Frequency of pharmacogenomic variation and medication exposures among All of Us Participants.

Pharmacogenomics promises improved outcomes through individualized prescribing. However, the lack of diversity in studies impedes clinical translation and equitable application of precision medicine. We evaluated the frequencies of PGx variants, predicted phenotypes, and medication exposures using whole genome sequencing and EHR data from nearly 100k diverse All of Us Research Program participants. We report 100% of participants carried at least one pharmacogenomics variant and nearly all (99.13%) had a predicted phenotype with prescribing recommendations. Clinical impact was high with over 20% having both an actionable phenotype and a prior exposure to an impacted medication with pharmacogenomic prescribing guidance. Importantly, we also report hundreds of alleles and predicted phenotypes that deviate from known frequencies and/or were previously unreported, including within admixed American and African ancestry groups.

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities.

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.

Defining and Reducing Variant Classification Disparities.

Background: Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style data may help resolve variant classification disparities between populations, especially for variants of uncertain significance (VUS). Methods: We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN . Results: Using two orthogonal statistical approaches, we show a higher prevalence ( p ≤5.95e-06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation ( p ≤2.5e-05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were higher in individuals of European-like genetic ancestry ( p ≤2.5e-05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry ( p =9.1e-03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency ( p =7.47e-06) and computational predictor ( p =6.92e-05) evidence codes for individuals of non-European-like genetic ancestry. Conclusions: Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.