Xiaohui Yao1,2, Jingwen Yan1,2, Kefei Liu2, Sungeun Kim2,3, Kwangsik Nho2, Shannon L Risacher2, Casey S Greene4, Jason H Moore5, Andrew J Saykin2, Li Shen1,2. 1. Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA. 2. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA. 3. Department of Electrical and Computer Engineering, SUNY Oswego, NY 13126, USA. 4. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. 5. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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 [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. 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 INFORMATION: Supplementary data are available at Bioinformatics online.
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 [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. 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 INFORMATION: Supplementary data are available at Bioinformatics online.
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