Xu Zhichao, Wei Peng
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
PLoS Genet. 2024 Nov 19;20(11):e1011483. doi: 10.1371/journal.pgen.1011483. eCollection 2024 Nov.
Meta-analysis is used to aggregate the effects of interest across multiple studies, while its methodology is largely underexplored in mediation analysis, particularly in estimating the total mediation effect of high-dimensional omics mediators. Large-scale genomic consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program, comprise multiple cohorts with diverse technologies to elucidate the genetic architecture and biological mechanisms underlying complex human traits and diseases. Leveraging the recent established asymptotic standard error of the R-squared (R2)-based mediation effect estimation for high-dimensional omics mediators, we have developed a novel meta-analysis framework requiring only summary statistics and allowing inter-study heterogeneity. Whereas the proposed meta-analysis can uniquely evaluate and account for potential effect heterogeneity across studies due to, for example, varying genomic profiling platforms, our extensive simulations showed that the developed method was more computationally efficient and yielded satisfactory operating characteristics comparable to analysis of the pooled individual-level data when there was no inter-study heterogeneity. We applied the developed method to 5 TOPMed studies with over 5800 participants to estimate the mediation effects of gene expression on age-related variation in systolic blood pressure and sex-related variation in high-density lipoprotein (HDL) cholesterol. The proposed method is available in R package MetaR2M on GitHub.
荟萃分析用于汇总多项研究中感兴趣的效应，而其方法在中介分析中很大程度上未得到充分探索，尤其是在估计高维组学中介变量的总中介效应方面。大规模基因组联盟，如精准医学全基因组关联研究（TOPMed）项目，包含多个采用不同技术的队列，以阐明复杂人类性状和疾病背后的遗传结构和生物学机制。利用最近为高维组学中介变量建立的基于决定系数（R2）的中介效应估计的渐近标准误差，我们开发了一种新颖的荟萃分析框架，该框架仅需要汇总统计量，并允许研究间存在异质性。虽然所提出的荟萃分析能够独特地评估并考虑因例如不同基因组分析平台等研究间潜在的效应异质性，但我们广泛的模拟表明，当不存在研究间异质性时，所开发的方法计算效率更高，并且产生的操作特性与合并个体水平数据的分析相当，令人满意。我们将所开发的方法应用于5项TOPMed研究，涉及超过5800名参与者，以估计基因表达对收缩压年龄相关变异和高密度脂蛋白（HDL）胆固醇性别相关变异的中介效应。所提出的方法可在GitHub上的R包MetaR2M中获取。
