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The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities. Commun Biol 2024 Feb 19;7(1):174

Date

02/20/2024

Pubmed ID

38374434

Pubmed Central ID

PMC10876563

DOI

10.1038/s42003-023-05708-y

Scopus ID

2-s2.0-85185702226 (requires institutional sign-in at Scopus site)   9 Citations

Abstract

Disparities in data underlying clinical genomic interpretation is an acknowledged problem, but there is a paucity of data demonstrating it. The All of Us Research Program is collecting data including whole-genome sequences, health records, and surveys for at least a million participants with diverse ancestry and access to healthcare, representing one of the largest biomedical research repositories of its kind. Here, we examine pathogenic and likely pathogenic variants that were identified in the All of Us cohort. The European ancestry subgroup showed the highest overall rate of pathogenic variation, with 2.26% of participants having a pathogenic variant. Other ancestry groups had lower rates of pathogenic variation, including 1.62% for the African ancestry group and 1.32% in the Latino/Admixed American ancestry group. Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer or Hypercholesterolemia. Variant frequencies in many genes were consistent with the data from the public gnomAD database, with some notable exceptions resolved using gnomAD subsets. Differences in pathogenic variant frequency observed between ancestral groups generally indicate biases of ascertainment of knowledge about those variants, but some deviations may be indicative of differences in disease prevalence. This work will allow targeted precision medicine efforts at revealed disparities.

Author List

Venner E, Patterson K, Kalra D, Wheeler MM, Chen YJ, Kalla SE, Yuan B, Karnes JH, Walker K, Smith JD, McGee S, Radhakrishnan A, Haddad A, Empey PE, Wang Q, Lichtenstein L, Toledo D, Jarvik G, Musick A, Gibbs RA, All of Us Research Program Investigators

Authors

William R. Hogan MD Director, Professor in the Data Science Institute department at Medical College of Wisconsin
Jeffrey Whittle MD Professor in the Medicine department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Genetic Predisposition to Disease
Genomics
Humans
Population Health
United States