Mendelian Randomization Applied to Neurology: Promises and Challenges
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Updated: Jul 5, 2025

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
Published on: June 15, 2011
Eloi Gagnon1, Iyas Daghlas1, Loukas Zagkos1
1From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada.
Abstract:
The Mendelian randomization (MR) paradigm allows for causal inferences to be drawn using genetic data. In recent years, the expansion of well-powered publicly available genetic association data related to phenotypes such as brain tissue gene expression, brain imaging, and neurologic diseases offers exciting opportunities for the application of MR in the field of neurology. In this review, we discuss the basic principles of MR, its myriad applications to research in neurology, and potential pitfalls of injudicious applications. Throughout, we provide examples where MR-informed findings have shed light on long-standing epidemiologic controversies, provided insights into the pathophysiology of neurologic conditions, prioritized drug targets, and informed drug repurposing opportunities. With the ever-expanding availability of genome-wide association data, we project MR to become a key driver of progress in the field of neurology. It is therefore paramount that academics and clinicians within the field are familiar with the approach.
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