{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:35:25Z","timestamp":1792510525310,"version":"4.5.0"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000269","name":"Economic and Social Research Council","doi-asserted-by":"publisher","award":["ES\/P000630\/1"],"award-info":[{"award-number":["ES\/P000630\/1"]}],"id":[{"id":"10.13039\/501100000269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Mendelian randomisation (MR) is the use of genetic variants as instrumental variables. Mode-based estimators (MBE) are one of the most popular types of estimators used in univariable-MR studies and is often used as a sensitivity analysis for pleiotropy. However, because there are no plurality valid regression estimators, modal estimators for multivariable-MR have been under-explored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We use the residual framework for multivariable-MR to introduce two multivariable modal estimators: multivariable-MBE, which uses IVW to create residuals fed into a traditional plurality valid estimator, and an estimator which instead has the residuals fed into the contamination mixture method (CM), multivariable-CM. We then use Monte-Carlo simulations to explore the performance of these estimators when compared to existing ones and re-analyse the data used by Grant and Burgess (2021) looking at the causal effect of intelligence, education, and household income on Alzheimer\u2019s disease as an applied example.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In our simulation, we found that multivariable-MBE was generally too variable to be much use. Multivariable-CM produced more precise estimates on the other hand. Multivariable-CM performed better than MR-Egger in almost all settings, and Weighted Median under balanced pleiotropy. However, it underperformed Weighted Median when there was a moderate amount of directional pleiotropy. Our re-analysis supported the conclusion of Grant and Burgess (2021), that intelligence had a protective effect on Alzheimer\u2019s disease, while education, and household income do not have a causal effect.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Here we introduced two, non-regression-based, plurality valid estimators for multivariable MR. Of these, \u201cmultivariable-CM\u201d which uses IVW to create residuals fed into a contamination-mixture model, performed the best. This estimator uses a plurality of variants valid assumption, and appears to provide precise and unbiased estimates in the presence of balanced pleiotropy and small amounts of directional pleiotropy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0291183","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T13:34:48Z","timestamp":1715088888000},"page":"e0291183","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["MVMRmode: Introducing an R package for plurality valid estimators for multivariable Mendelian randomisation"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1505-2570","authenticated-orcid":true,"given":"Benjamin","family":"Woolf","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7312-7078","authenticated-orcid":true,"given":"Dipender","family":"Gill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6422-583X","authenticated-orcid":true,"given":"Andrew J.","family":"Grant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Burgess","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"pone.0291183.ref001","doi-asserted-by":"crossref","first-page":"R89","DOI":"10.1093\/hmg\/ddu328","article-title":"Mendelian randomization: genetic anchors for causal inference in epidemiological studies","volume":"23","author":"G Davey Smith","year":"2014","journal-title":"Human Molecular Genetics"},{"key":"pone.0291183.ref002","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10654-020-00622-7","article-title":"Mendel\u2019s laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues","volume":"35","author":"G Davey Smith","year":"2020","journal-title":"Eur J 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