4
Requisites for Sustained Change
INTRODUCTION
The work of the committee has highlighted the need to attend to some overarching and long-standing issues that, if not more deliberately and fully addressed, will continue to impede progress in genetics and genomics research. The three topics of focus in this chapter—typological thinking, environmental factors, and community engagement—are not an exhaustive list and are not new, but the committee believes addressing them will be paramount to the long-term success of the best practices and implementation of recommendations in Chapters 5 and 6. In recent years, there has been increasing attention to environmental factors and community engagement in genetics research broadly (Green et al., 2020). Researchers in the field, as well as many others, however, have largely overlooked typological thinking, arguably the crux of the matter with regard to the use of descent-associated population descriptors. The consequences of failing to intentionally confront these topics are grave. Recognizing this, the committee has prioritized addressing typological thinking, environmental factors, and community engagement in its proposed framework for transforming the use of population descriptors in genetics and genomics research. The committee trusts that this will accelerate the expansion of current efforts in these critical areas and stimulate the development of new ones.
For each of the topics in this chapter, the committee indicates the guiding principles pertaining to the respective recommendations. These recommendations complement those in Chapter 5, and together they position researchers to lead the transformation of not only how population de-
scriptors are conceptualized and used, but also how genetics and genomics research is implemented and interpreted. The recommendations in Chapter 6 are intended to provide the support system that researchers will need to facilitate this outcome.
TYPOLOGICAL THINKING
Erroneous categorical assumptions are scientifically and ethically detrimental, particularly when applied to studies of human history, identity, variation, and traits and diseases (Lee et al., 2008; Shim et al., 2014; Weiss and Lambert, 2014). There is a pervasive misconception and belief that humans can be grouped into discrete innate categories (Jorde and Wooding, 2004). The illusion of discontinuity between racialized groups has supported a history of typological and hierarchical thinking, which both classify individuals by type—ignoring variation—and rank people by status (for full definitions see Appendix B). These modes of thinking often spill over to other descent-associated population descriptors such as ethnicity and ancestry (Byeon et al., 2021; Fang et al., 2019). The structure of human genetic variation, though, is the result of human population movement and mixing and so is more related to geography than to any racial or ethnic classification (see the section “Features of Human Genome Variation” in Chapter 1) (Jorde and Wooding, 2004; Lewontin, 1972; Manica et al., 2005).
In particular, describing geography with continental-scale labels, such as continental ancestry, can reinforce typological thinking. These labels can bolster the disproven view that the human species can naturally be divided into a small number of groups, akin to races, that are genetically homogeneous within each (Romualdi et al., 2002). Assumptions of homogeneity at the continental level reinforce the myth of original “pure” populations and buttress the belief in a racial hierarchy. A common and long-standing example of typological categories is Blumenbach’s hierarchical taxonomy (Marks, 1995; Painter, 2010), including the term Caucasian, a problematic term which is still frequently used in science and society (Popejoy, 2021). Moreover, the influence of Linnaeus’ system of human classification can still be seen today in the categories used in the U.S. Census (Harawa and Ford, 2009). In providing the following conclusions and three recommendations, the committee intends to draw attention to pervasive aspects of typological thinking and especially problematic terminology. Terms that imply a biological classification of race should not be used. This is not simply a matter of replacing some terms with other, more palatable terms. The objective of the committee was not to provide an improved nomenclature or vocabulary but to challenge misconceptions and push the dialogue forward. Anyone working in this area must think carefully and make judgments with clear rationale as to which population descriptors (or classification schemes) to
use, which labels to use, and why. The question of which descriptors to use is nuanced and addressed in subsequent recommendations in this report. Guidance and tools for making these selections are provided in Chapter 5.
Conclusions and Recommendations
Conclusion 4-1. Race is neither useful nor scientifically valid as a measure of the structure of human genetic variation.
