Article thumbnail

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

By Ailin Song, Jingwen Yan, Sungeun Kim, Shannon Leigh Risacher, Aaron K. Wong, Andrew J. Saykin, Li Shen and Casey S. Greene

Abstract

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

Publisher: 'Springer Science and Business Media LLC'
Year: 2016
DOI identifier: 10.1186/s13040-016-0082-8
OAI identifier: oai:scholarworks.iupui.edu:1805/9856
Provided by: IUPUIScholarWorks

Suggested articles


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.