diff --git a/src/gentropy/assets/schemas/l2g_predictions.json b/src/gentropy/assets/schemas/l2g_predictions.json
index 238ff4087..57247a49a 100644
--- a/src/gentropy/assets/schemas/l2g_predictions.json
+++ b/src/gentropy/assets/schemas/l2g_predictions.json
@@ -18,6 +18,17 @@
       "type": "double",
       "nullable": false,
       "metadata": {}
+    },
+    {
+      "metadata": {},
+      "name": "locusToGeneFeatures",
+      "nullable": true,
+      "type": {
+        "keyType": "string",
+        "type": "map",
+        "valueContainsNull": true,
+        "valueType": "float"
+      }
     }
   ]
 }
diff --git a/src/gentropy/dataset/l2g_prediction.py b/src/gentropy/dataset/l2g_prediction.py
index 169f5a846..64ce964c7 100644
--- a/src/gentropy/dataset/l2g_prediction.py
+++ b/src/gentropy/dataset/l2g_prediction.py
@@ -126,3 +126,54 @@ def to_disease_target_evidence(
                 "studyLocusId",
             )
         )
+
+    def add_locus_to_gene_features(
+        self: L2GPrediction, feature_matrix: L2GFeatureMatrix
+    ) -> L2GPrediction:
+        """Add features to the L2G predictions.
+
+        Args:
+            feature_matrix (L2GFeatureMatrix): Feature matrix dataset
+
+        Returns:
+            L2GPrediction: L2G predictions with additional features
+        """
+        # Testing if `locusToGeneFeatures` column already exists:
+        if "locusToGeneFeatures" in self.df.columns:
+            self.df = self.df.drop("locusToGeneFeatures")
+
+        # Columns identifying a studyLocus/gene pair
+        prediction_id_columns = ["studyLocusId", "geneId"]
+
+        # L2G matrix columns to build the map:
+        columns_to_map = [
+            column
+            for column in feature_matrix._df.columns
+            if column not in prediction_id_columns
+        ]
+
+        # Aggregating all features into a single map column:
+        aggregated_features = (
+            feature_matrix._df.withColumn(
+                "locusToGeneFeatures",
+                f.create_map(
+                    *sum(
+                        [
+                            (f.lit(colname), f.col(colname))
+                            for colname in columns_to_map
+                        ],
+                        (),
+                    )
+                ),
+            )
+            # from the freshly created map, we filter out the null values
+            .withColumn(
+                "locusToGeneFeatures",
+                f.expr("map_filter(locusToGeneFeatures, (k, v) -> v is not null)"),
+            )
+            .drop(*columns_to_map)
+        )
+        return L2GPrediction(
+            _df=self.df.join(aggregated_features, on=prediction_id_columns, how="left"),
+            _schema=self.get_schema(),
+        )
diff --git a/src/gentropy/l2g.py b/src/gentropy/l2g.py
index 79be2385a..cc1b5a5d1 100644
--- a/src/gentropy/l2g.py
+++ b/src/gentropy/l2g.py
@@ -190,9 +190,9 @@ def run_predict(self) -> None:
             hf_token=access_gcp_secret("hfhub-key", "open-targets-genetics-dev"),
             download_from_hub=self.download_from_hub,
         )
-        predictions.df.write.mode(self.session.write_mode).parquet(
-            self.predictions_path
-        )
+        predictions.add_locus_to_gene_features(self.feature_matrix).df.write.mode(
+            self.session.write_mode
+        ).parquet(self.predictions_path)
         self.session.logger.info("L2G predictions saved successfully.")
 
     def run_train(self) -> None: