BayesC TWAS weights (rounded-spike prior)

Use a rounded spike prior (low-variance Gaussian).

Usage

bayesCWeights(X, y, Z = NULL, pi = 0.1, ...)

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.

pi

Numeric in (0, 1). Prior proportion of non-null effects for the BayesC mixture. Default 0.1.

...

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
bayesCWeights(X, y)
#> Type of analysis performed: st-blr-individual-level-dense-ld
#>  [1] -6.062337e-03 -6.303635e-03  9.766574e-04 -3.031176e-03 -7.840823e-04
#>  [6] -3.266466e-03 -1.837009e-03  6.628204e-03  3.840737e-03  7.850173e-04
#> [11] -1.232330e-03 -3.948358e-03 -2.699437e-03  1.302681e-03 -2.273051e-03
#> [16]  9.277720e-03 -3.607041e-05 -1.323504e-03  6.528830e-03  1.991301e-03
#> [21] -3.943260e-03 -1.175765e-03 -4.121400e-03 -4.146699e-04 -6.881103e-04
#> [26] -2.497057e-03 -1.791950e-02 -3.388903e-03  7.565189e-03  1.931224e-03