{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:39:25Z","timestamp":1792456765709,"version":"4.5.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF1000100"],"award-info":[{"award-number":["2023YFF1000100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Key Research and Development Program of China","award":["2024BAA008"],"award-info":[{"award-number":["2024BAA008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Understanding disease-related causal mechanisms is essential for biomedical research, yet the practical use of Mendelian Randomization (MR) remains constrained by fragmented analytical workflows and a strong dependence on specialized expertise. These challenges limit the efficient, standardized, and reproducible execution of end-to-end MR analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose MR-MAS, an end-to-end automated MR analysis system built on a multi-agent architecture. MR-MAS leverages the reasoning and coordination capabilities of large language models (LLMs) and integrates external tools to unify data acquisition, causal effect estimation, results interpretation, and manuscript preparation into a single workflow, thereby reducing manual effort. MR-MAS further incorporates a structured Mendelian Randomization Knowledge Base (MRKB) to support MR method implementation and targeted repair of MR analysis code. Across diverse test scenarios, MR-MAS demonstrated stable workflow execution, reproduced several literature-supported causal signals, and maintained consistent causal estimates across repeated runs and different model backends. Additional evaluations showed robust trait retrieval and improved code-repair performance with MRKB guidance. These results suggest that MR-MAS may provide practical support for scalable, standardized, and reproducible MR analyses in biomedical research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>The source code, MRKB, and benchmark data are freely available at https:\/\/github.com\/Zaiwen\/MR-MAS, with the version used in this study archived at https:\/\/doi.org\/10.5281\/zenodo.20342636.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag722","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T10:13:20Z","timestamp":1792404800000},"source":"Crossref","is-referenced-by-count":0,"title":["MR-MAS: an end-to-end multi-agent system for autonomous Mendelian randomization analysis"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5622-2694","authenticated-orcid":false,"given":"Wendi","family":"Wei","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan, 430070,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuling","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan, 430070,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8519-5833","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Plant Science & Technology, Huazhong Agricultural University , Wuhan, 430070,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thuc Duy","family":"Le","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, Adelaide University , Adelaide, SA 5095,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomei","family":"Li","sequence":"additional","affiliation":[{"name":"Business Banking, Commonwealth Bank , Sydney, NSW 2000,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debo","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hainan University , Haikou, 570228,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1618-3553","authenticated-orcid":false,"given":"Zaiwen","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan, 430070,","place":["China"]},{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University , Wuhan, 430070,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,20]]},"reference":[{"key":"2026101919515767700_btag722-B1","author":"Achiam","year":"2023"},{"key":"2026101919515767700_btag722-B2","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/S0306-4573(02)00021-3","article-title":"An information-theoretic perspective of TF\u2013IDF measures","volume":"39","author":"Aizawa","year":"2003","journal-title":"Inform Process Manage"},{"key":"2026101919515767700_btag722-B3","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1093\/ije\/dyv080","article-title":"Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression","volume":"44","author":"Bowden","year":"2015","journal-title":"Int J Epidemiol"},{"key":"2026101919515767700_btag722-B4","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1002\/gepi.21965","article-title":"Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator","volume":"40","author":"Bowden","year":"2016","journal-title":"Genet Epidemiol"},{"key":"2026101919515767700_btag722-B5","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s42256-024-00832-8","article-title":"Augmenting large language models with chemistry tools","volume":"6","author":"Bran","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2026101919515767700_btag722-B6","doi-asserted-by":"publisher","first-page":"D1005","DOI":"10.1093\/nar\/gky1120","article-title":"The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019","volume":"47","author":"Buniello","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2026101919515767700_btag722-B7","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"key":"2026101919515767700_btag722-B8","volume-title":"The NCBI Handbook","author":"Canese"},{"key":"2026101919515767700_btag722-B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3641289","article-title":"A survey on evaluation of large language models","volume":"15","author":"Chang","year":"2024","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2026101919515767700_btag722-B10","author":"DeepSeek-AI","year":"2025"},{"key":"2026101919515767700_btag722-B11","doi-asserted-by":"publisher","author":"Elsworth","DOI":"10.1101\/2020.08.10.244293"},{"key":"2026101919515767700_btag722-B12","doi-asserted-by":"publisher","first-page":"2413523","DOI":"10.1002\/adma.202413523","article-title":"SciAgents: automating scientific discovery through bioinspired multi-agent intelligent graph reasoning","volume":"37","author":"Ghafarollahi","year":"2025","journal-title":"Adv