Repository: monarch-initiative/gpsea (language: python)

Issue:
Create a predicate that tests if a variant affects a Region given by the User.
Rather than only allowing users to pick a specific domain, create a predicate that will allow a user to give a region of amino acids on the protein and look for whether a variant affects an amino acid in that region. 

Make sure there is a matching predicate for comparing two regions and for comparing a recessive variant. 


Discussion / hints:

Fix patch:
diff --git a/notebooks/ANKRD11/KBG.ipynb b/notebooks/ANKRD11/KBG.ipynb
index eb067748..b23ae8a9 100644
--- a/notebooks/ANKRD11/KBG.ipynb
+++ b/notebooks/ANKRD11/KBG.ipynb
@@ -88,7 +88,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Patients Created: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 337/337 [00:00<00:00, 371.30it/s]\n",
+      "Patients Created: 100%|████████████████████████████████████████████████████| 337/337 [00:00<00:00, 407.92it/s]\n",
       "Validated under none policy\n",
       "Showing errors and warnings\n",
       "337 phenopacket(s) found at `phenopackets`\n",
@@ -714,26 +714,26 @@
        "                    </tr>\n",
        "                \n",
        "                <tr>\n",
-       "                    <td class=\"lft\">16_89283314_89283318_CCTTT_C</td>\n",
-       "                    <td>c.3224_3227del</td>\n",
+       "                    <td class=\"lft\">16_89274958_89274958_C_G</td>\n",
+       "                    <td>c.7570-1G>C</td>\n",
        "                    <td>3</td>\n",
        "                    </tr>\n",
        "                \n",
        "                <tr>\n",
-       "                    <td class=\"lft\">16_89274958_89274958_C_G</td>\n",
-       "                    <td>c.7570-1G>C</td>\n",
+       "                    <td class=\"lft\">16_89282136_89282136_C_T</td>\n",
+       "                    <td>c.4406G>A</td>\n",
        "                    <td>3</td>\n",
        "                    </tr>\n",
        "                \n",
        "                <tr>\n",
-       "                    <td class=\"lft\">16_89284358_89284360_GAT_G</td>\n",
-       "                    <td>c.2182_2183del</td>\n",
+       "                    <td class=\"lft\">16_89283314_89283318_CCTTT_C</td>\n",
+       "                    <td>c.3224_3227del</td>\n",
        "                    <td>3</td>\n",
        "                    </tr>\n",
        "                \n",
        "                <tr>\n",
-       "                    <td class=\"lft\">16_89282710_89282710_T_A</td>\n",
-       "                    <td>c.3832A>T</td>\n",
+       "                    <td class=\"lft\">16_89284358_89284360_GAT_G</td>\n",
+       "                    <td>c.2182_2183del</td>\n",
        "                    <td>3</td>\n",
        "                    </tr>\n",
        "                \n",
@@ -830,7 +830,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x2000 with 1 Axes>"
       ]
@@ -861,6 +861,7 @@
    "source": [
     "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n",
     "from genophenocorr.analysis.predicate import PatientCategories\n",
+    "from genophenocorr.model.genome import Region\n",
     "\n",
     "analysis_config = CohortAnalysisConfiguration()\n",
     "analysis_config.missing_implies_excluded = False\n",
@@ -974,46 +975,46 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormality of the forehead [HP:0000290]</th>\n",
-       "      <td>40/40</td>\n",
+       "      <th>Abnormal muscle tone [HP:0003808]</th>\n",
+       "      <td>14/14</td>\n",
        "      <td>100%</td>\n",
-       "      <td>15/15</td>\n",
+       "      <td>7/7</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal musculoskeletal physiology [HP:0011843]</th>\n",
-       "      <td>37/37</td>\n",
+       "      <th>Growth delay [HP:0001510]</th>\n",
+       "      <td>75/75</td>\n",
        "      <td>100%</td>\n",
-       "      <td>17/17</td>\n",
+       "      <td>45/45</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal dental morphology [HP:0006482]</th>\n",
-       "      <td>115/115</td>\n",
+       "      <th>Abnormality of head or neck [HP:0000152]</th>\n",
+       "      <td>153/153</td>\n",
        "      <td>100%</td>\n",
-       "      <td>55/55</td>\n",
+       "      <td>74/74</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormality of limbs [HP:0040064]</th>\n",
-       "      <td>95/95</td>\n",
+       "      <th>Non-motor seizure [HP:0033259]</th>\n",
+       "      <td>1/1</td>\n",
        "      <td>100%</td>\n",
-       "      <td>60/60</td>\n",
+       "      <td>2/2</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal eye morphology [HP:0012372]</th>\n",
-       "      <td>34/34</td>\n",
+       "      <th>Abnormality of the frontal hairline [HP:0000599]</th>\n",
+       "      <td>40/40</td>\n",
        "      <td>100%</td>\n",
-       "      <td>25/25</td>\n",
+       "      <td>15/15</td>\n",
        "      <td>100%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
@@ -1032,11 +1033,11 @@
        "Low anterior hairline [HP:0000294]                  40/58     69%  15/30   \n",
        "Intellectual disability [HP:0001249]               99/119     83%  59/64   \n",
        "...                                                   ...     ...    ...   \n",
-       "Abnormality of the forehead [HP:0000290]            40/40    100%  15/15   \n",
-       "Abnormal musculoskeletal physiology [HP:0011843]    37/37    100%  17/17   \n",
-       "Abnormal dental morphology [HP:0006482]           115/115    100%  55/55   \n",
-       "Abnormality of limbs [HP:0040064]                   95/95    100%  60/60   \n",
-       "Abnormal eye morphology [HP:0012372]                34/34    100%  25/25   \n",
+       "Abnormal muscle tone [HP:0003808]                   14/14    100%    7/7   \n",
+       "Growth delay [HP:0001510]                           75/75    100%  45/45   \n",
+       "Abnormality of head or neck [HP:0000152]          153/153    100%  74/74   \n",
+       "Non-motor seizure [HP:0033259]                        1/1    100%    2/2   \n",
+       "Abnormality of the frontal hairline [HP:0000599]    40/40    100%  15/15   \n",
        "\n",
        "FRAMESHIFT_VARIANT on NM_013275.6                                   \\\n",
        "                                                 Percent   p value   \n",
@@ -1046,11 +1047,11 @@
        "Low anterior hairline [HP:0000294]                   50%  0.105274   \n",
        "Intellectual disability [HP:0001249]                 92%  0.115195   \n",
        "...                                                  ...       ...   \n",
-       "Abnormality of the forehead [HP:0000290]            100%  1.000000   \n",
-       "Abnormal musculoskeletal physiology [HP:0011843]    100%  1.000000   \n",
-       "Abnormal dental morphology [HP:0006482]             100%  1.000000   \n",
-       "Abnormality of limbs [HP:0040064]                   100%  1.000000   \n",
-       "Abnormal eye morphology [HP:0012372]                100%  1.000000   \n",
+       "Abnormal muscle tone [HP:0003808]                   100%  1.000000   \n",
+       "Growth delay [HP:0001510]                           100%  1.000000   \n",
+       "Abnormality of head or neck [HP:0000152]            100%  1.000000   \n",
+       "Non-motor seizure [HP:0033259]                      100%  1.000000   \n",
+       "Abnormality of the frontal hairline [HP:0000599]    100%  1.000000   \n",
        "\n",
        "FRAMESHIFT_VARIANT on NM_013275.6                                   \n",
        "                                                 Corrected p value  \n",
@@ -1060,11 +1061,11 @@
        "Low anterior hairline [HP:0000294]                             1.0  \n",
        "Intellectual disability [HP:0001249]                           1.0  \n",
        "...                                                            ...  \n",
-       "Abnormality of the forehead [HP:0000290]                       1.0  \n",
-       "Abnormal musculoskeletal physiology [HP:0011843]               1.0  \n",
-       "Abnormal dental morphology [HP:0006482]                        1.0  \n",
-       "Abnormality of limbs [HP:0040064]                              1.0  \n",
-       "Abnormal eye morphology [HP:0012372]                           1.0  \n",
+       "Abnormal muscle tone [HP:0003808]                              1.0  \n",
+       "Growth delay [HP:0001510]                                      1.0  \n",
+       "Abnormality of head or neck [HP:0000152]                       1.0  \n",
+       "Non-motor seizure [HP:0033259]                                 1.0  \n",
+       "Abnormality of the frontal hairline [HP:0000599]               1.0  \n",
        "\n",
        "[190 rows x 6 columns]"
       ]
@@ -1392,47 +1393,29 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Febrile seizure (within the age range of 3 months to 6 years) [HP:0002373]</th>\n",
-       "      <td>3/5</td>\n",
-       "      <td>60%</td>\n",
-       "      <td>2/3</td>\n",
-       "      <td>67%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Strabismus [HP:0000486]</th>\n",
-       "      <td>12/51</td>\n",
-       "      <td>24%</td>\n",
-       "      <td>4/19</td>\n",
-       "      <td>21%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Anteverted nares [HP:0000463]</th>\n",
-       "      <td>41/58</td>\n",
-       "      <td>71%</td>\n",
-       "      <td>22/30</td>\n",
-       "      <td>73%</td>\n",
+       "      <th>Attention deficit hyperactivity disorder [HP:0007018]</th>\n",
+       "      <td>34/44</td>\n",
+       "      <td>77%</td>\n",
+       "      <td>19/24</td>\n",
+       "      <td>79%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Long philtrum [HP:0000343]</th>\n",
-       "      <td>66/82</td>\n",
-       "      <td>80%</td>\n",
-       "      <td>38/48</td>\n",
-       "      <td>79%</td>\n",
+       "      <th>Generalized non-motor (absence) seizure [HP:0002121]</th>\n",
+       "      <td>1/4</td>\n",
+       "      <td>25%</td>\n",
+       "      <td>2/4</td>\n",
+       "      <td>50%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Attention deficit hyperactivity disorder [HP:0007018]</th>\n",
-       "      <td>34/44</td>\n",
-       "      <td>77%</td>\n",
-       "      <td>19/24</td>\n",
-       "      <td>79%</td>\n",
+       "      <th>Hypotonia [HP:0001252]</th>\n",
+       "      <td>14/35</td>\n",
+       "      <td>40%</td>\n",
+       "      <td>7/18</td>\n",
+       "      <td>39%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -1446,20 +1429,20 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Generalized non-motor (absence) seizure [HP:0002121]</th>\n",
-       "      <td>1/4</td>\n",
-       "      <td>25%</td>\n",
-       "      <td>2/4</td>\n",
-       "      <td>50%</td>\n",
+       "      <th>Anteverted nares [HP:0000463]</th>\n",
+       "      <td>41/58</td>\n",
+       "      <td>71%</td>\n",
+       "      <td>22/30</td>\n",
+       "      <td>73%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Hypotonia [HP:0001252]</th>\n",
-       "      <td>14/35</td>\n",
-       "      <td>40%</td>\n",
-       "      <td>7/18</td>\n",
-       "      <td>39%</td>\n",
+       "      <th>Febrile seizure (within the age range of 3 months to 6 years) [HP:0002373]</th>\n",
+       "      <td>3/5</td>\n",
+       "      <td>60%</td>\n",
+       "      <td>2/3</td>\n",
+       "      <td>67%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -1473,6 +1456,24 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>Long philtrum [HP:0000343]</th>\n",
+       "      <td>66/82</td>\n",
+       "      <td>80%</td>\n",
+       "      <td>38/48</td>\n",
+       "      <td>79%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Strabismus [HP:0000486]</th>\n",
+       "      <td>12/51</td>\n",
+       "      <td>24%</td>\n",
+       "      <td>4/19</td>\n",
+       "      <td>21%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Generalized-onset seizure [HP:0002197]</th>\n",
        "      <td>8/9</td>\n",
        "      <td>89%</td>\n",
@@ -1515,15 +1516,15 @@
        "Protruding ear [HP:0000411]                           32/44     73%  16/23   \n",
        "Autistic behavior [HP:0000729]                        26/48     54%  14/24   \n",
        "Macrodontia [HP:0001572]                            115/145     79%  55/68   \n",
-       "Febrile seizure (within the age range of 3 mont...      3/5     60%    2/3   \n",
-       "Strabismus [HP:0000486]                               12/51     24%   4/19   \n",
-       "Anteverted nares [HP:0000463]                         41/58     71%  22/30   \n",
-       "Long philtrum [HP:0000343]                            66/82     80%  38/48   \n",
        "Attention deficit hyperactivity disorder [HP:00...    34/44     77%  19/24   \n",
-       "Prominent nose [HP:0000448]                           12/28     43%   5/13   \n",
        "Generalized non-motor (absence) seizure [HP:000...      1/4     25%    2/4   \n",
        "Hypotonia [HP:0001252]                                14/35     40%   7/18   \n",
+       "Prominent nose [HP:0000448]                           12/28     43%   5/13   \n",
+       "Anteverted nares [HP:0000463]                         41/58     71%  22/30   \n",
+       "Febrile seizure (within the age range of 3 mont...      3/5     60%    2/3   \n",
        "Global developmental delay [HP:0001263]               86/91     95%  46/48   \n",
+       "Long philtrum [HP:0000343]                            66/82     80%  38/48   \n",
+       "Strabismus [HP:0000486]                               12/51     24%   4/19   \n",
        "Generalized-onset seizure [HP:0002197]                  8/9     89%    4/5   \n",
        "\n",
        "FRAMESHIFT_VARIANT on NM_013275.6                                     \\\n",
@@ -1555,15 +1556,15 @@
        "Protruding ear [HP:0000411]                            70%  0.783007   \n",
        "Autistic behavior [HP:0000729]                         58%  0.804713   \n",
        "Macrodontia [HP:0001572]                               81%  0.856244   \n",
-       "Febrile seizure (within the age range of 3 mont...     67%  1.000000   \n",
-       "Strabismus [HP:0000486]                                21%  1.000000   \n",
-       "Anteverted nares [HP:0000463]                          73%  1.000000   \n",
-       "Long philtrum [HP:0000343]                             79%  1.000000   \n",
        "Attention deficit hyperactivity disorder [HP:00...     79%  1.000000   \n",
-       "Prominent nose [HP:0000448]                            38%  1.000000   \n",
        "Generalized non-motor (absence) seizure [HP:000...     50%  1.000000   \n",
        "Hypotonia [HP:0001252]                                 39%  1.000000   \n",
+       "Prominent nose [HP:0000448]                            38%  1.000000   \n",
+       "Anteverted nares [HP:0000463]                          73%  1.000000   \n",
+       "Febrile seizure (within the age range of 3 mont...     67%  1.000000   \n",
        "Global developmental delay [HP:0001263]                96%  1.000000   \n",
+       "Long philtrum [HP:0000343]                             79%  1.000000   \n",
+       "Strabismus [HP:0000486]                                21%  1.000000   \n",
        "Generalized-onset seizure [HP:0002197]                 80%  1.000000   \n",
        "\n",
        "FRAMESHIFT_VARIANT on NM_013275.6                                     \n",
@@ -1595,15 +1596,15 @@
        "Protruding ear [HP:0000411]                                 1.000000  \n",
        "Autistic behavior [HP:0000729]                              1.000000  \n",
        "Macrodontia [HP:0001572]                                    1.000000  \n",
-       "Febrile seizure (within the age range of 3 mont...          1.000000  \n",
-       "Strabismus [HP:0000486]                                     1.000000  \n",
-       "Anteverted nares [HP:0000463]                               1.000000  \n",
-       "Long philtrum [HP:0000343]                                  1.000000  \n",
        "Attention deficit hyperactivity disorder [HP:00...          1.000000  \n",
-       "Prominent nose [HP:0000448]                                 1.000000  \n",
        "Generalized non-motor (absence) seizure [HP:000...          1.000000  \n",
        "Hypotonia [HP:0001252]                                      1.000000  \n",
+       "Prominent nose [HP:0000448]                                 1.000000  \n",
+       "Anteverted nares [HP:0000463]                               1.000000  \n",
+       "Febrile seizure (within the age range of 3 mont...          1.000000  \n",
        "Global developmental delay [HP:0001263]                     1.000000  \n",
+       "Long philtrum [HP:0000343]                                  1.000000  \n",
+       "Strabismus [HP:0000486]                                     1.000000  \n",
        "Generalized-onset seizure [HP:0002197]                      1.000000  "
       ]
      },
@@ -2016,15 +2017,6 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Febrile seizure (within the age range of 3 months to 6 years) [HP:0002373]</th>\n",
-       "      <td>1/1</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>4/7</td>\n",
-       "      <td>57%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>Intrauterine growth retardation [HP:0001511]</th>\n",
        "      <td>1/7</td>\n",
        "      <td>14%</td>\n",
@@ -2034,20 +2026,11 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Strabismus [HP:0000486]</th>\n",
-       "      <td>2/10</td>\n",
-       "      <td>20%</td>\n",
-       "      <td>14/60</td>\n",
-       "      <td>23%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Inguinal hernia [HP:0000023]</th>\n",
-       "      <td>0/4</td>\n",
-       "      <td>0%</td>\n",
-       "      <td>6/44</td>\n",
-       "      <td>14%</td>\n",
+       "      <th>Prominent nose [HP:0000448]</th>\n",
+       "      <td>2/4</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>15/37</td>\n",
+       "      <td>41%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -2061,6 +2044,24 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>Microcephaly [HP:0000252]</th>\n",
+       "      <td>4/16</td>\n",
+       "      <td>25%</td>\n",
+       "      <td>26/97</td>\n",
+       "      <td>27%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Febrile seizure (within the age range of 3 months to 6 years) [HP:0002373]</th>\n",
+       "      <td>1/1</td>\n",
+       "      <td>100%</td>\n",
+       "      <td>4/7</td>\n",
+       "      <td>57%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Focal-onset seizure [HP:0007359]</th>\n",
        "      <td>1/1</td>\n",
        "      <td>100%</td>\n",
@@ -2070,6 +2071,15 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>Inguinal hernia [HP:0000023]</th>\n",
+       "      <td>0/4</td>\n",
+       "      <td>0%</td>\n",
+       "      <td>6/44</td>\n",
+       "      <td>14%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Cryptorchidism [HP:0000028]</th>\n",
        "      <td>5/12</td>\n",
        "      <td>42%</td>\n",
@@ -2079,15 +2089,6 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Prominent nose [HP:0000448]</th>\n",
-       "      <td>2/4</td>\n",
-       "      <td>50%</td>\n",
-       "      <td>15/37</td>\n",
-       "      <td>41%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>Macrodontia [HP:0001572]</th>\n",
        "      <td>22/27</td>\n",
        "      <td>81%</td>\n",
@@ -2097,11 +2098,11 @@
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Microcephaly [HP:0000252]</th>\n",
-       "      <td>4/16</td>\n",
-       "      <td>25%</td>\n",
-       "      <td>26/97</td>\n",
-       "      <td>27%</td>\n",
+       "      <th>Strabismus [HP:0000486]</th>\n",
+       "      <td>2/10</td>\n",
+       "      <td>20%</td>\n",
+       "      <td>14/60</td>\n",
+       "      <td>23%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -2138,16 +2139,16 @@
        "Hypertelorism [HP:0000316]                                5/10     50%   \n",
        "Feeding difficulties [HP:0011968]                         6/15     40%   \n",
        "Delayed skeletal maturation [HP:0002750]                 10/20     50%   \n",
-       "Febrile seizure (within the age range of 3 mont...         1/1    100%   \n",
        "Intrauterine growth retardation [HP:0001511]               1/7     14%   \n",
-       "Strabismus [HP:0000486]                                   2/10     20%   \n",
-       "Inguinal hernia [HP:0000023]                               0/4      0%   \n",
+       "Prominent nose [HP:0000448]                                2/4     50%   \n",
        "Triangular face [HP:0000325]                               6/8     75%   \n",
+       "Microcephaly [HP:0000252]                                 4/16     25%   \n",
+       "Febrile seizure (within the age range of 3 mont...         1/1    100%   \n",
        "Focal-onset seizure [HP:0007359]                           1/1    100%   \n",
+       "Inguinal hernia [HP:0000023]                               0/4      0%   \n",
        "Cryptorchidism [HP:0000028]                               5/12     42%   \n",
-       "Prominent nose [HP:0000448]                                2/4     50%   \n",
        "Macrodontia [HP:0001572]                                 22/27     81%   \n",
-       "Microcephaly [HP:0000252]                                 4/16     25%   \n",
+       "Strabismus [HP:0000486]                                   2/10     20%   \n",
        "\n",
        ">=1 allele of the variant 16_89284634_89284639_GTGTTT_G       No          \\\n",
        "                                                           Count Percent   \n",
@@ -2177,16 +2178,16 @@
        "Hypertelorism [HP:0000316]                                 54/91     59%   \n",
        "Feeding difficulties [HP:0011968]                         53/119     45%   \n",
        "Delayed skeletal maturation [HP:0002750]                   44/94     47%   \n",
-       "Febrile seizure (within the age range of 3 mont...           4/7     57%   \n",
        "Intrauterine growth retardation [HP:0001511]               12/55     22%   \n",
-       "Strabismus [HP:0000486]                                    14/60     23%   \n",
-       "Inguinal hernia [HP:0000023]                                6/44     14%   \n",
+       "Prominent nose [HP:0000448]                                15/37     41%   \n",
        "Triangular face [HP:0000325]                               62/91     68%   \n",
+       "Microcephaly [HP:0000252]                                  26/97     27%   \n",
+       "Febrile seizure (within the age range of 3 mont...           4/7     57%   \n",
        "Focal-onset seizure [HP:0007359]                            7/13     54%   \n",
+       "Inguinal hernia [HP:0000023]                                6/44     14%   \n",
        "Cryptorchidism [HP:0000028]                                30/70     43%   \n",
-       "Prominent nose [HP:0000448]                                15/37     41%   \n",
        "Macrodontia [HP:0001572]                                 148/186     80%   \n",
-       "Microcephaly [HP:0000252]                                  26/97     27%   \n",
+       "Strabismus [HP:0000486]                                    14/60     23%   \n",
        "\n",
        ">=1 allele of the variant 16_89284634_89284639_GTGTTT_G            \\\n",
        "                                                          p value   \n",
