetics research  are currently convening a taskforce on the appropriate usage of race, ethnicity and ancestry in genetics research . The appresearch . As Wagnresearch .Nature Genetics and interviews, Panofsky and Bliss explore geneticists’ use of population labels, demonstrating the increasing use of continental labels, which they argue are fundamentally ambiguous because they “blur racial and geographic understandings of population difference”  were the least likely to provide a rationale for its use . Interviference” . In an eference” . Focusinference” .Researchers across multiple fields employ the concept of ancestry; it is not straightforwardly a concept that is “owned” by genetics. The concept of ancestry is often employed in everyday conversation, carrying sociocultural implications outside of its usage in genetics and health research. This use across contexts gives many opportunities for miscommunication about what ancestry is and is not, and what we can learn about or from it. Genetics has a special role in our understanding of ancestry, but, as mentioned above, this concept then diffuses out to translational research, to the practice of medicine, and to popular conceptions about the human family tree.In this study, we employ a mixed methodology—a systematic literature analysis and semi-structured interviews—to offer a comprehensive examination of how ancestry is used by researchers. Because diverse domains use the concept of ancestry, and because they are of mutual relevance to each other, we include research from multiple disciplines. We seek to answer five questions about researchers’ use of ancestry. First, what types of research use ancestry? Second, when and why does ancestry enter the research process, i.e., what are the use cases for the concept? Third, what does ancestry mean to researchers, i.e., what definitions do researchers offer for the concept? Fourth, how is ancestry operationalized, i.e., how is this abstract concept made into a measurable observation? And finally, what types of population labels are used for ancestry categories? Answers to the first two questions help indicate just how important the concept of ancestry is in structuring both research questions and methodologies; answers to the remaining questions shed light on whether ancestry as currently conceptualized and operationalized can bear this heavy weight.This study employed two different methodologies to understand the use of ancestry by researchers: a systematic literature analysis and semi-structured interviews. The systematic literature analysis was performed on an original dataset composed of a sample of research articles in the population sciences. We designed this corpus of original research articles to capture as much of the diversity of the ways that ancestry is currently used by researchers as possible, in terms of divergent research questions and methodologies across disciplines. Given the motivation of our work to inform the use of ancestry and genetic ancestry across the biomedical sciences, we constrained the articles in our corpus to have at least some tangential relevance to human health. The study also aimed at diversity in terms of publication journals such that not only articles published in high impact factor journals were selected for the study, as this might represent a selection bias towards particular types of studies . We also identified a subset of these articles as engaging with the concept of ancestry particularly closely, and invited the first and last authors of these articles to participate in a semi-structured interview. This multi-method approach was designed to allow us to develop a robust understanding of researchers’ use of ancestry, with the systematic literature analysis revealing patterns of usage, and the interviews allowing us to understand why we observed these patterns. This strategy yielded 205 articles and 44 interviewees.All searches were based on Web Of Science (WOS) and conducted in February 2021. We restricted all searches to articles that contained “ancestry” in the title, abstract, or keywords, and then deployed two search strategies. First, we restricted articles to those concerning certain phenotypes, published from 2019 on. This focus on phenotypes ensured that we obtained diversity along other dimensions that we cared about, specifically research methodology. We chose a range of different types of conditions, all of which  present known health disparities: COVID-19, prostate cancer, chronic kidney disease, and schizophrenia. These articles were filtered to just retain original research articles where the phenotype of interest was a central explanandum of the article. Second, additional searches were conducted in order to obtain a sufficient number of anthropology, social science, and public health articles. The start year was adjusted to ensure an adequate sample size; this meant starting in 2010 for sociology , 2015 for anthropology, and 2020 for public health. For the anthropology and sociology searches, we also required that the term “health” appear in the title, abstract, or keywords. The results from all three searches were then filtered to retain articles that had either a connection to “health” broadly defined, or to human evolution, or to the characterization of human populations. The number of articles from each of the searches before and after filtering are given in The search results were exported, and the PDFs of the articles and their Supplementary Information were downloaded. We used the abstract, journal, and affiliations of first and last authors to assign a primary subfield and field to each article . The majority (29) were based in the United States, with 7 based in Europe, 6 in Central and South America, one in Canada, and one in India. The interviewees were assigned a subfield based on their training as inferred through their professional biographies, publication record, and in conversation during the interview. We achieved disciplinary diversity within our interviewees: 7 from anthropology, 14 from biology, 11 from medicine, 6 from public health, and 6 from sociology.In the interviews, we utilized the article by virtue of which the participant was recruited to probe in greater depth how they use and think about ancestry. The semi-structured interview guide covered five areas: their background and the focus of their work; the justifications for their choices related to ancestry, and how they understood the limitations of these; conceptual questions, including “What does ancestry mean to you?’’; publishing and the mechanisms of funding; and their views on the status of the field.Interviews were 1 hour long, and conducted by two interviewers, one with a biology background (AL) and one sociologist (SM). The interviews were conducted on a video conferencing platform, recorded, and auto-transcribed. These transcripts were then updated based on the recordings. Identifying information was removed. The interview study was deemed exempt by the Harvard University-Area Committee on the Use of Human Subjects (protocol ID IRB21-0496).The documents—the full text of the 205 articles and the 44 anonymized transcripts—were first coded. This involves identifying, highlighting, and annotating the appropriate sections of text corresponding to a set of codes. Around 3,000 sections of text from the articles were tagged with 27 codes, and about 2,300 to 34 codes from the interview transcripts. The list of codes covered are given in On a code-by-code basis we then analyzed the sections of coded text for emergent themes. In addition to this qualitative analysis, we established two features of the articles which enable quantitative presentation of results. First, for those articles that operationalize ancestry, we coded the data type(s) they use to do so. Second, we categorized the type of population labels used in the articles. We used the types of population labels as previously analyzed in Panofsky and Bliss , with soThe 205 articles in our corpus had diverse research aims, which we group into five categories, giving examples.via Genome Wide Association Studies (GWAS)  ), ident)      additionally include a between-group comparison . We observed that many articles do not clearly lay out their aims, with the relationship between research question and research motivation somewhat diffuse.Ancestry can enter the research process at multiple stages, representing different use cases for the concept.As seen in the previous section, ancestry can be used to frame the research question when the focus is on the relevance of ancestry to a trait or outcome. The motivation for this is the assumption that ancestry reflects distinctive patterns of genetic difference, and that using it as a key variable will help identify whether genetic factors could be contributing to differences in incidence and prevalence rates of disease, or differences in traits, between groups . This cexacerbate existing health disparities” , to answer, because there are of course different ways of thinking about it… each project, to some extent, helps you to rethink what (ancestry) means”. Consistent with ancestry being a concept from everyday life, many interviewees drew from their own personal stories in the answers that they gave.We identified two dimensions along which answers differed. The first is what ancestry is a property of: DNA; the individual; their family/kin; a population. The second is the criteria by which ancestry is shared: geographical origin; genealogical connections; culture; biology. Examples are given in Many individuals gave multipart answers spanning different cells in the matrix represented by Some interviewees were keen to emphasize the distinction of ancestry from race and/or ethnicity. For example, “race is a self identified construct and ancestry is biology”, or “it is very far from concepts like, race or ethnicity or something like that.” But others directly related the concepts, for example “African American ancestry - that racial group that, you know, would essentially have common genotypic and phenotypic characteristics.” In some cases there was a direct conflation which was masked by language, “Previously I used ethnicity. But then my mentor told me that nowadays people use ancestry.’’, or as a term to be used “instead of race, because we cannot use, like ‘mixed race’ because… the connotation is not good.”Before it is used, ancestry first has to be operationalized, i.e. a process has to be defined to ascribe an ancestry to individuals.Out of the 205 articles examined, 56 (27%) did not operationalize ancestry in their methodology or analysis. Many articles use the word “ancestry” haphazardly and interchange the term with other population descriptors. For example, an article states “More than half of our patients are from African ancestry” , but useEuropean ancestry, African ancestry and other categories  of these we could not determine what type of information was used in this operationalization . For example, when articles simply refer to “men of African ancestry” . In sometegories . In anotaucasian . Some reaucasian .Of the 110 articles where it was possible to tell what type of data was used to operationalize ancestry, 72 (65%) used genetic information to do so, 53 (49%) used non-genetic data, and 15 (14%) used both genetic and non-genetic data, see For those articles for which it was possible to determine the type of information used to operationalize ancestry, this information was often hard to find. For example, in Harrison et al., the main text referred to their Most of the articles that used self-identified information to operationalize ancestry are consistent with participants being asked to report their race and/or ethnicity rather than their ancestry. Suggestive evidence of this includes interchangeable use of ethnicity and ancestry, reference to “racial ancestry” , and demThere was also diversity in how geographic data was used to operationalize ancestry: birth country of parents ; birth cAncestry was also operationalized using surnames, including: inferred place of origin of the surname of the individual ; whetherThe articles in our corpus that used genetic data to operationalize ancestry used a variety of methodologies to do so. Some used analysis of Ancestry Informative Markers (AIMs), which are genetic variants specifically picked because they have very large frequency differences in different groups, where the groups of interest are chosen during design of the AIMs. The majority of articles used standard genotype chips and one of a small handful of methodologies. One common method is Principal Component Analysis (PCA), a dimension reduction technique that defines a space in which the first dimension (PC) captures the most variance in the inputted data, the second the next most, etc. Another common method is a version of the STRUCTURE/ADMIXTURE algorithms, which assign each individual a percentage of ancestry in different variation distribution clusters referred to as populations. These algorithms can be run in an unsupervised fashion, where the user provides only the number of