Structural Variation Interpretation in the Genome Sequencing Era: Lessons from Cytogenetics

Structural variation (SV), defined as balanced and unbalanced chromosomal rearrangements >1 kb, is a major contributor to germline and neoplastic disease. Large variants have historically been evaluated by chromosome analysis and now are commonly recognized by chromosomal microarray analysis (CMA). The increasing application of genome sequencing (GS) in the clinic and the relatively high incidence of chromosomal abnormalities in sick newborns and children highlights the need for accurate SV interpretation and reporting. In this review, we describe SV patterns of common cytogenetic abnormalities for laboratorians who review GS data.

GS has the potential to detect diverse chromosomal abnormalities and sequence breakpoint junctions to clarify variant structure. No single GS analysis pipeline can detect all SV, and visualization of sequence data is crucial to recognize specific patterns. Here we describe genomic signatures of translocations, inverted duplications adjacent to terminal deletions, recombinant chromosomes, marker chromosomes, ring chromosomes, isodicentric and isochromosomes, and mosaic aneuploidy. Distinguishing these more complex abnormalities from simple deletions and duplications is critical for phenotypic interpretation and recurrence risk recommendations.

Unlike single-nucleotide variant calling, identification of chromosome rearrangements by GS requires further processing and multiple callers. SV databases have caveats and limitations depending on the platform (CMA vs sequencing) and resolution (exome vs genome). In the rapidly evolving era of clinical genomics, where a single test can identify both sequence and structural variants, optimal patient care stems from the integration of molecular and cytogenetic expertise.