@@ -2216,16 +2217,16 @@
        "Hypertelorism [HP:0000316]                               0.737500   \n",
        "Feeding difficulties [HP:0011968]                        0.789475   \n",
        "Delayed skeletal maturation [HP:0002750]                 0.810453   \n",
-       "Febrile seizure (within the age range of 3 mont...       1.000000   \n",
        "Intrauterine growth retardation [HP:0001511]             1.000000   \n",
-       "Strabismus [HP:0000486]                                  1.000000   \n",
-       "Inguinal hernia [HP:0000023]                             1.000000   \n",
+       "Prominent nose [HP:0000448]                              1.000000   \n",
        "Triangular face [HP:0000325]                             1.000000   \n",
+       "Microcephaly [HP:0000252]                                1.000000   \n",
+       "Febrile seizure (within the age range of 3 mont...       1.000000   \n",
        "Focal-onset seizure [HP:0007359]                         1.000000   \n",
+       "Inguinal hernia [HP:0000023]                             1.000000   \n",
        "Cryptorchidism [HP:0000028]                              1.000000   \n",
-       "Prominent nose [HP:0000448]                              1.000000   \n",
        "Macrodontia [HP:0001572]                                 1.000000   \n",
-       "Microcephaly [HP:0000252]                                1.000000   \n",
+       "Strabismus [HP:0000486]                                  1.000000   \n",
        "\n",
        ">=1 allele of the variant 16_89284634_89284639_GTGTTT_G                    \n",
        "                                                        Corrected p value  \n",
@@ -2255,16 +2256,16 @@
        "Hypertelorism [HP:0000316]                                       1.000000  \n",
        "Feeding difficulties [HP:0011968]                                1.000000  \n",
        "Delayed skeletal maturation [HP:0002750]                         1.000000  \n",
-       "Febrile seizure (within the age range of 3 mont...               1.000000  \n",
        "Intrauterine growth retardation [HP:0001511]                     1.000000  \n",
-       "Strabismus [HP:0000486]                                          1.000000  \n",
-       "Inguinal hernia [HP:0000023]                                     1.000000  \n",
+       "Prominent nose [HP:0000448]                                      1.000000  \n",
        "Triangular face [HP:0000325]                                     1.000000  \n",
+       "Microcephaly [HP:0000252]                                        1.000000  \n",
+       "Febrile seizure (within the age range of 3 mont...               1.000000  \n",
        "Focal-onset seizure [HP:0007359]                                 1.000000  \n",
+       "Inguinal hernia [HP:0000023]                                     1.000000  \n",
        "Cryptorchidism [HP:0000028]                                      1.000000  \n",
-       "Prominent nose [HP:0000448]                                      1.000000  \n",
        "Macrodontia [HP:0001572]                                         1.000000  \n",
-       "Microcephaly [HP:0000252]                                        1.000000  "
+       "Strabismus [HP:0000486]                                          1.000000  "
       ]
      },
      "execution_count": 13,
@@ -2571,11 +2572,11 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Low-set ears [HP:0000369]</th>\n",
-       "      <td>19/37</td>\n",
-       "      <td>51%</td>\n",
-       "      <td>2/4</td>\n",
-       "      <td>50%</td>\n",
+       "      <th>Attention deficit hyperactivity disorder [HP:0007018]</th>\n",
+       "      <td>41/53</td>\n",
+       "      <td>77%</td>\n",
+       "      <td>12/15</td>\n",
+       "      <td>80%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -2589,11 +2590,20 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Strabismus [HP:0000486]</th>\n",
-       "      <td>14/62</td>\n",
-       "      <td>23%</td>\n",
-       "      <td>2/8</td>\n",
-       "      <td>25%</td>\n",
+       "      <th>Mandibular prognathia [HP:0000303]</th>\n",
+       "      <td>11/38</td>\n",
+       "      <td>29%</td>\n",
+       "      <td>2/9</td>\n",
+       "      <td>22%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low-set ears [HP:0000369]</th>\n",
+       "      <td>19/37</td>\n",
+       "      <td>51%</td>\n",
+       "      <td>2/4</td>\n",
+       "      <td>50%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -2607,20 +2617,11 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Attention deficit hyperactivity disorder [HP:0007018]</th>\n",
-       "      <td>41/53</td>\n",
-       "      <td>77%</td>\n",
-       "      <td>12/15</td>\n",
-       "      <td>80%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Mandibular prognathia [HP:0000303]</th>\n",
-       "      <td>11/38</td>\n",
-       "      <td>29%</td>\n",
-       "      <td>2/9</td>\n",
-       "      <td>22%</td>\n",
+       "      <th>Strabismus [HP:0000486]</th>\n",
+       "      <td>14/62</td>\n",
+       "      <td>23%</td>\n",
+       "      <td>2/8</td>\n",
+       "      <td>25%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -2658,12 +2659,12 @@
        "Hypertelorism [HP:0000316]                            52/90     58%   7/11   \n",
        "Low anterior hairline [HP:0000294]                    47/74     64%   8/14   \n",
        "Macrodontia [HP:0001572]                            150/189     79%  20/24   \n",
-       "Low-set ears [HP:0000369]                             19/37     51%    2/4   \n",
-       "Intrauterine growth retardation [HP:0001511]          11/52     21%   2/10   \n",
-       "Strabismus [HP:0000486]                               14/62     23%    2/8   \n",
-       "Inguinal hernia [HP:0000023]                           5/38     13%   1/10   \n",
        "Attention deficit hyperactivity disorder [HP:00...    41/53     77%  12/15   \n",
+       "Intrauterine growth retardation [HP:0001511]          11/52     21%   2/10   \n",
        "Mandibular prognathia [HP:0000303]                    11/38     29%    2/9   \n",
+       "Low-set ears [HP:0000369]                             19/37     51%    2/4   \n",
+       "Inguinal hernia [HP:0000023]                           5/38     13%   1/10   \n",
+       "Strabismus [HP:0000486]                               14/62     23%    2/8   \n",
        "\n",
        "Variant in exon 9 on NM_013275.6                                      \\\n",
        "                                                   Percent   p value   \n",
@@ -2694,12 +2695,12 @@
        "Hypertelorism [HP:0000316]                             64%  0.758847   \n",
        "Low anterior hairline [HP:0000294]                     57%  0.765379   \n",
        "Macrodontia [HP:0001572]                               83%  0.791300   \n",
-       "Low-set ears [HP:0000369]                              50%  1.000000   \n",
-       "Intrauterine growth retardation [HP:0001511]           20%  1.000000   \n",
-       "Strabismus [HP:0000486]                                25%  1.000000   \n",
-       "Inguinal hernia [HP:0000023]                           10%  1.000000   \n",
        "Attention deficit hyperactivity disorder [HP:00...     80%  1.000000   \n",
+       "Intrauterine growth retardation [HP:0001511]           20%  1.000000   \n",
        "Mandibular prognathia [HP:0000303]                     22%  1.000000   \n",
+       "Low-set ears [HP:0000369]                              50%  1.000000   \n",
+       "Inguinal hernia [HP:0000023]                           10%  1.000000   \n",
+       "Strabismus [HP:0000486]                                25%  1.000000   \n",
        "\n",
        "Variant in exon 9 on NM_013275.6                                      \n",
        "                                                   Corrected p value  \n",
@@ -2730,12 +2731,12 @@
        "Hypertelorism [HP:0000316]                                       1.0  \n",
        "Low anterior hairline [HP:0000294]                               1.0  \n",
        "Macrodontia [HP:0001572]                                         1.0  \n",
-       "Low-set ears [HP:0000369]                                        1.0  \n",
+       "Attention deficit hyperactivity disorder [HP:00...               1.0  \n",
        "Intrauterine growth retardation [HP:0001511]                     1.0  \n",
-       "Strabismus [HP:0000486]                                          1.0  \n",
+       "Mandibular prognathia [HP:0000303]                               1.0  \n",
+       "Low-set ears [HP:0000369]                                        1.0  \n",
        "Inguinal hernia [HP:0000023]                                     1.0  \n",
-       "Attention deficit hyperactivity disorder [HP:00...               1.0  \n",
-       "Mandibular prognathia [HP:0000303]                               1.0  "
+       "Strabismus [HP:0000486]                                          1.0  "
       ]
      },
      "execution_count": 14,
@@ -2998,56 +2999,29 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Low-set ears [HP:0000369]</th>\n",
-       "      <td>2/4</td>\n",
-       "      <td>50%</td>\n",
-       "      <td>19/37</td>\n",
-       "      <td>51%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Anteverted nares [HP:0000463]</th>\n",
-       "      <td>7/9</td>\n",
-       "      <td>78%</td>\n",
-       "      <td>56/79</td>\n",
-       "      <td>71%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Focal-onset seizure [HP:0007359]</th>\n",
-       "      <td>1/2</td>\n",
-       "      <td>50%</td>\n",
-       "      <td>7/12</td>\n",
-       "      <td>58%</td>\n",
+       "      <th>Exaggerated cupid's bow [HP:0002263]</th>\n",
+       "      <td>1/5</td>\n",
+       "      <td>20%</td>\n",
+       "      <td>12/42</td>\n",
+       "      <td>29%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Prominent nose [HP:0000448]</th>\n",
-       "      <td>2/4</td>\n",
-       "      <td>50%</td>\n",
-       "      <td>15/37</td>\n",
-       "      <td>41%</td>\n",
+       "      <th>Thick eyebrow [HP:0000574]</th>\n",
+       "      <td>12/15</td>\n",
+       "      <td>80%</td>\n",
+       "      <td>94/116</td>\n",
+       "      <td>81%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Mandibular prognathia [HP:0000303]</th>\n",
+       "      <th>Microretrognathia [HP:0000308]</th>\n",
        "      <td>1/5</td>\n",
        "      <td>20%</td>\n",
-       "      <td>12/42</td>\n",
-       "      <td>29%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Sleep abnormality [HP:0002360]</th>\n",
-       "      <td>2/5</td>\n",
-       "      <td>40%</td>\n",
-       "      <td>17/52</td>\n",
-       "      <td>33%</td>\n",
+       "      <td>15/47</td>\n",
+       "      <td>32%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -3070,20 +3044,38 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Thick eyebrow [HP:0000574]</th>\n",
-       "      <td>12/15</td>\n",
-       "      <td>80%</td>\n",
-       "      <td>94/116</td>\n",
-       "      <td>81%</td>\n",
+       "      <th>Prominent nose [HP:0000448]</th>\n",
+       "      <td>2/4</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>15/37</td>\n",
+       "      <td>41%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Microretrognathia [HP:0000308]</th>\n",
+       "      <th>Mandibular prognathia [HP:0000303]</th>\n",
        "      <td>1/5</td>\n",
        "      <td>20%</td>\n",
-       "      <td>15/47</td>\n",
-       "      <td>32%</td>\n",
+       "      <td>12/42</td>\n",
+       "      <td>29%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Anteverted nares [HP:0000463]</th>\n",
+       "      <td>7/9</td>\n",
+       "      <td>78%</td>\n",
+       "      <td>56/79</td>\n",
+       "      <td>71%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sleep abnormality [HP:0002360]</th>\n",
+       "      <td>2/5</td>\n",
+       "      <td>40%</td>\n",
+       "      <td>17/52</td>\n",
+       "      <td>33%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -3097,11 +3089,20 @@
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Exaggerated cupid's bow [HP:0002263]</th>\n",
-       "      <td>1/5</td>\n",
-       "      <td>20%</td>\n",
-       "      <td>12/42</td>\n",
-       "      <td>29%</td>\n",
+       "      <th>Focal-onset seizure [HP:0007359]</th>\n",
+       "      <td>1/2</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>7/12</td>\n",
+       "      <td>58%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low-set ears [HP:0000369]</th>\n",
+       "      <td>2/4</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>19/37</td>\n",
+       "      <td>51%</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -3144,18 +3145,18 @@
        "Triangular face [HP:0000325]                                                                                  7/11   \n",
        "Intellectual disability [HP:0001249]                                                                         20/22   \n",
        "Cryptorchidism [HP:0000028]                                                                                   6/12   \n",
-       "Low-set ears [HP:0000369]                                                                                      2/4   \n",
-       "Anteverted nares [HP:0000463]                                                                                  7/9   \n",
-       "Focal-onset seizure [HP:0007359]                                                                               1/2   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                           1/5   \n",
+       "Thick eyebrow [HP:0000574]                                                                                   12/15   \n",
+       "Microretrognathia [HP:0000308]                                                                                 1/5   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                             1/2   \n",
+       "Hypotonia [HP:0001252]                                                                                         3/7   \n",
        "Prominent nose [HP:0000448]                                                                                    2/4   \n",
        "Mandibular prognathia [HP:0000303]                                                                             1/5   \n",
+       "Anteverted nares [HP:0000463]                                                                                  7/9   \n",
        "Sleep abnormality [HP:0002360]                                                                                 2/5   \n",
-       "Generalized non-motor (absence) seizure [HP:000...                                                             1/2   \n",
-       "Hypotonia [HP:0001252]                                                                                         3/7   \n",
-       "Thick eyebrow [HP:0000574]                                                                                   12/15   \n",
-       "Microretrognathia [HP:0000308]                                                                                 1/5   \n",
        "Global developmental delay [HP:0001263]                                                                      11/11   \n",
-       "Exaggerated cupid's bow [HP:0002263]                                                                           1/5   \n",
+       "Focal-onset seizure [HP:0007359]                                                                               1/2   \n",
+       "Low-set ears [HP:0000369]                                                                                      2/4   \n",
        "Generalized-onset seizure [HP:0002197]                                                                         1/1   \n",
        "\n",
        "Variant that affects Important for protein degradation feature on protein encoded by transcript NM_013275.6          \\\n",
@@ -3183,18 +3184,18 @@
        "Triangular face [HP:0000325]                                                                                    64%   \n",
        "Intellectual disability [HP:0001249]                                                                            91%   \n",
        "Cryptorchidism [HP:0000028]                                                                                     50%   \n",
-       "Low-set ears [HP:0000369]                                                                                       50%   \n",
-       "Anteverted nares [HP:0000463]                                                                                   78%   \n",
-       "Focal-onset seizure [HP:0007359]                                                                                50%   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                            20%   \n",
+       "Thick eyebrow [HP:0000574]                                                                                      80%   \n",
+       "Microretrognathia [HP:0000308]                                                                                  20%   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                              50%   \n",
+       "Hypotonia [HP:0001252]                                                                                          43%   \n",
        "Prominent nose [HP:0000448]                                                                                     50%   \n",
        "Mandibular prognathia [HP:0000303]                                                                              20%   \n",
+       "Anteverted nares [HP:0000463]                                                                                   78%   \n",
        "Sleep abnormality [HP:0002360]                                                                                  40%   \n",
-       "Generalized non-motor (absence) seizure [HP:000...                                                              50%   \n",
-       "Hypotonia [HP:0001252]                                                                                          43%   \n",
-       "Thick eyebrow [HP:0000574]                                                                                      80%   \n",
-       "Microretrognathia [HP:0000308]                                                                                  20%   \n",
        "Global developmental delay [HP:0001263]                                                                        100%   \n",
-       "Exaggerated cupid's bow [HP:0002263]                                                                            20%   \n",
+       "Focal-onset seizure [HP:0007359]                                                                                50%   \n",
+       "Low-set ears [HP:0000369]                                                                                       50%   \n",
        "Generalized-onset seizure [HP:0002197]                                                                         100%   \n",
        "\n",
        "Variant that affects Important for protein degradation feature on protein encoded by transcript NM_013275.6       No  \\\n",
@@ -3222,18 +3223,18 @@
        "Triangular face [HP:0000325]                                                                                   61/88   \n",
        "Intellectual disability [HP:0001249]                                                                         138/161   \n",
        "Cryptorchidism [HP:0000028]                                                                                    29/70   \n",
-       "Low-set ears [HP:0000369]                                                                                      19/37   \n",
-       "Anteverted nares [HP:0000463]                                                                                  56/79   \n",
-       "Focal-onset seizure [HP:0007359]                                                                                7/12   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                           12/42   \n",
+       "Thick eyebrow [HP:0000574]                                                                                    94/116   \n",
+       "Microretrognathia [HP:0000308]                                                                                 15/47   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                               2/6   \n",
+       "Hypotonia [HP:0001252]                                                                                         18/46   \n",
        "Prominent nose [HP:0000448]                                                                                    15/37   \n",
        "Mandibular prognathia [HP:0000303]                                                                             12/42   \n",
+       "Anteverted nares [HP:0000463]                                                                                  56/79   \n",
        "Sleep abnormality [HP:0002360]                                                                                 17/52   \n",
-       "Generalized non-motor (absence) seizure [HP:000...                                                               2/6   \n",
-       "Hypotonia [HP:0001252]                                                                                         18/46   \n",
-       "Thick eyebrow [HP:0000574]                                                                                    94/116   \n",
-       "Microretrognathia [HP:0000308]                                                                                 15/47   \n",
        "Global developmental delay [HP:0001263]                                                                      121/128   \n",
-       "Exaggerated cupid's bow [HP:0002263]                                                                           12/42   \n",
+       "Focal-onset seizure [HP:0007359]                                                                                7/12   \n",
+       "Low-set ears [HP:0000369]                                                                                      19/37   \n",
        "Generalized-onset seizure [HP:0002197]                                                                         11/13   \n",
        "\n",
        "Variant that affects Important for protein degradation feature on protein encoded by transcript NM_013275.6          \\\n",
@@ -3261,18 +3262,18 @@
        "Triangular face [HP:0000325]                                                                                    69%   \n",
        "Intellectual disability [HP:0001249]                                                                            86%   \n",
        "Cryptorchidism [HP:0000028]                                                                                     41%   \n",
-       "Low-set ears [HP:0000369]                                                                                       51%   \n",
-       "Anteverted nares [HP:0000463]                                                                                   71%   \n",
-       "Focal-onset seizure [HP:0007359]                                                                                58%   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                            29%   \n",
+       "Thick eyebrow [HP:0000574]                                                                                      81%   \n",
