Incorporating CNV analysis improves the yield of exome sequencing for rare monogenic disorders—an important consideration for resource-constrained settings

Abstract

Exome sequencing (ES) is a recommended first-tier diagnostic test for many rare monogenic diseases. It allows for the detection of both single-nucleotide variants (SNVs) and copy number variants (CNVs) in coding exonic regions of the genome in a single test, and this dual analysis is a valuable approach, especially in limited resource settings. Single-nucleotide variants are well studied; however, the incorporation of copy number variant analysis tools into variant calling pipelines has not been implemented yet as a routine diagnostic test, and chromosomal microarray is still more widely used to detect copy number variants. Research shows that combined single and copy number variant analysis can lead to a diagnostic yield of up to 58%, increasing the yield with as much as 18% from the single-nucleotide variant only pipeline. Importantly, this is achieved with the consideration of computational costs only, without incurring any additional sequencing costs. This mini review provides an overview of copy number variant analysis from exome data and what the current recommendations are for this type of analysis. We also present an overview on rare monogenic disease research standard practices in resource-limited settings. We present evidence that integrating copy number variant detection tools into a standard exome sequencing analysis pipeline improves diagnostic yield and should be considered a significantly beneficial addition, with relatively low-cost implications. Routine implementation in underrepresented populations and limited resource settings will promote generation and sharing of CNV datasets and provide momentum to build core centers for this niche within genomic medicine.

Limited Added Diagnostic Value of Whole Genome Sequencing in Genetic Testing of Inherited Retinal Diseases in a Swiss Patient Cohort

“…The integration of CNV analysis in WES bioinformatic pipelines is challenging due to the plethora of tool options, the low concordance in calls from different tools, the relatively high false-positive calls, and the low sensitivity [ 34 ]. Despite these limitations, CNV analysis from WES data has led to an increase in diagnostic yield in mixed cohorts [ 53 , 54 ], as well as in IRD cohorts [ 55 , 56 ]. The implementation of CNV analysis in the exome bioinformatic pipeline may, therefore, reduce the added diagnostic value of WGS.…”