diff --git a/cellxgene_schema_cli/cellxgene_schema/schema_definitions/schema_definition.yaml b/cellxgene_schema_cli/cellxgene_schema/schema_definitions/schema_definition.yaml
index 28a3fad54..d14a0442e 100644
--- a/cellxgene_schema_cli/cellxgene_schema/schema_definitions/schema_definition.yaml
+++ b/cellxgene_schema_cli/cellxgene_schema/schema_definitions/schema_definition.yaml
@@ -582,3 +582,15 @@ components:
           - "cell culture"
           - "organoid"
           - "tissue"
+      genetic_ancestry_African:
+        type: genetic_ancestry_value
+      genetic_ancestry_East_Asian:
+        type: genetic_ancestry_value
+      genetic_ancestry_European:
+        type: genetic_ancestry_value
+      genetic_ancestry_Indigenous_American:
+        type: genetic_ancestry_value
+      genetic_ancestry_Oceanian:
+        type: genetic_ancestry_value
+      genetic_ancestry_South_Asian:
+        type: genetic_ancestry_value
diff --git a/cellxgene_schema_cli/cellxgene_schema/validate.py b/cellxgene_schema_cli/cellxgene_schema/validate.py
index 024a51ec0..01a2d1401 100644
--- a/cellxgene_schema_cli/cellxgene_schema/validate.py
+++ b/cellxgene_schema_cli/cellxgene_schema/validate.py
@@ -416,6 +416,109 @@ def _count_matrix_nonzero(self, matrix_name: str, matrix: Union[np.ndarray, spar
         self.number_non_zero[matrix_name] = nnz
         return nnz
 
+    def _validate_genetic_ancestry(self):
+        """
+        Performs row-based validation of the genetic_ancestry_X fields. This ensures that a valid row must be:
+        - all float('nan') if organism is not homo sapiens or info is unavailable
+        - sum to 1.0
+
+        Additionally, verifies that all rows with the same donor_id must have the same genetic ancestry values
+        """
+        ancestry_columns = [
+            "genetic_ancestry_African",
+            "genetic_ancestry_East_Asian",
+            "genetic_ancestry_European",
+            "genetic_ancestry_Indigenous_American",
+            "genetic_ancestry_Oceanian",
+            "genetic_ancestry_South_Asian",
+        ]
+
+        organism_column = "organism_ontology_term_id"
+        donor_id_column = "donor_id"
+
+        # Skip any additional validation if the genetic ancestry or organism columns are not present
+        # An error for missing columns will be raised at a different point
+        required_columns = ancestry_columns + [organism_column, donor_id_column]
+        for column in required_columns:
+            if column not in self.adata.obs.columns:
+                return
+
+        donor_id_to_ancestry_values = dict()
+
+        def is_valid_row(row):
+            ancestry_values = row[ancestry_columns]
+
+            # If ancestry values are different for the same donor id, then this row is invalid
+            donor_id = row[donor_id_column]
+            if donor_id in donor_id_to_ancestry_values:
+                if not donor_id_to_ancestry_values[donor_id].equals(ancestry_values):
+                    return False
+            else:
+                donor_id_to_ancestry_values[donor_id] = ancestry_values
+
+            # All values are NaN. This is always valid, regardless of organism
+            if ancestry_values.isna().all():
+                return True
+
+            # If any values are NaN, and we didn't return in the earlier all NaN check, then
+            # this is invalid
+            if ancestry_values.isna().any():
+                return False
+
+            # If organism is not homo sapiens, and we didn't return in the earlier all NaN check,
+            # then this row is invalid
+            if row[organism_column] != "NCBITaxon:9606":
+                return False
+
+            # The sum of genetic ancestry values should be approximately 1.0
+            if (
+                ancestry_values.apply(lambda x: isinstance(x, (float, int))).all()
+                and abs(ancestry_values.sum() - 1.0) <= 1e-6
+            ):
+                return True
+
+            return False
+
+        invalid_rows = ~self.adata.obs.apply(is_valid_row, axis=1)
+
+        if invalid_rows.any():
+            invalid_indices = self.adata.obs.index[invalid_rows].tolist()
+            self.errors.append(
+                f"obs rows with indices {invalid_indices} have invalid genetic_ancestry_* values. All "
+                f"observations with the same donor_id must contain the same genetic_ancestry_* values. If "
+                f"organism_ontolology_term_id is NOT 'NCBITaxon:9606' for Homo sapiens, then all genetic"
+                f"ancestry values MUST be float('nan'). If organism_ontolology_term_id is 'NCBITaxon:9606' "
+                f"for Homo sapiens, then the value MUST be a float('nan') if unavailable; otherwise, the "
+                f"sum of all genetic_ancestry_* fields must be equal to 1.0"
+            )
+
+    def _validate_individual_genetic_ancestry_value(self, column: pd.Series, column_name: str):
+        """
+        The following fields are valid for genetic_ancestry_value columns:
+        - float values between 0 and 1
+        - float('nan')
+        """
+        if column.dtype != float:
+            self.errors.append(f"Column '{column_name}' in obs must be float, not '{column.dtype.name}'.")
+            return
+
+        def is_individual_value_valid(value):
+            if isinstance(value, (float, int)) and 0 <= value <= 1:
+                return True
+            # Ensures only float('nan') or numpy.nan is valid, None is invalid
+            if isinstance(value, float) and pd.isna(value):
+                return True
+            return False
+
+        # Identify invalid values
+        invalid_values = column[~column.map(is_individual_value_valid)]
+
+        if not invalid_values.empty:
+            self.errors.append(
+                f"Column '{column_name}' in obs contains invalid values: {invalid_values.to_list()}. "
+                f"Valid values are floats between 0 and 1 or float('nan')."
+            )
+
     def _validate_column_feature_is_filtered(self, column: pd.Series, column_name: str, df_name: str):
         """
         Validates the "is_feature_filtered" in adata.var. This column must be bool, and for genes that are set to
@@ -505,6 +608,9 @@ def _validate_column(self, column: pd.Series, column_name: str, df_name: str, co
         if column_def.get("type") == "feature_is_filtered":
             self._validate_column_feature_is_filtered(column, column_name, df_name)
 
