Integrated Genomic and Metabolomic Profiles in Newborn Infants

描述(由申请人提供)：凯利·瑞克曼博士是爱荷华大学卡弗医学院的博士后研究员。她的主要研究重点是复杂生殖疾病的遗传学和流行病学，包括早产和相关并发症。她的短期职业目标是增强她对新生儿复杂生殖和围产期疾病的遗传学知识，并获得更多分析方法和技术方面的技能，以阐明复杂疾病的风险。她的长期目标是成为遗传学、统计学和流行病学领域的一名有资金的独立科学家，在一所大学担任终身教职。她的研究计划在结合基因组和新陈代谢图谱以了解复杂的疾病风险方面具有创新性。这项研究将利用爱荷华州新生儿代谢筛查计划(INMSP)，那里有丰富的表型、环境和遗传成分资源。这项研究有可能确定与新生儿疾病有关的新的生物学途径，并对普遍的新生儿筛查计划产生直接的临床影响。导师和顾问：杰夫·默里博士将担任该奖项的主要导师。他是爱荷华大学资金充足的全职教授，在培养成功的研究生和博士后方面有25年的经验。统计遗传学领域的专家Mary Marazita博士将担任该奖项的共同导师。斯坦顿·伯伯里奇博士和克里斯蒂·博罗夫斯基博士将担任该项目的顾问，提供临床和统计遗传学专业知识。Sara Copeland博士、John Dagle博士和Jonathan Haines博士将担任外部顾问成员，并将在整个授奖期间咨询、建议和评估Ryackman博士的进展，并在新生儿生化环境的代谢组学、早产并发症和基因组分析方面拥有互补的专业知识。环境：爱荷华大学在成功培养研究生和博士后方面享有盛誉，尤其是在研究和临床领域。该大学提供一系列学习和发展研讨会以及其他侧重于职业发展和晋升的计划，特别是受益于CTSA奖项对其临床与翻译科学研究所(ICTS)的支持带来的培训机会。职业发展计划：赖克曼博士计划定期与她的导师、顾问和外部顾问成员会面，以获得必要的知识，以实现她的职业和研究目标，并参加教程、课程、研讨会和相关会议。研究研究：本研究的重点是使用爱荷华州的新生儿筛查数据作为研究新生儿代谢和基因组图谱的模型，并将这些图谱与人类疾病，特别是早产及其并发症联系起来。 公共卫生相关性：了解复杂遗传病的病因只能通过研究包含大量环境、表型和遗传变量的大型数据库来完成。这项工作的重点将是使用来自爱荷华州新生儿代谢筛查数据库的丰富数据资源作为研究新生儿基因组和代谢组合图谱的模型。这项研究将有助于提高新生儿筛查测试的敏感性和准确性，同时确定可能影响早产等复杂疾病的关键环境、代谢和遗传因素。

DESCRIPTION (provided by applicant): Dr. Kelli Ryckman is a Postdoctoral Research Fellow for the Carver College of Medicine at the University of Iowa. Her primary research focus is on the genetics and epidemiology of complex reproductive disorders including preterm birth and related complications. Her short-term career goals are to enhance her knowledge of the genetics of complex reproductive and perinatal conditions in newborns and to gain additional skills in analytical methods and techniques for elucidating risks of complex diseases. Her long-term goals are to become a funded independent scientist in the fields of genetics, statistics and epidemiology as tenure-track faculty at a University. Her research plan is innovative in combining genomic and metabolomic profiles to understand complex disease risks. This research will utilize the Iowa Newborn Metabolic Screening Program (INMSP) where a deep resource of phenotypic, environmental and genetic components is available. This research has the potential to identify new biological pathways involved in neonatal disease and to have direct clinical impact on universal newborn screening programs. Mentors and Consultants: Dr. Jeff Murray will serve as primary mentor on this award. He is a well-funded full professor at the University of Iowa with 25 years of experience in training successful graduate and postdoctoral students. Dr. Mary Marazita, an expert in the field of statistical genetics, will serve as co-mentor of this award. Drs. Stanton Berberich and Kristi Borowski will serve as consultants on this project by providing clinical and statistical genetic expertise. Drs. Sara Copeland, John Dagle and Jonathan Haines will serve as external advisory members and will consult with, advise and evaluate the progress of Dr. Ryckman throughout the award period and have complementary expertise in the metabolomics of the neonatal biochemical milieu, the complications of preterm birth and genomic analysis. Environment: The University of Iowa has an established reputation for the successful training of graduate and postdoctoral students, particularly in research and clinical areas. The University offers a range of learning and development seminars as well as other programs focused on career development and advancement and particularly benefits from training opportunities deriving from its ICTS (Institute for Clinical and Translational Sciences) support by a CTSA award. Career Development Plan: Dr. Ryckman plans to meet regularly with her mentors, consultants and external advisory members to gain the knowledge necessary to obtain her career and research goals as well as attending tutorials, courses, seminars and relevant conferences. Research Study: The focus of this study is to use the Iowa newborn screening data as a model for studying the metabolomic and genomic profiles of newborns and relate the profiles to human disease, particularly prematurity and its complications. PUBLIC HEALTH RELEVANCE: Understanding the etiology of complex genetic diseases can only be accomplished by studying large database repositories containing a wealth of environmental, phenotypic and genetic variables. The focus of this work will be to use the rich resource of data from the Iowa newborn metabolic screening database as a model for studying the combined genomic and metabolomic profiles of newborns. This research will aid in increasing the sensitivity and accuracy of newborn screening tests while identifying key environmental, metabolic and genetic factors that may impact complex diseases such as preterm birth.