Interdisciplinary training: Statistical Genetics/Genomics & Computational Biology

 描述(申请人提供)：这是哈佛大学公共卫生学院(HSPH)统计遗传学/基因组学和计算生物学跨学科培训计划的续展申请。受训者将是HSPH生物统计学和流行病学系的博士前学生，这两个系将共同管理这笔拨款。该计划建议为七名博士前学生提供支持。该计划的目标是培养下一代量化基因组科学家，使他们对统计遗传学/基因组学和生物信息学/计算生物学及其在遗传流行病学、分子生物学和基因组医学中的应用方面的尖端方法学和合作研究有很强的理解和承诺。我们致力于培养学员成为未来的量化领导者，开发和应用先进的、可扩展的统计和计算方法，以管理、分析、集成和解释基础科学、流行病学和临床研究中的大量遗传和基因组数据，促进跨学科研究，并在基因和基因组研究中与主题科学家有效沟通和合作。在这次更新中，我们将扩大培训计划的范围，以加强大组学数据科学和可再生研究方面的量化培训。培训计划涉及经验丰富的多学科教员的积极参与，包括生物统计学家、生物信息学家和计算生物学家、遗传流行病学家和分子生物学家。它结合了课程培训、生物科学“湿”实验室和生物统计学“干”实验室的轮换， 计算生物学和遗传流行病学，指导方法学和协作研究，以及在激励和培养的跨学科环境中提供丰富的职业发展机会，这将为毕业生成为领先的量化基因组科学家做好准备。

 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.