Uncovering the contribution of copy number variants to disorders of insulin secretion

This project aims to identify new genetic causes of disorders of monogenic insulin secretion: disorders of decreased (diabetes) or excessive (hyperinsulinism) insulin secretion. A genetic diagnosis guides patient management and recurrence risk.

Testing of the known genetic causes fails to provide a diagnosis for many patients, including half of those with hyperinsulinism. Copy number variants (CNVs) are structural variants where large regions of the genome are duplicated or deleted.

They are a known cause of these disorders but are not detected genome-wide by routine screening using targeted gene panel sequencing.

We have developed a new computational tool, SavvyCNV, which enables CNVs to be detected genome-wide using ‘off-target’ reads that fall outside the targeted genes.

My preliminary data demonstrates this novel approach can detect both known and novel disease-causing CNVs from re-analysis of routinely generated targeted gene panel data.

CNVs can cause disease by disrupting gene regulation, giving crucial clues to identifying and understanding non-coding causes of disease.

This project will identify CNVs in existing sequencing data from >6500 patients with disorders of insulin secretion without a genetic diagnosis.

It will: - Quantify the contribution of known disease-causing CNVs to disorders of insulin secretion - Discover novel disease-causing CNVs in patients with disorders of insulin secretion - Identify the causative gene/region through the interrogation of genetic and epigenomic data This will provide important new genetic diagnoses and improve our knowledge of the genetic regulation of insulin secretion - acting as a model to study CNVs across all rare diseases.