strength_rmvmr

Calculates Q-statistics quantifying instrument strength. Each exposure is treated as an outcome sequentially, fitting the remaining exposures within a radial MVMR model. High Q-statistics indicate a high instrument strength, comparable to the Q_x statistic in conventional MVMR analyses. The function outputs a list of plots, global Q-statistics, and individual Q-contributions indexed by the exposure number ordered using the format_rmvmr function. Named exposures in each list refer to the remaining exposures in the strength RMVMR model.

Usage

strength_rmvmr(r_input, gencov = 0)

Arguments

r_input

A formatted data frame using the format_rmvmr function or an object of class MRMVInput from MendelianRandomization::mr_mvinput

gencov

Calculating heterogeneity statistics using the MVMR package requires the covariance between the effect of the genetic variants on each exposure to be known. This can either be estimated from individual level data, be assumed to be zero, or fixed at zero using non-overlapping samples of each exposure GWAS. A value of 0 is used by default.

Value

An object of class "S_RMVMR" containing the following components:

plot

A list containing plots for RMVMR analyses regressing each exposure sequentially upon remaining exposures in the r_input object. Plots are indexed by the exposure number serving as the outcome for the RMVMR analysis

qstat

A list containing global Q-statistics for RMVMR analyses regressing each exposure sequentially upon remaining exposures in the r_input object. Indexing follows that of plots and p-values for global heterogeneity are provided

qall

A list containing the individual Q-statistics and data for RMVMR analyses regressing each exposure sequentially upon remaining exposures in the r_input object. Indexing follows that of plots

References

Spiller, W., et al., Estimating and visualising multivariable Mendelian randomization analyses within a radial framework. Forthcoming.

Author

Wes Spiller; Eleanor Sanderson; Jack Bowden.

Examples

f.data <- format_rmvmr(
    BXGs = rawdat_rmvmr[,c("ldl_beta","hdl_beta","tg_beta")],
    BYG = rawdat_rmvmr$sbp_beta,
    seBXGs = rawdat_rmvmr[,c("ldl_se","hdl_se","tg_se")],
    seBYG = rawdat_rmvmr$sbp_se,
    RSID = rawdat_rmvmr$snp)
output <- strength_rmvmr(f.data)
#> Warning: Covariance between effect of genetic variants on each exposure not specified. Fixing covariance at 0.
#> Warning: Covariance between effect of genetic variants on each exposure not specified. Fixing covariance at 0.

# The following shows the strength plot and Q statistics for exposure 2,
# regressing exposure 2 upon exposures 1 and 3 (which are labeled exposure 1
# and exposure 2 based on ordering in the RMVMR model).

output$plot[[2]]

output$qstat[[2]]
#>            q_statistic       p_value
#> Exposure_1    1377.211 6.618994e-230
#> Exposure_2    1057.544 8.171778e-177