# Copy Number Variation: Methods and Clinical Applications
## 4 Clinical Interpretation of CNVs  
As detailed above, CNVs are an important source of normal and pathogenic variation. Pathogenic CNVs are typically large and contain multiple genes, significantly enriched in developmental genes and genes with greater evolutionary copy number conservation across mammals. On the other hand, genes found in benign CNVs have more variable copy numbers, suggesting that dosage sensitivity of genes is a predominant causative factor for CNV pathogenicity [94]. In everyday practice, laboratory diagnosticians, genetic counselors, and clinical geneticists need to distinguish pathogenic CNVs from benign ones in their patients, and such interpretation can be challenging. Many recurring CNVs are already classified into one of the five main classes of clinical impact (benign, likely benign, VUS, likely pathogenic, and pathogenic), a uniformized system commonly used for the interpretation of other sequence variants as well [95]. However, progress in the detection of CNVs resulted in a growing amount of novel CNVs that need further analysis to determine their potential clinical impact, while between the two clear extremes (benign and pathogenic), a wide spectrum of CNVs lacking evidence to support their clinical significance are classified as VUS [4]. This led to a demand for a more convenient annotation and classification of such CNVs. Even though the prediction of the clinical impact of CNVs is a challenge, there are several in silico prediction or decision support tools (Table 2) for CNV classification to help laboratory diagnosticians, genetic counselors and clinicians [96]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Tool** & **Description** & **Operating System** & **Availability** & **Reference** \\ \hline \multirow{4}{*}{AnnotSV} & A standalone program designed for annotating and ranking SVs. & \multirow{4}{*}{MS Windows Mac OS X Linux} & \multirow{4}{*}{Free-software license} & \multirow{4}{*}{[97]} \\  & The tool compiles functionally, regulatory and clinically relevant information and aims at providing annotations useful to (i) interpret the potential pathogenicity of SVs and (ii) filter out potential false positives. & & \\ \hline \multirow{4}{*}{iCopyDAV iCopyDAV} & Integrated platform for CNV detection, annotation and visualization enabling the user to identify CNVs in whole-genome NGS data. & \multirow{4}{*}{Mac OS X Linux} & \multirow{4}{*}{Freely available} & \multirow{4}{*}{[98]} \\  & icopyDAV consists of seven modules for (i) calculating optimal bin size; (ii) data preparation; (iii) data pre-treatment; (iv) segmentation; (v) variant calling; (vi) CNV annotation; (vii) plotting CNVs across the chromosome. & & \\ \hline \multirow{4}{*}{AluScanCNV2} & An R package for CNV calling and machine learning-based cancer risk prediction with NGS data. It uses Geary-Hinkley Transformation-based comparison of the read-depth. & MS Windows Mac OS X Linux & Free-software license & \multirow{4}{*}{[99]} \\  & A web service that displays genomic overlaps of the input coordinates with built-in databases of CNVs and SNPs from genome-wide association studies and additional features such as ENCODE regulatory elements, cytudands, segmental duplications, genome fragile sites, pseudogenes, promoters, enhancers, CpG islands, and methylation sites. & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Decision support tools for annotation and/or classification of CNVs. SV (structural variation); WGS (whole-genome sequencing); WES (whole-exome sequencing); TADs (topologically associated domains). It is essential to produce consistent, evidence-based clinical classification across laboratories and accurate clinical interpretation of CNVs, which requires not only appropriate methods to evaluate genomic content but also correlating clinical findings with reports in the medical literature.