{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:09:23Z","timestamp":1788786563485,"version":"build-2803163510"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures\u2014HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments\u2014to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR\u2019s superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.<\/jats:p>","DOI":"10.1093\/bib\/bbag473","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T11:45:11Z","timestamp":1786535111000},"source":"Crossref","is-referenced-by-count":0,"title":["TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries"],"prefix":"10.1093","volume":"27","author":[{"given":"Lei","family":"Hou","sequence":"first","affiliation":[{"name":"Healthcare Big Data Research Institute, School of Public Health, Cheeloo College of Medicine, Shandong University , No. 12550 Erhuan Donglu, Jinan 250002, Shandong ,","place":["P.R. China"]},{"name":"Department of Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University , No. 44 West Wenhua Road, Jinan 250012, Shandong ,","place":["P.R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Hua","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing International Center for Mathematical Research and Department of Biostatistics, Peking University , No. 5 Yiheyuan Road, Haidian District, Beijing 100871 ,","place":["P.R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0378-7956","authenticated-orcid":false,"given":"Fuzhong","family":"Xue","sequence":"additional","affiliation":[{"name":"Healthcare Big Data Research Institute, School of Public Health, Cheeloo College of Medicine, Shandong University , No. 12550 Erhuan Donglu, Jinan 250002, Shandong ,","place":["P.R. 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