diff --git a/biolearn/data/GP-age_model_10_cpgs.json.zip b/biolearn/data/GP-age_model_10_cpgs.json.zip
new file mode 100644
index 0000000..8a37130
Binary files /dev/null and b/biolearn/data/GP-age_model_10_cpgs.json.zip differ
diff --git a/biolearn/data/GP-age_model_30_cpgs.json.zip b/biolearn/data/GP-age_model_30_cpgs.json.zip
new file mode 100644
index 0000000..f4e3b4e
Binary files /dev/null and b/biolearn/data/GP-age_model_30_cpgs.json.zip differ
diff --git a/biolearn/data/GP-age_model_71_cpgs.json.zip b/biolearn/data/GP-age_model_71_cpgs.json.zip
new file mode 100644
index 0000000..2b4ad36
Binary files /dev/null and b/biolearn/data/GP-age_model_71_cpgs.json.zip differ
diff --git a/biolearn/data/GP-age_model_a.json.zip b/biolearn/data/GP-age_model_a.json.zip
new file mode 100644
index 0000000..4c6b3ab
Binary files /dev/null and b/biolearn/data/GP-age_model_a.json.zip differ
diff --git a/biolearn/data/GP-age_model_b.json.zip b/biolearn/data/GP-age_model_b.json.zip
new file mode 100644
index 0000000..6865ff1
Binary files /dev/null and b/biolearn/data/GP-age_model_b.json.zip differ
diff --git a/biolearn/data/GP-age_model_c.json.zip b/biolearn/data/GP-age_model_c.json.zip
new file mode 100644
index 0000000..6dbe361
Binary files /dev/null and b/biolearn/data/GP-age_model_c.json.zip differ
diff --git a/biolearn/model.py b/biolearn/model.py
index d529e53..becb7da 100644
--- a/biolearn/model.py
+++ b/biolearn/model.py
@@ -102,6 +102,272 @@ def preprocess(df):
         "output": "Age (Years)",
         "model": {"type": "LinearMethylationModel", "file": "Hannum.csv"},
     },
+    "GPAge10": {
+        "year": 2023,
+        "species": "Human",
+        "tissue": "Blood",
+        "source": "https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00211-4",
+        "output": "Age (Years)",
+        "model": {
+            "type": "GPAgeModel",
+            "file": "GP-age_model_10_cpgs.json.zip",
+            "sites": [
+                "cg16867657",
+                "cg06639320",
+                "cg04875128",
+                "cg19283806",
+                "cg07553761",
+                "cg08128734",
+                "cg12934382",
+                "cg00573770",
+                "cg23479922",
+                "cg10501210",
+            ],
+            "default_imputation": "none",
+        },
+    },
+    "GPAge30": {
+        "year": 2023,
+        "species": "Human",
+        "tissue": "Blood",
+        "source": "https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00211-4",
+        "output": "Age (Years)",
+        "model": {
+            "type": "GPAgeModel",
+            "file": "GP-age_model_30_cpgs.json.zip",
+            "sites": [
+                "cg16867657",
+                "cg22454769",
+                "cg06639320",
+                "cg04875128",
+                "cg19283806",
+                "cg24724428",
+                "cg07553761",
+                "cg24079702",
+                "cg08128734",
+                "cg12934382",
+                "cg08468401",
+                "cg20816447",
+                "cg00573770",
+                "cg06335143",
+                "cg06155229",
+                "cg03032497",
+                "cg06619077",
+                "cg17804348",
+                "cg00329615",
+                "cg23479922",
+                "cg10501210",
+                "cg19991948",
+                "cg27312979",
+                "cg23186333",
+                "cg25413977",
+                "cg22078805",
+                "cg17621438",
+                "cg21878650",
+                "cg04503319",
+                "cg09809672",
+            ],
+            "default_imputation": "none",
+        },
+    },
+    "GPAge71": {
+        "year": 2023,
+        "species": "Human",
+        "tissue": "Blood",
+        "source": "https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00211-4",
+        "output": "Age (Years)",
+        "model": {
+            "type": "GPAgeModel",
+            "file": "GP-age_model_71_cpgs.json.zip",
+            "sites": [
+                "cg16867657",
+                "cg22454769",
+                "cg06639320",
+                "cg04875128",
+                "cg19283806",
+                "cg24724428",
+                "cg07553761",
+                "cg24079702",
+                "cg14556683",
+                "cg07547549",
+                "cg08128734",
+                "cg23500537",
+                "cg12934382",
+                "cg08468401",
+                "cg05404236",
+                "cg20816447",
+                "cg17110586",
+                "cg24466241",
+                "cg18473521",
+                "cg00573770",
+                "cg06335143",
+                "cg06155229",
+                "cg03032497",
+                "cg12899747",
+                "cg06619077",
+                "cg17804348",
+                "cg00329615",
+                "cg23479922",
+                "cg09017434",
+                "cg10501210",
+                "cg19991948",
+                "cg03738025",
