Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America.
Pac Symp Biocomput. 2022;27:73-84.
The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity (WM) on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.
同时在大型研究队列中收集成像遗传学数据的出现为通过将遗传变异作为工具变量来评估大脑成像特征对外部测量的实验结果（例如认知测试）的因果效应提供了前所未有的机会。然而，经典的孟德尔随机化方法在处理作为暴露因素的高通量成像特征以识别因果效应时受到限制。我们提出了一种新的孟德尔随机化框架，用于联合选择工具变量和成像暴露因素，然后估计多变量成像数据对结果的因果效应。我们通过广泛的数据分析验证了所提出的方法，并将其与现有方法进行了比较。我们进一步应用我们的方法来评估白质微观结构完整性 (WM) 对认知功能的因果效应。研究结果表明，与单独评估单个暴露因素的因果效应以及在没有降维的情况下联合评估多个暴露因素的因果效应相比，我们的方法在灵敏度、偏差和假发现率方面表现更好。我们的应用结果表明，来自英国生物库的参与者的不同轨迹的 WM 测量值具有联合因果效应，对认知功能有显著影响。
