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Genetic & Genomic MedicineThe American Journal of Human Genetics
Peer-reviewed journal article

Extracting and calibrating evidence of variant pathogenicity from population biobank data

Vineel BhatiD, Tian Yu, Lara Brown, Vikas Rao PejaveriD, Matthew S. LeboiD, Steven M. HarrisoniD, Christopher A. CassaiD

Abstract

Genomic medicine requires a robust evidence base of variant phenotypic impacts, which remains incomplete even in extensively studied monogenic disease genes. Here, we evaluated the broad potential of using population cohort data to identify evidence that can be used in variant assessment. Across 41 genes related to 18 clinically actionable monogenic phenotypes, we calculated variant-level odds ratios of disease enrichment using data from 469,803 UK Biobank participants. We found significant differences in odds ratio values between ClinVar-labeled pathogenic and benign variants in 11 phenotypes, spanning both common and rare disorders. To facilitate clinical translation, we calibrated the strength of evidence provided by variant-level odds ratios to align with American College of Medical Genetics and Genomics (ACMG/AMP) interpretation guidelines (PS4 criterion), and found that odds ratios may reach moderate, strong, or very strong evidence, varying by phenotype and gene. Overall, we found that 2.6% (N = 12,350) of participants harbor a rare VUS with at least moderate evidence of pathogenicity - an indication of potentially unrecognized disease risk. Finally, by incorporating computational and functional data alongside population-based odds ratios, we identified variants that met criteria for clinical reclassification. Notably, using this approach, we identified that 12.4% of rare VUS in LDLR seen in participants meet diagnostic criteria to be classified as likely pathogenic, demonstrating its potential to scale the reclassification of VUS. HighlightsO_LIWe identify rare coding variants that alter risk across 41 genes related to 18 actionable phenotypes. C_LIO_LIWe find enrichments of cases in variants of uncertain significance related to rare and common disorders. C_LIO_LIWe calibrate the strength of biobank population evidence for use in sequence variant interpretation. C_LIO_LIWe combine evidence to identify uncertain variants that can be reclassified as likely pathogenic. C_LI

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