Computational Biology & Bioinformatics

PHD in Computational Biology & Bioinformatics

Program Principles & Goals

CBB students and faculty at the annual retreat

Computational Biology and Bioinformatics (CBB) at Duke University is an integrative, multi-disciplinary Ph.D. program that trains future leaders at the interdisciplinary intersection of quantitative and biomedical sciences.

CBB brings together 55 faculty from 18 departments—including computer science, statistics, mathematics, physics, engineering, biology, chemistry, and medical departments—to conduct cutting-edge research across a wide range of topics in computational biology and to prepare students to engage in innovative solutions to modern problems in the biomedical sciences.

CBB provides high-quality training in both quantitative and biomedical sciences through coursework; research rotations; journal clubs; weekly seminars; and hands-on mentoring from advisors, co-advisors, and dissertation committees. Students are trained to work independently and as part of collaborative teams. They learn to conduct research responsibly, with a commitment to data sharing and reproducible analysis, and they have professional development and teaching opportunities as part of their individual development plans.

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Meet A Faculty Member

  • Elizabeth Hauser
    Professor of Biostatistics and Bioinformatics

    Elizabeth Hauser, Professor in the Department of Medicine with secondary appointments in Biostatistics and Bioinformatics, Statistical Science, and Nursing, is a Statistical Geneticist with graduate degrees in Biostatistics and Epidemiology. Her research interests include statistical methods development for the analysis of complex genetic traits, genetic analysis of family data, identification of gene-environment interactions, and integrated analysis of metabolomics and genomic data. She has worked on studies of cardiovascular disease, diabetes, kidney disease, aging, and cancer. 

    My research interests are focused on developing and applying statistical methods to search for genes causing common human diseases. Recent work has been in the development of statistical methods for genetic studies and in identifying optimal study designs for genetic studies of complex traits. As application of these methods to specific diseases has progressed it has become apparent that etiologic and genetic heterogeneity is a major stumbling block in the research for genes for common diseases.  I am interested in developing methods to detect and account for genetic heterogeneity as one type of complex genetic model.  I am also interested in the extent to which environmental exposures interact with genetic variants to modify risks of complex diseases.  Both genetic heterogeneity and gene-environment interactions must be taken into account to achieve the goals of personalized medicine.

    Collaborative studies under way at Duke University, the Durham VA and elsewhere provide the opportunity to apply new methods to ongoing studies. My main area of application is in identifying genes for cardiovascular conditions using a variety of study designs.  One such study is the GENECARD study to identify genes for early onset coronary artery disease in families. Another study is the AGENDA study based on the CATHGEN cohort of patients from the Duke Cardiac Catheterization Lab. These two studies have been used to successfully identify a number of novel genes for coronary artery disease. I also work on studies of genetic effects in aging, kidney disease and colon cancer.  

    In turn these Collaborative studies continue to raise methodological research questions such as the effect of model misspecification on the results of linkage studies, the interpretation of confirmation studies to replicate linkage results, and the utility of a method for including additional phenotypic information when assessing linkage results.

    Keywords: linkage analysis, genetic association, gene mapping, genetic epidemiology, statistical genetics, biostatistics, cardiovascular disease, computational biology,  diabetes, aging, colon cancer, colon polyps, kidney disease
Christian Richardson

Christian Richardson

2nd Year CBB Student; Stefano Di Talia lab- Funded by T32 training grant 5T32-GM071340.
Sep 14
Manuel Rivas
Stanford University

CBB Seminar

Oct 5
Nuria Lopez-Bigas
IRB Barcelona

Computational Analysis of Cancer Genomes