Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
Neuroinformatics. 2021 Apr;19(2):285-303. doi: 10.1007/s12021-020-09486-4.
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.
大规模的病例对照全基因组关联研究(GWAS)已经揭示了与多种神经和精神疾病相关的遗传变异。最近在大型健康和患病队列的神经影像学和基因组数据库方面的进展,使研究能够描述已发现的遗传因素对大脑结构和功能的影响,从而暗示了神经通路和遗传机制在潜在生物学中的作用。然而,成像和基因组数据前所未有的规模和复杂性,需要新的先进的生物医学数据科学工具来管理、处理和分析这些数据。在这项工作中,我们引入了神经影像学 pheWAS(表型全基因组关联研究):这是一个基于网络的系统,用于搜索广泛的脑成像表型,以使用统一的基因型到表型策略发现真正的系统水平的基因-大脑关系。该设计具有一个用户友好的图形用户界面(GUI),用于匿名数据上传、研究定义和管理,以及交互式结果可视化,以及基于云的计算基础设施和多种用于统计关联分析和多重比较校正的最先进的方法。我们通过对阿尔茨海默病神经影像学倡议(ADNI)队列中载脂蛋白 E(APOE)基因对大脑各个部位各种大脑形态特征的影响进行案例分析,展示了神经影像学 pheWAS 的潜力。使用来自 UK Biobank 的数据进行了基准测试,以评估系统的性能。神经影像学 pheWAS 系统是免费提供的。它简化了 pheWAS 在神经影像学数据上的执行,并为成像遗传学研究提供了机会,以阐明特定遗传变异在疾病中的作用途径,同时提供详细的成像表型数据。