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Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts.

Publication ,  Journal Article
Song, A; Yan, J; Kim, S; Risacher, SL; Wong, AK; Saykin, AJ; Shen, L; Greene, CS; Alzheimer’s Disease Neuroimaging Initiative
Published in: BioData mining
January 2016

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.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.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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Published In

BioData mining

DOI

EISSN

1756-0381

ISSN

1756-0381

Publication Date

January 2016

Volume

9

Start / End Page

3

Related Subject Headings

  • 4605 Data management and data science
  • 3102 Bioinformatics and computational biology
  • 1303 Specialist Studies in Education
  • 1101 Medical Biochemistry and Metabolomics
  • 0801 Artificial Intelligence and Image Processing
 

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Song, A., Yan, J., Kim, S., Risacher, S. L., Wong, A. K., Saykin, A. J., … Alzheimer’s Disease Neuroimaging Initiative. (2016). Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts. BioData Mining, 9, 3. https://doi.org/10.1186/s13040-016-0082-8
Song, Ailin, Jingwen Yan, Sungeun Kim, Shannon Leigh Risacher, Aaron K. Wong, Andrew J. Saykin, Li Shen, Casey S. Greene, and Alzheimer’s Disease Neuroimaging Initiative. “Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts.BioData Mining 9 (January 2016): 3. https://doi.org/10.1186/s13040-016-0082-8.
Song A, Yan J, Kim S, Risacher SL, Wong AK, Saykin AJ, et al. Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts. BioData mining. 2016 Jan;9:3.
Song, Ailin, et al. “Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts.BioData Mining, vol. 9, Jan. 2016, p. 3. Epmc, doi:10.1186/s13040-016-0082-8.
Song A, Yan J, Kim S, Risacher SL, Wong AK, Saykin AJ, Shen L, Greene CS, Alzheimer’s Disease Neuroimaging Initiative. Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts. BioData mining. 2016 Jan;9:3.
Journal cover image

Published In

BioData mining

DOI

EISSN

1756-0381

ISSN

1756-0381

Publication Date

January 2016

Volume

9

Start / End Page

3

Related Subject Headings

  • 4605 Data management and data science
  • 3102 Bioinformatics and computational biology
  • 1303 Specialist Studies in Education
  • 1101 Medical Biochemistry and Metabolomics
  • 0801 Artificial Intelligence and Image Processing