Genetic ancestry testing is a billion-dollar industry, with more than 26 million tests sold by 2018, which raises concerns over how it might influence test-takers’ understandings of race. While social scientists argue that genetic ancestry tests may promote an essentialist view of race as fixed and determining innate abilities, others suggest it could reduce essentialist views by reinforcing a view of race as socially constructed. Essentialist views are a concern because of their association with racism, particularly in its most extreme forms. Here we report the first randomized controlled trial of genetic ancestry testing conducted to examine potential causal relationships between taking the tests and essentialist views of race. Native-born White Americans were randomly assigned to receive Admixture and mtDNA tests or no tests. While we find no significant average effect of genetic ancestry testing on essentialism, secondary analyses reveal that the impact of these tests on racial essentialism varies by type of genetic knowledge. Within the treatment arm, essentialist beliefs significantly declined after testing among individuals with high genetic knowledge, but increased among those with the least genetic knowledge. Additional secondary analysis show that essentialist beliefs do not change based on the specific ancestries reported in test-takers’ results. These results indicate that individuals’ interpretations of genetic ancestry testing results, and the links between genes and race, may depend on their understanding of genetics. Since the completion of the Human Genome Project in 2003, Direct-to-Consumer (DTC) genetic ancestry tests (GATs) have become a billion-dollar industry  with at How these tests influence test-takers’ understanding of race is of particular concern. While there are small genetic differences between populations that allow geneticists to trace their global migrations, these variations do not support the idea of discrete races that can be distinguished by genes alone –11. SociSome social scientists argue that GATs are likely to reinforce a genetic essentialist view of race ,17,19,20Others have speculated that taking the tests could have the opposite effect by reinforcing belief in the socially constructed nature of race –28. For The few studies seeking to adjudicate between these outcomes have focused on the effect of reading media articles about GATs and found that reading articles depicting the tests as able to reveal a person’s race, or emphasizing the degree of overall genetic difference between groups, increases belief in essential racial and ethnic differences ,32,33. HIn this paper we ask: does taking GATs increase or decrease an essentialist view of race? The overall aim of this study is to adjudicate between these competing hypotheses, which requires a causal analysis of the effect of taking GATs on essentialist views with a randomized controlled trial (RCT). We present the findings of the first such RCT here.In addition, we conduct secondary analyses to identify factors with plausible potential to influence the impact of GATs on genetic essentialism that could be valuably incorporated into subsequent experimental research. We anticipate that the interpretation of the results is a key mechanism influencing the impact these tests have on test-takers’ understanding of race. Those interpretations may be influenced by the test-takers’ genetic knowledge–their factual or scientific knowledge of basic genetics. For example, those with little genetic literacy may struggle to fully interpret their test results ,35 and mWe conducted this RCT with a sample of 802 native-born White Americans who were willing to take GATs. Half were randomly assigned to receive commercially-available admixture and mtDNA tests purchased from Family Tree DNA, one of the largest DTC ancestry testing companies . The othGiven aforementioned experimental studies on the impacts of reading media articles about the tests ,32,33, iIn additional secondary analysis of data from treatment arm participants, we hypothesize a null main effect of “confirming” or “discovering” a specific ancestry, but predict that any impact of the specific admixture test results on test-takers’ belief in genetic essentialism also varies by their type of genetic knowledge. To those with little understanding of genetics, test results “confirming” the ancestries respondents already know of may reinforce a belief that race is revealed in their genes . “DiscovParticipants were recruited through random-digit dialing and screened for eligibility. Eligible individuals were those born in the U.S., aged 19 or older, who self-identified as non-Hispanic White, and where neither they nor any relatives had taken GATs but they were willing to take one. Study costs and sample sizes required for analysis necessitated focusing on a single population group; we focused on non-Hispanic Whites because our informal communications with several testing companies indicated that they were the largest U.S. consumer group of the tests . Our tarStudy protocols were approved by the University of British Columbia Behavioral Research Ethics Board (H14-02090). Written informed consent was obtained from each participant as part of the registration process. The final analytical sample consists of 802 participants , using nine survey questions asked in both the pre- and post-test surveys . The iteWhile there are different facets of genetic literacy, we focused on the factual knowledge dimension which is highest in the knowledge hierarchy of genomics  and mostBecause the questions vary in difficulty , we weighted the responses based on the question’s difficulty and how close respondents came to the correct answer. As a proxy for difficulty, we used percentages answering correctly and multiplied this by the scores assigned to the response options: one point for the correct answer (the first option in both), a half point for those closer to the correct answer on the second question , and zero points for other responses, including “Don’t Know” which we treated as equivalent to “No Knowledge” . After aFor the treatment group, we compared the ancestries reported in their admixture test results to the ethnic identities and ancestral origins listed in the pre-test survey. Because people may list only some ethnic identities from all the ancestries they know of , we askeAnalyses were conducted using Stata  and SPSS Statistics . We examined pre-treatment equivalence across study arms and assessed attrition patterns for systematic difference relevant to our hypotheses  “confirming” an ancestry (reporting an ancestry of which the respondent had prior knowledge); 2) “discovering” a new ancestry (reporting an ancestry of which the respondent did not have prior knowledge); and 3) interacting with genetic knowledge. Ancestries are presented as European and non-European for clarity.All models, both experimental and non-experimental, control for living in the South, interaction with non-Whites and political party preference, as well as gender, age, and education. Additional details are provided in In our first set of analyses with LMM models comparing genetic essentialism between control and treatment groups, the results did not show any significant average difference between the pre-test and post-test essentialism scores of the control and treatment groups . The preHowever, our secondary analyses of within-group changes in genetic essentialism by baseline genetic knowledge found different patterns of change in genetic essentialism beliefs in the control and treatment arms, as depicted in In LMM models, the predicted average essentialism scores of control respondents with low genetic knowledge also increased , while there was no significant predicted change among treatment respondents with low knowledge . This increase in the control group may reflect changes in the U.S. political landscape between the survey waves. The low knowledge group may have been more susceptible to the rising salience of race-related discourse in the 2016 election campaigns. Yet, the treatment group, also subject to the same political changes, did not show similar increase, which we may read as the depressing effect of taking the tests.LMM results from sensitivity analyses using the dichotomous measure genetic knowledge measure, show that average predicted probabilities of genetic essentialism declined significantly in the treatment group with higher genetic knowledge , while control respondents with higher knowledge did not show a significant change  . There iIn sum, in analyses examining changes in genetic essentialism with a specific focus on levels of genetic knowledge within the control and treatment arms, our results for the observed decline in essentialism among treatment respondents with high genetic knowledge are fairly robust given that they are supported in the sensitivity analysis. While the observed increase in essentialist belief among treatment respondents with no genetic knowledge does not retain significance when ‘no’ and ‘low’ knowledge categories are grouped together, this suggests that changes may be specific to those with no genetic knowledge, an effect attenuated when combined with those with low knowledge. That a significant change from pre-test to post-test is observed among treatment respondents with ‘no knowledge’ in the analysis using the 4-category variable, despite a small number of cases, is noteworthy and warrants further attention.This graph plots the interaction of genetic knowledge measured as a 4-category ordinal variable and the study arm allocation group variable. It shows the predicted average change in the pre-test and post-test genetic essentialism scores of each of the four genetic knowledge groups within the control and treatment groups separately. We used Stata’s “margins” command to produce the graph.Finally, in additional non-experimental analyses examining whether the specific results test-takers receive affect their belief in genetic essentialism, we focused on the treatment group only. Descriptive statistics  and findHowever, we did not find any significant effects on treatment respondents’ essentialist beliefs from these specific test results or their interaction with genetic knowledge. Although “confirming” a non-European ancestry reduces genetic essentialism in This study analyzed whether taking GATs increases or decreases genetic essentialist beliefs about race, and whether changes in essentialism differ by baseline genetic knowledge. To our knowledge this is the first study to examine the effect of taking GATs on racial essentialism using an RCT, which is critical for separating the tests’ effects from the motivations of the people who buy them. Test-takers may purchase these tests for reasons related to their racial beliefs and ideologies; for White nationalists, GATs may be a means to validate racial purity , while pWhile our primary analysis testing whether taking GATs impacts individuals’ concepts of race showed no difference in effect between treatment and control arms, secondary analyses suggest the potential that taking the tests did not uniformly increase or decrease test-takers’ belief in racial essentialism. Instead, among the test takers in the treatment arm, changes in essentialist views were conditional on different levels of genetic knowledge. Those with high genetic knowledge taking GATs decreased their genetic essentialism. Results suggest that test-takers with very limited genetic knowledge may increase their belief in genetic essentialism, although additional research should explore this finding further.While we did not randomize participants according to their baseline levels of genetic knowledge, these secondary analyses suggest that genetic knowledge is a potentially important modifier of the impact that genetic testing has on essentialist views. Having sufficient genetic knowledge to interpret the results may be a critical precursor to how genetic essentialist views change after taking a test. While many DTC testing companies provide technical scientific descriptions of the tests on their websites, they rarely provide a clear explanation about the relationship between genes and race or offer assistance on interpretation. As a result, many test-takers have trouble comprehending their results or translating the technical, scientific information into meaningful information for their genealogy and identity . Indeed,While our secondary analyses focused on the role of genetic knowledge on changes in essentialist beliefs between the pre-test and post-test, genetic knowledge is also inversely associated with people’s baseline essentialist beliefs before testing. In the pre-test survey, those with higher genetic knowledge had lower genetic essentialism scores Click here for additional data file.S1 FigFigure Credit: Family tree DNA.(DOCX)Click here for additional data file.S2 FigFigure Credit: Family tree DNA.(DOCX)Click here for additional data file.S3 FigThis graph plots the interaction of genetic knowledge measured as a dichotomous variable and the study arm allocation group variables. It shows the predicted average change in the pre-test and post-test genetic essentialism scores of those with lower and higher genetic knowledge within the control and treatment groups separately. We used Stata’s “margins” command to produce the graph.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S12 Table(DOCX)Click here for additional data file.S13 Table(DOCX)Click here for additional data file.S14 Table(DOCX)Click here for additional data file. 21 Oct 2019PONE-D-19-21371Do genetic ancestry tests increase racial essentialism? A randomized controlled trial shows it depends on genetic knowledgePLOS ONEDear Dr Roth,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.The reviewers both had generally very positive comments about this article. However, they highlighted a number of issues that you should address. https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.We would appreciate receiving your revised manuscript by Nov 15, 2019. 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Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: I found this article very interesting and well written. A randomized trial to assess the effect of genetic ancestry testing on racial essentialism was much needed and the results are important.My only concern is that the secondary analyses evaluating the modifying effect of genetic knowledge on essentialism after the test can only be considered suggestive and should be presented more cautiously. The reason for my concern is that as the authors clearly state in the discussion section, they did not manipulate genetic knowledge experimentally and therefore there could be other reasons why they see the modifying effect of genetic knowledge .My recommendation is that they present the main results of the randomize trial, and then clearly state that they conducted secondary analyses within the test group to explore possible modifiers that might be interesting to test in additional randomization experiments. The current title, for example, could be misleading, since it states: “A randomized trial shows that it depends on genetic knowledge”. I do not think that the secondary analysis with lack of randomization of genetic knowledge can be conclusive in this respect. I do think is interesting and should be further explored in future research projects. I would be very interested in reading about a trial that randomizes genetic knowledge among people with the same level of education and social background.Reviewer #2: Summary:Roth and colleagues conducted a randomized controlled trial to understand the effects of genetic ancestry tests on racial essentialism. Interestingly, they find that the effect on racial essentialism depends on the participant’s level of genetic knowledge. Individuals with high genetic knowledge showed decreased racial essentialism, while individuals with low genetic knowledge showed increased racial essentialism after genetic testing. The study design is novel and the findings will contribute to the body of literature on the intersections of genetic science and society. However, several methods and results lack sufficient detail to evaluate the validity of the study conclusions.Major comments:1. Introduction: In their discussion of the meaning of race, the authors imply that the scientific community has concluded that race is purely a social construct. This is an oversimplified position that does not adequately reflect the complexity of race. While race is primarily a social construct, to say there are no biological or genetic differences between different ancestral groups is incorrect. In biomedical contexts, race can still be an important variable to consider. A better way to counter genetic essentialism is not to completely deny the existence of genetic differences, but to emphasize that genetics is not the only factor that influences race, and that these genetic differences do not give certain races superior abilities. Suggested references to improve the authors’ discussion of race are Ifekwunigwe et al. (PMID: 30078844), Wagner et al. (PMID: 27874171), Burchard et al. (PMID: 12646676), and Risch et al. (PMID: 12184798).2. Participants and procedure. Other types of direct-to-consumer genetic testing are available, though less common. It seems that providing the control group with some type of non-ancestry genetic results would have been a better comparison than giving them no genetic testing at all. Could the authors comment on this?3. Genetic essentialism scores: Both the introduction and methods reference the novelty of the genetic essentialism scale developed by the authors. Has this scale been previously validated?4. Measurement of genetic knowledge. The authors measure genetic knowledge using two questions from the Survey on Genomics Knowledge, Attitudes and Policy Views (GKAP). The rationale for using these two questions alone is unclear. Can the authors justify why they selected this survey rather than using validated surveys for genetic knowledge, such as the one developed by Jallinoja and Aro (PMID: 10848030)?5. Calculation of genetic knowledge score. The authors calculate a genetic knowledge score weighting each question by the percent of study participants who responded correctly. However, knowledge levels of study participants may not be representative of the larger general population. Could the authors use weights from the original GKAP survey to confirm they are not over- or under-estimating the levels of genetic knowledge of their study participants? A comparison of the percent answering correctly in this study vs. the original GKAP would also be informative regarding the overall genetic knowledge level of this study population.6. Analyses. The thorough attrition pattern analysis was valuable and a great addition to the paper.7. Table 1. This information is redundant with what is presented in Figure 3. Table 1 could be moved to the Supplement for those interested in the exact numbers. Please also add sample sizes of each knowledge level in the control and treatment groups to this table.8. Representativeness of study population. How representative is the study population of the U.S. non-Hispanic white population? The authors describe this as their goal during the study sampling, but never present information regarding how successful their sampling strategy was.9. Supplemental Table 6. This table is the only presentation of study population characteristics, but it is confusing. All variables are presented with a mean and standard deviation, even for variables where these are meaningless metrics . For the categorical variables, please present the number of participants in each category and the frequency instead. Furthermore, please add a brief description of the population characteristics in the main text of the paper to provide context for the findings.Minor comments:1. Measures. Please provide a citation for the GKAP.2. Statistical Analysis, Line 200. The authors use the abbreviation OLS, but do not define this abbreviation until line 221. Please provide abbreviation the first time it is used in the text.3. Analyses. The authors mention that their models adjust for living in the South. How was geographic region defined?4. Figures 2 & 3. These figures need more detailed figure legends. Figure 2 contains very little information of value and could be omitted or moved to the Supplement.5. Supplementary Tables. Several of the supplemental tables present results for 3 models. However, the details of these models are not clearly described. Models 1 and 2 are also referenced in line 265 of the text without explanation of what these models contain. Clearer definitions of the models would be helpful. A footnote should be provided with each table listing the variables included in each model to make it easier for the reader.6. Tables and Figures. Throughout the paper, the authors refer to figures and tables with the title followed by the number , except when referring to Supplemental tables, when they list the table number first . The Supplemental Tables should follow the same pattern as the figures and tables in the main text .