Polygenic Score Analysis for Schizophrenia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "improving-gravity",
   "metadata": {},
   "source": [
    "### Exploratory Analysis of Schizophrenia GWAS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "delayed-cocktail",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "separated-nomination",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHR</th>\n",
       "      <th>SNP</th>\n",
       "      <th>BP</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>FRQ_A_56418</th>\n",
       "      <th>FRQ_U_78818</th>\n",
       "      <th>INFO</th>\n",
       "      <th>OR</th>\n",
       "      <th>SE</th>\n",
       "      <th>P</th>\n",
       "      <th>ngt</th>\n",
       "      <th>Direction</th>\n",
       "      <th>HetISqt</th>\n",
       "      <th>HetDf</th>\n",
       "      <th>HetPVa</th>\n",
       "      <th>Nca</th>\n",
       "      <th>Nco</th>\n",
       "      <th>Neff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4553688</th>\n",
       "      <td>6</td>\n",
       "      <td>chr6:26518757</td>\n",
       "      <td>26518757</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0.99300</td>\n",
       "      <td>0.9910</td>\n",
       "      <td>0.442</td>\n",
       "      <td>1.78229</td>\n",
       "      <td>0.1005</td>\n",
       "      <td>8.912000e-09</td>\n",
       "      <td>0</td>\n",
       "      <td>+???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4584146</th>\n",
       "      <td>6</td>\n",
       "      <td>rs114476648</td>\n",
       "      <td>27633802</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.98100</td>\n",
       "      <td>0.9790</td>\n",
       "      <td>0.446</td>\n",
       "      <td>1.50606</td>\n",
       "      <td>0.0560</td>\n",
       "      <td>2.623000e-13</td>\n",
       "      <td>0</td>\n",
       "      <td>+???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46945</td>\n",
       "      <td>59700</td>\n",
       "      <td>52551.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4802236</th>\n",
       "      <td>6</td>\n",
       "      <td>chr6:30332254</td>\n",
       "      <td>30332254</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.97300</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>0.481</td>\n",
       "      <td>1.37355</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>8.745000e-12</td>\n",
       "      <td>0</td>\n",
       "      <td>+???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565589</th>\n",
       "      <td>6</td>\n",
       "      <td>rs71559051</td>\n",
       "      <td>27799470</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.94500</td>\n",
       "      <td>0.9350</td>\n",
       "      <td>0.758</td>\n",
       "      <td>1.30918</td>\n",
       "      <td>0.0263</td>\n",
       "      <td>1.267000e-24</td>\n",
       "      <td>0</td>\n",
       "      <td>+???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4559282</th>\n",
       "      <td>6</td>\n",
       "      <td>rs113780580</td>\n",
       "      <td>27555056</td>\n",
       "      <td>A</td>\n",
       "      <td>AACAC</td>\n",
       "      <td>0.94100</td>\n",
       "      <td>0.9300</td>\n",
       "      <td>0.861</td>\n",
       "      <td>1.27813</td>\n",
       "      <td>0.0240</td>\n",
       "      <td>1.534000e-24</td>\n",
       "      <td>0</td>\n",
       "      <td>+???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4553584</th>\n",
       "      <td>6</td>\n",
       "      <td>rs71557345</td>\n",
       "      <td>26680698</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.06170</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.78671</td>\n",
       "      <td>0.0229</td>\n",
       "      <td>1.116000e-25</td>\n",
       "      <td>0</td>\n",
       "      <td>-???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4537686</th>\n",
       "      <td>6</td>\n",
       "      <td>rs192804591</td>\n",
       "      <td>27729706</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0.06370</td>\n",
       "      <td>0.0756</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.78568</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>9.891000e-26</td>\n",
       "      <td>0</td>\n",
       "      <td>-???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4543692</th>\n",
       "      <td>6</td>\n",
       "      <td>rs35202262</td>\n",
