diff --git a/gnomad_constraint/pipeline/constraint_pipeline.py b/gnomad_constraint/pipeline/constraint_pipeline.py
index 97776922..edfaa6c2 100644
--- a/gnomad_constraint/pipeline/constraint_pipeline.py
+++ b/gnomad_constraint/pipeline/constraint_pipeline.py
@@ -244,6 +244,7 @@ def main(args):
     version = args.version
     test = args.test
     overwrite = args.overwrite
+    skip_downsamplings = args.skip_downsamplings
 
     max_af = args.max_af
     pops = args.pops
@@ -262,6 +263,10 @@ def main(args):
     )
     logger.info("The following downsamplings will be used: %s", downsamplings)
 
+    # If pops not specified, set to empty Tuple
+    if not pops:
+        pops = ()
+
     # Drop chromosome Y from version v4.0 (can add back in when obtain chrY
     # methylation data).
     if int(version[0]) >= 4:
@@ -422,10 +427,15 @@ def main(args):
                         "mane_select" if version_4_and_above else "canonical"
                     ),  # Switch to using MANE Select transcripts rather than canonical for gnomAD v4 and later versions.
                     global_annotation="training_dataset_params",
+                    skip_downsamplings=skip_downsamplings,
                 )
                 if use_v2_release_mutation_ht:
                     op_ht = op_ht.annotate_globals(use_v2_release_mutation_ht=True)
-                op_ht.write(getattr(res, f"train_{r}_ht").path, overwrite=overwrite)
+                # op_ht.write(getattr(res, f"train_{r}_ht").path, overwrite=overwrite)
+                op_ht.write(
+                    "gs://gnomad-kristen/constraint/gen_anc/train.ht",
+                    overwrite=overwrite,
+                )
             logger.info("Done with creating training dataset.")
 
         if args.build_models:
@@ -436,7 +446,10 @@ def main(args):
             # chromosome X, and chromosome Y.
             for r in regions:
                 # TODO: Remove repartition once partition_hint bugs are resolved.
-                training_ht = getattr(res, f"train_{r}_ht").ht()
+                # training_ht = getattr(res, f"train_{r}_ht").ht()
+                training_ht = hl.read_table(
+                    "gs://gnomad-kristen/constraint/gen_anc/train.ht"
+                )
                 training_ht = training_ht.repartition(args.training_set_partition_hint)
 
                 logger.info("Building %s plateau and coverage models...", r)
@@ -450,15 +463,16 @@ def main(args):
                 )
                 hl.experimental.write_expression(
                     plateau_models,
-                    getattr(res, f"model_{r}_plateau").path,
+                    "gs://gnomad-kristen/constraint/gen_anc/plateau_models.he",
+                    # getattr(res, f"model_{r}_plateau").path,
                     overwrite=overwrite,
                 )
-                if not args.skip_coverage_model:
-                    hl.experimental.write_expression(
-                        coverage_model,
-                        getattr(res, f"model_{r}_coverage").path,
-                        overwrite=overwrite,
-                    )
+                # if not args.skip_coverage_model:
+                #    hl.experimental.write_expression(
+                #        coverage_model,
+                #        getattr(res, f"model_{r}_coverage").path,
+                #        overwrite=overwrite,
+                #    )
                 logger.info("Done building %s models.", r)
 
         if args.apply_models:
@@ -486,7 +500,10 @@ def main(args):
                     exome_ht=getattr(res, f"preprocessed_{r}_exomes_ht").ht(),
                     context_ht=getattr(res, f"preprocessed_{r}_context_ht").ht(),
                     mutation_ht=mutation_ht,
-                    plateau_models=getattr(res, f"model_{r}_plateau").he(),
+                    plateau_models=hl.experimental.read_expression(
+                        "gs://gnomad-kristen/constraint/gen_anc/plateau_models.he"
+                    ),
+                    # plateau_models=getattr(res, f"model_{r}_plateau").he(),
                     coverage_model=(
                         getattr(res, "model_autosome_par_coverage").he()
                         if not args.skip_coverage_model
@@ -495,6 +512,7 @@ def main(args):
                     max_af=max_af,
                     pops=pops,
                     downsamplings=downsamplings,
+                    skip_downsamplings=skip_downsamplings,
                     obs_pos_count_partition_hint=args.apply_obs_pos_count_partition_hint,
                     expected_variant_partition_hint=args.apply_expected_variant_partition_hint,
                     custom_vep_annotation=custom_vep_annotation,
@@ -509,7 +527,8 @@ def main(args):
                 )
                 if use_v2_release_mutation_ht:
                     oe_ht = oe_ht.annotate_globals(use_v2_release_mutation_ht=True)
-                oe_ht.write(getattr(res, f"apply_{r}_ht").path, overwrite=overwrite)
+                # oe_ht.write(getattr(res, f"apply_{r}_ht").path, overwrite=overwrite)
+                oe_ht.write("gs://gnomad-kristen/constraint/gen_anc/apply.ht")
 
             logger.info(
                 "Done computing expected variant count and observed:expected ratio."
@@ -992,6 +1011,11 @@ def main(args):
         help="Export constraint metrics to tsv file.",
         action="store_true",
     )
+    parser.add_argument(
+        "--skip-downsamplings",
+        help="Whether to skip downsamplings when 'pops' is specified.",
+        action="store_true",
+    )
 
     compute_constraint_args._group_actions.append(populations)
 
diff --git a/gnomad_constraint/utils/constraint.py b/gnomad_constraint/utils/constraint.py
index 2f1701ab..819bec0c 100644
--- a/gnomad_constraint/utils/constraint.py
+++ b/gnomad_constraint/utils/constraint.py
@@ -1,4 +1,5 @@
 """Script containing utility functions used in the constraint pipeline."""
+
 import logging
 from typing import Dict, List, Optional, Tuple
 
