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      Shared genetics and causal associations between COVID‐19 and multiple sclerosis

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          Abstract

          Neuroinflammation caused by COVID‐19 negatively impacts brain metabolism and function, while pre‐existing brain pathology may contribute to individuals' vulnerability to the adverse consequences of COVID‐19. We used summary statistics from genome‐wide association studies (GWAS) to perform Mendelian randomization (MR) analyses, thus assessing potential associations between multiple sclerosis (MS) and two COVID‐19 outcomes (severe acute respiratory syndrome coronavirus 2 [SARS‐CoV‐2] infection and COVID‐19 hospitalization). Genome‐wide risk genes were compared between the GWAS datasets on hospitalized COVID‐19 and MS. Literature‐based analysis was conducted to construct molecular pathways connecting MS and COVID‐19. We found that genetic liability to MS confers a causal effect on hospitalized COVID‐19 (odd ratio [OR]: 1.09, 95% confidence interval: 1.03−1.16) but not on SARS‐CoV‐2 infection (1.03, 1.00−1.05). Genetic liability to hospitalized COVID‐19 confers a causal effect on MS (1.15, 1.02−1.30). Hospitalized COVID‐19 and MS share five risk genes within two loci, including TNFAIP8, HSD17B4, CDC37, PDE4A, and KEAP1. Pathway analysis identified a panel of immunity‐related genes that may mediate the links between MS and COVID‐19. Our study suggests that MS was associated with a 9% increased risk for COVID‐19 hospitalization, while hospitalized COVID‐19 was associated with a 15% increased risk for MS. Immunity‐related pathways may underlie the link between MS on COVID‐19.

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          Is Open Access

          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            Is Open Access

            Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

            Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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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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                Author and article information

                Contributors
                zhangfq@njmu.edu.cn
                Journal
                J Med Virol
                J Med Virol
                10.1002/(ISSN)1096-9071
                JMV
                Journal of Medical Virology
                John Wiley and Sons Inc. (Hoboken )
                0146-6615
                1096-9071
                29 December 2022
                January 2023
                29 December 2022
                : 95
                : 1 ( doiID: 10.1002/jmv.v95.1 )
                : e28431
                Affiliations
                [ 1 ] School of Systems Biology George Mason University Manassas USA
                [ 2 ] Research Centre for Medical Genetics Moscow Russia
                [ 3 ] Department of Biology Howard University Washington USA
                [ 4 ] Mind‐Body Interface Laboratory (MBI‐Lab), Department of Psychiatry China Medical University Hospital Taichung Taiwan
                [ 5 ] College of Medicine China Medical University Hospital Taichung Taiwan
                [ 6 ] An‐Nan Hospital China Medical University Hospital Tainan Taiwan
                [ 7 ] Institute of Neuropsychiatry The Affiliated Brain Hospital of Nanjing Medical University Nanjing China
                [ 8 ] Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Nanjing China
                Author notes
                [*] [* ] Correspondence Fuquan Zhang, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Rd, Nanjing 210029, China.

                Email: zhangfq@ 123456njmu.edu.cn

                Article
                JMV28431
                10.1002/jmv.28431
                9880714
                36571271
                5c3ee3a5-4937-429d-ac33-3c7b8d25a29d
                © 2022 Wiley Periodicals LLC.

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                History
                : 28 November 2022
                : 21 October 2022
                : 22 December 2022
                Page count
                Figures: 3, Tables: 2, Pages: 8, Words: 4531
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                January 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.4 mode:remove_FC converted:27.01.2023

                Microbiology & Virology
                covid‐19,mendelian randomization,multiple sclerosis
                Microbiology & Virology
                covid‐19, mendelian randomization, multiple sclerosis

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