Social and scientific motivations to move beyond groups in allele frequencies: The TOPMed experience

For the genomics community, allele frequencies within defined groups (or “strata”) are useful across multiple research and clinical contexts. Benefits include allowing researchers to identify populations for replication or “look up” studies, enabling researchers to compare population-specific frequencies to validate findings, and facilitating assessment of variant pathogenicity in clinical contexts. However, there are potential concerns with stratified allele frequencies. These include potential re-identification (determining whether or not an individual participated in a given research study based on allele frequencies and individual-level genetic data), harm from associating stigmatizing variants with specific groups, potential reification of race as a biological rather than a socio-political category, and whether presenting stratified frequencies—and the downstream applications that this presentation enables—is consistent with participants’ informed consents. The NHLBI Trans-Omics for Precision Medicine (TOPMed) program considered the scientific and social implications of different approaches for adding stratified frequencies to the TOPMed BRAVO (Browse All Variants Online) variant server. We recommend a novel approach of presenting ancestry-specific allele frequencies using a statistical method based upon local genetic ancestry inference. Notably, this approach does not require grouping individuals by either predominant global ancestry or race/ethnicity and, therefore, mitigates re-identification and other concerns as the mixture distribution of ancestral allele frequencies varies across the genome. Here we describe our considerations and approach, which can assist other genomics research programs facing similar issues of how to define and present stratified frequencies in publicly available variant databases. Open sharing of allele frequencies from diverse populations is important for genomic research and medicine. Here we explore social, ethical, and technical implications of different approaches for computing allele frequencies and ultimately recommend methods that do not require grouping individual participants into discrete categories by race, ethnicity, or ancestry.