populations (referred to as the “k” parameter). It can also be run in a supervised fashion, where the user defines the populations by providing reference genetic data for each. Of course, the labels for these populations are previously defined using some other form of data, typically geographic or self-identified. Methodologies other than these two were sometimes used, notably Multidimensional Scaling e.g., .As previously mentioned, many articles use PCA in their analysis; most of these refer to an individual’s location in Principal Component space as their ancestry, though exceptions include referring to this as ethnicity . In inteWhen PCs were used to define a population, this was typically done through drawing ellipses (“bubbles”) in PC space. Our interviewees reported that this was mostly either an “eyeball” process, or simply following a rule of thumb because that is what another paper had done. When PCs were used to control for confounding, the number used to do so varied widely, and, when asked, interviewees explained they were either choosing a number based on what a previous paper had used, or followed a rule of thumb . Few weWith the use of statistical software packages such as STRUCTURE/ADMIXTURE, which characterize ancestry of an individual in terms of their percentage similarity with different populations, many interviewees showed a greater awareness of the ways it could be arbitrary, reflecting the fact that choices must be made to run it . For example, one interviewee described the extent to which interesting observations could be made about their data as k varied . Some researchers, again those closest to population genetics, offered additional cautions about the use of this methodology to operationalize ancestry, including that it is very easy to over interpret the results, that newer admixture can be confused with identity by descent, and that there never have been “pure” populations. This last observation refers to the fact that the underlying population theoretic model motivating the design of STRUCTURE/ADMIXTURE models an individual as deriving proportions of ancestry from well-defined ancestral populations, but any such populations are themselves mixtures of other populations.Examples of the “Genetic Intersect Self Report” operationalizations are found in articles that used the United Kingdom Biobank “White British” data  Kolin e, which uWe observed inconsistent use of terminology to describe ancestry operationalized using genetic data. Articles were very mixed about whether they referred to this as “genetic ancestry”, with about equal numbers systematically using “genetic ancestry” or using just “ancestry”, and with many others using a mixture of these two. Other terms in use include “population ancestry”, “genomic ancestry”, “ancestral population structure’’, and “DNA ancestry”. Additionally, some articles distinguished global from local ancestry, but meant different things by this distinction. For some, “global ancestry” means the percentage of ancestral populations inferred by programs like ADMIXTURE  whereas “local ancestry” involves assigning population labels to sections of chromosome . For otWe observed a similar lack of clarity around how the term “population” is used by researchers. For some, a population is a model from population genetics implying random mating. For others, drawing from statistics, data from a sample should be chosen such that it is representative of a population. But for most, usage was more diffuse, implying simply a group of people with something (anything) in common, or as one interviewee put it, simply “large N” (where N is the sample size).We investigated how the data type used to operationalize ancestry varies across fields of study: anthropology, biology, medicine, public health, and sociology, see Throughout our analysis of the operationalization of ancestry we observed many sources of conflation between ancestry and race and ethnicity. As mentioned, it seems likely that many of the participants who were listed as self-reporting their ancestry actually self-reported their race or ethnicity. This is likely also true for the articles where it was not specified what type of data they used to operationalize ancestry. Other articles referred to their ancestry categories inferred from genetic data as ethnicities . One artWhite British ancestry, a label from the United Kingdom Biobank.Just as there is diversity in what type of data is used to operationalize ancestry, there is also diversity in the types of population labels used to describe the resulting categories see . ContineA combined analysis of population labels and type of data used to operationalize ancestry see  reveals Among the articles which use ancestry categories, most types of population labels are used by articles from all the fields of study see . ContineAn analysis of how population labels are used across the authors’ country/region of institutional affiliation see  indicateWe observed several instances of grand generalizations from samples from populations with ethnic or country labels to continental groups, for example from Japanese ancestry to Asian ancestry , and froIn this discussion we first summarize our main findings, and then reflect on the normative consequences of our findings for the type of research we included in this empirical work. We start by noting that our results are limited by the types of articles, and subsequently, researchers, that we included. There are some types of research questions, namely those that do not have even a tangential relationship to human health, that we do not cover.Our results indicate that ancestry is a concept that is drawn upon in multiple key ways across a broad range of types of research. This is particularly true when it comes to understanding traits and outcomes, from blood pressure to income. For researchers seeking to identify genetic variation linked to these traits, ancestry is evoked as a central part of the methodology, as something to be controlled for. For researchers interested in understanding social outcomes who are not interested in identifying genetic variants, ancestry is again something to be controlled for. For other researchers, ancestry shapes their research question; they hope that studying how a trait varies with ancestry can enable the identification of genetic contributions to between-group differences, including health disparities. Having established that researchers evoke ancestry in key ways, understanding what the concept means to them becomes central.We demonstrate a huge diversity of understandings of ancestry amongst researchers. Several observations stand out. First, the concept of ancestry encompasses much more than genetics. Many of our interviewees, including geneticists, gave definitions that were broader than what could be inferred from genetic data, stressing for example the narrative aspect of ancestry. The example of “indigenous ancestry” also illustrates that ancestry is more than genetics; several indigenous groups have explicitly rejected genetics as relevant to questions of ancestry, in favor of cultural affiliation . AttemptWe also report a huge diversity of operationalizations of the concept of ancestry. The process of operationalization involves taking a definition of an abstract concept and making it measurable. Given the diversity of definitions of ancestry, it is perhaps not surprising there are so many operationalizations. The fundamental ambiguity of the concept, as discussed above, is also evidenced by our result that a quarter of all papers that operationalize ancestry fail to state anything about how this was done, for example whether inclusion criteria were based on self-reported information, geography, or genetics. This was particularly the case in Biology and Medicine, with Anthropology and Sociology articles more frequently stating what type of data was used to operationalize ancestry, perhaps reflecting greater sensitivity in these fields to the ways in which these categories reflect decisions made by researchers. The failure to specify what type of data is used to operationalize ancestry allows for the introduction of further ambiguity between genetics and social identities, particularly given that researchers often use “ancestry” language (rather than “genetic ancestry”) when genetic data has been used to operationalize the concept, and particularly when ancestry is operationalized more than once in the same article, sometimes using genetic data and sometimes not.Our results also highlight that practices associated with using genetic data to operationalize ancestry rest on unclear conventions, and that there is a lack of clarity concerning the relationships between the different key concepts. This is particularly true for the use of Principal Components. PCs are sometimes referred to as ancestry, genetic ancestry, population structure, or some more qualified term, suggesting that they “capture” or “correlate” with one of the above. Researchers often justified their choices about the use of PCs by reference to prior papers, without being able to give their own justifications of why those choices were appropriate. This may be concerning, given the growing usage of PCs not only within statistical genetics but by those seeking “off the shelf” solutions to “control for genetics” . It shouThe predominance of continental ancestry labels, particularly when genetic data is used and when it is not specified what type of data is used, reinforces concerns that the turn to genetic ancestry may just essentialize the quasi-racial groupings represented by these categories . On the In repeated guidelines for the use of population descriptors, transparency of what is meant by the concepts and how they are operationalized is presented as a minimum bar . Our res. And whereas racial labels are a result of sociopolitical processes, it is researchers who choose to impose categories on genetic data, and then to attach labels to those categories. As Bonham et al. write, “It is critical to avoid creating fictitious, discrete genomic groups while recognizing that self-identified race and ethnicity are highly associated with genetic ancestry at the continental and population level”  Use of the term “ancestry” by itself should be avoided. Rather, this term should always be qualified, for example as “genealogical ancestry” or “genetic ancestry”.(2) Use of the term “population” should be avoided. It has no agreed upon meaning and only serves to make something sound more scientific than it in fact is. This is particularly true in genetics research when the term is apt to be confused with the term in population genetics theory for a group of individuals who are mating at random. Use of the term “group” is to be preferred, because it correctly draws attention to the question “by virtue of what are these individuals being grouped together” and because it evokes less scientific authority.(3) Operationalizations of genetic ancestry that reflect the continuous nature of genetic variation, such as PCA, should be encouraged wherever possible .(4) If genetic ancestry must be operationalized using categories, multiple sets of categories should be used, to reflect the fact that one can carve up the human family tree in multiple ways .(5) Genetically inferred continental ancestry categories should only be used if necessary .(6) Researchers need to make themselves familiar with the key limitations of the tools they use.While we agree with the dozens of other commentators on the importance of researchers transparently describing exactly who was studied, how they were classified, and why, we advocate for the following additional considerations.The hope of some is that ancestry represents the biological, objective counterpart to the social constructs of race and ethnicity, and that a turn to ancestry categories could help us get away from the bad science and damaging history of previous classification systems and practices. Our investigation of how ancestry is actually used indicates it is a far cry from the objective and straightforward concept hoped for. Nor is it a uniquely “biological construct”. Indeed, the concept is fundamentally ambiguous, and not more conceptually clear than race or ethnicity in practice. By just moving to a new term, there is a danger that research in this area fails to address the known issues in the uses of race and ethnicity as population descriptors. Part of the problem is precisely that some scientists are searching for a more objective term, thus treating this set of issues as purely semantic when in fact the problems run deeper: we need more careful attention to the different roles of genetic variants (which are not uniformly distributed) on the one hand and the myriad other contributors to health outcomes on the other. The move to “ancestry” just confuses the issue. Given the central role ancestry plays in genetics research and beyond, these deep-seated issues with how ancestry is conceptualized and operationalized should raise concern, and highlight the importance of strategies that will advance conceptual clarity.


