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. 2020 Jan 29;15(1):e0227399. doi: 10.1371/journal.pone.0227399

Do genetic ancestry tests increase racial essentialism? Findings from a randomized controlled trial

Wendy D Roth 1,2,*, Şule Yaylacı 1,2,3, Kaitlyn Jaffe 2, Lindsey Richardson 2,4
Editor: Mellissa H Withers5
PMCID: PMC6988910  PMID: 31995576

Abstract

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.

Introduction

Since the completion of the Human Genome Project in 2003, Direct-to-Consumer (DTC) genetic ancestry tests (GATs) have become a billion-dollar industry [1] with at least 74 companies selling tests online [2]. For a typical test, an individual submits a genetic sample from a cheek swab or saliva by mail and receives charts linking a particular family line to specific ancestry groups or regions and/or estimating what proportion of their ancestry is purportedly European, sub-Saharan African, Native American, or Asian–categories that map onto popular conceptions of race. The industry has grown exponentially; more than 26 million tests had been sold by 2018, and more people took a test in 2018 than in all previous years combined [3]. There are many critiques of the legitimacy and reliability of GATs [4–6], yet their continued popularity calls for investigation of how these tests affect users’ beliefs about race and their intergroup behaviors.

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 [7–11]. Social scientists have long asserted that race is socially constructed, for instance with classifications and their meaning changing over time and place, even if race refers in part to biological or descent-based characteristics [12,13]. Yet a genetic essentialist view of race, that holds that genes alone determine races and imbue them with distinct and immutable essences that are associated with different skills or abilities [12,13], continues to exist among the general public [13,14]. Genetic essentialism is one type of essentialism which locates the group’s core essence in its genes rather than elsewhere (e.g., in the group’s culture, as in cultural essentialism) [15]. One implication of this view is that discrete biological races exist within the human species and that genetic difference is the root of racial differences in behavior and outcomes [14]. Essentialist views of race have significant negative consequences for intergroup behavior, including less willingness to interact with other races, greater endorsement of racial stereotypes [16], and association with traditional and modern racism [14]. These beliefs have historically led to eugenicist movements, ethnic cleansing, apartheid, and genocide [12,14,17,18].

Some social scientists argue that GATs are likely to reinforce a genetic essentialist view of race [4,17,19,20]. Many people view scientific information as objective and authoritative and may therefore give it more consideration than social or environmental factors in understanding complex concepts [21–24]. The presentation of GAT results in the form of categories that overlap with commonly used racial groups reinforces a view that these tests report about race rather than biogeographical ancestry [24]. The methodology of ancestry testing itself may therefore reify races as essential genetic realities by suggesting that genetic tests can distinguish them [17].

Others have speculated that taking the tests could have the opposite effect by reinforcing belief in the socially constructed nature of race [25–28]. For example, a test-taker who receives evidence of an ancestry that is not part of her social experience of race may question the meaning of racial categories and recognize their foundation in social experience more than biology. Some journalistic accounts depict testing as reinforcing the views that all humans have mixed racial origins and that long-held identities are more than what genes reveal [29–31]. From this perspective, some question whether GATs might have the potential to break down racial categories and social distance [25–27].

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 [17,32,33]. However, the media articles in these studies included clear statements supporting or opposing a genetic basis to race; by contrast, the experience of taking a GAT requires the test-taker to actively interpret complex, personalized results.

In 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 [34,35] and may be more likely to view them as supporting “lay theories” of essential genetic differences between races [36]. We compare changes over time for people with different levels of genetic knowledge in the control and treatment arms. It is also possible that the specific ancestry results people receive–specifically the “confirmation” or “discovery” of new ancestries–influence their belief in racial essentialism. We therefore conduct additional non-experimental analyses within the treatment arm to explore these possibilities.

Overview of study goals and hypotheses

We 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 [37]. The other half received no tests, as our goal was to gauge the effect of taking GATs relative to those who do not. We developed a new scale to measure genetic essentialist beliefs about race, which includes nine items focusing on how people think about the relationship between genes and race, with four-point disagree/agree Likert scale response options. We included the scale items in both the pre-test and post-test surveys [15].