**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, AttachmentPLOSreview.docxSubmitted filename: Click here for additional data file. 19 Nov 2019We would like to begin by sincerely thanking the reviewers for their detailed and helpful comments. The reviewers offered valid critiques, which have led us to rethink and revise many aspects of the paper. We feel that the paper is much improved as a result of the reviewers’ comments, and are very grateful for the time and effort that they put in to helping us improve it. We are also extremely grateful for the many positive and encouraging comments from all the reviewers. We truly appreciate that you have found value in the paper and that you took the time to express it.Below we address the comments of the reviewers and the Editor. We have grouped the comments thematically. The line numbers refer to the version with track changes removed.Genetic Knowledge as a Modifying EffectReviewers 1 and 3 raised concerns about the presentation of the analysis of genetic knowledge. Reviewer 1 noted that because we did not manipulate genetic knowledge experimentally, we should emphasize that these findings are suggestive and they should be presented more cautiously. Similarly, Reviewer 3 cautioned against describing our findings as a moderation. In line with the reviewers’ suggestions, we have reframed the paper to distinguish between our primary analysis, based on the randomization to treatment and control groups, and the secondary analyses, which are not. We have changed the title of the paper accordingly, and have reorganized the description of the analysis in the introduction as well as the hypotheses to sequentially describe primary and secondary analyses. We had previously used the term ‘moderation’, intending it as a synonym for the effect we observed in our interaction models in the Ordinary Least Squares (OLS) regression, and did not mean to imply that we had conducted a formal moderation analysis. To clarify this, we have reworded our language to avoid readers interpreting it as a moderation analysis. We also adjusted the wording of the secondary analyses to remove causal language, and edited the discussion section to avoid implying that the findings surrounding genetic knowledge are causally related to the testing intervention. Reviewer 3 also expressed concern about the structure of the genetic knowledge variable and the analytic approach used to test interactions between genetic knowledge and the study arm allocation group variable, particularly the comparison of the no, low, and medium knowledge groups to the ‘high knowledge’ group. The reviewer was concerned about the strength of the effect observed for the ‘no knowledge’ group as compared to ‘high knowledge’ group in the interaction model because the finding, the reviewer suggested, is based on about half the sample and because it is only significant at the .05 level. We appreciate the reviewer raising these concerns because it forced us to think carefully about this issue. First, we would like to point out that this comment seems to focus only on the OLS analysis (now S10 Table), which uses interactions between the genetic knowledge groups and assignment to the treatment group. However, we include the OLS analysis to lend further support to the mixed-effects models (S8 Table) and the post-estimation predictions that are based on them as presented in the pairwise contrasts (S9 Table). In particular, the pairwise contrasts show the predicted average probabilities of change in genetic essentialism within each genetic knowledge group between the pre-test and post-test surveys. Here, too, we see that the average predicted probability of treatment respondents in the ‘no knowledge’ group declined significantly between the pre-test and the post-test, and this is not simply relative to the ‘high knowledge’ group.  However, we acknowledge and agree with the reviewer that the finding is still based on a small number of cases in the ‘no knowledge’ group, and we have made several changes to be clearer about this and to test the robustness of our findings. As Reviewer 3 suggested, we did run the analyses with a dichotomized version of the genetic knowledge variable. We combined the ‘no/low’ knowledge categories and the ‘medium/high’ knowledge categories to create a binary measure of ‘lower’ and ‘higher’ genetic knowledge. With this binary measure, we find that the average predicted probabilities of genetic essentialism declined significantly in the treatment group with higher genetic knowledge , while control respondents with higher genetic knowledge did not show a significant change  . This is in line with what we had found with the 4-category ordinal measure of genetic essentialism variable. We do not find a significant predicted change among treatment respondents with lower knowledge , however. Thus, the dichotomous variable shows that our findings that high(er) genetic knowledge is predicted to decrease genetic essentialism after testing is robust; as expected, this analysis does not support the predicted increase in genetic essentialism among those with the least genetic knowledge because the ‘lower knowledge’ category is numerically dominated by those in the 4-category ‘low knowledge’ group. The reviewer also suggested using a continuous version of the genetic knowledge variable; however, we do not think a continuous version of this variable would be viable given that there are only four effective categories out of the six in the full range, as two of the categories have very few cases in them (S6 Table). When categories are few, it is not recommended to treat a categorical variable as continuous; the recommendation is to have at least 5 and preferably 7 categories to treat an ordinal variable as a continuous variable (Liddell and Krusche 2018). Furthermore, the variable is not normally distributed (see S6 Table). Also given that we are now aware of potential differences in our results based on different genetic knowledge levels, we do not think it is reasonable to assume that the one-unit effect will be the same across the different points of a continuous scale. We decided to include the analyses with both the 4-category ordinal and the dichotomous genetic knowledge variables in the paper. We believe that the preliminary indication of an expected increase in genetic essentialism for test-takers with no genetic knowledge is a noteworthy pattern that deserves further exploration. Our limitation is statistical power; in fact, it is noteworthy that we see a significant association despite the lack of statistical power. Because this is the first RCT of genetic ancestry testing on genetic essentialism (to our knowledge), we believe it is valuable to present the indication of a possible increase in genetic essentialism among those with the least genetic knowledge as something that might be interesting to explore in future research, while still using caution to avoid suggesting that this is a conclusive finding. We have edited our discussion to reflect this . We have also added more information about these considerations in the supplement under the ‘Genetic Knowledge Variables’ section (p.5).Representativeness of SampleReviewer 2 asked how representative the study population is of the U.S. non-Hispanic White population and said “The authors describe this as their goal during the study sampling, but never present information regarding how successful their sampling strategy was.” We apologize if our language was misleading, but want to clarify that the representativeness of the study population was not our goal. Our target population is not all native-born White non-Hispanics, but specifically native-born White non-Hispanics who are willing to take genetic ancestry tests (GATs) and have never received personalized genetic ancestry information. We expect this population to differ from the overall population of native-born White non-Hispanics, yet we were unable to find a representative sample of this particular target population before the study began. We have clarified this in the manuscript . Nevertheless, we take the reviewer’s point that it is valuable for readers to know how this sample compares with the larger population of native-born White non-Hispanics. We have therefore added a table to the supplement (S3 Table) that compares our study sample to a representative sample of native-born White non-Hispanics from the 2015 American Community Survey. In the supplement (p.3-4), we discuss differences between our sample and this broader population, and note that these differences likely reflect patterns in who is willing to take genetic ancestry tests, as well as trends in who is willing to participate in research studies.Reviewer 2 also asked how our sample’s knowledge of genetics compared to the larger general population. We therefore also added to S3 Table the responses to the survey items from the subsample of native-born White non-Hispanics in the Survey on Genomics Knowledge, Attitudes and Policy Views (GKAP). Again we compare our sample to this population, and note that those who are willing to take genetic ancestry tests (our target population) likely has higher genetic knowledge than all native-born White non-Hispanics.Study DesignReviewer 2 asked us to comment on why we chose to give our control group respondents no stimulus rather than some type of non-ancestry genetic test results. We made this decision because our goal is to simulate the effect of taking GATs relative to those who do not (as we now state on lines 106-107). We are interested in the potential effects of GATs