This project will support the training of six predoctoral students each year in the area of biostatistics, with an emphasis on applications to modern problems in genomic science. The training combines rigorous coursework in statistical methods and theory, additional courses in bioinformatics and genomic science, and an extensive laboratory training experience. For the latter, trainees will begin as supervised statistical consultants for a matched genomics lab, then over the course of a year progress into active collaborators in one or more lab projects. Most students will be supported for the first three years of their graduate programs. The scientific training will be supplemented with training in the responsible conduct of research developed specifically to meet the needs of researchers in this area. The training involves collaboration among biostatistics, genomics, and philosophy faculty members. An active recruiting plan is described for enhancing the diversity of our training and graduate programs, including a summer program bringing faculty and undergraduate students from minority serving institutions to NC State during the summer to initiate collaborative work with training faculty.

Public Health Relevance

This project will train doctoral students to analyze the biological data that forms the basis of experimentation in public health. The unique aspect of our program is providing students with the necessary skills to analyze and interpret genetic data of the sort that will soon be used for personalized medicine.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Institutional National Research Service Award (T32)
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Special Emphasis Panel (ZGM1-BRT-X (TR))
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Flicker, Paula F
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North Carolina State University Raleigh
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Hertz, Daniel L; Roy, Siddharth; Jack, John et al. (2014) Genetic heterogeneity beyond CYP2C8*3 does not explain differential sensitivity to paclitaxel-induced neuropathy. Breast Cancer Res Treat 145:245-54
Wilson, Ander; Reif, David M; Reich, Brian J (2014) Hierarchical dose-response modeling for high-throughput toxicity screening of environmental chemicals. Biometrics 70:237-46
McWhinney-Glass, Sarah; Winham, Stacey J; Hertz, Daniel L et al. (2013) Cumulative genetic risk predicts platinum/taxane-induced neurotoxicity. Clin Cancer Res 19:5769-76
Hertz, D L; Roy, S; Motsinger-Reif, A A et al. (2013) CYP2C8*3 increases risk of neuropathy in breast cancer patients treated with paclitaxel. Ann Oncol 24:1472-8
Hertz, Daniel L; Motsinger-Reif, Alison A; Drobish, Amy et al. (2012) CYP2C8*3 predicts benefit/risk profile in breast cancer patients receiving neoadjuvant paclitaxel. Breast Cancer Res Treat 134:401-10
Winham, Stacey; Wang, Chong; Motsinger-Reif, Alison A (2011) A comparison of multifactor dimensionality reduction and L1-penalized regression to identify gene-gene interactions in genetic association studies. Stat Appl Genet Mol Biol 10:Article 4
Thomas, F; Motsinger-Reif, A A; Hoskins, J M et al. (2011) Methylenetetrahydrofolate reductase genetic polymorphisms and toxicity to 5-FU-based chemoradiation in rectal cancer. Br J Cancer 105:1654-62
Winham, Stacey J; Motsinger-Reif, Alison A (2011) The effect of retrospective sampling on estimates of prediction error for multifactor dimensionality reduction. Ann Hum Genet 75:46-61
Koehler, Megan L; Bondell, Howard D; Tzeng, Jung-Ying (2010) Evaluating haplotype effects in case-control studies via penalized-likelihood approaches: prospective or retrospective analysis? Genet Epidemiol 34:892-911
Winham, Stacey J; Slater, Andrew J; Motsinger-Reif, Alison A (2010) A comparison of internal validation techniques for multifactor dimensionality reduction. BMC Bioinformatics 11:394

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