Neuroimaging genetics has become an important research topic since it can reveal complex associations between genetic variants (i.e. single nucleotide polymorphisms (SNPs) and the structures or functions of the human brain. However, existing kernel mapping is difficult to directly use the sparse representation method in the kernel feature space, which makes it difficult for most existing sparse canonical correlation analysis (SCCA) methods to be directly promoted in the kernel feature space. To bridge this gap, we adopt a novel alternating projected gradient approach, gradient KCCA (gradKCCA) model to develop a powerful model for exploring the intrinsic associations among genetic markers, imaging quantitative traits (QTs) of interest. Specifically, this model solves kernel canonical correlation (KCCA) with an additional constraint that projection directions have pre-images in the original data space, a sparsity-inducing variant of the model is achieved through controlling the ℓ1-norm of the preimages of the projection directions. We evaluate this model using Alzheimer’s disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from Alzheimer’s disease (AD) risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging (MRI) scans. Our results show that the algorithm not only outperforms the traditional KCCA method in terms of Root Mean Square Error (RMSE) and Correlation Coefficient (CC) but also identify the meaningful and relevant biomarkers of SNPs (e.g. rs157594 and rs405697), which are positively related to right Postcentral and right SupraMarginal brain regions in this study. Empirical results indicate its promising capability in revealing biologically meaningful neuroimaging genetics associations and improving the disease-related mechanistic understanding of AD.

中文翻译：

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通过一种新的结构化 KCCA 方法检测阿尔茨海默病中与脑成像表型的遗传关联

神经影像遗传学已成为一个重要的研究课题，因为它可以揭示遗传变异（即单核苷酸多态性（SNPs）与人脑结构或功能之间的复杂关联。然而，现有的核映射很难直接使用稀疏表示方法在核特征空间，这使得大多数现有的稀疏典型相关分析（SCCA）方法很难在核特征空间中直接推广。为了弥补这一差距，我们采用了一种新颖的交替投影梯度方法，梯度KCCA（gradKCCA）模型开发一个强大的模型来探索遗传标记、感兴趣的数量性状 (QT) 成像之间的内在关联。具体来说，该模型解决了核典型相关 (KCCA) 问题，并附加了投影方向在原始数据空间中具有原像的约束，通过控制ℓ1-投影方向原像的范数。我们使用阿尔茨海默病神经影像学倡议 (ADNI) 队列评估此模型，以发现来自阿尔茨海默病 (AD) 风险基因 APOE 的 SNP 之间的关系，以及从结构磁共振成像 (MRI) 扫描中提取的成像 QT。我们的结果表明，该算法不仅在均方根误差 (RMSE) 和相关系数 (CC) 方面优于传统的 KCCA 方法，而且还识别出有意义且相关的 SNP 生物标志物（例如 rs157594 和 rs405697），它们正相关在这项研究中，右后中央和右超边缘大脑区域。实证结果表明，它在揭示具有生物学意义的神经影像遗传学关联和改善对 AD 的疾病相关机制的理解方面具有广阔的前景。