diff --git a/Figures_manuscript/scripts/Fig_SUPnote4_simulation_test_boundaries.R b/Figures_manuscript/scripts/Fig_SUPnote4_simulation_test_boundaries.R
index 274f2793a3d739f64b16a68526bc0950b4f629fb..99d6e4c9fe612cf4a4acfee464c241839ceb6eaf 100644
--- a/Figures_manuscript/scripts/Fig_SUPnote4_simulation_test_boundaries.R
+++ b/Figures_manuscript/scripts/Fig_SUPnote4_simulation_test_boundaries.R
@@ -6,7 +6,6 @@ h1 = 0.4
 h2 = 0.4
 M = 1000
 
-pgm= 0.4 * min(h1,h1) # unused
 pgh = 0.5 * min(h1,h2)
 
 epsgh = matrix(c(h1, pgh, pgh, h2), ncol=2)/ M
@@ -34,20 +33,14 @@ BG_mat <- append(BG_mat, list("G_cov_high"=  data.frame(mvrnorm(M, c(0,0),epsgh)
 #BG_mat <- append(BG_mat, list("G_bicov" = data.frame(rbind(mvrnorm(M/2, c(0,0),epsg1), mvrnorm(M/2, c(0,0), epsg2)))))	# genetic effects with a given mixed (local) genetic covariance
 
 
-sumZ <- function(Z, w){ return((as.matrix(Z) %*% matrix(w))^2 /(as.matrix(t(w))%*%sigr %*%as.matrix(w))[1,1])} # unused
 Univ <- function(Z){max(abs(Z))}
 
-hypothesis ="G_cov_high" # unused
-N= 10000 # unused
-overlap=  0.5 # unused
 sign_threshold = 5 *(10^-8) #/M
 C_env = seq(-0.9, 0.9, len=5) # Control the environmental correlation.
 Ns_frac = c(1, 0, 0.5) # Sample overlap
 N_samp = c(50000)
 
 
-ce=0.9 # unused
-
 for(hypothesis in names(BG_mat)){
   print(hypothesis)