Abstract:
The increasingly commonplace generation of genome-scale data provides us with a wealth of biological knowledge that captures global molecular-level changes in diverse model organisms and humans. However, these large data are often noisy, highly heterogenous, and lack the resolution required to study key aspects of metazoan complexity, such as tissue and cell-type specificity. Furthermore, in order to efficiently apply the results of such studies towards understanding the etiology of complex human diseases, we must consider integrative approaches that fuse computational predictions and model organism experimentation in the context of human data.
Here, we apply this core idea to the development of three different network-based approaches, with example applications to the study of neurodegenerative diseases. First, we will discuss diseaseQUEST, an integrative computational-experimental framework that combines human quantitative genetics with in silico functional network representations of model organism biology to systematically identify disease gene candidates. This framework leverages a novel semi-supervised Bayesian network integration approach to predict tissue- and cell-type specific functional relationships between genes in model organisms. We use diseaseQUEST to predict candidate genes for 25 different human diseases and traits using C. elegans as a model system, with a particular focus on Parkinson’s disease. Second, we will describe an approach that takes advantage of high-quality neuron-specific molecular profiles of cells that vary in vulnerability to Alzheimer’s disease obtained in mouse to generate neuron-specific functional networks in human. We then combine these network models with human quantitative genetics data to prioritize likely Alzheimer’s disease candidates. Finally, we will present a network-based approach that systematically identifies differential isoform interactions. We apply this approach to the study of tissue and environment dynamics in Alzheimer’s disease.