Biostatistics in Genetics and Genomics Training Program

遗传学和基因组学生物统计学培训计划

• 批准号: 10435510
• 负责人: Saonli Basu
• 金额: $ 31.22万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-09 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10435510
• 关键词: Biometry; Genetic; Genomics; Training Programs

Summary With the recent advances in high-throughput biotechnologies and associated flooding of Big Data, there is an urgent demand and shortage of the next-generation PhD-level scientists who have strong biostatistical and computational skills to handle Big Data while understanding biology, especially genetics and genomics. However, traditional training in biostatistics, which emphasizes almost exclusively on statistical theory and methods, no longer meets the need; similarly, to meet Big Data challenge, traditional training in genetics could be expanded and strengthened with more training in biostatistics/data science. As a response, this application continues the development and refinement of an innovative and interdisciplinary pre-doctoral T32 “Biostatistics in Genetics and Genomics” (BiG2) training program. The primary mission of this training grant is to prepare Biostatistics, Genetics and other predoctoral trainees for leadership roles in biomedical research through excellent training and mentorship in both biostatistics and genetics/genomics. To meet the overarching objective specified by the NIGMS's RFA, our training program requires completion by each trainee of required and elective coursework in an affiliated PhD program with extra training in Biostatistics and/or Genetics, mentored learning through interdisciplinary research projects through lab rotations, development of critical thinking, communication and networking skills, attendance at and participation in journal clubs, seminars and national meetings, successful completion of a program of interdisciplinary research through their dissertation, and future career development, all of which give our trainees broad training in a rapidly expanding field. The most distinctive feature of the proposed training program is its interdisciplinary nature integrating both a biostatistical (or methodological/computational) aspect and a genetic/biomedical aspect. A key is to ensure each trainee learns how to carry out critical, reproducible, cohesive and interdisciplinary research program, including collaborating with various biomedical researchers. We request support for 4-5-6-6-6 slots in years 1-5 for predoctoral trainees from Biostatistics, Genetics and other affiliated PhD programs. Each trainee is to be funded for two years at the early stage of his/her PhD study, after which his/her support will switch to projects funded by the faculty mentors' research grants. As measurable outcomes, in a short term, we will examine the success rates of the trainee's timely completion of their PhD studies and their job placements; in a long term, it will be determined by the success and impact of their future careers in biomedical research; in between, it will be their publications, awards and career transitions around the beginning of their careers.

总结 随着高通量生物技术的最新进展和相关的大数据泛滥， 有一个迫切的需求和短缺的下一代博士水平的科学家谁拥有强大的 生物统计和计算技能，以处理大数据，同时了解生物学，特别是 遗传学和基因组学。然而，传统的生物统计学培训强调几乎 仅仅依靠统计理论和方法，已经不能满足需求;同样，为了满足大数据的需求， 传统的遗传学培训可以扩大和加强， 生物统计学/数据科学。作为回应，该应用程序继续开发和完善 一个创新的和跨学科的博士前T32“生物统计学在遗传学和基因组学” （BiG 2）培训计划。该培训补助金的主要使命是准备生物统计学， 遗传学和其他博士前实习生的领导作用，在生物医学研究，通过优秀的 生物统计学和遗传学/基因组学方面的培训和指导。为了满足总体需求， 根据NIGMS RFA规定的目标，我们的培训计划要求每个学员完成以下内容： 必修和选修课程，在附属博士课程与生物统计学的额外培训 和/或遗传学，通过实验室轮换跨学科研究项目指导学习， 发展批判性思维、沟通和网络技能，出席和参与 在期刊俱乐部，研讨会和国家会议上，成功完成了一项计划， 跨学科研究，通过他们的论文，和未来的职业发展，所有这些都给 我们的学员在一个迅速扩大的领域接受广泛的培训。建议的最大特点是 培训计划是其跨学科的性质，整合了生物统计学（或 方法/计算）方面和遗传/生物医学方面。关键是要确保每个 学员学习如何开展关键的，可重复的，有凝聚力的和跨学科的研究计划， 包括与各种生物医学研究人员合作。我们请求支持4-5-6-6-6插槽， 生物统计学，遗传学和其他附属博士课程的博士预科生1-5年。 每个受训者将在其博士研究的早期阶段获得两年的资助，之后，他/她将获得 资助项目将转由导师研究赠款资助。为可测量 在短期内，我们将检查学员及时完成 他们的博士研究和他们的就业安置;从长远来看，这将取决于成功， 他们未来的职业生涯在生物医学研究的影响;在这之间，这将是他们的出版物，奖项 以及职业生涯初期的职业转型。