Overview of cloud-based GWAS data resources (IMAGE)

FAR Publishing Limited

Overview of cloud-based GWAS data resources

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The diagram illustrates a framework for cloud-based GWAS data resources, structured in a hub-and-spoke architecture with "Cloud-Based GWAS Data Resources" at its core—interconnected with six multi-omics domains. Phenomics domain​contains disease-related data, physical activity metrics, and behavioral characteristics. Research purposes are classified as disease mechanism research, physiological function assessment, and molecular biomarker discovery. This domain provides clinical phenotypes, disease diagnoses, and physiological indicators, encompassing over 10,000 variables. Neuroscience domain​integrates multi-source neuroimaging data, focusing on brain anatomy, cerebral cortex, and brain MRI. This domain includes cortical thickness measurements and more than 200 neuroimaging-derived features, supporting genome-wide association studies across .various MRI data types (as illustrated by the accompanying bar charts). Proteomics domain​provides plasma proteomic datasets quantified through multiplex immunoassay techniques, covering major studies such as the UKB-PPP, Finnish, and Icelandic Decode cohorts. Represented ancestral groups include European and East Asian populations. Microbiomics domain​covers intestinal flora, oral microbiota, and skin microbiota derived from metagenomic sequencing of over 10,000 samples. Analyses include α-diversity measurements and genus abundance profiling. Metabolomics domain incorporates a wide range of metabolic and immune profiling data, featuring multiple analytical categories including immune cells, lipoprotein, and blood biomarker. Nutrigenomics domain​contains long-term dietary habit data including dietary composition and food consumption patterns. This domain covers multiple food categories including fish, meat, and other dietary components. This domain provides over 120 food-related features for diet-genotype interaction studies. Each domain is visually differentiated by color coding for clear identification.

 

Credit

Xiaohong Ke, Kailai Li, Aimin Jiang, Yasi Zhang, Qi Wang, Zhengrui Li, Jian Zhang, András Hajd, Weniie Shi, Ulf Kahlerts, Anqi Lin, Pengpeng Zhang, Peng Luo

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