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.

/pdf/mendelian-randomization-with-invalid-instruments-effect-1pqi6rglyj.pdf

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

Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence

/pdf/reading-mendelian-randomisation-studies-a-guide-glossary-and-3nqnedeay2.pdf

Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

Although intermittent increases in inflammation are critical for survival during physical injury and infection, recent research has revealed that certain social, environmental and lifestyle factors can promote systemic chronic inflammation (SCI) that can, in turn, lead to several diseases that collectively represent the leading causes of disability and mortality worldwide, such as cardiovascular disease, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease and autoimmune and neurodegenerative disorders. In the present Perspective we describe the multi-level mechanisms underlying SCI and several risk factors that promote this health-damaging phenotype, including infections, physical inactivity, poor diet, environmental and industrial toxicants and psychological stress. Furthermore, we suggest potential strategies for advancing the early diagnosis, prevention and treatment of SCI.

/pdf/chronic-inflammation-in-the-etiology-of-disease-across-the-3ahtzxhxp7.pdf

Chronic inflammation in the etiology of disease across the life span

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Importance Mendelian randomization (MR) studies use genetic variation associated with modifiable exposures to assess their possible causal relationship with outcomes and aim to reduce potential bias from confounding and reverse causation. Objective To develop the STROBE-MR Statement as a stand-alone extension to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline for the reporting of MR studies. Design, Setting, and Participants The development of the STROBE-MR Statement followed the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework guidance and used the STROBE Statement as a starting point to draft a checklist tailored to MR studies. The project was initiated in 2018 by reviewing the literature on the reporting of instrumental variable and MR studies. A group of 17 experts, including MR methodologists, MR study design users, developers of previous reporting guidelines, and journal editors, participated in a workshop in May 2019 to define the scope of the Statement and draft the checklist. The draft checklist was published as a preprint in July 2019 and discussed on the preprint platform, in social media, and at the 4th Mendelian Randomization Conference. The checklist was then revised based on comments, further refined through 2020, and finalized in July 2021. Findings The STROBE-MR checklist is organized into 6 sections (Title and Abstract, Introduction, Methods, Results, Discussion, and Other Information) and includes 20 main items and 30 subitems. It covers both 1-sample and 2-sample MR studies that assess 1 or multiple exposures and outcomes, and addresses MR studies that follow a genome-wide association study and are reported in the same article. The checklist asks authors to justify why MR is a helpful method to address the study question and state prespecified causal hypotheses. The measurement, quality, and selection of genetic variants must be described and attempts to assess validity of MR-specific assumptions should be well reported. An item on data sharing includes reporting when the data and statistical code required to replicate the analyses can be accessed. Conclusions and Relevance STROBE-MR provides guidelines for reporting MR studies. Improved reporting of these studies could facilitate their evaluation by editors, peer reviewers, researchers, clinicians, and other readers, and enhance the interpretation of their results.

/pdf/strengthening-the-reporting-of-observational-studies-in-yvrbayvtvi.pdf

Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement

Background Mendelian randomization (MR) studies investigate the effect of genetic variation in levels of an exposure on an outcome, thereby using genetic variation as an instrumental variable (IV). We provide a meta-epidemiological overview of the methodological approaches used in MR studies, and evaluate the discussion of MR assumptions and reporting of statistical methods. Methods We searched PubMed, Medline, Embase and Web of Science for MR studies up to December 2013. We assessed (i) the MR approach used; (ii) whether the plausibility of MR assumptions was discussed; and (iii) whether the statistical methods used were reported adequately. Results Of 99 studies using data from one study population, 32 used genetic information as a proxy for the exposure without further estimation, 44 performed a formal IV analysis, 7 compared the observed with the expected genotype-outcome association, and 1 used both the latter two approaches. The 80 studies using data from multiple study populations used many different approaches to combine the data; 52 of these studies used some form of IV analysis; 44% of studies discussed the plausibility of all three MR assumptions in their study. Statistical methods used for IV analysis were insufficiently described in 14% of studies. Conclusions Most MR studies either use the genotype as a proxy for exposure without further estimation or perform an IV analysis. The discussion of underlying assumptions and reporting of statistical methods for IV analysis are frequently insufficient. Studies using data from multiple study populations are further complicated by the combination of data or estimates. We provide a checklist for the reporting of MR studies.

/pdf/mendelian-randomization-studies-a-review-of-the-approaches-3lrk2d5its.pdf

Mendelian randomization studies: a review of the approaches used and the quality of reporting

Instrumental variable analysis is a recently propagated method to deal with confounding and to estimate therapeutic effects in observational studies. An instrumental variable is a factor which affects treatment but is not related to patient prognosis. A theoretical advantage of a well-chosen instrumental variable is that both measured and unmeasured confounders do not influence the effect estimator. Examples of instrumental variables previously used are regional differences in treatment and physician prescribing preference. Application of instrumental variable analysis seems most suited to large patient registries with considerable expected residual confounding in case standard analytical methods are applied.

Instrumental variable analysis

Sample size importantly limits the usefulness of instrumental variable methods, depending on instrument strength and level of confounding.

© 2015 Wolters Kluwer Health, Inc. All rights reserved. www.epidem.com | e15 the authors report no conflicts of interest. Supported by the Netherlands Organisation for Health Research and Development (ZonMw, Grant Number 152002040). Mendelian Randomization Studies in the Elderly focus of this study was to investigate which set of covariates should be included in the above-mentioned evaluation procedures.

Mendelian Randomization Studies in the Elderly

Interleukin-10 (IL-10) production is under tight genetic control in populations living in affluent environments. However, little is known about the role of IL10 genetics on cytokine production in populations living in environments with high infectious pressure. We have previously reported that, in a rural Ghanaian population, the most common IL10 haplotype associates with a pro-inflammatory response. Here, we aim to replicate these findings in an independent sample of the same population 2 years later. IL-10 and tumour necrosis factor-α (TNF-α) protein concentrations were determined in whole-blood samples ex vivo stimulated with lipopolysaccharide and zymosan in 2006 (n=615) and 2008 (n=647). The association between IL10 single nucleotide polymorphisms and Z-scores of IL-10 and TNF-α levels was analysed in each population subset. The most common IL10 haplotype was associated with a significantly lower IL-10 production and nonsignificantly increased TNF-α levels. The correlation between repeated cytokine assays, based on 111 individuals with measurements in both 2006 and 2008, was r=0.53 (P<0.001) for IL-10 and r=0.36 (P<0.001) for TNF-α. The replication of our previously found effect of variation in the IL10 gene on IL-10 production and the correlation between repeated cytokine stimulation assays provide evidence that IL10 genetics have an important role in regulating the host response under high infectious pressure.

The influence of genetic variation on innate immune activation in an environment with high infectious pressure

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