Bristol Dental School, University of Bristol, Bristol, UK.
Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
Eur J Hum Genet. 2021 Feb;29(2):300-308. doi: 10.1038/s41431-020-00734-4. Epub 2020 Oct 3.
Hypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power, sample overlap or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments across human phenome. We developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed causal architecture plot. We apply this pipeline to body mass index (BMI) and lipid traits as exemplars of traits where there is strong prior expectation for causal effects, and to dental caries and periodontitis as exemplars of traits where there is a need for causal inference. The results for lipids and BMI suggest that these traits are best viewed as contributing factors on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. The automated pipeline is implemented in the Complex-Traits Genetics Virtual Lab ( https://vl.genoma.io ) and results are available in https://view.genoma.io . We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a new way visualizing the overall causal map of the human phenome.
无假设孟德尔随机化研究为评估人类表型范围内的特征的因果相关性提供了一种方法，但可能受到统计能力、样本重叠或水平多效性的限制。最近描述的潜在因果变量 (LCV) 方法提供了一种替代因果推断的方法，在人类表型的无假设实验中可能很有用。我们开发了一种使用 LCV 方法进行全表型测试的自动化管道，包括估计部分遗传因果关系、过滤到有意义的估计集、应用多重测试校正然后以因果结构图形式呈现发现的步骤。我们将该管道应用于体重指数 (BMI) 和脂质特征作为具有强烈因果效应预期的特征的范例，以及用于龋齿和牙周炎作为需要因果推断的特征的范例。脂质和 BMI 的结果表明，这些特征最好被视为多种特征和条件的促成因素，从而提供了更多支持将这些特征视为改善健康干预目标的证据。另一方面，龋齿和牙周炎最好被视为其他特征和疾病的下游后果，而不是健康不良的原因。自动化管道在复杂特征遗传学虚拟实验室 (https://vl.genoma.io) 中实现，结果可在 https://view.genoma.io 上获得。我们提出基于全表型部分遗传因果关系估计的因果结构图作为可视化人类表型整体因果图的新方法。
