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Novel integrative imaging genetics analysis for Alzheimer's disease riskand progression

Novel integrative imaging genetics analysis for Alzheimer's disease riskand progression
针对阿尔茨海默病风险和进展的新型综合成像遗传学分析
批准号:
10210551
负责人:
Yize Zhao
金额:
$187.49万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-05-15 至 2024-04-30

项目摘要

项目成果

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中文摘要
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摘要
英文摘要
Abstract Our overarching goal is to construct heritability enhanced multimodal imaging endophenotypes, dissect their associated genetic underpinnings, and characterize the pathological mechanism along with clinical profiles for Alzheimer’s disease (AD) susceptibility and progression through a suite of statistically powerful, biologically plausible and computationally efficient Bayesian models. AD is complex: its etiology is in no way clear and with no available cure. Genetics play a dominate role in AD. However, genome wide association studies based on AD susceptibility have only discovered a small number of independent genetic factors. Compared with categorical diagnoses, imaging quantitative trait (QT) has distinct advantages to capture disease etiology and their association on genetic variants has yielded some prominent finding. However, existing imaging genetics studies suffer with i) inadequate power under current atlas-based imaging traits, ii) oversight of genetic underpinnings across different brain processes and potential role of SNP-SNP interactions, and iii) unclear pathological mechanism among genetics, imaging and clinical profiles, which limit their further advances to AD research. To overcome these barriers, this project proposes the following four aims: 1) construct novel multimodal imaging endophenotypes under “brain heritability parcellation'” for AD risk and progression; 2) identify pleiotropic SNPs and SNP-SNP interactions under constructed multimodal brain biomarkers; 3) investigate the fundamental pathological mechanism between genetic variations and AD pathology mediated by multimodal imaging endophenotypes; and 4) perform systematic evaluation of the proposed methods through real data analyses and simulations, and develop user-friendly analytical pipelines. Our proposed methods are innovative in multiple aspects for and beyond AD biomedical research including a) to construct a novel brain heritability parcellation, b) to integrate multimodal imaging on their association to genetic bases, c) to explore risk SNP- SNP interaction along with common genetic variants in a coherent and scalable way, d) to consider genetic pleiotrophy in a fine mapping framework, e) to establish pathological mechanism among individual’s genetic variants and polygenic profile, brain activities and disease symptoms, f) to extend the proposed methods to longitudinal settings with respect to AD progression, and g) to develop efficient and user friendly pipelines for our products. A successful completion of this proposal will facilitate the identification of novel genetic variants and characterize their impacts on AD pathogenesis, mediated by the constructed multimodal imaging biomarkers. The development of innovative statistical methods and computational tools, and their implementation on the ADNI and Yale ADRC cohort will offer a great potential for understanding the genetic and neurobiological mechanisms of AD and advancing targeted prevention and treatment, paving the way for the development of neurological and psychiatric research in general and benefit public health outcomes.
期刊论文(2)
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会议论文
Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.
阿尔茨海默病纤维密度连接体在多个分区尺度上的图论测量的一致性。
DOI: 10.1109/bibm55620.2022.9995657
发表时间: 2022
期刊: Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
影响因子: --
作者: [Xu,Frederick, Garai,Sumita, Duong-Tran,Duy, Saykin,AndrewJ, Zhao,Yize, Shen,Li, ADNI]
通讯作者: ADNI
海外基金