       "      <td>27799514</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.06460</td>\n",
       "      <td>0.0766</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.78553</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>2.066000e-26</td>\n",
       "      <td>0</td>\n",
       "      <td>-???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4597569</th>\n",
       "      <td>6</td>\n",
       "      <td>chr6:27244243</td>\n",
       "      <td>27244243</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.02760</td>\n",
       "      <td>0.0311</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.75828</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>1.502000e-10</td>\n",
       "      <td>0</td>\n",
       "      <td>-???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46945</td>\n",
       "      <td>59700</td>\n",
       "      <td>52551.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4763487</th>\n",
       "      <td>6</td>\n",
       "      <td>rs114486118</td>\n",
       "      <td>30346826</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.00894</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.50535</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>2.274000e-08</td>\n",
       "      <td>0</td>\n",
       "      <td>-???????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33640</td>\n",
       "      <td>43456</td>\n",
       "      <td>37923.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15445 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CHR            SNP        BP A1     A2  FRQ_A_56418  FRQ_U_78818  \\\n",
       "4553688    6  chr6:26518757  26518757  A      T      0.99300       0.9910   \n",
       "4584146    6    rs114476648  27633802  T      C      0.98100       0.9790   \n",
       "4802236    6  chr6:30332254  30332254  T      C      0.97300       0.9700   \n",
       "4565589    6     rs71559051  27799470  A      G      0.94500       0.9350   \n",
       "4559282    6    rs113780580  27555056  A  AACAC      0.94100       0.9300   \n",
       "...      ...            ...       ... ..    ...          ...          ...   \n",
       "4553584    6     rs71557345  26680698  A      G      0.06170       0.0734   \n",
       "4537686    6    rs192804591  27729706  A      T      0.06370       0.0756   \n",
       "4543692    6     rs35202262  27799514  T      C      0.06460       0.0766   \n",
       "4597569    6  chr6:27244243  27244243  T      C      0.02760       0.0311   \n",
       "4763487    6    rs114486118  30346826  T      C      0.00894       0.0104   \n",
       "\n",
       "          INFO       OR      SE             P  ngt Direction  HetISqt  HetDf  \\\n",
       "4553688  0.442  1.78229  0.1005  8.912000e-09    0  +???????      0.0      0   \n",
       "4584146  0.446  1.50606  0.0560  2.623000e-13    0  +???????      0.0      0   \n",
       "4802236  0.481  1.37355  0.0465  8.745000e-12    0  +???????      0.0      0   \n",
       "4565589  0.758  1.30918  0.0263  1.267000e-24    0  +???????      0.0      0   \n",
       "4559282  0.861  1.27813  0.0240  1.534000e-24    0  +???????      0.0      0   \n",
       "...        ...      ...     ...           ...  ...       ...      ...    ...   \n",
       "4553584  0.903  0.78671  0.0229  1.116000e-25    0  -???????      0.0      0   \n",
       "4537686  0.866  0.78568  0.0230  9.891000e-26    0  -???????      0.0      0   \n",
       "4543692  0.882  0.78553  0.0227  2.066000e-26    0  -???????      0.0      0   \n",
       "4597569  0.475  0.75828  0.0432  1.502000e-10    0  -???????      0.0      0   \n",
       "4763487  0.235  0.50535  0.1221  2.274000e-08    0  -???????      0.0      0   \n",
       "\n",
       "         HetPVa    Nca    Nco      Neff  \n",
       "4553688     1.0  33640  43456  37923.10  \n",
       "4584146     1.0  46945  59700  52551.45  \n",
       "4802236     1.0  33640  43456  37923.10  \n",
       "4565589     1.0  33640  43456  37923.10  \n",
       "4559282     1.0  33640  43456  37923.10  \n",
       "...         ...    ...    ...       ...  \n",
       "4553584     1.0  33640  43456  37923.10  \n",
       "4537686     1.0  33640  43456  37923.10  \n",
       "4543692     1.0  33640  43456  37923.10  \n",
       "4597569     1.0  46945  59700  52551.45  \n",
       "4763487     1.0  33640  43456  37923.10  \n",
       "\n",
       "[15445 rows x 19 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schizophrenia_df = pd.read_csv(\"daner_natgen_eas_eur_autosomes\", sep='\\t')\n",
