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

4 Citations (Scopus)

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

Fingerprint

Genome-Wide Association Study
Amygdala
Phenotype
Genome
Genes
Imaging
Tissue
Imaging techniques
Module
Interaction
Gene
Three-step Methods
Neuroimaging
Alzheimer's Disease
Information Services
Biological Networks
Computational Biology
Specificity
Motivation
Bioinformatics

ASJC Scopus subject areas

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

Cite this

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules. / Alzheimer's Disease Neuroimaging Initiative.

In: Bioinformatics, Vol. 33, No. 20, 15.10.2017, p. 3250-3257.

Research output: Contribution to journalArticle

Alzheimer's Disease Neuroimaging Initiative. / Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules. In: Bioinformatics. 2017 ; Vol. 33, No. 20. pp. 3250-3257.
@article{ee0effff62ba47598b7107c0e7ceb92d,
title = "Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules",
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.",
author = "{Alzheimer's Disease Neuroimaging Initiative} and Xiaohui Yao and Jingwen Yan and Kefei Liu and Sungeun Kim and Kwangsik Nho and Risacher, {Shannon L.} and Greene, {Casey S.} and Moore, {Jason H.} and Andrew Saykin and Li Shen",
year = "2017",
month = "10",
day = "15",
doi = "10.1093/bioinformatics/btx344",
language = "English (US)",
volume = "33",
pages = "3250--3257",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "20",

}

TY - JOUR

T1 - Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

AU - Alzheimer's Disease Neuroimaging Initiative

AU - Yao, Xiaohui

AU - Yan, Jingwen

AU - Liu, Kefei

AU - Kim, Sungeun

AU - Nho, Kwangsik

AU - Risacher, Shannon L.

AU - Greene, Casey S.

AU - Moore, Jason H.

AU - Saykin, Andrew

AU - Shen, Li

PY - 2017/10/15

Y1 - 2017/10/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85031782790&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85031782790&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btx344

DO - 10.1093/bioinformatics/btx344

M3 - Article

C2 - 28575147

AN - SCOPUS:85031782790

VL - 33

SP - 3250

EP - 3257

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 20

ER -