A scalable approach to resolving variants of uncertain significance
Authors:
- Malvika TejuraMTMalvika Tejura
- Yile ChenYCYile Chen
- Abbye E. McEwenAbbye E. McEwen
- Ross StewartRSRoss Stewart
- Yuriy SverchkovYuriy Sverchkov
- Florent LavalFlorent Laval
- Ivan WooIWIvan Woo
- Daniel ZeibergDZDaniel Zeiberg
- Runxi ShenRSRunxi Shen
- Shawn FayerShawn Fayer
- Jeremy StoneJSJeremy Stone
- Nahum SmithNSNahum Smith
Preprint
Abstract
Over 90% of missense variants across ∼4,000 disease-associated genes are variants of uncertain significance (VUS). Experimental variant effect measurements provide critical evidence about pathogenicity and inform disease biology, but most variants lack data and clinical translation has been limited. The Impact of Genomic Variation on Function Consortium generated experimental data for 62,215 variants across ten genes using multiplexed assays and 1,407 variants across 163 genes using arrayed assays, curated 193,139 additional community-generated variant effect measurements across 30 additional genes, and developed automated calibration methods for translating experimental data and variant effect predictions into clinical evidence. To reduce current VUS, we developed a scalable workflow using only experimental and predictive evidence, enabling reclassification of 75% of the 16,115 VUS in these genes as pathogenic or benign with 90,000 unobserved variants; 62% had enough evidence to be “preclassified” as pathogenic or benign. We validated our data, evidence and classifications using All of Us and created interactive resources to enable clinical use of the calibrated data. Thus, for 40 genes, representing 1% of the clinical genome, we resolve most existing VUS and future variants, illustrating how systematic use of scalable evidence can empower genomic medicine.Keywords
Funding Information
- NIH NHGRI (UM1HG011969)
- NIH NHGRI (U01HG012022)
- NIH NHGRI (U01HG012039)
- NIH NHGRI (UM1HG011989)
- NIH NHGRI (U24HG012012)
This publication has 105 references indexed in Scilit:
- Rates and Classification of Variants of Uncertain Significance in Hereditary Disease Genetic TestingJAMA Network Open, 2023
- Variant interpretation using population databases: Lessons from gnomADHuman Mutation, 2021
- Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelinesHuman Mutation, 2020
- The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomicsHuman Mutation, 2018
- ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene‐level specification of the ACMG/AMP guidelines for sequence variant interpretationHuman Mutation, 2018
- Prevalence of Variant Reclassification Following Hereditary Cancer Genetic TestingJAMA, 2018
- VariantValidator: Accurate validation, mapping, and formatting of sequence variation descriptionsHuman Mutation, 2017
- HGVS Recommendations for the Description of Sequence Variants: 2016 UpdateHuman Mutation, 2016
- Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test resultsHuman Mutation, 2008
- SNPs, protein structure, and diseaseHuman Mutation, 2001