Overlapping-sample Mendelian randomisation with multiple exposures: A Bayesian approach

Background: Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate putative causal relationship between an exposure and an outcome. Traditional MR methods have dominantly focussed on a two-sample setting in which IV-exposure association study and IV-outcome association study are independent. However, it is not uncommon that participants from the two studies fully overlap (one-sample) or partly overlap (overlapping-sample). Methods: We proposed a method that is applicable to all the three sample settings. In essence, we converted a two- or overlapping- sample problem to a one-sample problem where data of some or all of the individuals were incomplete. Assume that all individuals were drawn from the same population and unmeasured data were missing at random. Then the unobserved data were treated au pair with the model parameters as unknown quantities, and thus, could be imputed iteratively conditioning on the observed data and estimated parameters using Markov chain Monte Carlo. We generalised our model to allow for pleiotropy and multiple exposures and assessed its performance by a number of simulations using four metrics: mean, standard deviation, coverage and power. Results: Higher sample overlapping rate and stronger instruments led to estimates with higher precision and power. Pleiotropy had a notably negative impact on the estimates. Nevertheless, overall the coverages were high and our model performed well in all the sample settings. Conclusions: Our model offers the flexibility of being applicable to any of the sample settings, which is an important addition to the MR literature which has restricted to one- or two- sample scenarios. Given the nature of Bayesian inference, it can be easily extended to more complex MR analysis in medical research.Comment: 11 pages, 5 figure

A Bayesian approach to Mendelian randomization with multiple pleiotropic variants.

We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by unknown parameters, and by imposing a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero, we obtain a proper posterior distribution for the causal effect of interest. This posterior can be sampled via Markov chain Monte Carlo methods of inference to obtain point and interval estimates. The model priors require a minimal input from the user. We explore the performance of our method by means of a simulation experiment. Our results show that the method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the instruments. One section of the article elaborates the model to deal with two exposures, and illustrates the possibility of using MR to estimate direct and indirect effects in this situation. A main objective of the article is to create a basis for developments in MR that exploit the potential offered by a Bayesian approach to the problem, in relation with the possibility of incorporating external information in the prior, handling multiple sources of uncertainty, and flexibly elaborating the basic model

Women’s Pregnancy Life History and Alzheimer’s Risk: Can Immunoregulation Explain the Link?

Background: Pregnancy is associated with improvement in immunoregulation that persists into the geriatric phase. Impaired immunoregulation is implicated in Alzheimer’s disease (AD) pathogenesis. Hence, we investigate the relationship between pregnancy and AD. Methods: Cross-sectional cohort of British women (N = 95). Cox proportional hazards modeling assessed the putative effects of cumulative months pregnant on AD risk and the mutually adjusted effects of counts of first and third trimesters on AD risk. Results: Cumulative number of months pregnant, was associated with lower AD risk (β = −1.90, exp(β) = 0.15, P = .02). Cumulative number of first trimesters was associated with lower AD risk after adjusting for third trimesters (β = −3.83, exp(β) = 0.02, P \u3c .01), while the latter predictor had no significant effect after adjusting for the former. Conclusions: Our observation that first trimesters (but not third trimesters) conferred protection against AD is more consistent with immunologic effects, which are driven by early gestation, than estrogenic exposures, which are greatest in late gestation. Results may justify future studies with immune biomarkers

Deep determinism and the assessment of mechanistic interaction between categorical and continuous variables

Our aim is to detect mechanistic interaction between the effects of two causal factors on a binary response, as an aid to identifying situations where the effects are mediated by a common mechanism. We propose a formalization of mechanistic interaction which acknowledges asymmetries of the kind "factor A interferes with factor B, but not viceversa". A class of tests for mechanistic interaction is proposed, which works on discrete or continuous causal variables, in any combination. Conditions under which these tests can be applied under a generic regime of data collection, be it interventional or observational, are discussed in terms of conditional independence assumptions within the framework of Augmented Directed Graphs. The scientific relevance of the method and the practicality of the graphical framework are illustrated with the aid of two studies in coronary artery disease. Our analysis relies on the "deep determinism" assumption that there exists some relevant set V - possibly unobserved - of "context variables", such that the response Y is a deterministic function of the values of V and of the causal factors of interest. Caveats regarding this assumption in real studies are discussed.Comment: 20 pages including the four figures, plus two tables. Submitted to "Biostatistics" on November 24, 201

