The genetic epidemiology and clinical course of cancer is increasingly being challenged by big data and high complexity. We aim to develop novel statistical methods to address some of the major problems facing cancer genetic epidemiologists in the “omic” era and to illustrate their use for novel discovery, characterization, and prediction in various cancer studies. These methods address a wide range of analysis challenges, including feature selection, mediation, interaction, and characterization, all in the context of integrating prior biological knowledge with epidemiological or clinical data. All these projects are motivated by an overall objective to provide tools for evaluating the impact of potential preventive or therapeutic interventions based on modifiable risk factors.

Happy Scientist Seminars

Happy Scientist Seminars are educational seminars sponsored by Core D of the Biostats Program Project award (P01 CA196569). This series, the “Happy Scientist” seminar series, is aimed at providing educational material for members of Biostats, both students and faculty, about a variety of tools and methods that might prove useful to them.


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.

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Core A: Administrative Core

The administrative core provides scientific oversight, enhances communications among investigators, and supports all of the activities of this program. It works proactively to assure complete synergy across the Research Projects and Cores around the theme of “integrative genomics” that is the focus of the program. Led by Jim Gauderman, PhD and Kimberly Siegmund, PhD

Funding by the National Cancer Institute P01 CA196569-07.