+        if column_def.get("type") == "genetic_ancestry_value":
+            self._validate_individual_genetic_ancestry_value(column, column_name)
+
         if "enum" in column_def:
             bad_enums = [v for v in column.drop_duplicates() if v not in column_def["enum"]]
             if bad_enums:
@@ -781,6 +887,7 @@ def _validate_dataframe(self, df_name: str):
                                 f"Column '{column_name}' in dataframe '{df_name}' contains a category '{category}' with "
                                 f"zero observations. These categories will be removed when `--add-labels` flag is present."
                             )
+                    self._validate_genetic_ancestry()
                 categorical_types = {type(x) for x in column.dtype.categories.values}
                 # Check for columns that have illegal categories, which are not supported by anndata 0.8.0
                 # TODO: check if this can be removed after upgading to anndata 0.10.0
diff --git a/cellxgene_schema_cli/tests/fixtures/examples_validate.py b/cellxgene_schema_cli/tests/fixtures/examples_validate.py
index 470c165ce..accbecfcd 100644
--- a/cellxgene_schema_cli/tests/fixtures/examples_validate.py
+++ b/cellxgene_schema_cli/tests/fixtures/examples_validate.py
@@ -48,6 +48,12 @@
             "HsapDv:0000003",
             "donor_1",
             "nucleus",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
         [
             "CL:0000192",
@@ -62,6 +68,12 @@
             "MmusDv:0000003",
             "donor_2",
             "na",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
     ],
     index=["X", "Y"],
@@ -78,6 +90,12 @@
         "development_stage_ontology_term_id",
         "donor_id",
         "suspension_type",
+        "genetic_ancestry_African",
+        "genetic_ancestry_East_Asian",
+        "genetic_ancestry_European",
+        "genetic_ancestry_Indigenous_American",
+        "genetic_ancestry_Oceanian",
+        "genetic_ancestry_South_Asian",
     ],
 )
 
@@ -144,6 +162,12 @@
             "donor_1",
             "na",
             0,
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
         [
             2,
@@ -161,6 +185,12 @@
             "donor_2",
             "na",
             1,
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
     ],
     index=["X", "Y"],
@@ -180,6 +210,12 @@
         "donor_id",
         "suspension_type",
         "in_tissue",
+        "genetic_ancestry_African",
+        "genetic_ancestry_East_Asian",
+        "genetic_ancestry_European",
+        "genetic_ancestry_Indigenous_American",
+        "genetic_ancestry_Oceanian",
+        "genetic_ancestry_South_Asian",
     ],
 )
 
@@ -203,6 +239,12 @@
             "HsapDv:0000003",
             "donor_1",
             "na",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
         [
             "CL:0000192",
@@ -217,6 +259,12 @@
             "MmusDv:0000003",
             "donor_2",
             "na",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
     ],
     index=["X", "Y"],
@@ -233,6 +281,12 @@
         "development_stage_ontology_term_id",
         "donor_id",
         "suspension_type",
+        "genetic_ancestry_African",
+        "genetic_ancestry_East_Asian",
+        "genetic_ancestry_European",
+        "genetic_ancestry_Indigenous_American",
+        "genetic_ancestry_Oceanian",
+        "genetic_ancestry_South_Asian",
     ],
 )
 
