diff --git a/R/regularized_regression.R b/R/regularized_regression.R
index 2d21335a..2557115e 100644
--- a/R/regularized_regression.R
+++ b/R/regularized_regression.R
@@ -370,6 +370,56 @@ susie_weights <- function(X = NULL, y = NULL, susie_fit = NULL, ...) {
   }
 }
 
+#' @importFrom susieR coef.susie
+#' @export
+susie_ash_weights <- function(X = NULL, y = NULL, susie_ash_fit = NULL, ...) {
+  if (is.null(susie_ash_fit)) {
+    # get susie_ash_fit object
+    susie_ash_fit <- susie_wrapper(X, y, unmappable_effects = "ash", standardize = FALSE, intercept = FALSE, ...)
+  }
+  if (!is.null(X)) {
+    if (length(susie_ash_fit$pip) != ncol(X)) {
+      stop(paste0(
+        "Dimension mismatch on number of variant in susie_ash_fit ", length(susie_ash_fit$pip),
+        " and TWAS weights ", ncol(X), ". "
+      ))
+    }
+  }
+  if ("alpha" %in% names(susie_ash_fit) && "mu" %in% names(susie_ash_fit) && "X_column_scale_factors" %in% names(susie_ash_fit)) {
+    # This is designed to cope with output from pecotmr::susie_post_processor()
+    # We set intercept to 0 and later trim it off anyways
+    susie_ash_fit$intercept <- 0
+    return(coef.susie(susie_ash_fit)[-1])
+  } else {
+    return(rep(0, length(susie_ash_fit$pip)))
+  }
+}
+
+#' @importFrom susieR coef.susie
+#' @export
+susie_inf_weights <- function(X = NULL, y = NULL, susie_inf_fit = NULL, ...) {
+  if (is.null(susie_inf_fit)) {
+    # get susie_inf_fit object
+    susie_inf_fit <- susie_wrapper(X, y, unmappable_effects = "inf", standardize = FALSE, intercept = FALSE, ...)
+  }
+  if (!is.null(X)) {
+    if (length(susie_inf_fit$pip) != ncol(X)) {
+      stop(paste0(
+        "Dimension mismatch on number of variant in susie_inf_fit ", length(susie_inf_fit$pip),
+        " and TWAS weights ", ncol(X), ". "
+      ))
+    }
+  }
+  if ("alpha" %in% names(susie_inf_fit) && "mu" %in% names(susie_inf_fit) && "X_column_scale_factors" %in% names(susie_inf_fit)) {
+    # This is designed to cope with output from pecotmr::susie_post_processor()
+    # We set intercept to 0 and later trim it off anyways
+    susie_inf_fit$intercept <- 0
+    return(coef.susie(susie_inf_fit)[-1])
+  } else {
+    return(rep(0, length(susie_inf_fit$pip)))
+  }
+}
+
 #' @importFrom mr.mash.alpha coef.mr.mash
 #' @export
 mrmash_weights <- function(mrmash_fit = NULL, X = NULL, Y = NULL, ...) {
@@ -829,43 +879,3 @@ bayes_c_rss_weights <- function(sumstats, LD, ...) {
 bayes_r_rss_weights <- function(sumstats, LD, ...) {
   return(bayes_alphabet_rss_weights(sumstats, LD, method = "bayesR", ...))
 }
-
-#' @export
-susie_ash_weights <- function(susie_ash_fit, X = NULL, y = NULL, ...) {
-  # If the fit object is missing or NULL, try to recover it from the parent frame.
-  if (missing(susie_ash_fit) || is.null(susie_ash_fit)) {
-    susie_ash_fit <- get0("susie_ash_fit", envir = parent.frame())
-    if (is.null(susie_ash_fit)) {
-      stop("A susie_ash_fit object is required.")
-    }
-  }
-  if (!is.null(X)) {
-    if (length(susie_ash_fit$marginal_PIP) != ncol(X)) {
-      stop(paste0("Dimension mismatch on number of variants in susie_ash_fit ",
-                  length(susie_ash_fit$marginal_PIP), " and TWAS weights ", ncol(X), ". "))
-    }
-  }
-  # Calculate coefficients as per the provided formula.
-  weights <- rowSums(susie_ash_fit$mu * susie_ash_fit$PIP) + susie_ash_fit$theta
-  return(weights)
-}
-
-#' @export
-susie_inf_weights <- function(susie_inf_fit, X = NULL, y = NULL, ...) {
-  # If the fit object is missing or NULL, try to recover it from the parent frame.
-  if (missing(susie_inf_fit) || is.null(susie_inf_fit)) {
-    susie_inf_fit <- get0("susie_inf_fit", envir = parent.frame())
-    if (is.null(susie_inf_fit)) {
-      stop("A susie_inf_fit object is required.")
-    }
-  }
-  if (!is.null(X)) {
-    if (length(susie_inf_fit$marginal_PIP) != ncol(X)) {
-      stop(paste0("Dimension mismatch on number of variants in susie_inf_fit ",
-                  length(susie_inf_fit$marginal_PIP), " and TWAS weights ", ncol(X), ". "))
-    }
-  }
-  # Calculate coefficients as per the provided formula.
-  weights <- rowSums(susie_inf_fit$mu * susie_inf_fit$PIP) + susie_inf_fit$alpha
-  return(weights)
-}
diff --git a/R/twas_weights.R b/R/twas_weights.R
index b1bfd190..73ddcc05 100644
--- a/R/twas_weights.R
+++ b/R/twas_weights.R
@@ -448,7 +448,9 @@ twas_weights_pipeline <- function(X,
                                     bayes_r_weights = list(),
                                     bayes_l_weights = list(),
                                     mrash_weights = list(init_prior_sd = TRUE, max.iter = 100),
-                                    susie_weights = list(refine = FALSE, init_L = 5, max_L = 20)
+                                    susie_weights = list(refine = FALSE, init_L = 5, max_L = 20),
+                                    susie_ash_weights = list(refine = FALSE, init_L = 5, max_L = 20),
+                                    susie_inf_weights = list(refine = FALSE, init_L = 5, max_L = 20)
                                   ),
                                   max_cv_variants = -1,
                                   cv_threads = 1,
diff --git a/man/twas_weights_pipeline.Rd b/man/twas_weights_pipeline.Rd
index f3e6e3ed..302af2f7 100644
--- a/man/twas_weights_pipeline.Rd
+++ b/man/twas_weights_pipeline.Rd
@@ -12,7 +12,9 @@ twas_weights_pipeline(
   sample_partition = NULL,
   weight_methods = list(enet_weights = list(), lasso_weights = list(), bayes_r_weights =
     list(), bayes_l_weights = list(), mrash_weights = list(init_prior_sd = TRUE, max.iter
-    = 100), susie_weights = list(refine = FALSE, init_L = 5, max_L = 20)),
+    = 100), susie_weights = list(refine = FALSE, init_L = 5, max_L = 20),
+    susie_ash_weights = list(refine = FALSE, init_L = 5, max_L = 20), susie_inf_weights =
+    list(refine = FALSE, init_L = 5, max_L = 20)),
   max_cv_variants = -1,
   cv_threads = 1,
   cv_weight_methods = NULL