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Guidelines for clinical interpretation of variant pathogenicity using RNA phenotypes.

Hum. Mutat. 43, 1056-1070 (2022)
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Over the last five years, RNA sequencing (RNA-seq) has been established and is increasingly applied as an effective approach complementary to DNA sequencing in molecular diagnostics. Currently, three RNA phenotypes, aberrant expression, aberrant splicing, and allelic imbalance, are considered to provide information about pathogenic variants. By providing a high-throughput, transcriptome-wide functional readout on variants causing aberrant RNA phenotypes, RNA-seq has increased diagnostic rates by about 15% over whole-exome sequencing. This breakthrough encouraged the development of computational tools and pipelines aiming to streamline RNA-seq analysis for implementation in clinical diagnostics. Although a number of studies showed the added value of RNA-seq for the molecular diagnosis of individuals with Mendelian disorders, there is no formal consensus on assessing variant pathogenicity strength based on RNA phenotypes. Taking RNA-seq as a functional assay for genetic variants, we evaluated the value of statistical significance and effect size of RNA phenotypes as evidence for the strength of variant pathogenicity. This was determined by the analysis of 394 pathogenic variants, of which 198 were associated with aberrant RNA phenotypes and 723 benign variants. Overall, this study seeks to establish recommendations for integrating functional RNA-seq data into the ACMG/AMP guidelines classification system. This article is protected by copyright. All rights reserved.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Rna Sequencing ; Guidelines ; Rare Disorders ; Variant Interpretation
Language english
Publication Year 2022
HGF-reported in Year 2022
ISSN (print) / ISBN 1059-7794
e-ISSN 1098-1004
Journal Human Mutation
Quellenangaben Volume: 43, Issue: 8, Pages: 1056-1070 Article Number: , Supplement: ,
Publisher Wiley
Reviewing status Peer reviewed
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Genetics and Epidemiology
PSP Element(s) G-503292-001
Grants Bundesministerium für Bildung und Forschung
E-Rare
Bavarian State Ministry of Health and Care
Scopus ID 85132883851
PubMed ID 35645004
Erfassungsdatum 2022-09-23