diff --git a/R/multivariate_pipeline.R b/R/multivariate_pipeline.R
index 913d9c42..217ee9f3 100644
--- a/R/multivariate_pipeline.R
+++ b/R/multivariate_pipeline.R
@@ -21,6 +21,7 @@
 #' @param sample_partition Optional data frame with Sample and Fold columns for cross-validation.
 #' @param mrmash_max_iter The maximum number of iterations for mr.mash. Default is 5000.
 #' @param mvsusie_max_iter The maximum number of iterations for mvSuSiE. Default is 200.
+#' @param estimate_residual_variance Passed to \code{mvsusieR::mvsusie()}. Default is TRUE.
 #' @param min_cv_maf The minimum minor allele frequency for variants to be included in cross-validation. Default is 0.05.
 #' @param max_cv_variants The maximum number of variants to be included in cross-validation. Defaults to -1 which means no limit.
 #' @param cv_folds The number of folds to use for cross-validation. Set to 0 to skip cross-validation. Default is 5.
@@ -87,6 +88,7 @@ multivariate_analysis_pipeline <- function(
     canonical_prior_matrices = TRUE,
     mrmash_max_iter = 5000,
     mvsusie_max_iter = 200,
+    estimate_residual_variance = TRUE,
     # fine-mapping results summary
     signal_cutoff = 0.025,
     coverage = c(0.95, 0.7, 0.5),
@@ -287,7 +289,7 @@ multivariate_analysis_pipeline <- function(
   res$mvsusie_fitted <- mvsusieR::mvsusie(X, Y,
     L = L, L_greedy = L_greedy,
     prior_variance = mvsusie_reweighted_mixture_prior$data_driven_prior_matrices,
-    residual_variance = resid_Y, estimate_residual_variance = TRUE,
+    residual_variance = resid_Y, estimate_residual_variance = estimate_residual_variance,
     max_iter = mvsusie_max_iter,
     verbose = verbose, coverage = coverage[1]
   )
diff --git a/R/univariate_pipeline.R b/R/univariate_pipeline.R
index 47eeb704..16e97638 100644
--- a/R/univariate_pipeline.R
+++ b/R/univariate_pipeline.R
@@ -26,6 +26,7 @@
 #' @param finemapping_extra_opts Additional options passed to \code{susieR::susie()}.
 #'   SuSiE-inf is always fitted with \code{refine = FALSE}; the ordinary SuSiE
 #'   fit keeps these options and is initialized with \code{model_init}.
+#' @param estimate_residual_variance Passed to \code{susieR::susie()}. Default is TRUE.
 #' @param add_susie_inf Whether to fit SuSiE-inf before ordinary SuSiE. Default
 #'   is TRUE for existing pipeline compatibility. If FALSE, ordinary SuSiE is
 #'   fitted directly and SuSiE-inf fitted objects/results are not returned.
@@ -63,6 +64,7 @@ univariate_analysis_pipeline <- function(
     coverage = c(0.95, 0.7, 0.5),
     min_abs_corr = 0.8,
     finemapping_extra_opts = list(refine = TRUE),
+    estimate_residual_variance = TRUE,
     add_susie_inf = TRUE,
     # TWAS weights and CV for TWAS weights
     twas_weights = TRUE,
@@ -126,7 +128,8 @@ univariate_analysis_pipeline <- function(
 
   susie_args <- modifyList(
     finemapping_extra_opts,
-    list(L = L, L_greedy = L_greedy, coverage = coverage[1])
+    list(L = L, L_greedy = L_greedy, coverage = coverage[1],
+         estimate_residual_variance = estimate_residual_variance)
   )
   if (isTRUE(add_susie_inf)) {
     message("Fitting SuSiE-inf model on input data ...")
diff --git a/man/multivariate_analysis_pipeline.Rd b/man/multivariate_analysis_pipeline.Rd
index fdd504f9..1fc0c503 100644
--- a/man/multivariate_analysis_pipeline.Rd
+++ b/man/multivariate_analysis_pipeline.Rd
@@ -24,6 +24,7 @@ multivariate_analysis_pipeline(
   canonical_prior_matrices = TRUE,
   mrmash_max_iter = 5000,
   mvsusie_max_iter = 200,
+  estimate_residual_variance = TRUE,
   signal_cutoff = 0.025,
   coverage = c(0.95, 0.7, 0.5),
   min_abs_corr = 0.8,
@@ -62,6 +63,8 @@ multivariate_analysis_pipeline(
 
 \item{mvsusie_max_iter}{The maximum number of iterations for mvSuSiE. Default is 200.}
 
+\item{estimate_residual_variance}{Passed to \code{mvsusieR::mvsusie()}. Default is TRUE.}
+
 \item{signal_cutoff}{Cutoff value for signal identification in PIP values. Default is 0.025.}
 
 \item{coverage}{A vector of coverage probabilities, with the first element being the primary coverage and the rest being secondary coverage probabilities for credible set refinement. Defaults to c(0.95, 0.7, 0.5).}
diff --git a/man/univariate_analysis_pipeline.Rd b/man/univariate_analysis_pipeline.Rd
index 06043f9a..ed4cc340 100644
--- a/man/univariate_analysis_pipeline.Rd
+++ b/man/univariate_analysis_pipeline.Rd
@@ -24,6 +24,7 @@ univariate_analysis_pipeline(
   coverage = c(0.95, 0.7, 0.5),
   min_abs_corr = 0.8,
   finemapping_extra_opts = list(refine = TRUE),
+  estimate_residual_variance = TRUE,
   add_susie_inf = TRUE,
   twas_weights = TRUE,
   sample_partition = NULL,
@@ -75,6 +76,8 @@ which is stricter than the susieR default of 0.5.}
 SuSiE-inf is always fitted with \code{refine = FALSE}; the ordinary SuSiE
 fit keeps these options and is initialized with \code{model_init}.}
 
+\item{estimate_residual_variance}{Passed to \code{susieR::susie()}. Default is TRUE.}
+
 \item{add_susie_inf}{Whether to fit SuSiE-inf before ordinary SuSiE. Default
 is TRUE for existing pipeline compatibility. If FALSE, ordinary SuSiE is
 fitted directly and SuSiE-inf fitted objects/results are not returned.}