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Mendelian Randomization Applied to Neurology: Promises and Challenges
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.
Mendelian randomization (MR) uses genetic data for causal inference in neurology. This powerful approach aids in understanding neurologic diseases, drug discovery, and offers insights into pathophysiology.
Area of Science:
- Neurology
- Genetics
- Epidemiology
Background:
- Mendelian randomization (MR) leverages genetic data for causal inference.
- Expanding genetic association data for brain phenotypes and neurologic diseases enhances MR applications.
Purpose of the Study:
- To review the principles of Mendelian randomization (MR).
- To discuss the applications and potential pitfalls of MR in neurology research.
- To highlight MR's role in advancing neurologic disease understanding and treatment.
Main Methods:
- Review of Mendelian randomization principles.
- Discussion of applications using genetic association data (gene expression, brain imaging, neurologic diseases).
- Case examples illustrating MR's impact on epidemiologic controversies and pathophysiology.
Main Results:
- MR has illuminated epidemiologic controversies in neurology.
- MR provided insights into the pathophysiology of neurologic conditions.
- MR has successfully prioritized drug targets and informed drug repurposing.
Conclusions:
- Mendelian randomization (MR) is a key driver for progress in neurology.
- Familiarity with MR is essential for neurologists and researchers.
- MR facilitates causal inference for neurologic diseases and therapeutic strategies.
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