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      Genetics of height and risk of atrial fibrillation: A Mendelian randomization study

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          Abstract

          Background

          Observational studies have identified height as a strong risk factor for atrial fibrillation, but this finding may be limited by residual confounding. We aimed to examine genetic variation in height within the Mendelian randomization (MR) framework to determine whether height has a causal effect on risk of atrial fibrillation.

          Methods and findings

          In summary-level analyses, MR was performed using summary statistics from genome-wide association studies of height (GIANT/UK Biobank; 693,529 individuals) and atrial fibrillation (AFGen; 65,446 cases and 522,744 controls), finding that each 1-SD increase in genetically predicted height increased the odds of atrial fibrillation (odds ratio [OR] 1.34; 95% CI 1.29 to 1.40; p = 5 × 10 −42). This result remained consistent in sensitivity analyses with MR methods that make different assumptions about the presence of pleiotropy, and when accounting for the effects of traditional cardiovascular risk factors on atrial fibrillation. Individual-level phenome-wide association studies of height and a height genetic risk score were performed among 6,567 European-ancestry participants of the Penn Medicine Biobank (median age at enrollment 63 years, interquartile range 55–72; 38% female; recruitment 2008–2015), confirming prior observational associations between height and atrial fibrillation. Individual-level MR confirmed that each 1-SD increase in height increased the odds of atrial fibrillation, including adjustment for clinical and echocardiographic confounders (OR 1.89; 95% CI 1.50 to 2.40; p = 0.007). The main limitations of this study include potential bias from pleiotropic effects of genetic variants, and lack of generalizability of individual-level findings to non-European populations.

          Conclusions

          In this study, we observed evidence that height is likely a positive causal risk factor for atrial fibrillation. Further study is needed to determine whether risk prediction tools including height or anthropometric risk factors can be used to improve screening and primary prevention of atrial fibrillation, and whether biological pathways involved in height may offer new targets for treatment of atrial fibrillation.

          Abstract

          Scott Damrauer and colleagues investigate genetic evidence for a potential causal relationship between height and risk of atrial fibrillation.

          Author summary

          Why was this study done?
          • Studies have identified height as a risk factor for atrial fibrillation, a common abnormal heart rhythm.

          • Whether being taller actually elevates risk of atrial fibrillation, or if this association is an artifact of prior study designs, remains unclear.

          What did the researchers do and find?
          • We examined randomly allocated genetic variants associated with height within the Mendelian randomization framework to study the effects of height on risk of atrial fibrillation.

          • Genetic variants associated with taller stature were also associated with increased risk of atrial fibrillation. This finding was consistent across multiple analysis methods, including when accounting for other known atrial fibrillation risk factors.

          What do these findings mean?
          • Taller individuals are likely to be at increased risk of atrial fibrillation.

          • Future research is needed to better define the pathways connecting height to atrial fibrillation.

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          Most cited references38

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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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              Bias due to participant overlap in two‐sample Mendelian randomization

              ABSTRACT Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one‐sample analysis (in which data are taken from a single data source) with weak instrumental variables is biased in the direction of the observational association between the risk factor and outcome, whereas the estimate from a two‐sample analysis (in which data on the risk factor and outcome are taken from non‐overlapping datasets) is less biased and any bias is in the direction of the null. When using genetic consortia that have partially overlapping sets of participants, the direction and extent of bias are uncertain. In this paper, we perform simulation studies to investigate the magnitude of bias and Type 1 error rate inflation arising from sample overlap. We consider both a continuous outcome and a case‐control setting with a binary outcome. For a continuous outcome, bias due to sample overlap is a linear function of the proportion of overlap between the samples. So, in the case of a null causal effect, if the relative bias of the one‐sample instrumental variable estimate is 10% (corresponding to an F parameter of 10), then the relative bias with 50% sample overlap is 5%, and with 30% sample overlap is 3%. In a case‐control setting, if risk factor measurements are only included for the control participants, unbiased estimates are obtained even in a one‐sample setting. However, if risk factor data on both control and case participants are used, then bias is similar with a binary outcome as with a continuous outcome. Consortia releasing publicly available data on the associations of genetic variants with continuous risk factors should provide estimates that exclude case participants from case‐control samples.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                8 October 2020
                October 2020
                : 17
                : 10
                : e1003288
                Affiliations
                [1 ] Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                [2 ] Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                [3 ] Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
                [4 ] Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                [5 ] Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
                [6 ] Centre for Pharmacology & Therapeutics, Department of Medicine, Imperial College London, London, United Kingdom
                [7 ] Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
                [8 ] Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, United Kingdom
                [9 ] Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom
                [10 ] Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                [11 ] Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                [12 ] Tarrytown, New York, United States of America
                [13 ] Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                [14 ] Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
                University of Groningen, University Medical Center Groningen, NETHERLANDS
                Author notes

