tetano
Editor, Senior Moderator
Genet Epidemiol
. 2022 Feb 22.
doi: 10.1002/gepi.22445. Online ahead of print.
A two-sample robust Bayesian Mendelian Randomization method accounting for linkage disequilibrium and idiosyncratic pleiotropy with applications to the COVID-19 outcomes
Anqi Wang[SUP] 1 [/SUP], Wei Liu[SUP] 1 [/SUP], Zhonghua Liu[SUP] 1 [/SUP]
Affiliations
Abstract
Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest. Despite wide uses of various popular two-sample MR methods based on genome-wide association study summary level data, however, those methods could suffer from potential power loss or/and biased inference when the chosen genetic variants are in linkage disequilibrium (LD), and also have relatively large direct effects on the outcome whose distribution might be heavy-tailed which is commonly referred to as the idiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a novel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more robust multivariate generalized t
-distribution to model such direct effects in a probabilistic model framework which can also incorporate the LD structure explicitly. The generalized t
-distribution can be represented as a Gaussian scaled mixture so that our model parameters can be estimated by the expectation maximization (EM)-type algorithms. We compute the standard errors by calibrating the evidence lower bound using the likelihood ratio test. Through extensive simulation studies, we show that our RBMR has robust performance compared with other competing methods. We further apply our RBMR method to two benchmark data sets and find that RBMR has smaller bias and standard errors. Using our proposed RBMR method, we find that coronary artery disease is associated with increased risk of critically ill coronavirus disease 2019. We also develop a user-friendly R package RBMR (https://github.com/AnqiWang2021/RBMR) for public use.
Keywords: COVID-19 outcomes; EM-type algorithm; Mendelian randomization; idiosyncratic pleiotropy; linkage disequilibrium; multivariate generalized t-distribution.
. 2022 Feb 22.
doi: 10.1002/gepi.22445. Online ahead of print.
A two-sample robust Bayesian Mendelian Randomization method accounting for linkage disequilibrium and idiosyncratic pleiotropy with applications to the COVID-19 outcomes
Anqi Wang[SUP] 1 [/SUP], Wei Liu[SUP] 1 [/SUP], Zhonghua Liu[SUP] 1 [/SUP]
Affiliations
- PMID: 35192729
- DOI: 10.1002/gepi.22445
Abstract
Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest. Despite wide uses of various popular two-sample MR methods based on genome-wide association study summary level data, however, those methods could suffer from potential power loss or/and biased inference when the chosen genetic variants are in linkage disequilibrium (LD), and also have relatively large direct effects on the outcome whose distribution might be heavy-tailed which is commonly referred to as the idiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a novel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more robust multivariate generalized t
-distribution to model such direct effects in a probabilistic model framework which can also incorporate the LD structure explicitly. The generalized t
-distribution can be represented as a Gaussian scaled mixture so that our model parameters can be estimated by the expectation maximization (EM)-type algorithms. We compute the standard errors by calibrating the evidence lower bound using the likelihood ratio test. Through extensive simulation studies, we show that our RBMR has robust performance compared with other competing methods. We further apply our RBMR method to two benchmark data sets and find that RBMR has smaller bias and standard errors. Using our proposed RBMR method, we find that coronary artery disease is associated with increased risk of critically ill coronavirus disease 2019. We also develop a user-friendly R package RBMR (https://github.com/AnqiWang2021/RBMR) for public use.
Keywords: COVID-19 outcomes; EM-type algorithm; Mendelian randomization; idiosyncratic pleiotropy; linkage disequilibrium; multivariate generalized t-distribution.