Conclusion 4-2. Using socially constructed groupings indiscriminately in human genetics research can be harmful. Their use reinforces the misconception that differences in social inequities or other factors are caused by innate biological differences and diverts attention from addressing the root causes of those social differences, which compromises the rigor and potential positive effect of the research.
Conclusion 4-3. Current practices in genetics studies often seem to reinforce typological views of human genetic ancestry (e.g., use of continental ancestry groups). Therefore, new models that reflect a more complex and realistic portrait of genetic ancestry are needed (e.g., genetic similarity).
Conclusion 4-4. The requirement to report participant demographics using OMB categories has perpetuated misconceptions or exacerbated typological thinking and can undermine the selection of variables that are most appropriate for a given genomics study.
Conclusion 4-5. Although perhaps useful for some analyses, the concept of genetically differentiated, discrete populations that are static in place and time does not apply to humans. For example, the racial and ethnic categories established by the OMB presume stable, fixed populations (even though the OMB categories themselves have changed over time), which makes them both inadequate and inaccurate for the purpose of representing human genetic variation.
Recommendation 1. Researchers should not use race as a proxy for human genetic variation. In particular, researchers should not assign genetic ancestry group labels to individuals or sets of individuals based on their race, whether self-identified or not.
Recommendation 2. When grouping people in studies of human genetic variation, researchers should avoid typological thinking, including the assumption and implication of hierarchy, homogeneity, distinct categories, or stability over time of the groups.
Recommendation 3. Researchers, as well as those who draw on their findings, should be attentive to the connotations and impacts of the terminology they use to label groups.
- As an example, the term Caucasian should not be used because it was originally coined to convey white supremacy,1 and is often mistakenly interpreted today as a “scientific” term, thus erroneously conferring empirical legitimacy to the notion of a biological white race.
- Another example of a term that should not be used is black race because it wrongly implies the existence of a discrete group of human beings, or race, who could be objectively identified as “black.”
These recommendations invoke the guiding principles of respect, beneficence, equity and justice, and validity and reproducibility. To promote validity through the use of accurate descriptors, the committee does not advocate the use of typological categories, such as the racial and ethnic categories established by the OMB in its Statistical Directive 15, for most purposes in human genomics research (see Chapter 5 for more specific guidance on the use of population descriptors). While the committee recognizes that the use of these categories, such as white or Hispanic, may be required of researchers under certain circumstances (for example, in describing participants in studies receiving U.S. federal funding), the fundamentally sociopolitical origins of these categories make them a poor fit for capturing human biological diversity (OMB, 1997). Even so, the required uses of OMB and other typological categories for certain reporting purposes need not dictate their use as analytical tools in human genomics research.2 If nothing else, the OMB categories are impractical because they change over time in the wake of administrative decisions and cultural shifts.3 Instead, the committee recommends that to evoke respect and beneficence, researchers who identify race as a valid population descriptor in a given study should reflect carefully on (1) the information that racial labels ostensibly provide, (2) whether such information—for example, on exposure to racial discrimination—might be better captured by other data (e.g., self-reports of such experiences), and (3) which labels or groupings would be most useful and
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1 Johann Friedrich Blumenbach (1752-1840) named Europeans “Caucasian” because he felt the most beautiful skull in his collection came from the Caucasus region and was thus a fitting symbol for a superior race (Marks, 1995; Painter, 2010).
2 For a comprehensive study of such “categorical alignment”—e.g., “the merging of social categories from the worlds of medicine, social movements, and state administration”—see Epstein, Steven. Inclusion: The Politics of Difference in Medical Research, University of Chicago Press, 2007.
3 For a longer history of changing U.S. Census racial categories, see Lee (1993) and Liebler et al. (2017).
informative for the study at hand. For example, the “black” racial label would not be useful in a study within the United States, because people who self-identify as “black” have a wide variety of national origins, class, and even linguistic backgrounds (Hamilton, 2019). In addition, this recommendation addresses problems inherent to the use of continental ancestry groupings. To adhere to equity and justice, alternative procedures that do not assume discrete continental ancestries are more valid and conceptually coherent; such alternatives are preferable and have the benefit of not reifying race. In Chapter 5, recommended use for specific population descriptors will be explored in greater detail.
