The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: (1) acquisition of REA data via clinical laboratory requisition forms, and (2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites ascertained from annotations in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about clinically relevant variants in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with “pathogenic” and “likely pathogenic” expert‐reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population‐level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed through partnerships and collaborations and adopted across clinical genomics.

Genetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in clinical genetics practice. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (n = 448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.

Genome-wide association studies (GWAS) have revealed important links between genetic markers across the human genome and phenotypic traits, including risk factors for disease. Studies have shown that GWAS continue to be overwhelmingly conducted on people of primarily European descent, despite the fact that the vast majority of human genomic variation is present in non-European populations such as those in Africa. To enhance our understanding of diversity in the pharmacogenomics and precision medicine literature, this review provides a window into the representation of biogeographical populations that have been studied for pharmacogenetic traits, such as enzyme metabolism and adverse drug response. Using the Medical Subject Headings (MeSH) ontology search terms in PubMed, studies were identified that are either population-based, or include a description of the study population on the basis of biological or environmental diversity. The results of this scoping review indicate that the majority of relevant papers (>95% of studies tagged in PubMed with MeSH terms “precision medicine” or “pharmacogenetics”, N=23,701) are not annotated with the “population group” MeSH term, suggesting that the majority of studies in this literature are not population-based, or the authors chose not to describe the study population. Among those studies related to pharmacogenetics or precision medicine that are specific to human population groups (N=1006) and were included in the analysis after filtering and screening on eligibility criteria (N=192), the majority of single-population studies included individuals of African, Asian, and European origins, or genetic ancestry. Combining studies of single and multiple populations, 33% involve participants of Asian origin or ancestry; 30% European; 24% African; 10% Hispanic or Latino; and < 3% American Indian or Alaska Native. These data provide a baseline for future comparison, indicating which biogeographic groups have informed the pharmacogenomic knowledgebase specific to diverse human populations. Challenges and potential solutions to improve diversity in the field and in genetics research more broadly are discussed.

Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model. We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them. Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.

“Structural racism in the COVID-19 pandemic: moving forward” (Sabatello et al. 2021) offers a nuanced and important critique of racial and ethnic health disparities starkly illuminated by COVID-19....