diff --git a/R/sufficient_stats_methods.R b/R/sufficient_stats_methods.R
index c5078193..e2d364b1 100644
--- a/R/sufficient_stats_methods.R
+++ b/R/sufficient_stats_methods.R
@@ -45,7 +45,7 @@ initialize_susie_model.ss <- function(data, params, var_y, ...) {
   if (params$unmappable_effects %in% c("inf", "ash")) {
 
     # Initialize omega quantities for unmappable effects
-    omega_res               <- compute_omega_quantities(data, tau2 = 0, sigma2 = 1)
+    omega_res               <- compute_omega_quantities(data, tau2 = 0, sigma2 = var_y)
     model$omega_var         <- omega_res$omega_var
     model$predictor_weights <- omega_res$diagXtOmegaX
     model$XtOmegay          <- data$eigen_vectors %*% (data$VtXty / omega_res$omega_var)
@@ -291,10 +291,8 @@ neg_loglik.ss <- function(data, params, model, V_param, ser_stats, ...) {
 #' @keywords internal
 update_fitted_values.ss <- function(data, params, model, l) {
   if (params$unmappable_effects != "none") {
-    model$XtXr <- compute_Xb(data$XtX, colSums(model$alpha * model$mu) + model$theta)
+    model$XtXr <- as.vector(data$XtX %*% (colSums(model$alpha * model$mu) + model$theta))
   } else {
-    # Fix: Use direct matrix multiplication to match original implementation
-    # Original: s$XtXr = s$XtXr + XtX %*% (s$alpha[l,] * s$mu[l,])
     model$XtXr <- model$fitted_without_l + as.vector(data$XtX %*% (model$alpha[l, ] * model$mu[l, ]))
   }
   return(model)
@@ -344,7 +342,7 @@ update_variance_components.ss <- function(data, params, model, ...) {
       ))
     }
 
-    # Remove the sparse effects
+    # Remove the sparse effects to compute residuals for mr.ash
     b <- colSums(model$alpha * model$mu)
     residuals <- data$y - data$X %*% b
 
diff --git a/R/susie_constructors.R b/R/susie_constructors.R
index 355caa13..8681d3c2 100644
--- a/R/susie_constructors.R
+++ b/R/susie_constructors.R
@@ -67,6 +67,18 @@ individual_data_constructor <- function(X, y, L = min(10, ncol(X)),
   }
   mean_y <- mean(y)
 
+  # Force required preprocessing for unmappable effects methods
+  if (unmappable_effects != "none") {
+    if (!intercept) {
+      warning_message("Unmappable effects methods require centered data. Setting intercept=TRUE.")
+      intercept <- TRUE
+    }
+    if (!standardize) {
+      warning_message("Unmappable effects methods require scaled data. Setting standardize=TRUE.")
+      standardize <- TRUE
+    }
+  }
+
   # Handle null weights
   if (is.numeric(null_weight) && null_weight == 0) {
     null_weight <- NULL
diff --git a/R/susie_utils.R b/R/susie_utils.R
index ad6b48aa..144e00ab 100644
--- a/R/susie_utils.R
+++ b/R/susie_utils.R
@@ -596,8 +596,8 @@ add_null_effect <- function(model_init, V) {
 # and log Bayes factor calculations.
 #
 # Functions: compute_eigen_decomposition, add_eigen_decomposition,
-# compute_omega_quantities, compute_theta_blup, lbf_stabilization,
-# compute_posterior_weights, compute_lbf_gradient
+# compute_omega_quantities, scale_design_matrix, compute_theta_blup, 
+# lbf_stabilization, compute_posterior_weights, compute_lbf_gradient
 # =============================================================================
 
 # Compute eigenvalue decomposition for unmappable methods
@@ -617,19 +617,6 @@ compute_eigen_decomposition <- function(XtX, n) {
 # Add eigen decomposition to ss data objects for unmappable methods
 #' @keywords internal
 add_eigen_decomposition <- function(data, params, individual_data = NULL) {
-  # Standardize y to unit variance for all unmappable effects methods
-  y_scale_factor <- 1
-
-  if (params$unmappable_effects != "none") {
-    var_y <- data$yty / (data$n - 1)
-    if (abs(var_y - 1) > 1e-10) {
-      sd_y           <- sqrt(var_y)
-      data$yty       <- data$yty / var_y
-      data$Xty       <- data$Xty / sd_y
-      y_scale_factor <- sd_y
-    }
-  }
-
   # Compute eigen decomposition
   eigen_decomp <- compute_eigen_decomposition(data$XtX, data$n)
 
@@ -638,19 +625,47 @@ add_eigen_decomposition <- function(data, params, individual_data = NULL) {
   data$eigen_values  <- eigen_decomp$Dsq
   data$VtXty         <- t(eigen_decomp$V) %*% data$Xty
 
-  # SuSiE.ash requires the X matrix and standardized y vector
   if (params$unmappable_effects == "ash") {
     if (is.null(individual_data)) {
       stop("Adaptive shrinkage (ash) requires individual-level data")
     }
-    data$X    <- individual_data$X
-    data$y    <- individual_data$y / y_scale_factor
-    data$VtXt <- t(data$eigen_vectors) %*% t(individual_data$X)
+
+    X_scaled <- scale_design_matrix(
+      individual_data$X,
+      center = attr(individual_data$X, "scaled:center"),
+      scale = attr(individual_data$X, "scaled:scale")
+    )
+
+    data$X    <- X_scaled               
+    data$y    <- individual_data$y       
+    data$VtXt <- t(data$eigen_vectors) %*% t(X_scaled)
   }
 
   return(data)
 }
 
+#' Scale design matrix using centering and scaling parameters
+#'
+#' Applies column-wise centering and scaling to match the space used by
+#' compute_XtX() and compute_Xty() for unmappable effects methods.
+#'
+#' @param X Matrix to scale (n × p)
+#' @param center Vector of column means to subtract (length p), or NULL
+#' @param scale Vector of column SDs to divide by (length p), or NULL
+#'
+#' @return Scaled matrix with centered and scaled columns
+#'
+#' @keywords internal
+scale_design_matrix <- function(X, center = NULL, scale = NULL) {
+  if (is.null(center)) center <- rep(0, ncol(X))
+  if (is.null(scale)) scale <- rep(1, ncol(X))
+
+  X_centered <- sweep(X, 2, center, "-")
+  X_scaled <- sweep(X_centered, 2, scale, "/")
+
+  return(X_scaled)
+}
+
 # Compute Omega-weighted quantities for unmappable effects methods
 #' @keywords internal
 compute_omega_quantities <- function(data, tau2, sigma2) {