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. 2023 Jul 21;14(7):1490.
doi: 10.3390/genes14071490.

Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases

Faidon-Nikolaos Tilemis  1 , Nikolaos M Marinakis  1   2 , Danai Veltra  1   2 , Maria Svingou  1 , Kyriaki Kekou  1 , Anastasios Mitrakos  1   2 , Maria Tzetis  1 , Konstantina Kosma  1 , Periklis Makrythanasis  1   3   4 , Joanne Traeger-Synodinos  1 , Christalena Sofocleous  1
Affiliations

Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases

Faidon-Nikolaos Tilemis et al. Genes (Basel). .

Abstract

Whole-Exome Sequencing (WES) has proven valuable in the characterization of underlying genetic defects in most rare diseases (RDs). Copy Number Variants (CNVs) were initially thought to escape detection. Recent technological advances enabled CNV calling from WES data with the use of accurate and highly sensitive bioinformatic tools. Amongst 920 patients referred for WES, 454 unresolved cases were further analysed using the ExomeDepth algorithm. CNVs were called, evaluated and categorized according to ACMG/ClinGen recommendations. Causative CNVs were identified in 40 patients, increasing the diagnostic yield of WES from 50.7% (466/920) to 55% (506/920). Twenty-two CNVs were available for validation and were all confirmed; of these, five were novel. Implementation of the ExomeDepth tool promoted effective identification of phenotype-relevant and/or novel CNVs. Among the advantages of calling CNVs from WES data, characterization of complex genotypes comprising both CNVs and SNVs minimizes cost and time to final diagnosis, while allowing differentiation between true or false homozygosity, as well as compound heterozygosity of variants in AR genes. The use of a specific algorithm for calling CNVs from WES data enables ancillary detection of different types of causative genetic variants, making WES a critical first-tier diagnostic test for patients with RDs.

Keywords: Copy Number Variants; ExomeDepth; Whole-Exome Sequencing; complex genotypes; diagnostic yield; rare diseases.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow followed to result to a final diagnostic yield for WES.
Figure 2
Figure 2
(A) Distribution of patients with respect to the clinical features of the primary referral. The cohort concerns patients remaining undiagnosed after WES analysis and the numbers are presented in percentages. (Β) Diagnostic yield (%) of the WES-based CNV algorithm (ExomeDepth) per category of disorders.
See this image and copyright information in PMC

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