Although consumers and experts often express concerns regarding the questionable business practices of direct-to-consumer (DTC) genetic testing services , the DTC genetic testing market keeps expanding rapidly. We employ retail fairness as our theoretical lens to address this seeming paradox and conduct a discrete choice experiment with 16 attributes to better understand consumers’ fairness perceptions of DTC genetic testing business models. Our results suggest that, while consumers perceive privacy-preserving DTC genetic testing services fairer, price is the main driver for fairness perception. We contribute to research on consumer perceptions of DTC genetic testing by investigating consumer preferences of DTC genetic testing business models and respective attributes. Further, this research contributes to knowledge about disruptive business models in healthcare and retail fairness by contextualizing the concept of retail fairness in the DTC genetic testing market. We also demonstrate how to utilize discrete choice experiments to elicit perceived fairness.The online version contains supplementary material available at 10.1007/s12525-022-00571-x. Driven by the dwindling costs for collecting and analyzing genetic data, numerous direct-to-consumer (DTC) genetic testing services emerged in the mid-2000s  available on the market. This is achieved by asking individuals to state their preferred choice from different hypothetical alternatives, allowing researchers to reveal how individuals value selected attributes of a product or service  . Since both solutions only require eight choice sets per respondent, we chose the smaller option with six varying attributes. The last important decision concerning the design was selecting the number of hypothetical alternatives each choice set should contain. While there is no upper limit, respondents need to be able to decide between the offered alternatives reasonably. In practice, most DCEs include two to five alternatives   dropped out before completion of the survey, and an additional 794 (25.80%) respondents were automatically rejected due to quality termination . Lastly, of the successful completes, 55 (7.51%) respondents answered every single question with the non-choice. While this may be a viable preference statement, non-choices do not influence the choice model as they do not provide any additional information on the trade-off of attribute levels  or WTP  are significant for the choice model at p < 0.01 significance level. Price is the most significant effect, followed by the no choice indicator. Hence, price has the largest impact on a respondent’s fairness perception toward a certain business model. In contrast, data storage  has no significant impact on consumer fairness perception.Analysis of respondent data provides insights into the relative importance and utility of the examined attributes. As can be seen, the model only analyzes the main effects . For an analysis considering two-way interactions including no choice indicator  reduces all criteria values by more than 8,900 (ca. 43%). Because the removal of observations tends to reduce the values of model comparison criteria and our sample consisted of 1949 (23.99%) non-choice observations, this finding is not surprising. For example, as can be seen in Supplementary Material JMP further provides comparison criteria for different models, namely the Akaike’s Information Criterion (AICc), Bayesian Information Criterion (BIC), -2 *Loglikelihood, and − 2*Firth Loglikelihood, with smaller statistics indicating a better fit for the model. The used choice model has an AICc of 20,944.51, a BIC of 21,126.4, a -2 *Loglikelihood of 20,892.33, and a -2*Firth Loglikelihood of 20,683.86. Although the criteria are not comparable amongst each other, a value of the same size indicates a stable model was found. Nonetheless, the values of the criteria are considerably large. The model is potentially still a good fit, as the values tend to increase with additional effects and a larger sample size due to more variation factors  must have the same distance from the mean (+ 0.128) with a positive impact. Thus, the nonprofit level has a positive utility of + 0.256 compared to the for-profit level. One notable exception to this is price. Since this attribute is numerical and continuous, the estimate describes the negative utility for every unit  added. For example, the part-worth utility for the $100 ($1000) level is -0.13 (-1.3) over the $0 level. However, this also brings the benefit of measuring utility for every price possible, even though it was not modeled in the survey. In addition to the marginal utility, Table sampling site, this means, considering this attribute, a respondent will select home or lab collection over the other levels with a probability of 39.4%.The marginal utility table Table  providestest purpose attribute. Since the marginal probability describes the likelihood of choosing a level over the other levels, we can presume that health tests are the most probable tests purchased by consumers with 40%, followed by lifestyle tests with 30.4%, and lastly, relationship tests with 29.6%. Notably, the highest marginal probability is given for no reselling of genome data, with a probability of 63.6% for consumers choosing this level over the other.The marginal probabilities provide some rather interesting insights for the price attribute  is defined as continuous, this allows for calculating the WTP. Since our survey did not ask for a preference to purchase but the preference for fairness, the WTP analysis can be seen as the monetary trade-off for a fairer attribute level. Utilizing the unfairest business model as the baseline, the WTP analysis can unveil how much a consumer is willing to pay for a fairer level of each attribute.When the business purpose of the service provider with $200.32 if it is nonprofit instead of for-profit. Consequently, the fairest business model has a value of $3,087.69 to consumers (with the unfairest set to a WTP of $0.00). Table For example, according to our analysis, a potential customer values the no choice indicator  causes a constant additional negative utility of -0.4730 to the overall utility  business model is simply calculated by adding the part-worth utilities of every selected level together. This allows for a comparison of business models in terms of overall perceived fairness by consumers. Consequently, the fairest business model with the attribute levels shown in Table price $0, no reselling of genome data, a health test purpose, and a nonprofit business purpose. On the contrary, the unfairest business model costs $1000, does resell genetic data, offers a relationship test, and has a for-profit business purpose.Naturally, the fairest business model represents all the attribute levels that benefit consumers financially or service-wise the most, such as To further examine the utility of DTC genetic testing business models, we have conducted a brief investigation of real DTC genetic testing service providers and the utility they would have according to our choice model. For this, we have investigated two service providers per archetype found by Thiebes et al.  in SupplAnalysis of the DCE results delivers interesting insights into consumer perceptions of DTC genetic testing and the fairness of their business models. While our DCE provides hypothetical fairest and unfairest business models, these are highly unlikely to exist in a real-world setting . This finding, however, is not surprising, as consumers naturally prefer the best service for the least amount of cost. Thus, it is necessary that consumers and service providers make certain trade-offs to find a balance between the service offered and the costs involved. Nonetheless, overall, the hypothetical models strengthen the notion of consumers’ preference for more privacy-preserving services. In a similar vein, the results from our analysis of real DTC genetic testing business models suggest that privacy-preserving services found in archetype two (high-privacy DTC genomics for enthusiasts) in general achieve a higher utility than the similar test from archetype one (low-cost DTC genomics for enthusiasts). However, as the name suggests, this trade-off comes with a price difference, as the service providers from archetype two are more expensive. Another interesting finding highlighting how price is the most important factor when considering the fairness of DTC genetic testing business models is that consumers seem to prefer simple health tests (archetype four) over comprehensive tests and low data processing (archetype six) in terms of utility, mainly because these health tests are more expensive and often require additional interpretation.Reselling of genome data is the attribute with the third-highest significance (LogWorth = 46.493). This is in line with previous research, which shows that consumers desire transparent and privacy-preserving business models  is the most significant factor for consumers’ perception of fairness. Therefore, it is likely that consumers prefer lower costs over higher overall service quality. The part-worth utility from $0 to $1000 (-1.3) vastly outperforms every other attribute. Moreover, our additional analysis of two-way interactions including price  is mainly responsible for the costs that occur for a service provider while also determining test comprehensibility. Hence, it seems plausible that consumers take the interaction of genome test type and price into account when determining fairness perception. The third most significant attribute is reselling of genome data, which only contributes − 0.56 negative utility impact if genetic data is resold. This is in line with findings on willingness to disclose personal genetic data, which show that consumers are willing to provide access to their personal (genetic) health information for monetary incentives  is the second most significant effect of the model. It could therefore be assumed that many consumers opt to refrain from DTC genetic testing altogether, as they do not perceive it to be a fair and valuable business market.The proposition that consumers perceive privacy-preserving DTC genetic testing business models as fairer is opposed by the fact that $1000 -1. vastly oreselling of genome data (LogWorth = 11.855) has a higher significance than the no choice indicator (LogWorth = 7.499). Because these respondents have taken or purchased a DTC genetic test before, it makes sense that they may be more open to perceiving DTC genetic testing business models as fair or fair enough to purchase, thus explaining the lower significance of the no choice indicator. On the other hand, having provided their genetic data previously, these respondents also may be more concerned about the reselling of their own genetic data, explaining the higher significance of reselling of genome data. Together with the large number of non-choices (ca. 23.99%) for the entire US population-centered respondent sample, these results suggest that certain consumer groups, certain age or ethnicity groups, might show a high aversion toward DTC genetic testing in general, while others are more adept.To investigate whether consumers who have taken a DTC genetic test before exhibit different fairness perceptions, we further conducted a separate analysis of 59 respondents who stated having taken a DTC genetic test . This may indicate that, while consumers do not want their data to be sold, they are not bothered if the service provider stores and uses their genetic data for service improvements. Although internal data usage does not involve the sharing of data with third parties, these practices can as well pose a threat to personal genetic privacy. Therefore, these results somewhat deviate from previous research on consumers’ concerns regarding their privacy and indicate that not all genetic privacy attributes are important for the fairness perception of DTC genetic testing. Finally, the significance of region of operation (LogWorth = 2.079) suggests that consumers do not mind whether the service provider operates only locally or worldwide . This stands in conflict with findings of previous studies  to extend the taxonomy of Thiebes et al. ESM 2(PDF 126 KB)ESM 3(PDF 160 KB)ESM 4(PDF 150 KB)ESM 5(PDF 214 KB)Below is the link to the electronic supplementary material.


