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However, MR methods, operating under classical assumptions, may yield biased estimates and inflated false-positive causal relationships when faced with realistic and complex correlated horizontal pleiotropy (CHP). While numerous MR methods have emerged to address CHP effects, limited methods can effectively handle relatively large direct effects, commonly known as idiosyncratic pleiotropy. Results: To address this gap, we propose an efficient and robust MR method to account for idiosyncratic and correlated pleiotropy, named RMR-ICP. Furthermore, Our method incorporates linkage disequilibrium structure using paralleled Gibbs sampling to enhance statistical power. The robustness and efficiency of our method are demonstrated through extensive simulation studies and applications. RMR-ICP is first used to analyze the effects of plasma proteins on stroke, followed by its application to conventional stroke risk factors. Our analysis reveals that Selectin E (SELE) exhibits a positive causal effect on the occurrence of any stroke. Only those specifically designed to account for idiosyncratic and CHP identified a significant positive causal effect of myeloperoxidase on ischemic stroke, with RMR-ICP providing stronger statistical evidence. Elevated Natriuretic Peptide B (BNP) levels specifically increase the risk of cardioembolic stroke (CES), though not with other stroke subtypes. This finding is consistent with previous studies suggesting that plasma BNP levels may help distinguish CES from other stroke types. Higher Waist-hip ratio (WHR) levels raise the risk across all stroke types. These findings provide new insights into identifying stroke-related risk factors. Availability and implementation: RMR-ICP is publicly available at https:\/\/github.com\/QingCheng0218\/RMR.ICP.<\/jats:p>","DOI":"10.1093\/bib\/bbaf508","type":"journal-article","created":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T14:33:53Z","timestamp":1759070033000},"source":"Crossref","is-referenced-by-count":0,"title":["RMR-ICP: robust Mendelian randomization method accounting for idiosyncratic and correlated pleiotropy with applications to stroke outcomes"],"prefix":"10.1093","volume":"26","author":[{"given":"Qing","family":"Cheng","sequence":"first","affiliation":[{"name":"Center of Statistical Research, School of Statistics and Data Science, Southwestern University of Finance and Economics , Chengdu 611130 ,","place":["China"]}]},{"given":"Wenxin","family":"Xu","sequence":"additional","affiliation":[{"name":"Center of Statistical Research, School of Statistics and Data Science, Southwestern University of Finance and Economics , Chengdu 611130 ,","place":["China"]}]},{"given":"Chan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Population Health, Division of Biostatistics, New York University Grossman School of Medicine , New York, NY 10016 ,","place":["United States"]}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong-Shenzhen , Shenzhen 518172 ,","place":["China"]}]},{"given":"Yanyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Public Health, Cheeloo College of Medicine, Shandong University , Jinan 250012 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,9,28]]},"reference":[{"key":"2025092810334827900_ref1","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1002\/sim.3034","article-title":"Mendelian randomization: using genes as instruments for making causal inferences in 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