Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank

Abstract

Recently, the development of biobanks linked to electronic medical records has presented new opportunities for genetic and epidemiological research. Studies based on these resources, however, present unique challenges, including the accurate assignment of individual-level population ancestry. In this work we examine the accuracy of administratively-assigned race in diverse populations by comparing assigned races to genetically-defined ancestry estimates. Using 220 ancestry informative markers, we generated principal components for patients in our dataset, which were used to cluster patients into groups based on genetic ancestry. Consistent with other studies, we find a strong overall agreement (Kappa = 0.872) between genetic ancestry and assigned race, with higher rates of agreement for African-descent and European-descent assignments, and reduced agreement for Hispanic, East Asian-descent, and South Asian-descent assignments. These results suggest caution when selecting study samples of non-African and non-European backgrounds when administratively-assigned race from biobanks is used.

Figure 1. Comparison of administratively-assigned race and genetic ancestry, based on principal component analysis.

A) All pairwise combinations of principle components (PCs) 1 through 3, by administratively assigned race. B) All pairwise combinations of PCs 1 through 3, by cluster assignments corresponding to genetic ancestry. Comparison of Frames 1A and1B indicate individuals with administratively assigned race different than their genetically defined ancestry cluster. For example, the East Asian-descent cluster (1B; blue) contains individuals with administratively-assigned race (1A) of Caucasian (green), Hispanic (purple), and Other (orange).