diff --git a/notebooks/SUOX/SUOX.ipynb b/notebooks/SUOX/SUOX.ipynb
index 29f87378d..cc712385e 100644
--- a/notebooks/SUOX/SUOX.ipynb
+++ b/notebooks/SUOX/SUOX.ipynb
@@ -77,7 +77,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Patients Created: 100%|██████████| 35/35 [01:26<00:00,  2.46s/it]\n",
+      "Patients Created: 100%|██████████| 35/35 [00:00<00:00, 691.09it/s]\n",
       "Validated under none policy\n",
       "No errors or warnings were found\n"
      ]
@@ -214,26 +214,26 @@
        "                </tr>\n",
        "            \n",
        "            <tr>\n",
-       "                <td class=\"lft\">Abnormality of extrapyramidal motor function</td>\n",
-       "                <td>HP:0002071</td>\n",
+       "                <td class=\"lft\">Hypertonia</td>\n",
+       "                <td>HP:0001276</td>\n",
        "                <td>11</td>\n",
        "                </tr>\n",
        "            \n",
        "            <tr>\n",
-       "                <td class=\"lft\">Hypertonia</td>\n",
-       "                <td>HP:0001276</td>\n",
+       "                <td class=\"lft\">Abnormality of extrapyramidal motor function</td>\n",
+       "                <td>HP:0002071</td>\n",
        "                <td>11</td>\n",
        "                </tr>\n",
        "            \n",
        "            <tr>\n",
-       "                <td class=\"lft\">Hypohomocysteinemia</td>\n",
-       "                <td>HP:0020222</td>\n",
+       "                <td class=\"lft\">Microcephaly</td>\n",
+       "                <td>HP:0000252</td>\n",
        "                <td>10</td>\n",
        "                </tr>\n",
        "            \n",
        "            <tr>\n",
-       "                <td class=\"lft\">Microcephaly</td>\n",
-       "                <td>HP:0000252</td>\n",
+       "                <td class=\"lft\">Hypohomocysteinemia</td>\n",
+       "                <td>HP:0020222</td>\n",
        "                <td>10</td>\n",
        "                </tr>\n",
        "            \n",
@@ -282,6 +282,14 @@
        "                \n",
        "                <tr>\n",
        "                    <td>3</td>\n",
+       "                    <td class=\"lft\">12_56004039_56004039_G_A</td>\n",
+       "                    <td>c.650G>A</td>\n",
+       "                    <td>p.Arg217Gln</td>\n",
+       "                    <td>MISSENSE_VARIANT</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <td>3</td>\n",
        "                    <td class=\"lft\">12_56004485_56004485_C_T</td>\n",
        "                    <td>c.1096C>T</td>\n",
        "                    <td>p.Arg366Cys</td>\n",
@@ -297,14 +305,6 @@
        "                </tr>\n",
        "                \n",
        "                <tr>\n",
-       "                    <td>3</td>\n",
-       "                    <td class=\"lft\">12_56004039_56004039_G_A</td>\n",
-       "                    <td>c.650G>A</td>\n",
-       "                    <td>p.Arg217Gln</td>\n",
-       "                    <td>MISSENSE_VARIANT</td>\n",
-       "                </tr>\n",
-       "                \n",
-       "                <tr>\n",
        "                    <td>2</td>\n",
        "                    <td class=\"lft\">12_56004933_56004933_A_ACAATGTGCAGCCAGACACCGTGGCCC</td>\n",
        "                    <td>c.1549_1574dup</td>\n",
@@ -314,14 +314,6 @@
        "                \n",
        "                <tr>\n",
        "                    <td>2</td>\n",
-       "                    <td class=\"lft\">12_56004771_56004771_A_T</td>\n",
-       "                    <td>c.1382A>T</td>\n",
-       "                    <td>p.Asp461Val</td>\n",
-       "                    <td>MISSENSE_VARIANT</td>\n",
-       "                </tr>\n",
-       "                \n",
-       "                <tr>\n",
-       "                    <td>2</td>\n",
        "                    <td class=\"lft\">12_56004905_56004909_ATTGT_A</td>\n",
        "                    <td>c.1521_1524del</td>\n",
        "                    <td>p.Cys508ArgfsTer109</td>\n",
@@ -337,19 +329,27 @@
        "                </tr>\n",
        "                \n",
        "                <tr>\n",
+       "                    <td>2</td>\n",
+       "                    <td class=\"lft\">12_56004771_56004771_A_T</td>\n",
+       "                    <td>c.1382A>T</td>\n",
+       "                    <td>p.Asp461Val</td>\n",
+       "                    <td>MISSENSE_VARIANT</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                <tr>\n",
        "                    <td>1</td>\n",
-       "                    <td class=\"lft\">12_56004183_56004183_C_A</td>\n",
-       "                    <td>c.794C>A</td>\n",
