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Low-level mosaic variants causing the pancreatic disease congenital hyperinsulinism can be detected from blood DNA

Bennett, J. J.; Laver, T. W.; Mannisto, J. M. E.; Houghton, J. A. L.; De Franco, E.; Kalyon, O.; Wright, S.; Johnson, A.-M.; De Leon, D. D.; Globa, E.; Kummer, S.; Banerjee, I.; Dastamani, A.; International Congenital Hyperinsulinism Consortium, ; Wakeling, M. N.; Johnson, M. B.; Flanagan, S. E.

2026-01-15 genetic and genomic medicine
10.64898/2026.01.13.26344002 medRxiv
Show abstract

A substantial proportion of individuals with a well-defined monogenic disorder remain without a genetic diagnosis. Low-level mosaic pathogenic variants are increasingly recognised as an underappreciated cause of monogenic disease but are technically challenging to detect, particularly in organ-specific conditions when affected tissue is inaccessible. We systematically investigated low-level mosaic variants in individuals with congenital hyperinsulinism (CHI: n=1,252) or neonatal diabetes (NDM: n=312), two opposing pancreatic disorders of insulin secretion. We screened for established pathogenic variants with variant allele fraction (VAF) <8% in dominant CHI (ABCC8, GCK, GLUD1, HK1) or dominant NDM (ABCC8, KCNJ11, INS) genes in targeted next generation sequencing (tNGS) data using Mutect2. This called 40 variants across the four genes in 39 individuals with CHI. No candidate variants were found in the NDM cohort. Orthogonal validation of 35 variants using TaqMan-based droplet digital PCR (ddPCR) confirmed 26/35 variants. The median VAF for confirmed variants was 3.6% (1.1-7.8%), while false positives (9/35) predominantly had a VAF <1% with some overlap in VAF with true positives. This study shows that disease-causing low-level mosaic variants in dominant CHI genes can be detected in blood using tNGS but require orthogonal validation. These results provide a framework to improve diagnostic yield in organ-specific conditions where mosaic variants may represent an important missed cause of disease.

Published in eBioMedicine (predicted rank #12) · training set

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