+       "Microretrognathia [HP:0000308]                                                                                  32%   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                              33%   \n",
+       "Hypotonia [HP:0001252]                                                                                          39%   \n",
        "Prominent nose [HP:0000448]                                                                                     41%   \n",
        "Mandibular prognathia [HP:0000303]                                                                              29%   \n",
+       "Anteverted nares [HP:0000463]                                                                                   71%   \n",
        "Sleep abnormality [HP:0002360]                                                                                  33%   \n",
-       "Generalized non-motor (absence) seizure [HP:000...                                                              33%   \n",
-       "Hypotonia [HP:0001252]                                                                                          39%   \n",
-       "Thick eyebrow [HP:0000574]                                                                                      81%   \n",
-       "Microretrognathia [HP:0000308]                                                                                  32%   \n",
        "Global developmental delay [HP:0001263]                                                                         95%   \n",
-       "Exaggerated cupid's bow [HP:0002263]                                                                            29%   \n",
+       "Focal-onset seizure [HP:0007359]                                                                                58%   \n",
+       "Low-set ears [HP:0000369]                                                                                       51%   \n",
        "Generalized-onset seizure [HP:0002197]                                                                          85%   \n",
        "\n",
        "Variant that affects Important for protein degradation feature on protein encoded by transcript NM_013275.6            \\\n",
@@ -3300,18 +3301,18 @@
        "Triangular face [HP:0000325]                                                                                 0.736399   \n",
        "Intellectual disability [HP:0001249]                                                                         0.743276   \n",
        "Cryptorchidism [HP:0000028]                                                                                  0.753567   \n",
-       "Low-set ears [HP:0000369]                                                                                    1.000000   \n",
-       "Anteverted nares [HP:0000463]                                                                                1.000000   \n",
-       "Focal-onset seizure [HP:0007359]                                                                             1.000000   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                         1.000000   \n",
+       "Thick eyebrow [HP:0000574]                                                                                   1.000000   \n",
+       "Microretrognathia [HP:0000308]                                                                               1.000000   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                           1.000000   \n",
+       "Hypotonia [HP:0001252]                                                                                       1.000000   \n",
        "Prominent nose [HP:0000448]                                                                                  1.000000   \n",
        "Mandibular prognathia [HP:0000303]                                                                           1.000000   \n",
+       "Anteverted nares [HP:0000463]                                                                                1.000000   \n",
        "Sleep abnormality [HP:0002360]                                                                               1.000000   \n",
-       "Generalized non-motor (absence) seizure [HP:000...                                                           1.000000   \n",
-       "Hypotonia [HP:0001252]                                                                                       1.000000   \n",
-       "Thick eyebrow [HP:0000574]                                                                                   1.000000   \n",
-       "Microretrognathia [HP:0000308]                                                                               1.000000   \n",
        "Global developmental delay [HP:0001263]                                                                      1.000000   \n",
-       "Exaggerated cupid's bow [HP:0002263]                                                                         1.000000   \n",
+       "Focal-onset seizure [HP:0007359]                                                                             1.000000   \n",
+       "Low-set ears [HP:0000369]                                                                                    1.000000   \n",
        "Generalized-onset seizure [HP:0002197]                                                                       1.000000   \n",
        "\n",
        "Variant that affects Important for protein degradation feature on protein encoded by transcript NM_013275.6                    \n",
@@ -3339,18 +3340,18 @@
        "Triangular face [HP:0000325]                                                                                              1.0  \n",
        "Intellectual disability [HP:0001249]                                                                                      1.0  \n",
        "Cryptorchidism [HP:0000028]                                                                                               1.0  \n",
-       "Low-set ears [HP:0000369]                                                                                                 1.0  \n",
-       "Anteverted nares [HP:0000463]                                                                                             1.0  \n",
-       "Focal-onset seizure [HP:0007359]                                                                                          1.0  \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                                      1.0  \n",
+       "Thick eyebrow [HP:0000574]                                                                                                1.0  \n",
+       "Microretrognathia [HP:0000308]                                                                                            1.0  \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                                        1.0  \n",
+       "Hypotonia [HP:0001252]                                                                                                    1.0  \n",
        "Prominent nose [HP:0000448]                                                                                               1.0  \n",
        "Mandibular prognathia [HP:0000303]                                                                                        1.0  \n",
+       "Anteverted nares [HP:0000463]                                                                                             1.0  \n",
        "Sleep abnormality [HP:0002360]                                                                                            1.0  \n",
-       "Generalized non-motor (absence) seizure [HP:000...                                                                        1.0  \n",
-       "Hypotonia [HP:0001252]                                                                                                    1.0  \n",
-       "Thick eyebrow [HP:0000574]                                                                                                1.0  \n",
-       "Microretrognathia [HP:0000308]                                                                                            1.0  \n",
        "Global developmental delay [HP:0001263]                                                                                   1.0  \n",
-       "Exaggerated cupid's bow [HP:0002263]                                                                                      1.0  \n",
+       "Focal-onset seizure [HP:0007359]                                                                                          1.0  \n",
+       "Low-set ears [HP:0000369]                                                                                                 1.0  \n",
        "Generalized-onset seizure [HP:0002197]                                                                                    1.0  "
       ]
      },
@@ -3364,6 +3365,622 @@
     "prot_test.summarize(hpo,PatientCategories.YES)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "650371ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Variant that affects Region(start=400, end=800) region on protein encoded by transcript NM_013275.6</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Yes</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">No</th>\n",
+       "      <th colspan=\"2\" halign=\"left\"></th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Percent</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Percent</th>\n",
+       "      <th>p value</th>\n",
+       "      <th>Corrected p value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Hypertelorism [HP:0000316]</th>\n",
+       "      <td>11/29</td>\n",
+       "      <td>38%</td>\n",
+       "      <td>48/72</td>\n",
+       "      <td>67%</td>\n",
+       "      <td>0.013375</td>\n",
+       "      <td>0.481483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Autistic behavior [HP:0000729]</th>\n",
+       "      <td>6/17</td>\n",
+       "      <td>35%</td>\n",
+       "      <td>34/55</td>\n",
+       "      <td>62%</td>\n",
+       "      <td>0.092227</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Long philtrum [HP:0000343]</th>\n",
+       "      <td>35/39</td>\n",
+       "      <td>90%</td>\n",
+       "      <td>69/91</td>\n",
+       "      <td>76%</td>\n",
+       "      <td>0.093622</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sleep abnormality [HP:0002360]</th>\n",
+       "      <td>9/19</td>\n",
+       "      <td>47%</td>\n",
+       "      <td>10/38</td>\n",
+       "      <td>26%</td>\n",
+       "      <td>0.141742</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Febrile seizure (within the age range of 3 months to 6 years) [HP:0002373]</th>\n",
+       "      <td>3/3</td>\n",
+       "      <td>100%</td>\n",
+       "      <td>2/5</td>\n",
+       "      <td>40%</td>\n",
+       "      <td>0.196429</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Anteverted nares [HP:0000463]</th>\n",
+       "      <td>20/32</td>\n",
+       "      <td>62%</td>\n",
+       "      <td>43/56</td>\n",
+       "      <td>77%</td>\n",
+       "      <td>0.218905</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hypotonia [HP:0001252]</th>\n",
+       "      <td>9/17</td>\n",
+       "      <td>53%</td>\n",
+       "      <td>12/36</td>\n",
+       "      <td>33%</td>\n",
+       "      <td>0.232247</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Strabismus [HP:0000486]</th>\n",
+       "      <td>8/25</td>\n",
+       "      <td>32%</td>\n",
+       "      <td>8/45</td>\n",
+       "      <td>18%</td>\n",
+       "      <td>0.236287</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Macrodontia [HP:0001572]</th>\n",
+       "      <td>52/69</td>\n",
+       "      <td>75%</td>\n",
+       "      <td>118/144</td>\n",
+       "      <td>82%</td>\n",
+       "      <td>0.277684</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Focal-onset seizure [HP:0007359]</th>\n",
+       "      <td>2/2</td>\n",
+       "      <td>100%</td>\n",
+       "      <td>6/12</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>0.472527</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prominent nose [HP:0000448]</th>\n",
+       "      <td>6/11</td>\n",
+       "      <td>55%</td>\n",
+       "      <td>11/30</td>\n",
+       "      <td>37%</td>\n",
+       "      <td>0.475668</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exaggerated cupid's bow [HP:0002263]</th>\n",
+       "      <td>5/14</td>\n",
+       "      <td>36%</td>\n",
+       "      <td>8/33</td>\n",
+       "      <td>24%</td>\n",
+       "      <td>0.486071</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Generalized-onset seizure [HP:0002197]</th>\n",
+       "      <td>3/4</td>\n",
+       "      <td>75%</td>\n",
+       "      <td>9/10</td>\n",
+       "      <td>90%</td>\n",
+       "      <td>0.505495</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Microcephaly [HP:0000252]</th>\n",
+       "      <td>9/41</td>\n",
+       "      <td>22%</td>\n",
+       "      <td>21/72</td>\n",
+       "      <td>29%</td>\n",
+       "      <td>0.507893</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Abnormality of the hand [HP:0001155]</th>\n",
+       "      <td>55/73</td>\n",
+       "      <td>75%</td>\n",
+       "      <td>100/142</td>\n",
+       "      <td>70%</td>\n",
+       "      <td>0.521712</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feeding difficulties [HP:0011968]</th>\n",
+       "      <td>18/45</td>\n",
+       "      <td>40%</td>\n",
+       "      <td>41/89</td>\n",
+       "      <td>46%</td>\n",
+       "      <td>0.581799</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cryptorchidism [HP:0000028]</th>\n",
+       "      <td>10/26</td>\n",
+       "      <td>38%</td>\n",
+       "      <td>25/56</td>\n",
+       "      <td>45%</td>\n",
+       "      <td>0.638877</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triangular face [HP:0000325]</th>\n",
+       "      <td>21/29</td>\n",
+       "      <td>72%</td>\n",
+       "      <td>47/70</td>\n",
+       "      <td>67%</td>\n",
+       "      <td>0.643431</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Global developmental delay [HP:0001263]</th>\n",
+       "      <td>46/49</td>\n",
+       "      <td>94%</td>\n",
+       "      <td>86/90</td>\n",
+       "      <td>96%</td>\n",
+       "      <td>0.697067</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delayed skeletal maturation [HP:0002750]</th>\n",
+       "      <td>20/40</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>34/74</td>\n",
+       "      <td>46%</td>\n",
+       "      <td>0.698833</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Microretrognathia [HP:0000308]</th>\n",
+       "      <td>5/14</td>\n",
+       "      <td>36%</td>\n",
+       "      <td>11/38</td>\n",
+       "      <td>29%</td>\n",
+       "      <td>0.738480</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EEG abnormality [HP:0002353]</th>\n",
+       "      <td>4/15</td>\n",
+       "      <td>27%</td>\n",
+       "      <td>12/34</td>\n",
+       "      <td>35%</td>\n",
+       "      <td>0.743392</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low-set ears [HP:0000369]</th>\n",
+       "      <td>6/13</td>\n",
+       "      <td>46%</td>\n",
+       "      <td>15/28</td>\n",
+       "      <td>54%</td>\n",
+       "      <td>0.744212</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Recurrent otitis media [HP:0000403]</th>\n",
+       "      <td>8/16</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>16/37</td>\n",
+       "      <td>43%</td>\n",
+       "      <td>0.766498</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Motor delay [HP:0001270]</th>\n",
+       "      <td>17/25</td>\n",
+       "      <td>68%</td>\n",
+       "      <td>38/52</td>\n",
+       "      <td>73%</td>\n",
+       "      <td>0.788253</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delayed speech and language development [HP:0000750]</th>\n",
+       "      <td>42/48</td>\n",
+       "      <td>88%</td>\n",
+       "      <td>79/93</td>\n",
+       "      <td>85%</td>\n",
+       "      <td>0.801727</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low anterior hairline [HP:0000294]</th>\n",
+       "      <td>16/27</td>\n",
+       "      <td>59%</td>\n",
+       "      <td>39/61</td>\n",
+       "      <td>64%</td>\n",
+       "      <td>0.811763</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Thick eyebrow [HP:0000574]</th>\n",
+       "      <td>33/40</td>\n",
+       "      <td>82%</td>\n",
+       "      <td>73/91</td>\n",
+       "      <td>80%</td>\n",
+       "      <td>0.814526</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Synophrys [HP:0000664]</th>\n",
+       "      <td>19/33</td>\n",
+       "      <td>58%</td>\n",
+       "      <td>34/63</td>\n",
+       "      <td>54%</td>\n",
+       "      <td>0.830016</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bulbous nose [HP:0000414]</th>\n",
+       "      <td>25/38</td>\n",
+       "      <td>66%</td>\n",
+       "      <td>49/72</td>\n",
+       "      <td>68%</td>\n",
+       "      <td>0.833232</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Attention deficit hyperactivity disorder [HP:0007018]</th>\n",
+       "      <td>13/17</td>\n",
+       "      <td>76%</td>\n",
+       "      <td>40/51</td>\n",
+       "      <td>78%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Generalized non-motor (absence) seizure [HP:0002121]</th>\n",
+       "      <td>0/1</td>\n",
+       "      <td>0%</td>\n",
+       "      <td>3/7</td>\n",
+       "      <td>43%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Intrauterine growth retardation [HP:0001511]</th>\n",
+       "      <td>3/16</td>\n",
+       "      <td>19%</td>\n",
+       "      <td>10/46</td>\n",
+       "      <td>22%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Protruding ear [HP:0000411]</th>\n",
+       "      <td>14/19</td>\n",
+       "      <td>74%</td>\n",
+       "      <td>34/48</td>\n",
+       "      <td>71%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mandibular prognathia [HP:0000303]</th>\n",
+       "      <td>3/13</td>\n",
+       "      <td>23%</td>\n",
+       "      <td>10/34</td>\n",
+       "      <td>29%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Inguinal hernia [HP:0000023]</th>\n",
+       "      <td>1/11</td>\n",
+       "      <td>9%</td>\n",
+       "      <td>5/37</td>\n",
+       "      <td>14%</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Variant that affects Region(start=400, end=800) region on protein encoded by transcript NM_013275.6    Yes  \\\n",
+       "                                                                                                     Count   \n",
+       "Hypertelorism [HP:0000316]                                                                           11/29   \n",
+       "Autistic behavior [HP:0000729]                                                                        6/17   \n",
+       "Long philtrum [HP:0000343]                                                                           35/39   \n",
+       "Sleep abnormality [HP:0002360]                                                                        9/19   \n",
+       "Febrile seizure (within the age range of 3 mont...                                                     3/3   \n",
+       "Anteverted nares [HP:0000463]                                                                        20/32   \n",
+       "Hypotonia [HP:0001252]                                                                                9/17   \n",
+       "Strabismus [HP:0000486]                                                                               8/25   \n",
+       "Macrodontia [HP:0001572]                                                                             52/69   \n",
+       "Focal-onset seizure [HP:0007359]                                                                       2/2   \n",
+       "Prominent nose [HP:0000448]                                                                           6/11   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                  5/14   \n",
+       "Generalized-onset seizure [HP:0002197]                                                                 3/4   \n",
+       "Microcephaly [HP:0000252]                                                                             9/41   \n",
+       "Abnormality of the hand [HP:0001155]                                                                 55/73   \n",
+       "Feeding difficulties [HP:0011968]                                                                    18/45   \n",
+       "Cryptorchidism [HP:0000028]                                                                          10/26   \n",
+       "Triangular face [HP:0000325]                                                                         21/29   \n",
+       "Global developmental delay [HP:0001263]                                                              46/49   \n",
+       "Delayed skeletal maturation [HP:0002750]                                                             20/40   \n",
+       "Microretrognathia [HP:0000308]                                                                        5/14   \n",
+       "EEG abnormality [HP:0002353]                                                                          4/15   \n",
+       "Low-set ears [HP:0000369]                                                                             6/13   \n",
+       "Recurrent otitis media [HP:0000403]                                                                   8/16   \n",
+       "Motor delay [HP:0001270]                                                                             17/25   \n",
+       "Delayed speech and language development [HP:000...                                                   42/48   \n",
+       "Low anterior hairline [HP:0000294]                                                                   16/27   \n",
+       "Thick eyebrow [HP:0000574]                                                                           33/40   \n",
+       "Synophrys [HP:0000664]                                                                               19/33   \n",
+       "Bulbous nose [HP:0000414]                                                                            25/38   \n",
+       "Attention deficit hyperactivity disorder [HP:00...                                                   13/17   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                     0/1   \n",
+       "Intrauterine growth retardation [HP:0001511]                                                          3/16   \n",
+       "Protruding ear [HP:0000411]                                                                          14/19   \n",
+       "Mandibular prognathia [HP:0000303]                                                                    3/13   \n",
+       "Inguinal hernia [HP:0000023]                                                                          1/11   \n",
+       "\n",
+       "Variant that affects Region(start=400, end=800) region on protein encoded by transcript NM_013275.6          \\\n",
+       "                                                                                                    Percent   \n",
+       "Hypertelorism [HP:0000316]                                                                              38%   \n",
+       "Autistic behavior [HP:0000729]                                                                          35%   \n",
