Mendelian randomization and pleiotropy analysis
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REVIEW Mendelian randomization and pleiotropy analysis Xiaofeng Zhu* Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA * Correspondence: [email protected] Received April 1, 2020; Revised May 16, 2020; Accepted May 21, 2020 Background: Mendelian randomization (MR) analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies. Many statistical approaches have been developed and each of these methods require specific assumptions. Results: In this article, we review the pros and cons of these methods. We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation. Conclusion: The current available MR approaches allow us to study causality among risk factors and outcomes. However, novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.
Keywords: Mendelian randomization; causality; summary statistics; confounding; instrumental variable Author summary: Mendelian randomization analysis is a popular approach to studying the causality of exposures on an outcome, and it shares similarities with randomized controlled trials. Since MR is based on observational data, it requires assumptions that are difficult to validate. We review the current developed MR approaches and the challenges in performing MR analysis and interpreting the results.
INTRODUCTION Randomized controlled trials (RCTs) are considered as the gold standard to establish a causal relationship between an exposure and an outcome in epidemiology studies. Many associations observed in epidemiological studies have failed to be replicated in RCTs, such as fiber and colon cancer [1], vitamin E, cardiovascular disease and lung cancer [2,3], and vitamin C and cardiovascular disease [4]. The failed replications in RCTs can be potentially attributed to confounding, reverse causation, and various biases [5,6]. Thus, RCTs are the primary tool to establish a causation between a risk factor and an outcome, but they come with a high cost. To circumvent the high cost in RCTs, Mendelian randomization (MR) has become a widely used epidemiological approach to infer causality of an exposure to a disease outcome [7–9]. This is benefitted from the rapid identifications of genetic variants associated
with complex traits in large genome wide associations (GWAS) [10]. Intuitively, MR shares a similarity with RCTs (Fig. 1). In RCTs, the enrolled patients are randomly assigned to a treatment or a control group to eliminate potential confounding associated with both the treatment and the outcome. Therefore, causal effect can be estimated in an unbiased fashion. In contrast, MR assigns subjects based on their carried alleles, which are inherited from their parents. Since the alleles are transmitted from parents to offspring randomly, individuals are therefore divided i
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