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

Crambe (Crambe abyssinica Hochst): A non-food oilseed crop with great potential: A review

Crambe (Crambe abyssinica Hochst) is an oilseed crop in the Brassicaceae family. Crambe\u2019s ability to survive in diverse environmental conditions, its unique oil composition, the high oil content, suitability for the production of slip agents for plasticizers, the capacity to be easily included in common crop rotations, and its adaptability to equipment used for small grain cultivation has renewed the interest in this emerging crop. Crambe is considered one of the main sources of erucic acid, which can be up to 60% of its seed oil content. Erucic acid (C22:1) is a fatty acid with industrial importance since it is used to produce erucamide, key ingredient in the plastic industry. Inclusion of crambe into crop rotations can be beneficial because of its short life cycle, low fertility requirements, resistance to pest and diseases, and relative drought tolerance. Currently high erucic acid rapeseed (Brassica napus L.) (HEAR) is the principal source for erucic acid. However, the risk of contaminating food quality rapeseed (i.e., canola) by cross-pollination and the negative impact on climate, due to high inputs, are potential limitations to expand HEAR cultivation. Crambe has thus great potential to, at least, partially replace HEAR as a source of erucic acid, if the current knowledge-gap in agronomic management and crop improvement (seed yield and quality) can be addressed. Seed yield needs to be increased to be able to compete with HEAR. In addition, reducing glucosinolates and fiber in crambe meal may increase its inclusion in monogastrics rations. The objective of this review was to compile and summarize new and existing information on agricultural practices in crambe production and management to identify gaps in knowledge and areas for future research to increase the cultivation of crambe

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

Subgradient-Based Markov Chain Monte Carlo Particle Methods for Discrete-Time Nonlinear Filtering

This work shows how a carefully designed instrumental distribution can improve the performance of a Markov chain Monte Carlo (MCMC) filter for systems with a high state dimension. We propose a special subgradient-based kernel from which candidate moves are drawn. This facilitates the implementation of the filtering algorithm in high dimensional settings using a remarkably small number of particles. We demonstrate our approach in solving a nonlinear non-Gaussian high-dimensional problem in comparison with a recently developed block particle filter and over a dynamic compressed sensing (l1 constrained) algorithm. The results show high estimation accuracy

Prevalence and 9-year incidence of hepatitis E virus infection among north italian blood donors: Estimated transfusion risk

Recent European guidelines recommend that screening policiesfor Hepatitis E virus (HEV) in blood donors should be based on local risk assessments. We determined the prevalence of current and past HEV infection in donors from Lombardy, the Italian region providing 24% of the Italian blood supply. We also calculated the incidence of infection over a period of 10 years, and estimated the risk of transfusion-related transmission. The study was conducted within the framework of BOTIA, an EU-funded project. HEV RNA was detected by individual donation testing, and the prevalence and incidence of anti-HEV antibodies were determined in two subgroups. The risk of receiving an infected blood unit was estimated on the basis of HEV RNA yields and serology. RESULTS: One of the 9726 donors was truly viremic. The prevalence of confirmed anti-HEV IgG reactivity was 52/767 (6.8%; 95%CI 5.1-8.8%). The incidence of HEV infection was 7.6/10000 per year (95%CI 2.1-2.5 per year). The estimated transfusion-related risk of infection was 1/10000 blood donations on the basis of HEV RNA yield (upper limit of the 95%CI 1:1666), and 1/16666 donations on the basis of the incidence data (95%CI 1:435-1:57000).In conclusion, The frequency of current and past HEV infection in blood donors living in Northern Italy is among the lowest so far reported in Europe. The estimated transfusion-related risk of infection was similar regardless of whether it was calculated on the basis of HEV RNA yield or serological incidence, thus suggesting stable infection pressure over the last ten years