One of the central tenets of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that genes and genetic variants work together in complex regulatory networks that help define phenotypes and mediate phenotypic transitions. We have found that the networks, and their structure, provide unique insight into how genetic elements interact with each other and the structure of the network has predictive power for identifying critical processes in health and disease and for identifying potential therapeutic targets. Drawing on examples from TCGA, GTEx, and other large data sets, we will explore the ways in which modeling regulatory networks provides insight into functional changes that can drive cancers and point to therapies.

Learning Objectives: 

1. Understand why genetic differences alone are not enough to determine phenotype.

2. Describe the difference between a gene regulatory network and a correlation network.

3. Describe "data shift" and "under-specification" and how prior knowledge of a system can help address the latter.