{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:26:48Z","timestamp":1770888408527,"version":"3.50.1"},"reference-count":87,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100007270","name":"University of Michigan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Mendelian randomization (MR) is a common analytic tool for exploring the causal relationship among complex traits. Existing MR methods require selecting a small set of single nucleotide polymorphisms (SNPs) to serve as instrument variables. However, selecting a small set of SNPs may not be ideal, as most complex traits have a polygenic or omnigenic architecture and are each influenced by thousands of SNPs. Here, motivated by the recent omnigenic hypothesis, we present an MR method that uses all genome-wide SNPs for causal inference. Our method uses summary statistics from genome-wide association studies as input, accommodates the commonly encountered horizontal pleiotropy effects and relies on a composite likelihood framework for scalable computation. We refer to our method as the omnigenic Mendelian randomization, or OMR. We examine the power and robustness of OMR through extensive simulations including those under various modeling misspecifications. We apply OMR to several real data applications, where we identify multiple complex traits that potentially causally influence coronary artery disease (CAD) and asthma. The identified new associations reveal important roles of blood lipids, blood pressure and immunity underlying CAD as well as important roles of immunity and obesity underlying asthma.<\/jats:p>","DOI":"10.1093\/bib\/bbab322","type":"journal-article","created":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T19:11:47Z","timestamp":1627326707000},"source":"Crossref","is-referenced-by-count":16,"title":["Mendelian randomization under the omnigenic architecture"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1361-5146","authenticated-orcid":false,"given":"Lu","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China"},{"name":"Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Boran","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Yue","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"School of Public Health, Health Science Center of Xi'an Jiaotong University, Xi\u2019an, Shaanxi 710061, China"}]},{"given":"Fuzhong","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China"}]},{"given":"Xiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, 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