AI Insight
Researchers developed GAVURD (Genomic Analysis of Variants in Unresolved Rare Disease), a computational system designed to identify disease-causing variants in non-coding regions of DNA that are missed by standard genetic testing. The system analyzes whole genome sequencing data from patients and their parents, uses topologically associated domain information to link variants to relevant genes, and prioritizes candidates based on how well they match patient symptoms. In a proof-of-concept study of ten unresolved rare disease cases, GAVURD identified six potentially causal non-coding variants that warrant further investigation.
Why it matters
Approximately 50% of rare disease patients remain without a genetic diagnosis even after comprehensive sequencing, and pathogenic variants in non-coding DNA regions may explain many of these cases. This tool provides a systematic approach to analyze the vast non-coding genome and could help identify diagnoses for millions of people currently lacking genetic explanations for their conditions.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Unresolved rare disease is a major public health challenge affecting ~300 million people worldwide. At least 50% of these individuals remain genetically unresolved after applying exome sequencing and/or whole genome sequencing. One source of these missing diagnoses is the presence of rare variants within the non-coding genome that are detected but not interpreted by whole genome sequencing. In order to systematically evaluate candidate pathogenic non-coding variants, we created the Genomic Analysis of Variants in Unresolved Rare Disease (GAVURD) system. GAVURD leverages trio whole genome sequencing alignment data to produce a short list of putative pathogenic non-coding variants for a given proband. GAVURD uses best practices for de novo and rare inherited variant identification, links variants to human disease genes harnessing topologically associated domain (TAD) data, and rank prioritizes variants based on phenotypic overlap. As a proof-of-concept, we applied GAVURD to ten probands with unresolved rare disease and implicated six potentially causal non-coding variants based on a confluence of evidence supportive of pathogenicity. The GAVURD system serves an important role in prioritizing candidate non-coding causal variants for unresolved rare disease that can serve as the high value and informed focus of additional functional follow-up studies.