@@ -255,6 +309,12 @@
             "HsapDv:0000003",
             "donor_1",
             "na",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
         [
             "CL:0000192",
@@ -269,6 +329,12 @@
             "MmusDv:0000003",
             "donor_2",
             "na",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
     ],
     index=["X", "Y"],
@@ -285,6 +351,12 @@
         "development_stage_ontology_term_id",
         "donor_id",
         "suspension_type",
+        "genetic_ancestry_African",
+        "genetic_ancestry_East_Asian",
+        "genetic_ancestry_European",
+        "genetic_ancestry_Indigenous_American",
+        "genetic_ancestry_Oceanian",
+        "genetic_ancestry_South_Asian",
     ],
 )
 
@@ -493,6 +565,12 @@
             "tissue:1",
             "sre:1",
             "development_stage:1",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
         [
             "cell_type:1",
@@ -503,6 +581,12 @@
             "tissue:1",
             "sre:1",
             "development_stage:1",
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
+            float("nan"),
         ],
     ],
     index=["X", "Y"],
@@ -515,6 +599,12 @@
         "tissue_ontology_term_id",
         "self_reported_ethnicity_ontology_term_id",
         "development_stage_ontology_term_id",
+        "genetic_ancestry_African",
+        "genetic_ancestry_East_Asian",
+        "genetic_ancestry_European",
+        "genetic_ancestry_Indigenous_American",
+        "genetic_ancestry_Oceanian",
+        "genetic_ancestry_South_Asian",
     ],
 )
 
diff --git a/cellxgene_schema_cli/tests/fixtures/h5ads/example_valid.h5ad b/cellxgene_schema_cli/tests/fixtures/h5ads/example_valid.h5ad
index ec5f0aee2..a1b121bdf 100644
Binary files a/cellxgene_schema_cli/tests/fixtures/h5ads/example_valid.h5ad and b/cellxgene_schema_cli/tests/fixtures/h5ads/example_valid.h5ad differ
diff --git a/cellxgene_schema_cli/tests/test_schema_compliance.py b/cellxgene_schema_cli/tests/test_schema_compliance.py
index 425086fcd..7ee65a6df 100644
--- a/cellxgene_schema_cli/tests/test_schema_compliance.py
+++ b/cellxgene_schema_cli/tests/test_schema_compliance.py
@@ -1510,6 +1510,124 @@ def test_nan_values_must_be_rejected(self, validator_with_adata):
             in validator.errors
         )
 
