Implications of self-identified race, ethnicity, and genetic ancestry on genetic association studies in biobanks within health systems

TL;DR

This paper discusses how race, ethnicity, and genetic ancestry influence genetic association studies in biobanks linked to electronic health records, emphasizing the importance of understanding biases and complex factors in precision medicine.

Contribution

It provides a comprehensive overview of current practices, highlights challenges and biases, and offers guidance for researchers conducting genetic association studies in EHR-linked biobanks.

Findings

Genetic ancestry significantly influences disease risk analysis.

Biases in race and ethnicity concepts can affect study outcomes.

Complex environmental and sociocultural factors also impact disease risk.

Abstract

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.