diff --git a/R/susie_wrapper.R b/R/susie_wrapper.R
index ff6d225c..1aa0eac4 100644
--- a/R/susie_wrapper.R
+++ b/R/susie_wrapper.R
@@ -296,40 +296,10 @@ susie_rss_pipeline <- function(sumstats, LD_mat, n = NULL, var_y = NULL, L = 5,
 
 #' @noRd
 get_cs_index <- function(snps_idx, susie_cs) {
-  # Use pmap to iterate over each vector in susie_cs
-  idx_lengths <- tryCatch(
-    {
-      pmap(list(x = susie_cs), function(x) {
-        # Check if snps_idx is in the CS and return the length of the CS if it is
-        if (snps_idx %in% x) {
-          return(length(x))
-        } else {
-          return(NA_integer_)
-        }
-      }) %>% unlist()
-    },
-    error = function(e) NA_integer_
-  )
-  idx <- which(!is.na(idx_lengths))
-  # idx length should be either 1 or 0
-  # But in some rare cases there will be a convergence issue resulting in a variant belong to multiple CS
-  # In which case we will keep one of them, with a warning
-  if (length(idx) > 0) {
-    if (length(idx) > 1) {
-      smallest_cs_idx <- which.min(idx_lengths[idx])
-      selected_cs <- idx[smallest_cs_idx]
-      selected_length <- idx_lengths[selected_cs]
-
-      warning(sprintf(
-        "Variable %d found in multiple CS: %s. Keeping smallest: CS %d (length %d).",
-        snps_idx, paste(idx, collapse = ", "), selected_cs, selected_length
-      ))
-      idx <- selected_cs # Keep index with smallest length
-    }
-    return(idx)
-  } else {
-    return(NA_integer_)
-  }
+  # Return ALL CS indices that contain this variant (not just one)
+  idx <- which(vapply(susie_cs, function(x) snps_idx %in% x, logical(1)))
+  if (length(idx) == 0) return(NA_integer_)
+  return(idx)
 }
 #' @noRd
 get_top_variants_idx <- function(susie_output, signal_cutoff) {
@@ -338,11 +308,21 @@ get_top_variants_idx <- function(susie_output, signal_cutoff) {
     sort()
 }
 #' @noRd
+#' Returns a data.frame(variant_idx, cs_idx) with one row per (variant, CS) pair.
+#' Variants in multiple CSs get multiple rows.
 get_cs_info <- function(susie_output_sets_cs, top_variants_idx) {
-  cs_info_pri <- map_int(top_variants_idx, ~ get_cs_index(.x, susie_output_sets_cs))
-  ifelse(is.na(cs_info_pri), 0, as.numeric(str_replace(names(susie_output_sets_cs)[cs_info_pri], "L", "")))
+  cs_names <- names(susie_output_sets_cs)
+  rows <- lapply(top_variants_idx, function(vi) {
+    idx <- get_cs_index(vi, susie_output_sets_cs)
+    if (length(idx) == 1 && is.na(idx)) {
+      data.frame(variant_idx = vi, cs_idx = 0L, stringsAsFactors = FALSE)
+    } else {
+      cs_nums <- as.integer(str_replace(cs_names[idx], "L", ""))
+      data.frame(variant_idx = rep(vi, length(cs_nums)), cs_idx = cs_nums, stringsAsFactors = FALSE)
+    }
+  })
+  do.call(rbind, rows)
 }
-
 #' @noRd
 get_cs_and_corr <- function(susie_output, coverage, data_x, mode = c("susie", "susie_rss", "mvsusie"), min_abs_corr = NULL) {
   if (mode %in% c("susie", "mvsusie")) {
@@ -434,30 +414,34 @@ susie_post_processor <- function(susie_output, data_x, data_y, X_scalar, y_scala
   if (length(eff_idx) > 0) {
     # Prepare for top loci table
     top_variants_idx_pri <- get_top_variants_idx(susie_output, signal_cutoff)
-    cs_pri <- get_cs_info(susie_output$sets$cs, top_variants_idx_pri)
+    # get_cs_info returns data.frame(variant_idx, cs_idx) with one row per (variant, CS) pair
+    top_loci_pri <- get_cs_info(susie_output$sets$cs, top_variants_idx_pri)
+    if (is.null(top_loci_pri)) top_loci_pri <- data.frame(variant_idx = integer(0), cs_idx = integer(0))
     susie_output$cs_corr <- if (mode %in% c("susie", "mvsusie")) get_cs_correlation(susie_output, X = data_x) else get_cs_correlation(susie_output, Xcorr = data_x)
-    top_loci_list <- list("coverage_0.95" = data.frame(variant_idx = top_variants_idx_pri, cs_idx = cs_pri, stringsAsFactors = FALSE))
+    top_loci_list <- list("coverage_0.95" = top_loci_pri)
 
