BayesR TWAS weights (hierarchical mixture prior)

Use a hierarchical Bayesian mixture model with four Gaussian components. Variances are scaled by 0, 0.0001, 0.001, and 0.01.

Usage

bayesRWeights(X, y, Z = NULL, ...)

Arguments

X

Numeric genotype / design matrix (samples x variants).

y

Numeric response (phenotype) vector of length nrow(X).

Z

Optional numeric matrix of fixed-effect covariates, or NULL.

...

Additional arguments forwarded to bayesAlphabetWeights / qgg.

Value

A numeric vector of effect-size weights, one per variant (column of X); columns dropped for zero variance receive weight 0.

Examples

data(eqtlRegionExample)
X <- eqtlRegionExample$X[, 1:30]
y <- eqtlRegionExample$yRes
bayesRWeights(X, y)
#> Type of analysis performed: st-blr-individual-level-dense-ld
#>  [1] -0.0100162117 -0.0111664934  0.0008800154 -0.0012441645 -0.0004041811
#>  [6] -0.0043454462 -0.0026965027  0.0139654181 -0.0012713296  0.0021199867
#> [11] -0.0041203922 -0.0059923465 -0.0023420437  0.0042129764 -0.0017206831
#> [16]  0.0112690257 -0.0017164872 -0.0026293484  0.0117500086 -0.0010841084
#> [21] -0.0209834116 -0.0013096610  0.0184870769 -0.0006088956 -0.0034754816
#> [26] -0.0095015492 -0.0303975566 -0.0064906148  0.0127323249  0.0022716519