生物医学工程学杂志

生物医学工程学杂志

阿尔茨海默症的影像遗传组学研究进展

查看全文

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。

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 of 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.

关键词: 影像遗传组学; 阿尔茨海默症; 全基因组关联分析; 生物标记物

Key words: imaging genomics; Alzheimer's disease; genome-wide association analysis; biomarker

登录后 ,请手动点击刷新查看图表内容。 没有账号,
1. Uflacker A, Doraiswamy P M. Alzheimer’s disease: an overview of recent developments and a look to the future. Focus, 2017, 15(1): 13-17.
2. Caselli R J, Beach T G, Knopman D S. Alzheimer disease: scientific breakthroughs and translational challenges. Mayo Clinic Proceedings, 2017, 92(6): 978-994.
3. Farrer L A. Expanding the genomic roadmap of Alzheimer's disease. Lancet Neurol, 2015, 14(8): 783-785.
4. Khondoker M, Newhouse S, Westman E, et al. Linking genetics of brain changes to Alzheimer's disease: sparse whole genome association scan of regional MRI volumes in the ADNI and AddNeuroMed cohorts. Journal of Alzheimers Disease, 2015, 45(3): 851-864.
5. Weiner M W, Veitcha D P, Aisen P S, et al. The Alzheimer’s disease neuroimaging initiative: A review of papers published since its inception. Alzheimer's and Dementia, 2012, 8(1): S1-S68.
6. Huang H, Shen L I, Thompson P M, et al. Imaging genomics. Pacific Symposium, 2018, 23: 304.
7. Shen L I, Cooper L A D, Shen L I, et al. Imaging genomics. Pacific Symposium, 2017: 51-57.
8. Yao Xiaohui, Yan Jingwen, Kim S, et al. Two-dimensional enrichment analysis for mining high-level imaging genetic associations. Brain Informatics, 2017, 4(1): 27-37.
9. Habes M, Toledo J B, Resnick S M, et al. Relationship between APOE genotype and structural MRI measures throughout adulthood in the SHIP population-based cohort. AJNR Am J Neuroradiol, 2016, 37(9): 1636.
10. Ramirez L M, Goukasian N, Porat S, et al. Common variants in ABCA7 and MS4A6A are associated with cortical and hippocampal atrophy. Neurobiol Aging, 2016, 39(5): 82-89.
11. Frackowiak R S J, Friston K J, Frith C, et al. Human brain function. 2nd ed. Amsterdam: Elsevier, 2004: 43-58.
12. Gibson J, Russ T C, Adams M J, et al. Assessing the presence of shared genetic architecture between Alzheimer's disease and major depressive disorder using genome-wide association data. Transl Psychiatry, 2017, 7(4): e1094.
13. Nho K, Horgusluoglu E, Kim S, et al. Integration of bioinformatics and imaging informatics for identifying rare PSEN1 variants in Alzheimer's disease. BMC Med Genomics, 2016, 9(1): 12-18.
14. Kim D, Basile A O, Bang L, et al. Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease. BMC Med Inform Decis Mak, 2017, 17(1): 61.
15. Mormino E C, Sperling R A, Holmes A J, et al. Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology, 2016, 87(5): 481-488.
16. Desikan R S, Fan C C, Wang Yunpeng, et al. Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLoS Med, 2017, 14(3): 1-17.
17. Chauhan G, Adams H H H, Bis J C, et al. Association of Alzheimer's disease GWAS loci with MRI markers of brain aging. Neurobiol Aging, 2015, 36(4): e7-e16.
18. Foley S F, Tansey K E, Caseras X, et al. Multimodal brain imaging reveals structural differences in Alzheimer's disease polygenic risk carriers: a study in healthy young adults. Biol Psychiatry, 2017, 81(2): 154-161.
19. Darst B F, Koscik R L, Racine A M, et al. Pathway-specific polygenic risk scores as predictors of amyloid-beta deposition and cognitive function in a sample at increased risk for Alzheimer's disease. Journal of Alzheimers Disease, 2017, 55(2): 473-484.
20. Kong D, Giovanello K S, Wang Y, et al. Predicting Alzheimer's disease using combined imaging-whole genome SNP data. Journal of Alzheimer’s Disease, 2015, 46(3): 695-702.
21. Pan Q. Epistasis, complexity, and multifactor dimensionality reduction. Methods Mol Biol, 2013, 1019(1019): 465-477.
22. Jombart T, Pontier D, Dufour A B. Genetic markers in the playground of multivariate analysis. Heredity (Edinb), 2009, 102(4): 330-341.
23. Zhang Xiaolong, Yu Jintai, Li Jin, et al. Bridging integrator 1 (BIN1) genotype effects on working memory, hippocampal volume, and functional connectivity in young healthy individuals. Neuropsychopharmacology, 2015, 40(7): 1794-1803.
24. Zhang N, Liu H, Qin W, et al. APOE and KIBRA interactions on brain functional connectivity in healthy young adults. Cerebral Cortex, 2017, 27(10): 4797-4805.
25. Du Lei, Huang Heng, Yan Jingwen, et al. Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method. Bioinformatics, 2016, 32(10): 1544-1551.
26. Zille P, Calhoun V D, Wang Y P. Enforcing co-expression within a brain-imaging genomics regression framework. IEEE Trans Med Imaging, 2017, 37(12): 2561-2571.
27. Yan J, Risacher S L, Nho K, et al. Identification of discriminative brain imaging and genomic associations: an Alzheimer’s disease study. Alzheimer's and Dementia, 2016, 12(7): 205-206.