ArticleScientific reports2019
Selecting variants of unknown significance through network-based gene-association significantly improves risk prediction for disease-control cohorts.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Role of Genetics in the Management of Heart Failure Patients.International journal of molecular sciences · 2023Review
- Basic science methods for the characterization of variants of uncertain significance in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2023Review
- Targeting mutations in cancer.The Journal of clinical investigation · 2022Review
- Multicenter Consensus Approach to Evaluation of Neonatal Hypotonia in the Genomic Era: A Review.JAMA neurology · 2022Review
- Exome sequencing and RNA analysis identify two novel CPLANE1 variants causing Joubert syndrome.Molecular genetics & genomic medicine · 2022Article
- Clinical and analytical validation of FoundationOne®CDx, a comprehensive genomic profiling assay for solid tumors.PloS one · 2022Article
- How Functional Genomics Can Keep Pace With VUS Identification.Frontiers in cardiovascular medicine · 2022Review
- Whole genome sequencing of low input circulating cell-free DNA obtained from normal human subjects.Physiological reports · 2021Article
- Proteotyping of knockout mouse strains reveals sex- and strain-specific signatures in blood plasma.NPJ systems biology and applications · 2021Article
- Prioritizing variants of uncertain significance for reclassification using a rule-based algorithm in inherited retinal dystrophies.NPJ genomic medicine · 2021Article
- Collision-Induced Unfolding Differentiates Functional Variants of the KCNQ1 Voltage Sensor Domain.Journal of the American Society for Mass Spectrometry · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Variants of unknown/uncertain significance (VUS) pose a huge dilemma in current genetic variation screening methods and genetic counselling. Driven by methods of next generation sequencing (NGS) such as whole exome sequencing (WES), a plethora of VUS are being detected in research laboratories as well as in the health sector. Motivated by this overabundance of VUS, we propose a novel computational methodology, termed VariantClassifier (VarClass), which utilizes gene-association networks and polygenic risk prediction models to shed light into this grey area of genetic variation in association with disease. VarClass has been evaluated using numerous validation steps and proves to be very successful in assigning significance to VUS in association with specific diseases of interest. Notably, using VUS that are deemed significant by VarClass, we improved risk prediction accuracy in four large case-studies involving disease-control cohorts from GWAS as well as WES, when compared to traditional odds ratio analysis. Biological interpretation of selected high scoring VUS revealed interesting biological themes relevant to the diseases under investigation. VarClass is available as a standalone tool for large-scale data analyses, as well as a web-server with additional functionalities through a user-friendly graphical interface.
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