Abstract
Homozygous deletions (HDs) may be the cause of rare diseases and cancer, and their discovery in targeted sequencing is a challenging task. Different tools have been developed to disentangle HD discovery but a sensitive caller is still lacking. We present VarGenius-HZD, a sensitive and scalable algorithm that leverages breadth-of-coverage for the detection of rare homozygous and hemizygous single-exon deletions (HDs). To assess its effectiveness, we detected both real and synthetic rare HDs in fifty exomes from the 1000 Genomes Project obtaining higher sensitivity in comparison with state-of-the-art algorithms that each missed at least one event. We then applied our tool on targeted sequencing data from patients with Inherited Retinal Dystrophies and solved five cases that still lacked a genetic diagnosis. We provide VarGenius-HZD either stand-alone or integrated within our recently developed software, enabling the automated selection of samples using the internal database. Hence, it could be extremely useful for both diagnostic and research purposes.
Highlights
We developed a new algorithm for the detection of rare single-exon Homozygous deletions (HDs) that exploit breadth-of-coverage (BoC), and we named it VarGenius-HZD
One of the five HDs found in the 1KGP Variant Calling Format (VCF) file appeared to be a false positive
We developed VarGenius-HZD, which searches for HDs within the single sample and leverages multi-sample information to corroborate such calls, and we integrated it within our recently developed VarGenius
Summary
Next-generation sequencing (NGS) is commonly used to unveil genetic causes of diseases and whole-exome-sequencing (WES) has become one of the most commonly used diagnostic tools both in the clinic and in several programs investigating rare genetic diseases. The standard protocol to investigate rare diseases includes multiple clinical diagnostics assays. One of the reasons for this is the limited knowledge of how to detect Copy Number Variation (CNV) from sequencing data. It is estimated that about 12% of the genome in the human population is subject to copy number changes [6,7]. To detect CNVs, diagnostic laboratories often use multiplex ligation-dependent probe amplification (MLPA) and array comparative genomics hybridization analysis (ArrayCGH) prior to executing NGS-based analysis [8]
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