The statistical genetics faculty at the University of Washington propose renewal of the predoctoral training program in biostatistics that emphasizes applications to genetics. The faculty members belong to the very strong departments of Biostatistics, Genome Sciences or Statistics at the University of Washington. The trainees will pursue Ph.D. degrees in one of the three departments. The training program will include the current Ph.D. tracks in statistical genetics offered by the Departments of Biostatistics and Statistics but it will have the additional feature of rotations in experimental laboratories and opportunities for internships in local companies. In addition to formal courses taught by international experts in statistical genetics, the training program features journal club, seminars and retreats. Trainees will also be able to attend modules in the annual Summer Institute in Statistical Genetics held at the University of Washington.

Public Health Relevance

Biology in general and genetics in particular, is becoming increasingly quantitative. Many major advances in medical science are being made possible by the massive amounts of data from the Human Genome Project, the HapMap project, the 1000 Genomes project and related activities. Managing and interpreting these data requires the development and application of new statistical methodologies. This training grant will support highly talented students in the Departments of Biostatistics, Genome Sciences or Statistics and ensure that they are trained to lead such development and application.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
4T32GM081062-10
Application #
9090124
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Marcus, Stephen
Project Start
2007-07-01
Project End
2017-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
10
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Washington
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
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Valcarcel, Alessandra; Grinde, Kelsey; Cook, Kaitlyn et al. (2016) A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests. BMC Proc 10:349-355

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