    "schizophrenia_df.sort_values('P',ascending= False)\n",
    "schizo_df_filt= schizophrenia_df.loc[schizophrenia_df['P']<=5e-8]\n",
    "schizo_df_filt.sort_values('OR',ascending= False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "greatest-lambda",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHR</th>\n",
       "      <th>SNP</th>\n",
       "      <th>BP</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>FRQ_A_56418</th>\n",
       "      <th>FRQ_U_78818</th>\n",
       "      <th>INFO</th>\n",
       "      <th>OR</th>\n",
       "      <th>SE</th>\n",
       "      <th>P</th>\n",
       "      <th>ngt</th>\n",
       "      <th>Direction</th>\n",
       "      <th>HetISqt</th>\n",
       "      <th>HetDf</th>\n",
       "      <th>HetPVa</th>\n",
       "      <th>Nca</th>\n",
       "      <th>Nco</th>\n",
       "      <th>Neff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>rs14403</td>\n",
       "      <td>243663893</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.94064</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>8.718000e-10</td>\n",
       "      <td>17</td>\n",
       "      <td>----+---</td>\n",
       "      <td>10.9</td>\n",
       "      <td>7</td>\n",
       "      <td>0.34550</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>rs1198588</td>\n",
       "      <td>98552832</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.90150</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>1.399000e-16</td>\n",
       "      <td>24</td>\n",
       "      <td>----????</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.63580</td>\n",
       "      <td>48223</td>\n",
       "      <td>60717</td>\n",
       "      <td>53635.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>rs2949006</td>\n",
       "      <td>200715388</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.958</td>\n",
       "      <td>1.09615</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>2.884000e-18</td>\n",
       "      <td>2</td>\n",
       "      <td>++-+++++</td>\n",
       "      <td>32.1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.17170</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>rs778371</td>\n",
       "      <td>233743109</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.93146</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>6.879000e-12</td>\n",
       "      <td>1</td>\n",
       "      <td>----+---</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.83140</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>rs4687552</td>\n",
       "      <td>52838402</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.990</td>\n",
       "      <td>1.05781</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>1.715000e-09</td>\n",
       "      <td>2</td>\n",
       "      <td>++-++++?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.89820</td>\n",
       "      <td>51274</td>\n",
       "      <td>64443</td>\n",
       "      <td>56864.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>rs2910032</td>\n",
       "      <td>152540354</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.94753</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>5.968000e-09</td>\n",
       "      <td>25</td>\n",
       "      <td>-++++-+-</td>\n",
       "      <td>20.4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.26810</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>rs6461049</td>\n",
       "      <td>2017445</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.961</td>\n",
       "      <td>1.07691</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>2.582000e-17</td>\n",
       "      <td>22</td>\n",
       "      <td>++-+++++</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.63360</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>rs11995572</td>\n",
       "      <td>89592083</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.961</td>\n",
       "      <td>1.06481</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>2.429000e-08</td>\n",
       "      <td>0</td>\n",
       "      <td>+--+++++</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.92030</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>rs4129585</td>\n",
       "      <td>143312933</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.958</td>\n",
       "      <td>1.07047</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>4.831000e-14</td>\n",
       "      <td>51</td>\n",