ENVIRONMENTAL FACTORS
Nongenetic factors and contexts must be considered when examining genetic effects. Nongenetic factors—that is, anything that is not captured by inherited DNA variation—include environmental factors. Environmental factors are variable across individuals and include physical, chemical, and biological exposures; behavioral patterns; social context; and “life events,” among others (Glass and McAtee, 2006; Ottman, 1996). Such environmental exposures can act on a phenotype on their own or do so by interplay with genetic effects (Seabrook and Avison, 2010) (see Chapter 2). These environmental effects, whether genome independent or dependent, may be themselves additive or multiplicative to a postulated genetic effect (Hunter, 2005). The critical importance of identifying environmental effects is that they improve or even alter researchers’ understanding of the causal pathways to human genetic disease, thereby curtailing the common practice of assuming genetic causes for unexplained population differences in outcomes (Duello et al., 2021).
Environmental exposures are not always easy to identify or measure. As a shortcut, some epidemiologic and genetics studies have used race or ethnicity as a proxy for the environment without directly measuring specific factors (Benmarhnia et al., 2021; Duello et al., 2021; Evans et al., 2021). This is unfortunate since any two groups vary with respect to both the frequency of genetic variants and pertinent environmental exposures, and just as there are genetic differences between any two humans so are there individual-specific exposures and other environmental differences (Boardman et al., 2013; Johannesson et al., 2011).
In recent times, there have been efforts to understand the “exposome,” representing the total suite of exposures and being the other major contributor to phenotypes besides the genome (Zhou and Lee, 2021). Additionally, the ability to quantify the epigenome, which can also be a measure of past environmental exposures, has improved (Cazaly et al., 2019). Considerable research is needed though to assess the usefulness of epigenetic markers as
specific exposure sentinels. The 2011 National Academies report on precision medicine incorporated the exposome in its integrative knowledge network that provided the foundation for the U.S. Precision Medicine Initiative (NRC, 2011). Exposome studies are still rare and underused in genetics.
Conclusions and Recommendation
Conclusion 4-6. Genetic variation alone is not sufficient to describe or understand human phenotypes; environmental variation should also be examined when feasible.
Conclusion 4-7. Race, ethnicity, and other descent-associated population descriptors are poor proxies for environmental variables in human genetics studies.
Conclusion 4-8. In the absence of measured environmental factors, researchers often wrongly attribute unexplained phenotypic variance between populations to unmeasured genetic differences.
Recommendation 4. Researchers conducting human genetics studies should directly evaluate the environmental factors or exposures that are of potential relevance to their studies, rather than rely on population descriptors as proxies. If it is not possible to make these direct measurements and it is necessary to use population descriptors as proxies, researchers should explicitly identify how the descriptors are employed and explain why they are used and are relevant. Genetics and genomics researchers should collaborate with experts in the social sciences, epidemiology, environmental sciences, or other relevant disciplines to aid in these studies, whenever possible.
These recommendations are supported by the guiding principles of equity and justice, validity and reproducibility, and transparency and replicability. To promote validity and reproducibility, genetics and genomics researchers should collaborate with experts in the social sciences, epidemiology, environmental sciences, or other relevant disciplines if they are unsure about whether or how to include environmental variables in their studies. Transparency and replicability are key if descent-associated population descriptors are deemed necessary as proxies for the environment. In these cases, investigators must be transparent about how the descriptors are being used and why environmental factors were not able to be measured by other means. Equity and justice are evoked when researchers are clear about how and why a descent-associated population descriptor is being used as
a proxy for environment. Such clarification will reduce the likelihood that the descriptor, rather than environment, will be viewed as the causal factor in any identified variance.
