ArticleHGG advances2026
A personalized genomic-medicine approach to rare genomic disorders associated with simple chromosomal structural variants.
Article in HGG advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Chromosomal structural-variant (CSV)-associated conditions, or genomic disorders (GDs), remain a diagnostic challenge. Our cohort included 26 individuals with severe unselected phenotypes associated with simple CSVs, as well as two clinically unaffected individuals. Long-insert short-read (LI-SR) and short-insert whole-genome sequencing (SI-WGS) were applied to capture the full spectrum of structural and small sequence variants, complemented by whole-blood transcriptome profiling in 12 individuals. Genome-wide, pathway-stratified, and candidate gene-based phenotype-overlap analyses, together with pathway enrichment analysis, were performed to define the spectrum of pathogenic variants and underlying molecular mechanisms, thereby enabling comprehensive phenotype-genotype correlations and to assess the diagnostic yield and suitability of the applied WGS technologies within a genome-first personalized genomic-medicine (PGM) framework. Among disease-causing genes, eight were affected by gene disruption or position effects leading to autosomal dominant (AD) neurodevelopmental GDs, six were identified in individuals with benign simple CSVs with AD conditions caused by small sequence variants, and one case was attributed to a digenic combination of distinct variant types. Candidate disease-associated genes included FLT1 (RASopathies), SSBP3 (chromatinopathies), the ARL14EP-DT/FSHB locus, and position-effect-affected genes ZC4H2 and EDA2R, supported by phenotypic, transcriptomic, and pathway enrichment data. This integrative PGM approach-combining WGS, transcriptomic profiling providing orthogonal functional evidence, and detailed phenotyping-improves diagnostic yield, shortens the diagnostic odyssey, facilitates functional genome annotation, and enables the identification of previously unreported disease associations. These findings support SI-WGS as a first-tier diagnostic strategy for SV-associated conditions and highlight its potential utility in prenatal diagnosis and personalized medicine.
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