Multi-Sample Structural Variation Detection and Single Cell Analysis of Human Neurons

Deoxyribonucleic acid (DNA) is the inheritance molecule, storing genetic information in its sequence of nucleic acids. The aggregate of an organism’s genetic material, or DNA, is called its genome. The genome contains the complete set of instructions to create and maintain an organism. Initially, it was thought that genomes were static entities, changing slowly over very large timescales; however, insights into evolution and discoveries made by sequencing DNA have dispelled this notion. The current understanding asserts that genomes are highly plastic and dynamic structures. Genomic alterations are broadly classified as variations and can even be found occurring among cells in the same tissue. Given the importance of DNA in the functioning of a cell, detecting and characterizing DNA variation is paramount in understanding disease, especially cancer. In this bipartite dissertation, I will describe a population-based method for detecting variation and characterizing the prevalence of copy number variation in human neurons. In Chapter 1, I will provide a brief overview of genetics and variation followed by an explanation of current methods of detecting variation in the genome. In Chapter 2, I will detail a novel framework that increases the sensitivity and specificity of genomic structural variation detection by using multiple samples. In Chapter 3, I will describe how single cell sequencing has been used to uncover mosaic copy number variation in human neurons. Finally, in Chapter 4, I will conclude with a discussion of future directions and ongoing experiments.