Given aforementioned experimental studies on the impacts of reading media articles about the tests [17,32,33], in primary analysis we hypothesize that taking GATs would increase levels of genetic essentialism. In secondary analyses, we hypothesize that the direction of the tests’ effect on genetic essentialism will depend on the genetic knowledge of the test-taker since basic genetic knowledge is crucial for interpreting the results [34,35,38]. Many White Americans have limited genetic literacy [39]; even those with high educational attainment have difficulty understanding certain aspects of genetic information [40]. Research on health-focused genetic testing shows that many people incorrectly interpret the test results [41,42], and that lower levels of genetic literacy are associated with lower comprehension of results [34,35]. For ancestry tests as well, many test consumers have difficulty translating the information they receive into usable knowledge [1], a fact acknowledged by DTC companies [43]. Admixture test results often include a breakdown of geographic ancestral origins as percentages (S1 Fig), while mtDNA and Y-DNA test results often present a map of historic migration routes (S2 Fig). These results can be interpreted in different ways. For instance, the map depicting humanity as branching off the same origins and spreading across the globe may be perceived as evidence of one human race or as emphasizing distinctions based on where the routes end, depending on genetic literacy. Test-takers without a basic understanding of genetics (e.g. not aware that 99.9% of the human genetic code is identical in every person) are more likely to refer to “lay theories”–genetic explanations for perceived race differences [36]–in their interpretations, and read the biogeographical categories as races, which may foster more essentialist views. We therefore expect test-takers lacking basic genetic knowledge to develop more essentialist views after testing. 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.

In 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 [44]. “Discovering” a new ancestry unknown to respondents may also influence those test-takers’ beliefs in essentialism. Receiving results reporting known or new ancestries may feel like confirmation or discovery to these test-takers, even though the results are easily misunderstood and their interpretations may be incorrect. We expect that those with weaker knowledge of genetics will be more likely to believe that the ancestries reported are their “true” race, increasing essentialism. By contrast, we expect that a stronger knowledge of genetics, and thus a greater ability to interpret the results, is likely to reduce any impact on essentialism that the test’s “confirmation” of an existing identity or reporting of a new ancestry may otherwise have.

Materials and methods

Participants and procedure

Participants 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 [45]. Our target population is not all native-born non-Hispanic Whites, but those who were willing to take GATs and had not previously received personalized genetic ancestry information, a group for which we were unable to find representative data. While we cannot claim that our data represent the target population, we stratified our sampling by gender, age, education, and region based on the national population of native-born non-Hispanic Whites aged 19 and older to improve the demographic diversity of our sample beyond that of a convenience or regional sample. See S1 Appendix for a comparison of our data with existing data representing the larger population of non-Hispanic Whites.

Study 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 (control N = 425; treatment N = 377; Fig 1). The sample is 36.7% male and 63.3% female. For age, 9.9% of participants are age 19–34, 37.3% are age 35–54, and 52.9% are 55 or older. For educational attainment, 10.7% of participants have a high school degree or less, 27.9% have some college education, 28.9% have a college degree, and 32.4% have more than a college education. S3 Table, column 1, shows the sample’s demographic characteristics, while S2 Table, columns 5 and 6 (“Remaining Participants”) shows descriptive statistics for all variables used in analysis separately for the control and treatment groups.

Fig 1. CONSORT flow diagram of study design and response.

Fig 1

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Participants completed an online pre-test survey between October 2014 and February 2015. After that, the treatment group received a test kit by mail with instructions to send a DNA sample to the testing company. We asked control participants not to take GATs and offered them a discount coupon to purchase the same tests at half price after the study. Admixture and mtDNA tests were conducted on the treatment group’s samples and results were e-mailed to them. To ensure treatment participants engaged with the results, we asked them to spend at least 30 minutes reviewing the results online and then take a short “First Reactions” survey, conducted between Jan.-July 2015. A final online post-test survey was conducted between Sept. 2015-Mar. 2016 among all participants. We offered small financial incentives for completing each survey. Participants were invited to complete the post-test survey 11 months after completing the pre-test survey, and approximately eight months after treatment respondents received their results. This ensured that sufficient time had passed for the test-takers to cognitively absorb their results and potentially share the information with others; any observed changes are hence more likely to be lasting effects rather than immediate reactions.

