Abstract
Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.
Highlights
Conventional epidemiology has made enormous contributions to identifying certain significant exposures associated with common diseases, like fine particle air pollution was found to increase the risk of lung cancer mortality (Knowler et al, 2002; Pope et al, 2002)
Just as the role that IVs play in econometrics, setting genetic variants, e.g., single-nucleotide polymorphisms (SNPs), as instrumental variables, Mendelian randomization (MR) is capable of excluding the unknown confounders which often interfere with the conventional epidemiological analyses
We explore and compare the estimation results and statistical properties of MR-LDP, RAPS, MR-Egger, and LDA MR-Egger with pleiotropy and linkage disequilibrium adaptive Mendelian randomization (PLDMR) in nine combinations of three patterns of pleiotropy and three magnitudes of linkage disequilibrium
Summary
Conventional epidemiology has made enormous contributions to identifying certain significant exposures associated with common diseases, like fine particle air pollution was found to increase the risk of lung cancer mortality (Knowler et al, 2002; Pope et al, 2002). Even if RCTs can correct the bias, despite the high cost of RCTs, the randomization of some potential confounders like nutrition and physical activity may be unfeasible (Smith and Ebrahim, 2003), some statistical methods were developed and employed to infer the causal relationship of interested exposures and diseases in epidemiology studies. Mendelian randomization (MR) applies the method of instrumental variables (IVs) to estimate the causal effect of a non-genetic exposure on a disease outcome (Lawlor et al, 2008). Immense results of GWASs are available through various online databases, such as Gene ATLAS and GWAS Catalog (Buniello et al, 2018; Canela-Xandri et al, 2018), from where we can get summary statistics like effects of SNPs on exposures and outcomes. There are some methods developed to infer causal relationships in individual-level data (Kang et al, 2016; Windmeijer et al, 2019), in addition to the general two-sample MR methods, which can be conducted and only require one-sample individual-level data
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