with the recent advances in high-throughput genomic and proteomic technologies, there is an urgent demand and shortage of the next-generation PhD biostatisticians who understand biology, especially genetics and genomics because they are so essential in understanding mechanisms and risk of disease, and how people respond to different therapeutic interventions. However, traditional training in biostatistics, which emphasizes almost exclusively on statistical theory and methods, no longer meets the need. As a response, this application develops an innovative and interdisciplinary pre- doctoral T32 Biostatistics in Genetics and Genomics (BiG2) training program and request support for implementing it. The primary mission of this training grant is to prepare Biostatistics predoctoral trainees for leadership roles in biomedical and public health research through excellent training and mentorship in genetics and genomics. Our specific objectives include completion by each trainee of required and elective coursework in our PhD program and in other departments, mentored learning through interdisciplinary research projects, development of communication and networking skills, attendance at and participation in journal clubs and seminars, and successful completion of a program of interdisciplinary research through their dissertation, all of which give our trainees broad training in a rapidly expanding field. The distinctive feature of the proposed training program is that each objective has both a biostatistical aspect and a biomedical aspect. A key is to ensure each trainee learns how to carry out a cohesive and interdisciplinary research program, including working with biomedical researchers, and writing, submitting, editing, and resubmitting scholarly papers. The program is designed to support students for up to three years at the early stages of their PhD studies, after which their support will switch to projects funded by the faculty mentors' research grants. We request salary, tuition, and limited travel support for 1 new predoctoral trainee in each of the fist two years of a 5-year funding period, and for 2 new predoctoral trainees in each of the following three years. This training program will contribute to our nation's ability to analyze important biomedical data, thus improving the health and well-being of the public in the long run.

Public Health Relevance

Biostatisticians play an indispensable role in the design, implementation and analysis of biomedical and public health studies. They are essential collaborators from the initial concept and study design through final analyses and dissemination of results. As a result, training of the next generation biostatisticians is crucial o the ongoing health of the nation's biomedical research enterprise. In this application, we request stipend, tuition, and limited travel support for the interdisciplinary training of one to two new predoctoral students in biostatistics and genetics/genomics per year for five years.

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)
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Marcus, Stephen
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University of Minnesota Twin Cities
Biostatistics & Other Math Sci
Schools of Public Health
United States
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Coombes, Brandon J; Basu, Saonli; McGue, Matt (2018) A linear mixed model framework for gene-based gene-environment interaction tests in twin studies. Genet Epidemiol 42:648-663
Robinson-Cohen, Cassianne; Lutsey, Pamela L; Kleber, Marcus E et al. (2017) Genetic Variants Associated with Circulating Parathyroid Hormone. J Am Soc Nephrol 28:1553-1565
Arbet, Jaron; McGue, Matt; Chatterjee, Snigdhansu et al. (2017) Resampling-based tests for Lasso in genome-wide association studies. BMC Genet 18:70
Coombes, Brandon; Basu, Saonli; McGue, Matt (2017) A combination test for detection of gene-environment interaction in cohort studies. Genet Epidemiol 41:396-412
Coombes, Brandon; Basu, Saonli; Guha, Sharmistha et al. (2015) Weighted Score Tests Implementing Model-Averaging Schemes in Detection of Rare Variants in Case-Control Studies. PLoS One 10:e0139355
Kim, Junghi; Bai, Yun; Pan, Wei (2015) An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics. Genet Epidemiol 39:651-63