Trema orientalis based on two genome-wide high-throughput diversity array technology (DArT) markers; silicoDArT and single nucleotide polymorphisms (SNPs). Genotyping of 119 individuals generated 40,650 silicoDArT and 4767 SNP markers. Both marker types had a high average scoring reproducibility (>99%). Genetic relationships explored by principal coordinates analysis (PCoA) showed that the first principal coordinate axis explained most of the variation in both the SilicoDArT (34.2%) and SNP (89.6%) marker data. The average polymorphic information content did not highly differ between silicoDArT (0.22) and SNPs (0.17) suggesting minimal differences in informativeness in the two groups of markers. The, mean observed (Ho) and expected (He) heterozygosity were low and differed between the silicoDArT and SNPs respectively, estimated at Ho = 0.08 and He = 0.05 for silicoDArT and Ho = 0.23 and He = 0.19 for SNPs. The population of T. orientalis was moderately differentiated (FST  =  0.20–0.53) and formed 2 distinct clusters based on maximum likelihood and principal coordinates analysis. Analysis of molecular variance revealed that clusters contributed more to the variation (46.3–60.8%) than individuals (32.9–31.2%). Overall, the results suggest a high relatedness of the individuals sampled and a threatened genetic potential of T. orientalis in the wild. Therefore, genetic management activities such as ex-situ germplasm management are required for the sustainability of the species. Ex-situ conservation efforts should involve core collection of individuals from different populations to capture efficient diversity. This study demonstrates the importance of silicoDArT and SNP makers in population structure and genetic diversity analysis of Trema orientalis, useful for future genome wide studies in the species.Establishing the genetic diversity and population structure of a species can guide the selection of appropriate conservation and sustainable utilization strategies. Next-generation sequencing (NGS) approaches are increasingly being used to generate multi-locus data for genetic structure determination. This study presents the genetic structure of a fodder species - Calliandra calothyrsus, Leucaena trichandra or Gliricidia sepium have been the most promoted fodder species  and -023 [SNPs].Based on the two clusters identified in STRUCTURE, the silicoDArT markers also showed lower estimates of total genetic diversity (Ht) and genetic diversity (Dst) among groups/populations (H = 0.21) . The est = 0.53) . The lowOverall, results indicated the presence of higher variation (AMOVA results) contained between clusters inferred using silicoDArTs (46.3%) and SNPs (60.8%) than individuals. Variation among individuals was 32.9% and 31.2% based on silicoDArTs and SNPs respectively. The consistency of these results is also reflected in the Mantel test that revealed strong association  between both markers.To put the resulting SNPs in the context of other sequences produced using other sequencing methods, the length of the short sequence reads corresponding with SilicoDArT markers ranged from 20 to 69 nucleotides (nt), with an average of 55.2nt and for SNPs the range was 22–69 (average 64.6 nt).Cannabis sativum (Cannabaceae) sequences, 9 sequences matched Morus notabilis (Moraceae) while the rest were more similar to sequences T. orientale, Prunus dulcis, Juglans regia, Ziziphus jujuba, Fragaria vesca, Corylus avellana, Vigna radiata, Quercus lobata, Populus eupratica, Pistacia vera, Chenopodium quinoa and Nymphaea colorata. The genetic relationship among the sequences of T. orientalis and the above species is illustrated in Blasting the 100 sequences selected over the branches of the dendrogram, 52 SNPs could not match any other sequence, while 15 SNPs matched T. orientalis. Such high call rate and reproducibility has been recorded for DArT technologies in different plant species [The importance of understanding the genetic diversity of fodder species is critical for conservation and utilization of their germplasm in breeding programs. While most studies that have used the DArT platform have mainly worked with cultivated species ,33,34, o species ,36 indicT. orientalis with potential consequences on the species ability to recover from demographic, environmental and genetic stochasticity [T. orientalis. The PIC values were in the range of those established for other trees like Macadamia, where PIC for silicoDArT and SNP markers were 0.29 and 0.21 respectively, although the distribution was different [The results from the silicoDArT and SNP markers indicated low genetic variation in asticity . Geneticasticity ,38. The ifferent . The PICifferent ,39–41 poHo for the markers was low but was in range of what has been reported in other tropical forest trees the same region [Ho <He, which is normally indicative of inbreeding, our study showed Ho >He, for both SNP and silicoDArT markers. This suggests presence of an isolate-breaking effect (the mixing of two previously isolated populations or presence of hybrids) [T. orientalis is moderately differentiated and formed 2 distinct clusters. The SNP data clustered the groups more tightly, with less overlap and explained more variation in the samples possibly because SNPs are abundant in plant genomes. This clustering was supported by results of the genetic differentiation metric (Fst = 0.20–0.53) between pairs of clusters. Ideally, Fst values below 0.05 indicate low genetic differentiation, while values between 0.05–0.15, 0.15–0.25, and above 0.25 indicate moderate, high, and very high genetic differentiation respectively [t = 0.06–0.40) across markers was lower than what has been established for forest trees in the wild [T. orientalis [T. orientalis disperses its seed by birds [The average observed heterozygosity e region ,43 whiche region ,43 that hybrids) , consisthybrids) ; includiectively . The totthe wild . Althougientalis  should rientalis . The degby birds  and pollTrema orientalis exhibits low genetic diversity and a potentially threatened genetic integrity. The strong population structure suggests that collection of germplasm should be done in different populations to maximise genetic variation in the collections. Characterisation of other populations is also recommended as well as studies on the population structure and recruitment of this species. The statistical analysis of DArT data sets showed high consistency with the results based on SNPs highlighting the suitability of DArT platforms for genomic dissection of T. orientalis.S1 FigA similar graph was obtained for the SNP markers (graph not shown).(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Massive defaunation and high extinction rates have become characteristic of the Anthropocene. Genetic effects of population decline can lead populations into an extinction vortex, where declining populations show lower genetic fitness, in turn leading to lower populations still. The lower genetic fitness in a declining population due to a shrinking gene pool is known as genetic erosion. Three different types of genetic erosion are highlighted in this review: overall homozygosity, genetic load and runs of homozygosity (ROH), which are indicative of inbreeding. The ability to quantify genetic erosion could be a very helpful tool for conservationists, as it can provide them with an objective, quantifiable measure to use in the assessment of species at risk of extinction. The link between conservation status and genetic erosion should become more apparent. Currently, no clear correlation can be observed between the current conservation status and genetic erosion. However, the high quantities of genetic erosion in wild populations, especially in those species dealing with habitat fragmentation and habitat decline, may be early signs of deteriorating populations. Whole genome sequencing data is the way forward to quantify genetic erosion. Extra screening steps for genetic load and hybridization can be included, since they could potentially have great impact on population fitness. This way, the information yielded from genetic sequence data can provide conservationists with an objective genetic method in the assessment of species at risk of extinction. However, the great complexity of genome erosion quantification asks for consensus and bridging science and its applications, which remains challenging. The Anthropocene is seeing a tremendous amount of defaunation. In the last 500 years alone, at least 322 species of terrestrial vertebrates have gone extinct, with remaining population sizes showing an average decline of 25%. Invertebrates show a similar pattern, with two thirds of monitored populations showing a mean 45% abundance decline . Recent inbreeding depression”  and inbreeding depression caused by accumulation of deleterious genes. Increasing the genetic diversity of a population is essential to maintain the ability to adapt to changing environments, since genetic heterozygosity is essential for recombination and evolution to occur . InbreedA population with low genetic diversity can be “genetically rescued” by crossing it with a different population, masking the effect of fixed harmful mutations . This shQuantifying genetic erosion can be used as an indicator for viable population size and extinction risk. Especially when used temporally, this could offer conservationists a quantifiable measure to assess at risk populations . This cae = 50) and long term effects based on theoretical prevention of genetic drift and massive loss of gene variance (Ne = 500). This reasoning has proven extremely important for the conservation community, however a genomics-informed equivalent to these rules would be preferred in conservation efforts, adopting quantified methods from sequence data, to help indicate at risk species.In order to assess the extinction risk status, a standardized unit needs to be implemented in order to link a certain amount of genetic erosion to risk of extinction. Genomic data holds potential to reflect past and current effective population size, as well as masked and realized genetic load. Clear threshold values of effective population size and risk of extinction, derived from genomic data, could serve as indicator for IUCN red list assessment . ThreshoProviding conservationists with a quantifiable measure for genetic erosion to assess species at risk of extinction would be very helpful in conservation efforts. In order to use genetic erosion measurements in the assessment of at risk species, the link between a declining population and the presence of genetic erosion needs to be established first.Ideally, all of the genetic erosion measures are taken into account to calculate an effective population size. The effective population size can then be used to assess the extinction risk of a species. By using a measure like the 50/500 rule, as proposed by Franklin, tiers can be created to give a certain genetic erosion score. This erosion score is in the form of effective population size, which corresponds to a certain extinction risk and specific measures that can be taken. However, in order to do this, the 50/500 rule for MVP is in need of revising . AlthougNe). Although the concept of effective population size is rather intuitive, and extremely important for conservation genomics, its exact definition is can be less valuable from a practical point of view  are long continuous tracts of consecutive homozygous single nucleotide polymorphisms (SNPs) . These hThe resulting homozygous segments can be detected using various data types, as long as the density is sufficient to separate heterozygous from homozygous stretches. SNP chips are a valuable and easy source to acquire genetic data for ROH analysis . The datAn essential requirement for the use of SNP chips is a standardized method of SNP data collection and processing. Peripolli and colleagues showed significant differences between different analytical tools used to assess SNP data for ROH frequencies . Many fap < 0.001) different ROH frequencies. Across mammals, trophic level, body mass, and latitude have significant effects on θ and ROH burden  unconditionally deleterious variants, and 