on the population that takes them, rather than the relative effect of GATs vs. other genetic tests. To have compared those who take GATs to those who take health-based genetic tests, for instance, would be similar to more conventional non-inferiority clinical RCTs that test the equivalence of two treatments – whether one is inferior to the other for the treatment of a disease outcome. We expect that most people who do not take GATs likely do not take other genetic tests either. Therefore, we have maintained our control group based on the exploratory, innovative nature of the study and the fact that this type of RCT has not been conducted before (to our knowledge). Comparing the relative effect of different tests would be a reasonable next step after establishing the effect that concerns us here. MeasuresBoth Reviewers 2 and 3 asked for additional information about the essentialism scale. We added in more information about the development and validity of the scale. We also added in the Cronbach's alpha for this sample, and provided a clearer reference to the publication where the scale's development and validation are described in detail (lines 190-208).Reviewer 2 noted that our models adjust for living in the South and asked how geographic region was defined. We use the same definition as the U.S. Census Bureau statistical regions. Specifically, the South census region includes Alabama, Arkansas, Delaware, the District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. We added a description for how the geographic region 'South' was defined to the Supplement, under 'Control Variables' (pp.6-7).Reviewer 2 also asked us why we developed the genetic knowledge scale out of the two questions from the GKAP survey rather than using validated surveys for genetic knowledge like the one developed by Jallinoja and Aro. Our answer is that when we began this study, we did not expect genetic knowledge to emerge as a major focus and predicted that educational attainment would capture the differences in respondents’ understandings of their GAT results. We view this as a limitation of the study, and believe that future studies need to build on this work by using a more robust scale. Although we had already mentioned this as a limitation of the study in our discussion section, we have further emphasized this point and have specifically mentioned the Jallinoja and Aro scale to direct potential future researchers towards it (lines 414-419).Organization and WritingIn the Introduction, Reviewer 2 asked for a more nuanced discussion of the meaning of race that more adequately reflects its complexity. We thank the reviewer for pointing out the shortcomings of this section, as we did not intend to imply that the entire scientific community had concluded that race is purely a social construct or that it has no association with biological factors. We have edited this text (lines 49-56) to acknowledge this point and to clarify that socially constructionist views of race still acknowledge that those categories are partly based in biological or descent-based characteristics. We have also added in several additional references and thank the reviewer for providing them.Reviewer 3 asked us to introduce earlier in the paper that we would examine specific ancestry results on GATs as an influence on genetic essentialism. We added this information to the abstract and also to the Introduction (lines 97-100).Adding or Clarifying Statistics/AnalysesBased on Reviewer 2’s helpful comments, we have revised our presentation of study population characteristics. We added a brief description of the sample’s characteristics to the main manuscript (lines 166-171) to provide context for the findings. We also edited the previously-confusing supplement table (now S2 Table) that presented these descriptive statistics. It now shows frequencies for categorical variables and means and standard deviations only for continuous variables.We have clarified our hypotheses both about the role of genetic knowledge, and about the “confirmation” and “discovery” of new ancestry. We had only included what we expect the low genetic knowledge to experience as a result of seeing the test results, which is an increase in genetic essentialism. To complement the section, we added the following sentence in lines 130-133: "By contrast, we expect test-takers with high knowledge of genetics to be more likely to read the results as evidence that races and racial traits are not determined solely by genetics, and thereby develop less essentialist views after testing." We also specified that with regard to the specific ancestry test results, we expect a null main effect, but that high genetic literacy will lead to less essentialism while low genetic literacy will lead to more genetic essentialism (lines 134-146).At Reviewer 3’s suggestion, we clarified the claim, regarding the additional survey item measuring genetic knowledge, that “models that included this item showed similar results…” (now lines 220-221) to specify that these are models that we ran with this item. We decided not to include the statistics for those additional models, both because PLOS One does not allow footnotes and because including this information would require adding a somewhat lengthy explanation of how we coded genetic knowledge using three items rather than two. Because the third genetic knowledge item (how much of the genes of a Black person are the same as those of a White person) has considerable theoretical overlap with our dependent variable, we feel that adding a lengthy discussion of this does not add value to the paper. If the Editor feels it is more appropriate, we can simply delete this sentence.We have edited our ‘Analysis’ section (lines 246-268) to be clearer about the statistical techniques we are using and which models they draw from. We specified “Ordinary Least Squares” the first time we provided the abbreviation, on line 249. We also clarified that the statistics procedure used for the second hypothesis  is OLS regression. We say this on line 337 in the updated manuscript.We confirmed that all our models include control variables. In the text, we stated this on lines 266-268 but changed the wording a bit to make it clearer: “All models, both experimental and non-experimental, control for living in the South, interaction with non-Whites and political party preference, as well as gender, age, and education.”We thank Reviewer 3 for noting the omission of the statistic being reported in several places on the former page 10. The numbers refer to average predicted probabilities, also known as marginal effects. We changed the text by adding “predicted probabilities” in the necessary parts in the results section. We also would like to state that the average predicted probabilities capture the effect sizes that are relevant to our analysis.We added information to the notes of S1 Table and S10 Table to explain what variables are added to each of these nested regression models. All variables in the model are shown in the left-hand column, so we did not add a footnote with this information. We also added information to the manuscript to explain what Models 1 and 2 of S13 Table are (now lines 344-345).Figure Legends and Duplication of Information in Figures/TableThe reviewers noted that there was some duplication of information in the figures, text, and the table in the manuscript. However, they differed on what should be done about it. Based on our best judgment, we decided to move the table (formerly Table 1) to the supplement (now S9 Table). We also added the sample sizes of each knowledge level in the control and treatment group to this table. We have kept the changes in essentialism scores in the text because the exact numbers may not be clear from the figures. We added a note to Figure 3 and clarified what the values refer to in the text. We also made slight changes by bringing some details from the notes of Figure 2 to the main text (lines 273-266).Minor ConcernsWe did not intend to set up the expectation that we would be examining the impact of GATs on multiple types of essentialism. We edited the wording in the Introduction (lines 56-58) to clarify that here we were distinguishing between genetic essentialism and cultural essentialism or other types of essentialism that we do not study. We have also removed the names of the three sub-factors that contribute to the full genetic essentialism scale (lines 195-198). We wanted to indicate that this is a complex concept that a second-order model helps to capture, but removed these titles to avoid setting up readers to expect that we would be analyzing the sub-factors individually.Reviewer 2 asked for a citation for the GKAP study. The Co-Principal Investigator sent us the reference that we include at the bottom of this memo. However, because PLOS One does not allow citations to unpublished works, we decided to instead cite a publication by the Co-PIs that uses the GKAP and discusses the survey details . We have made all the minor editorial corrections pointed out by the reviewers, with the exception of referring to the Supplemental tables using the same pattern as for the main tables and figures (e.g. Table S1 instead of S1 Table). Here we are following the PLOS One formatting guidelines. Editors’ CommentWe have provided permission from the copyright holder to reproduce S1 Fig and S2 Fig.Once again, we would like to thank the Reviewers for reading the paper so carefully and for their very helpful comments. We very much hope that the revisions to the manuscript adequately address the issues that were raised.References:Liddell, Torrin M. & John K.Kruschke. 2018. Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology 79: 328-348.Survey on Genomics Knowledge, Attitudes, and Policies (GKAP), by Jennifer Hochschild and Maya Sen. GKAP 1 in 2011; GKAP 2 in 2017. GKAP 1 was funded by Robert Wood Johnson Foundation, as part of a Investigator Award in Health Policy Research, 2010-2013 (with Maya Sen). Surveys were conducted by Knowledge Networks (now GfK).AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 19 Dec 2019Do genetic ancestry tests increase racial essentialism? Findings from a randomized controlled trialPONE-D-19-21371R1Dear Dr. Roth,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Mellissa H Withers, PhD, MHSAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: All comments have been addressedReviewer #2: All comments have been addressed**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: (No Response)**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Authors have addressed my concerns thoughtfully, as well as the concerns of other reviewers. I have no additional comments for the Authors.Reviewer #2: Roth and colleagues present a significantly revised version of their manuscript regarding the effect of genetic ancestry testing on racial essentialism. The authors have made substantial changes to the manuscript, and we appreciate how thoroughly and thoughtfully they have addressed the comments raised. The revised manuscript is significantly improved. Overall, we feel the authors have addressed our concerns and recommend the manuscript be accepted for publication.Comments:1. Introduction: The authors have revised their discussion of the meaning of race to present a more nuanced view, as requested. These changes better represent the complexity of race and provide better context for the article.2. Participants and procedure. We had questioned why the reviewers chose to provide no genetic tests to the control group. The authors have clarified this in their response and have incorporated language into the manuscript to emphasize that their goal was to compare people who receive genetic ancestry testing with those who do not. Their rationale that people who do not receive ancestry testing also rarely receive other genetic testing seems valid, and we agree that this work is an important first step in understanding the role of genetic ancestry testing on racial essentialism.3. Genetic essentialism scores. In the original manuscript, it was unclear how the authors had developed the genetic essentialism scale and whether it had been validated. The authors have added more detail into the methods section and provided a reference to their previous paper where they developed the score. We appreciate the clarification and feel it appropriately addresses our concerns.4. Measurement of genetic knowledge. We were unclear why the authors had used just a few questions from the Survey on Genomics Knowledge, Attitudes and Policy Views (GKAP) to measure genetic knowledge. However, the authors’ substantial revisions to the manuscript to emphasize that the genetic knowledge analyses were a secondary goal of the work and the revised title de-emphasizing the genetic knowledge results make this less of a concern. While better metrics of genetic knowledge would be valuable to this research, we understand that this was not the authors’ initial goal. The changes they have made to the manuscript and the addition of a more thorough discussion of measurements of genetic knowledge sufficiently address our previous concerns. We also appreciate the authors providing references to the GKAP to the Reviewers and to readers of the manuscript.5. Figures and tables. We appreciate how carefully the authors have thought about which tables and figures to include in the revised manuscript. The revisions made to the study population characteristics table make it much easier to understand. We agree with the authors’ decision to move the original Table 1 to the supplement, as it was not adding much information beyond the main figures. We also appreciate the addition of sample sizes to this table. The updated figure legends also add much needed detail that was previously lacking.6. Representativeness of study population. We thank the authors for adding comparisons between the study population and the overall non-Hispanic white population in both the demographics and genetic knowledge tables. The revised language around the study sampling goals and the added discussion regarding the representativeness of the study population address our previous concerns. 3 Jan 2020PONE-D-19-21371R1 Do genetic ancestry tests increase racial essentialism? Findings from a randomized controlled trial Dear Dr. Roth:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. For any other questions or concerns, please email Thank you for submitting your work to PLOS ONE.With kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. Mellissa H Withers Academic EditorPLOS ONE


Boechera stricta is a perennial relative of Arabidopsis thaliana native to largely undisturbed habitats with two geographic and ecologically divergent subspecies. Here, we delineate the evolutionary processes driving the genetic diversity and population differentiation in this species.Genomic variation is widespread, and both neutral and selective processes can generate similar patterns in the genome. These processes are not mutually exclusive, so it is difficult to infer the evolutionary mechanisms that govern population and species divergence. B. stricta accessions, we identify four genetic groups that diverged around 30–180 thousand years ago, with long-term small effective population sizes and recent population expansion after the Last Glacial Maximum. We find three genomic regions with elevated nucleotide diversity, totaling about 10% of the genome. These three regions of elevated nucleotide diversity show excess of intermediate-frequency alleles, higher absolute divergence (dXY), and lower relative divergence (FST) than genomic background, and significant enrichment in immune-related genes, reflecting long-term balancing selection. Scattered across the genome, we also find regions with both high FST and dXY among the groups, termed FST-islands. Population genetic signatures indicate that FST-islands with elevated divergence, which have experienced directional selection, are derived from divergent sorting of ancient polymorphisms.Using whole-genome re-sequencing data from 517 Our results suggest that long-term balancing selection on disease resistance genes may have maintained ancestral haplotypes across different geographical lineages, and unequal sorting of balanced polymorphisms may have generated genomic regions with elevated divergence. This study highlights the importance of ancestral balanced polymorphisms as crucial components of genome-wide variation.The online version of this article (10.1186/s13059-019-1729-9) contains supplementary material, which is available to authorized users. How evolutionary processes drive genetic divergence and eventually lead to speciation is a fundamental question in evolutionary biology . Taking π) in descendant species and low relative divergence (FST) between species [FST and absolute divergence (dXY) [FST and dXY) [FST but unchanged or decreased dXY [Balancing selection maintains multiple advantageous polymorphisms in populations and increases genetic diversity . In cont species . Alterna species . In thisce (dXY) , 7, 13. and dXY) , 11, 13.ased dXY , 4–6, 11ased dXY . Additioased dXY . Therefoased dXY .Table 1PBoechera stricta (Brassicaceae), a perennial relative of Arabidopsis thaliana, is native to largely undisturbed sites in western North America [East and West) of B. stricta, with further subdivision within the East subspecies [East and West is significantly influenced by ecological adaptation [Western genotypes are typically found in sites with more constant and abundant water supply, suggested that local water availability may be the selective force underlying ecological speciation between East and West subspecies [East-West recombinant inbred line population segregates for many quantitative trait loci (QTLs) for ecologically important traits, including flowering time, herbivore resistance, fecundity, and lifetime fitness [B. stricta. These characteristics, along with the sequenced genome [B. stricta to understand how complex evolutionary forces drive divergence and speciation. America –17. Prevbspecies –21. Whilbspecies , the divaptation . These sbspecies . In addibspecies , and an  fitness , which mB. stricta accessions representing much of the species range. First, we investigated the population structure and history of species divergence. Next, we identified the signatures of long-term balancing selection influencing ~ 10% of the genome. Finally, we looked for genomic regions that distinguish lineages and assessed the roles of different evolutionary processes in driving divergence. Our study provides an example for disentangling the multitude of evolutionary processes that may have shaped the patterns of genetic variation across the genome and improves our understanding of the cause and consequence of genomic divergence during speciation.To understand how different evolutionary processes contribute to current genomic variation, we re-sequenced the whole genomes of 517 B. stricta accessions using Illumina Hiseq2000/2500 short-read technology  models. We found significant correlations between pair-wise genetic distance and geographical distance within each group  using coalescent simulation in fastsimcoal2 v.2.6.0.3 [−9 substitutions per site per year [Estimates of demographic history and gene flow provide a sketch of population history and enable demographically informed simulations of population genetic variation under the assumption of selectively neutral evolution. Accordingly, we inferred the past demographic history of .2.6.0.3 . To avoi.2.6.0.3 . We usedper year , we estiEast subspecies groups) was ~ 179 thousand years ago , and divergence within the East subspecies dates to 30–70 KYA  was small in B. stricta; the estimated Ne values for the common ancestor of all groups, ancestor of East subspecies, and ancestor of UTA and NOR are 2.4 × 104, 3.6 × 104, and 3.9 × 104, respectively. After splitting, the WES and NOR groups both had small initial populations  comparable with their ancestors, while the COL and UTA groups had relatively larger initial populations . The long-term small effective population sizes of WES and NOR are consistent with the relatively high level of linkage disequilibrium (LD)  between observed and simulated data. We found that SFS and summary statistics predicted under neutrality are well-matched to the data  and recent population expansions within 15 KYA  in 20-kb non-overlapping windows across the genome. Then, we computed Z-transformations (Z-π) separately in each group  were identified as outlier windows  