Dissecting genotype-phenotype relationships through integration and analysis of differential genetic interaction maps

Epistasis, refers to the phenomenon, in which the phenotypic effect of one gene depends on or is modified by a secondary gene. High-throughput screening of genetic interactions has been made possible through a variety of methods such as Synthetic Genetic Array, combinatorial RNAi and genome-wide association studies. However, thus far the majority of data has been generated in standard laboratory conditions. Yet in the course of their lives, cells are exposed to a wide-array of environmental stresses. How genetic interaction networks are re-wired in response to such stimuli remains an open question. In this thesis, I describe the generation and analysis of differential genetic interaction data, in response to numerous genotoxic stresses and demonstrate how this data can be used to elucidate cellular pathways required for the response to these stresses. In Chapter 2, I describe the development of computational and visualization algorithms designed to integrate physical and differential genetic interaction data. This integrative approach enables the automatic assembly of raw interactions into pathway models and maps the higher-order functional relationships between such pathways. In Chapter 3, I map changes in the cell's genetic network across a panel of mechanistically distinct DNA-damaging agents. This multi- conditional genetic interaction map identifies both agent- specific and general DNA damage response pathways. More over, we anticipate that this data will be an important resource for the study of the DDR and its associated diseases. In Chapters 4 and 5, I describe our efforts to analyze genetic interactions derived from forward genetic screening approaches, such as genome-wide association studies (GWAS). We develop a novel computational algorithm, which greatly increases our power to detect such interactions and furthermore, through projection of these genetic interactions within and across protein complexes, demonstrate that such pathway-based interpretations of GWAS data provide novel hypothesis regarding the mechanism through which combinations of polymorphisms may affect a phenotyp