基于孟德尔随机化的脑成像表型和双相情感障碍因果网络的推导。

IF 4.8
Shane O'Connell, Brielin C Brown, Dara M Cannon, Pilib Ó Broin, Nadine Parker, Dag Alnæs, Lars T Westlye, Saikat Banerjee, Leila Nabulsi, Emma Corley, Ole A Andreassen, David A Knowles, Niamh Mullins
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引用次数: 0

摘要

背景:在观察性研究中,双相情感障碍(BD)患者的神经解剖学变异已经被描述过。然而,这些关系的因果动态仍未被探索。方法:我们使用GWAS汇总统计对来自UK Biobank和BD的297种结构和功能神经影像学表型进行孟德尔随机化。我们进行了一系列敏感性分析,并检查了对双相障碍影响最大的表型类别。我们应用了一种新的逆稀疏回归模型,该模型考虑了相关效应集之间的协方差,以估计“直接因果效应”(DCE),即一种表型条件对所有其他效应的影响。我们利用来自三个临床队列的神经影像学数据,使用DCE权重来创建双相障碍的因果评分。结果:在包含BD作为术语的多次测试修正后,我们发现了28个显著的因果关系对,其中27个描述了神经成像表型对BD的影响。在MR测试和估计的直接因果效应解决方案中,白质束表型对BD的绝对影响大于反之。我们发现,在网络解决方案中,白质表型明显大于非白质表型。使用神经影像学因果估计构建的因果评分是青少年队列中双相障碍的重要预测因子(O.R.=0.79)。结论:孟德尔随机化分析表明,神经解剖学变异,特别是在白质束如纵向束,可能是双相障碍的原因,而不是结果。验证估计的因果关系需要使用其他研究设计的证据方法的复制和三角测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deriving Mendelian Randomization-based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder.

Background: Neuroanatomical variation in individuals with bipolar disorder (BD) has been previously described in observational studies. However, the causal dynamics of these relationships remain unexplored.

Methods: We performed Mendelian Randomization of 297 structural and functional neuroimaging phenotypes from the UK Biobank and BD using GWAS summary statistics. We carried out a suite of sensitivity analyses and examined phenotypic categories with the greatest effect on BD. We applied a novel inverse sparse regression model which accounts for covariance between sets of correlated effects to estimate 'direct causal effects' (DCE), representing the effect of one phenotype conditional on all other effects. We used DCE weights to create causal scores for BD using neuroimaging data from three clinical cohorts.

Results: We found 28 significant causal relationship pairs after multiple testing corrections containing BD as a term, 27 of which described neuroimaging phenotype effects on BD. White matter tract phenotypes have larger absolute effects on BD than vice versa in MR tests and estimated direct causal effect solutions. We found that white matter phenotypes had significantly larger out-degrees than non-white matter tract phenotypes across network solutions. A causal score constructed using neuroimaging causal estimates was a significant predictor of BD in an adolescent cohort (O.R.=0.79).

Conclusion: Mendelian randomization analyses suggest that neuroanatomical variation, specifically in white matter tracts such as the longitudinal fasciculi, is likely a cause rather than a consequence of BD. Verification of estimated causal relationships requires replication and triangulation of evidence approaches using other study designs.

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