If investigators are unable to collaborate with experts who measure environmental effects, they can use existing resources to facilitate incorporation of exposure assessment into their studies. Some examples of these resources include the Human Health Exposure Analysis Resource,4 National Institute of Environmental Health Sciences (Cui et al., 2022), the Centers for Disease Control and Prevention and National Institute for Occupational Safety and Health’s resources on exposome and exposomics,5 PhenX Toolkit,6 and the All of Us Research Program’s survey questions.7 In future studies that collect new data, investigators should collect rich information on environmental exposures and social contexts. Some examples of information to collect may include geospatial data, socioeconomic position, dietary practices, education, and frequency of medical care. Moreover, whenever possible, the spatial and temporal distributions of measured environmental variables should be accounted for in combination with individual characteristics.
COMMUNITY ENGAGEMENT
My primary care clinician refers to me as an African American woman, yet I’ve never had a discussion with her or been asked about my ethnicity. So when I think about the absence of this discussion, I also begin to feel unseen and question what has been missed in the exchanges that I have with my provider because of the lack of having this conversation.
—Julia Ortega, testimony to the committee
in a public session on April 4, 2022
In a sense, it doesn’t really matter what you call it, it matters who is doing the naming and who is in charge of providing agency, who is in control of the data, and who is in governance of that data, and how does that work… So deferring to communities to self-identify their belongingness is…a great step forward.
—Keolu Fox, testimony to the committee
in a public session on April 4, 2022
When we think about what’s really called for and why we cannot abandon this very important project, it’s because we need not only the contribution
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4https://hhearprogram.org/ (accessed January 3, 2023).
5https://www.cdc.gov/niosh/topics/exposome/default.html (accessed January 3, 2023).
6https://www.phenxtoolkit.org/ (accessed January 3, 2023).
7https://databrowser.researchallofus.org/ (accessed January 3, 2023).
of genetic data information from people to research, but from research back to people who can use it.
—Donna Cryer, testimony to the committee
in a public session on April 4, 2022
In this report, a community is considered a group of people who have a common characteristic or are living in the same place. The committee acknowledges that how a community defines itself is dynamic. How researchers and community members define the community has implications for who is included in community engagement efforts. Community engagement can be a challenging and labor-intensive process, requiring resources including time, dedicated funding, and people with the necessary knowledge and skills (CTSA Consortium, 2011). It should be noted that the increased demands of community-engaged research also fall on research participants, potentially increasing the burden on their time.
Community engagement goals will vary depending on the research question, the participating community, and the researchers. Community engagement processes are diverse, and partners can include various organized groups such as agencies, institutions, or individuals (CTSA Consortium, 2011). The process may also be seen as a continuum of community involvement, stemming from study conception to translation and communication of findings, and the specific practices may also vary at different stages of the study, such as during research approval or guidance, sharing information, or consent (CTSA Consortium, 2011). Communities not only vary in how individuals and groups self-identify but also in their preferences for involvement (CTSA Consortium, 2011). Collaboration with individuals or groups begins by working with relevant parties from the community to identify their preferences on how, when, and to what extent they would like to be involved (CTSA Consortium, 2011). From there, researchers and community members can develop an engagement plan for the duration of the research, drawing on existing models and best practices to support community-engaged research (Beaton et al., 2017; Lemke et al., 2022; Minkler and Wallerstein, 2011; Wallerstein and Duran, 2010). Other resources include the National Institutes of Health (NIH) and Centers for Disease Control and Prevention Principles of Community Engagement8 (NIH, 2015), the NIH Tribal Health Research Office,9 and the Patient-Centered Outcomes Research Institute’s engagement assessment tools10 for ways to evaluate engagement at key stages of research (Sheridan et al., 2017). When community engagement is difficult or impossible (as with legacy samples),
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8https://www.atsdr.cdc.gov/communityengagement/pdf/PCE_Report_508_FINAL.pdf (accessed January 3, 2023).