+                "cg27312979",
+                "cg14766700",
+                "cg23186333",
+                "cg21184711",
+                "cg22730004",
+                "cg19421125",
+                "cg15894389",
+                "cg10835286",
+                "cg25413977",
+                "cg11807280",
+                "cg22078805",
+                "cg02872426",
+                "cg00303541",
+                "cg04295144",
+                "cg19729744",
+                "cg24794228",
+                "cg17621438",
+                "cg05017994",
+                "cg21878650",
+                "cg15804973",
+                "cg07797372",
+                "cg27152890",
+                "cg17471939",
+                "cg18887458",
+                "cg15243034",
+                "cg04503319",
+                "cg03350900",
+                "cg09809672",
+                "cg14577707",
+                "cg24892069",
+                "cg00602811",
+                "cg05991454",
+                "cg23126342",
+                "cg09988805",
+                "cg19344626",
+                "cg20059012",
+                "cg21826784",
+                "cg07164639",
+                "cg11693709",
+            ],
+            "default_imputation": "none",
+        },
+    },
+    "GPAgeA": {
+        "year": 2023,
+        "species": "Human",
+        "tissue": "Blood",
+        "source": "https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00211-4",
+        "output": "Age (Years)",
+        "model": {
+            "type": "GPAgeModel",
+            "file": "GP-age_model_a.json.zip",
+            "sites": [
+                "cg16867657",
+                "cg19283806",
+                "cg24724428",
+                "cg07547549",
+                "cg05404236",
+                "cg06155229",
+                "cg03738025",
+                "cg14766700",
+                "cg23186333",
+                "cg21184711",
+                "cg15894389",
+                "cg02872426",
+                "cg15804973",
+                "cg18887458",
+                "cg15243034",
+                "cg03350900",
+                "cg23126342",
+                "cg07164639",
+            ],
+            "default_imputation": "none",
+        },
+    },
+    "GPAgeB": {
+        "year": 2023,
+        "species": "Human",
+        "tissue": "Blood",
+        "source": "https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00211-4",
+        "output": "Age (Years)",
+        "model": {
+            "type": "GPAgeModel",
+            "file": "GP-age_model_b.json.zip",
+            "sites": [
+                "cg22454769",
+                "cg06639320",
+                "cg07553761",
+                "cg24079702",
+                "cg08128734",
+                "cg12934382",
+                "cg08468401",
+                "cg20816447",
+                "cg24466241",
+                "cg00573770",
+                "cg06335143",
+                "cg03032497",
+                "cg12899747",
+                "cg06619077",
+                "cg17804348",
+                "cg00329615",
+                "cg10501210",
+                "cg22730004",
+                "cg10835286",
+                "cg25413977",
+                "cg11807280",
+                "cg22078805",
+                "cg00303541",
+                "cg19729744",
+                "cg07797372",
+                "cg17471939",
+                "cg09809672",
+                "cg14577707",
+                "cg00602811",
+                "cg05991454",
+                "cg09988805",
+                "cg21826784",
+            ],
+            "default_imputation": "none",
+        },
+    },
+    "GPAgeC": {
+        "year": 2023,
+        "species": "Human",
+        "tissue": "Blood",
+        "source": "https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00211-4",
+        "output": "Age (Years)",
+        "model": {
+            "type": "GPAgeModel",
+            "file": "GP-age_model_c.json.zip",
+            "sites": [
+                "cg04875128",
+                "cg14556683",
+                "cg23500537",
+                "cg17110586",
+                "cg18473521",
+                "cg23479922",
+                "cg09017434",
+                "cg19991948",
+                "cg27312979",
+                "cg19421125",
+                "cg04295144",
+                "cg24794228",
+                "cg17621438",
+                "cg05017994",
+                "cg21878650",
+                "cg27152890",
+                "cg04503319",
+                "cg24892069",
+                "cg19344626",
+                "cg20059012",
+                "cg11693709",
+            ],
+            "default_imputation": "none",
+        },
+    },
     "Lin": {
         "year": 2016,
         "species": "Human",
@@ -1930,6 +2196,56 @@ def methylation_sites(self):
         return self.required_cpgs if self.required_cpgs else []
 
 
+class GPAgeModel:
+    """Gaussian Process regression model for age prediction (GP-age clock)."""