+       "Long philtrum [HP:0000343]                                                                              90%   \n",
+       "Sleep abnormality [HP:0002360]                                                                          47%   \n",
+       "Febrile seizure (within the age range of 3 mont...                                                     100%   \n",
+       "Anteverted nares [HP:0000463]                                                                           62%   \n",
+       "Hypotonia [HP:0001252]                                                                                  53%   \n",
+       "Strabismus [HP:0000486]                                                                                 32%   \n",
+       "Macrodontia [HP:0001572]                                                                                75%   \n",
+       "Focal-onset seizure [HP:0007359]                                                                       100%   \n",
+       "Prominent nose [HP:0000448]                                                                             55%   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                    36%   \n",
+       "Generalized-onset seizure [HP:0002197]                                                                  75%   \n",
+       "Microcephaly [HP:0000252]                                                                               22%   \n",
+       "Abnormality of the hand [HP:0001155]                                                                    75%   \n",
+       "Feeding difficulties [HP:0011968]                                                                       40%   \n",
+       "Cryptorchidism [HP:0000028]                                                                             38%   \n",
+       "Triangular face [HP:0000325]                                                                            72%   \n",
+       "Global developmental delay [HP:0001263]                                                                 94%   \n",
+       "Delayed skeletal maturation [HP:0002750]                                                                50%   \n",
+       "Microretrognathia [HP:0000308]                                                                          36%   \n",
+       "EEG abnormality [HP:0002353]                                                                            27%   \n",
+       "Low-set ears [HP:0000369]                                                                               46%   \n",
+       "Recurrent otitis media [HP:0000403]                                                                     50%   \n",
+       "Motor delay [HP:0001270]                                                                                68%   \n",
+       "Delayed speech and language development [HP:000...                                                      88%   \n",
+       "Low anterior hairline [HP:0000294]                                                                      59%   \n",
+       "Thick eyebrow [HP:0000574]                                                                              82%   \n",
+       "Synophrys [HP:0000664]                                                                                  58%   \n",
+       "Bulbous nose [HP:0000414]                                                                               66%   \n",
+       "Attention deficit hyperactivity disorder [HP:00...                                                      76%   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                       0%   \n",
+       "Intrauterine growth retardation [HP:0001511]                                                            19%   \n",
+       "Protruding ear [HP:0000411]                                                                             74%   \n",
+       "Mandibular prognathia [HP:0000303]                                                                      23%   \n",
+       "Inguinal hernia [HP:0000023]                                                                             9%   \n",
+       "\n",
+       "Variant that affects Region(start=400, end=800) region on protein encoded by transcript NM_013275.6       No  \\\n",
+       "                                                                                                       Count   \n",
+       "Hypertelorism [HP:0000316]                                                                             48/72   \n",
+       "Autistic behavior [HP:0000729]                                                                         34/55   \n",
+       "Long philtrum [HP:0000343]                                                                             69/91   \n",
+       "Sleep abnormality [HP:0002360]                                                                         10/38   \n",
+       "Febrile seizure (within the age range of 3 mont...                                                       2/5   \n",
+       "Anteverted nares [HP:0000463]                                                                          43/56   \n",
+       "Hypotonia [HP:0001252]                                                                                 12/36   \n",
+       "Strabismus [HP:0000486]                                                                                 8/45   \n",
+       "Macrodontia [HP:0001572]                                                                             118/144   \n",
+       "Focal-onset seizure [HP:0007359]                                                                        6/12   \n",
+       "Prominent nose [HP:0000448]                                                                            11/30   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                    8/33   \n",
+       "Generalized-onset seizure [HP:0002197]                                                                  9/10   \n",
+       "Microcephaly [HP:0000252]                                                                              21/72   \n",
+       "Abnormality of the hand [HP:0001155]                                                                 100/142   \n",
+       "Feeding difficulties [HP:0011968]                                                                      41/89   \n",
+       "Cryptorchidism [HP:0000028]                                                                            25/56   \n",
+       "Triangular face [HP:0000325]                                                                           47/70   \n",
+       "Global developmental delay [HP:0001263]                                                                86/90   \n",
+       "Delayed skeletal maturation [HP:0002750]                                                               34/74   \n",
+       "Microretrognathia [HP:0000308]                                                                         11/38   \n",
+       "EEG abnormality [HP:0002353]                                                                           12/34   \n",
+       "Low-set ears [HP:0000369]                                                                              15/28   \n",
+       "Recurrent otitis media [HP:0000403]                                                                    16/37   \n",
+       "Motor delay [HP:0001270]                                                                               38/52   \n",
+       "Delayed speech and language development [HP:000...                                                     79/93   \n",
+       "Low anterior hairline [HP:0000294]                                                                     39/61   \n",
+       "Thick eyebrow [HP:0000574]                                                                             73/91   \n",
+       "Synophrys [HP:0000664]                                                                                 34/63   \n",
+       "Bulbous nose [HP:0000414]                                                                              49/72   \n",
+       "Attention deficit hyperactivity disorder [HP:00...                                                     40/51   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                       3/7   \n",
+       "Intrauterine growth retardation [HP:0001511]                                                           10/46   \n",
+       "Protruding ear [HP:0000411]                                                                            34/48   \n",
+       "Mandibular prognathia [HP:0000303]                                                                     10/34   \n",
+       "Inguinal hernia [HP:0000023]                                                                            5/37   \n",
+       "\n",
+       "Variant that affects Region(start=400, end=800) region on protein encoded by transcript NM_013275.6          \\\n",
+       "                                                                                                    Percent   \n",
+       "Hypertelorism [HP:0000316]                                                                              67%   \n",
+       "Autistic behavior [HP:0000729]                                                                          62%   \n",
+       "Long philtrum [HP:0000343]                                                                              76%   \n",
+       "Sleep abnormality [HP:0002360]                                                                          26%   \n",
+       "Febrile seizure (within the age range of 3 mont...                                                      40%   \n",
+       "Anteverted nares [HP:0000463]                                                                           77%   \n",
+       "Hypotonia [HP:0001252]                                                                                  33%   \n",
+       "Strabismus [HP:0000486]                                                                                 18%   \n",
+       "Macrodontia [HP:0001572]                                                                                82%   \n",
+       "Focal-onset seizure [HP:0007359]                                                                        50%   \n",
+       "Prominent nose [HP:0000448]                                                                             37%   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                    24%   \n",
+       "Generalized-onset seizure [HP:0002197]                                                                  90%   \n",
+       "Microcephaly [HP:0000252]                                                                               29%   \n",
+       "Abnormality of the hand [HP:0001155]                                                                    70%   \n",
+       "Feeding difficulties [HP:0011968]                                                                       46%   \n",
+       "Cryptorchidism [HP:0000028]                                                                             45%   \n",
+       "Triangular face [HP:0000325]                                                                            67%   \n",
+       "Global developmental delay [HP:0001263]                                                                 96%   \n",
+       "Delayed skeletal maturation [HP:0002750]                                                                46%   \n",
+       "Microretrognathia [HP:0000308]                                                                          29%   \n",
+       "EEG abnormality [HP:0002353]                                                                            35%   \n",
+       "Low-set ears [HP:0000369]                                                                               54%   \n",
+       "Recurrent otitis media [HP:0000403]                                                                     43%   \n",
+       "Motor delay [HP:0001270]                                                                                73%   \n",
+       "Delayed speech and language development [HP:000...                                                      85%   \n",
+       "Low anterior hairline [HP:0000294]                                                                      64%   \n",
+       "Thick eyebrow [HP:0000574]                                                                              80%   \n",
+       "Synophrys [HP:0000664]                                                                                  54%   \n",
+       "Bulbous nose [HP:0000414]                                                                               68%   \n",
+       "Attention deficit hyperactivity disorder [HP:00...                                                      78%   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                      43%   \n",
+       "Intrauterine growth retardation [HP:0001511]                                                            22%   \n",
+       "Protruding ear [HP:0000411]                                                                             71%   \n",
+       "Mandibular prognathia [HP:0000303]                                                                      29%   \n",
+       "Inguinal hernia [HP:0000023]                                                                            14%   \n",
+       "\n",
+       "Variant that affects Region(start=400, end=800) region on protein encoded by transcript NM_013275.6            \\\n",
+       "                                                                                                      p value   \n",
+       "Hypertelorism [HP:0000316]                                                                           0.013375   \n",
+       "Autistic behavior [HP:0000729]                                                                       0.092227   \n",
+       "Long philtrum [HP:0000343]                                                                           0.093622   \n",
+       "Sleep abnormality [HP:0002360]                                                                       0.141742   \n",
+       "Febrile seizure (within the age range of 3 mont...                                                   0.196429   \n",
+       "Anteverted nares [HP:0000463]                                                                        0.218905   \n",
+       "Hypotonia [HP:0001252]                                                                               0.232247   \n",
+       "Strabismus [HP:0000486]                                                                              0.236287   \n",
+       "Macrodontia [HP:0001572]                                                                             0.277684   \n",
+       "Focal-onset seizure [HP:0007359]                                                                     0.472527   \n",
+       "Prominent nose [HP:0000448]                                                                          0.475668   \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                 0.486071   \n",
+       "Generalized-onset seizure [HP:0002197]                                                               0.505495   \n",
+       "Microcephaly [HP:0000252]                                                                            0.507893   \n",
+       "Abnormality of the hand [HP:0001155]                                                                 0.521712   \n",
+       "Feeding difficulties [HP:0011968]                                                                    0.581799   \n",
+       "Cryptorchidism [HP:0000028]                                                                          0.638877   \n",
+       "Triangular face [HP:0000325]                                                                         0.643431   \n",
+       "Global developmental delay [HP:0001263]                                                              0.697067   \n",
+       "Delayed skeletal maturation [HP:0002750]                                                             0.698833   \n",
+       "Microretrognathia [HP:0000308]                                                                       0.738480   \n",
+       "EEG abnormality [HP:0002353]                                                                         0.743392   \n",
+       "Low-set ears [HP:0000369]                                                                            0.744212   \n",
+       "Recurrent otitis media [HP:0000403]                                                                  0.766498   \n",
+       "Motor delay [HP:0001270]                                                                             0.788253   \n",
+       "Delayed speech and language development [HP:000...                                                   0.801727   \n",
+       "Low anterior hairline [HP:0000294]                                                                   0.811763   \n",
+       "Thick eyebrow [HP:0000574]                                                                           0.814526   \n",
+       "Synophrys [HP:0000664]                                                                               0.830016   \n",
+       "Bulbous nose [HP:0000414]                                                                            0.833232   \n",
+       "Attention deficit hyperactivity disorder [HP:00...                                                   1.000000   \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                   1.000000   \n",
+       "Intrauterine growth retardation [HP:0001511]                                                         1.000000   \n",
+       "Protruding ear [HP:0000411]                                                                          1.000000   \n",
+       "Mandibular prognathia [HP:0000303]                                                                   1.000000   \n",
+       "Inguinal hernia [HP:0000023]                                                                         1.000000   \n",
+       "\n",
+       "Variant that affects Region(start=400, end=800) region on protein encoded by transcript NM_013275.6                    \n",
+       "                                                                                                    Corrected p value  \n",
+       "Hypertelorism [HP:0000316]                                                                                   0.481483  \n",
+       "Autistic behavior [HP:0000729]                                                                               1.000000  \n",
+       "Long philtrum [HP:0000343]                                                                                   1.000000  \n",
+       "Sleep abnormality [HP:0002360]                                                                               1.000000  \n",
+       "Febrile seizure (within the age range of 3 mont...                                                           1.000000  \n",
+       "Anteverted nares [HP:0000463]                                                                                1.000000  \n",
+       "Hypotonia [HP:0001252]                                                                                       1.000000  \n",
+       "Strabismus [HP:0000486]                                                                                      1.000000  \n",
+       "Macrodontia [HP:0001572]                                                                                     1.000000  \n",
+       "Focal-onset seizure [HP:0007359]                                                                             1.000000  \n",
+       "Prominent nose [HP:0000448]                                                                                  1.000000  \n",
+       "Exaggerated cupid's bow [HP:0002263]                                                                         1.000000  \n",
+       "Generalized-onset seizure [HP:0002197]                                                                       1.000000  \n",
+       "Microcephaly [HP:0000252]                                                                                    1.000000  \n",
+       "Abnormality of the hand [HP:0001155]                                                                         1.000000  \n",
+       "Feeding difficulties [HP:0011968]                                                                            1.000000  \n",
+       "Cryptorchidism [HP:0000028]                                                                                  1.000000  \n",
+       "Triangular face [HP:0000325]                                                                                 1.000000  \n",
+       "Global developmental delay [HP:0001263]                                                                      1.000000  \n",
+       "Delayed skeletal maturation [HP:0002750]                                                                     1.000000  \n",
+       "Microretrognathia [HP:0000308]                                                                               1.000000  \n",
+       "EEG abnormality [HP:0002353]                                                                                 1.000000  \n",
+       "Low-set ears [HP:0000369]                                                                                    1.000000  \n",
+       "Recurrent otitis media [HP:0000403]                                                                          1.000000  \n",
+       "Motor delay [HP:0001270]                                                                                     1.000000  \n",
+       "Delayed speech and language development [HP:000...                                                           1.000000  \n",
+       "Low anterior hairline [HP:0000294]                                                                           1.000000  \n",
+       "Thick eyebrow [HP:0000574]                                                                                   1.000000  \n",
+       "Synophrys [HP:0000664]                                                                                       1.000000  \n",
+       "Bulbous nose [HP:0000414]                                                                                    1.000000  \n",
+       "Attention deficit hyperactivity disorder [HP:00...                                                           1.000000  \n",
+       "Generalized non-motor (absence) seizure [HP:000...                                                           1.000000  \n",
+       "Intrauterine growth retardation [HP:0001511]                                                                 1.000000  \n",
+       "Protruding ear [HP:0000411]                                                                                  1.000000  \n",
+       "Mandibular prognathia [HP:0000303]                                                                           1.000000  \n",
+       "Inguinal hernia [HP:0000023]                                                                                 1.000000  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prot_test = analysis.compare_by_protein_region(Region(400, 800), tx_id)\n",
+    "prot_test.summarize(hpo,PatientCategories.YES)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a12b8117",
diff --git a/src/genophenocorr/analysis/_gp_impl.py b/src/genophenocorr/analysis/_gp_impl.py
index 8c61347d..4861a791 100644
--- a/src/genophenocorr/analysis/_gp_impl.py
+++ b/src/genophenocorr/analysis/_gp_impl.py
@@ -4,11 +4,12 @@
 import hpotk
 