-       "                    <td>p.Ala265Asp</td>\n",
+       "                    <td class=\"lft\">12_56004525_56004525_A_G</td>\n",
+       "                    <td>c.1136A>G</td>\n",
+       "                    <td>p.Lys379Arg</td>\n",
        "                    <td>MISSENSE_VARIANT</td>\n",
        "                </tr>\n",
        "                \n",
        "                <tr>\n",
        "                    <td>1</td>\n",
-       "                    <td class=\"lft\">12_56004669_56004669_C_A</td>\n",
-       "                    <td>c.1280C>A</td>\n",
-       "                    <td>p.Ser427Ter</td>\n",
-       "                    <td>STOP_GAINED</td>\n",
+       "                    <td class=\"lft\">12_56004120_56004124_CTCTT_C</td>\n",
+       "                    <td>c.734_737del</td>\n",
+       "                    <td>p.Leu245ProfsTer27</td>\n",
+       "                    <td>FRAMESHIFT_VARIANT</td>\n",
        "                </tr>\n",
        "                \n",
        "            </tbody>\n",
@@ -390,13 +390,13 @@
        "                </tr>\n",
        "            \n",
        "              <tr>\n",
-       "                <td class=\"lft\">STOP_GAINED</td>\n",
-       "                <td>10</td>\n",
+       "                <td class=\"lft\">FRAMESHIFT_VARIANT</td>\n",
+       "                <td>9</td>\n",
        "                </tr>\n",
        "            \n",
        "              <tr>\n",
-       "                <td class=\"lft\">FRAMESHIFT_VARIANT</td>\n",
-       "                <td>9</td>\n",
+       "                <td class=\"lft\">STOP_GAINED</td>\n",
+       "                <td>10</td>\n",
        "                </tr>\n",
        "            \n",
        "           </tbody>\n",
@@ -428,7 +428,16 @@
    "execution_count": 5,
    "id": "615010fa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_36711/3306755006.py:7: DeprecationWarning: Use `configure_default_protein_metadata_service` instead\n",
+      "  pms = configure_protein_metadata_service()\n"
+     ]
+    }
+   ],
    "source": [
     "from gpsea.model.genome import GRCh38\n",
     "from gpsea.preprocessing import configure_protein_metadata_service, VVMultiCoordinateService\n",
@@ -458,7 +467,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x2000 with 1 Axes>"
       ]
@@ -611,15 +620,223 @@
    "id": "8357f23c",
    "metadata": {},
    "source": [
-    "## Genotype Phenotype Correlation Analysis analysis"
+    "## Genotype phenotype correlation analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8684a75c",
+   "metadata": {},
+   "source": [
+    "### Genotype predicate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9e1f53e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Which genotype group does the patient fit in: HOM_REF, HET, BIALLELIC_ALT'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from gpsea.model import VariantEffect\n",
+    "from gpsea.analysis.predicate.genotype import VariantPredicates, ModeOfInheritancePredicate\n",
+    "\n",
+    "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, SUOX_transcript_id)\n",
+    "moi_predicate = ModeOfInheritancePredicate.autosomal_recessive(\n",
+    "    variant_predicate=is_missense,\n",
+    ")\n",
+    "moi_predicate.display_question()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83fb2a82",
+   "metadata": {},
+   "source": [
+    "These are the categorizations that can be produced:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3564694a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Categorization(category=HOM_REF),\n",
+       " Categorization(category=HET),\n",
+       " Categorization(category=BIALLELIC_ALT))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cats = moi_predicate.get_categorizations()\n",
+    "cats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc32bb65",
+   "metadata": {},
+   "source": [
+    "However, we are only interested in comparing `HET` vs `BIALLELIC_ALT`, so we will filter the other category away:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "50f42273",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Which genotype group does the patient fit in: HET, BIALLELIC_ALT'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from gpsea.analysis.predicate.genotype import filtering_predicate\n",
+    "\n",
+    "cats_of_interest = (cats[1], cats[2])\n",
+    "gt_predicate = filtering_predicate(\n",
+    "    predicate=moi_predicate,\n",
+    "    targets=cats_of_interest,\n",
+    ")\n",
+    "gt_predicate.display_question()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ec1d80e",
+   "metadata": {},
+   "source": [
+    "### Phenotype predicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "21d4e4cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from gpsea.analysis.predicate.phenotype import prepare_predicates_for_terms_of_interest\n",
