Overview:
Racial and ethnic classification in research and medicine is rooted in a centuries-long practice of categorizing humans into distinct groups to justify colonization, slavery, and genocide. The legacy of these harms to communities lingers today, as the U.S. still uses six distinct categories in racial and ethnic classification for official reporting. Just as the human genome’s complexity necessitates an updated reference, the spectrum of human diversity requires a dynamic and inclusive model to account for social, cultural, geographic, and genomic populations.

Here we present a graph-based data model and mobile application that facilitate the collection, storage, aggregation, and use of open-ended, as well as structured, data types. Categorical and free-text data from different studies can be combined to reveal complexity in human populations that racialized frameworks have erased.

Objectives:

• Characterize the history of racial and ethic classification in research and medicine
• Investigate a novel approach using graph-based data model
• Describe the available pre-doctoral and post-doctoral positions in translational data science and health and data equity

Speaker Information: Alice Popejoy, Ph.D., is an assistant professor in the Epidemiology Division of the Department of Public Health Sciences at the University of California, Davis (UC Davis Health). She is also an associate member of the UC Davis Comprehensive Cancer Center. Dr. Popejoy's research program in public health genetics is situated at the intersections of evolutionary genomics, biomedical data science, statistical genetics, and the attending ethical, legal, and social implications (ELSI). She is currently focused on innovation and methods development to fundamentally shift the way human populations are categorized in biomedical research, epidemiology, and precision medicine. She received her B.A. from Hamilton College and her Ph.D. in public health genetics from the University of Washington.