ArticleBioinformatics (Oxford, England)2017
Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Module-Level Polygenic Risk Score-Based NetWAS Framework for Identifying AD Genetic Modules Mediated by Amygdala: An ADNI Study.International journal of molecular sciences · 2025Article
- Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop.Circulation. Cardiovascular imaging · 2021Article
- Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest.Genes · 2021Article
- System-Level Analysis of Alzheimer's Disease Prioritizes Candidate Genes for Neurodegeneration.Frontiers in genetics · 2021Article
- Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease.BMC genomics · 2020Article
- Volumetric GWAS of medial temporal lobe structures identifies an ERC1 locus using ADNI high-resolution T2-weighted MRI data.Neurobiology of aging · 2020Article
- Regional imaging genetic enrichment analysis.Bioinformatics (Oxford, England) · 2020Article
- DEEP NETWORK-BASED FEATURE SELECTION FOR IMAGING GENETICS: APPLICATION TO IDENTIFYING BIOMARKERS FOR PARKINSON'S DISEASE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus.Nature communications · 2019Article
- Mining Regional Imaging Genetic Associations via Voxel-wise Enrichment Analysis.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2019Article
- Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease.Current Alzheimer research · 2019Article
- GPU Accelerated Browser for Neuroimaging Genomics.Neuroinformatics · 2018Article
- Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine.Molecules (Basel, Switzerland) · 2018Review
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11 authors.
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Abstract
motivationNetwork-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.
resultsWe 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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