The generation of diverse genome-scale data across organisms and experimental conditions is becoming increasingly commonplace, creating unprecedented opportunities for understanding the molecular underpinnings of human disease. However, realization of these opportunities relies on the development of novel computational approaches, as these large data are often noisy, highly heterogeneous, and lack the resolution required to study key aspects of metazoan complexity, such as tissue and cell-type specificity. Furthermore, targeted data collection and experimental verification is often infeasible in humans, underscoring the need for methods that can integrate -omics data, computational predictions, and biological knowledge across organisms. To address these challenges, I have developed 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 construct 203 tissue- and cell-type specific functional networks and predict candidate genes for 25 different human diseases and traits using C. elegans as a model system and focus on Parkinson's disease as a case study. To further model the role of cell-type specificity in human disease, I developed the first network models of Alzheimer's-relevant neurons, in particular, the neuron type most vulnerable to the disease. We then use these models to predict and interpret processes that are critical to the pathological cascade of Alzheimer's. Finally, I will discuss my future plans for developing a systems-level understanding of the environmental and temporal effects on disease, building on my current work, including network modeling of environmental effects of splicing, temporal tracking of molecular markers in rheumatoid arthritis, and developing interactive analytical and visualization systems.
Vicky Yao is a postdoctoral fellow at the Lewis-Sigler Institute for Integrative Genomics. She received her PhD in Computer Science from Princeton University, advised by Olga Troyanskaya, and her Masters in Statistics from the University of Chicago. Her research focuses on the development of machine learning and statistical methods to improve understanding of the biological circuitry that underlies living organisms and how its dysregulation may lead to disease.