regions”  including 91 (70%) shared outliers  in 20-kb windows across the genome using LDhelmet [ρ by genetic diversity (π) to account for the confounding effects of local Ne and compared scaled ρ (ρ/π) between the BLS regions and the rest of the genome. Relative to the genomic background, we found similar recombination rate in the BLS regions of groups WES and NOR  but significant higher and lower recombination in the BLS regions of groups COL and UTA, respectively  and lower population differentiation (FST) in the BLS regions compared to the genomic background  and also relative node depth , much longer than the level of LD in this species  gene family, can be targets of balancing selection, and genetic variation in these gene regions could be maintained for long periods by transient or frequency-dependent selection . While Nthaliana , 42 and Capsella . In the a genome , we identhaliana , with 27A. thaliana found that regions syntenic to these BLS regions also show enrichment for NBS-LRR genes and elevated nucleotide polymorphism [An early population genomic study in morphism   scores separately in each group pair [Z-FST ≥ 2, corresponding to 3.04% of windows from all comparisons, were identified as outlier windows , but only 22 in a comparison between sympatrically distributed WES and NOR groups  and absolute divergence (dXY) in FST-islands are compatible with a model in which these islands were derived from divergent sorting of ancient polymorphisms [B. stricta groups  showed the highest FST and the lowest number of FST-islands among comparisons  together with the low frequency of stricta × stricta admixed genotypes, also suggest that gene flow has been limited among the groups. Therefore, our observations indicate that reduced levels of recent gene flow are unlikely to be an important contributor to the formation of FST-islands in B. stricta.To examine which factors have contributed to the formation of -islands , 11, 13,orphisms . ConsistBoechera . The geoFST-islands could be due to the ongoing background selection or recurrent selective sweeps. These two processes tend to reduce genetic polymorphism and to elevate FST in low-recombination regions [FST-islands and the rest of the genome and tested the correlation between differentiation (FST and dXY) and recombination in FST-islands. Among the 12 comparisons , only two comparisons showed a significant lower recombination in FST-islands than in background , excess of low frequency alleles (more negative Tajima’s D) ,and high-frequency derived alleles (more negative Fay and Wu’s H) in one or both groups of each comparison  among the four lineages may be derived from divergent sorting of ancient polymorphisms, instead of heterogeneous gene flow or recurrent selective sweeps. These findings provide evidence that elevated genetic diversity due to balancing selection also may increase population differentiation by sorting balanced polymorphisms during divergence processes. This study highlights the importance of ancestral balanced polymorphisms as crucial constituents of genome-wide variation and incipient speciation.We used population genomic analyses to track the divergence processes of B. stricta accessions across the species range in the western USA . Plate-based DNA library preparation for Illumina sequencing was performed on the PerkinElmer Sciclone NGS robotic liquid handling system using KAPA Biosystems Library Preparation Kit. About 200 ng DNA was sheared to 475–600 bp using a Covaris LE220 Focused-ultrasonicator. The sheared DNA fragments were size selected by double-SPRI, and then the selected fragments were end-repaired, A-tailed, and ligated with Illumina compatible sequencing adaptors from IDT containing a unique molecular index barcode for each sample library. The prepared libraries were quantified using KAPA Biosystem’s next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The quantified libraries were then multiplexed with other libraries, and the pool of libraries was then prepared for sequencing on the Illumina HiSeq sequencing platform utilizing a TruSeq paired-end cluster kit and Illumina’s cBot instrument to generate a clustered flowcell for sequencing. Sequencing of the flowcell was performed on the Illumina HiSeq2000/2500 sequencer using a TruSeq SBS sequencing kit, following a 2 × 100 or 2 × 150 indexed run recipe.B. stricta reference genome v1.2 [We used trimmomatic v0.36  to removome v1.2  with BWAome v1.2 . We usedome v1.2  for baseB. stricta is predominantly inbred; hence, high heterozygosity may indicate paralogous loci), or if reference or variant alleles were indels. We further removed 20 samples with missing rate > 0.60, five duplicated samples from the same inbred family, six samples with divergent morphology or identified as outliers by PCA, and two samples without geographical information  homozygous genotypes were assigned as missing if supported by less than two reads; (2) heterozygous genotypes were assigned as missing if supported by less than 20 reads or reference ratio  < 0.25, or > 0.75; and (3) SNPs were discarded if they met any one of the following criteria: genotyped in fewer than 50% of individuals, mean depth > 20, more than one variant allele was observed, sites where the proportion of heterozygous genotypes was > 15%  previously assayed by Sanger sequencing , 50. TweB. stricta, because it is an inbreeding species with extremely low heterozygous rate [A. thaliana, another inbreeding species [To further monitor the accuracy of genotypes over different coverage depths, we downloaded reads of two genotypes that were sequenced at very high depth, ~ 400×  and ~ 170×  . Next, wous rate . A simil species .B. stricta, we (1) conducted admixture analyses using Admixture v1.3.0 [K values ranging from 1 to 10 and repeated the process 20 times with different seeds. The best K  was chosen by the ΔK method [K values in Admixture and sNMF, respectively. Genotype likelihoods estimated by ANGSD [To investigate population structure in e v1.3.0 , FastSTRe v1.3.0 , sNMF [2e v1.3.0 , and NGSe v1.3.0 ; (2) pere v1.3.0 ; and (3)e v1.3.0  with 100K method . A tenfoby ANGSD  were useby ANGSD . To do tTo investigate the pattern of isolation by distance in each group, we calculated genetic and geographic distances between each pair of genotypes and tested the correlation of genetic and geographic matrices by Mantel test with 10,000 permutations (permuting rows and columns) implemented in the R package VEGAN . We furtr2) for each pair of common SNPs within 50-kb windows using plink v1.90 [We estimated genome-wide LD species-wide , as well as for each group . We extracted common SNPs with MAF above 0.05 and calculated the mean-squared correlation  whether gene flow was present among groups and (2) how population size changed within groups  39, 78, 63, and 52 genotypes from WES, COL, UTA, and NOR groups, respectively, to maximize the number of segregating SNPs. Sites were discarded if the sample size (non-missing genotypes) was less than the threshold in any group. We excluded SNPs from three genomic regions under long-term balancing selection  between simulated and observed data.We inferred the demographic history of .2.6.0.3 . We testegard to  whether thaliana  were useNe over time in each genetic group. We used default settings to run Stairway, including 2/3 of the data for training and four random break points at (nseq-2)/4, (nseq-2)/2, (nseq-2)*3/4, and nseq-2. As suggested by the authors, we created 200 input files using the provided script and estimated the median and 95% confidence interval of demographic parameters based on these files. We converted estimates to absolute values based on a generation time of 2 years and a mutation rate of 7 × 10−9 substitutions per site per year.We applied the Stairway plot v2  method wD and FST); and (3) at least 2000 bases available from both ingroup and outgroup species, for outgroup statistics requiring outgroup information, such as Fay and Wu’s H and RND.We partitioned scaffolds into 20-kb windows, and calculated the sequence coverage by counting the number of available sites in each window. To obtain all available sites, we used the “-allSites” argument in GATK and filtered non-segregating sites using the same quality thresholds as for segregating sites see the “” sectionB. stricta and the species as a whole, we estimated SFS and related population genetic statistics using a probabilistic method implemented in ANGSD v0.919 [P value higher than 1 × 10−4 , (4) assigning genotypes as missing if the depth was less than two for an individual, and (5) removing sites that did not pass filtering criteria above (see the “π) and Tajima’s D on the basis of folded SFS and calculated Fay and Wu’s H from the unfolded SFS. We used B. retrofracta [For each group of D v0.919 . We filtsee the “” sectionrofracta , 36 as tFST) [dXY) [da) [dXY of each group pair with their mean divergence to an outgroup species (B. retrofracta) [FST, we calculated two variance components (the numerator and denominator) for each segregating site, averaged them separately, and obtained the window-based estimates as a “ratio of average” [dXY and RND, we obtained window-based values by averaging per-site estimates across all sites (both variable and monomorphic) passing the initial quality filters for each window.We used custom Python scripts to calculate the relative genetic differentiation (Weir and Cockerham’s weighted FST) , absolutT) [dXY) , and netXY) [da)  for six XY) [da)  by dividofracta) , 36. In average” . For dXYρ = 4Nec) were estimated for each group using the program LDhelmet v.1.10 [θ = 0.001 (close to estimates of genomic background) and θ = 0.01 (close to estimates in three genomic region with elevated diversity) yielded quantitatively identical results ; thus, only the results based on θ = 0.001 were reported. To account for the influence of effective population size on estimated ρ, we divided ρ by diversity (π) in each 20-kb window following Wang et al. [ρ/π between islands and the rest of the genome. Gene density was estimated as the total length of coding sequences within each of the 20-kb windows.Population-scaled recombination rates (t v.1.10 . We ran π), we standardized π in each group for each window and identified high-diversity