Our overall goal in this proposal is to functionally analyze mutations in human genes associated with a set of model complex disorders for which a large number of uncharacterized genetic variants have been obtained. With the prospect of knowing the complete genotype of multiple individuals and with increasingly sophisticated ways of measuring phenotypes, biomedicine can now explore genotype-phenotype relationships in mechanistic detail. A fundamental issue to be resolved in the characterization of genotypes is how genetic variation directly relates to phenotype. Our premise is that sequence alone is not sufficient. What is needed is a disruptive shift to better understand the functional and mechanistic molecular consequences of genotypic differences. Our solution to this challenging problem is to investigate the complex macromolecular networks, or "interactomes", formed by large numbers of interacting genes and gene products inside cells and to the perturbations of these networks that occur as a consequence of genetic variation. In characterizing genotype- to-phenotype relationships via an interactome network approach, genotypic variation can lead to either a complete gene knockout, modeled as removal of a node and all of its edges in the network, or alternatively, as interaction-specific perturbation, leading to the removal or strengthening of specific interactions, modeled as edge-specific, or "edgetic" perturbations. We propose that to better understand genotype-phenotype relationships, "edgotypes" should be characterized by systematically establishing the state of node removal versus edgetic perturbations for every biophysical interaction. These strategies will be applied to a small set of complex clinical phenotypes chosen because they exhibit extensive genetic heterogeneity, pleiotropy and phenotypic overlap. These four clinical phenotypes (Usher syndrome;retinitis pigmentosa;Hirschsprung disease;Bardet-Biedl syndrome) have also been studied enough that ample numbers of mutations are known to enable edgotyping profiling at sufficient depth to generate informative disease networks. Study of these four clinical phenotypes should accordingly provide fundamental insights into genotype-phenotype relationships, the impact of DNA sequence variants on specific biological functions, disease modules, and disease classification.
Our specific aims are to: i) Generate deep and robust interactome network maps for the selected set of clinical phenotypes, ii) Generate edgotypic maps of perturbed physical and biochemical interactions amongst gene products implicated in the selected set of clinical phenotypes, iii) Exploit edgotyping data computationally to derive mechanistic molecular insights into genotype-phenotype relationships for the selected set of clinical phenotypes.
New information that will emerge from the proposed work should lead to advanced understanding of the molecular mechanisms involved in complex human diseases. We propose to improve understanding of these human diseases by analyzing how disease-causing mutations perturb important molecular networks within cells. Important outcomes of this work will be methods and approaches that can better establish causality of genetic variations or mutations statistically associated with diseases as well as mechanistic insights that can better direct therapeutic intervention.