Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization.

Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as a simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We apply our method first to a subset of the UK Biobank consisting of blood traits and inflammatory disease, and then to a broader set of 411 heritable phenotypes. We detect many effects with strong literature support, as well as numerous behavioral effects that appear to stem from physician advice given to people at high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis in ever-growing databases of genotypes and phenotypes.

Mendelian randomisation for nutritional psychiatry.

Nutritional psychiatry is a growing area of research, with several nutritional factors implicated in the cause of psychiatric ill-health. However, nutritional research is highly complex, with multiple potential factors involved, highly confounded exposures and small effect sizes for individual nutrients. This Personal View considers whether Mendelian randomisation provides a solution to these difficulties, by investigating causality in a low-risk and low-cost way. We reviewed studies using Mendelian randomisation in nutritional psychiatry, along with the potential opportunities and challenges of using this approach for investigating the causal effects of nutritional exposures. Several studies have identified nutritional exposures that are potentially causal by using Mendelian randomisation in psychiatry, offering opportunities for further mechanistic research, intervention development, and replication. The use of Mendelian randomisation as a foundation for intervention development facilitates the best use of resources in an emerging discipline in which opportunities are rich, but resources are often poor.

[Mendelian randomisation - a genetic approach to an epidemiological method]. / Mendelsk randomisering - genetisk tilnærming til epidemiologisk metode.

BACKGROUND Genetic information is becoming more easily available, and rapid progress is being made in developing methods of illuminating issues of interest. Mendelian randomisation makes it possible to study causes of disease using observational data. The name refers to the random distribution of gene variants in meiosis. The methodology makes use of genes that influence a risk factor for a disease, without influencing the disease itself. In this review article I explain the principles behind Mendelian randomisation and present the areas of application for this methodology.MATERIAL AND METHOD Methodology articles describing Mendelian randomisation were reviewed. The articles were found through a search in PubMed with the combination «mendelian randomization¼ OR «mendelian randomisation¼, and a search in McMaster Plus with the combination «mendelian randomization¼. A total of 15 methodology articles were read in full text. Methodology articles were supplemented by clinical studies found in the PubMed search.RESULTS In contrast to traditional observational studies, Mendelian randomisation studies are not affected by two important sources of error: conventional confounding variables and reverse causation. Mendelian randomisation is therefore a promising tool for studying causality. Mendelian randomisation studies have already provided valuable knowledge on the risk factors for a wide range of diseases. It is nevertheless important to be aware of the limitations of the methodology. As a result of the rapid developments in genetics research, Mendelian randomisation will probably be widely used in future years.INTERPRETATION If Mendelian randomisation studies are conducted correctly, they may help to reveal both modifiable and non-modifiable causes of disease.

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

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.

Asociación de la uricemia con la cardiopatía isquémica y la presión arterial: análisis mediante aleatorización mendeliana de 2 grandes cohortes / No disponible

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Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions.

As with other instrumental variable (IV) analyses, Mendelian randomization (MR) studies rest on strong assumptions. These assumptions are not routinely systematically evaluated in MR applications, although such evaluation could add to the credibility of MR analyses. In this article, the authors present several methods that are useful for evaluating the validity of an MR study. They apply these methods to a recent MR study that used fat mass and obesity-associated (FTO) genotype as an IV to estimate the effect of obesity on mental disorder. These approaches to evaluating assumptions for valid IV analyses are not fail-safe, in that there are situations where the approaches might either fail to identify a biased IV or inappropriately suggest that a valid IV is biased. Therefore, the authors describe the assumptions upon which the IV assessments rely. The methods they describe are relevant to any IV analysis, regardless of whether it is based on a genetic IV or other possible sources of exogenous variation. Methods that assess the IV assumptions are generally not conclusive, but routinely applying such methods is nonetheless likely to improve the scientific contributions of MR studies.

Missing data methods in Mendelian randomization studies with multiple instruments.

Mendelian randomization studies typically have low power. Where there are several valid candidate genetic instruments, precision can be gained by using all the instruments available. However, sporadically missing genetic data can offset this gain. The authors describe 4 Bayesian methods for imputing the missing data based on a missing-at-random assumption: multiple imputations, single nucleotide polymorphism (SNP) imputation, latent variables, and haplotype imputation. These methods are demonstrated in a simulation study and then applied to estimate the causal relation between C-reactive protein and each of fibrinogen and coronary heart disease, based on 3 SNPs in British Women's Heart and Health Study participants assessed at baseline between May 1999 and June 2000. A complete-case analysis based on all 3 SNPs was found to be more precise than analyses using any 1 SNP alone. Precision is further improved by using any of the 4 proposed missing data methods; the improvement is equivalent to about a 25% increase in sample size. All methods gave similar results, which were apparently not overly sensitive to violation of the missing-at-random assumption. Programming code for the analyses presented is available online.

The homocysteine controversy.

Mild to moderate hyperhomocysteinemia has been identified as a strong predictor of cardiovascular disease, independent from classical atherothrombotic risk factors. In the last decade, a number of large intervention trials using B vitamins have been performed and have shown no benefit of homocysteine-lowering therapy in high-risk patients. In addition, Mendelian randomization studies failed to convincingly demonstrate that a genetic polymorphism commonly associated with higher homocysteine levels (methylenetetrahydrofolate reductase 677 C>T) is a risk factor for cardiovascular disease. Together, these findings have cast doubt on the role of homocysteine in cardiovascular disease pathogenesis, and the homocysteine hypothesis has turned into a homocysteine controversy. In this review, we attempt to find solutions to this controversy. First, we explain that the Mendelian randomization analyses have limitations that preclude final conclusions. Second, several characteristics of intervention trials limit interpretation and generalizability of their results. Finally, the possibility that homocysteine lowering is in itself beneficial but is offset by adverse side effects of B vitamins on atherosclerosis deserves serious attention. As we explain, such side effects may relate to direct adverse effects of the B-vitamin regimen (in particular, the use of high-dose folic acid) or to proinflammatory and proproliferative effects of B vitamins on advanced atherosclerotic lesions.

An introduction to instrumental variables--part 2: Mendelian randomisation.

In the first part of this series, it was highlighted how even though randomised controlled trials can provide robust evidence for therapeutic interventions, for many types of exposure it may not be either practical or ethical to randomise patients to such studies (see part 1). Instrumental variables (IV) analyses have been increasingly employed in recent times in epidemiology to investigate the potential causal effects of an exposure. An IV is a variable that can realistically mimic the treatment allocation process in a randomised study and is assumed to be not directly related to outcome, except through the direct effect of treatment and not related to outcome through either measured or unmeasured confounders. As discussed in the first article, IV analyses can be useful in estimating direct treatment effects provided that the chosen instrument is strong. A particular type of IV analysis where a specific genetic variant has been used as the instrument known as 'Mendelian randomisation' has become increasingly common. The aim of the second part of this statistical primer is to outline the approach to Mendelian randomisation and some of the advantages and disadvantages of this approach.