Integrating Polygenic Risk and Environmental Exposures to Uncover Biological Mechanisms Underlying Dementia in a Diverse Cohort

整合多基因风险和环境暴露来揭示不同人群中痴呆症的生物机制

• 批准号: 10560160
• 负责人: Diane Xue
• 金额: $ 4.83万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-16 至 2025-03-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10560160
• 关键词: Address; Aging; Air Pollution; Algorithms; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Ancillary Study; Award; Biological; Biological Markers; Biological Process; Biology; Cause of Death; Cognitive; Computing Methodologies; Data; Data Analyses; Dementia; Developed Countries; Diagnosis; Disease; Dose; Education; Environment; Environmental Exposure; Environmental Risk Factor; Ethnic group; Exposure to; Fellowship; Gene Expression; Genes; Genetic; Genetic Risk; Genetic Variation; Genetic study; Genomics; Goals; Health; Heritability; Impaired cognition; Individual; Intervention; Late Onset Alzheimer Disease; Lead; Location; Longitudinal cohort; Machine Learning; Mediating; Mediator of activation protein; Methods; Modeling; Molecular; Multi-Ethnic Study of Atherosclerosis; Multiomic Data; Neighborhoods; Nerve Degeneration; Neurodegenerative Disorders; Nitrogen Dioxide; Outcome; Parents; Participant; Pathogenesis; Pathway interactions; Pattern; Persons; Pharmacologic Substance; Phase; Policies; Population; Postdoctoral Fellow; Precision therapeutics; Predictive Value; Prevention; Prevention strategy; Proteins; Proteomics; Reporting; Research; Research Personnel; Risk; Risk Factors; Safety; Scoring Method; Socioeconomic Status; Subgroup; Testing; Training; Work; adjudication; ambient air pollution; brain health; career; career development; cohort; data integration; dementia risk; design; differential expression; diverse data; ethnic diversity; fine particles; gene environment interaction; genome wide association study; genomic locus; genomic profiles; hazard; human old age (65+); improved; interest; longitudinal analysis; modifiable risk; multi-ethnic; multiple omics; non-genetic; polygenic risk score; proteomic signature; racial diversity; research and development; resilience; response; risk prediction; social; social cohesion; social health determinants; transcriptomics; treatment strategy

PROJECT SUMMARY Alzheimer’s disease (AD) is a leading cause of death in developed countries. Over 50 genetic loci have been associated with late-onset AD risk, yet we still do not fully understand the disease pathogenesis and have failed to find successful solutions for prevention or treatment of dementia or cognitive decline. AD is influenced by genetic and environmental (social, built, and physical) factors. Understanding the interplay between these genetic and non-genetic factors is crucial to address the underlying biology of the disease. Many studies have focused on identifying either genetic causes or modifiable risk factors associated with AD, and most gene- environment studies of AD have been restricted to single-gene x single-environmental factor studies (primarily focusing on the gene APOE). Few studies have addressed how upstream factors like socioeconomic status and ambient air pollution interact with risk across many genetic locations (polygenic) to influence gene expression and proteomic changes that lead to AD and related dementias. The specific aims of this study are to 1. (F99 phase) determine social, built, and physical environmental variables associated with dementia risk and/or cognitive decline, independent of and modified by polygenic risk and 2. (K00 phase) identify transcriptomic and proteomic signatures that mediate the effect of environmental exposure on dementia/cognitive decline. During my dissertation phase, I will train in polygenic risk score computation, predictive analysis, mixed-effect modeling, and machine learning to characterize the effects of genetic and environmental determinants on AD and related dementias. I will employ polygenic risk score methods that have been designed to improve predictive accuracy in multi-ethnic populations. As a post-doctoral fellow, I will expand my training to multi-omic data integration and analysis to move our understanding of risk factors for AD and related dementias beyond studies of association to an understanding of causal pathways and the biological mediators of environmental exposures. This research will leverage the Multi-Ethnic Study of Atherosclerosis parent and ancillary studies (MESA Neighborhood and Aging, MESA MIND, and MESA Air), a longitudinal cohort unprecedented in its scope of social determinants of health along with dementia adjudication and multi-omic data. This fellowship application aligns with the NIA Strategic Directions for Research Goal D-1 “to determine how genetic, molecular, cellular, and social/environmental factors interact for brain health and neurodegeneration.” As a result of this work, we will have identified upstream (policy-level) and downstream (biological mechanisms) points of intervention and prevention. In addition, the research and career development provided by this award will help me launch my career as an independent investigator of AD prediction and prevention.

项目摘要 阿尔茨海默氏病（AD）是发达国家死亡的主要原因。超过50个遗传基因座 与迟到的AD风险相关，但我们仍然没有完全了解疾病的发病机理，并且 未能找到用于预防或治疗痴呆或认知能力下降的成功解决方案。广告受到影响 通过遗传和环境（社会，建筑和物理）因素。了解这些之间的相互作用 遗传和非遗传因素对于解决该疾病的基本生物学至关重要。许多研究有 专注于确定与AD相关的遗传原因或可修改的危险因素以及大多数基因 AD的环境研究仅限于单基因X单环境因素研究（主要是 专注于基因apoe）。很少有研究解决了社会经济地位等上游因素如何 环境空气污染与许多遗传位置（pollygenic）的风险相互作用，以影响基因 导致AD和相关痴呆的表达和蛋白质组学变化。这项研究的具体目的是 到1。（F99阶段）确定与痴呆症风险相关的社交，建筑和身体环境变量 和/或认知能力下降，独立于多基因风险和2。（k00阶段）确定 转录组和蛋白质组学特征，介导环境暴露对 痴呆/认知能力下降。在论文阶段，我将进行多基因风险评分计算训练， 预测分析，混合效应建模和机器学习，以表征遗传和 AD和相关痴呆症的环境决定者。我将采用多基因风险评分方法 旨在提高多种族人群的预测准确性。作为博士后家伙，我会 将我的培训扩展到多OMIC数据集成和分析，以转移我们对AD风险因素的理解 以及相关的痴呆症超出对因果途径和因果途径的理解和 环境暴露的生物介体。这项研究将利用多民族研究 动脉粥样硬化父母和辅助研究（Mesa邻居和衰老，Mesa Mind和Mesa Air），A 纵向队列与痴呆症的社会决定者的范围前所未有 裁决和多摩变数据。该奖学金申请与NIA的战略指导一致 研究目标D-1“确定遗传，分子，细胞和社会/环境因素如何相互作用 大脑健康和神经变性。”由于这项工作，我们将确定上游（政策级别） 和下游（生物学机制）干预和预防点。此外，研究和 该奖项提供的职业发展将帮助我启动我的职业生涯 广告预测和预防。