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

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

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish (US)
JournalBioData Mining
DOIs
StateAccepted/In press - Jan 19 2016

Fingerprint

Neuroimaging
Alzheimer's Disease
Genome-Wide Association Study
Alzheimer Disease
Genes
Genome
Genetic Databases
Tissue
Organ Specificity
Gene
Gene Regulatory Networks
Licensure
Computational Biology
Neurodegenerative Diseases
Neurodegenerative diseases
Dementia
Disease Progression
Hippocampus
Databases
Bioinformatics

ASJC Scopus subject areas

  • Genetics
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease : A study of ADNI cohorts. / Song, Ailin; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon Leigh; Wong, Aaron K.; Saykin, Andrew; Shen, Li; Greene, Casey S.

In: BioData Mining, 19.01.2016.

Research output: Contribution to journalArticle

Song, Ailin ; Yan, Jingwen ; Kim, Sungeun ; Risacher, Shannon Leigh ; Wong, Aaron K. ; Saykin, Andrew ; Shen, Li ; Greene, Casey S. / Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease : A study of ADNI cohorts. In: BioData Mining. 2016.
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