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

复杂疾病的生物、医学和影像信息学整合

• 批准号: 8720062
• 负责人: Kwangsik Timothy Nho
• 金额: $ 21.78万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8720062
• 关键词: Address; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Area; Award; Behavioral; Bioinformatics; Biological; Biological Markers; Brain; Cause of Death; Clinical; Clinical Data; Clinical Trials; Cognitive; Complex; Computerized Medical Record; Coupled; Data; Data Set; Databases; Detection; Development; Diagnosis; Discipline; Disease; Early Diagnosis; Early treatment; Fellowship; Future; Genes; Genetic; Genetic Predisposition to Disease; Genome; Genomics; Genotype; Goals; Health; Healthcare; Heritability; Hippocampus (Brain); Image; Image Analysis; Imaging Techniques; Indiana; Informatics; Institutes; Knowledge; Lead; Learning; Magnetic Resonance Imaging; Medical Imaging; Medical Informatics; Medical Records; Medicine; Mental disorders; Methodology; Methods; Modality; Molecular; Neurodegenerative Disorders; Participant; Pathway interactions; Patient Care; Patients; Phenotype; Physics; Pilot Projects; Play; Positron-Emission Tomography; Predisposition; Public Health; Public Health Informatics; Quantitative Trait Loci; Rare Diseases; Recruitment Activity; Research; Research Personnel; Role; Sampling; Science; Structure; Surface; Susceptibility Gene; Technology; Thick; Training Programs; Translating; United States; United States National Institutes of Health; United States National Library of Medicine; Universities; Variant; Work; base; biomedical informatics; career; case control; clinical phenotype; deep sequencing; density; exome sequencing; experience; functional genomics; genetic analysis; genetic association; genetic variant; genome wide association study; genome-wide; gray matter; imaging informatics; improved; informatics training; insight; medical schools; mild cognitive impairment; neuroimaging; next generation; next generation sequencing; novel; rare variant; risk variant; skills; statistics

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.

描述（由申请人提供）：I是国家医学图书馆（NLM）生物医学信息学（BMI）研究所的研究所和印第安纳大学医学院（IUSM）神经影像中心的研究员。我的职业目标是成为一名成功的独立研究者，重点是整合生物，医学和成像信息学方法，包括下一代测序（NGS）和先进的神经影像学，以增强对复杂疾病机制的理解，从而促进新治疗的发展。 NLM BMI培训计划一直是一种无价的体验，使我能够成功地将职业从计算物理学转变为生物医学信息学。在三年的NLM BMI奖学金中，我在高级医学成像分析，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，以使我为实现自己的职业目标做好准备。为了巩固我的跨学科技能并完成向独立研究人员的过渡，我的直接职业目标是运用和扩展我的方法论技巧，并对BMI中当前的生物学知识和未来计算挑战有了更广泛的了解。我将使用正在进行的阿尔茨海默氏病神经影像学计划（ADNI）作为初始学习平台的全外观测序项目数据，我将以综合方式采用生物，医学和成像信息学策略，以识别与中间多模型的功能性稀有变体与与中等多模型相关的功能性稀有变体（MCICERIPERS and CONTIDISS to Adivience tate Impirim and McI）。先前的研究已经确定了大量常见的遗传变异，但是AD的遗传力的很大一部分仍无法解释已知的敏感性基因。 NGS可以通过稀有变体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协会研究（GWAS）上使用成像和医学信息学表型的工作，并将我的专业知识扩展到NGS。丰富的ADNI数据集为解决我的特定目标中提出的挑战提供了一个独特的机会，而IU神经影像中心则是ADNI遗传学核心，极大地促进了数据访问和专家帮助。具体目的：（1）在完整的ADNI样品集中确定与MCI/AD诊断有关的中间表型生物标志物的子集； （2）在ADNI全异位测序试验项目中，对稀有变体进行基于基因的稀有变体分析； （3）通过在整个ADNI样品集中归纳和基因分型靶区域来验证新的变体和风险基因。与公共卫生相关：AD是一种日益普遍的神经退行性疾病，也是美国第六大死亡原因。用 功能对MCI进展的影响将增强有关AD途径的机械知识，并可以实现早期的诊断和治疗。