Mendelian Randomization with Incomplete Exposure Data: a Bayesian Approach

We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent individuals (eg components of a family). Our method rests on the Bayesian MR framework proposed by Berzuini et al (2018), which we apply in a study of multiplex Multiple Sclerosis (MS) Sardinian families to characterise the role of certain plasma proteins in MS causation. The method is robust to presence of pleiotropic effects in an unknown number of instruments, and is able to incorporate inter-individual kinship information. Introduction of missing data allows us to overcome the bias introduced by the (reverse) effect of treatment (in MS cases) on level of protein. From a substantive point of view, our study results confirm recent suspicion that an increase in circulating IL12A and STAT4 protein levels does not cause an increase in MS risk, as originally believed, suggesting that these two proteins may not be suitable drug targets for MS

Acid sensing ion channel 2: A new potential player in the pathophysiology of multiple sclerosis

Acid-sensing ion channels (ASICs) are proton-gated channels involved in multiple biological functions such as: pain modulation, mechanosensation, neurotransmission, and neurodegeneration. Earlier, we described the genetic association, within the Nuoro population, between Multiple Sclerosis (MS) and rs28936, located in ASIC2 3′UTR. Here we investigated the potential involvement of ASIC2 in MS inflammatory process. We induced experimental autoimmune encephalomyelitis (EAE) in wild-type (WT), knockout Asic1 −/− and Asic2 −/− mice and observed a significant reduction of clinical score in Asic1 −/− mice and a significant reduction in the clinical score in Asic2 −/− mice in a limited time window (i.e., at days 20–23 after immunization). Immunohistochemistry confirmed the reduction in adaptive immune cell infiltrates in the spinal cord of EAE Asic1 −/− mice. Analysis of mechanical allodynia, showed a significant higher pain threshold in Asic2 −/− mice under physiological conditions, before immunization, as compared to WT mice and Asic1 −/−. A significant reduction in pain threshold was observed in all three strains of mice after immunization. More importantly, analysis of human autoptic brain tissue in MS and control samples showed an increase of ASIC2 mRNA in MS samples. Subsequently, in vitro luciferase reporter gene assays, showed that ASIC2 expression is under possible miRNA regulation, in a rs28936 allele-specific manner. Taken together, these findings suggest a potential role of ASIC2 in the pathophysiology of MS

Systematic Analysis of Circulating Soluble Angiogenesis-Associated Proteins in ICON7 Identifies Tie2 as a Biomarker of Vascular Progression on Bevacizumab

background: There is a critical need for predictive/resistance biomarkers for VEGF inhibitors to optimise their use. methods: Blood samples were collected during and following treatment and, where appropriate, upon progression from ovarian cancer patients in ICON7, a randomised phase III trial of carboplatin and paclitaxel with or without bevacizumab. Plasma concentrations of 15 circulating angio-biomarkers were measured using a validated multiplex ELISA, analysed through a novel network analysis and their relevance to the PFS then determined. results: Samples (n=650) were analysed from 92 patients. Bevacizumab induced correlative relationships between Ang1 and Tie2 plasma concentrations, which reduced after initiation of treatment and remained decreased until progressive disease occurred. A 50% increase from the nadir in the concentration of circulating Tie2 (or the product of circulating Ang1 and Tie2) predicted tumour progression. Combining Tie2 with GCIG-defined Ca125 data yielded a significant improvement in the prediction of progressive disease in patients receiving bevacizumab in comparison with Ca125 alone (74.1% vs 47.3%, P<1 × 10−9). conclusions: Tie2 is a vascular progression marker for bevacizumab-treated ovarian cancer patients. Tie2 in combination with Ca125 provides superior information to clinicians on progressive disease in patients with VEGFi-treated ovarian cancers