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      Use of Mendelian Randomization to assess the causal status of modifiable exposures for rheumatic diseases

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          Abstract

          The explosion in Mendelian randomization (MR) publications is hard to ignore and shows no signs of slowing. Clinician readers, who may not be familiar with jargon-ridden methods, are expected to discern the good from the many low-quality studies that make overconfident claims of causality or stretch the plausibility of what MR can investigate. We aim to equip readers with foundational concepts, contextualized using examples in rheumatology, to appraise the many MR papers that are or will appear in their journals. We highlight the importance of assessing whether exposures are under plausibly specific genetic influence, whether the hypothesized causal pathways make biological sense, and whether results stand up to replication and use of control outcomes. Quality of research can vary substantially using MR as with any design, and all methods have inherent limitations. MR studies have provided and can still contribute valuable insights in the context of evidence triangulation.

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          Most cited references60

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          The MR-Base platform supports systematic causal inference across the human phenome

          Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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            Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

            Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence
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              Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization : The STROBE-MR Statement

              Mendelian randomization (MR) studies use genetic variation associated with modifiable exposures to assess their possible causal relationship with outcomes and aim to reduce potential bias from confounding and reverse causation.
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                Author and article information

                Journal
                101121149
                Best Pract Res Clin Rheumatol
                Best Pract Res Clin Rheumatol
                Best practice & research. Clinical rheumatology
                1521-6942
                1532-1770
                30 June 2024
                30 June 2024
                09 July 2024
                20 September 2024
                : 101967
                Affiliations
                [1 ]Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Science, School of Biological Sciences, Faculty of Biological Medicine and Health, University of Manchester ( https://ror.org/027m9bs27) , Manchester Academic Health Science Centre ( https://ror.org/04rrkhs81) , Manchester, UK
                [2 ]MRC Biostatistics Unit ( https://ror.org/046vje122) , University of Cambridge ( https://ror.org/013meh722) , Cambridge, United Kingdom
                [3 ]British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge ( https://ror.org/013meh722) , Cambridge, United Kingdom
                Author notes
                Correspondence to: Dr Sizheng S Zhao. Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biological Medicine and Health, The University of Manchester, Manchester, Academic Health Science Centre, Oxford Road, Manchester, M13 9LJ, UK., Phone: +44 7824860535, sizheng.zhao@ 123456manchester.ac.uk
                Article
                EMS197419
                10.1016/j.berh.2024.101967
                7616521
                38951047
                90e26de0-b30b-425a-9adc-e9232bb5dba8

                This work is licensed under a BY 4.0 International license.

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                Categories
                Article

                Rheumatology
                instrumental variable,genetics,causal inference,rheumatology,rheumatoid arthritis
                Rheumatology
                instrumental variable, genetics, causal inference, rheumatology, rheumatoid arthritis

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