This is a renewal application of the Interdisciplinary Training Program in Statistical Genetics/Genomics and Computational Biology at the Harvard School of Public Health (HSPH). Trainees will be pre-doctoral students at HSPH in the Departments of Biostatistics and Epidemiology, which will jointly administer the grant. The program proposes support for seven pre-doctoral students. The goal of the program is to train the next generation of quantitative genomic scientists to have a strong understanding of, and commitment to, cutting- edge methodological and collaborative research in statistical genetics/genomics and bioinformatics/computational biology with applications in genetic epidemiology, molecular biology and genomic medicine. We are committed to train trainees to become future quantitative leaders to develop and apply advanced, scalable statistical and computational methods to manage, analyze, integrate, and interpret massive genetic and genomic data in basic science, epidemiological and clinical studies, to promote interdisciplinary research, and to effectively communicate and collaborate with subject-matter scientists in genetic and genomic research. In this renewal, we will expand the scope of the training program to enhance quantitative training in big `omics data science and reproducible research. The training program involves active participation by accomplished and experienced multidisciplinary faculty members, including biostatisticians, bioinformaticians and computational biologists, genetic epidemiologists, and molecular biologists. It combines elements of training in coursework, lab rotations in both wet labs in biological science and dry labs in biostatistics, computational biology, and genetic epidemiology, directed methodological and collaborative research, and rich career development opportunities in a stimulating and nurturing interdisciplinary environment, that will prepare graduates to become leading quantitative genomic scientists.

Public Health Relevance

8. Groundbreaking research and discovery in the life sciences in the 21st century are more interdisciplinary than ever. To expedite scientific advances in the 'omics' era, it is critical to train the next generation of quantitative health science students wh are strong in biostatistics, computational biology, molecular biology and genetic epidemiology, and who have enough basic knowledge that they can easily communicate and work with colleagues who have complementary areas of expertise.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
5T32GM074897-12
Application #
9103132
Study Section
Training and Workforce Development Subcommittee - D (TWD)
Program Officer
Marcus, Stephen
Project Start
2005-07-01
Project End
2020-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
12
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
Barnett, Ian; Mukherjee, Rajarshi; Lin, Xihong (2017) The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies. J Am Stat Assoc 112:64-76
Tsoucas, Daphne; Yuan, Guo-Cheng (2017) Recent progress in single-cell cancer genomics. Curr Opin Genet Dev 42:22-32
Barfield, Richard; Shen, Jincheng; Just, Allan C et al. (2017) Testing for the indirect effect under the null for genome-wide mediation analyses. Genet Epidemiol 41:824-833
Lu, Donghao; Sinnott, Jennifer A; Valdimarsdóttir, Unnur et al. (2016) Stress-Related Signaling Pathways in Lethal and Nonlethal Prostate Cancer. Clin Cancer Res 22:765-772
Slade, Emily; Kraft, Peter (2016) Leveraging Methylome-Environment Interaction to Detect Genetic Determinants of Disease. Hum Hered 81:26-34
Yung, Godwin; Lin, Xihong (2016) Validity of using ad hoc methods to analyze secondary traits in case-control association studies. Genet Epidemiol 40:732-743
Krier, Joel; Barfield, Richard; Green, Robert C et al. (2016) Reclassification of genetic-based risk predictions as GWAS data accumulate. Genome Med 8:20
Hsu, Jessie J; Finkelstein, Dianne M; Schoenfeld, David A (2015) Outcome-Driven Cluster Analysis with Application to Microarray Data. PLoS One 10:e0141874
Martin, Neil E; Gerke, Travis; Sinnott, Jennifer A et al. (2015) Measuring PI3K Activation: Clinicopathologic, Immunohistochemical, and RNA Expression Analysis in Prostate Cancer. Mol Cancer Res 13:1431-40
Rider, Jennifer R; Fiorentino, Michelangelo; Kelly, Rachel et al. (2015) Tumor expression of adiponectin receptor 2 and lethal prostate cancer. Carcinogenesis 36:639-47

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