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      Telomere Length and Development of Systemic Lupus Erythematosus: A Mendelian Randomization Study

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          Abstract

          Objective

          Previous observational studies demonstrated that a subset of patients with systemic lupus erythematosus (SLE) have markedly short telomere length in leukocytes. This study was undertaken to test whether leukocyte telomere length is causally associated with risk of SLE.

          Methods

          A 2‐sample Mendelian randomization (MR) analysis was conducted to estimate causality of telomere length on SLE in European populations. A replication 2‐sample MR study using Asian genetic data was also conducted. A reverse MR analysis was then performed to test the effects of SLE on telomere length. The autoantibodies targeting telomere‐associated protein (telomeric repeat–binding factor 1 [TERF1] autoantibodies) were detected in patients with SLE, healthy controls, and patients with rheumatoid arthritis.

          Results

          The results of the inverse variance–weighted method (odds ratio [OR] 2.96 [95% confidence interval (95% CI) 1.58–5.55], P < 0.001) showed strong evidence for a causal relationship between longer telomere length and risk of SLE in people with European ancestry. The outcomes of MR‐Egger regression analysis (OR 29.46 [95% CI 3.02–287.60], P = 0.033) and MR pleiotropy residual sum and outlier analysis (OR 3.62 [95% CI 2.03–6.46], P = 0.002) also showed that longer telomere length was significantly associated with increased risk of SLE in a European population. Sensitivity analyses using different methods and summary data sets showed that the results were still broadly consistent. A replication MR study using Asian genetic data yielded similar findings. However, the reverse MR analysis showed that genetically predicted SLE was not causally associated with telomere length. In addition, we found that TERF1 autoantibodies were present in 2 of 40 SLE patients (5.0%).

          Conclusion

          In contrast with previous observational studies, MR analyses show that longer telomere length is significantly associated with increased risk of SLE.

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          Most cited references23

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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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            Updating the American college of rheumatology revised criteria for the classification of systemic lupus erythematosus

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              Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators

              Mendelian randomization (MR) is a method for estimating the causal relationship between an exposure and an outcome using a genetic factor as an instrumental variable (IV) for the exposure. In the traditional MR setting, data on the IV, exposure, and outcome are available for all participants. However, obtaining complete exposure data may be difficult in some settings, due to high measurement costs or lack of appropriate biospecimens. We used simulated data sets to assess statistical power and bias for MR when exposure data are available for a subset (or an independent set) of participants. We show that obtaining exposure data for a subset of participants is a cost-efficient strategy, often having negligible effects on power in comparison with a traditional complete-data analysis. The size of the subset needed to achieve maximum power depends on IV strength, and maximum power is approximately equal to the power of traditional IV estimators. Weak IVs are shown to lead to bias towards the null when the subsample is small and towards the confounded association when the subset is relatively large. Various approaches for confidence interval calculation are considered. These results have important implications for reducing the costs and increasing the feasibility of MR studies.
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                Author and article information

                Contributors
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                Journal
                Arthritis & Rheumatology
                Arthritis & Rheumatology
                Wiley
                2326-5191
                2326-5205
                December 2022
                November 04 2022
                December 2022
                : 74
                : 12
                : 1984-1990
                Affiliations
                [1 ] Department of Epidemiology and Biostatistics School of Public Health, Anhui Medical University, and Inflammation and Immune Mediated Diseases Laboratory of Anhui Province Hefei China
                [2 ] Department of Rheumatology and Immunology, the First Affiliated Hospital of Anhui Medical University Hefei China
                [3 ] Department of Clinical Laboratory, the First Affiliated Hospital of Anhui Medical University Hefei China
                [4 ] Department of Rheumatology and Immunology, the First Affiliated Hospital of University of Science and Technology of China Hefei China
                [5 ] National Health Commission of the People's Republic of China, Key Laboratory of Enteric Pathogenic Microbiology (Jiangsu Provincial Center for Disease Control and Prevention) Nanjing China
                Article
                10.1002/art.42304
                35830513
                63d76a71-40c7-4e2e-9e84-029997754251
                © 2022

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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