TEMR: Trans-ethnic Mendelian Randomization Method using Large-scale GWAS Summary Datasets

Available large-scale GWAS summary datasets predominantly stem from European populations, while sample sizes for other ethnicities, notably Central/South Asian, East Asian, African, Hispanic, etc. remain comparatively limited, which induces the low precision of causal effect estimation within these ethnicities using Mendelian Randomization (MR). In this paper, we propose a Trans-ethnic MR method called TEMR to improve statistical power and estimation precision of MR in the target population using trans-ethnic large-scale GWAS summary datasets. TEMR incorporates trans-ethnic genetic correlation coefficients through a conditional likelihood-based inference framework, producing calibrated p-values with substantially improved MR power. In the simulation study, TEMR exhibited superior precision and statistical power in the causal effects estimation within the target populations than other existing MR methods. Finally, we applied TEMR to infer causal relationships from 17 blood biomarkers to four diseases (hypertension, ischemic stroke, type 2 diabetes and schizophrenia) in East Asian, African and Hispanic/Latino populations leveraging the biobank-scale GWAS summary data from European. We found that causal biomarkers were mostly validated by previous MR methods, and we also discovered 13 new causal relationships that were not identified using previously published MR methods. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement HL was supported by the National Key Research and Development Program of Chinaunder (Grant 2022YFC3502100), National Natural Science Foundation of China (Grant 82003557) and Shandong Province Key R&D Program Project (2021SFGC0504). FX was supported by the National Natural Science Foundation of China (Grant 82173625). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The GWAS summary data in UK Biobank are publicly available at http://www.nealelab.is/uk-biobank. The GWAS summary data in Biobank Japan are publicly available at https://pheweb.jp/. The GWAS summary data in Pan-UKB are publicly available at https://pan.ukbb.broadinstitute.org/. Other GWAS summary data are publicly available at IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/) and GWAS Catalog (https://www.ebi.ac.uk/gwas/). All the analysis in our article were implemented by R software (version 4.3.2). R packages used in our analysis include TwoSampleMR, MendelianRandomization, ggplot2, plinkbinr and ieugwasr. TEMR package can be implemented by https://github.com/hhoulei/TEMR. All the codes for simulation are uploaded in https://github.com/hhoulei/TEMR_Simul. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The GWAS summary data in UK Biobank are publicly available at http://www.nealelab.is/uk-biobank. The GWAS summary data in Biobank Japan are publicly available at https://pheweb.jp/. The GWAS summary data in Pan-UKB are publicly available at https://pan.ukbb.broadinstitute.org/. Other GWAS summary data are publicly available at IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/) and GWAS Catalog (https://www.ebi.ac.uk/gwas/). All the analysis in our article were implemented by R software (version 4.3.2). R packages used in our analysis include TwoSampleMR, MendelianRandomization, ggplot2, plinkbinr and ieugwasr. TEMR package can be implemented by https://github.com/hhoulei/TEMR. All the codes for simulation are uploaded in https://github.com/hhoulei/TEMR_Simul.