diff --git a/src/indra_cogex/sources/disgenet/__init__.py b/src/indra_cogex/sources/disgenet/__init__.py
index 1dc6782d5..1ce14e24a 100644
--- a/src/indra_cogex/sources/disgenet/__init__.py
+++ b/src/indra_cogex/sources/disgenet/__init__.py
@@ -24,12 +24,10 @@
 CURATED_DISEASE_GENES_ASSOCIATIONS_URL = (
     f"{DOWNLOAD_BASE}/curated_gene_disease_associations.tsv.gz"
 )
-# TODO Tenzin!
 CURATED_DISEASE_VARIANT_ASSOCIATIONS_URL = (
     f"{DOWNLOAD_BASE}/curated_variant_disease_associations.tsv.gz"
 )
 
-
 TARGET_KEYS = {
     "NofSnps": int,
     "DSI": str,
@@ -65,19 +63,22 @@ class DisgenetGeneProcessor(Processor):
 
     def __init__(self):
         """Initialize the DisGeNet processor."""
-        self.df = load_disgenet_disease_gene(CURATED_DISEASE_GENES_ASSOCIATIONS_URL)
+        self.df = load_disgenet_disease_gene(
+            CURATED_DISEASE_GENES_ASSOCIATIONS_URL)
 
     def get_nodes(self):  # noqa:D102
         diseases = {
             tuple(row)
-            for row in self.df[["disease_prefix", "disease_id", "disease_name"]].values
+            for row in
+            self.df[["disease_prefix", "disease_id", "disease_name"]].values
         }
         for prefix, identifier, name in diseases:
             yield Node.standardized(
                 db_ns=prefix, db_id=identifier, name=name, labels=["BioEntity"]
             )
         for hgnc_id in self.df["hgnc_id"].unique():
-            yield Node.standardized(db_ns="HGNC", db_id=hgnc_id, labels=["BioEntity"])
+            yield Node.standardized(db_ns="HGNC", db_id=hgnc_id,
+                                    labels=["BioEntity"])
 
     def get_relations(self):  # noqa:D102
         columns = [
@@ -90,35 +91,14 @@ def get_relations(self):  # noqa:D102
         for hgnc_id, disease_prefix, disease_id, snps, papers in self.df[
             columns
         ].values:
-            data = {"snps:int": snps, "source": self.name, "papers:int": papers}
+            data = {"snps:int": snps, "source": self.name,
+                    "papers:int": papers}
             yield Relation(
-                "HGNC", hgnc_id, disease_prefix, disease_id, self.relation, data
+                "HGNC", hgnc_id, disease_prefix, disease_id, self.relation,
+                data
             )
 
 
-def load_disgenet_disease_gene(url, force: bool = False) -> pd.DataFrame:
-    """Export disease-gene association file."""
-    df = SUBMODULE.ensure_csv(
-        url=url,
-        read_csv_kwargs=dict(dtype={"geneId": str}),
-        force=force,
-    )
-
-    df["hgnc_id"] = df["geneId"].map(
-        lambda s: hgnc_client.get_hgnc_from_entrez(s.strip())
-    )
-    df = df[df["hgnc_id"].notna()]
-
-    umls_mapper = UmlsMapper()
-    (
-        df["disease_prefix"],
-        df["disease_id"],
-        df["disease_name"],
-    ) = zip(*df["diseaseId"].map(umls_mapper.standardize))
-    df = df[df["disease_prefix"].notna()]
-    return df
-
-
 class DisgenetVariantProcessor(Processor):
     """Processor for the DisGeNet database."""
 
@@ -127,4 +107,79 @@ class DisgenetVariantProcessor(Processor):
     node_types = ["BioEntity"]
     relation = "variant_disease_association"
 
-    ...
+    def __init__(self):
+        """Initialize the DisGeNet processor."""
+        self.df = load_disgenet_disease_gene(
+            CURATED_DISEASE_VARIANT_ASSOCIATIONS_URL, variant=True)
+
+    def get_nodes(self):  # noqa:D102
+        # Since there exists no column in the disease-variant association tsv
+        # that describes disease prefix, opted to use diseaseClass
+        diseases = {
+            tuple(row)
+            for row in
+            self.df[["diseaseClass", "diseaseId", "diseaseName"]].values
+        }
+        for disease_class, identifier, name in diseases:
+            yield Node.standardized(
+                db_ns=disease_class, db_id=identifier, name=name,
+                labels=["BioEntity"]
+            )
+        for snp_id in self.df["snpId"].unique():
+            yield Node.standardized(db_ns="DBSNP", db_id=snp_id,
+                                    labels=["BioEntity"])
+
+    def get_relations(self):  # noqa:D102
+        # Use diseaseName instead of diseasePrefix as diseasePrefix does not
+        # exist in df for disease-variant tsv
+        columns = [
+            "snpId",
+            "DSI",
+            "DPI",
+            "diseaseName",
+            "diseaseId",
+            "NofPmids",
+        ]
+
+        for snp_id, dsi, dpi, disease_name, disease_id, papers in self.df[
+            columns
+        ].values:
+            data = {"snp_id:str": snp_id, "source": self.name,
+                    "dsi:float": dsi, "dpi:float": dpi,
+                    "papers:int": papers}
+            yield Relation(
+                "DBSNP", snp_id, disease_name, disease_id, self.relation,
+                data
+            )
+
+
+def load_disgenet_disease_gene(url, force: bool = False,
+                               variant: bool = False) -> (pd.DataFrame):
+    """Export disease-gene association file."""
+    df = SUBMODULE.ensure_csv(
+        url=url,
+        read_csv_kwargs=dict(dtype={"geneId": str,
+                                    "snpId": str}),
+        force=force,
+    )
+
+    if not variant:
+        df["hgnc_id"] = df["geneId"].map(
+            lambda s: hgnc_client.get_hgnc_from_entrez(s.strip())
+        )
+        df = df[df["hgnc_id"].notna()]
+        umls_mapper = UmlsMapper()
+        (
+            df["disease_prefix"],
+            df["disease_id"],
+            df["disease_name"],
+        ) = zip(*df["diseaseId"].map(umls_mapper.standardize))
+        df = df[df["disease_prefix"].notna()]
+        return df
+    elif variant:
+        # several dpi and dsi fields are NaN
+        df = df[df["DSI"].notna()]
+        df = df[df["DPI"].notna()]
+        return df
+
+