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      BMI and low vitamin D are causal factors for multiple sclerosis : A Mendelian Randomization study

      research-article
      , BM BCh, , PhD, MRCP, , PhD, , PhD
      Neurology® Neuroimmunology & Neuroinflammation
      Lippincott Williams & Wilkins

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          Abstract

          Objective

          To update the causal estimates for the effects of adult body mass index (BMI), childhood BMI, and vitamin D status on multiple sclerosis (MS) risk.

          Methods

          We used 2-sample Mendelian randomization to determine causal estimates. Summary statistics for SNP associations with traits of interest were obtained from the relevant consortia. Primary analyses consisted of random-effects inverse-variance-weighted meta-analysis, followed by secondary sensitivity analyses.

          Results

          Genetically determined increased childhood BMI (OR MS 1.24, 95% CI 1.05–1.45, p = 0.011) and adult BMI (OR MS 1.14, 95% CI 1.01–1.30, p = 0.042) were associated with increased MS risk. The effect of genetically determined adult BMI on MS risk lessened after exclusion of 16 variants associated with childhood BMI (OR MS 1.11, 95% CI 0.97–1.28, p = 0.121). Correcting for effects of serum vitamin D in a multivariate analysis did not alter the direction or significance of these estimates. Each genetically determined unit increase in the natural-log-transformed vitamin D level was associated with a 43% decrease in the odds of MS (OR 0.57, 95% CI 0.41–0.81, p = 0.001).

          Conclusions

          We provide novel evidence that BMI before the age of 10 is an independent causal risk factor for MS and strengthen evidence for the causal role of vitamin D in the pathogenesis of MS.

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          Most cited references20

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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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            LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

            Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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              An Atlas of Genetic Correlations across Human Diseases and Traits

              Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
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                Author and article information

                Contributors
                Journal
                Neurol Neuroimmunol Neuroinflamm
                Neurol Neuroimmunol Neuroinflamm
                nnn
                NEURIMMINFL
                Neurology® Neuroimmunology & Neuroinflammation
                Lippincott Williams & Wilkins (Hagerstown, MD )
                2332-7812
                March 2020
                14 January 2020
                14 January 2020
                : 7
                : 2
                : e662
                Affiliations
                From the Preventive Neurology Unit (B.M.J., A.J.N., G.G., R.D.), Wolfson Institute of Preventive Medicine, Barts and Queen Mary University of London; and Royal London Hospital (B.M.J., A.J.N., G.G., R.D.), Barts Health NHS Trust.
                Author notes
                Correspondence Dr. Dobson ruth.dobson@ 123456qmul.ac.uk

                Go to Neurology.org/NN for full disclosures. Funding information is provided at the end of the article.

                The Article Processing Charge was funded by the authors.

                Article
                NEURIMMINFL2019023861
                10.1212/NXI.0000000000000662
                6975169
                31937597
                c940d320-9ab9-4418-9752-ca7044c5593b
                Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

                History
                : 03 September 2019
                : 26 November 2019
                Funding
                Funded by: Barts Charity
                Award ID: MGU0365
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