Interdisciplinary training: Statistical Genetics/Genomics & Computational Biology

跨学科培训：统计遗传学/基因组学

• 批准号: 9285804
• 负责人: Curtis Huttenhower
• 金额: $ 27.97万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9285804
• 关键词: Computational Biology; Genetic; Genomics; Training

 DESCRIPTION (provided by applicant): This is a renewal 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 seven pre-doctoral students. 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. In this renewal, we will expand the scope of the training program to enhance quantitative training in big `omics data science and reproducible research. The training program involves active participation by accomplished and experienced multidisciplinary faculty members, including biostatisticians, bioinformaticians and computational biologists, genetic epidemiologists, and molecular biologists. It combines elements of training in coursework, lab rotations in both "wet" labs in biological science and "dry" labs in biostatistics, 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.