# Interdisciplinary training: Statistical Genetics/Genomics and Computational Biology

> **NIH NIH T32** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2020 · $433,850

## Abstract

PROJECT SUMMARY/ABSTRACT
This is an 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 8 predoctoral students in years 1-2 and 10 predoctoral students in years 3-5.
This is the only program at Harvard School of Public Health that provides integrative training in statistical
genetics/genomics and computational biology. 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. Trainees receive
quantitative training in big `omics data science and reproducible research. The training program involves active
participation by 26 multidisciplinary faculty members who are recognized scientific leaders, including
biostatisticians, bioinformaticians and computational biologists, genetic epidemiologists, and molecular
biologists, and clinical genomicists. It combines elements of training in coursework, lab rotations in both wet
labs in biological science and dry labs in statistical genetics and genomics, 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. Trainees will be provided with extensive individualized mentoring tailored
towards their career objectives and are required to develop Individual Development Plans. The rich career
development programs help trainees gain skills in scientific communication, teaching, grant and paper writing,
teamwork, collaboration, and leadership. Trainee progress is closely monitored to ensure that those who are
struggling can be quickly identified and receive timely support. The Program evaluation process involves both
internal and external feedbacks from all the stakeholders, including current and past trainees, faculty and the
External Advisory Committee. Recruitment and retention plans are carefully developed to promote diversity
and ensure participation and full inclusion of underrepresented ...

## Key facts

- **NIH application ID:** 9855711
- **Project number:** 1T32GM135117-01
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Curtis Huttenhower
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $433,850
- **Award type:** 1
- **Project period:** 2020-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9855711

## Citation

> US National Institutes of Health, RePORTER application 9855711, Interdisciplinary training: Statistical Genetics/Genomics and Computational Biology (1T32GM135117-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9855711. Licensed CC0.

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