Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants

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.

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.

We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing.

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.