2) structural variants that create genetic incompatibility when outcrossed. Long-read and high throughput sequencing technologies now open up exciting possibilities to actually pinpoint potentially harmful mutations and structural variants in individual genomes . The delTemporal use of genomic data offers the possibility to monitor loss-of-function (LoF) variants and synonymous gene variants. LoF variants, like stop-gain, splice site, stop-loss and frame-shift mutations can be uncovered using genetic techniques and represent part of the genetic load in an individual. Although not all LoF have negative fitness effects, it is assumed that an increase in such mutations likely contribute to the lowered fitness of an individual. The quantification of LoF mutations can serve as a good proxy for genetic load quantification and is likely the best method we currently have . By lookKnowledge on the temporal increase in genetic load is indicative of the overall genetic erosion in a population. The theoretical increase in genetic load in a diminishing population has also been empirically tested and found to uphold this hypothesis e.g., . It is tA very apparent example of genetic load is hybrid load. Hybrid load is the effect of hybridization of closely related species, with offspring with lower fitness than both parents. Either the combination of species-specific genes can lead to a deleterious phenotype, or the resulting mixed phenotype is of a lower fitness than either parent species. The effect of natural and anthropogenic hybridization has been shown in several fish, but overall effects of hybridization events vary from increased fitness to non-viable offspring . A decreGenetic erosion has so far been concluded for species in several classes of animals: mammals, fish, amphibians, insects and birds e.g., . It seemAlthough genetic erosion has been concluded for several different classes of animals, the same goes for a lack of genetic erosion . HoweverThe relationship between population decline or habitat fragmentation as reflected in genome erosion, and IUCN conservation status is far from clear . This caThe complexity of genetic erosion and the factors influencing it ask for a robust standardized method, that can be confidently used for conservation purposes. All three different quantification methods, discussed before, each with their own benefits and limitations should be included, to deal with the complexity of genetic erosion. For example, a population that has been small for a long time has high potential to become extinct due to a lack of overall heterozygosity and therefore a lack of adaptive potential. However, this population might not show a lot of inbreeding or high levels of deleterious mutations, due to high selection pressure, purging strongly deleterious recessive alleles . AnotherIdeally, generalized methods should include the effective population size, calculated from both the ROH and the overall heterozygosity. This allows the inference of the past as well as current population size. Since genetic load can not be linked to effective population size, it is currently hard to incorporate this in the quantification of genetic erosion and at risk populations in the same manner as overall heterozygosity and inbreeding. However, it is a strong indicator of genetic erosion and should not be left out, especially since the effects of inbreeding depression are increased by higher genetic load. For now, it can therefore be used as an additional indicator of genetic erosion. However, with our knowledge about evolution and trajectories of harmful mutations in populations, we obtain increasing understanding of the fate of such alleles in populations, given their demographic history . So evenThe difficulty of incorporating genetic load is the determination of recessive deleterious alleles, which is currently hampered by technological limitations. In pioneer studies the method to determine whether deleterious mutations are accumulating in a population is by collecting phenotypic data and linking it to specific alleles. This labor intensive process is not realistic for every population. A more realistic method is to establish a system of model species with well-established traits and their genetic markers. Model species can be used to validate the prediction of genetic load from the genome sequence. Once this purely sequence-based method is validated, it could be used for non-model species. Although this is a system that is not yet established, it would be an important indicator for at risk species. It allows the incorporation of genetic load in the risk assessment of species, without the need to collect phenotypic data for each species individually.The importance of incorporating genetic load is most apparent in the prevention of deleterious gene variant accumulation, since these are the driving force behind inbreeding depression. However, despite the low abundance of lethal recessive alleles or hybridization events, their importance should not be underestimated. Deleterious mutations with high fitness impact, like recessive lethal mutations can cause the extension of a population bottleneck, or even the extinction of a species . TherefoDrosophila, but these studies did not quantify harmful mutations in their experimental populations   . The autinereus) . Recent inereus) , leading


For the reason that the systematic breeding programs and pedigree records are unavailable in the indigenous livestock breeds, it is hard to evaluate their genetic diversity and population structures using the traditional pedigree-based and demographic approaches. The Xieka cattle are an indigenous breed geographically distributed in the southeastern Sichuan, China, and have the long-term evolutionary adaptation to local subtropical highland environments. To explore efficient programs on genetic resources conservation and utilization, the genetic diversity and population structures of Xieka cattle were investigated in this study using genomic information. Our analyses revealed that this indigenous cattle breed have remained a relatively high degree of genetic diversity and have not suffered from the recently generated inbreeding. Furthermore, some candidate genomic regions and genes were suggested to be likely associated with the diverse production traits in cattle.In aiming to achieve sustainable development goals in the livestock industry, it is becoming increasingly necessary and important for the effective conservation of genetic resources. There are some indigenous cattle breeds in Sichuan, southwest China, for which, however, the genetic diversity and population structures still remain unknown because of the unavailability of systematic breeding programs and pedigree information. Xieka cattle are an indigenous breed locally distributed in southeastern Sichuan and have a long-term evolutionary adaptation to local environments and climates. In this study, we obtained 796,828 single nucleotide polymorphisms (SNPs) through sequencing the genomes of 30 Xieka cattle and used them for analyzing the genetic diversity and runs of homozygosity (ROH). The mean nucleotide diversity was 0.28 and 72% of SNPs were found to be in the heterozygous states. A total of 4377 ROH were detected with even distribution among all autosomes, and 74% of them were lower than 1 Mb in length. Meanwhile, only five ROH were found longer than 5 Mb. We further determined 19 significant genomic regions that were obviously enriched by ROH, in which 35 positional candidate genes were found. Some of these genes have been previously reported to be significantly associated with various production traits in cattle, such as meat quality, carcass performances, and diseases. In conclusion, the relatively high degree of genetic diversity of Xieka cattle was revealed using the genomic information, and the proposed candidate genes will help us optimize the breeding programs regarding this indigenous breed. Bos taurus), as well as some unregistered breeds and populations. Wang and colleagues (2018)  iUsing the genome-wide short tandem-repeat polymorphisms, Broman and Weber 1999)  first fo99  firstAiming to understand the current genetic landscape of the gene pool of Xieka cattle, in this study, we employed the genome resequencing data for ROH analyses and further investigating the genomic diversity and population structure regarding this indigenous breed in China.All blood samples involved in this study were collected by veterinarians at the annual health inspection, which means that no ethical approval is required.A total of 30 adult Xieka cattle were collected in this study, consisting of 12 males and 18 females. To guarantee our sampling as being representative as possible, we recruited the candidate animals according to the two considerations. First, these sampled animals were separately raised on rural farmers and did not have known pedigree relationships with each other. Second, individual morphological characteristics were carefully checked to avoid possible hybrid offspring most likely from the exotic cattle breeds. Blood samples from the external jugular vein were collected and used for the genome sequencing.Genomic DNA was extracted using the Axy-Prep Genomic DNA Miniprep Kit . The paired-end sequencing libraries with 350 bp in length were constructed according to Illumina’s protocol . In brief, 0.5 μg of genomic DNA was fragmented, end-paired, and ligated to adaptors, respectively. The ligated fragments were subsequently fractionated on the agarose gels and purified by PCR amplification to produce sequencing libraries. The successfully constructed libraries were sequenced on the Illumina HiSeq platform and the 150 bp paired-end reads were finally generated .The raw sequencing reads in FASTQ format were subjected to quality filtering to discard low-quality reads using the fastp software v0.23.2 , which wp > 10−6). Finally, the missing genotypes were also imputed using Beagle software v5.3 with the default parameters  aBased on the ROH analysis, nine genomic regions were found to be significantly enriched in low-fertility when compared with high-fertility bulls . In ChinCORIN (corin serine pepsidase) gene, a member of the trypsin superfamily located on BTA6, was reported to be associated with meat colour traits in Nellore cattle [SPAST (spastin) gene was proposed to be the affected gene of spinal dysmielination disease in cattle [TNFSF4 (TNF superfamily member 4) gene were significantly associated with the primary Sjögren’s syndrome [TMEM201 (transmembrane protein 201) gene was suggested to be related to selection signatures [INPP4B (inositol polyphosphate-4-phosphatase type II B) as a candidate gene. Other functional genes revealed in this study were also previously reported to be associated with diverse production traits, including the RCN2 , PSTPIP1 (proline-serine-threonine phosphatase interacting protein 1), ADRB3 (adrenoceptor beta 3) genes with carcass traits in Hanwoo and Qinchuan cattle [GABRB3 (gamma-aminobutyric acid type A receptor subunit beta3) and GABRB5  genes with temperament in beef cattle [ZNF703 (zinc finger protein 703) and ERLIN2 (ER lipid raft associated 2) genes with average daily gain in Nellore and with residual concentrate intake in Holstein [GOT1L1  as a cell indicator of stress in the cattle semen [We further conducted literature searches for investigating the possible biological functions of candidate genes revealed by ROH analysis in this study. Among them, the e cattle . The SPAn cattle . In humasyndrome . In Origgnatures . Buchanagnatures  analyzedn cattle ,36, GABRf cattle , ZNF703 Holstein , and GOTle semen . Interesle semen , regulatle semen . On the In this study, we employed the genomic information and ROH analyses for investigating genetic diversity and population structure regarding an indigenous cattle breed that are geographically distributed in Southwest China. Our results revealed the comparable genetic diversity to other geographically adjacent indigenous cattle breeds. Furthermore, we determined the significant genomic regions that are enriched by ROH, in which some known and novel genes were suggested to be associated with production traits in cattle.


