Research in the Gillis lab centers on using computational methods to understand gene function. Computational biology has taken up the challenge of determining gene function mainly by looking for commonalities in the function of genes in the context of networks derived from gene association data, such as expression profiles. Gene network analysis intended to provide insight into diverse levels of functional activity is a major interest, typically starting with regulatory interactions and moving up to more diffuse association important for understanding systemic dynamics. Gene associations (of various sorts) are believed to encode functional interaction, and this interaction is frequently shown to be able to substantially reproduce ‘gold standard’ functional annotation. This approach, commonly called “Guilt by Association”, is embedded in everything from prioritization of de novo variants to uncovering novel regulatory interactions or mechanisms of disease. Our research focuses on identifying limitations in the GBA approach and making fundamental improvements to its operation for the interpretation of neuropsychiatric genomics data. Broadly, our research can be divided into gene network development and analysis, meta-analysis of function prediction algorithms, and targeted methods applications focusing on neuropsychiatric data.