| Literature DB >> 34159400 |
Jonas Elsner1, Martin A Mensah1,2, Stefan Mundlos3,4, Malte Spielmann5,6,7, Manuel Holtgrewe8, Jakob Hertzberg9, Stefania Bigoni10, Andreas Busche11, Marie Coutelier1,12, Deepthi C de Silva13, Nursel Elçioglu14,15, Isabel Filges16, Erica Gerkes17, Katta M Girisha18, Luitgard Graul-Neumann1, Aleksander Jamsheer19, Peter Krawitz20, Ingo Kurth21, Susanne Markus22, Andre Megarbane23, André Reis24, Miriam S Reuter24, Daniel Svoboda25, Christopher Teller26, Beyhan Tuysuz27, Seval Türkmen1,28, Meredith Wilson29, Rixa Woitschach30, Inga Vater31, Almuth Caliebe31, Wiebke Hülsemann32, Denise Horn1.
Abstract
The extensive clinical and genetic heterogeneity of congenital limb malformation calls for comprehensive genome-wide analysis of genetic variation. Genome sequencing (GS) has the potential to identify all genetic variants. Here we aim to determine the diagnostic potential of GS as a comprehensive one-test-for-all strategy in a cohort of undiagnosed patients with congenital limb malformations. We collected 69 cases (64 trios, 1 duo, 5 singletons) with congenital limb malformations with no molecular diagnosis after standard clinical genetic testing and performed genome sequencing. We also developed a framework to identify potential noncoding pathogenic variants. We identified likely pathogenic/disease-associated variants in 12 cases (17.4%) including four in known disease genes, and one repeat expansion in HOXD13. In three unrelated cases with ectrodactyly, we identified likely pathogenic variants in UBA2, establishing it as a novel disease gene. In addition, we found two complex structural variants (3%). We also identified likely causative variants in three novel high confidence candidate genes. We were not able to identify any noncoding variants. GS is a powerful strategy to identify all types of genomic variants associated with congenital limb malformation, including repeat expansions and complex structural variants missed by standard diagnostic approaches. In this cohort, no causative noncoding SNVs could be identified.Entities:
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Year: 2021 PMID: 34159400 DOI: 10.1007/s00439-021-02295-y
Source DB: PubMed Journal: Hum Genet ISSN: 0340-6717 Impact factor: 4.132