标题： CHIMA：一种相关感知的高维中介分析及其在生命大脑项目研究中的应用 显示英文标题

标题： CHIMA: a correlation-aware high-dimensional mediation analysis with its application to the living brain project study

摘要： 中介分析研究中介变量将暴露因素的影响传递到结果变量的路径。 在高维情况下，通常使用变量筛选后进行联合显著性检验。 然而，当中介变量高度相关时，现有方法可能由于筛选不准确和渐近推断中的残余偏差而导致统计功效降低。 为解决这些问题，我们提出了 CHIMA（相关性感知的高维中介分析），这是最近开发的高维中介分析框架的扩展，通过整合两项改进来提高相关性下的性能：(i) 在相关性下进行准确筛选的高维普通最小二乘投影；以及 (ii) 近似正交化以减少偏差。 模拟研究显示，即使存在强相关性，CHIMA也能有效识别活跃的中介变量，并在各种设置中优于竞争方法。 我们进一步将CHIMA应用于Living Brain Project的核糖核酸测序（RNA-seq）数据，识别出介导帕金森病对脑细胞组成影响的基因，从而揭示了疾病在细胞类型特异性机制中的作用。 显示英文摘要

摘要： Mediation analysis examines the pathways through which mediators transmit the effect of an exposure to an outcome. In high-dimensional settings, the joint significance test is commonly applied using variable screening followed by statistical inference. However, when mediators are highly correlated, existing methods may experience reduced statistical power due to inaccurate screening and residual bias in asymptotic inference. To address these issues, we propose CHIMA (Correlation-aware High-dimensional Mediation Analysis), an extension of a recently developed high-dimensional mediation analysis framework that enhances performance under correlation by integrating two advances: (i) high-dimensional ordinary least squares projection for accurate screening under correlation; and (ii) approximate orthogonalization for bias reduction. Simulation studies demonstrate that CHIMA effectively identifies active mediators even in the presence of strong correlations and outperforms competing methods across various settings. We further apply CHIMA to ribonucleic acid sequencing (RNA-seq) from the Living Brain Project, identifying genes that mediate the effect of Parkinson's disease on brain cell composition, thereby revealing cell-type-specific mechanisms of disease.