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.
