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

• Published in: Bioinformatics (Oxford, England) England), 2017-10, Vol.33 (20), p.3250-3257
• Main Authors: Yao, Xiaohui, Yan, Jingwen, Liu, Kefei, Kim, Sungeun, Nho, Kwangsik, Risacher, Shannon L, Greene, Casey S, Moore, Jason H, Saykin, Andrew J, Shen, Li
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Alzheimer disease; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer Disease - pathology; amygdala; Amygdala - diagnostic imaging; Amygdala - pathology; artificial intelligence; bioinformatics; Computational Biology - methods; computer software; Female; genes; Genetic Predisposition to Disease; genome-wide association study; Genome-Wide Association Study - methods; Humans; image analysis; Machine Learning; Male; Middle Aged; Original Papers; Phenotype; Polymorphism, Genetic; Positron Emission Tomography Computed Tomography; Software; topology
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btx344

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. 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 [18F]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. The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/. [e-mail]. Supplementary data are available at Bioinformatics online.