About

As the (re)production of group harm from biorepository researchers often has multiple contributing factors, the CHIRON toolkit intervenes at several points in the research lifecycle. While group harm is rarely reducible to one particular touchpoint in a research study’s trajectory, this piece will look at several instances of harmful research from different perspectives to illustrate potential points of intervention.

Planning

When researchers are planning their approach to working with biorepository data, at times they choose research questions that are of low priority to the population group being studied. Kim et al., 20121 used data from the multiethnic cohort study (MEC), which is a repository of samples from Japanese Americans, Native Hawaiians, African Americans, Latinos, and whites, broadly collected for cancer research. Kim and colleagues merged a subset of MEC data with a subset of Human Genome Diversity Project (HGDP) panel data, another major sample repository which will be discussed later in this piece. Kim and colleagues used this aggregated data to study the “genetic origins” of Native Hawaiians, making claims about population movement and contact with other populations. This line of research inquiry is not unusual; indeed, genetic origin research is a widely published field. However, Native Hawaiian researchers report that these research questions are of low priority for Native Hawaiian participants, who prefer that biorepository samples are used to further research on diseases that disproportionately affect Native Hawaiians, like heart disease and diabetes. In order to address the problem of low-priority research, CHIRON tool 3 asks researchers to look into priority-setting literature on the population in their dataset(s).

Given the stated reasons for collection of MEC and HGDP data, one may wonder whether genetic origin studies actually stretch the boundaries of research types consented to by participants. Bird and Carlson, 20242 raise this question in their extensive analysis of how “racial hereditarian researchers” use biomedical datasets to publish ideologically-motivated papers on intelligence and educational attainment in black and white Americans. These papers provide fodder for far-right social forums, as Carlson’s work has shown. Although informed consent for biorepositories is characteristically broad, “study participants were recruited with the understanding that results would be used for medical research,”2 calling into question the acceptability of some stigmatizing types of research questions. The CHIRON toolkit has questions that address participant consent, and the project team also recommends further innovation in this area by data oversight boards.

Execution & Analysis

The way in which biorepository data is curated and analyzed shapes the conclusions that can be drawn and the meanings that researchers, clinicians, and patients can make. At times, researchers choose labeling and categorization schemas that are unscientific, stigmatizing, or just inappropriate. Rosenberg et al., 20023 presents the first major characterization of genetic variation in the HGDP panel dataset.4 This paper’s impact transcends what most scientists experience in their career; “it was the most-cited paper in Science in its year and continues to feature in scientific, philosophical, and cultural debates” long after its publication.5 Rosenberg and colleagues identified major genetic clusters that correspond with continental regions, which has been interpreted by readers to reify a concept of biological race.4,5 While Rosenberg and colleagues later argued that their work should not be used to support a concept of biological race,6 critical scholarship has argued that this is far from a case of runaway interpretation.5,7 The paper presents the sample in two to six population clusters, which Rosenberg’s team selected using the program structure.3 At five population clusters, Rosenberg’s team produced a result corresponding to the regions of Africa, Eurasia, East Asia, Oceania, and America, which created the legacy associated with this paper.7 Indeed, the popular science writer Nicholas Wade used this finding as foundational evidence in his articles and book that promulgate arguments rooted in genetic essentialism.5,7 While Rosenberg, his colleagues, and many other geneticists have disavowed Wade’s work,5 renowned scholar of scientific racism Dorothy Roberts argues that Rosenberg and team’s choice of clustering schema gave rise to this popular interpretation: “There is nothing in the team’s findings to suggest that six clusters represent human population structure better than ten, or fifteen, or twenty.”7

To expand the scope beyond the specifics of Rosenberg et al., 2002, the choices that researchers make in their curation and analysis of biorepository data are not neutral and are not without consequence. The CHIRON toolkit includes a number of questions that address the labeling, lumping, splitting, and “other”ing of population groups, as well as supplementary reading and guides for these processes.

Dissemination & Ongoing Project Maintenance

As researchers write their discussion, submit their paper, and publicize their work, at times they fail to contextualize their findings within social and cultural structures. At a 2006 academic conference in Australia, a group of researchers [cited as Hall et al.8 as part of a research team containing Rod Lea and Geoffrey Chambers,9,10 although finding an abstract for this presentation is challenging due to link degradation] presented their findings that their sample of Māori people, a group indigenous to New Zealand, had higher levels of a gene which codes for monoamine oxidase, an enzyme linked to tobacco and alcohol dependency.8,9 The term “warrior gene” appears to have been applied to this particular genetic sequence prior to its usage by this group of researchers,10 although this language could also be problematized as it conflates a gene with a phenotype or displayed set of behaviors.9 In popular press, members of the research team made sense of their findings in that “historically Maori were fearless warriors”10 and linking it to alleged criminality, aggression, and violence in Māori people—claims they later walked back following controversy.8 Claims like these—that carrying a given gene “predisposes people to be more likely to be criminals” in the words of Rod Lea9—reveal a genetic essentialist bias in the research team. They provide a genetic “explanation” for socially situated constructs such as criminality, which are influenced by definitions of crime, policing and biased application therein, and social inequality. In failing to contextualize their findings with histories of colonialism and discrimination that are highly relevant to research involving Māori people, Lea and Chambers and their colleagues actually present an incomplete picture. The CHIRON toolkit asks researchers to contextualize their findings with historical, environmental, social, and cultural factors that may influence researchers’ interpretation of their results, see especially CHIRON tools 7 and 10.

