Center for Statistical Genomics

This project will develop statistical approaches leveraging latent structures or mediating relationships for the integration of multiple omics data to better understand gene-to-phenotype relationships. The methods will be applicable to either individual-level data or summary statistics, and they will have a direct impact on applied investigations by facilitating a better understanding of potential mechanisms driving underlying cancer etiology.

The availability of high-volume ‘omic’ data, including gene expression, metabolome, methylation, and microbiome, provides new opportunities to identify gene-environment (G×E) and omic × E interactions. This project will develop statistical methods to leverage omic data to improve power for identifying novel interactions as well as to inform the biological mechanism by which genes and exposures affect cancer outcomes.

Understanding the role that genes play in life is a key issue in biomedical sciences, yet the overwhelming majority of sequences in public databases remain uncharacterized. Functional annotation is important for a variety of downstream analyses of genetic data, yet experimental characterization of function remains costly and slow. This project therefore proposes three Aims focused on improving our understanding of functional genomics, thereby allowing better translation of data to knowledge impacting human health.