Detecting pathogenic structural variation in families with undiagnosed rare disease in a national genome project

Summary

Some rare diseases are caused by changes in a person’s DNA, but these changes can be difficult to find. In this study, researchers looked at 24 families who had already had genetic testing but still did not have an answer.
The team used a method called long-read sequencing. This reads much longer sections of DNA than standard tests, which can make it easier to spot large changes where pieces of DNA have been deleted, moved or flipped around.
The researchers found disease-causing, or probably disease-causing, DNA changes in three of the 24 families. These changes involved the AUTS2 gene, the DLX5/6 gene and the FN1 gene. When the team looked again at the older sequencing data using newer analysis methods, they could also find these changes there.
The study shows that looking carefully for large changes in DNA can help some families with rare diseases get closer to a genetic diagnosis.

Keywords:
Rare disease, genetic diagnosis, DNA, genome sequencing, long-read sequencing, structural variants, genetic testing, AUTS2, DLX5, DLX6, FN1

Abstract

Whole-genome sequencing (WGS) projects for rare disease diagnosis typically yield a diagnostic rate of 25–41%, depending on the methods for patient selection and the extent of prior genetic testing. The Scottish Genomes Partnership (SGP) is a collaborative programme using genome sequencing to diagnose rare disease patients with presumed monogenic aetiology in the Scottish NHS. Within SGP, short-read sequencing (SRS) had previously achieved a diagnostic rate of 23% in affected families. To increase diagnostic yield, we applied Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) to a cohort of 24 SGP families (74 individuals) that remained undiagnosed after SRS-based SNV/indel analysis. We also retrospectively reviewed SRS-derived structural variant (SV) calls to assess whether LRS results could have been detected in SRS data. After quality, rarity, panel-based and inheritance-based filtering, 392 candidate SVs were retained across de novo, homozygous recessive, compound heterozygous and X-linked inheritance models, of which 8 overlapped PanelApp diagnostic-grade “green” genes. Pathogenic or likely pathogenic de novo SVs were identified in 3 of 24 families: an AUTS2 inversion, a DLX5/6 locus inversion, and an FN1 deletion. All three SVs were independently confirmed by retrospective SRS re-analysis using SVRare. These findings demonstrate that LRS-based SV analysis, supported by orthogonal SRS re-analysis, can resolve clinically significant SVs in families who remain unsolved after standard rare disease testing.