3

Guiding Principles

INTRODUCTION

The committee identified guiding principles intended to foster the highest ethical and empirical practices and to support trustworthy research to inform its recommendations. For scientific research to be trustworthy, it must be both ethically conducted and empirically valid (Beskow et al., 2021; Emanuel et al., 2000; Goering et al., 2008; NASEM, 2019); both aspects of trustworthiness reinforce each other. Indeed, as discussed in Chapters 1 and 2, the history of racialization and typological thinking about differences between human groups in genetics research produced unethical and invalid findings, which hindered the advancement of the science. The guiding principles discussed here aim to achieve these requirements of trustworthy research. Each recommendation in this report is motivated by at least one guiding principle and reflects commitments necessary from the scientific enterprise. In creating its recommendations and best practices, the committee has identified a range of studies and research contexts in which each would be applied. However, the research enterprise is dynamic, and it is impractical for this report to fully capture the range of possible use cases in future genetics and genomics research as technologies and perspectives change. Thus, the guiding principles provide a foundation and common vocabulary for interested parties to engage in future decision making for contexts that may not be addressed directly in this report.

The guiding principles outlined here address the ethical responsibilities of respect, beneficence, equity, and justice, as well as scientific standards of validity, reproducibility, transparency, and replicability. These principles together operate to build trust among researchers and the many relevant par-

ties in the genomics research ecosystem. It is especially important to foster trust between researchers and study participants and with the general public (Beskow et al., 2021; Faure et al., 2021). By identifying these principles, the committee emphasizes ethical integrity and scientific best practices as foundational to the selection of population descriptors in human genetics and genomics research.

PRINCIPLES

Respect

Respect for individual and community preferences, norms, and values should inform approaches when determining population descriptors. Respect begins with the recognition that a person has the right to make decisions as an autonomous individual and is deserving of dignity (Lee, 2021). Acknowledging the important role of communities, the committee extends this principle to groups who have a stake in how they are characterized in genetics research. This principle of respect requires protecting the autonomy of individuals and communities in determining what data are collected and how data are characterized (Lee, 2021). For example, individuals and groups may prefer descriptors that reflect multiple and/or situationally specific identities. Respectful research requires engaging, understanding, and acting upon the perspectives, preferences, and lived experiences of individuals and communities (Goering et al., 2008; Lee, 2021). Respect should also address the desire among participants for nondisclosure of data and/or the possibility of an exit from studies (Beskow et al., 2021; Emanuel, et al., 2000). Trustworthiness of research depends on investigators demonstrating respect toward participants throughout the life course of a study, including study design, informed consent, managing samples, protecting private information, data safety monitoring, and dissemination (Beskow et al., 2021). Researchers and research institutions should explore strategies that can promote trustworthiness through collaboration and accountability mechanisms (see the section “Community Engagement” in Chapter 4).

Beneficence

Trustworthy and equitable science requires that research produces benefit for individuals, communities, and the public as well as promotes human dignity, although such benefits may not be immediate nor be tangible to the participant (Beskow et al., 2021; Emanuel et al., 2000). The principle of beneficence calls on researchers to assess how the selection of population descriptors may generate not only potential good but also potential harm. Fulfilling goals of beneficence requires consideration of the needs and inter-

ests of all relevant parties, including researchers, participants, communities, and the public at large (Claw et al., 2018; Lee, 2021). When considering the use of population descriptors, researchers have an obligation to use labels and methods that benefit individual participants and communities. Researchers must also avoid potential negative effects on groups, such as stigma, discrimination and exacerbation of racial and ethnic inequities, reinforcement of hierarchical and typological thinking, and inequitable distribution of benefits (Beskow et al., 2021; de Vries et al., 2012; Emanuel et al., 2000; Martin et al., 2022).

Equity and Justice¹

Decisions about population descriptors should recognize the structural inequities in society, the histories of exploitation and abuse of politically marginalized groups participating in genetics research, and the effect and potential harm that research and the labels used will have on these groups. A commitment to justice and the goal of equity requires that researchers avoid reproducing hierarchical thinking embedded in the historical use of classification systems such as race in science. This commitment requires addressing whom researchers choose to study (sampling biases) and confronting the power imbalances in science, which have contributed to the lack of participation of historically marginalized groups (Martin et al., 2022; Mills and Rahal, 2020) and the disrespect of those who participate. Embedded within these commitments is a responsibility to ensure representative sampling wherever possible to answer specific research questions or achieve particular research goals.

A commitment to equity and justice, though, should be more than assessing representation in research participation; it should include addressing inequities in the scientific workforce and who is conducting research on whom, and their effects on the use of population descriptors in genetics research. Researchers should strive toward engaging communities early in the research process and to consider historical, political, and societal biases that may inform their descriptions. Researchers should avoid procedures that privilege particular viewpoints at the exclusion of those who have been historically marginalized and disempowered (Goering et al., 2012). In addition, a commitment to equity and justice requires an assessment of whether and how the selection and use of population descriptors will

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¹ Equity “recognizes that each person has different circumstances and allocates the exact resources and opportunities needed to reach an equal outcome. Equity is a solution for addressing imbalanced social systems” (GWU, 2020). “Justice, or social justice, is the view that everyone deserves to enjoy the same economic, political, and social rights, regardless of race, socioeconomic status, gender, and other characteristics” (Begg, 2021).

produce equitable benefits and avoid reinforcing existing inequities or introducing new ones.

