Combining callers improves the detection of copy number variants from whole-genome sequencing

Copy Number Variants (CNVs) are deletions, duplications or insertions larger than 50 base pairs. They account for a large percentage of the normal genome variation and play major roles in human pathology. While array-based approaches have long been used to detect them in clinical practice, whole-genome sequencing (WGS) bears the promise to allow concomitant exploration of CNVs and smaller variants. However, accurately calling CNVs from WGS remains a difficult computational task, for which a consensus is still lacking. In this paper, we explore practical calling options to reach the best compromise between sensitivity and sensibility. We show that callers based on different signal (paired-end reads, split reads, coverage depth) yield complementary results. We suggest approaches combining four selected callers (Manta, Delly, ERDS, CNVnator) and a regenotyping tool (SV2), and show that this is applicable in everyday practice in terms of computation time and further interpretation. We demonstrate the superiority of these approaches over array-based Comparative Genomic Hybridization (aCGH), specifically regarding the lack of resolution in breakpoint definition and the detection of potentially relevant CNVs. Finally, we confirm our results on the NA12878 benchmark genome, as well as one clinically validated sample. In conclusion, we suggest that WGS constitutes a timely and economically valid alternative to the combination of aCGH and whole-exome sequencing.

Automated summary

Copy Number Variants (CNVs) are deletions, duplications, or insertions larger than 50 base pairs that contribute to normal genome variation and have significant implications in human pathology. While traditional array-based approaches have been used for CNV detection, whole-genome sequencing (WGS) offers the potential for comprehensive exploration of CNVs and smaller variants. This study evaluates practical calling options for CNV detection from WGS data, highlighting the need to strike a balance between sensitivity and sensibility. Combining multiple callers and tools like SV2 shows promising results in terms of computation time and accuracy compared to array-based methods like aCGH.