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      Implementing MR‐PRESSO and GCTA‐GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner's perspective

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          Abstract

          With the advent of very large scale genome‐wide association studies (GWASs), the promise of Mendelian randomization (MR) has begun to be fulfilled. However, whilst GWASs have provided essential information on the single nucleotide polymorphisms (SNPs) associated with modifiable risk factors needed for MR, the availability of large numbers of SNP instruments raises issues of how best to use this information and how to deal with potential problems such as pleiotropy. Here we provide commentary on some of the recent advances in the MR analysis, including an overview of the different genetic architectures that are being uncovered for a variety of modifiable risk factors and how users ought to take that into consideration when designing MR studies.

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          Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

          Stephen Burgess, Robert A H Scott, Nicholas J Timpson … (2015)
          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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            Recent Developments in Mendelian Randomization Studies

            Jie Zheng, Denis Baird, Maria-Carolina Borges … (2017)
            Purpose of Review Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel’s First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions. Recent Findings In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR. Summary In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.
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              Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption

              Jack Bowden, Fabiola Del Greco M., Cosetta Minelli … (2019)
              Abstract Background Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated. Methods Causal estimation and heterogeneity assessment in MR require an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular ‘first-order’ weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate ‘second-order’ weights can dramatically increase the chances of failing to detect heterogeneity when it is truly present. We derive modified weights to mitigate both of these adverse effects. Results Using Monte Carlo simulations, we show that the modified weights outperform first- and second-order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using first- and second-order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared with first-order weighting. Moreover, first-order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary-data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk. Conclusions We propose the use of modified weights within two-sample summary-data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with first-order weights) but further research is required to understand their strengths and weaknesses in specific settings.
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                Author and article information

                Contributors
                Stuart MacGregor: stuart.macgregor@qimrberghofer.edu.au
                Journal
                Journal ID (nlm-ta): Genet Epidemiol
                Journal ID (iso-abbrev): Genet. Epidemiol
                Journal ID (doi): 10.1002/(ISSN)1098-2272
                Journal ID (publisher-id): GEPI
                Title: Genetic Epidemiology
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Print): 0741-0395
                ISSN (Electronic): 1098-2272
                Publication date (Electronic): 02 May 2019
                Publication date (Print): September 2019
                Volume: 43
                Issue: 6 ( doiID: 10.1002/gepi.2018.43.issue-6 )
                Pages: 609-616
                Affiliations
                [ 1 ] Statistical Genetics Laboratory, Genetics and Computational Biology Department QIMR Berghofer Medical Research Institute Brisbane Australia
                Author notes
                [*] [* ] Correspondence Stuart MacGregor, Statistical Genetics Lab, Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane, QLD 4006, Australia. Email: stuart.macgregor@ 123456qimrberghofer.edu.au

                Author information
                http://orcid.org/0000-0002-6062-710X
                Article
                Publisher ID: GEPI22207
                DOI: 10.1002/gepi.22207
                PMC ID: 6767464
                PubMed ID: 31045282
                SO-VID: abd75d51-43d5-46ac-91a6-4df7cece64db
                Copyright © © 2019 The Authors. Genetic Epidemiology Published by Wiley Periodicals, Inc.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 25 October 2018
                Date revision received : 01 April 2019
                Date accepted : 04 April 2019
                Page count
                Figures: 3, Tables: 3, Pages: 8, Words: 4054
                Funding
                Funded by: National Health and Medical Research Council
                Award ID: 1123248
                Funded by: Australian National Health and Medical Research Council (NHMRC)
                Award ID: 1123248
                Funded by: University of Queensland
                Funded by: QIMR Berghofer Medical Research Institute
                Categories
                Subject: Brief Report
                Subject: Brief Report
                Custom metadata
                source-schema-version-number 2.0
                component-id gepi22207
                cover-date September 2019
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.9 mode:remove_FC converted:30.09.2019

                ScienceOpen disciplines: Public health
                Keywords: causal inference,genome‐wide complex trait analysis‐generalized summary mendelian randomization (gcta‐gsmr),mendelian randomization,mendelian randomization pleiotropy residual sum and outlier (mr‐presso),pleiotropy assessment
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: causal inference, genome‐wide complex trait analysis‐generalized summary mendelian randomization (gcta‐gsmr), mendelian randomization, mendelian randomization pleiotropy residual sum and outlier (mr‐presso), pleiotropy assessment

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