Abstract
SummaryStructural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DDs). Individuals presenting with DDs are often referred for diagnostic testing with chromosomal microarrays (CMAs) to identify large copy-number variants (CNVs) and/or with single-gene, gene-panel, or exome sequencing (ES) to identify single-nucleotide variants, small insertions/deletions, and CNVs. However, individuals with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the tool InDelible, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DDs recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 63 rare, damaging variants in genes previously associated with DDs missed by standard SNV, indel, or CNV discovery approaches. Clinical review of these 63 variants determined that about half (30/63) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21 and 500 bp in size and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.9%. Of particular interest were seven confirmed de novo variants in MECP2, which represent 35.0% of all de novo protein-truncating variants in MECP2 among DDD study participants. InDelible provides a framework for the discovery of pathogenic SVs that are most likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases.
Highlights
We considered variants with a non-Finnish European minor allele frequency of R1 3 10À4 (19/260; 7.3%) in the Genome Aggregation Database[1,24] or presence in other unrelated individuals within Developmental Disorders (DDD) (20/260; 7.7%) as unlikely to be the cause of the child’s disorder
We found that InDelible missed variants for three primary reasons
We identified six genes with multiple previously undetected Structural variation (SV) among unrelated individuals, of which the most recurrently affected was MECP2, the causal gene of Rett syndrome (Figure 2E).[26]
Summary
Structural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. To quantify the added diagnostic value of running InDelible across different settings by a user seeking to run a minimal number of algorithms, we estimated the proportion of unique PTVs InDelible would find if used alone or jointly with other algorithms targeting a breadth of variant types (SNVs, indels, large deletions, and MEIs; supplemental material and methods).[3,9,11] Overall, and when using other approaches, InDelible-specific variants will likely represent between 2%–3% of all PTVs in a given cohort (Figure 4) This observation strongly implies that workflows that do not incorporate algorithms capable of detecting this class of cryptic variation are likely to achieve only 97%–98% sensitivity for pathogenic PTVs. we note that InDelible is unlikely to be more effective than currently available tools when applied to genome-sequencing data. Our results show that through a combination of enhanced algorithm design, variant annotation, and clinical interpretation, ongoing interrogation of well-studied datasets will continue to yield improved diagnoses
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