mvmr.srivw: Perform spectral regularized inverse-variance weighted...

In tye27/mr.divw: debiased inverse-variance weighted estimator for univariable and multivariable Mendelian randomization

Perform spectral regularized inverse-variance weighted (SRIVW) estimator for summary-data multivariable Mendelian randomization

Description

Perform spectral regularized inverse-variance weighted (SRIVW) estimator for summary-data multivariable Mendelian randomization

Usage

mvmr.srivw(
  beta.exposure,
  se.exposure,
  beta.outcome,
  se.outcome,
  phi_cand = 0,
  over.dispersion = FALSE,
  overlap = FALSE,
  gen_cor = NULL
)

Arguments

beta.exposure	A data.frame or matrix. Each row contains the estimated marginal effect of a SNP on K exposures, usually obtained from a GWAS
se.exposure	A data.frame or matrix of estimated standard errors of beta.exposure
beta.outcome	A vector of the estimated marginal effect of a SNP on outcome, usually obtained from a GWAS
se.outcome	A vector of estimated standard errors of beta.outcome
phi_cand	A vector of tuning parameters for SRIVW estimator. Default is 0. To use the recommended set for the tuning parameter, simply set phi_cand = NULL.
over.dispersion	Should the model consider balanced horizontal pleiotropy? Default is FALSE.
overlap	Should the model consider overlapping exposure and outcome datasets? Default is FALSE.
gen_cor	If overlap = FALSE, provide a K-by-K matrix for the estimated shared correlation matrix between the effect of the genetic variants on each exposure, where K is the number of exposure. If overlap = TRUE, provide a (K+1)-by-(K+1) matrix for the estimated shared correlation matrix between the effect of the genetic variants on each exposure and the outcome, where the last index position corresponds to the outcome. The correlations can either be estimated, be assumed to be zero, or fixed at zero. Default input is NULL, meaning that an identity matrix is used as the correlation matrix.

Value

A list with elements

beta.hat	Estimated direct effects of each exposure on the outcome
beta.se	Estimated standard errors of beta.hat
iv_strength_parameter	The minimum eigenvalue of the sample IV strength matrix, which quantifies the IV strength in the sample
phi_selected	The selected tuning parameter for the SRIVW estimator
tau.square	Overdispersion parameter if over.dispersion=TRUE

Examples

data("hdl_subfractions")
# We are going to estimate the effect of S-HDL-P on the risk of CAS with adjustments of HDL, LDL, and TG levels
beta.exposure <- hdl_subfractions$data[,c("gamma_exp1","gamma_exp2","gamma_exp3","gamma_exp4")] # corresponding to SNP effects on respectively  HDL, LDL, TG, and S-HDL-P
se.exposure <- hdl_subfractions$data[,c("se_exp1","se_exp2","se_exp3","se_exp4")]
beta.outcome <- hdl_subfractions$data$gamma_out1
se.outcome <- hdl_subfractions$data$se_out1
P <- hdl_subfractions$cor.mat[c(1:4,10),c(1:4,10)] # make sure the last index corresponds to the outcome
mvmr.srivw(beta.exposure = beta.exposure,
se.exposure = se.exposure,
beta.outcome = beta.outcome,
se.outcome = se.outcome,
gen_cor = P,
phi_cand = NULL,
over.dispersion = FALSE,
overlap = TRUE)

tye27/mr.divw documentation built on Oct. 18, 2024, 12:06 a.m.