                The authors have declared that no competing interests exist.

                ¶ Membership of the Regeneron Genetics Center is provided in the Acknowledgments.

                Author information
                http://orcid.org/0000-0002-9937-9932
                http://orcid.org/0000-0001-7312-7078
                http://orcid.org/0000-0003-4924-5714
                http://orcid.org/0000-0001-5216-4670
                http://orcid.org/0000-0002-1208-1720
                http://orcid.org/0000-0002-4050-6925
                http://orcid.org/0000-0002-8369-0259
                http://orcid.org/0000-0002-9245-9876
                http://orcid.org/0000-0002-6205-9994
                http://orcid.org/0000-0001-8009-1632
                Article
                PMEDICINE-D-20-00041
                10.1371/journal.pmed.1003288
                7544133
                33031386
                01a5eae5-8c68-49d0-b86d-9ef98051d52b
                © 2020 Levin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 January 2020
                : 3 September 2020
                Page count
                Figures: 4, Tables: 1, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: R01-DK101478
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000738, U.S. Department of Veterans Affairs;
                Award ID: IK2-CX001780
                Award Recipient :
                Funded by: Wellcome Trust (GB)
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100007928, Perelman School of Medicine, University of Pennsylvania;
                Award ID: Linda Pechenik Montague Investigator Award
                Award Recipient :
                BFV was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R01-DK101478; https://www.niddk.nih.gov/) and a Linda Pechenik Montague Investigator Award (upenn.edu). SMD was supported by the US Department of Veterans Affairs (IK2-CX001780; VA.gov). This publication does not represent the views of the Department of Veterans Affairs or the United States government. DG was supported by funding from the Wellcome Trust ( https://wellcome.ac.uk/). Genetic sequencing of Penn Medicine Biobank participants was performed in collaboration with Regeneron Genetics Center ( https://www.regeneron.com/genetics-center), who also reviewed the manuscript but had no role in study design, data analysis, or decision to publish. The other funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Cardiology
                Arrhythmia
                Atrial Fibrillation
                Medicine and Health Sciences
                Medical Conditions
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Medicine and Health Sciences
                Cardiology
                Cardiovascular Medicine
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Biology and Life Sciences
                Genetics
                Biology and Life Sciences
                Genetics
                Genetics of Disease
                Medicine and Health Sciences
                Medical Conditions
                Cardiovascular Diseases
                Coronary Heart Disease
                Medicine and Health Sciences
                Cardiology
                Cardiovascular Medicine
                Cardiovascular Diseases
                Coronary Heart Disease
                Medicine and Health Sciences
                Vascular Medicine
                Coronary Heart Disease
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Medical Conditions
                Metabolic Disorders
                Diabetes Mellitus
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Instrumental Variable Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Instrumental Variable Analysis
                Custom metadata
                GWAS summary statistics for height are publicly available, and can be downloaded from the GIANT consortium website ( https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files). Summary statistics for atrial fibrillation were contributed by the AFGen consortium ( http://afgen.org), are publicly available, and may be downloaded from the Variant to Function Knowledge Portal ( http://www.kp4cd.org/datasets/v2f). Individual-level data from the Penn Medicine BioBank are not publicly available due to their sensitive nature, however the data may be made available with the appropriate ethical approval and data sharing agreements ( biobank@ 123456upenn.edu ).

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