       "      <td>++--+++-</td>\n",
       "      <td>27.8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20620</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>rs7085104</td>\n",
       "      <td>104628873</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.962</td>\n",
       "      <td>1.08426</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>3.649000e-20</td>\n",
       "      <td>24</td>\n",
       "      <td>+-+--++-</td>\n",
       "      <td>60.9</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01233</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>rs17691888</td>\n",
       "      <td>18734528</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.91448</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>1.495000e-10</td>\n",
       "      <td>2</td>\n",
       "      <td>-+------</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.98410</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>rs11532322</td>\n",
       "      <td>123731423</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.958</td>\n",
       "      <td>1.09111</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>2.141000e-20</td>\n",
       "      <td>0</td>\n",
       "      <td>+-++++++</td>\n",
       "      <td>16.8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.29790</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>rs1006737</td>\n",
       "      <td>2345295</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.962</td>\n",
       "      <td>1.10043</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>1.848000e-19</td>\n",
       "      <td>30</td>\n",
       "      <td>+--+++-+</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.74140</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>rs4801131</td>\n",
       "      <td>52752700</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.95180</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>2.944000e-08</td>\n",
       "      <td>2</td>\n",
       "      <td>--+-+-+-</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.46360</td>\n",
       "      <td>56418</td>\n",
       "      <td>78818</td>\n",
       "      <td>64441.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CHR         SNP         BP A1 A2  FRQ_A_56418  FRQ_U_78818   INFO  \\\n",
       "9     1     rs14403  243663893  T  C        0.272        0.325  0.963   \n",
       "12    1   rs1198588   98552832  A  T        0.154        0.171  0.998   \n",
       "0     2   rs2949006  200715388  T  G        0.266        0.306  0.958   \n",
       "6     2    rs778371  233743109  A  G        0.703        0.649  0.956   \n",
       "8     3   rs4687552   52838402  T  C        0.648        0.637  0.990   \n",
       "4     5   rs2910032  152540354  T  C        0.579        0.545  0.963   \n",
       "5     7   rs6461049    2017445  T  C        0.542        0.536  0.961   \n",
       "1     8  rs11995572   89592083  T  G        0.240        0.296  0.961   \n",
       "3     8   rs4129585  143312933  A  C        0.426        0.446  0.958   \n",
       "7    10   rs7085104  104628873  A  G        0.603        0.580  0.962   \n",
       "10   10  rs17691888   18734528  A  G        0.181        0.261  0.934   \n",
       "2    12  rs11532322  123731423  A  G        0.321        0.348  0.958   \n",
       "13   12   rs1006737    2345295  A  G        0.312        0.368  0.962   \n",
       "11   18   rs4801131   52752700  T  C        0.459        0.461  0.960   \n",
       "\n",
       "         OR      SE             P  ngt Direction  HetISqt  HetDf   HetPVa  \\\n",
       "9   0.94064  0.0100  8.718000e-10   17  ----+---     10.9      7  0.34550   \n",
       "12  0.90150  0.0125  1.399000e-16   24  ----????      0.0      3  0.63580   \n",
       "0   1.09615  0.0105  2.884000e-18    2  ++-+++++     32.1      7  0.17170   \n",
       "6   0.93146  0.0103  6.879000e-12    1  ----+---      0.0      7  0.83140   \n",
       "8   1.05781  0.0093  1.715000e-09    2  ++-++++?      0.0      6  0.89820   \n",
       "4   0.94753  0.0093  5.968000e-09   25  -++++-+-     20.4      7  0.26810   \n",
       "5   1.07691  0.0088  2.582000e-17   22  ++-+++++      0.0      7  0.63360   \n",
       "1   1.06481  0.0113  2.429000e-08    0  +--+++++      0.0      7  0.92030   \n",
       "3   1.07047  0.0090  4.831000e-14   51  ++--+++-     27.8      7  0.20620   \n",
       "7   1.08426  0.0088  3.649000e-20   24  +-+--++-     60.9      7  0.01233   \n",