Measures

We operationalized genetic essentialism with a newly-developed scale, the Genetic Essentialism Scale for Race (GESR), using nine survey questions asked in both the pre- and post-test surveys [15]. The items focus on how people think about the relationship between genes and race, and have four-point disagree/agree Likert scale response options (S4 Table). Using exploratory and confirmatory factor analysis, we developed GESR as a second-order construct that incorporates several first-order factors comprising subsets of belief in genetic essentialism (i.e., the belief that races are discrete, immutable categories determined by genes; the belief that genes cause races to have distinct and innate essences associated with different traits, skills, or abilities; and the belief that, rather than sharing common roots, races evolved from two or more distinct ancestry groups) [15]. Our tests showed that the second-order factor, GESR, which incorporated these components into a broader concept of genetic essentialism had high validity and reliability (Composite Reliability (CR) = 0.838 and Average Variance Extracted (AVE) = 0.635; the thresholds for these measures are > = 0.7, and > = 0.5, respectively, and convergent validity is achieved when CR > AVE [46–48]), whereas the individual first-order factors, representing only parts of the concept, did not perform as well. We also confirmed the construct validity of the GESR, hypothesizing and confirming that the higher the GESR, the higher the opposition to affirmative action policies. A detailed description of the development and validation of the GESR scale has been published previously [15]. The Cronbach's alpha coefficients for the 9 items in the scale for the current sample were 0.70 in the pre-test and 0.71 in the post-test. A lower score on the scale is consistent with a social constructionist view of race.

While there are different facets of genetic literacy, we focused on the factual knowledge dimension which is highest in the knowledge hierarchy of genomics [49] and most pertinent to the cognitive steps of moving from reading results to forming attitudes. To measure basic genetic knowledge, we used two pre-test survey questions drawn from the Survey on Genomics Knowledge, Attitudes and Policy Views (GKAP) [50]: 1) “Based on what you know, would you say that DNA can be found in every cell in the human body or only in specific organs and cells in the human body?” 2) “Based on what you know, would you say that more than half, about half, or less than half of a human being’s genes are identical to those of a mouse?” (See S1 Appendix & S3 Table for comparison of our sample’s responses to those of GKAP respondents). The GKAP and our survey also included a third item: “Based on what you know, would you say that more than half, about half, or less than half of a White person’s genes are identical to those of a Black person?” We excluded this item from this analysis because of its association with racial essentialism. However, models that we ran which included this item showed similar results to those presented in the paper.

Because the questions vary in difficulty (91.86% answered the first correctly, 32.87% for the second), 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 (“about half”), and zero points for other responses, including “Don’t Know” which we treated as equivalent to “No Knowledge” (S5 Table). After applying these weights, we constructed the genetic knowledge scale by adding individuals’ response scores. This produced a raw scale with six points; however, two of the six points in the range had very few cases in them. We recoded these with the closest category, which yielded four effective categories. We use this variable as a 4-category ordinal measure of genetic knowledge, ranging from “No Knowledge” to “High Knowledge.” While this measure best fits the pattern of responses to the genetic knowledge questions, we were concerned that our findings might not be robust due to the sample sizes within these categories (in particular, the ‘no knowledge’ group has 31 and 21 cases in the control and treatment groups respectively). We therefore also created an alternative dichotomous variable that grouped ‘no/low knowledge’ and ‘medium/high knowledge’ together (see S6 Table). We ran sensitivity analyses using this dichotomous version of the measure as well.

For 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 [51], we asked them to list all their ethnic identities and also all the ancestral origins they knew of for each biological parent. We examined whether their admixture results “confirmed” any ancestry listed for themselves or their parents, and whether they reported any new ancestry that was not listed for themselves or their parents (see S1 Appendix for discussion of coding).

Analyses

Analyses were conducted using Stata (Version 14.2; StataCorp) and SPSS Statistics (Version 25; IBM). We examined pre-treatment equivalence across study arms and assessed attrition patterns for systematic difference relevant to our hypotheses (presented in S1 Appendix). Following this, our first set of analyses uses linear mixed-effects models, to examine the causal effect of taking GATs on genetic essentialism, comparing control and treatment groups. In the text, we present our findings as average predicted probabilities of genetic essentialism estimated by the linear mixed-effects models (LMM). We supplement these analyses with Ordinary Least Squares (OLS) regression.

Next, using the same techniques, we conducted secondary analysis examining within-group changes in genetic essentialism for control and treatment groups, subdividing participants by baseline genetic knowledge to determine whether such knowledge was associated with changes in genetic essentialism. Our second set of analyses uses the 4-category ordinal measure of genetic knowledge, while our third set is a sensitivity analysis using the dichotomous measure of genetic knowledge.

Finally, our fourth set of analyses presents non-experimental results focusing only on the treatment group to examine the effect of specific GAT results on genetic essentialism, while controlling for and interacting test results with baseline genetic knowledge. We used OLS regression to examine the effects of the admixture test results 1) “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 S1 Appendix.

Results

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 (Fig 2). The predicted probability of the control group’s genetic essentialism score increases by 0.005 points while that of the treatment group decreases by 0.006, yet neither difference is statistically significant (p = 0.379 and p = 0.322, respectively). That is, contrary to our hypothesis, taking GATs did not impact genetic essentialist views on average.

Fig 2. Pre-test and post-test genetic essentialism for control and treatment groups (N = 794).

Fig 2

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This graph plots the average marginal change in genetic essentialism scores of both the control and treatment groups. The pre-test genetic essentialism scores of the control and treatment groups are 0.468 and 0.469 respectively. The post-test genetic essentialism scores are 0.473 for the control group and 0.463 for the treatment group. We used Stata’s “margins” command to produce the graph.

However, 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 Fig 3. Specifically, in our analyses using the 4-category ordinal measure of genetic knowledge, the no knowledge and high knowledge groups show contrasting changes between pre- and post-test in the treatment group but not in the control group. In the LMM, control respondents with high knowledge of genetics did not exhibit a significant change in the average predicted probabilities of their essentialism scores (Δ = -0.015; p = 0.157) and, similarly, control group respondents with no genetic knowledge did not show a significant change (Δ = 0.013; p = 0.543). Conversely, the average predicted probabilities for the treatment respondents with high knowledge of genetics declined significantly after taking the test (Δ = -0.040; p < 0.001). Treatment respondents with no knowledge of genetics on our scale showed an increase in their predicted essentialism scores after taking the test (Δ = 0.058; p = 0.026). That is, among participants who took genetic tests, belief in genetic essentialism decreased among those with high genetic knowledge and increased among those with no genetic knowledge (S9 Table). OLS regression models further confirmed these interactions by showing that the difference between the treatment effect of taking GATs on respondents with no genetic knowledge (β = 0.086) and with high genetic knowledge (β = -0.032) was statistically significant (S10 Table).

Fig 3. Pre-test and post-test genetic essentialism by genetic knowledge (4-category ordinal) for control (N = 419) and treatment groups (N = 375).

Fig 3

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In LMM models, the predicted average essentialism scores of control respondents with low genetic knowledge also increased (Δ = 0.018; p = 0.035), while there was no significant predicted change among treatment respondents with low knowledge (Δ = 0.004; p = 0.667). 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 (Δ = -0.025; p = 0.007), while control respondents with higher knowledge did not show a significant change (Δ = -0.010; p = 0.280) (S11 Table and S3 Fig). There is a significant increase in the predicted average essentialism scores of control respondents with lower genetic knowledge (Δ = 0.017; p = 0.030), while there is no significant predicted change among treatment respondents with lower knowledge (Δ = 0.009; p = 0.265).

In 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 (S7 Table) and findings from OLS regressions (S12–S14 Tables) are shown in the supplementary information. Most respondents (96%) listed a specific European ancestry, and 92.3% “confirmed” a known European ancestry with the test results. Fewer (15.9%) listed a specific non-European ancestry but only 1.1% “confirmed” it. A substantial majority received results reporting a new European ancestry (91%), while 61% “discovered” a new non-European ancestry (S7 Table).

