Integration of Bio-, Medical, and Imaging Informatics for Complex Diseases

描述（由申请人提供）：我是美国国家医学图书馆（NLM）生物医学信息学（BMI）研究员，就职于印第安纳大学医学院（ism）的Regenstrief研究所和神经成像中心。我的职业目标是成为一名成功的独立研究者，专注于整合生物、医学和成像信息学方法，包括下一代测序（NGS）和先进的神经成像，以增强对复杂疾病机制的理解，从而促进新疗法的发展。NLM BMI培训项目是一段无价的经历，使我能够成功地从计算物理学转向生物医学信息学。在我三年的NLM BMI奖学金期间，我在高级医学影像分析、医学信息学、生物信息学、基因组学和统计学方面获得了丰富的经验，为我实现我的职业目标做好了准备。为了巩固我的跨学科技能并完成向独立研究人员的过渡，我的近期职业目标是应用和扩展我的方法技能，并对BMI中当前的生物学知识和未来的计算挑战有更广泛的了解。利用正在进行的阿尔茨海默病神经影像学倡议（ADNI）的全外显子组测序试点项目数据作为初始学习平台，我将采用生物、医学和成像信息学策略，以综合的方式识别与从轻度认知障碍（MCI）到阿尔茨海默病（AD）进展相关的中间多模态表型相关的功能罕见变异。先前的研究已经确定了大量常见的遗传变异，但仍有很大一部分阿尔茨海默病的遗传能力无法用已知的易感基因来解释。NGS可以通过罕见变异检测来帮助缩小差距。拟议的研究计划建立在我以前在全基因组关联研究（GWAS）中使用成像和医学信息学表型的工作基础上，并将我的专业知识扩展到NGS。丰富的ADNI数据集提供了一个独特的机会来解决我的具体目标中提出的挑战，IU神经成像中心作为ADNI遗传学核心，极大地促进了数据访问和专家协助。具体目标：(1)确定与MCI/AD诊断相关的中间表型生物标志物子集，用于完整ADNI样本集的遗传关联分析；(2)在ADNI全外显子组测序试点项目中，对罕见变异与MCI/ ad相关的中间表型生物标志物进行基于基因集的关联分析；(3)通过在全ADNI样本集中输入和分型检测到的基因的靶区来验证新的变异和风险基因。与公共卫生相关：阿尔茨海默病是一种越来越常见的神经退行性疾病，是美国第六大死亡原因。鉴定新的易感位点

DESCRIPTION (provided by applicant): I am a National Library of Medicine (NLM) Biomedical Informatics (BMI) fellow at both the Regenstrief Institute and the Center for Neuroimaging at Indiana University School of Medicine (IUSM). My career goal is to become a successful independent investigator with a focus on integrating bio-, medical, and imaging informatics approaches, including next generation sequencing (NGS) and advanced neuroimaging, to enhance the understanding of complex disease mechanisms and thereby facilitate development of novel treatments. The NLM BMI training program has been a priceless experience that enabled me to successfully transition my career from computational physics to biomedical informatics. During my three year NLM BMI fellowship, I gained extensive experience in advanced medical imaging analysis, medical informatics, bioinformatics, genomics, and statistics to prepare me to achieve my career goal. In order to consolidate my transdisciplinary skills and complete the transition to an independent researcher, my immediate career objective is to apply and expand my methodological skills and gain a broader understanding of current biological knowledge and future computational challenges in BMI. Using whole- exome sequencing pilot project data from the ongoing Alzheimer's Disease Neuroimaging Initiative (ADNI) as an initial learning platform, I will employ bio-, medical, and imaging informatics strategies in an integrated manner to identify functional rare variants associated with intermediate multi-modality phenotypes related to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Prior studies have identified a large number of common genetic variants but a substantial proportion of the heritability of AD remains unexplained by the known susceptibility genes. NGS can help close the gap by rare variant detection. The proposed research plan builds on my previous work in genome-wide association studies (GWAS) using imaging and medical informatics phenotypes and will expand my expertise to NGS. The rich ADNI data set provides a unique opportunity to address the challenges proposed in my specific aims and the IU Center for Neuroimaging serves as the ADNI Genetics Core which greatly facilitates data access and expert assistance. Specific aims: (1) identify a subset of intermediate phenotypic biomarkers associated with a diagnosis of MCI/AD for genetic association analysis in the full ADNI sample set; (2) perform gene set- based association analysis of rare variants with MCI/AD-related intermediate phenotypic biomarkers in the ADNI whole-exome sequencing pilot project; and (3) validate novel variants and risk genes by imputing and genotyping target regions of detected genes in the full ADNI sample set. RELEVANCE TO PUBLIC HEALTH: AD is an increasingly common neurodegenerative disease and the sixth leading cause of death in the United States. Identifying new susceptibility loci with functional impact on MCI progression will enhance mechanistic knowledge regarding AD pathways and may enable earlier diagnosis and treatment.