Integrating genomic data to build more predictive models of biology. - Fine, Alexander. (Tufts University. Tisch Library., 2019)
By measuring multiple characteristics of a biological system at once, one can get a more complete picture of how a perturbation affects that system. Single-omic measurements sometimes fail to correlate well with other genome-wide measurements, demonstrating the value of measuring a biological system at multiple levels. However, knowing which aspects are informative and how to best integrate them into a comprehensive model is a challenge that persists within the field of systems genetics. Here, we utilize induced and naturally-occurring perturbations across multiple biological systems to build more predictive models of biology across three distinct projects. For each project, we integrated a combination of genetic, epigenetic, transcriptional, and cellular measurements to better understand their role in the complex biological system, gaining a more comprehensive understanding than could be accomplished through examining any single measurement alone. First, we disrupted normal germ cell differentiation by knocking out the key meiotic protein PRDM9, which deposits epigenetic modifications throughout the genome to select sites for meiotic recombination. We integrated cellular and molecular phenotypes to determine how cytological and transcriptional programs remain coupled in case of developmental arrest. We found that these two developmental programs became uncoupled in the perturbed system, demonstrating the need to pair transcriptional analyses with classical molecular biology in order to put gene expression data within their proper context. Next, we employed genetic variation within and between the C57BL/6J and CAST/EiJ genomes to test the effect on binding affinity of variants within the binding site of the PRDM9 long zinc-finger array. We showed that, in addition to their individual effects, combinations of certain base-pairs across the binding site of the zinc-finger had epistatic effects on binding affinity. Finally, we integrated genetic, epigenetic, and transcriptomic data from hepatocytes of nine diverse strains of mice to build a multi-omic model of gene expression. Our work quantified the inter-dependency among genetic, epigenetic, and gene expression variation across these strains. These projects used existing and novel tools for data integration and analysis to go beyond the scope of any single-omic analysis, demonstrating the power and necessity of multi-omic analyses in the field of modern genetics.; Thesis (Ph.D.)--Tufts University, 2019.; Submitted to the Dept. of Genetics.; Advisors: Gregory Carter, and Jennifer Trowbridge.; Committee: Mary Ann Handel, Steven Munger, and Amy Yee.; Keywords: Genetics, Computer science, and Biology.