Guidelines for clinical interpretation of variant pathogenicity using RNA phenotypes

Abstract

Over the last 5 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 the American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines classification system.

Diagnostic potential of the amniotic fluid cells transcriptome in deciphering mendelian disease: a proof-of-concept

“…5b ). With this new evidence from AF cells RNA-seq analysis, together with the recently updated guidelines 43 , NM_000089.3:c.2133 + 5 G > A can be considered as pathogenic rather than a VUS (PS3, PM2, PM4 and PM6).

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“…3c ). Integrating the recently updated guidelines for clinical interpretation of variant pathogenicity using RNA-seq data from Smirnov et al 43 , PS3 evidence can be used after AF cells RNA-seq to upgrade the variant from likely pathogenic to pathogenic.…”

“…The rationale of this approach is based on the comparison of one patient sample against the rest of the samples in the cohort as internal controls. Therefore, the sequencing read depth of this study was aim high at 100 million reads, for the proper detection of aberrant outliers 22 , 43 . This comparison allows for the identification of significant aberrant outliers, AE, AS and monoallelic expression (MAE), to stand out due to the causal genetic defects in a sample.…”

“…While we demonstrated the ability of AF cells RNA-seq and our adapted DROP v1.1.0 pipeline to identify pathogenic splicing variants in three families, their corresponding expression is not being affected. According to a recent paper by Smirnov et al 43 that overviewed eight recent studies applying large-scale RNA‐seq, 64%, 62% and 27% of the diagnosed cases were identified by AE, AS and MAE, respectively, half of the splicing outliers did not lead to aberrant expression. In addition, detection as expression outliers could only provide strong evidence of pathogenicity in biallelic splice variants but not monoallelic splice variants; therefore, for a monoallelic splice variant, abnormal expression is not a useful functional consequence to support its pathogenicity.…”

“…On the other hand, detection as splicing outlier could provide strong evidence of pathogenicity to monoallelic splice variants. Recently, efforts have been made to incorporate RNA-seq evidence into the ACMG Variant Interpretation Guidelines 43 , 56 , 57 . Using a set of 723 benign variants and 198 pathogenic variants with known aberrant RNA phenotypes such as aberrant splicing or expression, Smirnov et al calculated the odds of pathogenicity to evaluate different thresholds for assessing variant pathogenicity strength, with the aim of integrating data from DROP to the ACMG Variant Interpretation Guidelines 43 .…”