+
+    def __init__(self, model_file, sites):
+        self.model_file = model_file
+        self._sites = sites
+        self._model = None
+        self._training_means = None
+
+    def _load_model(self):
+        if self._model is not None:
+            return
+        try:
+            import GPy
+        except ImportError:
+            raise ImportError(
+                "GPy is required for GP-age models. "
+                "Install with: pip install biolearn[gpage]"
+            )
+        model_path = get_data_file(self.model_file)
+        self._model = GPy.models.GPRegression.load_model(model_path)
+        self._training_means = np.mean(self._model.X, axis=0)
+
+    @classmethod
+    def from_definition(cls, clock_definition):
+        model_def = clock_definition["model"]
+        return cls(model_def["file"], model_def["sites"])
+
+    def predict(self, geo_data):
+        self._load_model()
+        dnam = geo_data.dnam
+        methylation_data = pd.DataFrame(
+            np.nan, index=dnam.columns, columns=self._sites
+        )
+        existing = [s for s in self._sites if s in dnam.index]
+        for site in existing:
+            methylation_data[site] = dnam.loc[site].values
+        X = methylation_data.values
+        if np.isnan(X).any():
+            for i, mean_val in enumerate(self._training_means):
+                X[np.isnan(X[:, i]), i] = mean_val
+        predictions = self._model.predict(X)[0].squeeze()
+        return pd.DataFrame(
+            predictions, index=dnam.columns, columns=["Predicted"]
+        )
+
+    def methylation_sites(self):
+        return self._sites
+
+
 class ImputationDecorator:
     def __init__(self, clock, imputation_method):
         self.clock = clock
diff --git a/biolearn/model_gallery.py b/biolearn/model_gallery.py
index 2037d35..2ca8971 100644
--- a/biolearn/model_gallery.py
+++ b/biolearn/model_gallery.py
@@ -13,6 +13,7 @@
     PCLinearTransformationModel,
     MiAgeModel,
     HurdleAPIModel,
+    GPAgeModel,
 )
 from biolearn.imputation import (
     hybrid_impute,
@@ -40,6 +41,7 @@ class ModelGallery:
         "PCLinearTransformationModel": PCLinearTransformationModel.from_definition,
         "MiAgeModel": MiAgeModel.from_definition,
         "HurdleAPIModel": HurdleAPIModel.from_definition,
+        "GPAgeModel": GPAgeModel.from_definition,
     }
 
     def __init__(self, models=model_definitions):
diff --git a/biolearn/test/data/expected_model_outputs/GPAge10.csv b/biolearn/test/data/expected_model_outputs/GPAge10.csv
new file mode 100644
index 0000000..0f34e5b
--- /dev/null
+++ b/biolearn/test/data/expected_model_outputs/GPAge10.csv
@@ -0,0 +1,11 @@
+,Predicted
+GSM1009660,31.89704727031237
+GSM1009661,23.903188943555232
+GSM1009662,74.73425675682917
+GSM1009663,18.578239549985653
+GSM1009664,26.700354717080252
+GSM1009665,25.511381170918273
+GSM1009666,15.411114544706832
+GSM1009667,24.447221724018352
+GSM1009668,20.506828510955785
+GSM1009669,19.82849328756421
diff --git a/biolearn/test/data/expected_model_outputs/GPAge30.csv b/biolearn/test/data/expected_model_outputs/GPAge30.csv
new file mode 100644
index 0000000..83ab435
--- /dev/null
+++ b/biolearn/test/data/expected_model_outputs/GPAge30.csv
@@ -0,0 +1,11 @@
+,Predicted
