Extract weights from mr.ash.rss (susieR)

Usage

mrAshRssWeights(stat, LD, varY, sigma2E, s0, w0, z = numeric(0), ...)

Arguments

stat: A list of summary statistics with elements b (effect sizes), seb (standard errors) and n (per-variant sample sizes).
LD: Numeric LD (correlation) matrix aligned to the variants in stat.
varY: Numeric. Variance of the phenotype.
sigma2E: Numeric. Residual error variance estimate.
s0: Numeric vector of prior mixture standard deviations (the sigma0 grid).
w0: Numeric vector of prior mixture weights (summing to 1).
z: Optional numeric vector of z-scores; defaults to numeric(0) (derived from stat).
...: Additional arguments forwarded to mr.ash.rss.

Value

A numeric vector of the posterior mean of the coefficients.

Examples

data(eqtlRegionExample)
X <- eqtlRegionExample$X[, 1:30]
y <- eqtlRegionExample$yRes
ss <- lapply(seq_len(ncol(X)), function(j) {
  coef(summary(lm(y ~ X[, j])))[2, 1:2]
})
stat <- list(b = vapply(ss, `[`, numeric(1), 1L),
  seb = vapply(ss, `[`, numeric(1), 2L), n = rep(nrow(X), ncol(X)))
mrAshRssWeights(stat, cor(X), varY = var(y), sigma2E = var(y),
  s0 = c(0, 0.1, 0.5), w0 = c(0.8, 0.1, 0.1))
#>                [,1]
#>  [1,] -1.255350e-09
#>  [2,] -1.255350e-09
#>  [3,]  2.607000e-10
#>  [4,] -1.612172e-10
#>  [5,] -1.612172e-10
#>  [6,] -3.437262e-10
#>  [7,] -5.630480e-10
#>  [8,]  1.013868e-09
#>  [9,] -1.133489e-10
#> [10,] -1.133489e-10
#> [11,] -2.414229e-10
#> [12,] -4.445716e-10
#> [13,] -1.256939e-10
#> [14,] -9.525855e-11
#> [15,] -2.414229e-10
#> [16,]  1.013868e-09
#> [17,] -2.110191e-10
#> [18,] -2.371480e-10
#> [19,]  1.304826e-09
#> [20,] -2.371480e-10
#> [21,] -2.371480e-10
#> [22,] -3.437262e-10
#> [23,] -2.371480e-10
#> [24,]  2.860192e-11
#> [25,] -1.256940e-10
#> [26,] -3.437262e-10
#> [27,] -4.113177e-09
#> [28,] -1.184402e-09
#> [29,]  6.175135e-10
#> [30,]  4.776471e-10