Major Causes of Conflicting Interpretations of Variant Pathogenicity in Rare Disease: A Systematic Analysis

The identification of the genetic causes of inherited disorders from next-generation sequencing (NGS) data remains a complicated process, in particular due to challenges in interpretation of the vast amount of generated data and hundreds of candidate variants identified. Inconsistencies in variant classification, where genetic centers classify the same variant differently, can hinder accurate diagnoses for rare diseases. Publicly available databases that collect data on human genetic variations and their association with diseases provide ample opportunities to discover conflicts in variant interpretation worldwide. In this study, we explored patterns of variant classification discrepancies using data from ClinVar, a public archive of variant interpretations. We found that 5.7% of variants have conflicting interpretations (COIs) reported, and the vast majority of interpretation conflicts arise for variants of uncertain significance (VUS). As many as 78% of clinically relevant genes harbor variants with COIs, and genes with high COI rates tended to have more exons and longer transcripts, with a greater proportion of genes linked to several distinct conditions. The enrichment analysis of COI-enriched genes revealed that the products of these genes are involved in cardiac disorders, muscle development, and function. To improve diagnoses, we believe that specific variant interpretation rules could be developed for such genes. Additionally, our findings underscore the need for the publication of variant pathogenicity evidence and the importance of considering every variant as VUS unless proven otherwise.

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Publication Details

Journal
Journal of Personalized Medicine
Published
2024-08-15
DOI
https://doi.org/10.3390/jpm14080864
Citations
3
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
1.65

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article

Major Causes of Conflicting Interpretations of Variant Pathogenicity in Rare Disease: A Systematic Analysis

Tatyana E. Lazareva, Yury A. Barbitoff, Yulia A. Nasykhova, Аndrey S. Glotov
3 citations
Journal of Personalized Medicine
Genomics and Rare Diseases
1.65
article

Major Causes of Conflicting Interpretations of Variant Pathogenicity in Rare Disease: A Systematic Analysis

Tatyana E. Lazareva, Yury A. Barbitoff, Yulia A. Nasykhova, Аndrey S. Glotov
article en
3 citations

Abstract

The identification of the genetic causes of inherited disorders from next-generation sequencing (NGS) data remains a complicated process, in particular due to challenges in interpretation of the vast amount of generated data and hundreds of candidate variants identified. Inconsistencies in variant classification, where genetic centers classify the same variant differently, can hinder accurate diagnoses for rare diseases. Publicly available databases that collect data on human genetic variations and their association with diseases provide ample opportunities to discover conflicts in variant interpretation worldwide. In this study, we explored patterns of variant classification discrepancies using data from ClinVar, a public archive of variant interpretations. We found that 5.7% of variants have conflicting interpretations (COIs) reported, and the vast majority of interpretation conflicts arise for variants of uncertain significance (VUS). As many as 78% of clinically relevant genes harbor variants with COIs, and genes with high COI rates tended to have more exons and longer transcripts, with a greater proportion of genes linked to several distinct conditions. The enrichment analysis of COI-enriched genes revealed that the products of these genes are involved in cardiac disorders, muscle development, and function. To improve diagnoses, we believe that specific variant interpretation rules could be developed for such genes. Additionally, our findings underscore the need for the publication of variant pathogenicity evidence and the importance of considering every variant as VUS unless proven otherwise.

Journal of Personalized MedicineVol. 14(8)
Research Institute of Obstetrics and Gynecology named after D.O. Ott (RU), Bioinformatics Institute (RU)
Ministry of Science and Higher Education of the Russian Federation
Openalex Percentile: Top 15%
Genomics and Rare Diseases
1.65
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