Validity and Reproducibility

Fulfilling the principle of validity requires judicious evaluation of research objectives, the use of resources, and potential risks and benefits of research. A study is valid if it is designed to yield reproducible data (NASEM, 2019), whereas studies that are poorly designed to answer research questions are scientifically invalid and unethical (Emanuel et al., 2000; NASEM, 2019). Researchers should be intentional in selecting population descriptors that will answer specific research questions. This will require assessing the appropriateness and purpose of including population descriptors for the type of study being conducted. Assessment of the validity of the use of population descriptors as well as the risks and benefits associated with the research should include the expertise of the communities involved (Claw et al., 2018). In addition, the principle of validity requires that study design and decision making on population descriptors enable reproducibility by independent researchers. For example, providing detailed definitions and descriptions of methodologies for selecting and applying population descriptors will support reproducibility of study findings across studies and the potential for accurately understanding study results.

Transparency and Replicability²

The principle of transparency includes the obligation to provide clear rationale for the selection and use of population descriptors and to explain decision-making processes in an open and accessible manner. This includes articulation of the conceptual assumptions and operational considerations that lead to the adoption of population descriptors. For example, procedures for achieving relative “homogeneity” in a data set or procedures of “binning” that combine data should be clearly described, including a rationale of why they are necessary. Such transparency is critical for rigor and replicability (NASEM, 2019). Articulation of methodological logic in a comprehensible manner can further empirical validation. In addition, participants in research should reasonably expect that researchers will communicate research goals and processes (Claw et al., 2018) and explain the selection of population descriptors used to describe them. Thus, adhering to the principle of transparency supports the principle of respect of individuals

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² Replicability refers to obtaining consistent results across studies, as compared to reproducibility, which refers to obtaining consistent results from the same data set (NASEM, 2019).

and communities and their ability to assert autonomy and informed decision making about participation in research.

SYNERGY AMONG AND TENSION BETWEEN GUIDING PRINCIPLES

These principles reflect values and goals that are overlapping, intertwined, and mutually reinforcing. All of the principles aim at a unified goal of engaging in scientifically valid and trustworthy research. Researchers should aim to achieve affinity among the principles. Nevertheless, tension between principles may arise when researchers prioritize competing interests. For example, group preferences for population descriptors may be in conflict with goals of reproducibility in which researchers use alternative descriptors to maximize data aggregation or harmonization (Lee et al., 2019). In such cases, researchers should make explicit their rationale for selecting specific population descriptors and assess how these decisions affect principles of responsible research and their potential effect on trustworthiness and equity. Ultimate decisions about the use of population descriptors may vary depending on the specific context of the research, responsiveness to community preferences, and evolving best practices over time. This concept will be explored further in Chapter 5.

The committee encourages all relevant parties, including researchers, institutions, funders, professional organizations, journals, media, and communities, to assess and engage in the discussion of how practices contribute to synergies among, and tensions between, guiding principles. The committee underscores the importance of collaboration when resolving competing interests. Power imbalances inherent in research, and research institutions, and the potential vulnerability of individuals and groups enrolled in research create challenges for equitable partnership and can undermine the trustworthiness of science (Faure et al., 2021; Parker and Kingori, 2016; Powers and Faden, 2019). Researchers should employ these guiding principles and explore strategies, such as community engagement, to support shared decision making about population descriptors.

REFERENCES

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Emanuel, E. J., D. Wendler, and C. Grady. 2000. What makes clinical research ethical? JAMA 283(20):2701-2711.

Faure, M. C., N. S. Munung, N. A. B. Ntusi, B. Pratt, and J. de Vries. 2021. Mapping experiences and perspectives of equity in international health collaborations: A scoping review. International Journal for Equity in Health 20:28.

Goering, S., S. Holland, and K. Fryer-Edwards. 2008. Transforming genetic research practices with marginalized communities: A case for responsive justice. Hastings Center Report 38(2):43-53.

GWU (George Washington University). 2020. Equity vs. equality: What’s the difference? https://onlinepublichealth.gwu.edu/resources/equity-vs-equality/ (accessed December 6, 2022).

Lee, S. S.-J. 2021. The ethics of consent in a shifting genomic ecosystem. Annual Review of Biomedical Data Science 4(1):145-164.

Lee, S. S.-J., S. M. Fullerton, A. Saperstein, and J. K. Shim. 2019. Ethics of inclusion: Cultivate trust in precision medicine. Science 364(6444):941-942.

Martin, A. R., R. E. Stroud II, T. Abebe, D. Akena, M. Alemayehu, L. Atwoli, S. B. Chapman, K. Flowers, B. Gelaye, S. Gichuru, S. M. Kariuki, S. Kinyanjui, K. J. Korte, N. Koen, K. C. Koenen, C. Newton, A. M. Olivares, S. Pollock, K. Post, I. Singh, D. J. Stein, S. Teferra, Z. Zingela, and L. B. Chibnik. 2022. Increasing diversity in genomics requires investment in equitable partnerships and capacity building. Nature Genetics 54(6):740-745.

Mills, M. C., and C. Rahal. 2020. The GWAS diversity monitor tracks diversity by disease in real time. Nature Genetics 52:242-243.

NASEM (National Academies of Sciences, Engineering, and Medicine). 2019. Reproducibility and replicability in science. Washington, DC: The National Academies Press.

Parker, M., and P. Kingori. 2016. Good and bad research collaborations: Researchers’ views on science and ethics in global health research. PLoS ONE 11(10):e0163579.

Powers, M., and R. Faden. 2019. Structural injustice: Power, advantage, and human rights. Oxford, UK: Oxford University Press.