期刊
CURRENT ALZHEIMER RESEARCH
卷 16, 期 13, 页码 1163-1174出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1567205016666191121142558
关键词
Alzheimer's disease; amyloid imaging phenotype; genome-wide association; network analysis; pathway enrichment; consensus modules; neurodegenerative disease
资金
- National Natural Science Foundation of China [61773134, 61803117]
- Fundamental Research Funds for the Central Universities [3072019CFM0403, HEUCFG201824]
- Natural Science Foundation of Heilongjiang Province of China at Harbin Engineering University [YQ2019F003]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences at the Changzhou Institute of Technology [19YJCZH120]
- NIH at the Indiana University [R01 EB022574, R01 LM011360, U01 AG024904, RC2 AG036535, R01 AG19771, P30 AG10133]
- University of Pennsylvania
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research AMP
- Development, LLC
- JohnsonAMP
- Johnson Pharmaceutical Research AMP
- Development LLC
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
Background: The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. Objective: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network-assisted strategy. Methods: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. Results: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported A1) genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. Conclusion: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.
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