| his dissertation is focused on the development of technologies to increase our understanding of the correlation between genomes and phenomes. Particularly, it describes advances in sequencing technology and targeted capture of genomic regions of interest to increase the efficiency of collecting genomic information. Additionally, it discusses improvements in interpreting these genomic regions using existing databases. The introduction starts by describing the nature of our genetic individuality and our understanding of it, beginning with the mapping of genes using extensive pedigrees, followed by the mapping of common diseases to common variants using large populations, and finally the sequencing of genetic material from large populations to correlate phenotypes with rare variants. It then provides a brief review of Second Generation Sequencing (SGS), genomic targeting methods and the clinical applicability of disease-related variants found in healthy individuals. Chapter 2 describes a web-based software tool and its performance in annotating all genomic variants identified in 25 genomes using several general mutation databases, together with an algorithm for identifying those variants with the potential for clinical utility. Additionally, arguments for clinical geneticists prioritizing various sets of genes are presented and the error rates of different sequencing platforms are discussed. Chapter 3 describes molecular inversion probes (MIPs) and their use in targeting exons for sequencing from the first ten participants from the Personal Genome Project. Additionally, it describes various tools developed to analyze this data. Chapter 4 describes different biases of MIPs and presents design criteria for future experiments targeting genomic regions with MIPs. Appendices A, B and C describe technical improvements to the open source Polonator SGS platform, including the development of a microfluidics flow-cell (Appendix A), the replacement of emulsion PCR amplified beads with rolling circle amplified colonies ("rolonies", Appendix B) and the use of ordered arrays to increase the density of sequenced features (Appendix C). Taken together, these technical improvements represent a >400x decrease in sequencing cost. Appendix D describes the first open-source SGS platform, Appendix E improvements to MW targeting, Appendix F the MIPTAG Pro algorithm for designing MIPs and Appendix G the analysis of a genome sequenced for a consumables cost of less than... |