However, 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 S13 Table, Model 1 (controlling for demographic variables) & Model 2 (controlling for demographic variables and genetic knowledge), this effect disappears once interactions between “confirmed” non-European ancestry and genetic knowledge are added in Model 3, reinforcing the role of genetic knowledge identified above. Further, the small number of treatment respondents “confirming” non-European ancestry does not offer confidence that this is a meaningful effect. Neither “confirmation” of a known ancestry (S12 and S13 Tables) nor “discovery” of a new one (S14 Table) from an admixture test significantly influenced genetic essentialism, supporting our hypothesis about null main effects. However, our hypothesis of an interaction between these test results and genetic knowledge was not supported.

Discussion

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 [52], while progressive White people may seek racial mixture [24], approaching these tests as “methods for performing racial harmony and assuaging white guilt” [53]. Without randomization, such motivations from test-takers would bias the findings of the tests’ effects. Further, a control group permits us to test internal validity; amidst growing media exposure about GATs and the genomic revolution [17,33], increases in genetic essentialism overall could reflect general changes in public views over time rather than the testing experience.

While 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 [1]. Indeed, treatment participants often expressed confusion over how to make sense of test results, both immediately after viewing them and in the post-test survey. In open-ended comments, one respondent wrote: “I need narrative results, not just a chart with numbers. I consider myself pretty intelligent but honestly the numbers and results made very little sense to me.” Others noted it would take too much time to read up and fully understand them. Individuals who lack a foundation in basic genetics may be less likely to critically evaluate the tests’ limitations. With admixture tests’ biogeographical categories mapping onto commonly used racial groups, the tests appear to tell test-takers their race. This superficial interpretation may be the default for those who lack the required knowledge to engage with the results and not make erroneous inferences.

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 (shown on the left axis of panels in Fig 3). For those with high genetic knowledge, the testing experience provides an opportunity to reflect critically on what these tests purport to show. Taking a test thus has a polarizing effect, magnifying differences in essentialist beliefs even further between those with weaker and stronger understandings of the science behind them.

Despite the low number of observations of test-takers with very limited genetic knowledge, we believe the observed increase in genetic essentialism is important to report and should be followed up. As the first RCT in the area of genetic ancestry testing and genetic essentialism, results from this study are valuable both for the robust significant findings they provide and also in shaping an agenda for future research. We hope that the observed contrast between the ‘no knowledge’ and ‘high knowledge’ groups will instigate further research, and a sample with a greater number of observations with limited genetic knowledge can further test whether limited genetic knowledge plays a magnifying role in increasing genetic essentialism as a result of taking GATs. We also believe this study provides a rationale for future RCTs that randomize test-takers based on differing levels of genetic knowledge. Our secondary analysis provides indication of potential moderation effect by genetic knowledge. A formal moderation analysis in future research might confirm this signal of an effect.

There are other limitations to our findings. First, the study described here focused only on native-born White Americans. Furthermore, because our target population was people willing to take GATs, it does not represent all native-born American Whites; however, large proportions of people state their willingness to take free GATs [54]. Second, the questions that tested genetic knowledge were limited in number. As our findings suggest that genetic knowledge may play an important role as a potential moderator of GATs’ effect on essentialism, future researchers will benefit significantly from incorporating more developed scales of genetic knowledge, such as that developed by Jallinoja and Aro [55]. We expect that future studies incorporating such scales will show an even more robust effect than that found here. Third, we used one company for the tests, while DTC companies differ in the way their tests are analyzed, presented, and marketed, as well as their willingness to interpret individual findings or provide genetic counselling [56,57]. However, we note that among the largest DTC companies, presentation and interpretation services are comparable, and we expect that genetic literacy will be a major mediator in interpreting the results in all DTC companies that do not offer genetic counselling.

