Ding Shengxian, Liu Rongjie, Srivastava Anuj, Nowakowski Richard S, Shen Li, Thompson Paul M, Zhang Heping, Huang Chao
Department of Biostatistics, Yale University, New Haven, Connecticut, USA.
Department of Statistics, University of Georgia, Athens, Georgia, USA.
Hum Brain Mapp. 2025 Aug 1;46(11):e70297. doi: 10.1002/hbm.70297.
Mediation analysis is widely utilized in neuroscience to investigate the role of brain image phenotypes in the neurological pathways from genetic exposures to clinical outcomes. However, it is still difficult to conduct mediation analyses with whole genome-wide exposures and brain subcortical shape mediators due to several challenges including (i) large-scale genetic exposures, that is, millions of single-nucleotide polymorphisms (SNPs); (ii) nonlinear Hilbert space for shape mediators; and (iii) statistical inference on the direct and indirect effects. To tackle these challenges, this paper proposes a genome-wide mediation analysis framework with brain subcortical shape mediators. First, to address the issue caused by the high dimensionality in genetic exposures, a fast genome-wide association analysis is conducted to discover potential genetic variants with significant genetic effects on the clinical outcome. Second, the square-root velocity function representations are extracted from the brain subcortical shapes, which fall in an unconstrained linear Hilbert subspace. Third, to identify the underlying causal pathways from the detected SNPs to the clinical outcome implicitly through the shape mediators, we utilize a shape mediation analysis framework consisting of a shape-on-scalar model and a scalar-on-shape model. Furthermore, the bootstrap resampling approach is adopted to investigate both global and spatial significant mediation effects. Finally, our framework is applied to the corpus callosum shape data from the Alzheimer's Disease Neuroimaging Initiative.
中介分析在神经科学中被广泛应用，以研究脑影像表型在从基因暴露到临床结局的神经通路中的作用。然而，由于包括以下几个挑战，对全基因组暴露和脑皮质下形状中介进行中介分析仍然很困难：（i）大规模基因暴露，即数百万个单核苷酸多态性（SNP）；（ii）形状中介的非线性希尔伯特空间；以及（iii）对直接和间接效应的统计推断。为应对这些挑战，本文提出了一个具有脑皮质下形状中介的全基因组中介分析框架。首先，为解决基因暴露中高维度问题导致的影响，进行快速全基因组关联分析，以发现对临床结局具有显著遗传效应的潜在基因变异。其次，从脑皮质下形状中提取平方根速度函数表示，这些形状属于无约束线性希尔伯特子空间。第三，为通过形状中介隐含地识别从检测到的SNP到临床结局的潜在因果通路，我们使用了一个由标量对形状模型和形状对标量模型组成的形状中介分析框架。此外，采用自助重采样方法来研究全局和空间显著的中介效应。最后，我们的框架应用于来自阿尔茨海默病神经影像倡议的胼胝体形状数据。
