We propose to establish a new interdisciplinaryresearch trainingprogram in Computational Genetics as a collaborative effort between MIT, the Whitehead Institute, and the Broad Institute of MIT and Harvard. The goal of this program is to train MIT students to be effective interdisciplinaryscientists, working as team members with biologists to develop new algorithms, tools, and approaches for analyzing genomic and genetic data and expressing this analysis in the form of principled predictive models. The program faculty will consist of five MIT EECS and Mathematics faculty, four Whitehead faculty members, and four members of the Broad Institute of MIT and Harvard. The major research disciplines of this program include: 1) the development of new approaches and algorithms for the analysis of data from genomics and genetics based experiments and studies; 2) approaches for the principled design of studies based upon past data; 3) the construction of computational models that explaincomplex phenotypes and biological phenomenon; 4) and the development of approaches for interpreting genomic, genetic, and clinical data relevant to human health and disease. It is proposed that four pre-doctoral trainees be supported in this program, each for a period of two years (a total of 8 slots). We have been runninga training program in this area for over seven years, and our students to date have made substantial contributionsto the field. Among our recent graduates are faculty at Stanford, Berkeley, Univ. of Washington, Princeton, Duke, and CMU. Our pool of applicants is unusually strong, with 592 applicants in 2008 in relevant sub-areas of Computer Science. Trainees in our proposed research training program will have a very rigorous technical and quantitativefoundation from the MIT graduate program in Computer Science, combined formal interdisciplinary course work and a co mentorship arrangement between a Computer Science and a Biology faculty member. The strong technical skills present in our pre doctoral students have provided an excellent foundation for the creation of ground breaking new approaches and algorithms in Computational Genetics.

Public Health Relevance

We will train scientists who can discover links between genetic informationand risks for human disease. These studies can suggest appropriate therapies for certain diseases and give clues towards the development of new therapeutics. As more data form Genome Wide Association Studies becomes available, we expect that genetic information will become an important component of preventative medicine.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Institutional National Research Service Award (T32)
Project #
3T32HG004947-05S2
Application #
9014598
Study Section
Ethical, Legal, Social Implications Review Committee (GNOM)
Program Officer
Gatlin, Christine L
Project Start
2009-07-08
Project End
2016-04-30
Budget Start
2015-05-01
Budget End
2016-04-30
Support Year
5
Fiscal Year
2015
Total Cost
$2,634
Indirect Cost
$416
Name
Massachusetts Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
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Loh, Po-Ru; Lipson, Mark; Patterson, Nick et al. (2013) Inferring admixture histories of human populations using linkage disequilibrium. Genetics 193:1233-54