In another case of lacking context, the work of Dhejne et al., 2011,11 to investigate mortality and criminality in transgender people in Sweden has provided extensive fodder to anti-trans activists that seek to reduce access to gender affirming care. Journalist Erin Reed details how this study, which found that transgender people in Sweden who received gender affirming care had higher risks of mortality and suicidal behavior as compared to cisgender people, has been used in congressional hearings, lawsuits, and by right-wing press to argue against providing gender affirming care. As Reed notes, Dhejne and colleagues do not provide a point of comparison with trans people who have not received gender affirming care, only the cisgender population writ large. Given the virulence of transphobia, it is not surprising, then, that trans people even after gender affirming surgery have higher rates of suicidal behavior and psychiatric illness than cis people. Dhejne has expressed frustration over what she describes as misrepresentations of her work, refuting some of the most egregious anti-trans claims that have been made using her study as “evidence.” While Dhejne’s endeavors to clarify bad faith interpretations of her work are appropriate, one wonders if the authors could have done more in their paper to compare their findings with published work or other elements of their dataset on the effect of obtaining gender affirming surgery versus lack thereof to the health and wellbeing of trans people. Given other work exhibiting how mental health improves in transgender youth following gender affirming care, perhaps additional context to this end could have staved off the 2011 study’s worst interpretations. The CHIRON toolkit prompts researchers early on to plan for contextualizing their findings, especially in a sensitive political climate, see especially CHIRON tool 4.

References

1. Kim SK, Gignoux CR, Wall JD, et al. Population Genetic Structure and Origins of Native Hawaiians in the Multiethnic Cohort Study. PLOS ONE. 2012;7(11):e47881. doi:10.1371/journal.pone.0047881

2. Bird KA, Carlson J. Typological thinking in human genomics research contributes to the production and prominence of scientific racism. Front Genet. 2024;15. doi:10.3389/fgene.2024.1345631

3. Rosenberg NA, Pritchard JK, Weber JL, et al. Genetic Structure of Human Populations. Science. 2002;298(5602):2381-2385. doi:10.1126/science.1078311

4. Fullerton SM, Lee SSJ. Secondary uses and the governance of de-identified data: Lessons from the human genome diversity panel. BMC Med Ethics. 2011;12:16. doi:10.1186/1472-6939-12-16

5. Wills M. Are Clusters Races? A Discussion of the Rhetorical Appropriation of Rosenberg et al.’s “Genetic Structure of Human Populations.” Philos Theory Pract Biol. 2017;9(12). doi:10.3998/ptb.6959004.0009.012

6. Rosenberg NA, Mahajan S, Ramachandran S, Zhao C, Pritchard JK, Feldman MW. Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure. PLOS Genet. 2005;1(6):e70. doi:10.1371/journal.pgen.0010070

7. Roberts D. Fatal Invention: How Science, Politics, and Big Business Re-Create Race in the Twenty-First Century. New Press; 2011.

8. Wensley D, King M. Scientific responsibility for the dissemination and interpretation of genetic research: lessons from the “warrior gene” controversy. J Med Ethics. 2008;34(6):507-509. doi:10.1136/jme.2006.019596

9. Perbal L. The “warrior gene” and the Mãori people: the responsibility of the geneticists. Bioethics. 2013;27(7):382-387. doi:10.1111/j.1467-8519.2012.01970.x

10. Horgan J. Code rage: The “warrior gene” makes me mad! (Whether I have it or not). Scientific American. Published April 26, 2011. Accessed May 1, 2024. https://www.scientificamerican.com/blog/cross-check/code-rage-the-warrior-gene-makes-me-mad-whether-i-have-it-or-not/

11. Dhejne C, Lichtenstein P, Boman M, Johansson ALV, Långström N, Landén M. Long-Term Follow-Up of Transsexual Persons Undergoing Sex Reassignment Surgery: Cohort Study in Sweden. PLOS ONE. 2011;6(2):e16885. doi:10.1371/journal.pone.0016885

What is CHIRON? 

Community Health Interests for Researchers & Oversight Networks (CHIRON) is a research project. Our goal is to develop and pilot a toolkit that will encourage researchers, ethics boards, and data access committees to consider group interests as they plan, execute, and report on big health data research. 

What is big health data research? 

