This Interdisciplinary Training Grant in Biostatistics and Computational Biology proposal from the Harvard School of Public Health (HSPH) is a renewal application that represents an expansion of the existing interdisciplinary training grant in biostatistics at HSPH.
It aims at addressing the critical need in the """"""""omits"""""""" era for well-trained quantitative genomic scientists who have a strong understanding of, and commitment to, cutting-edge methodological and collaborative research at the intersection of molecular biology, biostatistics, bioinformatics, computational biology, and genetic epidemiology to analyze, integrate and interpret high- dimensional genomic and environmental data. The training program will involve active participation by over thirty accomplished and experienced multidisciplinary faculty members, including biostatisticians, bioinformaticians and computational biologists, genetic epidemiologists, and molecular biologists with the goal of providing our trainees with experience in all essential elements of this emerging area. The goals of our proposed training program are: * To train high-quality quantitative researchers who have excellent biological, statistical and computational knowledge, and are capable of conducting cutting-edge methodological and collaborative research at the intersection of biostatistics, bioinformatics and computational biology, genetic epidemiology, and molecular biology; * To train quantitative researchers to become strong leaders and effective communicators in an interdisciplinary research environment, and to enable them to conduct translational genomic research from basic sciences to population and clinical sciences focused on developing effective strategies for disease prevention, intervention, and treatments. Trainees will be pre-doctoral students at HSPH in the Departments of Biostatistics and Epidemiology, which will jointly administer the grant. The program proposes initial support of eight students in year 1, and two additional trainees in years 2-5. This training program combines elements of training in both """"""""wet"""""""" labs in biological science and """"""""dry"""""""" labs in biostatistics, computational biology, and genetic epidemiology, accomplished through lab rotations and directed interdisciplinary research that will prepare graduates to become leading quantitative genomic scientists.

Public Health Relevance

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

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1-BRT-X (TR))
Program Officer
Brazhnik, Paul
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
Biostatistics & Other Math Sci
Schools of Public Health
United States
Zip Code
Tsoucas, Daphne; Yuan, Guo-Cheng (2018) GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection. Genome Biol 19:58
Cai, Tianxi; Zhang, Yichi; Ho, Yuk-Lam et al. (2018) Association of Interleukin 6 Receptor Variant With Cardiovascular Disease Effects of Interleukin 6 Receptor Blocking Therapy: A Phenome-Wide Association Study. JAMA Cardiol 3:849-857
Fang, Chao; Zhong, Huanzi; Lin, Yuxiang et al. (2018) Assessment of the cPAS-based BGISEQ-500 platform for metagenomic sequencing. Gigascience 7:1-8
Pan, Deng; Kobayashi, Aya; Jiang, Peng et al. (2018) A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359:770-775
Stein, Shayna; Zhao, Rui; Haeno, Hiroshi et al. (2018) Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients. PLoS Comput Biol 14:e1005924
Sinnott, Jennifer A; Cai, Tianxi (2018) Pathway aggregation for survival prediction via multiple kernel learning. Stat Med 37:2501-2515
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
Schwager, Emma; Mallick, Himel; Ventz, Steffen et al. (2017) A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput Biol 13:e1005852

Showing the most recent 10 out of 43 publications