+GSM1009660,34.77582182754103
+GSM1009661,27.320792200007833
+GSM1009662,68.58154920240196
+GSM1009663,21.221924955458782
+GSM1009664,27.543776269924667
+GSM1009665,26.083311597312022
+GSM1009666,21.77038103306041
+GSM1009667,22.65136967579668
+GSM1009668,20.18206633237497
+GSM1009669,19.249568421988524
diff --git a/biolearn/test/data/expected_model_outputs/GPAge71.csv b/biolearn/test/data/expected_model_outputs/GPAge71.csv
new file mode 100644
index 0000000..3db8323
--- /dev/null
+++ b/biolearn/test/data/expected_model_outputs/GPAge71.csv
@@ -0,0 +1,11 @@
+,Predicted
+GSM1009660,33.397193679701225
+GSM1009661,27.301255786703674
+GSM1009662,70.7486930314503
+GSM1009663,22.43499389016172
+GSM1009664,28.47807323841766
+GSM1009665,27.82262163954521
+GSM1009666,21.118096364885393
+GSM1009667,20.766219965455534
+GSM1009668,21.09132109177609
+GSM1009669,20.959442794228888
diff --git a/biolearn/test/data/expected_model_outputs/GPAgeA.csv b/biolearn/test/data/expected_model_outputs/GPAgeA.csv
new file mode 100644
index 0000000..500d58f
--- /dev/null
+++ b/biolearn/test/data/expected_model_outputs/GPAgeA.csv
@@ -0,0 +1,11 @@
+,Predicted
+GSM1009660,30.223491203897595
+GSM1009661,23.773177186897197
+GSM1009662,68.55257703888005
+GSM1009663,17.32071283788694
+GSM1009664,29.405484234863785
+GSM1009665,24.859859187152804
+GSM1009666,20.806872201548995
+GSM1009667,23.729867009949583
+GSM1009668,23.654339109005498
+GSM1009669,17.604339500089328
diff --git a/biolearn/test/data/expected_model_outputs/GPAgeB.csv b/biolearn/test/data/expected_model_outputs/GPAgeB.csv
new file mode 100644
index 0000000..674e022
--- /dev/null
+++ b/biolearn/test/data/expected_model_outputs/GPAgeB.csv
@@ -0,0 +1,11 @@
+,Predicted
+GSM1009660,38.053207193340114
+GSM1009661,26.838589826778367
+GSM1009662,65.40032765179201
+GSM1009663,21.929513088767344
+GSM1009664,28.204459426349594
+GSM1009665,28.490788190605667
+GSM1009666,18.970951609583576
+GSM1009667,22.634972677172925
+GSM1009668,16.882486073155313
+GSM1009669,21.025250055961784
diff --git a/biolearn/test/data/expected_model_outputs/GPAgeC.csv b/biolearn/test/data/expected_model_outputs/GPAgeC.csv
new file mode 100644
index 0000000..f092065
--- /dev/null
+++ b/biolearn/test/data/expected_model_outputs/GPAgeC.csv
@@ -0,0 +1,11 @@
+,Predicted
+GSM1009660,35.872956172522194
+GSM1009661,31.745596307927535
+GSM1009662,62.699845394737785
+GSM1009663,19.563242250310083
+GSM1009664,32.938776035415444
+GSM1009665,31.152218020521573
+GSM1009666,25.678496268631186
+GSM1009667,22.626146308618416
+GSM1009668,20.353462400398076
+GSM1009669,23.768585991739368
diff --git a/pyproject.toml b/pyproject.toml
index 708291d..1f248cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -55,6 +55,8 @@ test = [
 plotting = ["matplotlib>=3.3.0", "seaborn>=0.13.0"]
 # Examples external dependencies
 examples = ["lifelines"]
+# Required for GP-age clock (Gaussian Process regression)
+gpage = ["gpy>=1.10.0"]
 # Requirements necessary for building the documentation
 doc = [
     "biolearn[plotting,examples]",
@@ -75,7 +77,7 @@ doc = [
 ]
 # A combination of dependencies useful for developers
 dev = [
-	"biolearn[test,plotting,examples,doc]",
+	"biolearn[test,plotting,examples,doc,gpage]",
 	"jupyterlab",
 	"scikit-learn",
 	"torch",