MR_het_test {iGasso}

Test of Heterogeneity in MR using Principal Components

Description

MR_het_test performs tests of heterogeneity in MR.

Usage

MR_het_test(x.b, y.b, x.se, y.se, b0, k = NULL, cum.prop = 0.8)

Arguments

x.b

a vector of the estimated regression coefficients from the SNP-exposure GWAS

y.b

a vector of the estimated regression coefficients from the SNP-outcome GWAS

x.se

a vector of SEs for x.b

y.se

a vector of SEs for y.b

b0

a value used for the common effect size. It is used for the weighting matrix

k

the number of principal components used. It is used by the \tilde Q(b_0) statistic. The default is NULL

cum.prop

threshold for selecting k. It is void if k is specified. The default is 0.8, i.e., the proportion of variance explained by the top k principal components is 0.8

Value

A list containing the following components:

* P_\text{min}(b_0) statistic and its P-value.

* \tilde Q_\text{min}(b_0) statistic, its degrees of freedom, and its P-value.

Author(s)

Kai Wang <[e-mail]>

References

Wang, K, Alberding, Steven Y. (2024) Powerful test of heterogeneity in two-sample summary-data Mendelian randomization. Submitted.

Examples

p = 10
b = 0.5
gamma.true = runif(p, 0.34, 1.1)
x.se = runif(p, 0.06, 0.1)
y.se = runif(p, 0.015, 0.11)
x.b = rnorm(p, gamma.true, x.se)
y.b = rnorm(p, b*gamma.true, y.se)
b0 = 0.4

MR_het_test(x.b, y.b, x.se, y.se, b0)