windows with Z-π ≥ 2 . Following the same procedure, we standardized per-window FST in each pair of groups to a Z-score based on the formula Z-FST = (FST × FST′)/std-FST [FST is a per-window estimate, and FST′ and std-FST are the mean and standard deviation of FST across windows. We excluded the BLS regions from the analyses because they show evidence of long-term balancing selection and defined windows with Z-FST ≥ 2  as outliers (FST-island). Based on 100,000 permutations, we compared genomic islands vs. genome-wide background for possible differences in multiple summary statistics, including recombination rate, diversity, and FST.To identify the genome regions with elevated diversity  using demographic parameters of the best model estimated by fastsimcoal2  and FST (for FST-islands) in islands versus those from simulated data by using the Mann-Whitney U test.To test whether π-islands shared among all groups. These islands showed higher diversity and stronger signatures of balancing selection than islands specific to particular group(s). These islands were clustered into three genomic regions. We merged shared islands that were separated by less than 1.5 Mb into single genomic segments and delimited these three highly polymorphic genomic regions to 15.3–24.4 Mb on the short arm of chromosome 1 (LG1p), 0.47–3.4 Mb on the short arm of chromosome 5 (LG5p), and 0.45–5.1 Mb on the short arm of chromosome 7 (LG7p). To look for patterns of their homologous regions in A. thaliana genome, we used SyMAP v3.4 [B. stricta [A. thaliana genomes [B. stricta genomic regions onto the A. thaliana genome to delimit the homologous regions. To calculate genetic diversity in A. thaliana, we downloaded SNP data from http://1001genomes.org/. Of the 1135 sequenced accessions, we retained 972 non-relicts from the native range [A. thaliana into 10-kb, 20-kb, and 100-kb windows, calculated per-site nucleotide diversity (π) using custom Python scripts, and obtained the window-based values by averaging per-site estimates in each window. To correct for variation in mutation rate among genomic regions, we divided diversity by divergence to the outgroup species, Arabidopsis lyrata.We further focused on 130 MAP v3.4  to ident stricta  and A. t genomes  and furtve range , 32. Genπ-islands or FST-islands. We first calculated P values of Fisher’s test and subsequently corrected P values for multiple testing with Benjamini-Hochberg FDR [B. stricta genome [We performed GO analyses to test whether any functional classes of genes were over-represented in berg FDR . GO termberg FDR  and useda genome  as a refAdditional file 1:Supplementary figures and tables. (PDF 8011 kb)


Domestic animals play a key role in human survival and the development of civilization. However, the genetic resources of domestic animals are facing an alarming rate of erosion due to socioeconomic changes, economic globalization and financial constraints. In this study, through genome-wide SNP analysis, we estimated the heterozygosity, inbreeding coefficient, effective population size, and runs of homozygosity to identify the breeds facing the risk of extinction for sheep and cattle across the world. In particular, we quantified the contribution of 97 sheep breeds and 53 cattle breeds to genomic diversity  and prioritized the breeds for conservation. Additionally, we compared the average values of genomic diversity between breeds from regions (or countries) in different economic categories , and found that breeds in developed regions exhibit significantly higher levels of total genomic diversity than those in underdeveloped and developing regions. Altogether, our results suggested that conservation priority should be given to breeds in developed regions to secure the future genomic diversity hotspots of domestic animal resources. However, in the past two decades, farm animal genetic resources have faced an alarming rate of extinction and genetic erosion, as approximately 200 uniquely adapted breeds have become extinct and up to 30% of global livestock breeds  are currently endangered2. This loss of diversity is attributed to multiple causes, including regional economic forces such as the decline in the economic viability of traditional livestock production systems3, destruction of the native habitats  of livestock breeds4, rapid dissemination of uniform and highly productive breeds at the expense of native genetic resources5 and the extension of markets and economic globalization6. In this context, an integrated analysis of the genomic diversity of farm animals and the associated economies can aid an understanding of economic impact on the changes in farm animal genomic diversity in recent years. Additionally, it will contribute to the development of governmental interventions and the implementation of conservation priorities that are necessary to ensure that ongoing agricultural development will be compatible with the conservation and sustainable use of local livestock diversity7.Farm animals contribute in many ways to human survival and economic development, particularly as societies evolve from subsistence agriculture into market-based economies8 and allelic richness approaches9, we calculated the breed contribution to genomic diversity at three levels: within-population diversity (ΔGDWS), between-population diversity (ΔGDBS), and total genomic diversity (ΔGDT) for each breed after its removal  in terms of the annual gross domestic product (GDP) per capita (GDPPC). We also took into account the increase in the GDP per capita (ΔGDPPC) for the regions during the past two decades in a dual-regression analysis to correlate the contribution of the genomic diversity of the breeds and the past economic development in a region. To facilitate comparisons among the regions with different economies, we grouped the regions into three categories  according to the average GDPPC (A-GDPPC) during the years 1993–2013. Based on all the above analyses, we were finally able to determine the contribution of each breed to the maximum amount of genomic diversity and establish the core sets of breeds with higher priority for conservation.Here, we ranked the conservation priorities of 97 native sheep breeds and 53 native cattle breeds from around the world based on genome-wide single-nucleotide polymorphism (SNP) data for the first time. Firstly, we calculated heterozygosity, inbreeding coefficient, and runs of homozygosity (ROH) for each sheep and cattle breed to detect the small and inbred populations that are facing the risk of extinction. Moreover, we implemented the first association analysis between economic indexes and genomic diversity by studying a total of 2,941 animals from 150 native breeds of two globally distributed livestock species: sheep  and expected (HE) heterozygosity for each sheep and cattle breed. For sheep breeds, the highest HO value was observed in the RAA (0.394) breed from Spain, and the lowest was in the SPS breed (0.252) from Yunnan province of China. The highest and lowest expected (HE) heterozygosity were also observed in the RAA breed (0.385) and SPS breeds (0.258), respectively (Supplementary Table HO (0.362) and HE (0.343) values, respectively, and BALI breed from Indonesia presented the lowest HO (0.061) and HE (0.063) values (Supplementary Table FIS) of sheep breeds, BGE breed had the highest FIS value (0.157) and DSH breed had the lowest FIS value (−0.065)  for sheep and cattle were calculated based on the linkage disequilibrium method and presented in the Supplementary Tables Ne ranged from 3,074 (BYK) down to 3 (WNS), while in cattle the value ranged from 1,429 (ZBO) down to 1 (BALI). The high degree of inbreeding may be the reason for the extremely low Ne value of BALI, as evidenced by the high FIS and FROH value observed in this breed  was estimated. The FROH values ranged from 0.0004 (WZS) to 0.2016 (WIL) in sheep breeds (Supplementary Table ROH) above 500 kb, the sheep breed of WIL (NROH = 780) showed the highest NROH value and WZS (NROH = 2) had the lowest NROH value (Supplementary Table ROH = 1) and BALI  breeds showed the lowest and highest NROH value, respectively (Supplementary Table ROH). For sheep and cattle breeds, LROH values ranged from 26.9 Mb (HLS) to 9.0 Mb (SOA), and 21.8 Mb (BOR) to 5.1 Mb (ZEB), respectively , NROH categories  and LROH categories , and the number of breeds belong to each of the categories were shown in Table In this study, we calculated the ROH statistics for each breed of sheep and cattle, and subsequently the inbreeding coefficient based on ROH , and European sheep breeds  to 0.077 (RAA), −0.067 (KAZ) to 0.218 (SOA), and −0.044 (SPS) to 0.042 (DSH), respectively  breeds showed a ΔGDT < −0.1 in this study  showed a ΔGD1 and 11 0.8% breeT (0.259) value, Xinjiang breeds showed the highest ΔGDWS (0.278) value, and South African breeds were observed with the highest ΔGDBS (0.179) value. In contrast, Bangladeshi breeds showed the lowest ΔGDWS (−0.205) value, and Xinjiang breeds had the lowest ΔGDBS (−0.445) and ΔGDT (−0.167) values (Supplementary Table WS ranged from 2.271 (France) to −1.421 (Indonesia), ΔGDBS ranged 1.134 (Indonesia) to −1.225 (France), and ΔGDT ranged from 1.047 (France) to −0.293 (Pakistan)  higher ΔGDWS values than those in underdeveloped regions, whereas the ΔGDBS estimates for breeds from different regions within the three economic categories were not significantly different from each other , R2 = 0.278, P < 0.01 (ΔGDT); cattle: R2 = 0.235, P < 0.05 (ΔGDWS); R2 = 0.318, P < 0.01 (ΔGDT); Fig. WS and ΔGDT displayed a rapid increase for ΔGDPPC < 20,000 and a slow increase thereafter  during 1993 to 2013, we divided countries or regions into three economic categories: underdeveloped countries/regions, developing countries/regions, and developed countries/regions. For sheep, English breeds showed the highest ΔGDΔGDBS −0.45 and ΔGher Fig. . We also10. In this study, we found that the livestock in developing and developed regions have higher heterozygosity than those in underdeveloped regions or some island populations. For example, sheep breeds from Southwestern Europe and Northern China obviously harbor higher observed (HO) and expected (HE) heterozygosity than those from Africa and southwestern China, which belong to underdeveloped