 from genophenocorr.model import Cohort, VariantEffect, FeatureType
+from genophenocorr.model.genome import Region
 from genophenocorr.preprocessing import ProteinMetadataService
 from .predicate import GenotypePolyPredicate, GenotypeBooleanPredicate
-from .predicate.genotype import VariantEffectPredicate, VariantPredicate, ExonPredicate, ProtFeatureTypePredicate, ProtFeaturePredicate
-from .predicate.genotype import VariantEffectsPredicate, VariantsPredicate, ExonsPredicate, ProtFeaturesPredicate, ProtFeatureTypesPredicate
-from .predicate.genotype import RecessiveVariantPredicate, RecessiveProtFeaturePredicate, RecessiveExonPredicate, RecessiveProtFeatureTypePredicate, RecessiveVariantEffectPredicate
+from .predicate.genotype import VariantEffectPredicate, VariantPredicate, ExonPredicate, ProtFeatureTypePredicate, ProtFeaturePredicate, ProtRegionPredicate
+from .predicate.genotype import VariantEffectsPredicate, VariantsPredicate, ExonsPredicate, ProtFeaturesPredicate, ProtFeatureTypesPredicate, ProtRegionsPredicate
+from .predicate.genotype import RecessiveVariantPredicate, RecessiveProtFeaturePredicate, RecessiveExonPredicate, RecessiveProtFeatureTypePredicate, RecessiveVariantEffectPredicate, RecessiveProtRegionPredicate
 from .predicate.phenotype import PhenotypePolyPredicate, P, PropagatingPhenotypePredicate, DiseasePresencePredicate
 
