4.3 Article

Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies

期刊

NEUROINFORMATICS
卷 19, 期 2, 页码 285-303

出版社

HUMANA PRESS INC
DOI: 10.1007/s12021-020-09486-4

关键词

Discovery science; Magnetic resonance imaging; Genetics; High-performance computing; Web-based system

资金

  1. Big Data for Discovery Science (BDDS) (NIH) [U54EB020406]
  2. Laboratory of Neuro Imaging Resource (LONIR) (NIH) [P41EB015922]
  3. Genetic Influences on Human Neuroanatomical Shapes (NIH) [R01MH094343]

向作者/读者索取更多资源

Large-scale genome-wide association studies have identified genetic variations associated with neurological and psychiatric disorders, leading to the need for advanced biomedical data science tools to manage the complex imaging and genomic data. Neuroimaging PheWAS is introduced as a web-based system to search through brain-wide imaging phenotypes and discover true gene-brain relationships, offering a user-friendly interface and cloud-based computational infrastructure for statistical analysis and visualization. Its potential was demonstrated with a case study on the influences of the APOE gene on brain morphological properties in the context of detailed imaging phenotypic data.
Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.

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