The corpus callosum, the largest white matter structure in the brain, plays a critical role in interhemispheric communication. Variations in its morphology are associated with various neurological and psychological conditions, making it a key focus in neurogenetics. Age is known to influence the structure and morphology of the corpus callosum significantly, complicating the identification of specific genetic factors that contribute to its shape and size. We propose a conditional strong independence screening method to address these challenges for ultrahigh-dimensional predictors and non-Euclidean responses. Our approach incorporates prior knowledge, such as age. It introduces a novel concept of conditional metric dependence, quantifying non-linear conditional dependencies among random objects in metric spaces without relying on predefined models. We apply this framework to identify genetic factors associated with the morphology of the corpus callosum. Simulation results demonstrate the efficacy of this method across various non-Euclidean data types, highlighting its potential to drive genetic discovery in neuroscience.

翻译：胼胝体作为大脑中最大的白质结构，在半球间信息交流中发挥着关键作用。其形态变化与多种神经和精神疾病存在关联，这使其成为神经遗传学研究的核心焦点。已知年龄会显著影响胼胝体的结构和形态，这进一步增加了识别影响其形态与尺寸特定遗传因素的复杂性。为应对超高维预测变量与非欧几里得响应带来的挑战，我们提出了一种条件强独立性筛选方法。该方法融入了年龄等先验知识，并引入"条件度量依赖"这一新概念，无需预设模型即可量化度量空间中随机对象间的非线性条件依赖关系。我们应用该框架识别与胼胝体形态相关的遗传因素。模拟实验结果表明，该方法在多种非欧几里得数据类型上均展现出良好效能，凸显了其在推动神经科学遗传发现方面的潜力。