+    "\n",
+    "pheno_predicates = prepare_predicates_for_terms_of_interest(\n",
+    "    cohort=cohort,\n",
+    "    hpo=hpo,\n",
+    "    missing_implies_excluded=False,\n",
+    "    min_n_of_patients_with_term=2,\n",
+    ")\n",
+    "len(pheno_predicates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e9a9dd3",
+   "metadata": {},
+   "source": [
+    "### MTC phenotype filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ade36715",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gpsea.analysis.mtc_filter import HpoMtcFilter\n",
+    "mtc_filter = HpoMtcFilter.default_filter(\n",
+    "    hpo=hpo,\n",
+    "    term_frequency_threshold=0.2,\n",
+    ")\n",
+    "mtc_correction = 'fdr_bh'\n",
+    "mtc_alpha = 0.05"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efe92d03",
+   "metadata": {},
+   "source": [
+    "### Count statistic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b4c8a4d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gpsea.analysis.pcats.stats import FisherExactTest\n",
+    "\n",
+    "count_statistic = FisherExactTest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12af4002",
+   "metadata": {},
+   "source": [
+    "### Finalize the analysis"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "531e077d",
+   "id": "7a9ad29e",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "from gpsea.analysis.pcats import HpoTermAnalysis\n",
+    "\n",
+    "analysis = HpoTermAnalysis(\n",
+    "    count_statistic=count_statistic,\n",
+    "    mtc_filter=mtc_filter,\n",
+    "    mtc_correction=mtc_correction,\n",
+    "    mtc_alpha=mtc_alpha,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "531e077d",
+   "metadata": {},
    "source": [
     "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n",
     "\n",
@@ -649,247 +866,42 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "f4fb138e",
+   "id": "4847121e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = analysis.compare_genotype_vs_phenotypes(\n",
+    "    cohort=cohort,\n",
+    "    gt_predicate=gt_predicate,\n",
+    "    pheno_predicates=pheno_predicates,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "217afa4e",
    "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>MISSENSE_VARIANT on NM_001032386.2</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Both</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">One</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Neither</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>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>Cognitive regression [HP:0034332]</th>\n",
-       "      <td>6/12</td>\n",
-       "      <td>50%</td>\n",
-       "      <td>0/5</td>\n",
-       "      <td>0%</td>\n",
-       "      <td>0/8</td>\n",
-       "      <td>0%</td>\n",
-       "      <td>0.023343</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Hypotonia [HP:0001252]</th>\n",
-       "      <td>10/11</td>\n",
-       "      <td>91%</td>\n",
-       "      <td>2/5</td>\n",
-       "      <td>40%</td>\n",
-       "      <td>3/7</td>\n",
-       "      <td>43%</td>\n",
-       "      <td>0.039530</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Seizure [HP:0001250]</th>\n",
-       "      <td>12/18</td>\n",
-       "      <td>67%</td>\n",
-       "      <td>5/6</td>\n",
-       "      <td>83%</td>\n",
-       "      <td>11/11</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>0.082640</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abnormality of extrapyramidal motor function [HP:0002071]</th>\n",
-       "      <td>8/12</td>\n",
-       "      <td>67%</td>\n",
-       "      <td>1/5</td>\n",
-       "      <td>20%</td>\n",
-       "      <td>2/8</td>\n",
-       "      <td>25%</td>\n",
-       "      <td>0.139377</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Neurodevelopmental delay [HP:0012758]</th>\n",
-       "      <td>4/12</td>\n",
-       "      <td>33%</td>\n",
-       "      <td>0/5</td>\n",
-       "      <td>0%</td>\n",
-       "      <td>4/8</td>\n",
-       "      <td>50%</td>\n",
-       "      <td>0.151874</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Decreased head circumference [HP:0040195]</th>\n",
-       "      <td>4/4</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>4/4</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abnormality of the head [HP:0000234]</th>\n",
-       "      <td>4/4</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>4/4</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abnormal circulating homocysteine concentration [HP:0010919]</th>\n",
-       "      <td>7/7</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>1/1</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Abnormal urine metabolite level [HP:0033354]</th>\n",