Signal transducer and activator of transcription 3 (STAT3) is an oncogenic transcription factor that promotes cell proliferation and immunomodulation in untransformed cells and maintains stemness of transformed cells, facilitating invasion and metastasis. Numerous point mutations in the STAT3 protein have been identified that drive malignancy in various tumor entities. The missense mutation D427H localized in the STAT3 DNA-binding domain has been previously reported in patients with NK/T cell lymphomas. To assess the biological activity of this missense mutation, we compared the STAT3-D427H mutant to wild-type (WT) protein as well as the known hyper-active mutant F174A.Although previously reported as an activating mutation, the STAT3-D427H mutant neither showed elevated cytokine-induced tyrosine phosphorylation nor altered nuclear accumulation, as compared to the WT protein. However, the D427H mutant displayed enhanced binding to STAT-specific DNA-binding sites but a reduced sequence specificity and dissociation rate from DNA, which was demonstrated by electrophoretic mobility shift assays. This observation is consistent with the phenotype of the homologous E421K mutation in the STAT1 protein, which also displayed enhanced binding to DNA but lacked a corresponding increase in transcriptional activity.Based on our data, it is unlikely that the D427H missense mutation in the STAT3 protein possesses an oncogenic potential beyond the WT molecule.The online version contains supplementary material available at 10.1186/s12860-022-00422-9. Signal transducers and activators of transcription (STATs) are a group of cytokine-driven transcription factors that initiate and modulate numerous proliferative and immunological processes inside the cell . The higThrough activation by IL-6 and other cytokines, STAT3 has been implicated in many immunological processes, including the induction of hepatic acute phase proteins, stimulation of cell proliferation and differentiation in lymphocytes, maintenance of pluripotency in embryonic stem cells, and inhibition of apoptosis , 12. OncSTAT3 is among the most frequently mutated genes , 0.1% IGEPAL-CA-360, 0.4 mM Pefabloc [Sigma-Aldrich], and Complete Mini protease inhibitors [Roche]). The lysates were centrifuged for 15 s at a temperature of 4 °C and 16,000 g. The supernatants were spun again for 5 min and collected as cytoplasmic extracts. The pellets reserved from the first centrifugation step were resuspended in a volume of 50 µl nuclear extraction buffer  glycerol, 1 mM EDTA, 3 mM DTT, 0.1 mM Na3VO4, 0.4 mM Pefabloc, and Complete Mini protease inhibitors) and incubated for 30 min on ice. Subsequently, these samples were centrifuged for 15 min and 4 °C at 16,000 g and collected as nuclear extracts. Equal amounts of nuclear and the corresponding cytoplasmic extracts were mixed for each sample. The combined extracts were then boiled for 3 min in sodium dodecyl sulphate (SDS) sample buffer and resolved by 10% SDS–polyacrylamide gel electrophoresis (PAGE) with subsequent transfer onto poly-vinylidene difluoride (PVDF) membranes. The membranes were then blocked with 25% bovine serum albumin in Tris-buffered saline with 0.05% Tween-20. These blots were then incubated with either the monoclonal phospho-Tyr705-specific STAT3 antibody D3A7 or the monoclonal pan-STAT3 antibody D1B2J, both obtained from Cell Signaling, and after three washing steps exposed to the conjugated secondary anti-rabbit IRDye 800CW antibody (LI-COR). Bound immunoreactivity on the membrane was detected using the LI-COR Odyssey imaging system. The pSTAT3 band intensity was normalized to the corresponding amount of total STAT3 for each variant and stimulation condition.Cells expressing recombinant STAT3 tagged with either GFP- or SNAP were grown on 6-well dishes and lysed on ice for 5 min in 50 µl cytoplasmic extraction buffer  glycerol, 1 mM EDTA, 0.1 mM NaDirect fluorescence microscopy was employed to monitor the kinetics of IL-6- and IFNγ-induced nuclear accumulation of STAT3 variants in U3A and HeLa cells . Cells e33P]-labelled duplex oligonucleotide probe, which was generated by an end-filling reaction using the Klenow fragment (New England Biolabs). A duplex oligonucleotide M67 with a single, canonical SIE/GAS site was used to test the binding of STAT3 variants : 5´-TTTTCGACATTTCCCGTAAATCTG-´3. To test for changes in sequence-specific DNA binding of the STAT3 mutants, additional oligonucleotides with two complete GAS sites in tandem orientation (2xGAS) or one complete GAS site (GAS-nonGAS) or no GAS site (2xnon-GAS) were used:STAT3 proteins were examined by means of electrophoretic mobility shift assays (EMSA) for their binding to specific or mutant duplex oligonucleotides containing a single sis-inducible element (SIE)/GAS element. Cellular extracts (4 µl) from IL-6- or IFNγ-stimulated cells expressing recombinant STAT3 were incubated with EMSA reaction buffer (8 µl) containing the unspecific, synthetic competitor poly(deoxyinosinic-deoxycytidylic) acid and 1 ng of the -labelled duplex M67 oligonucleotides for 15 min at RT in EMSA buffer. Subsequently, a 750-fold molar excess of unlabelled M67 DNA was added for 10 min on ice. The reactions were loaded on a 4.8% acrylamide:bisacrylamide (29:1) gel at 4 °C and separated at 400 V. Sequence-specific DNA binding was visualized on vacuum-dried gels using the laser phosphorimaging system Typhoon FLA 9500 . The band intensity corresponding to the STAT3 variants on the autoradiograms was either measured as an absolute value or divided by the intensity of a fast-migrating unspecific band, which was regarded as a loading control and is labelled with an asterisk in the figures.For competition experiments, cellular extracts were incubated with [4, 4 mM EGTA, 0.4 mM Pefabloc, 3 mM DTT, pH 7.8, and Complete protease inhibitors. Reporter gene expression was assessed by means of a luciferase assay system from Promega using the luminometer Centro KS LB960 (Berthold Technologies). Luciferase expression was first normalized to the β-galactosidase activity, which was determined spectroscopically at a wavelength of 420 nm in the corresponding samples. Five independent transfections were tested for each STAT3 variant and stimulation mode, and the experiment was repeated in duplicate.Promoter activation mediated by STAT3 was studied in transfected U3A cells using a vector encoding a luciferase reporter with three copies of an IFNγ-inducible Ly6E GAS element in the promoter region upstream of the transcriptional start site , 21, as To test endogenous target gene induction, U3A cells expressing STAT3-GFP were cultured for 18 h in Dulbecco’s modified Eagle’s medium supplemented with 1% FCS, prior to stimulation for 3 h with IL-6 or IFNγ. RNA was isolated from the cells using the peqGold Total RNA kit (VWR Lifesciences). First-strand cDNA synthesis was carried out using the Verso cDNA Synthesis kit (Thermo Fisher Scientific). Real-time PCR reactions were performed in a volume of 20 µl, containing 25 ng cDNA, 70 nM of each specific primer, and 10 µl of Absolute Blue qPCR SYBR Green Mix (Thermo Fisher Scientific). The following primer pairs were used, according to sequence data available from the National Center for Biotechnology Information (NCBI):hCyclinD1F; 5'-CGG TGT CCT ACT TCA AAT GT-3',hCyclinD1R; 5'-ATG GAG TTG TCG GTG TAG AT-3',hc-MycF; 5´-GGTCTTCCCCTACCCTCTCAACGA-´3,hc-MycR; 5´-GGCAGCAGGATAGTCCTTCCGAGT-´3,hGAPDHF; 5´-GAAGGTGAAGGTCGGAGTC-´3, and.hGAPDHR; 5´-GAAGATGGTGATGGGATTTC-´3.GAPDH gene. The △△ Ct-method was used to determine comparative relative expression levels, based on the formula 2−(△Ct target − △Ct reference sample). All real-time PCR reactions were performed in two independent transfection experiments with duplicate reactions each.The PCR protocol included a denaturation step at 95 °C for 15 min and 40 cycles of denaturation at 95 °C for 15 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s. After the final amplification step, a melting curve analysis was performed on the Eppendorf Mastercycler ep realplex 2 using a temperature gradient from 60 °C to 95 °C in 0.5 °C increment steps and fluorescence being measured at each temperature for a period of 10 s. The relative expression of a target transcript was normalized to the expression of the t-test, and a p-value ≤ 0.05 was