Expanding diversity and representation in scientific research is critical to improving health outcomes and disparities for underserved populations, a conclusion voiced by an increasing number of biomedical researchers.
An initial, but difficult, step to accomplishing this is increasing the transparency of how scientists use concepts of race, ethnicity, and ancestry in various stages of the research process; a tall order but one that a group of genetic researchers at the University of Washington School of Public Health was motivated to pursue.
In a recently published paper in Cell Genomics, investigators affiliated with the National, Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program outlined a set of recommendations addressing these issues.
"Too often, lack of clarity in how genetics research is communicated can lead to a misunderstanding of its relationship to race. Attributing racial categories to genetic conditions is the opposite of precision medicine, leads to real harms for patients, and obscures the true causes of racial disparities in health," said Stephanie Gogarten, a research scientist with the Genetic Analysis Center (GAC) in the Department of Biostatistics who is first author of the paper along with fellow GAC researcher Alyna Khan.
As a consortium with more than 80 ongoing studies that include people of different races, ethnicities, geographic locations, and ancestries, TOPMed is well-positioned to address the challenges and opportunities presented by diverse and heterogeneous datasets.
The paper presents recommendations across four common stages of genetic research: terminology, harmonization (combining and standardizing race, ethnicity, and ancestry variables across datasets), analysis, and reporting.
Underpinning the recommendations is a call to clearly distinguish when genetic versus social concepts are used and why (i.e., ancestry is often a genetic concept whereas race and ethnicity are social concepts). This approach is crucial in order to avoid inaccurate conclusions such as attributing observed outcomes to genetic differences rather than social or structural determinants of health.
Investigators hope the recommendations encourage researchers to make well-founded and responsible analytical and methodological decisions when using race, ethnicity, and ancestry variables.
Co-authors include GAC researchers Adrienne Stilp, Michael Bowers, Quenna Wong, Matthew Conomos, and Sarah Nelson, and former GAC researchers Caitlin McHugh and Tamar Sofer.
Lead contact for further information and requests for resources: Alyna T. Khan, firstname.lastname@example.org.