Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer’s Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.

中文翻译：

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使用多变量 IWAS 和 GWAS 摘要数据揭示阿尔茨海默病的因果脑成像内表型


最近的证据表明，存在许多未发现的遗传性大脑表型与阿尔茨海默病（AD）发病机制有关。这一发现需要将磁共振成像测量和基因型数据相结合的方法来发现 AD 中大脑的因果变化。然而，在这种情况下进行因果推断的现有方法，例如单变量成像广泛关联研究（UV-IWAS），在存在遗传多效性（一种变异影响多个基因的现象）的情况下，会受到不一致的效果估计和夸大的 I 型错误的影响。因果中等风险表型。在这项研究中，我们对 IWAS 模型实施了多变量扩展，即 MV-IWAS，以一致地估计和测试阿尔茨海默氏病神经影像计划 (ADNI) 的多种脑成像内表型在存在多效性和可能相关的情况下的因果效应SNP。我们进一步扩展 MV-IWAS，以纳入对 AD 的变体特异性直接影响，类似于现有的 Egger 回归孟德尔随机化方法，该方法允许在调整多个中间途径后测试剩余的多效性。我们提出了一种实施 MV-IWAS 的便捷方法，该方法仅依赖于公开可用的 GWAS 摘要数据和参考面板。通过使用个体水平或汇总数据进行模拟，我们证明了在多效性 SNP 存在的情况下，MV-IWAS 比 UV-IWAS 具有良好控制的 I 型错误和更强大的能力。我们将基于统计的汇总测试应用于英国生物银行的 1578 个可遗传成像衍生表型 (IDP)。 MV-IWAS 检测到许多 IDP 可能是 UV-IWAS 的误报，同时发现了 AD 中许多其他因果神经影像表型，这些表型得到了现有文献的大力支持。