       "10  0.91448  0.0139  1.495000e-10    2  -+------      0.0      7  0.98410   \n",
       "2   1.09111  0.0094  2.141000e-20    0  +-++++++     16.8      7  0.29790   \n",
       "13  1.10043  0.0106  1.848000e-19   30  +--+++-+      0.0      7  0.74140   \n",
       "11  0.95180  0.0089  2.944000e-08    2  --+-+-+-      0.0      7  0.46360   \n",
       "\n",
       "      Nca    Nco      Neff  \n",
       "9   56418  78818  64441.44  \n",
       "12  48223  60717  53635.28  \n",
       "0   56418  78818  64441.44  \n",
       "6   56418  78818  64441.44  \n",
       "8   51274  64443  56864.72  \n",
       "4   56418  78818  64441.44  \n",
       "5   56418  78818  64441.44  \n",
       "1   56418  78818  64441.44  \n",
       "3   56418  78818  64441.44  \n",
       "7   56418  78818  64441.44  \n",
       "10  56418  78818  64441.44  \n",
       "2   56418  78818  64441.44  \n",
       "13  56418  78818  64441.44  \n",
       "11  56418  78818  64441.44  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schizo_snps= pd.read_csv(\"schizophrenia_snps.txt\", sep='\\t', names=['SNP'])\n",
    "schizo_df_final= schizo_df_filt.merge(schizo_snps, on=\"SNP\")\n",
    "schizo_df_final= schizo_df_final.sort_values('CHR',ascending=True)\n",
    "schizo_df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afraid-phenomenon",
   "metadata": {},
   "source": [
    "### Confirming that Schizophrenia SNP's are in vcf files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "binding-likelihood",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHR</th>\n",
       "      <th>SNP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>rs14403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>rs1198588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>rs2949006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>rs778371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>rs4687552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>rs2910032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>rs6461049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>rs11995572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>rs4129585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>rs7085104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>rs17691888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>rs11532322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>rs1006737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>rs4801131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CHR         SNP\n",
       "9     1     rs14403\n",
       "12    1   rs1198588\n",
       "0     2   rs2949006\n",
       "6     2    rs778371\n",
       "8     3   rs4687552\n",
       "4     5   rs2910032\n",
       "5     7   rs6461049\n",
       "1     8  rs11995572\n",
       "3     8   rs4129585\n",
       "7    10   rs7085104\n",
       "10   10  rs17691888\n",
       "2    12  rs11532322\n",
       "13   12   rs1006737\n",
       "11   18   rs4801131"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schizo_snp_list = schizo_df_final\n",
    "schizo_snp_list = schizo_snp_list.drop(schizo_snp_list.iloc[:, 2:19], axis=1)\n",
    "schizo_snp_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "disciplinary-netherlands",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 1, 2, 2, 3, 5, 7, 8, 8, 10, 10, 12, 12, 18],\n",
       " ['rs14403',\n",
       "  'rs1198588',\n",
       "  'rs2949006',\n",
       "  'rs778371',\n",
       "  'rs4687552',\n",
       "  'rs2910032',\n",
       "  'rs6461049',\n",
       "  'rs11995572',\n",
       "  'rs4129585',\n",
       "  'rs7085104',\n",
       "  'rs17691888',\n",
       "  'rs11532322',\n",
       "  'rs1006737',\n",
       "  'rs4801131']]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snp_chr = schizo_snp_list.transpose().values.tolist()\n",
    "snp_chr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "atmospheric-stability",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHROM</th>\n",
       "      <th>SNP</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>PARENT1</th>\n",
       "      <th>PARENT2</th>\n",
       "      <th>CHILD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>52752700</td>\n",
       "      <td>rs4801131</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>1|0</td>\n",
       "      <td>1|0</td>\n",
       "      <td>0|1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CHROM        SNP REF ALT PARENT1 PARENT2 CHILD\n",