9https://dpcpsi.nih.gov/thro (accessed January 3, 2023).
10 See https://www.pcori.org/engagement/engagement-resources/Engagement-Tool-Resource-Repository/engagement-assessment-tool (accessed January 3, 2023).
then proxy groups, who could be potential interested parties, could be used. Before legacy samples are used or relabeled, it is the researcher’s responsibility to ensure that the samples were collected ethically and that the current related or proxy community groups agree to the use of these descriptors within the research design.
Frequently, descent-associated population descriptors (such as race) are assigned to samples by a researcher or clinician, leaving individuals and communities out of the conversations about what labels are applied to or preferred for their data (Lemke et al., 2022). Community engagement can improve communication, study coordination, and long-term collaborations between researchers and communities for enhancing that research (CTSA Consortium, 2011). Falling short of engaging and understanding communities and the relevant parties can undermine trust and the trustworthiness of research and, importantly, hamper delivery of the research outcomes to the communities whom researchers are trying to serve (Lemke et al., 2022). Effectively engaging communities requires multidisciplinary approaches that draw on expertise in history, sociology, anthropology, communication, and other fields working alongside the study’s primary investigators in genetics and genomics (CTSA Consortium, 2011).
Conclusions and Recommendation
Conclusion 4-9. Community engagement recognizes the expertise of communities and relies on collaboration between researchers and the communities they are trying to serve.
Conclusion 4-10. Engaging participants in genomics research design increases the likelihood that population labels will respectfully describe participants, reduce potential harms, and lead to more beneficial science and translation to health and health care.
Conclusion 4-11. Lack of transparency by researchers threatens the trustworthiness of the entire research enterprise and may undermine goals of equity and justice by disenfranchising minoritized groups from participating.
Conclusion 4-12. Communities are dynamic and changing entities; therefore, with each new study, it is important to consider how the community being asked to participate in research could share in the selection of population descriptors.
Recommendation 5. Researchers, especially those who collect new data or propose new courses of study for a data set, should work in ongo
ing partnerships with study participants and community experts to integrate the perspectives of the relevant communities and to inform the selection and use of population descriptors.
This recommendation supports the guiding principles of respect, beneficence, equity and justice, validity and reproducibility, and transparency and replicability. Equity and justice are invoked by engaging communities in the research process to avoid reproducing hierarchical thinking and to consider biases that may produce inequities. Through partnerships with participants and communities, researchers will gain a better understanding of their perspectives, needs, and expectations, which can foster or enhance a commitment to beneficence, not only pertaining to the research, but also in other meaningful ways. Validity and reproducibility are instituted by including the expertise of communities in determining the valid use of population descriptors and the risks and benefits associated with the research. Researchers should also partner with experts on engagement approaches to ensure community engagement occurs in a culturally sensitive way. Integrating team members who have knowledge and understanding of community perspectives early in study conception and throughout the research process is critical for achieving the goals of trustworthy science.
Respect is demonstrated through the inclusion of the community in the decision-making and study design processes when either collecting new data or using legacy data and by seeking information directly from the community. Although consulting communities about population descriptors is easier in studies collecting new data, it is important to also engage communities when using legacy data. This engagement might look different since the participants cannot be identified; however, proxy groups can be formed to discuss appropriate and preferred terms and usage of descriptors. Using the population descriptor preferences of individual and community participants reflects the principle of respect. However, if deviations from these preferred descriptors occur, respect and transparency must go hand in hand. In this case, transparency and replicability with communities mean communicating and explaining why the selected population descriptors differ from the participants’ preferences, which helps preserve research relationships and allows individuals to make an informed decision about continued participation. Best practices for communicating technical and cultural concepts should be incorporated to increase transparent collaboration. Transparency and clear communication regarding the choices, rationale, and implications about decisions on descent-associated population descriptors promote trust and trustworthiness of the research and researchers. Individuals and communities called upon to participate in research must feel confident that researchers are committed to communicating their process.
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