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: 8352575
• 负责人: Kwangsik Timothy Nho
• 金额: $ 9万
• 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 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8352575
• 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; Risk; 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; density; exome; 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 neurocognitive impairment; neuroimaging; next generation; novel; 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）：我是国家医学图书馆（NLM）生物医学信息学（BMI）研究员，在Regenstrief研究所和印第安纳州大学医学院（IUSM）神经影像中心工作。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，从而促进新疗法的开发。NLM BMI培训计划是一次无价的经历，使我能够成功地将我的职业生涯从计算物理学过渡到生物医学信息学。在我三年的NLM BMI奖学金期间，我在先进的医学成像分析，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，为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，我的直接职业目标是应用和扩展我的方法学技能，并对BMI当前的生物学知识和未来的计算挑战有更广泛的了解。使用来自正在进行的阿尔茨海默病神经成像倡议（ADNI）的全外显子组测序试点项目数据作为初始学习平台，我将以综合的方式采用生物，医学和成像信息学策略来识别与轻度认知障碍（MCI）进展到阿尔茨海默病（AD）相关的中间多模态表型相关的功能性罕见变异。先前的研究已经确定了大量常见的遗传变异，但AD的遗传性的很大一部分仍然无法解释已知的易感基因。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使用成像和医学信息学表型进行全基因组关联研究（GWAS）的基础上，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神经影像中心作为ADNI遗传学核心，极大地促进了数据访问和专家援助。具体目标：（2）在ADNI全外显子组测序试点项目中进行罕见变体与MCI/AD相关的中间表型生物标志物的基于基因集的关联分析;和（3）通过在全ADNI样品组中对检测到的基因的靶区域进行插补和基因分型来验证新的变体和风险基因。与公共卫生的相关性：AD是一种日益常见的神经退行性疾病，是美国第六大死亡原因。鉴定新的易感基因座， 对MCI进展的功能影响将增强关于AD途径的机制知识，并可能实现早期诊断和治疗。