Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Motivation Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [ 18 F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype. Availability and implementation The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/ Contact shenli@iu.edu Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)3250-3257
Number of pages8
JournalBioinformatics
Volume33
Issue number20
DOIs
StatePublished - Oct 15 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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