 from ._api import CohortAnalysis, GenotypePhenotypeAnalysisResult
@@ -64,6 +65,10 @@ def compare_by_protein_feature_type(self, feature_type: FeatureType, tx_id: str)
     def compare_by_protein_feature(self, feature: str, tx_id: str) -> GenotypePhenotypeAnalysisResult:
         predicate = ProtFeaturePredicate(tx_id, feature, self._protein_service)
         return self._apply_boolean_predicate_on_hpo_terms(predicate)
+    
+    def compare_by_protein_region(self, protein_region: Region, tx_id: str) -> GenotypePhenotypeAnalysisResult:
+        predicate = ProtRegionPredicate(tx_id, protein_region)
+        return self._apply_boolean_predicate_on_hpo_terms(predicate)
 
     def compare_by_variant_effects(
             self,
@@ -100,6 +105,15 @@ def compare_by_protein_features(
         predicate = ProtFeaturesPredicate(tx_id, feature1, feature2, self._protein_service)
         return self._apply_poly_predicate_on_hpo_terms(predicate)
     
+    def compare_by_protein_regions(
+        self,
+        region1: Region,
+        region2: Region, 
+        tx_id: str,
+    ) -> GenotypePhenotypeAnalysisResult:
+        predicate = ProtRegionsPredicate(tx_id, region1, region2)
+        return self._apply_poly_predicate_on_hpo_terms(predicate)
+    
     def compare_by_recessive_variant_effect(self, effect: VariantEffect, tx_id: str) -> GenotypePhenotypeAnalysisResult:
         predicate = RecessiveVariantEffectPredicate(tx_id, effect)
         return self._apply_poly_predicate_on_hpo_terms(predicate)
@@ -119,6 +133,10 @@ def compare_by_recessive_protein_feature_type(self, feature_type: FeatureType, t
     def compare_by_recessive_protein_feature(self, feature: str, tx_id: str) -> GenotypePhenotypeAnalysisResult:
         predicate = RecessiveProtFeaturePredicate(tx_id, feature, self._protein_service)
         return self._apply_poly_predicate_on_hpo_terms(predicate)
+    
+    def compare_by_recessive_protein_region(self, region:Region, tx_id:str) -> GenotypePhenotypeAnalysisResult:
+        predicate = RecessiveProtRegionPredicate(tx_id, region)
+        return self._apply_poly_predicate_on_hpo_terms(predicate)
 