-    ## Loop over each secondary coverage value
+    ## Loop over each secondary coverage value independently
     sets_secondary <- list()
     if (!is.null(secondary_coverage) && length(secondary_coverage)) {
       for (sec_cov in secondary_coverage) {
         sets_secondary[[paste0("coverage_", sec_cov)]] <- get_cs_and_corr(susie_output, sec_cov, data_x, mode, min_abs_corr)
         top_variants_idx_sec <- get_top_variants_idx(sets_secondary[[paste0("coverage_", sec_cov)]], signal_cutoff)
-        cs_sec <- get_cs_info(sets_secondary[[paste0("coverage_", sec_cov)]]$sets$cs, top_variants_idx_sec)
-        top_loci_list[[paste0("coverage_", sec_cov)]] <- data.frame(variant_idx = top_variants_idx_sec, cs_idx = cs_sec, stringsAsFactors = FALSE)
+        top_loci_sec <- get_cs_info(sets_secondary[[paste0("coverage_", sec_cov)]]$sets$cs, top_variants_idx_sec)
+        if (is.null(top_loci_sec)) top_loci_sec <- data.frame(variant_idx = integer(0), cs_idx = integer(0))
+        top_loci_list[[paste0("coverage_", sec_cov)]] <- top_loci_sec
       }
     }
 
-    # Iterate over the remaining tables, rename and merge them
+    # Merge coverage tables via full_join
     names(top_loci_list[[1]])[2] <- paste0("cs_", names(top_loci_list)[1])
     top_loci <- top_loci_list[[1]]
     if (length(top_loci_list) > 1) {
       for (i in 2:length(top_loci_list)) {
         names(top_loci_list[[i]])[2] <- paste0("cs_", names(top_loci_list)[i])
-        top_loci <- full_join(top_loci, top_loci_list[[i]], by = "variant_idx")
+        top_loci <- dplyr::full_join(top_loci, top_loci_list[[i]], by = "variant_idx")
       }
     }
+
     if (nrow(top_loci) > 0) {
       top_loci[is.na(top_loci)] <- 0
       variants <- res$variant_names[top_loci$variant_idx]
diff --git a/tests/testthat/test_susie_wrapper.R b/tests/testthat/test_susie_wrapper.R
index e82b5b1b..571d9b95 100644
--- a/tests/testthat/test_susie_wrapper.R
+++ b/tests/testthat/test_susie_wrapper.R
@@ -201,16 +201,29 @@ test_that("get_cs_info maps variants to CS numbers", {
   susie_cs <- list(L1 = c(1, 2), L3 = c(4, 5, 6))
   top_idx <- c(1, 3, 5)
   result <- pecotmr:::get_cs_info(susie_cs, top_idx)
-  expect_equal(result[1], 1)
-  expect_equal(result[2], 0)
-  expect_equal(result[3], 3)
+  # Now returns data.frame(variant_idx, cs_idx) with one row per (variant, CS) pair
+  expect_true(is.data.frame(result))
+  expect_equal(result$variant_idx, c(1, 3, 5))
+  expect_equal(result$cs_idx, c(1L, 0L, 3L))
 })
 
 test_that("get_cs_info handles all variants outside CS", {
   susie_cs <- list(L1 = c(1, 2))
   top_idx <- c(5, 6, 7)
   result <- pecotmr:::get_cs_info(susie_cs, top_idx)
-  expect_true(all(result == 0))
+  expect_true(is.data.frame(result))
+  expect_true(all(result$cs_idx == 0))
+})
+
+test_that("get_cs_info reports variant in multiple CSs as multiple rows", {
+  susie_cs <- list(L1 = c(1, 2, 3), L3 = c(2, 3, 4))
+  top_idx <- c(1, 2, 4)
+  result <- pecotmr:::get_cs_info(susie_cs, top_idx)
+  expect_true(is.data.frame(result))
+  # variant 2 is in both L1 and L3, so it gets two rows
+  expect_equal(nrow(result), 4)
+  expect_equal(sum(result$variant_idx == 2), 2)
+  expect_equal(sort(result$cs_idx[result$variant_idx == 2]), c(1L, 3L))
 })
 
 # =============================================================================