University Center for Primary Care and Public Health, Lausanne, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Nat Commun. 2024 Feb 15;15(1):1420. doi: 10.1038/s41467-024-45655-8.
Mendelian Randomisation (MR) estimates causal effects between risk factors and complex outcomes using genetic instruments. Pleiotropy, heritable confounders, and heterogeneous causal effects violate MR assumptions and can lead to biases. To alleviate these, we propose an approach employing a Phenome-Wide association Clustering of the MR instruments (PWC-MR) and apply this method to revisit the surprisingly large apparent causal effect of body mass index (BMI) on educational attainment (EDU): [Formula: see text] = -0.19 [-0.22, -0.16]. First, we cluster 324 BMI-associated genetic instruments based on their association with 407 traits in the UK Biobank, which yields six distinct groups. Subsequent cluster-specific MR reveals heterogeneous causal effect estimates on EDU. A cluster enriched for socio-economic indicators yields the largest BMI-on-EDU causal effect estimate ([Formula: see text] = -0.49 [-0.56, -0.42]) whereas a cluster enriched for body-mass specific traits provides a more likely estimate ([Formula: see text] = -0.09 [-0.13, -0.05]). Follow-up analyses confirms these findings: within-sibling MR ([Formula: see text] = -0.05 [-0.09, -0.01]); MR for childhood BMI on EDU ([Formula: see text] = -0.03 [-0.06, -0.002]); step-wise multivariable MR ([Formula: see text] = -0.05 [-0.07, -0.02]) where socio-economic indicators are jointly modelled. Here we show how the in-depth examination of the BMI-EDU causal relationship demonstrates the utility of our PWC-MR approach in revealing distinct pleiotropic pathways and confounder mechanisms.
孟德尔随机化(MR)使用遗传工具估计风险因素与复杂结果之间的因果关系。多效性、可遗传性混杂因素和异质性因果效应违反了 MR 假设,可能导致偏差。为了缓解这些问题,我们提出了一种利用 MR 工具的表型全基因组关联聚类(PWC-MR)的方法,并应用该方法重新审视体质指数(BMI)对教育程度(EDU)的因果效应:[公式:见正文] = -0.19 [-0.22, -0.16]。首先,我们根据英国生物库中 407 个特征与 324 个 BMI 相关遗传工具的关联对这些工具进行聚类,得到了六个不同的聚类。随后对特定聚类的 MR 揭示了对 EDU 的异质性因果效应估计。一个富集社会经济指标的聚类产生了最大的 BMI-EDU 因果效应估计值([公式:见正文] = -0.49 [-0.56, -0.42]),而一个富集与体重特异性特征的聚类则提供了更有可能的估计值([公式:见正文] = -0.09 [-0.13, -0.05])。后续分析证实了这些发现:同卵双胞胎 MR [公式:见正文] = -0.05 [-0.09, -0.01];儿童 BMI 对 EDU 的 MR [公式:见正文] = -0.03 [-0.06, -0.002];逐步多变量 MR [公式:见正文] = -0.05 [-0.07, -0.02],其中社会经济指标共同建模。在这里,我们展示了如何深入研究 BMI-EDU 因果关系,展示了我们的 PWC-MR 方法在揭示不同的多效性途径和混杂机制方面的效用。