-       "      <td>9/9</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>3/3</td>\n",
-       "      <td>100%</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 circulating amino acid concentration [HP:0003112]</th>\n",
-       "      <td>8/8</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>5/5</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>64 rows × 8 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "MISSENSE_VARIANT on NM_001032386.2                   Both           One  \\\n",
-       "                                                    Count Percent Count   \n",
-       "Cognitive regression [HP:0034332]                    6/12     50%   0/5   \n",
-       "Hypotonia [HP:0001252]                              10/11     91%   2/5   \n",
-       "Seizure [HP:0001250]                                12/18     67%   5/6   \n",
-       "Abnormality of extrapyramidal motor function [H...   8/12     67%   1/5   \n",
-       "Neurodevelopmental delay [HP:0012758]                4/12     33%   0/5   \n",
-       "...                                                   ...     ...   ...   \n",
-       "Decreased head circumference [HP:0040195]             4/4    100%   2/2   \n",
-       "Abnormality of the head [HP:0000234]                  4/4    100%   2/2   \n",
-       "Abnormal circulating homocysteine concentration...    7/7    100%   2/2   \n",
-       "Abnormal urine metabolite level [HP:0033354]          9/9    100%   3/3   \n",
-       "Abnormal circulating amino acid concentration [...    8/8    100%   2/2   \n",
-       "\n",
-       "MISSENSE_VARIANT on NM_001032386.2                         Neither          \\\n",
-       "                                                   Percent   Count Percent   \n",
-       "Cognitive regression [HP:0034332]                       0%     0/8      0%   \n",
-       "Hypotonia [HP:0001252]                                 40%     3/7     43%   \n",
-       "Seizure [HP:0001250]                                   83%   11/11    100%   \n",
-       "Abnormality of extrapyramidal motor function [H...     20%     2/8     25%   \n",
-       "Neurodevelopmental delay [HP:0012758]                   0%     4/8     50%   \n",
-       "...                                                    ...     ...     ...   \n",
-       "Decreased head circumference [HP:0040195]             100%     4/4    100%   \n",
-       "Abnormality of the head [HP:0000234]                  100%     4/4    100%   \n",
-       "Abnormal circulating homocysteine concentration...    100%     1/1    100%   \n",
-       "Abnormal urine metabolite level [HP:0033354]          100%     2/2    100%   \n",
-       "Abnormal circulating amino acid concentration [...    100%     5/5    100%   \n",
-       "\n",
-       "MISSENSE_VARIANT on NM_001032386.2                                              \n",
-       "                                                     p value Corrected p value  \n",
-       "Cognitive regression [HP:0034332]                   0.023343               1.0  \n",
-       "Hypotonia [HP:0001252]                              0.039530               1.0  \n",
-       "Seizure [HP:0001250]                                0.082640               1.0  \n",
-       "Abnormality of extrapyramidal motor function [H...  0.139377               1.0  \n",
-       "Neurodevelopmental delay [HP:0012758]               0.151874               1.0  \n",
-       "...                                                      ...               ...  \n",
-       "Decreased head circumference [HP:0040195]           1.000000               1.0  \n",
-       "Abnormality of the head [HP:0000234]                1.000000               1.0  \n",
-       "Abnormal circulating homocysteine concentration...  1.000000               1.0  \n",
-       "Abnormal urine metabolite level [HP:0033354]        1.000000               1.0  \n",
-       "Abnormal circulating amino acid concentration [...  1.000000               1.0  \n",
-       "\n",
-       "[64 rows x 8 columns]"
+       "9"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from gpsea.model import VariantEffect\n",
-    "from gpsea.analysis.predicate import PatientCategories\n",
-    "from gpsea.analysis.predicate.genotype import VariantPredicates\n",
-    "\n",
-    "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, SUOX_transcript_id)\n",
-    "missense = analysis.compare_hpo_vs_recessive_genotype(is_missense)\n",
-    "missense.summarize(hpo, PatientCategories.YES)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31eea804",
-   "metadata": {},
-   "source": [
-    "Test for presence of genotype-phenotype correlations between subjects with >=1 allele of a variant vs. the others."
