College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Sci Rep. 2017 Mar 14;7:44272. doi: 10.1038/srep44272.
Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.
神经影像学遗传学是一个新兴领域，旨在确定遗传变异（例如单核苷酸多态性（SNPs））与定量特征（如脑影像学表型）之间的关联。在最近的研究中，为了检测复杂的多 SNP-多 QT 关联，已经提出并在影像学遗传学研究中使用了双变量技术，如各种结构化稀疏典型相关分析（SCCA）算法。然而，现有双变量方法识别的遗传标记与影像学 QT 之间的关联可能并非全部是疾病特异性的。为了弥补这一差距，我们提出了一个基于三向稀疏典型相关分析（T-SCCA）的分析框架，以探索遗传标记、影像学 QT 和感兴趣的临床评分之间的内在关联。我们使用阿尔茨海默病神经影像学倡议（ADNI）队列进行了实证研究，以发现 AD 风险基因 APOE 中的 SNPs、从结构磁共振成像扫描中提取的影像学 QT 以及认知和诊断结果之间的关系。所提出的 T-SCCA 模型不仅在识别强关联方面优于传统的 SCCA 方法，而且还发现了稳健的与结果相关的影像学遗传模式，表明其有希望改善与疾病相关的机制理解。