     def compare_disease_vs_genotype(
         self, genotype_predicate: GenotypePolyPredicate, disease_ids:typing.Sequence[typing.Union[str, hpotk.TermId]] = None
diff --git a/src/genophenocorr/analysis/predicate/genotype/__init__.py b/src/genophenocorr/analysis/predicate/genotype/__init__.py
index 878410ba..a4b0655e 100644
--- a/src/genophenocorr/analysis/predicate/genotype/__init__.py
+++ b/src/genophenocorr/analysis/predicate/genotype/__init__.py
@@ -1,14 +1,15 @@
-from ._geno_bool import ProtFeaturePredicate, ProtFeatureTypePredicate
+from ._geno_bool import ProtFeaturePredicate, ProtFeatureTypePredicate, ProtRegionPredicate
 from ._geno_bool import VariantEffectPredicate, VariantPredicate, ExonPredicate
 from ._geno_group import VariantEffectsPredicate, VariantsPredicate, ExonsPredicate
-from ._geno_group import ProtFeaturesPredicate, ProtFeatureTypesPredicate
+from ._geno_group import ProtFeaturesPredicate, ProtFeatureTypesPredicate, ProtRegionsPredicate
 from ._geno_bool_rec import RecessiveExonPredicate, RecessiveProtFeatureTypePredicate, RecessiveVariantPredicate
-from ._geno_bool_rec import RecessiveProtFeaturePredicate, RecessiveVariantEffectPredicate
+from ._geno_bool_rec import RecessiveProtFeaturePredicate, RecessiveVariantEffectPredicate, RecessiveProtRegionPredicate
 
 __all__ = [
     'VariantEffectPredicate', 'VariantEffectsPredicate', 'RecessiveVariantEffectPredicate',
     'VariantPredicate', 'VariantsPredicate', 'RecessiveVariantPredicate',
     'ExonPredicate', 'ExonsPredicate', 'RecessiveExonPredicate',
     'ProtFeaturePredicate', 'ProtFeaturesPredicate', 'RecessiveProtFeaturePredicate',
-    'ProtFeatureTypePredicate', 'ProtFeatureTypesPredicate', 'RecessiveProtFeatureTypePredicate'
+    'ProtFeatureTypePredicate', 'ProtFeatureTypesPredicate', 'RecessiveProtFeatureTypePredicate',
+    'ProtRegionPredicate', 'ProtRegionsPredicate', 'RecessiveProtRegionPredicate'
 ]
diff --git a/src/genophenocorr/analysis/predicate/genotype/_geno_bool.py b/src/genophenocorr/analysis/predicate/genotype/_geno_bool.py
index 1b63601d..31990665 100644
--- a/src/genophenocorr/analysis/predicate/genotype/_geno_bool.py
+++ b/src/genophenocorr/analysis/predicate/genotype/_geno_bool.py
@@ -3,6 +3,7 @@
 import hpotk
 
 from genophenocorr.model import Patient, FeatureType, VariantEffect
+from genophenocorr.model.genome import Region
 from genophenocorr.preprocessing import ProteinMetadataService
 
 from .._api import Categorization, GenotypeBooleanPredicate
@@ -10,7 +11,7 @@
 
 class VariantEffectPredicate(GenotypeBooleanPredicate):
     """
-    `VariantEffectPredicate` tests if the `patient` has at least one variant that is predicted to have
+    `VariantEffectPredicate` tests if the patient has at least one variant that is predicted to have
     the functional `effect` on the transcript of interest.
 
     :param transcript_id: the accession of the transcript of interest.
@@ -60,7 +61,7 @@ def __repr__(self):
 
 class VariantPredicate(GenotypeBooleanPredicate):
     """
-    `VariantPredicate` tests if the `patient` has ar least one allele of the variant described by the `variant_key`.
+    `VariantPredicate` tests if the patient has ar least one allele of the variant described by the `variant_key`.
 
     .. note::
 
@@ -78,7 +79,7 @@ def get_question(self) -> str:
     def test(self, patient: Patient) -> typing.Optional[Categorization]:
         """A variant key, such as `X_12345_12345_C_G`, is given when initializing the class.
 
-        Given a `patient`, we test whether the patient does
+        Given a patient, we test whether the patient does
         or does not have the variant and returns the respective category.
 
         Args:
@@ -108,7 +109,7 @@ def __repr__(self):
 
 class ExonPredicate(GenotypeBooleanPredicate):
     """
-    `ExonPredicate` tests if the `patient` has a variant that affects *n*-th exon of the transcript of interest.
+    `ExonPredicate` tests if the patient has a variant that affects *n*-th exon of the transcript of interest.
 
     .. warning::
 
@@ -171,7 +172,7 @@ def __repr__(self):
 
 class ProtFeatureTypePredicate(GenotypeBooleanPredicate):
     """
-    `ProtFeatureTypePredicate` tests if the `patient` has a variant that affects a :class:`FeatureType`
+    `ProtFeatureTypePredicate` tests if the patient has a variant that affects a :class:`FeatureType`
     in the protein encoded by the transcript of interest.
 
     :param transcript_id: the accession of the transcript of interest.
@@ -229,7 +230,7 @@ def __repr__(self):
 
 class ProtFeaturePredicate(GenotypeBooleanPredicate):
     """
-    `ProtFeaturePredicate` tests if the `patient` has a variant that overlaps with a protein feature.
+    `ProtFeaturePredicate` tests if the patient has a variant that overlaps with a protein feature.
 
     The predicate needs the name of the protein feature.
     For instance, `EGF-like 2` for `FBN1 <https://www.uniprot.org/uniprotkb/P35555/entry#family_and_domains>`_
@@ -282,4 +283,56 @@ def __str__(self):
         return repr(self)
 
     def __repr__(self):
-        return f'ProtFeaturePredicate(tx_id={self._tx_id}, exon_number={self._pf_name})'
+        return f'ProtFeaturePredicate(tx_id={self._tx_id}, protein_feature_name={self._pf_name})'
+
+
+class ProtRegionPredicate(GenotypeBooleanPredicate):
+    """
+    `ProtRegionPredicate` tests if the patient has a variant that overlaps with a given region of the protein.
+
+    The predicate needs the start and end coordinate for the protein region, given as a `Region`.
+    For instance, Region(150, 175)
+
+    :param transcript_id: the accession of the transcript of interest.
+    :param protein_region: a `Region` with the start and end coordinates.
+    """
+
+    def __init__(self, transcript_id: str, protein_region: Region) -> None:
+        self._tx_id = transcript_id
+        self._prot_region = hpotk.util.validate_instance(protein_region, Region, 'protein_region')
+
+    def get_question(self) -> str:
+        return f'Variant that affects an amino acid between {self._prot_region.start} and {self._prot_region.end} on protein encoded by transcript {self._tx_id}'
+
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
+        """A protein_region is given when initializing the class.
+        Given a Patient class, this function tests whether the patient does
+        or does not have a variant in that protein region on the given protein and
+        returns the respective category.
+
+        Args:
+            patient (Patient): A Patient class representing a patient.
+
+        Returns:
+            typing.Optional[Categorization]: GenotypeBooleanPredicate, either "YES" or "NO"
+                                             if genotype is present or not.
+        """
+        self._check_patient(patient)
+        if len(patient.variants) == 0:
+            return None
+
+        for variant in patient.variants:
+            for ann in variant.tx_annotations:
+                if ann.transcript_id == self._tx_id:
+                    prot_loc = ann.protein_effect_location
+                    if prot_loc is not None:
+                        if prot_loc.overlaps_with(self._prot_region):
+                            return GenotypeBooleanPredicate.YES
+
+        return GenotypeBooleanPredicate.NO
+
+    def __str__(self):
+        return repr(self)
+
+    def __repr__(self):
+        return f'ProtRegionPredicate(tx_id={self._tx_id}, protein_region={self._prot_region})'
\ No newline at end of file
diff --git a/src/genophenocorr/analysis/predicate/genotype/_geno_bool_rec.py b/src/genophenocorr/analysis/predicate/genotype/_geno_bool_rec.py
index 131e6c6c..fe13225c 100644
--- a/src/genophenocorr/analysis/predicate/genotype/_geno_bool_rec.py
+++ b/src/genophenocorr/analysis/predicate/genotype/_geno_bool_rec.py
@@ -3,6 +3,7 @@
 import hpotk
 
 from genophenocorr.model import Patient, FeatureType, VariantEffect
+from genophenocorr.model.genome import Region
 from genophenocorr.preprocessing import ProteinMetadataService
 
 from .._api import Categorization, RecessiveGroupingPredicate
@@ -10,7 +11,7 @@
 
 class RecessiveVariantEffectPredicate(RecessiveGroupingPredicate):
     """
-    `VariantEffectPredicate` tests if the `patient` has at least one variant that is predicted to have
+    `VariantEffectPredicate` tests if the patient has at least one variant that is predicted to have
     the functional `effect` on the transcript of interest.
 
     :param transcript_id: the accession of the transcript of interest.
@@ -67,7 +68,7 @@ def __repr__(self):
 
 class RecessiveVariantPredicate(RecessiveGroupingPredicate):
     """
-    `VariantPredicate` tests if the `patient` has ar least one allele of the variant described by the `variant_key`.
+    `VariantPredicate` tests if the patient has ar least one allele of the variant described by the `variant_key`.
 
     .. note::
 
@@ -121,7 +122,7 @@ def __repr__(self):
 
 class RecessiveExonPredicate(RecessiveGroupingPredicate):
     """
-    `ExonPredicate` tests if the `patient` has a variant that affects *n*-th exon of the transcript of interest.
+    `ExonPredicate` tests if the patient has a variant that affects *n*-th exon of the transcript of interest.
 
     .. warning::
 
@@ -191,7 +192,7 @@ def __repr__(self):
 
 class RecessiveProtFeatureTypePredicate(RecessiveGroupingPredicate):
     """
-    `ProtFeatureTypePredicate` tests if the `patient` has a variant that affects a :class:`FeatureType`
+    `ProtFeatureTypePredicate` tests if the patient has a variant that affects a :class:`FeatureType`
     in the protein encoded by the transcript of interest.
 
     :param transcript_id: the accession of the transcript of interest.
@@ -258,7 +259,7 @@ def __repr__(self):
 
 class RecessiveProtFeaturePredicate(RecessiveGroupingPredicate):
     """
-    `ProtFeaturePredicate` tests if the `patient` has a variant that overlaps with a protein feature.
+    `ProtFeaturePredicate` tests if the patient has a variant that overlaps with a protein feature.
 