When people think of health research, they often imagine doctors testing out novel treatments, like new medicines, on their patients. Somewhat surprisingly, loads of health research no longer directly involves the people being researched. Health data from people who have given broad consent flows into massive biorepositories (also called biobanks, databanks, or data repositories). Scientists use the “big health data” in these biorepositories to make discoveries using techniques like machine learning and artificial intelligence. Scientists’ use of this data is often overseen by an ethics committee and/or a data access committee.  

Why are we building this toolkit? 

As data flows into these massive biorepositories, it gets further and further separated from the people it originally came from. This loss of context can cause a disconnect between researchers and communities. In the worst case, this disconnect can lead to group harm. Group harm happens when stigmatizing or otherwise harmful group-based generalizations or inferences are made (usually unintentionally) by scientists in the course of their research. Scientists can slip up and make these generalizations more easily when their research doesn’t have community context.

Right now, big health data researchers don’t have many formal tools to help them bring community context back into their research and avoid group harm. We are building the CHIRON toolkit to help researchers, ethics boards, and data access committees reconnect with community context as they conduct and oversee big health data research. 

Who is involved in this project? 

The CHIRON toolkit is being conceptualized, designed, and built by Sage Bionetworks, a non-profit open science organization based in Seattle, Washington (USA). Megan Doerr is the principal investigator (PI) of the CHIRON project. Meg is collaborating with research scientist Carly Marten and designers Stockard Simon and Adam Hindman at Sage.  

The team at Sage is joined by expert academic and community workgroups convened specifically for this project. The academic workgroup members are Anouk Ruhaak, Astha Kapoor, Maile M. Tauali’i, Melissa Creary, Jasmine McNealy, Joon-Ho Yu, and Samuel A. Moore. The community workgroups are led by Andrea Downing,David Andres, Kalei Glozier, and Odia Kane.

Once built, the CHIRON toolkit will be piloted by researchers, ethics boards, and data access committees as they plan, execute, and report on big health data research. The pilots will begin at the end of 2024 and last until the end of 2025. The CHIRON project team, including our academic and community experts, will study the pilots to see if and how the toolkit is working.

Who is funding this project? 

The Robert Wood Johnson Foundation has provided the funding for this project. 

The Workgroups

The power behind the Community Health Interests for Researchers & Oversight Networks (CHIRON) project comes from two groups: academic knowledge holders and community experts. These two workgroups have been leading our exploration since 2022 of how to consider group interests in biorepository research.

CHIRON Framing Questions

1. What constitutes community in a biorepository context? What roles do self-identification and algorithmic identification play? How is membership defined and by whom?

2. In what situations is it necessary to invoke consent of a predefined community?

3. If we assume that communities evolve, for how long is a definition of community applicable?

4. How do we ensure the representativeness of a given community? Are there limits?

5. What meaningful role(s) can communities play in biorepository research? What is the role and responsibilities of researchers in alerting/ engaging/addressing communities?

6. What indicators should researchers use to guide when/how to engage with communities?

7. Is community consent a ceiling or floor: does it provide, prevent, or require entry of individuals?

Collaboration

Through live conversations spanning 16 time zones (!) and asynchronous collaboration on shared idea boards and documents, our academic workgroup mined existing scholarship across their domains of expertise to establish the project’s academic grounding. 

As the academic workgroup set off to read, ideate, and collaborate, we planned a series of community engagement studios which roughly corresponded to each of the project’s framing questions listed above.

Community engagement studios (CE studios) are an approach to consulting affected communities developed by researchers at Vanderbilt University and Meharry Medical College. We recruited four community workgroups in the CE studio format, centering groups that have historically (and, unfortunately, not so historically) been harmed by medical research: people who identify as American Indian, Alaska Native, Native Hawaiian, and Pacific Islanders (AI/AN/NH/PI); LGBTQ+ people; Black people; and people with genetic variants that make them predisposed to certain inherited conditions, like Hereditary Breast and Ovarian Cancer syndrome (HBOC). These groups were facilitated by Andrea Downing, David Andres, Kalei Glozier, and Odia Kane.

While CE studios often do not involve meeting with the same groups on the same topics repeatedly, the 29 community experts in our groups had experiences as patients, patient advocates, clinicians, and active community members that proved essential to addressing concepts salient to group harm in biorepository research.

In advance of the first CE studio meetings, we created animated videos illustrating different challenges to group interests to get the conversation started. We used a digital whiteboard platform for brainstorming and documenting our conversations and held CE studios over Zoom to maintain community experts’ safety in a pandemic context. The groups met seven times for 90 minutes, with an academic workgroup member joining as a consultant for each meeting. As groups continued to meet, the intimacy and depth of discussion also increased. This was another feature of the continuity of groups; the structure of our CE studios created the space to discuss the spectrum from positive experiences, hopes, and aspirations, to medical trauma and negative encounters with research. The benefit to the CHIRON project of our summer of CE studios is greater than we can articulate. Our mandate now is to deliver a toolkit that complements the quality of these discussions.