text: 122,42,1194606557272645633,139122509,Stephen Burgess,"New paper on an approach for performing robust instrumental variable analyses by Andrew Grant and myself: ""An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting"" - <LINK>. Tweetorial follows!: Suppose genetic variants influence the outcome not via the risk factor (hence are not valid instruments), but pleiotropic pathways operate via a measured variable. Then we can perform multivariable Mendelian randomization including the variable to recover an unbiased estimate. However, there may be multiple different measured potentially pleiotropic variables. If we ignore them, then we get bias, but if we adjust for all of them, we will typically have an inefficient estimate as not all the pathways may influence the outcome. Additionally, if there are more potentially pleiotropic variables than genetic variants, then we can't include all variables in the analysis model. We have developed a lasso approach that only includes relevant pleiotropic variables in the analysis - hence the estimate is unbiased, but more efficient than that including all variables. The approach is implemented using summarized genetic association data. One problem is inference post-variable selection - we discuss this at length in the paper. Thanks to Stijn Vansteelandt for helpful conversations on this topic. We present as an example the effect of serum urate on coronary heart disease risk, showing that blood pressure is the key pleiotropic variable, and that the estimated direct effect of urate on CHD risk not via blood pressure is substantially attenuated. We recommend the method when it is believed that pleiotropic effects of genetic predictors of a risk factor could influence the outcome via measured pleiotropic pathways - could use as a main analysis or sensitivity. Comments welcome! @dpsg108 True, could be either direct (horizontal) or indirect (vertical/mediated) pleiotropy.",https://arxiv.org/abs/1911.00347,"Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method which uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can also be used in the case where there are more covariates under consideration than instruments, which is not possible using standard multivariable Mendelian randomization. We show the results of simulation studies which demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease. ","An efficient and robust approach to Mendelian randomization with