期刊
BIODATA MINING
卷 9, 期 -, 页码 -出版社
BIOMED CENTRAL LTD
DOI: 10.1186/s13040-016-0082-8
关键词
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资金
- Sophomore Science Scholarship Program of Dartmouth College
- Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative [GBMF4552]
- NIH [R01 LM010098, P20 GM103534, R01 LM011360, U01 AG024904, RC2 AG036535, R01 AG19771, P30 AG10133, UL1TR001108, DOD W81XWH-14-2-0151, NCAA 14132004]
- NSF [IIS-1117335]
- 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
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen Idec Inc.
- Bristol-Myers Squibb Company
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd
- Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Medpace, Inc.
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Synarc Inc.
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
Background: Alzheimer's disease (AD) is a neurodegenerative disease that causes dementia. While molecular basis of AD is not fully understood, genetic factors are expected to participate in the development and progression of the disease. Our goal was to uncover novel genetic underpinnings of Alzheimer's disease with a bioinformatics approach that accounts for tissue specificity. Findings: We performed genome-wide association studies (GWAS) for hippocampal volume in two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. We used these GWAS in a subsequent tissue-specific network-wide association study (NetWAS), which applied nominally significant associations in the initial GWAS to identify disease relevant patterns in a functional network for the hippocampus. We compared prioritized gene lists from NetWAS and GWAS with literature curated AD-associated genes from the Online Mendelian Inheritance in Man (OMIM) database. In the ADNI-1 GWAS, where we also observed an enrichment of low p-values, NetWAS prioritized disease-gene associations in accordance with OMIM annotations. This was not observed in the ADNI-2 dataset. We provide source code to replicate these analyses as well as complete results under permissive licenses. Conclusions: We performed the first analysis of hippocampal volume using NetWAS, which uses machine learning algorithms applied to tissue-specific functional interaction network to prioritize GWAS results. Our findings support the idea that tissue-specific networks may provide helpful context for understanding the etiology of common human diseases and reveal challenges that network-based approaches encounter in some datasets. Our source code and intermediate results files can facilitate the development of methods to address these challenges.
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