Mater"},{"key":"2026101919515767700_btag722-B13","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1097\/00001648-199901000-00008","article-title":"Causal diagrams for epidemiologic research","volume":"10","author":"Greenland","year":"1999","journal-title":"Epidemiology (Fairfax)"},{"key":"2026101919515767700_btag722-B14","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1093\/ije\/dyx102","article-title":"Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption","volume":"46","author":"Hartwig","year":"2017","journal-title":"Int J Epidemiol"},{"key":"2026101919515767700_btag722-B15","doi-asserted-by":"publisher","first-page":"e34408","DOI":"10.7554\/eLife.34408","article-title":"The MR-Base platform supports systematic causal inference across the human phenome","volume":"7","author":"Hemani","year":"2018","journal-title":"elife"},{"key":"2026101919515767700_btag722-B16","doi-asserted-by":"publisher","DOI":"10.1126\/science.adz4351"},{"key":"2026101919515767700_btag722-B17","doi-asserted-by":"publisher","author":"Hurst","year":"2024","DOI":"10.48550\/arXiv.2410.21276"},{"key":"2026101919515767700_btag722-B18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2412.16720"},{"key":"2026101919515767700_btag722-B19","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 epidemiology","volume":"27","author":"Lawlor","year":"2008","journal-title":"Stat Med"},{"key":"2026101919515767700_btag722-B20","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1038\/s41588-020-0631-4","article-title":"Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics","volume":"52","author":"Morrison","year":"2020","journal-title":"Nat Genet"},{"key":"2026101919515767700_btag722-B21","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.1038\/s41467-019-09432-2","article-title":"Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects","volume":"10","author":"Qi","year":"2019","journal-title":"Nat Commun"},{"key":"2026101919515767700_btag722-B22","first-page":"1","article-title":"CRISPR-GPT for agentic automation of gene-editing experiments","author":"Qu","year":"2025","journal-title":"Nat Biomed Eng"},{"key":"2026101919515767700_btag722-B23","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1080\/02698590701498084","article-title":"Interpreting causality in the health sciences","volume":"21","author":"Russo","year":"2007","journal-title":"Int Stud Philos Sci"},{"key":"2026101919515767700_btag722-B24","first-page":"5977","author":"Schmidgall","year":"2025"},{"key":"2026101919515767700_btag722-B25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/ije\/dyg070","article-title":"\u2018Mendelian randomization\u2019: can genetic epidemiology contribute to understanding environmental determinants of disease?","volume":"32","author":"Smith","year":"2003","journal-title":"Int J Epidemiol"},{"key":"2026101919515767700_btag722-B26","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1038\/s41586-025-09442-9","article-title":"The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies","volume":"646","author":"Swanson","year":"2025","journal-title":"Nature"},{"key":"2026101919515767700_btag722-B27","doi-asserted-by":"publisher","first-page":"btae639","DOI":"10.1093\/bioinformatics\/btae639","article-title":"Causal relationships between diseases mined from the literature improve the use of polygenic risk scores","volume":"40","author":"Toonsi","year":"2024","journal-title":"Bioinformatics"},{"key":"2026101919515767700_btag722-B28","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1038\/s41588-018-0099-7","article-title":"Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases","volume":"50","author":"Verbanck","year":"2018","journal-title":"Nat Genet"},{"key":"2026101919515767700_btag722-B29","doi-asserted-by":"publisher","first-page":"24824","DOI":"10.52202\/068431-1800","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026101919515767700_btag722-B30","doi-asserted-by":"publisher","first-page":"bbaf140","DOI":"10.1093\/bib\/bbaf140","article-title":"MRAgent: an LLM-based automated agent for causal knowledge discovery in disease via Mendelian randomization","volume":"26","author":"Xu","year":"2025","journal-title":"Brief Bioinform"},{"key":"2026101919515767700_btag722-B31","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2504.08066"},{"key":"2026101919515767700_btag722-B32","doi-asserted-by":"publisher","first-page":"btaf359","DOI":"10.1093\/bioinformatics\/btaf359","article-title":"AI-HOPE: an AI-driven conversational agent for enhanced clinical and genomic data integration in precision medicine research","volume":"41","author":"Yang","year":"2025","journal-title":"Bioinformatics"},{"key":"2026101919515767700_btag722-B33","author":"Yao","year":"2023"},{"key":"2026101919515767700_btag722-B34","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1093\/ije\/dyx034","article-title":"Mendelian randomization: an R package for performing Mendelian randomization analyses using summarized data","volume":"46","author":"Yavorska","year":"2017","journal-title":"Int J Epidemiol"},{"key":"2026101919515767700_btag722-B35","doi-asserted-by":"crossref","first-page":"e2407094","DOI":"10.1002\/advs.202407094","article-title":"An AI agent for fully automated multi-omic analyses","volume":"11","author":"Zhou","year":"2024","journal-title":"Adv Sci (Weinh)"},{"key":"2026101919515767700_btag722-B36","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41467-017-02317-2","article-title":"Causal associations between risk factors and common diseases inferred from GWAS summary data","volume":"9","author":"Zhu","year":"2018","journal-title":"Nat Commun"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag722\/72089679\/btag722.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/10\/btag722\/72089679\/btag722.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/10\/btag722\/72089679\/btag722.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:52:08Z","timestamp":1792453928000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag722\/8937278"}},"subtitle":[],"editor":[{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":36,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag722","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,10]]},"published":{"date-parts":[[2026,10]]},"article-number":"btag722"}}