BayesA TWAS weights (t-distribution prior)

Use t-distribution as prior.

Usage

bayesAWeights(
  X,
  y,
  Z = NULL,
  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.

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
bayesAWeights(X, y)
#> Type of analysis performed: st-blr-individual-level-dense-ld
#>  [1] -0.065038400 -0.064721426  0.021706693 -0.003878998 -0.009051476
#>  [6] -0.014767369 -0.007054643  0.055613170  0.022264242  0.022203763
#> [11] -0.016286800 -0.069228346 -0.023177103  0.027962285 -0.010926378
#> [16]  0.055226534 -0.008473986 -0.014885737  0.016963190 -0.006848259
#> [21] -0.005971904 -0.017227759 -0.011279069 -0.008267544 -0.028986622
#> [26] -0.021151352 -0.077751321 -0.010151363  0.036093338  0.011092512