{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Xu Z"],"funding":["NHLBI NIH HHS"],"pubmed_abstract":["Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M."],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2023.02.06.527391"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9934518"],"repository":["biostudies-literature"],"pubmed_title":["Speeding up interval estimation for R 2 -based mediation effect of high-dimensional mediators via cross-fitting."],"pmcid":["PMC9934518"],"funding_grant_id":["N01 HC025195","HHSN268201500001C","75N92019D00031","R01 HL116720","HHSN268201500001I"],"pubmed_authors":["Wei P","Li C","Chi S","Yang T","Xu Z"],"additional_accession":[]},"is_claimable":false,"name":"Speeding up interval estimation for R 2 -based mediation effect of high-dimensional mediators via cross-fitting.","description":"Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2026-04-08T13:22:40.389Z","creation":"2025-02-19T04:54:22.694Z"},"accession":"S-EPMC9934518","cross_references":{"pubmed":["36798366"],"doi":["10.1101/2023.02.06.527391"]}}