About the Center for Statistical Genetics and Genomics

Human genetics/genomics research is increasingly characterized by high-throughput assays that can economically measure millions of biologic states and/or processes on any given patient sample.

These technologies hold tremendous promise for identifying specific disruptions that lead to disease and tailoring treatments to compensate for a patient’s particular cause of disease. As a result, genomic information is expected to proliferate both in research and clinical applications. However, the data generated by these assays are extremely complex and the datasets produced are huge. Therefore, in order to realize the promise these technologies hold for medicine, sophisticated computational and statistical methods are required to advance our knowledge of disease biology as well as to identify important, treatment relevant, features of individual patient genomes.
 


Our Center

The Duke Center for Statistical Genetics and Genomics (StatGen) creates an environment in which computational and statistical researchers throughout Duke can come together to address critical gaps in our ability to deliver on the promises of the “genomic revolution” in medicine and, as a result, establish Duke as a leading center of genomic focused statistical and computational methods development. Additionally, as StatGen pulls together quantitatively oriented scientists from various disciplines, the presence of such diverse skill sets and perspectives makes for a particularly rich educational environment.

Trainees from various educational programs (Computational Biology & Bioinformatics, Biostatistics & Bioinformatics, Statistics, Computer Science, Engineering, etc.) unite in a common laboratory environment where they work together in teams to solve key problems. This mixing of various perspectives and backgrounds breaks down traditional barriers between disciplines and forms a potent model for the development of future genomic scientists.

Andrew Allen, PhD
Andrew Allen, PhD, Director
Dear friends and colleagues:

During the last decade, sequencing costs have fallen dramatically. In 2006, the cost to sequence a human genome was over $10,000,000; in 2022 that cost is under $1,000. This reduction in cost has led to a “genomic revolution” and with it, an unprecedented increase in the amount of information that can be accessed in any given sample. This holds great promise for a better understanding of disease processes, with the hope that with such understanding will come improved medical therapies and ultimately, a positive impact on public health initiatives.

A critical barrier to making progress in genomics is our ability to analyze the resulting datasets. Technologies are constantly changing. The data generated by current experiments is extremely complex and the resulting datasets are immense. Therefore, in an effort to bridge the analytic barrier, new sophisticated statistical and computational methodologies are needed to make the most (or really get anything out) of these data. This is the primary mission of Duke’s Center for Statistical Genetics and Genomics.

Fortunately, Duke is particularly rich in quantitative researchers investigating such approaches. They can be found in departments across Duke: Biostatistics and Bioinformatics, Statistics, Engineering, Mathematics, Biology, etc.  A fundamental objective of the Center for Statistical Genetics and Genomics is to provide a community for these scientists.  This community enhances infrastructures that facilitate collaboration between groups and maximizes opportunities for scientific discovery.

I invite you to explore our website and learn more about the Center, the people involved and the types of research projects we currently support.  If you are member of the Duke community — faculty, staff, or student — and are interested in participating or have ideas for future projects, please feel free to get in touch with us at statgen@duke.edu. Or if you are not at Duke and would like to learn more about the Center, please also send us an email.  I look forward to hearing from you and hope to see you around the Center in the near future.

Andrew

Problem solving defines the goals and structure of StatGen; key barriers in our ability to facilitate genetic discovery and translate those findings into patient care gives structure to the way expertise is developed and resources allocated. In consultation with the larger genomic community at Duke, we evaluate the areas of development that address key barriers. Currently, our focus is on four strategic areas to advance genomic medicine at Duke by consolidating, empowering, and developing expertise, including:

  1. Genomic characterization—methods for measuring, annotating, and prioritizing genomic sequences and the variation found within them;
  2. Genetic architecture—methods for characterizing the extent of genetic control of a disease phenotype as well as the identification of specific genetic factors influencing disease risk (gene discovery);
  3. Prediction/genomic interpretation—methods for estimating disease risk based on genomic and other risk factors as well as formalizing approaches for attributing the likely genetic cause of an individual case of disease;
  4. Functional studies—methods for the design and analysis of studies designed to evaluate the functional impact of mutations and the effect interventions have on normalizing disease phenotypes within these model systems.

StatGen strives to leverage the considerable expertise among quantitative and computational faculty working on similar problems — bringing them together to create a unique collaborative environment.  Unifying key stakeholders from the areas of Biostatistics, Statistics, Mathematics, Computer Science, Biology, and interdisciplinary groups, such as the Center for Genomics and Computational Biology, the Center for Advanced Genomic Technologies, and the Center for Applied Genomics and Precision Medicine, fosters growth in these related fields by pooling resources, providing enhanced support for students, and coordinating infrastructure by easing the management of these relationships.  Additionally, StatGen’s research philosophies and operational structure are detailed in our Scientific Culture and Accountability Plan (SCAP).