The spectrum of mosaic mutations in megalencephaly and other growth disorders by ultra-deep targeted next-generation sequencing (NGS)

Thesis (Master's)--University of Washington, 2021This is a retrospective study aimed at analyzing genetic variants and levels of mosaicism identified in a cohort of patients clinically tested for brain and body overgrowth phenotypes between 2014 and 2019 through the Megaplex multi-gene panel offered at the University of Washington. We analyzed the megaplex data to further characterize the molecular basis of overgrowth phenotypes and to optimize future interpretation and analysis of this panel. In this study, we examined samples from 180 clinical patients diagnosed with brain and body overgrowth disorders. An additional 33 samples were collected from parents to determine the inheritance of compelling variants. The panel consisted of 37 genes known to be associated with brain and body overgrowth disorders. DNA was extracted from peripheral blood in 169 (53.8%) of the samples, 69 (22.8%) in skin fibroblast, 68 (21.8%) in tissue, 3 (0.9%) in saliva, 2 (0.6%) in cell-free DNA (cfDNA), and 2 (0.6%) in unknown samples. Capture-based Next-Generation sequencing (NGS) was performed using custom-designed SureSelect probe libraries and analyzed using short read sequencing on Illumina HiSeq 2000 or MiSeq sequencers. Identified mutations were confirmed and analyzed using a custom in-house bioinformatics pipeline. Of the 213 individuals tested, 128 (41.0%) had pathogenic and likely pathogenic mutations. Most of these variants were in PIK3CA (N =49, 38%) and PTEN 17 (13.3%). There were no pathogenic or likely pathogenic mutations reported in 161 cases (51.6%). In this study, variants of uncertain significance were reported in 23 (7.4%) of cases. Ultra-deep NGS can efficiently identity mosaic mutations in megalencephaly and overgrowth disorders including detecting low levels of mosaicism, compared to Sanger sequencing and standard-depth NGS testing. Detecting mosaic mutations using deep NGS testing improves the clinical yield and provides a better understating of the spectrum of mosaic mutations underlying these phenotypes