Network-based genome wide study of hippocampal imaging phenotype in Alzheimer's Disease to identify functional interaction modules
Year: 2017
Type: conference-paper
Abstract: Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases.
Institutions: Indiana University Indianapolis, University of Pennsylvania
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G104586243, Indiana Alzheimer Disease Center, Bioinformatics Strategies for Multidimensional Brain Imaging Genetics, Genetic architecture of memory and executive functioning in Alzheimer's disease, Indiana Clinical and Translational Sciences Institute, Integration of Bio-, Medical, and Imaging Informatics for Complex Diseases, Integration of Bio-, Medical, and Imaging Informatics for Complex Diseases, Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics, Machine Learning Prediction of Cancer Susceptibility, Bioinformatics Strategies for Genome Wide Association Studies, Alzheimer's Disease Neuroimaging Initiative, Memory Circuitry in MCI and Early Alzheimerâs Disease Prodrome: Molecular Drivers, Amyloid Imaging, VMCI, and Analysis for ADNI, Metabolic Networks and Pathways in Alzheimer's Disease, Alzheimer's Disease Neuroimaging Initiative (ADNI4)
OpenAlex ID: W2636004249