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A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia

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  • Zhichao Xu
  • Peng Wei

Abstract

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.Author summary: We have developed a novel meta-analysis framework to combine the estimates of the total mediation effect of high-dimensional omics mediators on complex traits from multiple studies in large-scale genomic consortia. By applying the developed method to genome-wide gene expression data from five studies with over 5,800 participants, we were able to demonstrate that our approach is not only computationally efficient but also yields reliable results. We illustrate how certain genes and biological pathways can influence age-related changes in blood pressure and sex differences in high-density lipoprotein (HDL) cholesterol levels. Our new tool, available as an R package MetaR2M on GitHub, makes it easier for researchers to analyze such complex data. This could lead to a better understanding of the genetic architecture and biological mechanisms underlying complex human traits and diseases.

Suggested Citation

  • Zhichao Xu & Peng Wei, 2024. "A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia," PLOS Genetics, Public Library of Science, vol. 20(11), pages 1-23, November.
    • Handle: RePEc:plo:pgen00:1011483
    DOI: 10.1371/journal.pgen.1011483
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    1. Kurex Sidik & Jeffrey N. Jonkman, 2005. "Simple heterogeneity variance estimation for meta‐analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(2), pages 367-384, April.
    2. Imai, Kosuke & Yamamoto, Teppei, 2013. "Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments," Political Analysis, Cambridge University Press, vol. 21(2), pages 141-171, April.
    3. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    4. James Y. Dai & Janet L. Stanford & Michael LeBlanc, 2022. "A Multiple-Testing Procedure for High-Dimensional Mediation Hypotheses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 198-213, January.
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