+    "result.total_tests"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "743954bd",
+   "execution_count": 17,
+   "id": "0327c94f",
    "metadata": {},
    "outputs": [
     {
@@ -912,10 +924,9 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th>variant has ID of 12_56004589_56004589_C_G</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Both</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">One</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Neither</th>\n",
+       "      <th>Which genotype group does the patient fit in</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">HET</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">BIALLELIC_ALT</th>\n",
        "      <th colspan=\"2\" halign=\"left\"></th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -924,195 +935,168 @@
        "      <th>Percent</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",
+       "      <th>Corrected p values</th>\n",
+       "      <th>p values</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Hypotonia [HP:0001252]</th>\n",
-       "      <td>0/1</td>\n",
-       "      <td>0%</td>\n",
-       "      <td>1/4</td>\n",
-       "      <td>25%</td>\n",
-       "      <td>14/18</td>\n",
-       "      <td>78%</td>\n",
-       "      <td>0.032869</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Ectopia lentis [HP:0001083]</th>\n",
-       "      <td>0/1</td>\n",
-       "      <td>0%</td>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>5/15</td>\n",
-       "      <td>33%</td>\n",
-       "      <td>0.137255</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2/5</td>\n",
+       "      <td>40%</td>\n",
+       "      <td>10/11</td>\n",
+       "      <td>91%</td>\n",
+       "      <td>0.393665</td>\n",
+       "      <td>0.063187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Neurodevelopmental delay [HP:0012758]</th>\n",
-       "      <td>0/1</td>\n",
-       "      <td>0%</td>\n",
+       "      <th>Cognitive regression [HP:0034332]</th>\n",
        "      <td>0/5</td>\n",
        "      <td>0%</td>\n",
-       "      <td>8/19</td>\n",
-       "      <td>42%</td>\n",
-       "      <td>0.175758</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>6/12</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>0.393665</td>\n",
+       "      <td>0.102295</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abnormality of extrapyramidal motor function [HP:0002071]</th>\n",
-       "      <td>0/1</td>\n",
-       "      <td>0%</td>\n",
        "      <td>1/5</td>\n",
        "      <td>20%</td>\n",
-       "      <td>10/19</td>\n",
-       "      <td>53%</td>\n",
-       "      <td>0.340580</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>8/12</td>\n",
+       "      <td>67%</td>\n",
+       "      <td>0.393665</td>\n",
+       "      <td>0.131222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Cognitive regression [HP:0034332]</th>\n",
-       "      <td>0/1</td>\n",
-       "      <td>0%</td>\n",
+       "      <th>Ectopia lentis [HP:0001083]</th>\n",
+       "      <td>3/4</td>\n",
+       "      <td>75%</td>\n",
+       "      <td>3/11</td>\n",
+       "      <td>27%</td>\n",
+       "      <td>0.468908</td>\n",
+       "      <td>0.235165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Neurodevelopmental delay [HP:0012758]</th>\n",
        "      <td>0/5</td>\n",
        "      <td>0%</td>\n",
-       "      <td>6/19</td>\n",
-       "      <td>32%</td>\n",
-       "      <td>0.452851</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>4/12</td>\n",
+       "      <td>33%</td>\n",
+       "      <td>0.468908</td>\n",
+       "      <td>0.260504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <th>Seizure [HP:0001250]</th>\n",
+       "      <td>5/6</td>\n",
+       "      <td>83%</td>\n",
+       "      <td>12/18</td>\n",
+       "      <td>67%</td>\n",
+       "      <td>0.943000</td>\n",
+       "      <td>0.628667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormality of the head [HP:0000234]</th>\n",
-       "      <td>1/1</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>1/1</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>8/8</td>\n",
-       "      <td>100%</td>\n",
+       "      <th>Hypertonia [HP:0001276]</th>\n",
+       "      <td>2/5</td>\n",
+       "      <td>40%</td>\n",
+       "      <td>5/11</td>\n",
+       "      <td>45%</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Seizure [HP:0001250]</th>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>4/5</td>\n",
-       "      <td>80%</td>\n",
-       "      <td>22/28</td>\n",
-       "      <td>79%</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal circulating homocysteine concentration [HP:0010919]</th>\n",