This is a study of a randomized sample–to eliminate the effect of consumers’ motivations for buying the tests. Our findings do not necessarily apply to the self-selected people who currently purchase GATs, but speak to reactions of average individuals who are willing to take the tests but do not buy them. Yet sizable numbers of people receive this information without seeking it. Many receive tests as gifts or take them at a relative’s request. Others are primarily interested in health-related tests and receive genetic ancestry information incidentally. Others are tested for television programs, company promotions, or research [45,58]. Personal genomic information may become more widely available on a societal level [25] and if so, our findings may be relevant for an even larger population.

Qualitative research shows that those who purchased the tests to aid their genealogical pursuits were highly motivated to make sense of their results and devoted considerable time to learning the science of population genetics. In contrast, those who purchased the tests for other reasons–to uncover or confirm ancestral identities or out of curiosity–typically were less motivated to learn about the science behind them and sometimes misinterpreted their results [24]. Thus, even those who purchase the tests may vary in their levels of genetic knowledge, understanding of the results, and motivations to educate themselves.

Another important avenue for future research is to determine whether actively increasing test-takers’ genetic literacy will reduce their belief in genetic essentialism, even for those initially holding essentialist beliefs. We examined the impact of higher and lower starting points in genetic knowledge, but did not manipulate genetic knowledge itself, for example by teaching respondents more about genetics. People with higher pre-test genetic knowledge may also have other characteristics that contribute to the testing experience’s reduction in essentialism such as openness to new ideas or stronger critical thinking. Yet, just as teaching adolescents scientifically accurate information about genetic variation between and within races can reduce their racial biases [59], increasing any test-takers’ genetic knowledge may also help them put their test results in context. Such a finding might imply that educational materials or online genetics modules for future test-takers could help prevent these tests from advancing historically destructive views.

The popularity of GATs has generated considerable debate over how taking these tests will affect popular belief in racial essentialism. Some speculated that taking the tests would increase essentialist beliefs while others argued that the testing experience might decrease essentialism. We found that both processes appear to be relevant simultaneously, for different respondents. Because sub-group effects in different directions can cancel each other out, examining average effects alone risks drawing the mistaken conclusion that these tests do not affect essentialism. This study also points to the importance of respondents’ genetic knowledge in the testing process. Genetic ancestry testing appears to polarize test-takers by strengthening the beliefs about racial essentialism that they already held. Given the significant negative repercussions of essentialist beliefs about race [12–14,16–18], the reinforcement of those beliefs–even for some test-takers–should be a cause for concern.

Supporting information

S1 Appendix

(DOCX)

Click here for additional data file. (59.2KB, docx)
S1 Fig. Example of admixture test results.

Figure Credit: Family tree DNA.

(DOCX)

Click here for additional data file. (449.7KB, docx)
S2 Fig. Example of mtDNA test results, haplogroup migration map.

Figure Credit: Family tree DNA.

(DOCX)

Click here for additional data file. (418.5KB, docx)
S3 Fig. Pre-test and post-test genetic essentialism by genetic knowledge (dichotomous) for control (N = 419) and treatment groups (N = 375).

This 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. (1.7MB, tif)
S1 Table. Odds ratios of remaining in study (N = 995a).

(DOCX)

Click here for additional data file. (19.7KB, docx)
S2 Table. Baseline characteristics of all participants, those lost to follow-up, and those remaining in the study.

(DOCX)

Click here for additional data file. (20.9KB, docx)
S3 Table. Comparison of study sample to American Community Survey (ACS) sample and survey on Genomics Knowledge, Attitudes and Policy views (GKAP) sample.

(DOCX)

Click here for additional data file. (21KB, docx)
S4 Table. Genetic Essentialism Scale for Race (GESR).

(DOCX)

Click here for additional data file. (12.8KB, docx)
S5 Table. Genetic knowledge questions, frequencies, and point values (N = 802).

(DOCX)

Click here for additional data file. (13.5KB, docx)
S6 Table. Genetic knowledge scale distribution in raw scores, 4-category ordinal measure, and dichotomous measure.

(DOCX)

Click here for additional data file. (14.5KB, docx)
S7 Table. Descriptive statistics for treatment respondents’ test result variables (N = 377).

