MOTIVATION The detection of potential biomarkers of Alzheimer's disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues. RESULTS We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial-anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer's Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD/normal control subjects, and a high accuracy of AD/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD. AVAILABILITY AND IMPLEMENTATION https://github.com/Meiyan88/SASM-VGWAS.

中文翻译：

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将空间解剖相似性纳入 AD 生物标志物检测的 VGWAS 框架中。


动机 检测阿尔茨海默病 (AD) 的潜在生物标志物对于其早期预测、诊断和治疗至关重要。体素全基因组关联研究（VGWAS）是成像基因组学中常用的方法，通常用于检测成像和遗传数据中的 AD 生物标志物。然而，现有的 VGWAS 方法需要大量的计算成本，并且忽略成像数据内的空间相关性。提出了一种新方法来解决这些问题。结果我们引入了一种新方法，将空间相关性纳入 VGWAS 框架中，以检测潜在的 AD 生物标志物。为了考虑 AD 的特征，我们首先提出了一种简单线性迭代聚类方法的修改，用于以解剖学上有意义的方式进行空间分组。其次，我们提出了一个空间解剖相似性矩阵来合并体素之间的相关性。最后，我们使用快速 VGWAS 方法从成像和遗传数据中检测潜在的 AD 生物标志物，并在从阿尔茨海默病神经影像倡议数据集中获得的 708 名受试者上测试我们的方法。结果表明，我们的方法可以成功检测一些新的 AD 风险基因和簇。检测到的成像和遗传生物标志物用作预测因子对AD/正常对照受试者进行分类，并且实现了AD/正常对照分类的高精度。据我们所知，在建立对 AD 受试者进行分类的统计模型以在影像遗传学和 AD 之间建立联系时，成像和遗传数据之间的关联尚未得到系统研究。因此，我们的方法可能为深入了解 AD 的潜在病理机制提供一种新方法。可用性和实施​​ https://github.com/Meiyan88/SASM-VGWAS。