-       "      <td>0/0</td>\n",
-       "      <td>0%</td>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>8/8</td>\n",
-       "      <td>100%</td>\n",
+       "      <th>Microcephaly [HP:0000252]</th>\n",
+       "      <td>2/4</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>4/10</td>\n",
+       "      <td>40%</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal urine metabolite level [HP:0033354]</th>\n",
-       "      <td>0/0</td>\n",
+       "      <th>Xanthinuria [HP:0010934]</th>\n",
+       "      <td>0/2</td>\n",
        "      <td>0%</td>\n",
-       "      <td>2/2</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>12/12</td>\n",
-       "      <td>100%</td>\n",
+       "      <td>2/7</td>\n",
+       "      <td>29%</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Abnormal circulating amino acid concentration [HP:0003112]</th>\n",
-       "      <td>1/1</td>\n",
-       "      <td>100%</td>\n",
-       "      <td>2/2</td>\n",
+       "      <th>Abnormal nervous system physiology [HP:0012638]</th>\n",
+       "      <td>5/5</td>\n",
        "      <td>100%</td>\n",
-       "      <td>12/12</td>\n",
+       "      <td>17/17</td>\n",
        "      <td>100%</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>64 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "variant has ID of 12_56004589_56004589_C_G          Both           One  \\\n",
-       "                                                   Count Percent Count   \n",
-       "Hypotonia [HP:0001252]                               0/1      0%   1/4   \n",
-       "Ectopia lentis [HP:0001083]                          0/1      0%   2/2   \n",
-       "Neurodevelopmental delay [HP:0012758]                0/1      0%   0/5   \n",
-       "Abnormality of extrapyramidal motor function [H...   0/1      0%   1/5   \n",
-       "Cognitive regression [HP:0034332]                    0/1      0%   0/5   \n",
-       "...                                                  ...     ...   ...   \n",
-       "Abnormality of the head [HP:0000234]                 1/1    100%   1/1   \n",
-       "Seizure [HP:0001250]                                 2/2    100%   4/5   \n",
-       "Abnormal circulating homocysteine concentration...   0/0      0%   2/2   \n",
-       "Abnormal urine metabolite level [HP:0033354]         0/0      0%   2/2   \n",
-       "Abnormal circulating amino acid concentration [...   1/1    100%   2/2   \n",
+       "Which genotype group does the patient fit in         HET          \\\n",
+       "                                                   Count Percent   \n",
+       "Hypotonia [HP:0001252]                               2/5     40%   \n",
+       "Cognitive regression [HP:0034332]                    0/5      0%   \n",
+       "Abnormality of extrapyramidal motor function [H...   1/5     20%   \n",
+       "Ectopia lentis [HP:0001083]                          3/4     75%   \n",
+       "Neurodevelopmental delay [HP:0012758]                0/5      0%   \n",
+       "Seizure [HP:0001250]                                 5/6     83%   \n",
+       "Hypertonia [HP:0001276]                              2/5     40%   \n",
+       "Microcephaly [HP:0000252]                            2/4     50%   \n",
+       "Xanthinuria [HP:0010934]                             0/2      0%   \n",
+       "Abnormal nervous system physiology [HP:0012638]      5/5    100%   \n",
        "\n",
-       "variant has ID of 12_56004589_56004589_C_G                 Neither          \\\n",
-       "                                                   Percent   Count Percent   \n",
-       "Hypotonia [HP:0001252]                                 25%   14/18     78%   \n",
-       "Ectopia lentis [HP:0001083]                           100%    5/15     33%   \n",
-       "Neurodevelopmental delay [HP:0012758]                   0%    8/19     42%   \n",
-       "Abnormality of extrapyramidal motor function [H...     20%   10/19     53%   \n",
-       "Cognitive regression [HP:0034332]                       0%    6/19     32%   \n",
-       "...                                                    ...     ...     ...   \n",
-       "Abnormality of the head [HP:0000234]                  100%     8/8    100%   \n",
-       "Seizure [HP:0001250]                                   80%   22/28     79%   \n",
-       "Abnormal circulating homocysteine concentration...    100%     8/8    100%   \n",
-       "Abnormal urine metabolite level [HP:0033354]          100%   12/12    100%   \n",
-       "Abnormal circulating amino acid concentration [...    100%   12/12    100%   \n",
+       "Which genotype group does the patient fit in       BIALLELIC_ALT          \\\n",