considered to indicate statistical significance.ImageJ (NIH) software was used to process digital images and data figures were created using CorelDRAW Graphics Suite 2019. Means and standard deviations were calculated for each STAT3 variant and stimulation condition. Data were analysed using the program GraphPad PRISM. Differences between the groups were assessed using Student’s In the common STAT modular structure, the DNA-binding domain is quite conserved across all STAT proteins. Sequence alignment of STAT family members shows that a negatively charged amino acid residue is located at position 427 in STAT3 Fig. A. WithinTo investigate changes in tyrosine phosphorylation of STAT3 upon mutation of the D427 residue in the DNA-binding domain, we performed Western-blotting experiments using lysates from IL-6- or IFNγ-stimulated, STAT1-deficient U3A cells expressing the STAT3 variants , or one complete and one partial GAS element (GAS-nonGAS), or no GAS site (2xnonGAS). As described above, the STAT3-D427H showed an increased tetrameric and dimeric binding to GAS-nonGAS sequences as compared to the STAT3-WT, which bound weakly and predominantly as a dimer  experiments. U3A cells transfected with the STAT3 variants were serum-starved for 18 h prior to stimulation with IL-6 or IFNγ for 3 h. RNA was extracted from stimulated cells and converted to cDNA for qPCR experiments. The D427H mutant did not show an induction of the In this study, we characterized the D427H mutation in the DNA-binding domain of STAT3 that was previously identified in patients with mature T-cell lymphomas, wherein it displayed hyper-phosphorylation and increased transcriptional response . In an aWhile the position of the D427 residue could not be defined in the crystal structure of STAT3 due to the inherent structural flexibility of this region in the DNA-binding domain, the homologous residue E421 in the STAT1 molecule has been reported to be adjacent to the DNA molecule with its side chain pointing to the backbone of the DNA double helix . The STASimilarly, the enhanced DNA binding seen in STAT3-D427H can be explained by the increased affinity conferred by the positively charged histidine compared to the negatively charged, native aspartic acid residue. However, this amino acid exchange at position 427 neither altered the cytokine-induced tyrosine phosphorylation nor the nuclear accumulation of STAT3-D427H in our experiments, underscoring the different behaviour of STAT3 and STAT1 around homologous mutations. While enhanced DNA-binding activity was seen for both STAT3-D427H and STAT1-E421K mutants, we also observed a similar reduced dissociation rate of the mutant STAT3-DNA complex, indicating that these two homologous mutations display similar but not identical phenotypes. Furthermore, in an alternative crystal structure of an unphosphorylated STAT3 parallel dimer lacking reciprocal phosphotyrosine-SH2 interactions, the D427 residue does not appear to contribute to the stability of this structure .Interestingly, the enhanced affinity of STAT3-D427H to DNA did not reflect as an increase in its transcriptional activation. This phenomenon indicates that STAT-dependent gene transcription does not solely depend on the affinity of STAT proteins to GAS elements but suggests the presence of an additional mechanism that confers specificity to the STAT-DNA interaction. The STAT3-D427H mutant strongly binds to DNA not just on specific sites but also to non-specific sequences, which are usually present outside of promoter regions. Thus, the increased DNA binding displayed by the mutant is counterbalanced by unspecific binding to non-GAS sequences, resulting in grossly unchanged transcriptional activation of endogenous target genes. An example of such a mechanism has been discovered for STAT1, wherein residues E559 and E563 in the STAT1 linker domain regulate the dissociation of STAT1 from low-affinity sites and the residue K567 confers GAS recognition to STAT1 . HoweverActivating mutations reported in the STAT3 protein usually alter one or more of its downstream signalling activities and/or its interactions with other proteins and components of the JAK-STAT pathway. In our experiments with STAT3-D427H, except for an increased binding affinity to DNA, all other signal transduction processes were unaffected. Therefore, we hypothesize that the residue D427 partially regulates the strength of the STAT3-DNA complex at transcriptionally inert sites. This aspartic acid residue possibly helps to select high-affinity GAS sites that foster the formation of a STAT3-DNA complex through additional interactions with the neighbouring residues that contribute to GAS recognition. This mechanism may ensure that GAS-like or non-GAS sequences are excluded from the formation of a strong STAT3-DNA complex due to the lack of sequence recognition. The negatively charged aspartic acid at position 427 plays a role in the release of STAT3 from the negatively charged DNA backbone.In our experiments, we used U3A cells lacking endogenous STAT1 expression to eliminate any interference from STAT1/STAT3 heterodimers which are inevitably formed in cells with preserved STAT1 expression. We chose the STAT1-negative U3A cell line to eliminate any potential effect of STAT1 cross-interactions on the intracellular behavior of our recombinant STAT3 variants. Although the expression level of endogenous STAT3 in these cells is low compared to the transfected GFP- or SNAP-tagged variants, the fact that a residual expression of native STAT3 is nonetheless detectable must be considered an important limitation of our study.In all our experiments, the behaviour of STAT3-D427H was compared to the WT protein and a previously described activating mutation, F174A, in the coiled-coil domain of STAT3. The F174 residue (F172 in STAT1) interacts with residues in the DNA-binding domain that stabilizes the anti-parallel dimer of STAT3 . This muIn summary, it is evident from our data that the D427H mutation increases the DNA-binding affinity of STAT3 but lowers sequence specificity. Although the STAT3-D427H mutation has been reported in the pathogenesis of NK/T-cell lymphoma, it is unlikely that the sole presence of this mutation could significantly increase the oncogenic potential of the STAT3 molecule. We propose that the STAT3-D427H mutation has, if any, only a mild impact on the etiology of NK/T-cell lymphomas. Furthermore, our data support previous studies suggesting that an increased affinity to DNA does not directly translate into an increased target gene induction by STAT proteins. These transcription factors have additional mechanisms conferring sequence recognition which help discriminate against inert sites to ensure optimal gene induction in response to cytokine signalling.Using site-directed mutagenesis, we investigated the phenotype of the point mutation D427H in the DNA-binding domain of STAT3, which was previously reported to exhibit GOF behaviour in NK/T-cell lymphoma patients. Our study did not reveal a hyper-activation of this mutant when compared with the GOF mutant F174A in the coiled-coil domain of STAT3. The D427H mutation displayed unaltered cytokine-induced tyrosine phosphorylation, nuclear accumulation, and subsequent transcriptional activation of STAT3. Similar to its homologous mutation in STAT1, the STAT3-D427H showed a reduced specificity to STAT-specific, high-affinity GAS sites on DNA coupled with a compromised dissociation rate from these sites, indicating that the mutation did not harbour the potential to grossly affect signal transduction by STAT3. Based on these findings, we conclude that STAT3-D427H may not have an oncogenic potential that exceeds the WT protein and may have little significance or an ancillary role in the pathogenesis of NK/T-cell lymphomas.Additional file 1: Supplementary data 1. Raw data from the representative reporter gene assays performed on U3A cells transfected with the indicated STAT3-GFP or STAT3-SNAP variants, normalized to their respective co-transfected b-galactosidase activity. Additional file 2: Supplementary data 2. Raw data from the representative qPCR experiments performed on U3A cells transfected with the indicated STAT3-GFP variants, normalized to the expression of the house-keeping gene GAPDH.Additional file 3. Raw data from Western blots and gelshifts.