@@ -16,7 +17,7 @@
     compute_pli,
     count_variants_by_group,
     get_constraint_flags,
-    get_downsampling_freq_indices,
+    get_pop_freq_indices,
     oe_aggregation_expr,
     oe_confidence_interval,
     trimer_from_heptamer,
@@ -182,6 +183,7 @@ def create_observed_and_possible_ht(
     low_coverage_filter: int = None,
     transcript_for_synonymous_filter: str = None,
     global_annotation: Optional[str] = None,
+    skip_downsamplings: bool = False,
 ) -> hl.Table:
     """
     Count the observed variants and possible variants by substitution, context, methylation level, and additional `grouping`.
@@ -238,6 +240,7 @@ def create_observed_and_possible_ht(
     :param global_annotation: The annotation name to use as a global StructExpression
         annotation containing input parameter values. If no value is supplied, this
         global annotation will not be added. Default is None.
+    :param skip_downsamplings: Whether or not to skip pulling the downsampling data.
     :return: Table with observed variant and possible variant count.
     """
     if low_coverage_filter is not None:
@@ -292,6 +295,7 @@ def create_observed_and_possible_ht(
         count_downsamplings=pops,
         use_table_group_by=True,
         max_af=max_af,
+        skip_downsamplings=skip_downsamplings,
     )
 
     # TODO: Remove repartition once partition_hint bugs are resolved.
@@ -353,6 +357,7 @@ def apply_models(
     high_cov_definition: int = COVERAGE_CUTOFF,
     low_coverage_filter: int = None,
     use_mane_select: bool = True,
+    skip_downsamplings: bool = False,
 ) -> hl.Table:
     """
     Compute the expected number of variants and observed:expected ratio using plateau models and coverage model.
@@ -426,6 +431,7 @@ def apply_models(
     :param use_mane_select: Use MANE Select transcripts in grouping.
         Only used when `custom_vep_annotation` is set to 'transcript_consequences'.
         Default is True.
+    :param skip_downsamplings: Whether or not to skip pulling the downsampling data.
 
     :return: Table with `expected_variants` (expected variant counts) and `obs_exp`
         (observed:expected ratio) annotations.
@@ -477,6 +483,7 @@ def apply_models(
         partition_hint=obs_pos_count_partition_hint,
         filter_coverage_over_0=True,
         transcript_for_synonymous_filter=None,
+        skip_downsamplings=skip_downsamplings,
     )
 
     # NOTE: In v2 ht.mu_snp was incorrectly multiplied here by possible_variants, but this multiplication has now been moved,
@@ -524,15 +531,15 @@ def apply_models(
 
         # Store which downsamplings are obtained for each pop in a
         # downsampling_meta dictionary.
-        ds = hl.eval(get_downsampling_freq_indices(ht.freq_meta, pop=pop))
+        ds = hl.eval(get_pop_freq_indices(ht.freq_meta, pop=pop))
         key_names = {key for _, meta_dict in ds for key in meta_dict.keys()}
         genetic_ancestry_label = "gen_anc" if "gen_anc" in key_names else "pop"
         downsampling_meta[pop] = [
-            x[1]["downsampling"]
+            x[1].get("downsampling", "all")
             for x in ds
-            if (x[1][genetic_ancestry_label] == pop)
-            & (
-                int(x[1]["downsampling"]) in downsamplings
+            if x[1][genetic_ancestry_label] == pop
+            and (
+                int(x[1].get("downsampling", 0)) in downsamplings
                 if downsamplings is not None
                 else True
             )
@@ -897,9 +904,8 @@ def compute_constraint_metrics(
     # `annotation_dict` stats the rule of filtration for each annotation.
     annotation_dict = {
         # Filter to classic LoF annotations with LOFTEE HC or LC.
-        "lof_hc_lc": hl.literal(set(classic_lof_annotations)).contains(
-            ht.annotation
-        ) & ((ht.modifier == "HC") | (ht.modifier == "LC")),
+        "lof_hc_lc": hl.literal(set(classic_lof_annotations)).contains(ht.annotation)
+        & ((ht.modifier == "HC") | (ht.modifier == "LC")),
         # Filter to LoF annotations with LOFTEE HC.
         "lof": ht.modifier == "HC",
         # Filter to missense variants.
@@ -1009,6 +1015,29 @@ def compute_constraint_metrics(
             z_raw=raw_z_expr,
         )
 
+        gen_anc_lower_struct = {}
+        gen_anc_upper_struct = {}
+        gen_anc_z_raw_struct = {}
+
+        # Calculate lower and upper cis, and raw z scores for each pop, excluding downsamplings.
+        for pop in pops:
+            obs_expr = ht[ann]["gen_anc_obs"][pop][0]
+            exp_expr = ht[ann]["gen_anc_exp"][pop][0]
+            oe_ci_expr = oe_confidence_interval(obs_expr, exp_expr)
+            raw_z_expr = calculate_raw_z_score(obs_expr, exp_expr)
+
+            lower_struct[pop] = oe_ci_expr.lower
+            upper_struct[pop] = oe_ci_expr.upper
+            gen_anc_z_raw_struct[pop] = raw_z_expr
+
+    # Annotate the table with the structs.
+    ann_expr[ann] = ann_expr[ann].annotate(
+        gen_anc_oe_ci=hl.struct(
+            lower=hl.struct(**lower_struct), upper=hl.struct(**upper_struct)
+        ),
+        gen_anc_z_raw=hl.struct(**gen_anc_z_raw_struct),
+    )
+
     ann_expr["constraint_flags"] = add_filters_expr(filters=constraint_flags_expr)
     ht = ht.annotate(**ann_expr)
     ht = ht.checkpoint(