+       "                                                           Count Percent   \n",
+       "Hypotonia [HP:0001252]                                     10/11     91%   \n",
+       "Cognitive regression [HP:0034332]                           6/12     50%   \n",
+       "Abnormality of extrapyramidal motor function [H...          8/12     67%   \n",
+       "Ectopia lentis [HP:0001083]                                 3/11     27%   \n",
+       "Neurodevelopmental delay [HP:0012758]                       4/12     33%   \n",
+       "Seizure [HP:0001250]                                       12/18     67%   \n",
+       "Hypertonia [HP:0001276]                                     5/11     45%   \n",
+       "Microcephaly [HP:0000252]                                   4/10     40%   \n",
+       "Xanthinuria [HP:0010934]                                     2/7     29%   \n",
+       "Abnormal nervous system physiology [HP:0012638]            17/17    100%   \n",
        "\n",
-       "variant has ID of 12_56004589_56004589_C_G                                      \n",
-       "                                                     p value Corrected p value  \n",
-       "Hypotonia [HP:0001252]                              0.032869               1.0  \n",
-       "Ectopia lentis [HP:0001083]                         0.137255               1.0  \n",
-       "Neurodevelopmental delay [HP:0012758]               0.175758               1.0  \n",
-       "Abnormality of extrapyramidal motor function [H...  0.340580               1.0  \n",
-       "Cognitive regression [HP:0034332]                   0.452851               1.0  \n",
-       "...                                                      ...               ...  \n",
-       "Abnormality of the head [HP:0000234]                1.000000               1.0  \n",
-       "Seizure [HP:0001250]                                1.000000               1.0  \n",
-       "Abnormal circulating homocysteine concentration...  1.000000               1.0  \n",
-       "Abnormal urine metabolite level [HP:0033354]        1.000000               1.0  \n",
-       "Abnormal circulating amino acid concentration [...  1.000000               1.0  \n",
+       "Which genotype group does the patient fit in                           \\\n",
+       "                                                   Corrected p values   \n",
+       "Hypotonia [HP:0001252]                                       0.393665   \n",
+       "Cognitive regression [HP:0034332]                            0.393665   \n",
+       "Abnormality of extrapyramidal motor function [H...           0.393665   \n",
+       "Ectopia lentis [HP:0001083]                                  0.468908   \n",
+       "Neurodevelopmental delay [HP:0012758]                        0.468908   \n",
+       "Seizure [HP:0001250]                                         0.943000   \n",
+       "Hypertonia [HP:0001276]                                      1.000000   \n",
+       "Microcephaly [HP:0000252]                                    1.000000   \n",
+       "Xanthinuria [HP:0010934]                                     1.000000   \n",
+       "Abnormal nervous system physiology [HP:0012638]                   NaN   \n",
        "\n",
-       "[64 rows x 8 columns]"
+       "Which genotype group does the patient fit in                  \n",
+       "                                                    p values  \n",
+       "Hypotonia [HP:0001252]                              0.063187  \n",
+       "Cognitive regression [HP:0034332]                   0.102295  \n",
+       "Abnormality of extrapyramidal motor function [H...  0.131222  \n",
+       "Ectopia lentis [HP:0001083]                         0.235165  \n",
+       "Neurodevelopmental delay [HP:0012758]               0.260504  \n",
+       "Seizure [HP:0001250]                                0.628667  \n",
+       "Hypertonia [HP:0001276]                             1.000000  \n",
+       "Microcephaly [HP:0000252]                           1.000000  \n",
+       "Xanthinuria [HP:0010934]                            1.000000  \n",
+       "Abnormal nervous system physiology [HP:0012638]          NaN  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "is_variant = VariantPredicates.variant_key('12_56004589_56004589_C_G')\n",
+    "from gpsea.analysis.predicate import PatientCategories\n",
     "\n",
-    "by_variant = analysis.compare_hpo_vs_recessive_genotype(is_variant)\n",
-    "by_variant.summarize(hpo, PatientCategories.YES)"
+    "result.summarize(hpo, PatientCategories.YES).head(10)"
    ]
   },
   {
@@ -1140,7 +1124,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,