Livestock European diffusion followed different human migration waves from the Fertile Crescent. In sheep, at least two diffusion waves have shaped the current breeds’ biodiversity generating a complex genetic pattern composed by either primitive or fine-wool selected breeds. Among primitive breeds, aside from sharing common ancestral genomic components, they also show several traits such as the policeraty, large horns in the ram, short tail, and a moulting fleece, considered as ancestral. Although most of the primitive breeds characterized by these traits are confined on the very edge of Northern Europe, several residual populations are also scattered in the Mediterranean region. In fact, although in Italy a large number of local breeds are already extinct, others are listed as critically endangered, and among these there is the Quadricorna breed which is a four-horned sheep characterized by several ancestral traits. In this context we genotyped 47 individuals belonging to the Quadricorna sheep breed, a relict and endangered breed, from Central and Southern Italy. In doing so we used the Illumina OvineSNP50K array in order to explore its genetic diversity and to compare it with other 33 primitive traits-related, Mediterranean and Middle-East breeds, with the specific aim to reconstruct its origin. After retaining 35,680 SNPs following data filtering, the overall genomic architecture has been explored by using genetic diversity indices, Principal Component Analysis (PCA) and admixture analysis, while the genetic relationships and migration events have been inferred using a neighbor-joining tree based on Reynolds’ distances and by the maximum likelihood tree as implemented in treemix. Multiple convergent evidence from all our population genetics analyses, indicated that the two Quadricorna populations differ from all the other Italian breeds, while they resulted to be very close to the Middle Eastern and primitive European breeds. In addition, the genetic diversity indices highlighted values comparable with those of most of the other analyzed breeds, despite the two populations exhibit slightly different genetic indices suggesting different levels of genomic inbreeding and drift (FIS and FROH). The admixture analysis does not suggest any signal of recent gene exchange with other Italian local breeds, highlighting a rather ancestral purity of the two populations, while on the other hand the treemix analysis seems to suggest an ancient admixture with other primitive European breeds. Finally, all these evidences seem to trace back the residual Quadricorna sheep to an early Neolithic spread, probably following a Mediterranean route and that urgent conservation actions are needed in order to keep the breed and all related cultural products alive. Since Neolithic revolution, domestic animals followed human migration routes and spread throughout the world. For many livestock species, this processes have been shown to have occurred in distinct historical periods by following different colonization routes leading to a vast array of breed diversity well adapted to specific local environments –3, that Compared to other domestic species such as cattle, goat and dogs, sheep (Ovis aries) have been shown to have retained much higher genetic diversity conform with wider heterogeneous pre-domestication populations, combined with less severe post-domestication bottlenecks , 9. MoreIn Europe, most of the genome of the first migration wave (hair type sheep), has been later replaced, generating a well-defined geographic cline from Middle-East to Balkan and continental Europe . HoweverConcerning the Italian peninsula, a high level of genetic diversity with a strong phylogeographic structuring has been observed . All genAmong the most critically endangered Italian local breed is the Quadricorna sheep which is constituted by a very residual population reared in Central-Southern Italy, counting in 2022 only 2 farms, one located in Colliano  and the other in Monte San Giovanni Campano  with 33  and 23  heads respectively. The sheep population called “Pecora Quadricorna” has been registered since 2006 in the Regional Voluntary Register of Lazio established and from 2018 to the National Registry, but currently, the Herd Book is still lacking. The most characteristic trait of the breed is the polyceraty, hence the name Quadricorna which means four-horned. Worldwide, there are numerous sheep populations showing polycerism  being prTo disentangle such a complexity and to shed light on the origin of the critically endangered Quadricorna sheep, we genotyped two populations from Central and Southern Italy using the Illumina OvineSNP50K BeadChip. The genomic architecture was therefore compared with other primitive and Mediterranean breeds, in order to gather insights into a more complete post-domestication evolutionary picture.Blood samples were collected from sheep during normal health prophylaxis procedures by the Istituto Zooprofilattico Sperimentale del Lazio e della Toscana, in agreement with the recommendations of European Union Directive 2010/63/EU, to ensure appropriate animal care.A total of 47 blood samples belonging to the Quadricorna sheep breed  were stored and selected for the genotyping. These samples included 22 individuals belonging to Colliano (QUAD_SA) and 25 to Monte San Giovanni Campano (QUAD_FR). The two examined farms pursue similar breeding conditions in a semi-wild state with small additions of flaked feed when fresh forage is scarce.The Illumina OvineSNP50K BeadChip was used to genotype the Quadricorna sheep population while the raw genotyped data for 33 breeds  were retrieved from previous works , 30–33 .After merging all the genotyped samples, 53,854 SNPs were obtained. Using the software plink v1.9 , the genFor each breed expected (He) and observed (Ho) heterozygosity and the inbreeding coefficient (FIS) was calculated using the R package dartR . In addiFinally, the contemporary effective population size (Ne) for each breed was calculated using the software GONe  which usTo visualize the genetic differentiation among breeds, a principal component analysis (PCA) was performed using plink. The PCA was performed through a supervised approach using the pca-cluster flag in plink v1.9, in order to account for breed heterogeneity and outliers. Results of the centroids were then plotted using the R package ggplot2 .The maximum clustering likelihood approach as implemented in the software admixture , was useThe phylogenetic relationships among breeds were inferred using an in-house R script which calculates the genetic Reynolds’ distance matrix and uses a neighbor-joining approach to compute the tree.To assess the genetic drift and migration events among breeds, the maximum likelihood dendrogram was inferred using the software treemix . The anaAll genetic diversity indices are reported in The contemporary effective population size (Ne) is reported in The supervised PCA is reported in The genetic repartition as highlighted by the admixture analysis showed a best K value at 40  where alThe neighbor-joining tree based on Reynolds’ distances, basically confirms all the aforementioned results, showing a basal group which includes the Quadricorna breed nested within the Middle-East and primitive European breeds . A second group includes all the South-West Mediterranean breeds which do not form a monophyletic. In this analysis the Local Awassi clusters in a basal position of this group instead of grouping with the South-West Mediterranean breeds as highlighted in both the PCA and the admixture analyses Figs  and 3. FThese main topological clusters are also supported by the treemix analysis which showed the presence of these three main genetic groups according to the geography . Also, in this analysis the two Quadricorna populations fall close to the Middle-East breeds . AccordiThe genetic indices for the two Quadricorna populations in terms of expected and observed heterozygosity resulted in line with values of other local breeds in the Italian Peninsula .Also, the effective population size and inbreeding coefficients are comparable with those observed in most of the analyzed breeds. However, the two Quadricorna populations displayed slightly different genetic indices with the QUAD_SA showing higher diversity indices (Ho and He), effective population size (Ne) and lower inbreeding coefficients (FIS and FROH). Different patterns of genetic diversity indices between populations of the same breed are not surprising and have been commonly reported in many livestock species including local  and cosmIn all the population genetics analyses and phylogeographic reconstructions, the two Quadricorna populations from Central and Southern Italy cluster close to those breeds currently diffused near the sheep domestication centre . MoreoveAlthough our analysis seems to trace back those residual populations to an early Neolithic diffusion probably following a Mediterranean route, several alternative hypotheses cannot be denied. First of all, a more recent diffusion from the Middle-East probably as a consequence of the increased marine trade from the Hellenistic to the Roman periods cannot be completely excluded. For example, written sources about livestock trade are reported in the De re Rustica of Lucius Junius Moderatus Columella (AD 4 – c.70), documenting the presence in the Italian Peninsula of livestock varieties from the Middle-East , 48. SecFrom a morphological point of view, it is interesting to note that both Quadricorna and primitive traits-related breeds, aside from sharing a common genomic ancestral background, are also characterized by the frequent presence of four-horned individuals. Four-horned sheep were historically widely distributed in Europe, due to advantages in the size and arrangement of the horns in anti-predatory strategies . HoweverFuture studies would be desirable in order to shed light into the mode and the time of diffusion of this ancestral breed, moreover we believe that our results should be considered in light to plan adequate conservation measures in order to recover the population and to valorise their related cultural and commercial products.Sheep are among the first animals to be domesticated in the Fertile Crescent and as initially reared mainly for meat production, they reached Europe and Mediterranean areas around 8,000 YBP. However, subsequent migration waves mainly addressed at satisfying the wool production, have probably replaced the ancestral genetic signature. In spite of this, several scattered primitive populations characterized by both genetic and morphological ancestral traits are still scattered on the very edge of North Europe. Our analysis showed that a comparable ancestral genetic background is still present also in the Quadricorna sheep breed, indicating that the genetic diversity in terms of ancestral components of Southern Europe might be underestimated. This outcome is of considerable interest when considering the potential of genes that confer high "capacity for constructivism" to specific environments and to preserve and valorise local products alongside with their long-standing history in the Italian Peninsula.S1 Fig(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.