  Precision medicine aims to create biomedical solutions tailored to specific
factors that affect disease risk and treatment responses within the population.
The success of the genomics era and recent widespread availability of
electronic health records (EHR) has ushered in a new wave of genomic biobanks
connected to EHR databases (EHR-linked biobanks). This perspective aims to
discuss how race, ethnicity, and genetic ancestry are currently utilized to
study common disease variation through genetic association studies. Although
genetic ancestry plays a significant role in shaping the genetic landscape
underlying disease risk in humans, the overall risk of a disease is caused by a
complex combination of environmental, sociocultural, and genetic factors. When
using EHR-linked biobanks to interrogate underlying disease etiology, it is
also important to be aware of how the biases associated with commonly used
descent-associated concepts such as race and ethnicity can propagate to
downstream analyses. We intend for this resource to support researchers who
perform or analyze genetic association studies in the EHR-linked biobank
setting such as those involved in consortium-wide biobanking efforts. We
provide background on how race, ethnicity, and genetic ancestry play a role in
current association studies, highlight considerations where there is no
consensus about best practices, and provide transparency about the current
shortcomings.
