MendelianRandomization (version 0.10.0)

mr_mvcML: Multivariable constrained maximum likelihood method

Description

The mr_mvcML function performs multivariable Mendelian randomization via the constrained maximum likelihood method, which is robust to both correlated and uncorrelated pleiotropy.

Usage

mr_mvcML(
  object,
  n,
  DP = TRUE,
  rho_mat = diag(ncol(object@betaX) + 1),
  K_vec = 0:(ceiling(nrow(object@betaX)/2)),
  random_start = 0,
  num_pert = 100,
  min_theta_range = -0.5,
  max_theta_range = 0.5,
  maxit = 100,
  alpha = 0.05,
  seed = 314159265
)
# S4 method for MRMVInput
mr_mvcML(
  object,
  n,
  DP = TRUE,
  rho_mat = diag(ncol(object@betaX) + 1),
  K_vec = 0:(ceiling(nrow(object@betaX)/2)),
  random_start = 0,
  num_pert = 200,
  min_theta_range = -0.5,
  max_theta_range = 0.5,
  maxit = 100,
  alpha = 0.05,
  seed = 314159265
)

Value

The output from the function is an MVMRcML object containing:

Exposure

A character vector with the names given to the exposure.

Outcome

A character string with the names given to the outcome.

Estimate

A vector of causal estimates.

StdError

A vector of standard errors of the causal estimates.

CILower

The lower bounds of the causal estimates based on the estimated standard errors and the significance level provided.

CIUpper

The upper bounds of the causal estimates based on the estimated standard errors and the significance level provided.

Alpha

The significance level used when calculating the confidence intervals.

Pvalue

The p-values associated with the estimates (calculated as Estimate/StdError as per Wald test) using a normal distribution.

BIC_invalid

Set of selected invalid IVs by MVMRcML-BIC.

K_hat

The number of selected invalid IVs by MVMRcML-BIC, or a vector for each data perturbation in MVMRcML-DP.

eff_DP_B

The number of data perturbations with successful convergence in MVMRcML-DP.

SNPs

The number of genetic variants (SNPs) included in the analysis.

Arguments

object

An MRMVInput object.

n

Sample size. The smallest sample size among all (both exposures and outcome) GWAS used in the analysis is recommended.

DP

Whether data perturbation is applied or not. Default is TRUE.

rho_mat

The correlation matrix among the exposures and outcome GWAS estimates, which can be estimated by the intercept term from bivariate LDSC. See reference for more discussions. Default is the identify matrix, for example, in the absence of overlapping samples among GWAS datasets.

K_vec

Set of candidate K's, the constraint parameter representing number of invalid IVs. It can range from 0 up to #IV - (#exposure + 1). Default is from 0 to (#IV/2).

random_start

Number of random starting points for MVMRcML, default is 0.

num_pert

Number of perturbation when DP is TRUE, default is 200.

min_theta_range

The lower bound of the uniform distribution for each initial value for theta generated from, default is -0.5.

max_theta_range

The uppder bound of the uniform distribution for each initial value for theta generated from, default is 0.5.

maxit

Maximum number of iterations for each optimization. Default is 100.

alpha

Significance level for the confidence interval for estimate, default is 0.05.

seed

The random seed to use when generating the perturbed samples (for reproducibility). The default value is 314159265. If set to NA, the random seed will not be set (for example, if the function is used as part of a larger simulation).

Details

Multivariable MRcML (MVMRcML) is an extension of MRcML to deal with multiple exposures of interest. It is robust to both correlated and uncorrelated pleiotropy as its univariable version.

In practice, the data perturbation (DP) version is preferred in practice for a more robust inference as it can account for the uncertainty in model selection. However, it may take a longer time especially when the number of IVs is large (so the range of K_vec can be large too). One strategy is to try a small range of K (the number of invalid IVs) first (with a small num_pert), then adjust it if the number of selected invalid IVs fall close to the boundary. You can also use other methods, e.g. mr_mvlasso, to get a rough sense of the number of invalid IVs.

Similar to mr_cML, multiple random starting points could be used to find a global minimum.

References

Lin, Z., Xue, H., & Pan, W. (2023). Robust multivariable Mendelian randomization based on constrained maximum likelihood. The American Journal of Human Genetics, 110(4), 592-605.

Examples

# Perform MVMRcML-DP:
mr_mvcML(mr_mvinput(bx = cbind(ldlc, hdlc, trig), bxse = cbind(ldlcse, hdlcse, trigse),
   by = chdlodds, byse = chdloddsse), n = 17723, num_pert = 5, random_start = 5)
  # num_pert is set to 5 to reduce runtime for the mr_mvcML method,
  # At least 100 perturbations should be used and more is preferred for a stable result.

rho_mat = matrix(c(1,-0.1,0.2,0,-0.1,1,-0.3,0,
 				  0.2,-0.3,1,0,0,0,0,1),ncol=4) ## Toy example of rho_mat
mr_mvcML(mr_mvinput(bx = cbind(ldlc, hdlc, trig), bxse = cbind(ldlcse, hdlcse, trigse),
   by = chdlodds, byse = chdloddsse), n = 17723, num_pert = 5, rho_mat = rho_mat)

# Perform MVMRcML-BIC:
mr_mvcML(mr_mvinput(bx = cbind(ldlc, hdlc, trig), bxse = cbind(ldlcse, hdlcse, trigse),
   by = chdlodds, byse = chdloddsse), n = 17723, DP = FALSE)