regions . Also, GNS (HO = 0.299 and HE = 0.297) and JER (HO = 0.290 and HE = 0.280) breeds from Guernsey and Jersey islands near England harbor lower heterozygosity than breeds from the European continent  has been regarded as a popular parameter to investigate small populations and endangered species. According to the 50/500 rule of thumb16, Ne values under 50 means that the population is likely to face a serious genetic threat after 5 or more generations. This is because small populations cannot easily avoid inbreeding depression. Ne values above 500 means that the population can maintain an evolutionary potential for longer periods of time. However, Frankham et al.17 have proposed a new criterion that Ne values under 100 and above 1,000 are suitable to control inbreeding and maintain high genetic diversity, respectively17. Based on the new criterion, more than half of the breeds for both species in this study are facing the risk of extinction as their Ne values were under 100 , we have identified 10 sheep breeds  and three cattle breeds  with an FROH value above 0.10. In addition, these breeds also showed a large number of runs of homozygosity (NROH ≥ 200)  have a Ne value under 200, but they exhibit high ΔGDBS and positive ΔGDT values  from Europe have longer branches   , our most prominent finding was that breeds from developed regions exhibit higher total genomic diversity than those in underdeveloped and developing regions, and thus, should be given a higher conservation priority  and the Yunnan-Kweichow Plateau of China , where little or no financial support has been provided for local livestock conservation measures, showed extreme negative values of within-breed genomic diversity . Both species are transboundary animals, and many highly productive commercial breeds have been transferred worldwide to upgrade local breeds42. With the rapid economic development and increase in the human population in developing regions, the share of trade in genetic material from developed to developing countries increased from 20% in 1995 to 30% in 200544. Long-term intensive crossbreeding programs using imported exotic breeds have narrowed the diversity of local genetic materials and led to the loss of between-breed genomic diversity in developing regions45. In northern China, the estimates of both ΔGDBS and ΔGDT were negative, suggesting that northern Chinese sheep have suffered severe genetic erosion induced by exotic breeds in the past, which has resulted in the loss of both total and within-breed genomic diversity  should be ranked with high conservation priorities to preserve closed populations that will be capable of coping with future challenging environments or diversified food production. However, breeds with large divergence should also give protection measures to prevent their extinction due to their unique genetic resources and special adaptation to local environment.Another important finding regarding genetic diversity was that breeds in developing regions have been facing the erosion of between-breed genomic diversity  by using the LD-pruned data, and obtained similar results with those not controlling for LD  approved by the Committee for Animal Experiments of the Institute of Zoology, Chinese Academy of Sciences, China.49 or from blood using the General AllGen Kit  following the manufacturer’s instructions.Blood or tissue samples from 783 animals representing 40 native Chinese sheep breeds were collected in this study (Supplementary Table http://www.sheephapmap.org). Details regarding SNP discovery, the design of the ovine array and genotyping procedures for the BeadChips can be found at the following address: http://www.sheephapmap.org/hapmap.php, and in Kijas et al.33 for the SNP50 BeadChip and Anderson et al.50 for the HD SNP BeadChip.Of the 40 Chinese native sheep breeds/populations, 35 breeds .An Ovine SNP50 BeadChip dataset of 74 worldwide-distributed breeds/populations (Dataset III) was retrieved from a previous study51. The SNP quality control measures have been detailed elsewhere, in Kijas et al.52, Miller et al.53, Kijas et al.33 and Lv et al.54. Briefly, quality control was performed according the following criteria: (1) individuals with a call rate >95%, (2) SNPs with a minor allele frequency (MAF) >0.05, (3) SNPs with a >95% genotyping rate, and (4) SNPs with physical locations on autosomes. After removing the SNPs and individuals that failed to meet any of the above criteria, 39,641 SNPs and 1,910 individuals were retained in our working dataset.All of the ovine SNP BeadChip datasets  were merged to apply quality control procedures using Plink v.1.9 software34, which included 53 native cattle breeds across its main distributions in the world . After filtering, there were 37,827 SNPs and 1,031 animals available for further analyses.Bovine SNP50 BeadChip datasets were retrieved from previous publicationsHO) and expected (HE) heterozygosity of each sheep and cattle breed were estimated using Plink v.1.951. They are important within-breed genetic diversity parameters for the conservation priorities in domesticated animals. In addition, FIS was calculated by genepop R package55 based on the observed versus expected number of homozygous genotypes.The observed (Ne) is a well-known parameter in evaluating conservation priorities for a species. Estimates of contemporary Ne were performed with the linkage disequilibrium method using NE ESTIMATOR v.2.1 software56 with the following parameter settings: random mating model and a Pcrit value with 0.05 which as the criterion for excluding rare alleles. In order to insure all loci in the analysis are physically unlinked57, we removed pairwise comparisons with r2 ≥ 0.05 before calculating Ne using Plink v.1.9 software51 (indep-pairwise 100 25 0.05). In addition, we divided the Ne value into three categories: Ne ≤ 100, 100 < Ne < 200, and Ne ≥ 200.Effective population size  required minimum density (-homozyg-density 1000) and (2) number of heterozygotes allowed in a window (-homozyg-window-het 1). In this study, we detected ROH with the minimum length of 500 kb (-homozyg-window-kb 500) and a minimum SNP number of 50 SNPs (-homozyg-window-snp 50). The number (NROH) and average length (LROH) of ROH for each breed were estimated and the NROH and LROH were divided into three categories: NROH ≤ 100, 100 < NROH < 200, and NROH ≥ 200; LROH ≥ 20 Mb, 15 Mb < LROH < 20 Mb, and LROH ≤ 15 Mb. In addition, the inbreeding coefficient based on ROH (FROH) was calculated for each animal according the following formula59:60 and 2,610,000 kb61, respectively. For sheep and cattle, the mean of FROH of each breed were estimated separately. The breed FROH values were divided into three ranges: FROH ≥ 0.10, 0.05 < FROH < 0.10, and FROH ≤ 0.05.Runs of homozygosity (ROHs) were detected using the Plink v.1.9 software62. To avoid the SNPs with high levels of LD distorting the NJ tree, SNP were pruned using Plink v.1.951. For the sheep and cattle dataset, the 39,641 and 37,827 SNPs were pruned to 34,770, and 29,093 SNPs respectively, by application of LD pruning command line–indep-pairwise 100 25 0.25. Subsequently, Reynolds genetic distances63 were calculated by Arlequin 3.5.2.264, and the NJ trees were constructed using SplitsTree 462 and visualized by FigTree v.1.3.165.In order to understand the relationship within and between breeds across each major geographic group, neighbor-joining (NJ) trees were constructed by the software SplitTree 48 has proven to be efficient for defining conservation priorities for domestic animals. Unlike prioritization based on Weitzman diversity measures67, this method considers within-breed genetic variation, which is of great importance for the management of livestock68. The method assumes that there is a metapopulation including n populations with Ni breeding individuals. In the metapopulation, si denotes the average self-coancestry of the Ni individuals of population i; Dii is the average distance between individuals of the ith population; and Dij is Nei’s minimum distance between populations i and j69. The average global coancestry (70:70:WI and GDBI) gives the genetic diversity within populations (T is assumed to be the sum of the genetic diversity within populations (GDWS) and the genetic diversity between populations (GDBS), i.e., GDT = GDWS + GDBS.The method of molecular coancestryWS, ΔGDBS and ΔGDT) was estimated using Metapop v.2.0 software71 when each of the populations was removed from the dataset. The parameter of λ = 1, which indicates equal weight for within- and between-breed diversity, was applied in the Metapop calculations71  were calculated using Metapop v.2.0 software as described above . In order to obtain more accurate results, the average values of the estimates obtained using the two approaches were employed in the following statistical analysis. The average values of ΔGDWS, ΔGDBS and ΔGDT in sheep and cattle were divided into four fractions: <−0.1, −0.1–0, 0–0.1 and >0.1, and then plotted around the world using the software ArcGIS v.10.1  . Because the 40 native Chinese sheep populations originated from 13 provinces, which differ greatly in their economies and employ different breeding and conservation strategies for domestic animals76, the GDP per capita for the provinces, rather than the whole country of China, was collected for the period from 1993 to 2013  in genetic diversity for each breed (or group of breeds) and the economies of the countries (or regions) from which the breeds originated. The average GDP per capita of the countries (or regions) during the years 1993 to 2013 was collected from the World Bank database  and the increased average GDP per capita (ΔGDPPC) for 1993 to 2013 from each country (or region) using the dual-regression approach. The coefficient of determination (R2) and its significance  were calculated using SPSS v.22.0 software79.To evaluate the effect of economic development on the loss or gain of genetic diversity for the breeds, we performed regression analysis between the loss or gain of genetic diversity (within subpopulation, ΔGDThe data sets analyzed in the present study are available from the corresponding author on reasonable request.Supplementary InformationSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5