diff --git a/smaca/sma.py b/smaca/sma.py
index 954fe43..c14a494 100644
--- a/smaca/sma.py
+++ b/smaca/sma.py
@@ -120,15 +120,59 @@ def __init__(self, bam_list, ref, n_jobs=1):
             self.dup_id[:] = dup_id_memmap[:]
 
         self.r_ij = self.D1_ij + self.D2_ij
-        #TODO: consider using only "the bests" HK
-        self.z_ik = self.c_ix.sum(axis=1).reshape((self.n_bam, 1)) / self.H_ik
-        self.std_k = np.std(self.H_ik, axis=0)
-        self.std_i = np.std(self.H_ik, axis=1)
-        self.zmean_k = self.z_ik.sum(axis=0) / self.n_bam
-        self.theta_i = (self.z_ik / self.zmean_k).sum(axis=1) / len(
-            C.POSITIONS[ref]["GENES"])
-        self.pi_ij = self.theta_i.reshape(
-            (self.n_bam, 1)) * (self.D1_ij / self.r_ij)
+
+        # Detect samples with zero-coverage control genes and warn
+        gene_names = list(C.POSITIONS[ref]["GENES"].keys())
+        zero_cov_samples = np.any(self.H_ik == 0, axis=1)
+
+        for i in range(self.n_bam):
+            if zero_cov_samples[i]:
+                zero_genes = [gene_names[k] for k in range(len(gene_names))
+                              if self.H_ik[i, k] == 0]
+                click.echo(
+                    f"Warning: Sample '{self.bam_list[i]}' has zero coverage"
+                    f" in control gene(s): {', '.join(zero_genes)}. "
+                    f"Results for this sample will be set to NaN.",
+                    err=True
+                )
+
+        # Compute statistics with safe division
+        with np.errstate(divide='ignore', invalid='ignore'):
+            self.z_ik = np.where(
+                self.H_ik > 0,
+                self.c_ix.sum(axis=1).reshape((self.n_bam, 1)) / self.H_ik,
+                0.0
+            )
+            self.std_k = np.std(self.H_ik, axis=0)
+            self.std_i = np.std(self.H_ik, axis=1)
+            # Exclude zero-coverage samples from zmean_k so they don't
+            # corrupt statistics for healthy samples
+            valid_mask = ~zero_cov_samples
+            n_valid = valid_mask.sum()
+            if n_valid > 0:
+                self.zmean_k = (
+                    self.z_ik[valid_mask].sum(axis=0) / n_valid
+                )
+            else:
+                self.zmean_k = np.zeros(self.z_ik.shape[1])
+            self.theta_i = np.where(
+                self.zmean_k > 0,
+                self.z_ik / self.zmean_k,
+                0.0
+            ).sum(axis=1) / len(C.POSITIONS[ref]["GENES"])
+            self.pi_ij = np.where(
+                self.r_ij > 0,
+                self.theta_i.reshape((self.n_bam, 1))
+                * (self.D1_ij / np.where(self.r_ij > 0, self.r_ij, 1.0)),
+                np.nan
+            )
+
+        # Overwrite all computed stats to NaN for zero-coverage samples
+        for i in range(self.n_bam):
+            if zero_cov_samples[i]:
+                self.pi_ij[i, :] = np.nan
+                self.theta_i[i] = np.nan
+                self.z_ik[i, :] = np.nan
 
     def write_stats(self, output_file):
         """
diff --git a/smaca/test_results.py b/smaca/test_results.py
index b3c059c..df9648b 100644
--- a/smaca/test_results.py
+++ b/smaca/test_results.py
@@ -67,13 +67,17 @@ def test_bamclass(self):
     def test_sma_stats(self):
         s = SmaCalculator(BAM_LIST, ref=C.REF_HG19)
 
+        # Sample 0 (HG002) has full coverage; sample 1 (HG007) has zero
+        # coverage in 6 control genes and is excluded from zmean_k
         np.testing.assert_array_almost_equal(
-            s.pi_ij[0], [0.8027337510925755, 0.7296692915157783, 0.6580751624125392])
-        self.assertAlmostEqual(s.zmean_k[0], 0.6135924580475719)
+            s.pi_ij[0], [0.65682657, 0.59704251, 0.53846154])
+        self.assertAlmostEqual(s.zmean_k[0], 0.9887272198987905)
         np.testing.assert_array_almost_equal(
             s.std_i, [21.18266345, 5.07109509])
         self.assertAlmostEqual(s.std_k[0], 15.516854393351194)
         self.assertEqual(s.dup_id[0][0], b'T [[0], [0], [0], [106]]')
+        # Sample 1 should have NaN due to zero-coverage genes
+        self.assertTrue(np.all(np.isnan(s.pi_ij[1])))
 
     def test_get_chr_prefix(self):
         sam_file = pysam.AlignmentFile(BAM_hg19, "rb")
@@ -110,5 +114,19 @@ def test_hg19_hg38_coverages(self):
                 err_msg=";".join(C.POSITIONS[C.REF_HG19][ranges]))
 
 
+    def test_zero_coverage_gene_warning(self):
+        """Test that zero-coverage control genes produce warnings and NaN."""
+        s = SmaCalculator(BAM_LIST, ref=C.REF_HG19)
+
+        # Sample 0 (HG002) has full coverage — finite results
+        self.assertTrue(np.all(np.isfinite(s.pi_ij[0])))
+        self.assertTrue(np.isfinite(s.theta_i[0]))
+
+        # Sample 1 (HG007) has zero coverage in some control genes — NaN
+        self.assertTrue(np.all(np.isnan(s.pi_ij[1])))
+        self.assertTrue(np.isnan(s.theta_i[1]))
+        self.assertTrue(np.all(np.isnan(s.z_ik[1])))
+
+
 if __name__ == '__main__':
     unittest.main()