Somatic genomic mosaicism (SGM) has recently been identified in the brain, however there is not yet a clear consensus on the prevalence or functional ramifications of this phenomenon. In this thesis, I utilized the cutting-edge technology of single-cell whole-genome sequencing (scWGS) to assess the presence of somatic copy number variations (CNVs) throughout cerebral cortical development. I developed and thoroughly validated a robust CNV detection pipeline. This included the use of support vector machine learning algorithms trained on lymphocyte V(D)J recombination to model high quality CNV calls. My approach reduced false discovery rate (FDR) by 91% and identified thousands of somatic CNVs in the cerebral cortex—with 51% under 1 Mb. Analysis of ~400 cells revealed that CNVs are present throughout the genome of individual brain cells and exhibit a strong preference for deletions over amplifications. Importantly, CNV frequency triples from early- to mid-neurogenesis, which may suggest a prenatal origin of CNV diversity. Expanding upon this baseline has indicated that the incidence of somatic CNVs, particularly amplification events, is altered by fetal exposure to ethanol. I anticipate my work will inspire targeted research that will provide further insight into the importance of neuronal SGM in both healthy and diseased brains.
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