     The predicate needs the name of the protein feature.
     For instance, `EGF-like 2` for `FBN1 <https://www.uniprot.org/uniprotkb/P35555/entry#family_and_domains>`_
@@ -321,3 +322,62 @@ def __str__(self):
 
     def __repr__(self):
         return f'ProtFeaturePredicate(tx_id={self._tx_id}, exon_number={self._pf_name})'
+
+class RecessiveProtRegionPredicate(RecessiveGroupingPredicate):
+    """
+    `RecessiveProtRegionPredicate` tests if the patient has a variant that overlaps with a given region of the protein.
+
+    The predicate needs the start and end coordinate for the protein region, given as a `Region`.
+    For instance, Region(150, 175)
+
+    :param transcript_id: the accession of the transcript of interest.
+    :param protein_region: a `Region` with the start and end coordinates.
+    """
+
+    def __init__(self, transcript_id: str, protein_region: Region) -> None:
+        self._tx_id = transcript_id
+        self._prot_region = hpotk.util.validate_instance(protein_region, Region, 'protein_region')
+
+    def get_question(self) -> str:
+        return f'Variant that affects an amino acid between {self._prot_region.start} and {self._prot_region.end} on ' \
+            f'protein encoded by transcript {self._tx_id}'
+
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
+        """A protein_region is given when initializing the class.
+        Given a Patient class, this function tests whether the patient does
+        or does not have a variant within the protein region on the given protein and
+        returns the respective category.
+
+        Args:
+            patient (Patient): A Patient class representing a patient.
+
+        Returns:
+            typing.Optional[Categorization]: RecessiveGroupingPredicate, either "BOTH", "ONE" or "NEITHER"
+                                             if genotype is present on both alleles, one allele, or neither.
+        """
+        self._check_patient(patient)
+        if len(patient.variants) == 0:
+            return None
+        
+        results = []
+        for variant in patient.variants:
+            for ann in variant.tx_annotations:
+                if ann.transcript_id == self._tx_id:
+                    prot_loc = ann.protein_effect_location
+                    if prot_loc is not None:
+                        if prot_loc.overlaps_with(self._prot_region):
+                            results.append(True)
+
+
+        if len(results) == 2:
+            return RecessiveGroupingPredicate.BOTH
+        elif len(results) == 1:
+            return RecessiveGroupingPredicate.ONE
+        else:
+            return RecessiveGroupingPredicate.NEITHER
+
+    def __str__(self):
+        return repr(self)
+
+    def __repr__(self):
+        return f'RecessiveProtRegionPredicate(tx_id={self._tx_id}, protein_region={self._prot_region})'
\ No newline at end of file
diff --git a/src/genophenocorr/analysis/predicate/genotype/_geno_group.py b/src/genophenocorr/analysis/predicate/genotype/_geno_group.py
index 8a95f7db..1917e24e 100644
--- a/src/genophenocorr/analysis/predicate/genotype/_geno_group.py
+++ b/src/genophenocorr/analysis/predicate/genotype/_geno_group.py
@@ -3,9 +3,10 @@
 import hpotk
 
 from genophenocorr.model import Patient, FeatureType, VariantEffect
+from genophenocorr.model.genome import Region
 from genophenocorr.preprocessing import ProteinMetadataService
 
-from .._api import PatientCategory, GroupingPredicate
+from .._api import PatientCategory, GroupingPredicate, Categorization
 
 
 # TODO - should we remove these three?
@@ -31,7 +32,7 @@
 
 class VariantEffectsPredicate(GroupingPredicate):
     """
-    `VariantEffectsPredicate` tests if the `patient` has at least one variant that is predicted to have
+    `VariantEffectsPredicate` tests if the patient has at least one variant that is predicted to have
     one of the two functional `effects` on the transcript of interest.
 
     :param transcript_id: the accession of the transcript of interest.
@@ -48,7 +49,7 @@ def __init__(self, transcript_id: str,
     def get_question(self) -> str:
         return f'{self._effect1.name} or {self._effect2.name} on {self._tx_id}'
 
-    def test(self, patient: Patient) -> typing.Optional[PatientCategory]:
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
         """Two VariantEffects are given when initializing the class. 
         Given a Patient class, this function tests whether the patient has either 
         VariantEffects and returns a category based off which one it does have, 
@@ -92,7 +93,7 @@ def __repr__(self):
 
 class VariantsPredicate(GroupingPredicate):
     """
-    `VariantsPredicate` tests if the `patient` has at least one allele of one of the variants described by
+    `VariantsPredicate` tests if the patient has at least one allele of one of the variants described by
     the `variant_key1` and `variant_key2`.
     **If patient has both variant_keys, it is not included**
 
@@ -111,7 +112,7 @@ def __init__(self, variant_key1: str, variant_key2: str) -> None:
     def get_question(self) -> str:
         return f'>=1 allele of either variant {self._variant_key1} or variant {self._variant_key2}'
 
-    def test(self, patient: Patient) -> typing.Optional[PatientCategory]:
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
         """Two variant strings are given when initializing the class. 
         Given a Patient class, this function tests whether the patient has either 
         variant and returns a category based off which one it does have, 
@@ -152,7 +153,7 @@ def __repr__(self):
 
 class ExonsPredicate(GroupingPredicate):
     """
-    `ExonsPredicate` tests if the `patient` has a variant that affects one of the
+    `ExonsPredicate` tests if the patient has a variant that affects one of the
     two given *n*-th exons of the transcript of interest.
 
     .. warning::
@@ -182,7 +183,7 @@ def __init__(self, transcript_id: str,
     def get_question(self) -> str:
         return f'Variant in exon {self._exon1_number} vs in exon {self._exon2_number} on {self._tx_id}'
 
-    def test(self, patient: Patient) -> typing.Optional[PatientCategory]:
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
         """Two exon numbers are given when initializing the class. 
         Given a Patient class, this function tests whether the patient has a variant effecting
         either exon and returns a category based off which one it does have an effect on, 
@@ -227,7 +228,7 @@ def __repr__(self):
 
 class ProtFeatureTypesPredicate(GroupingPredicate):
     """
-    `ProtFeatureTypesPredicate` tests if the `patient` has a variant that affects one of the two given
+    `ProtFeatureTypesPredicate` tests if the patient has a variant that affects one of the two given
     :class:`FeatureType`s in the transcript of interest.
 
     :param transcript_id: the accession of the transcript of interest.
@@ -246,7 +247,7 @@ def __init__(self, transcript_id: str, feature_type1: FeatureType, feature_type2
     def get_question(self) -> str:
         return f'Variant that affects {self._feature_type1.name} protein feature type vs {self._feature_type2} on {self._tx_id}'
 
-    def test(self, patient: Patient) -> typing.Optional[PatientCategory]:
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
         """Two FeatureTypes and a ProteinMetadataService are given when initializing the class. 
         Given a Patient class, this function tests whether the patient has a variant effecting
         either protein feature type and returns a category based off which one it does have an effect on, 
@@ -298,7 +299,7 @@ def __repr__(self):
 
 class ProtFeaturesPredicate(GroupingPredicate):
     """
-    `ProtFeaturesPredicate` tests if the `patient` has a variant that overlaps with one of the two given
+    `ProtFeaturesPredicate` tests if the patient has a variant that overlaps with one of the two given
      protein features.
 
     The predicate needs the name of the protein feature.
@@ -320,7 +321,7 @@ def __init__(self, transcript_id: str, protein_feature1_name: str, protein_featu
     def get_question(self) -> str:
         return f'Variant that affects {self._pf1_name} protein feature vs {self._pf2_name} on {self._tx_id}'
 
-    def test(self, patient: Patient) -> typing.Optional[PatientCategory]:
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
         """Two protein features and a ProteinMetadataService are given when initializing the class. 
         Given a Patient class, this function tests whether the patient has a variant effecting
         either protein feature and returns a category based off which one it does have an effect on, 
@@ -330,7 +331,7 @@ def test(self, patient: Patient) -> typing.Optional[PatientCategory]:
             patient (Patient): A Patient class representing a patient.
 
         Returns:
-            typing.Optional[Categorization]: GroupingPredicate, either "FIRST" or "SECOND" 
+            typing.Optional[PatientCategory]: GroupingPredicate, either "FIRST" or "SECOND" 
                                              if it has the first given genotype or the
                                              second given genotype. 
         """
@@ -369,3 +370,65 @@ def __str__(self):
 
     def __repr__(self):
         return f'ProtFeaturePredicate(tx_id={self._tx_id}, protein_feature1_name={self._pf1_name}, protein_feature2_name={self._pf2_name}, protein_service={self._protein_service})'
+
+
+class ProtRegionsPredicate(GroupingPredicate):
+    """
+    `ProtRegionsPredicate` tests if the patient has a variant that overlaps with a given region of the protein.
+
+    The predicate needs the start and end coordinate for the protein region, given as a `Region`.
+    For instance, Region(150, 175)
+
+    :param transcript_id: the accession of the transcript of interest.
+    :param protein_region: a `Region` with the start and end coordinates.
+    """
+
+    def __init__(self, transcript_id: str, protein_region_1: Region, protein_region_2: Region) -> None:
+        self._tx_id = transcript_id
+        self._prot_region_1 = hpotk.util.validate_instance(protein_region_1, Region, 'protein_region_1')
+        self._prot_region_2 = hpotk.util.validate_instance(protein_region_2, Region, 'protein_region_2')
+
+    def get_question(self) -> str:
+        return f'Variant that affects an amino acid between {self._prot_region_1.start} and {self._prot_region_1.end} vs between {self._prot_region_2.start} and' \
+            f'{self._prot_region_2.end} on protein encoded by transcript {self._tx_id}'
+
+    def test(self, patient: Patient) -> typing.Optional[Categorization]:
+        """A protein_region is given when initializing the class.
+        Given a Patient class, this function tests whether the patient does
+        or does not have a variant withing the protein region on the given protein and
+        returns the respective category.
+
+        Args:
+            patient (Patient): A Patient class representing a patient.
+
+        Returns:
+            typing.Optional[Categorization]: GenotypeBooleanPredicate, either "YES" or "NO"
+                                             if genotype is present or not.
+        """
+        self._check_patient(patient)
+        if len(patient.variants) == 0:
+            return None
+        
+        results = [False, False]
+        for variant in patient.variants:
+            for ann in variant.tx_annotations:
+                if ann.transcript_id == self._tx_id:
+                    prot_loc = ann.protein_effect_location
+                    if prot_loc is not None:
+                        if prot_loc.overlaps_with(self._prot_region_1):
+                            results[0] = True
+                        if prot_loc.overlaps_with(self._prot_region_2):
+                            results[1] = True
+
+        if results == [True, False]:
+            return GroupingPredicate.FIRST
+        elif results == [False, True]:
+            return GroupingPredicate.SECOND
+        else:
+            return None
+
+    def __str__(self):
+        return repr(self)
+
+    def __repr__(self):
+        return f'ProtRegionsPredicate(tx_id={self._tx_id}, protein_region_1={self._prot_region_1}, protein_region_2={self._prot_region_2})'
\ No newline at end of file


Test patch:
diff --git a/tests/test_predicates.py b/tests/test_predicates.py
index 5f0c3b45..c70533a9 100644
--- a/tests/test_predicates.py
+++ b/tests/test_predicates.py
@@ -7,6 +7,7 @@
 from genophenocorr.analysis.predicate.phenotype import PropagatingPhenotypePredicate, DiseasePresencePredicate
 from genophenocorr.analysis.predicate.genotype import *
 from genophenocorr.model import Cohort, Patient, FeatureType, VariantEffect
+from genophenocorr.model.genome import Region
 
 
 def find_patient(pat_id: str, cohort: Cohort) -> typing.Optional[Patient]:
@@ -168,3 +169,14 @@ def test_ProteinFeaturePredicate(patient_id, feature, hasVarResult, toy_cohort,
     result = predicate.test(patient)
     assert result.category == hasVarResult
 
+@pytest.mark.parametrize('patient_id, region, hasVarResult',
+                         (['HetDoubleVar2', Region(2000, 2500), PatientCategories.YES],
+                          ['HetDoubleVar2', Region(1000, 1500), PatientCategories.NO],
+                          ['HomoVar', Region(2000, 2500), PatientCategories.YES],
+                          ['HetSingleVar', Region(2000, 2500), PatientCategories.YES],
+                          ['HetDoubleVar1', Region(600, 650), PatientCategories.YES]))
+def test_ProteinRegionPredicate(patient_id, region, hasVarResult, toy_cohort):
+    predicate = ProtRegionPredicate('NM_013275.6', region)
+    patient = find_patient(patient_id, toy_cohort)
+    result = predicate.test(patient)
+    assert result.category == hasVarResult
\ No newline at end of file