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An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.


ABSTRACT: Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

SUBMITTER: Lin CX 

PROVIDER: S-EPMC8769916 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Lin Cui-Xiang CX   Li Hong-Dong HD   Deng Chao C   Liu Weisheng W   Erhardt Shannon S   Wu Fang-Xiang FX   Zhao Xing-Ming XM   Guan Yuanfang Y   Wang Jun J   Wang Daifeng D   Hu Bin B   Wang Jianxin J  

Briefings in bioinformatics 20220101 1


Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data  ...[more]

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