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      Genetically predicted serum vitamin D and COVID-19: a Mendelian randomisation study

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          Abstract

          Objectives

          To investigate causality of the association of serum vitamin D with the risk and severity of COVID-19 infection.

          Design

          Two-sample Mendelian randomisation study.

          Setting

          Summary data from genome-wide analyses in the population-based UK Biobank and SUNLIGHT Consortium, applied to meta-analysed results of genome-wide analyses in the COVID-19 Host Genetics Initiative.

          Participants

          17 965 COVID-19 cases including 11 085 laboratory or physician-confirmed cases, 7885 hospitalised cases and 4336 severe respiratory cases, and 1 370 547 controls, primarily of European ancestry.

          Exposures

          Genetically predicted variation in serum vitamin D status, instrumented by genome-wide significant single nucleotide polymorphisms (SNPs) associated with serum vitamin D or risk of vitamin D deficiency/insufficiency.

          Main outcome measures

          Susceptibility to and severity of COVID-19 infection, including severe respiratory infection and hospitalisation.

          Results

          Mendelian randomisation analysis, sufficiently powered to detect effects comparable to those seen in observational studies, provided little to no evidence for an effect of genetically predicted serum vitamin D on susceptibility to or severity of COVID-19 infection. Using SNPs in loci related to vitamin D metabolism as genetic instruments for serum vitamin D concentrations, the OR per SD higher serum vitamin D was 1.04 (95% CI 0.92 to 1.18) for any COVID-19 infection versus population controls, 1.05 (0.84 to 1.31) for hospitalised COVID-19 versus population controls, 0.96 (0.64 to 1.43) for severe respiratory COVID-19 versus population controls, 1.15 (0.99 to 1.35) for COVID-19 positive versus COVID-19 negative and 1.44 (0.75 to 2.78) for hospitalised COVID-19 versus non-hospitalised COVID-19. Results were similar in analyses using SNPs with genome-wide significant associations with serum vitamin D (ie, including SNPs in loci with no known relationship to vitamin D metabolism) and in analyses using SNPs with genome-wide significant associations with risk of vitamin D deficiency or insufficiency.

          Conclusions

          These findings suggest that genetically predicted differences in long-term vitamin D nutritional status do not causally affect susceptibility to and severity of COVID-19 infection, and that associations observed in previous studies may have been driven by confounding. These results do not exclude the possibility of low-magnitude causal effects or causal effects of acute responses to therapeutic doses of vitamin D.

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          Most cited references67

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            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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              Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

              ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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                Author and article information

                Journal
                BMJ Nutr Prev Health
                BMJ Nutr Prev Health
                bmjnph
                bmjnph
                BMJ Nutrition, Prevention & Health
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2516-5542
                May 2021
                4 May 2021
                : bmjnph-2021-000255
                Affiliations
                [1 ]departmentDivision of Nutritional Sciences , Cornell University , Ithaca, New York, USA
                [2 ]departmentDepartment of Molecular Biology and Genetics , Cornell University , Ithaca, New York, USA
                [3 ]departmentGenOmics, Bioinformatics and Translational Research Center , Research Triangle Institute , Research Triangle Park, North Carolina, USA
                [4 ]departmentPopulation Health Sciences , Weill Cornell Medical College , New York, New York, USA
                Author notes
                [Correspondence to ] Dr Patricia A Cassano, Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853, USA; pac6@ 123456cornell.edu
                Author information
                http://orcid.org/0000-0003-3361-6744
                http://orcid.org/0000-0003-4827-5073
                Article
                bmjnph-2021-000255
                10.1136/bmjnph-2021-000255
                8098235
                355322cc-d61e-4fbb-abbc-449ad1e9629c
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 11 February 2021
                : 09 April 2021
                : 10 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Award ID: R01 HL149352
                Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;
                Award ID: R01 HG006849
                Funded by: FundRef http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: T32 DK007158
                Categories
                Original Research
                1506
                2474
                2527
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                covid-19,nutrient deficiencies
                covid-19, nutrient deficiencies

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