+    @pytest.mark.parametrize(
+        "genetic_ancestry_African, genetic_ancestry_East_Asian, genetic_ancestry_European, "
+        "genetic_ancestry_Indigenous_American, genetic_ancestry_Oceanian, genetic_ancestry_South_Asian",
+        [
+            (0.0, 0.0, 0.0, 0.0, 0.0, 1.0),
+            (0.5, 0.5, 0.0, 0.0, 0.0, 0.0),
+            (0.0, 0.25, 0.25, 0.25, 0.25, 0.0),
+            (float("nan"), float("nan"), float("nan"), float("nan"), float("nan"), float("nan")),
+            (numpy.nan, numpy.nan, numpy.nan, numpy.nan, numpy.nan, numpy.nan),
+        ],
+    )
+    def test_genetic_ancestry__OK(
+        self,
+        validator_with_adata,
+        genetic_ancestry_African,
+        genetic_ancestry_East_Asian,
+        genetic_ancestry_European,
+        genetic_ancestry_Indigenous_American,
+        genetic_ancestry_Oceanian,
+        genetic_ancestry_South_Asian,
+    ):
+        """
+        genetic_ancestry_X fields must all be floats between 0 and 1 and sum to 1
+        OR they can all be NaN
+        """
+        validator = validator_with_adata
+        # Second organism in adata is not homo sapiens
+        validator.adata.obs["genetic_ancestry_African"] = [genetic_ancestry_African, float("nan")]
+        validator.adata.obs["genetic_ancestry_East_Asian"] = [genetic_ancestry_East_Asian, float("nan")]
+        validator.adata.obs["genetic_ancestry_European"] = [genetic_ancestry_European, float("nan")]
+        validator.adata.obs["genetic_ancestry_Indigenous_American"] = [
+            genetic_ancestry_Indigenous_American,
+            float("nan"),
+        ]
+        validator.adata.obs["genetic_ancestry_Oceanian"] = [genetic_ancestry_Oceanian, float("nan")]
+        validator.adata.obs["genetic_ancestry_South_Asian"] = [genetic_ancestry_South_Asian, float("nan")]
+        validator.validate_adata()
+        assert validator.errors == []
+
+    @pytest.mark.parametrize(
+        "genetic_ancestry_African, genetic_ancestry_East_Asian, genetic_ancestry_European, "
+        "genetic_ancestry_Indigenous_American, genetic_ancestry_Oceanian, genetic_ancestry_South_Asian",
+        [
+            # Non-float value of "random string"
+            (0.0, 0.0, 0.0, 1.0, 0.0, "random string"),
+            # Non-float value of True
+            (0.0, 0.0, 0.0, 1.0, 0.0, True),
+            # Non-float value of None
+            (0.0, 0.0, 0.0, 1.0, 0.0, None),
+            # Non-float value of numpy True
+            (0.0, 0.0, 0.0, 1.0, 0.0, numpy.True_),
+            # Non-float value of numpy NaN
+            (0.0, numpy.nan, 0.0, 1.0, 0.0, 0.0),
+            # One value is > 1
+            (0.0, 0.0, 1.1, 0.0, 0.0, 0.0),
+            # One value is < 0.0
+            (0.0, 0.0, -0.25, 1.0, 0.25, 0.0),
+            # Sum is > 1.0
+            (0.0, 0.1, 1.0, 0.0, 0.0, 0.0),
+            # Sum is < 1.0
+            (0.0, 0.25, 0.25, 0.25, 0.0, 0.0),
+            # Only all NaN is valid
+            (float("nan"), 0.0, 0.0, 0.0, 0.0, 0.0),
+            # Only all NaN is valid
+            (numpy.nan, 0.0, 0.0, 0.0, 0.0, 0.0),
+        ],
+    )
+    def test_genetic_ancestry__invalid(
+        self,
+        validator_with_adata,
+        genetic_ancestry_African,
+        genetic_ancestry_East_Asian,
+        genetic_ancestry_European,
+        genetic_ancestry_Indigenous_American,
+        genetic_ancestry_Oceanian,
+        genetic_ancestry_South_Asian,
+    ):
+        validator = validator_with_adata
+        # Second organism in adata is not homo sapiens
+        validator.adata.obs["genetic_ancestry_African"] = [genetic_ancestry_African, float("nan")]
+        validator.adata.obs["genetic_ancestry_East_Asian"] = [genetic_ancestry_East_Asian, float("nan")]
+        validator.adata.obs["genetic_ancestry_European"] = [genetic_ancestry_European, float("nan")]
+        validator.adata.obs["genetic_ancestry_Indigenous_American"] = [
+            genetic_ancestry_Indigenous_American,
+            float("nan"),
+        ]
+        validator.adata.obs["genetic_ancestry_Oceanian"] = [genetic_ancestry_Oceanian, float("nan")]
+        validator.adata.obs["genetic_ancestry_South_Asian"] = [genetic_ancestry_South_Asian, float("nan")]
+        validator.validate_adata()
+        assert len(validator.errors) > 0
+
+    def test_genetic_ancestry_same_donor_id(self, validator_with_adata):
+        """
+        genetic_ancestry_X fields must be the same when the donor id is the same
+        """
+        validator = validator_with_adata
+        original_donor_id_column = validator.adata.obs["donor_id"].copy()
+
+        # Second row should have identical donor id + genetic ancestry values, so this should pass validation
+        validator.adata.obs.iloc[1] = validator.adata.obs.iloc[0].values
+        validator.validate_adata()
+        assert validator.errors == []
+
+        # Update the genetic ancestry values to be different. This should now fail validation
+        validator.adata.obs["genetic_ancestry_African"] = [1.0, 0.0]
+        validator.adata.obs["genetic_ancestry_East_Asian"] = [0.0, 1.0]
+        validator.adata.obs["genetic_ancestry_European"] = [0.0, 0.0]
+        validator.adata.obs["genetic_ancestry_Indigenous_American"] = [0.0, 0.0]
+        validator.adata.obs["genetic_ancestry_Oceanian"] = [0.0, 0.0]
+        validator.adata.obs["genetic_ancestry_South_Asian"] = [0.0, 0.0]
+        validator.validate_adata()
+        assert len(validator.errors) > 0
+
+        # Change the donor id back to two different donor id's. Now, this should pass validation
+        validator.adata.obs["donor_id"] = original_donor_id_column
+        validator.validate_adata()
+        assert validator.errors == []
+
 
 class TestVar:
     """