Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants
Middelkamp, Sjors; Vlaar, Judith M; Giltay, Jacques; Korzelius, Jerome; Besselink, Nicolle; Boymans, Sander; Janssen, Roel; de la Fonteijne, Lisanne; van Binsbergen, Ellen; van Roosmalen, Markus J; Hochstenbach, Ron; Giachino, Daniela; Talkowski, Michael E; Kloosterman, Wigard P; Cuppen, Edwin
(2019) Genome Medicine, volume 11, issue 1
(Article)
Abstract
BACKGROUND: Genomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients carrying de novo SVs are frequently unknown. METHODS: We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital
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abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing. RESULTS: In 7 of these cases (18%), whole-genome sequencing analysis revealed disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict the effects on genes directly affected by SVs and on genes indirectly affected likely due to the changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In 8 cases, evidence was found for the involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to two cohorts containing a total of 379 patients with previously detected and classified de novo SVs and identified candidate driver genes in 189 cases (50%), including 40 cases whose SVs were previously not classified as pathogenic. Pathogenic position effects were predicted in 28% of all studied cases with balanced SVs and in 11% of the cases with copy number variants. CONCLUSIONS: These results demonstrate an integrated computational and experimental approach to predict driver genes based on analyses of WGS data with phenotype association and chromatin organization datasets. These analyses nominate new pathogenic loci and have strong potential to improve the molecular diagnosis of patients with de novo SVs.
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Keywords: Copy number variants, Driver genes, Intellectual disability, Multiple congenital anomalies, Neurodevelopmental disorders, Position effects, Structural variation, Topologically associating domains, Transcriptome sequencing, Whole-genome sequencing, Genetics(clinical), Genetics, Molecular Medicine, Molecular Biology, Research Support, Non-U.S. Gov't, Journal Article, Research Support, N.I.H., Extramural
ISSN: 1756-994x
Publisher: BioMed Central
Note: Funding Information: This work is supported by the funding provided by the Netherlands Science Foundation (NWO) Vici grant (865.12.004) to Edwin Cuppen, as well as the National Institutes of Health (GM061354, MH115957, HD081256) to Michael Talkowski. Publisher Copyright: © 2019 The Author(s).
(Peer reviewed)