This 5 year program is designed to continue to train predoctoral (PhD) students in statistical genomics with the major emphasis in cancer genomics. The goal is to train biostatisticians in the biology, etiology, and genetics of cancer, as well as to train them to conduct state-of-the-art biostatistical methodologic research relevant to the genomics of cancer as well as in related areas of genomics. The goal is to also produce biostatisticians who can collaborate with other scientific researchers and oncologists on research issues related to genomics and cancer. The typical predoctoral trainee will be a college graduate or master's level graduate with an excellent academic record appropriate for this training area. Funding is requested for the support of 5 predoctoral trainees. The Department of Biostatistics at UNC is one of the largest in the world, and has highly qualified personnel and the available facilities to provide the most comprehensive predoctoral and postdoctoral training in this research area. Several members from the Department of Genetics and the Bioinformatics and Computational Biology (BCB) program will be deeply involved in all phases of this training program and will play an integral role in this training program.

Public Health Relevance

This training grant clearly has major relevance in public health as it is designed to train the next wave of biostatistical researchers in cancer genomics, which is one of the largest and growing areas of biomedical research, in which the demand for well trained biostatistical researchers is much greater than the supply.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Damico, Mark W
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
Zip Code
Ankerst, Donna P; Goros, Martin; Tomlins, Scott A et al. (2018) Incorporation of Urinary Prostate Cancer Antigen 3 and TMPRSS2:ERG into Prostate Cancer Prevention Trial Risk Calculator. Eur Urol Focus :
O'Brien, Jonathon J; Gunawardena, Harsha P; Qaqish, Bahjat F (2018) Row versus column correlations: avoiding the ecological fallacy in RNA/protein expression studies. Brief Bioinform 19:946-953
O'Brien, Jonathon J; Gunawardena, Harsha P; Paulo, Joao A et al. (2018) The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments. Ann Appl Stat 12:2075-2095
Bower, Jacquelyn J; Vance, Leah D; Psioda, Matthew et al. (2017) Patterns of cell cycle checkpoint deregulation associated with intrinsic molecular subtypes of human breast cancer cells. NPJ Breast Cancer 3:9
Qaqish, Bahjat F; O'Brien, Jonathon J; Hibbard, Jonathan C et al. (2017) Accelerating high-dimensional clustering with lossless data reduction. Bioinformatics 33:2867-2872
Lee, Clara Nan-Hi; Deal, Allison M; Huh, Ruth et al. (2017) Quality of Patient Decisions About Breast Reconstruction After Mastectomy. JAMA Surg 152:741-748
Bryant, Christopher; Zhu, Hongtu; Ahn, Mihye et al. (2017) LCN: a random graph mixture model for community detection in functional brain networks. Stat Interface 10:369-378
Carson, Johnny L; Zhou, Laura; Brighton, Luisa et al. (2017) Temporal structure/function variation in cultured differentiated human nasal epithelium associated with acute single exposure to tobacco smoke or E-cigarette vapor. Inhal Toxicol 29:137-144
Smith, Ikuko T; Townsend, Leah B; Huh, Ruth et al. (2017) Stream-dependent development of higher visual cortical areas. Nat Neurosci 20:200-208
Rao, Shangbang; Ibrahim, Joseph G; Cheng, Jian et al. (2016) SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging. J Comput Graph Stat 25:1195-1211

Showing the most recent 10 out of 48 publications