Study Reveals Racial Bias In Genomics Databases

October 12, 2016    |  

Led by investigators at the University of Maryland School of Medicine, a national group of researchers has confirmed for the first time that two of the top genomic databases, which are in wide use today by clinical geneticists, reflect a measurable bias toward genetic data based on European ancestry over that of African ancestry. The results of their study were published today in the journal Nature Communication.

The research team was led by Principal Investigator Timothy O'Connor, PhD, assistant professor at the University of Maryland School of Medicine and a faculty member of the school’s Institute for Genomic Sciences. He is also a specialist in the areas of Human Evolutionary Genomics, Genotype/Phenotype Architecture, and Computational Biology. Other members of the study included researchers from UM SOM’s Department of Medicine and the Program in Personalized and Genomic Medicine, and from Johns Hopkins University, the University of Colorado, and the Henry Ford Health System.

Timothy O'Connor, PhD

Timothy O'Connor, PhD

This deficit in African ancestry genomic data was identified during an 18-month-long study conducted under the auspices of the larger Consortium on Asthma among African-Ancestry Populations in the Americas (CAAPA). To create a benchmark for comparison to current database results, the researchers first created the largest, high-quality non-European genome data set ever assembled. Genetic samples of 642 subjects from the African diaspora, including representatives from U.S., African, and Afro-Caribbean populations, were sequenced in order to produce this unique data set. Then, when compared with current clinical genomic databases, researchers found a clearer preference in those databases for European genetic variants over non-European variants.

“By better understanding the important role of African ancestry in clinical genetics, we can begin to actually identify a disease that has been forgotten or is not part of an individual’s self-identification,” says O’Connor. “For example, if an African-American patient walks in the door, he might have 20 percent European ancestry, while another might have 20 percent African ancestry. That difference will dramatically change how many variants are found in their genome, and what disease risks they might encounter. That’s why we need to expand these databases to include a broader range of ancestries, in order to produce more accurate medical genetic diagnoses.”

O’Connor also points out that this shortfall in genomic data also comes at a financial cost. “If you translate the review time it takes for each one of these variants to be sequenced in terms of cost in a clinical setting, you’re looking at a difference of about $1,000 more to analyze an African-American’s genome than a European American’s genome — and you still receive less accurate results,” he notes.