(DOCX)

Click here for additional data file. (12.7KB, docx)
S8 Table. Mixed model results showing pre- and post-test differences in genetic essentialism between the control and treatment groups, disaggregated by genetic knowledge categories.

(DOCX)

Click here for additional data file. (14.1KB, docx)
S9 Table. Pairwise contrasts of genetic essentialism scores across the two waves for groups with different genetic knowledge levels (4-category ordinal measure) (N = 794).

(DOCX)

Click here for additional data file. (13.2KB, docx)
S10 Table. OLS regression on post-test genetic essentialism (N = 794).

(DOCX)

Click here for additional data file. (17.3KB, docx)
S11 Table. Pairwise contrasts of genetic essentialism scores across the two waves for groups with lower and higher genetic knowledge levels (dichotomous measure) (N = 794).

(DOCX)

Click here for additional data file. (14.9KB, docx)
S12 Table. OLS regression of European ancestry “confirmed” on treatment respondents’ post-test genetic essentialism (N = 360).

(DOCX)

Click here for additional data file. (16.8KB, docx)
S13 Table. OLS regression of non-European ancestry “confirmed” on treatment respondents’ post-test genetic essentialism (N = 60).

(DOCX)

Click here for additional data file. (16.9KB, docx)
S14 Table. OLS regression of “discovery” of ancestry on treatment respondents’ post-test genetic essentialism (N = 375).

(DOCX)

Click here for additional data file. (15.9KB, docx)

Acknowledgments

The authors would like to thank William C. Carlquist, Marcella Chan, Mesmin Destin, Meredith Dost, Greg Duncan, Steven Heine, Torsten Heinemann Voigt, Jennifer Hochschild, Arne Kalleberg, Catherine Lee, Ann Morning, Alondra Nelson, Ian Tietjen, Jay Van Bavel, and the Russell Sage Foundation.

Data Availability

The data used to create this analysis are available at this link in the Harvard Dataverse: https://doi.org/10.7910/DVN/DZ099T

Funding Statement

This research was funded by grants from the Social Sciences and Humanities Research Council of Canada (#435- 2014-0467; WR), the Canada Foundation for Innovation (#23744; WR), and a UBC Killam Faculty Research Fellowship (WR). Kaitlyn Jaffe is supported by a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research and by the University of British Columbia Public Scholars Initiative. Lindsey Richardson is supported by a Michael Smith Foundation for Health Research Scholar award and Canadian Institutes of Health Research New Investigator (MSH 217672) and Foundation (FDN-154320) awards.” The funders and Family Tree DNA had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Mellissa H Withers

21 Oct 2019

PONE-D-19-21371

Do genetic ancestry tests increase racial essentialism? A randomized controlled trial shows it depends on genetic knowledge

PLOS ONE

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Reviewer #2: Yes

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5. Review Comments to the Author

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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 (i.e. openness to new ideas or stronger critical thinking).

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 (eg, sex). 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 (eg, Fig 3), except when referring to Supplemental tables, when they list the table number first (eg, S5 Table). The Supplemental Tables should follow the same pattern as the figures and tables in the main text (eg, Table S5 or Supplementary Table 5).

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Reviewer #1: No

Reviewer #2: Yes: Melinda C. Aldrich, Victoria L. Martucci

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PLoS One. 2020 Jan 29;15(1):e0227399. doi: 10.1371/journal.pone.0227399.r002

Author response to Decision Letter 0

19 Nov 2019

We 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 Effect

Reviewers 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 (Δ = -0.025; p=0.007), while control respondents with higher genetic knowledge did not show a significant change (Δ = -0.010; p=0.280) (S11 Table and S3 Fig). 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 (Δ = 0.009; p=0.265), 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 (lines 230-237; 257-258; 280-318; 369-373; 399-410). We have also added more information about these considerations in the supplement under the ‘Genetic Knowledge Variables’ section (p.5).

Representativeness of Sample

Reviewer 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 (lines 103-104; 150-152; 155-162). 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 Design

Reviewer 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.

Measures

Both 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 Writing

In 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/Analyses

Based 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 (i.e., testing the effects of specific test results on genetic essentialism controlling for genetic literacy) 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/Table

The 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 Concerns

We 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 (line 213, citation 50).

