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      Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

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          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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          'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

          Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
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            Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

            Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.
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              Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S)

              Drug therapy for hypercholesterolaemia has remained controversial mainly because of insufficient clinical trial evidence for improved survival. The present trial was designed to evaluate the effect of cholesterol lowering with simvastatin on mortality and morbidity in patients with coronary heart disease (CHD). 4444 patients with angina pectoris or previous myocardial infarction and serum cholesterol 5.5-8.0 mmol/L on a lipid-lowering diet were randomised to double-blind treatment with simvastatin or placebo. Over the 5.4 years median follow-up period, simvastatin produced mean changes in total cholesterol, low-density-lipoprotein cholesterol, and high-density-lipoprotein cholesterol of -25%, -35%, and +8%, respectively, with few adverse effects. 256 patients (12%) in the placebo group died, compared with 182 (8%) in the simvastatin group. The relative risk of death in the simvastatin group was 0.70 (95% CI 0.58-0.85, p = 0.0003). The 6-year probabilities of survival in the placebo and simvastatin groups were 87.6% and 91.3%, respectively. There were 189 coronary deaths in the placebo group and 111 in the simvastatin group (relative risk 0.58, 95% CI 0.46-0.73), while noncardiovascular causes accounted for 49 and 46 deaths, respectively. 622 patients (28%) in the placebo group and 431 (19%) in the simvastatin group had one or more major coronary events. The relative risk was 0.66 (95% CI 0.59-0.75, p < 0.00001), and the respective probabilities of escaping such events were 70.5% and 79.6%. This risk was also significantly reduced in subgroups consisting of women and patients of both sexes aged 60 or more. Other benefits of treatment included a 37% reduction (p < 0.00001) in the risk of undergoing myocardial revascularisation procedures. This study shows that long-term treatment with simvastatin is safe and improves survival in CHD patients.
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                Author and article information

                Journal
                Genet Epidemiol
                Genet. Epidemiol
                10.1002/(ISSN)1098-2272
                GEPI
                Genetic Epidemiology
                John Wiley and Sons Inc. (Hoboken )
                0741-0395
                1098-2272
                07 April 2016
                May 2016
                : 40
                : 4 ( doiID: 10.1002/gepi.2016.40.issue-4 )
                : 304-314
                Affiliations
                [ 1 ] Integrative Epidemiology UnitUniversity of Bristol BristolUnited Kingdom
                [ 2 ] Department of Public Health and Primary CareUniversity of Cambridge CambridgeUnited Kingdom
                Author notes
                [*] [* ]Correspondence to: Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, 2 Worts Causeway, Cambridge CB1 8RN, UK. E‐mail: sb452@ 123456medschl.cam.ac.uk .
                Article
                GEPI21965
                10.1002/gepi.21965
                4849733
                27061298
                3e305a1d-dcd8-42bb-885f-4cf07d331dd6
                © 2016 The Authors. * Genetic Epidemiology Published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 September 2015
                : 03 December 2015
                : 04 February 2016
                Page count
                Pages: 11
                Funding
                Funded by: Medical Research Council
                Award ID: MR/N501906/1
                Funded by: Medical Research Council
                Award ID: MC_UU_12013/1‐9
                Funded by: Wellcome Trust
                Award ID: 100114
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                gepi21965
                May 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.8.6 mode:remove_FC converted:22.04.2016

                Public health
                mendelian randomization,instrumental variables,robust statistics,egger regression,pleiotropy

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