This training program is designed to develop a cadre of young biostatistical scientists to become leaders in integrative and team approaches to understanding biological science in the public health arena. Susceptibility to many complex diseases can be governed by the interactions between modifier genes and environmental factors. In the post-genomic era we are beginning to comprehend and compile the breadth of genetic variation within the human population. Refined use of this information requires the development of advanced methods of biostatistical analyses. In addition, to make significant progress in disease prevention, a hallmark of public health, there is a pressing need to translate advances in basic science into programs and policies focused on preventing common and costly chronic diseases. Only by integrating emerging scientific information into the design of clinical and public health interventions can we fully extract the value of these advances for public health. The established tools of probability and statistical inference remain critical to the success in the training of this new generation of biostatisticians, but in addition they must be conversant with the biological basis and data structures encountered in studies that incorporate genetic information, data produced by expression arrays, and that seek to establish connections between data collected in different approaches to the study of biological systems. They will also need a strong grounding in the information sciences preparing them to utilize computationally-intensive methods such as Markov Chain Monte Carlo procedures and semi-parametric models. This training program combines those elements with experiential training in laboratory science and computational biology and directed interdisciplinary research that will prepare graduates to meet this challenge. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
5T32GM074897-02
Application #
7089917
Study Section
Special Emphasis Panel (ZGM1-BRT-6 (BS))
Program Officer
Li, Jerry
Project Start
2005-07-01
Project End
2010-06-30
Budget Start
2006-07-01
Budget End
2007-06-30
Support Year
2
Fiscal Year
2006
Total Cost
$181,735
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
02115
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
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
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

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