[Progress in the study of the imaging genomics of Alzheimer's disease]
- PMID: 30887788
- PMCID: PMC9929890
- DOI: 10.7507/1001-5515.201805061
[Progress in the study of the imaging genomics of Alzheimer's disease]
Abstract
With the exacerbation of aging population in China, the number of patients with Alzheimer's disease (AD) is increasing rapidly. AD is a chronic but irreversible neurodegenerative disease, which cannot be cured radically at present. In recent years, in order to intervene in the course of AD in advance, many researchers have explored how to detect AD as early as possible, which may be helpful for effective treatment of AD. Imaging genomics is a kind of diagnosis method developed in recent years, which combines the medical imaging and high-throughput genetic omics together. It studies changes in cognitive function in patients with AD by extracting effective information from high-throughput medical imaging data and genomic data, providing effective guidance for early detection and treatment of AD patients. In this paper, the association analysis of magnetic resonance image (MRI) with genetic variation are summarized, as well as the research progress on AD with this method. According to complexity, the objects in the association analysis are classified as candidate brain phenotype, candidate genetic variation, genome-wide genetic variation and whole brain voxel. Then we briefly describe the specific methods corresponding to phenotypic of the brain and genetic variation respectively. Finally, some unsolved problems such as phenotype selection and limited polymorphism of candidate genes are put forward.
随着中国人口老龄化程度的加剧,阿尔茨海默症(AD)患者数量迅速增加。AD 是一种发病进程缓慢但不可逆的持续性神经功能障碍,目前无法根治。近年来大量研究者开始探索如何尽早发现 AD,从而提前干预 AD 患者病程,为 AD 的有效治疗提供帮助。影像遗传组学是近年来发展起来的一种将医学影像数据和遗传组学数据相结合的研究诊断方法,它可以从高通量医学影像数据和遗传组学数据中挖掘出有效信息来研究 AD 患者的认知功能状态变化情况,对 AD 患者的早期发现和治疗提供有效的引导。本文概述了磁共振图像(MRI)与遗传变异的关联分析及其在 AD 上的研究进展,具体根据关联分析对象的复杂程度将其分类为候选脑表型、候选遗传变异、全基因组遗传变异和全脑体素,并分别简述分类后的脑表型和遗传变异关联分析所对应的具体方法。最后提出了一些目前仍未解决的问题,如表型的选取以及候选基因多态性有限等。.
Keywords: Alzheimer's disease; biomarker; genome-wide association analysis; imaging genomics.
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