Systems biology and gene networks in Alzheimer's disease

被引:13
|
作者
Wang, Zuo-Teng [1 ]
Tan, Chen-Chen [1 ]
Tan, Lan [1 ]
Yu, Jin-Tai [1 ,2 ]
机构
[1] Qingdao Univ, Qingdao Municipal Hosp, Dept Neurol, Qingdao, Peoples R China
[2] Fudan Univ, Huashan Hosp, Dept Neurol, 12 Wulumuqi Rd, Shanghai, Peoples R China
来源
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS | 2019年 / 96卷
基金
中国国家自然科学基金;
关键词
Alzheimer's disease; Network; Transcriptome; Protein-protein interaction; Epigenome; PROTEIN-PROTEIN INTERACTION; PRECISION MEDICINE CLARITY; IDENTIFY CANDIDATE GENES; GLOBAL DNA METHYLATION; COEXPRESSION NETWORKS; AMYLOID-BETA; TRANSCRIPTIONAL LANDSCAPE; DRUG DISCOVERY; LEVEL ANALYSIS; EXPRESSION;
D O I
10.1016/j.neubiorev.2018.11.007
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
Gene mining has been a fruitful approach in the study of Alzheimer's disease (AD). As a new starting point for studying AD, genetic and genomic investigations consistently strive to discover causative variants that are related to disease pathophysiology. Currently, genetic and genomic approaches have identified numerous loci. However, the elaboration of AD mechanism lagged behind gene discovery. The extensive use of parallel, high throughput, next-generation sequencing techniques has improved our understanding of the roles of genetic variants in the brain at the highest level of functional hierarchy. We highlight three molecular systems (the transcriptome, proteome and epigenome) in this review to ascertain whether the methods used in systems biology studies of AD are useful. Here, we present many advantages of the high-throughput molecular, integrative and network methods, which may provide a good reference for future studies employing network biology approaches and large datasets.
引用
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页码:31 / 44
页数:14
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