BayesC TWAS weights (rounded-spike prior)

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

Usage

bayesCWeights(
  X,
  y,
  Z = NULL,
  pi = 0.1,
  h2 = NULL,
  nit = 5000,
  nburn = 1000,
  nthin = 5,
  methodArgs = QggOptions()
)

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.

h2

Numeric or NULL. Prior heritability; NULL leaves the engine's own default.

nit

Integer. Total MCMC iterations. Default 5000.

nburn

Integer. Burn-in iterations. Default 1000.

nthin

Integer. Thinning interval for retained MCMC samples. Default 5.

methodArgs

Options forwarded to qgg::gbayes, built with QggOptions. A bare list is refused: it cannot be checked against the engine, so a misspelled option would be silently ignored.

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] -5.884592e-03 -5.791870e-03  1.198015e-03 -3.040642e-03 -6.072188e-05
#>  [6] -3.329197e-03 -1.214103e-03  6.354900e-03  3.756463e-03  3.957296e-04
#> [11] -3.658074e-03 -3.844207e-03 -3.120776e-03  1.816600e-03 -2.381331e-03
#> [16]  8.854182e-03 -9.915386e-05 -1.278181e-03  1.033847e-02  1.762544e-03
#> [21] -3.620562e-03 -8.827520e-04 -4.046530e-03 -8.590346e-04 -1.017868e-03
#> [26] -2.293299e-03 -1.899420e-02 -2.871092e-03  6.695803e-03  1.873417e-03