diff --git a/tests/gentropy/conftest.py b/tests/gentropy/conftest.py
index 13992f8f2..78bd567da 100644
--- a/tests/gentropy/conftest.py
+++ b/tests/gentropy/conftest.py
@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Any
 
 import dbldatagen as dg
 import hail as hl
@@ -648,46 +647,3 @@ def sample_data_for_susie_inf() -> list[np.ndarray]:
     lbf_moments = np.loadtxt("tests/gentropy/data_samples/01_test_lbf_moments.csv")
     lbf_mle = np.loadtxt("tests/gentropy/data_samples/01_test_lbf_mle.csv")
     return [ld, z, lbf_moments, lbf_mle]
-
-
-@pytest.fixture()
-def sample_data_for_coloc(spark: SparkSession) -> list[Any]:
-    """Sample data for Coloc tests."""
-    overlap_df = spark.read.parquet(
-        "tests/gentropy/data_samples/coloc_test_data.snappy.parquet"
-    )
-    expected_df = spark.createDataFrame(
-        [
-            {
-                "h0": 1.3769995397857477e-18,
-                "h1": 2.937336451601565e-10,
-                "h2": 8.593226431647826e-12,
-                "h3": 8.338916748775843e-4,
-                "h4": 0.9991661080227981,
-            }
-        ]
-    )
-    single_snp_coloc = spark.createDataFrame(
-        [
-            {
-                "leftStudyLocusId": 1,
-                "rightStudyLocusId": 2,
-                "chromosome": "1",
-                "tagVariantId": "snp",
-                "left_logBF": 10.3,
-                "right_logBF": 10.5,
-            }
-        ]
-    )
-    expected_single_snp_coloc = spark.createDataFrame(
-        [
-            {
-                "h0": 9.254841951638903e-5,
-                "h1": 2.7517068829182966e-4,
-                "h2": 3.3609423764447284e-4,
-                "h3": 9.254841952564387e-13,
-                "h4": 0.9992961866536217,
-            }
-        ]
-    )
-    return [overlap_df, expected_df, single_snp_coloc, expected_single_snp_coloc]
diff --git a/tests/gentropy/data_samples/coloc_test_data.snappy.parquet b/tests/gentropy/data_samples/coloc_test_data.snappy.parquet
deleted file mode 100644
index 71b3913eb..000000000
Binary files a/tests/gentropy/data_samples/coloc_test_data.snappy.parquet and /dev/null differ
diff --git a/tests/gentropy/method/test_colocalisation_method.py b/tests/gentropy/method/test_colocalisation_method.py
index 1f52244be..d311d88ad 100644
--- a/tests/gentropy/method/test_colocalisation_method.py
+++ b/tests/gentropy/method/test_colocalisation_method.py
@@ -4,10 +4,12 @@
 
 from typing import Any
 
+import pytest
 from gentropy.dataset.colocalisation import Colocalisation
 from gentropy.dataset.study_locus_overlap import StudyLocusOverlap
 from gentropy.method.colocalisation import Coloc, ECaviar
-from pyspark.sql import functions as f
+from pandas.testing import assert_frame_equal
+from pyspark.sql import SparkSession
 
 
 def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None:
@@ -15,43 +17,92 @@ def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None:
     assert isinstance(Coloc.colocalise(mock_study_locus_overlap), Colocalisation)
 
 
-def test_coloc_colocalise(
-    sample_data_for_coloc: list[Any],
-    threshold: float = 1e-4,
-) -> None:
-    """Compare COLOC results with R implementation, using provided sample dataset from R package (StudyLocusOverlap)."""
-    test_overlap_df = sample_data_for_coloc[0]
-    test_overlap = StudyLocusOverlap(
-        _df=test_overlap_df, _schema=StudyLocusOverlap.get_schema()
-    )
-    test_result = Coloc.colocalise(test_overlap)
-    expected = sample_data_for_coloc[1]
-    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
-    for col in difference.columns:
-        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
-
-
-def test_single_snp_coloc(
-    sample_data_for_coloc: list[Any],
+@pytest.mark.parametrize(
+    "observed_data, expected_data",
+    [
+        # associations with a single overlapping SNP
+        (
+            # observed overlap
+            [
+                {
+                    "leftStudyLocusId": 1,
+                    "rightStudyLocusId": 2,
+                    "chromosome": "1",
+                    "tagVariantId": "snp",
+                    "statistics": {"left_logBF": 10.3, "right_logBF": 10.5},
+                },
+            ],
+            # expected coloc
+            [
+                {
+                    "h0": 9.254841951638903e-5,
+                    "h1": 2.7517068829182966e-4,
+                    "h2": 3.3609423764447284e-4,
+                    "h3": 9.254841952564387e-13,
+                    "h4": 0.9992961866536217,
+                },
+            ],
+        ),
+        # associations with multiple overlapping SNPs
+        (
+            # observed overlap
+            [
+                {
+                    "leftStudyLocusId": 1,
+                    "rightStudyLocusId": 2,
+                    "chromosome": "1",
+                    "tagVariantId": "snp1",
+                    "statistics": {"left_logBF": 10.3, "right_logBF": 10.5},
+                },
+                {
+                    "leftStudyLocusId": 1,
+                    "rightStudyLocusId": 2,
+                    "chromosome": "1",
+                    "tagVariantId": "snp2",
+                    "statistics": {"left_logBF": 10.3, "right_logBF": 10.5},
+                },
+            ],
+            # expected coloc
+            [
+                {
+                    "h0": 4.6230151407950416e-5,
+                    "h1": 2.749086942648107e-4,
+                    "h2": 3.357742374172504e-4,
+                    "h3": 9.983447421747411e-4,
+                    "h4": 0.9983447421747356,
+                },
+            ],
+        ),
+    ],
+)
+def test_coloc_semantic(
+    spark: SparkSession,
+    observed_data: list[Any],
+    expected_data: list[Any],
     threshold: float = 1e-5,
 ) -> None:
-    """Test edge case of coloc where only one causal SNP is present in the StudyLocusOverlap."""
-    test_overlap_df = sample_data_for_coloc[2]
-    test_overlap = StudyLocusOverlap(
-        _df=test_overlap_df.select(
-            "leftStudyLocusId",
-            "rightStudyLocusId",
-            "chromosome",
-            "tagVariantId",
-            f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
-        ),
+    """Test our COLOC with the implementation in R."""
+    observed_overlap = StudyLocusOverlap(
+        _df=spark.createDataFrame(observed_data, schema=StudyLocusOverlap.get_schema()),
         _schema=StudyLocusOverlap.get_schema(),
     )
-    test_result = Coloc.colocalise(test_overlap)
-    expected = sample_data_for_coloc[3]
-    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
-    for col in difference.columns:
-        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
+    observed_coloc_pdf = (
+        Coloc.colocalise(observed_overlap)
+        .df.select("h0", "h1", "h2", "h3", "h4")
+        .toPandas()
+    )
+    expected_coloc_pdf = (
+        spark.createDataFrame(expected_data)
+        .select("h0", "h1", "h2", "h3", "h4")
+        .toPandas()
+    )
+
+    assert_frame_equal(
+        observed_coloc_pdf,
+        expected_coloc_pdf,
+        check_exact=False,
+        check_dtype=True,
+    )
 
 
 def test_ecaviar(mock_study_locus_overlap: StudyLocusOverlap) -> None: