Guest blog by Vence L. Bonham, Jr., J.D., Acting Deputy Director, National Human Genome Research Institute and Sheri Schully, Ph.D., Deputy Chief Medical and Scientific Officer, All of Us Research Program
For decades, socially constructed population descriptors such as race, ethnicity, and ancestry have been used as a proxy to describe human diversity but do not accurately capture the full scientific scope of diversity, and their definitions have changed over time. For example, genetics and genomics researchers have mistakenly used these descriptors to describe human genetic variation. This practice is outdated and needs replacement moving forward.
Assuming that biological or genetic differences relate directly to racial or ethnic categories can lead to false scientific conclusions and perpetuate bias. Misuse of population descriptors has harmed marginalized groups and promoted scientific racism.
These limitations in existing population descriptors in genetics and genomics led 14 NIH Institutes, Centers, and Offices to sponsor the National Academies of Science, Engineering, and Medicine (NASEM) to establish an interdisciplinary committee of experts and explore the issue.
Over the last year, the committee assessed the benefits, challenges, and existing methodologies in the use of race, ethnicity, and ancestry as population descriptors in the genetics and genomics field. In March, the committee released a report with a set of recommendations and guiding principles for researchers, funders, and publishers. The report and resources also detail a framework for using population descriptors in genetic and genomic studies.
The report’s authors propose three overarching approaches for scientists and researchers navigating the use of population descriptors in genetics and genomics research: avoiding or dispelling typological thinking, engaging with communities of study participants, and integrating environmental factors in study design, when appropriate. The NASEM committee recommends tailoring population descriptors according to the type of study and explaining the reasoning behind choosing the specific descriptor. The committee also put together a flowchart to assist researchers in selecting appropriate population descriptors for their type of study. Determination of descriptors is important for harmonization of data, replicability between studies, and scientific precision.
The NIH community is now carefully considering how these recommendations could help guide more scientifically accurate design of genomic and genomic studies, maximize use of existing genomic datasets, and help ensure scientific language reflects our evolving understanding of human diversity. NHGRI also recently created an explainer sheet about the evolving use of population descriptors in genetics and genomic research. We look forward to collaborating with the broader community as we consider the National Academies recommendations.
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