Next-Generation Cytogenetics: An international, multi-center study of chromosomal aberrations in constitutional diseases and leukemia with Bionano genome imaging

Paris Descartes University / Radboud Medical Center
Laila El Khattabi, PharmD PhD | Alexander Hoischen, PhD | June 26, 2020

Structural variants (SVs) are an important source of genetic variation in the human genome and they are involved in a multitude of human diseases as well as cancer. SVs are enriched in repeat-rich regions of the human genome, and several remain undetected by conventional short-read sequencing technologies. Here we applied Bionano Genomics’ high-resolution optical mapping to comprehensively identify SVs, leveraging the most recent improvements: a) deep-genome coverage (400x) to enable somatic mutation detection in leukemia samples; b) highest resolution (≥500bp) and no sequencing bias allows detection of SVs refractory to sequencing in rare disease cases.

Deep-genome coverage was used to comprehensively detect somatic SVs on 52 leukemia samples, and allowed the 100% concordance for all aberrations with >10% variant allele fraction that previously required a combination of karyotyping, FISH and/or CNV-microarray. In addition, optical mapping allowed the identification of SVs that remained refractory to detection by classical methods including MLPA, Sanger sequencing, exome and/or genome sequencing. This allowed the identification of likely disease causing SVs in 5/20 research cases. Including a) a partial deletion of the NSF gene located in the distal segmental-duplication in 17q21.31, which likely disrupts NSF in a patients with intellectual disability; this event remained undetected even by long-read SMRT sequencing; b) a retrotransposon insertion in patient with a tumor-predisposition syndrome.

In summary, the full concordance with diagnostic standard assays in leukemia demonstrates the potential to replace classical cytogenetic tests. We furthermore show how the complementary use of mapping rather than sequencing approaches can unmask hidden structural variants.