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.

JRL has stock ownership in 23andMe, is a paid consultant for Regeneron Genetics Center, and is a coinventor on multiple U.S. and European patents related to molecular diagnostics for inherited neuropathies, eye diseases, and bacterial genomic fingerprinting. JRL serves on the Scientific Advisory Board of Baylor Genetics. EV, JRL, and RAG declare that Baylor Genetics is a Baylor College of Medicine affiliate that derives revenue from genetic testing. BCM and Miraca Holdings have formed a joint venture with shared ownership and governance of Baylor Genetics which performs clinical microarray analysis and other genomic studies (exome sequencing and whole genome sequencing) for patient and family care. EV is a co-founder of Codified Genomics, a provider of genetic interpretation.

This study originated within the Atlas of Variant Effects (AVE) and was further supported as a cross-consortia project via the Trans-Variant working group of the Impact of Genomic Variation on Function (IGVF) consortia of the United States National Human Genome Research Institute (NHGRI). Additional funding was provided in part by the NHGRI Genomics Research Elucidates Genetics of Rare Disease (BCM GREGoR Center, U01HG011758 for MD, JEP, JRL, RAG), NHGRI IGVF (University of Washington (UW) Center for Actionable Variant Analysis; UM1HG011969 for MD, SF, SP, MP, LAM, DMF, AFR, LMS), NHGRI Centers of Excellence in Genomic Sciences (UW Center for Multiplexed Assessment of Phenotypes; RM1HG010461 for MD, SF, SP, MP, LAM, DMF, AFR, LMS), NHGRI Clinical Genome (ClinGen) Resource (BCM/Stanford ClinGen Resource; U24HG009649 for SEP) and the NIH All of Us Program (The Baylor-Hopkins Clinical Genomics Center for All of Us; OT2OD002751 for MD, DK, KP, EV, RAG). MD was also supported by the Baylor College of Medicine Comprehensive Cancer Training Program of the Cancer Prevention Research Institute of Texas (CPRITRP210027). CDRE was supported by the Wellcome Trust through a Career Development Award (227228/Z/23/Z), the Melanoma Research Alliance (825924) and the Chan-Zuckerberg Initiative through the Ancestry Networks for the Human Cell Atlas grant program (CZI007). IGR was supported in part by Australian Research Council Discovery Project DP200101552, National Health and Medical Research Council Ideas Grant 2020501 and the European Union through the Horizon 2020 Research and Innovation Program under Grant No. 810645 and the European Union through the European Regional Development Fund Project No. MOBEC008. St Vincent's Institute acknowledges the infrastructure support it receives from the National Health and Medical Research Council Independent Research Institutes Infrastructure Support Program and from the Victorian Government through its Operational Infrastructure Support Program. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All input data derived from both gnomAD v2.1.1 and gnomAD v3.1.2 (non v2) is publicly available at https://gnomad.broadinstitute.org/. All of Us data used in this manuscript is publicly available through the All of Us Data v7 browser (https://databrowser.researchallofus.org/variants) but was accessed via the All of Us Researcher Workbench as Baylor College of Medicine is an approved institution. The project was declared on the All of Us Research Projects Directory in accordance with the All of Us data access model. This declaration can be publicly viewed at https://allofus.nih.gov/protecting-data-and-privacy/research-projects-all-us-data/ ("MAVEComparison").

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All input data derived from both gnomAD v2.1.1 and gnomAD v3.1.2 (non v2) is linked at the GitHub above and publicly available at https://gnomad.broadinstitute.org/. All of Us data used in this manuscript is publicly available through the All of Us Data v7 browser (https://databrowser.researchallofus.org/variants) but was accessed via the All of Us Researcher Workbench as Baylor College of Medicine is an approved institution. The project was declared on the All of Us Research Projects Directory in accordance with the All of Us data access model. This declaration can be publicly viewed at https://allofus.nih.gov/protecting-data-and-privacy/research-projects-all-us-data/ ("MAVEComparison"). Both the input data and associated code are accessible through the All of Us workbench and will be promptly shared with requesters with approved workbench access. The code used for analysis of the All of Us data is the same as in the above GitHub with minor modifications made that are specific to the All of Us Researcher Workbench. All the input data used from All of Us is publicly available at the All of Us Public Data Browser (v7; https://databrowser.researchallofus.org/variants). Complete rankings across all three databases of all clinically curated genes by VUS/PorLP/BorLB/CI/ND allele prevalence difference are also linked to the GitHub. All variant reclassifications will be submitted to ClinVar and made publicly available following peer review.

https://gnomad.broadinstitute.org/

https://databrowser.researchallofus.org/variants

https://github.com/MoezDawood/ReducingVariantClassificationInequities.git