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’ Comment

We 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).

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Decision Letter 1

Mellissa H Withers

19 Dec 2019

Do genetic ancestry tests increase racial essentialism? Findings from a randomized controlled trial

PONE-D-19-21371R1

Dear 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.

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With kind regards,

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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 addressed

Reviewer #2: All comments have been addressed

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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: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The 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.

Reviewer #1: Yes

Reviewer #2: (No Response)

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please 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. (Please upload your review as an attachment if it exceeds 20,000 characters)

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.

Acceptance letter

Mellissa H Withers

3 Jan 2020

PONE-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.

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on behalf of

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Appendix

    (DOCX)

    Click here for additional data file. (59.2KB, docx)
    S1 Fig. Example of admixture test results.

    Figure Credit: Family tree DNA.

    (DOCX)

    Click here for additional data file. (449.7KB, docx)
    S2 Fig. Example of mtDNA test results, haplogroup migration map.

    Figure Credit: Family tree DNA.

    (DOCX)

    Click here for additional data file. (418.5KB, docx)
    S3 Fig. Pre-test and post-test genetic essentialism by genetic knowledge (dichotomous) for control (N = 419) and treatment groups (N = 375).

    This 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. (1.7MB, tif)
    S1 Table. Odds ratios of remaining in study (N = 995a).

    (DOCX)

    Click here for additional data file. (19.7KB, docx)
    S2 Table. Baseline characteristics of all participants, those lost to follow-up, and those remaining in the study.

    (DOCX)

    Click here for additional data file. (20.9KB, docx)
    S3 Table. Comparison of study sample to American Community Survey (ACS) sample and survey on Genomics Knowledge, Attitudes and Policy views (GKAP) sample.

    (DOCX)

    Click here for additional data file. (21KB, docx)
    S4 Table. Genetic Essentialism Scale for Race (GESR).

    (DOCX)

    Click here for additional data file. (12.8KB, docx)
    S5 Table. Genetic knowledge questions, frequencies, and point values (N = 802).

    (DOCX)

    Click here for additional data file. (13.5KB, docx)
    S6 Table. Genetic knowledge scale distribution in raw scores, 4-category ordinal measure, and dichotomous measure.

    (DOCX)

    Click here for additional data file. (14.5KB, docx)
    S7 Table. Descriptive statistics for treatment respondents’ test result variables (N = 377).

    (DOCX)

    Click here for additional data file. (12.7KB, docx)
    S8 Table. Mixed model results showing pre- and post-test differences in genetic essentialism between the control and treatment groups, disaggregated by genetic knowledge categories.

    (DOCX)

    Click here for additional data file. (14.1KB, docx)
    S9 Table. Pairwise contrasts of genetic essentialism scores across the two waves for groups with different genetic knowledge levels (4-category ordinal measure) (N = 794).

    (DOCX)

    Click here for additional data file. (13.2KB, docx)
    S10 Table. OLS regression on post-test genetic essentialism (N = 794).

    (DOCX)

    Click here for additional data file. (17.3KB, docx)
    S11 Table. Pairwise contrasts of genetic essentialism scores across the two waves for groups with lower and higher genetic knowledge levels (dichotomous measure) (N = 794).

    (DOCX)

    Click here for additional data file. (14.9KB, docx)
    S12 Table. OLS regression of European ancestry “confirmed” on treatment respondents’ post-test genetic essentialism (N = 360).

    (DOCX)

    Click here for additional data file. (16.8KB, docx)
    S13 Table. OLS regression of non-European ancestry “confirmed” on treatment respondents’ post-test genetic essentialism (N = 60).

    (DOCX)

    Click here for additional data file. (16.9KB, docx)
    S14 Table. OLS regression of “discovery” of ancestry on treatment respondents’ post-test genetic essentialism (N = 375).

    (DOCX)

    Click here for additional data file. (15.9KB, docx)
    Attachment

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    Attachment

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    Data Availability Statement

    The data used to create this analysis are available at this link in the Harvard Dataverse: https://doi.org/10.7910/DVN/DZ099T

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