       "18  52752700  rs4801131   C   T     1|0     1|0   0|1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr18_df = pd.read_csv(\"chr18_HG00379_HG00155.txt\", sep='\\t', error_bad_lines=False, skiprows=2, names=['CHROM', 'SNP', 'REF', 'ALT', 'PARENT1', 'PARENT2', 'USELESS', 'CHILD'] )\n",
    "chr18_df = chr18_df.drop('USELESS', axis=1)\n",
    "schizo_snp_found = chr18_df.loc[chr18_df['SNP']==\"rs4801131\"]\n",
    "schizo_snp_found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "proved-information",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = schizo_df_filt['P']\n",
    "bp_values = schizo_df_filt['BP']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlimited-target",
   "metadata": {},
   "source": [
    "## Generating Manhattan and QQ plots for visual analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hundred-baseline",
   "metadata": {},
   "source": [
    "### Manhattan Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "nonprofit-semester",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "0           0.414426\n",
      "1           0.086186\n",
      "2           0.372942\n",
      "3           0.060531\n",
      "4           0.325966\n",
      "              ...   \n",
      "13558618    0.736364\n",
      "13558619    0.243060\n",
      "13558620    1.459420\n",
      "13558621    0.302596\n",
      "13558622    0.340654\n",
      "Name: P, Length: 13558623, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-8991270fa9f4>:23: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticks(), size=12)\n",
      "<ipython-input-40-8991270fa9f4>:24: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), size=12);\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "# Manhattan Plot\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches((10, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "p_values = schizophrenia_df['P']\n",
    "bp_values = schizophrenia_df['BP']\n",
    "\n",
    "#bp_values = bp_values.to_numpy()\n",
    "pvals = -np.log10(p_values)\n",
    "print(pvals)\n",
    "ax.scatter(bp_values, pvals)\n",
    "\n",
    "\n",
    "#raise NotImplementedError()\n",
    "\n",
    "ax.set_xlabel(\"Position on Chromosomes\", size=15)\n",
    "ax.set_ylabel(\"-log10 P\", size=15)\n",
    "ax.set_xlim(left=0, right=max(bp_values))\n",
    "ax.set_ylim(bottom=0, top=max(pvals))\n",
    "ax.set_xticklabels(ax.get_xticks(), size=12)\n",
    "ax.set_yticklabels(ax.get_yticks(), size=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "composed-eleven",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "379         7.768276\n",
      "42353       7.823041\n",
      "99597       7.862646\n",
      "99851       8.287014\n",
      "100535      8.524910\n",
      "              ...   \n",
      "13394708    8.471855\n",
      "13531911    7.364617\n",
      "13534498    7.400008\n",
      "13535893    7.344285\n",
      "13537154    7.466482\n",
      "Name: P, Length: 15445, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-39-a4cf404256f0>:20: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticks(), size=12)\n",
      "<ipython-input-39-a4cf404256f0>:21: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), size=12);\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAFFCAYAAACDjrseAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABLGklEQVR4nO3df7xVdZ0v/tf7/AAOCB5QsjgjHmUMi1BPkljMTGoppWknyQyxGefOzaau916zS4HhiEVC0TjN3Px2r15LSyQ0cQ+KRc0o3YmEgnvAM0ygYQJt/IHCQYEjHA7v7x9r7eM6+6y19vr9a7+ej8d+wF5r7bU/Z6+913qvz4/3R1QVRERERJQvDWkXgIiIiIj8YxBHRERElEMM4oiIiIhyiEEcERERUQ4xiCMiIiLKIQZxRERERDnUlHYBknbyySdre3t72sVIRXf5gOO6qW0nJlgSIiIi8mLTpk2vqup4u3V1F8S1t7dj48aNaRcjFTOWPIlyT++Q5W2tLVg37+IUSkRERERuRGSn0zo2p9aRuTMno6W5cdCyluZGzJ05OaUSERERUVAM4urM8Ka3DvnYkc1YfNVUdHa0pVgiIiIiCqLumlPrVamrjPkru9Hb1z+w7M2+4ymWiIiIiMJgTVydWLpm+6AADgB6+/qxdM32lEpEREREYTCIqxN7bAY0uC0nIiKibGMQVycmtLb4Wk5ERETZxiCuTnBkKhERUbFwYEOdqIxAXbpmO/b09GJCawvmzpzMkalEREQ5xSCujnR2tDFoIyIiKgg2pxIRERHlEIM4IiIiohxic2odKXWV2SeOiIioIBjE1YnqGRvKPb2Yv7IbABjIERER5RCbU+sEZ2wgIiIqFgZxdaLsMDOD03IiIiLKNjanFtyCUjeWb9jtuL5RJMHSEBERUVRYE1dgC0rdeGD9LvSrOm7Tr4pSVznBUhEREVEUGMQV2APrd3na7qYVm7Gg1B1zaYiIiChKDOIIgBHwsUaOiIgoPxjE0YDbH9uadhGIiIjIIwZxBRWkVm3/4b4YSkJERERxYBBXQKWuMub+ZEvaxSAiIqIYMYgroNsf24q+fucRqU6YbYSIiCg/GMQVUNBmUZdMJERERJQxDOJoABP/EhER5QeDOBrglhSYiIiIsoVBHBEREVEOMYgjIiIiyiEGcQXEvm1ERETFxyCugGZPPzXQ61qa+XUgIiLKC161C2hR51TMmDTO9+veO7E1+sIQERFRLBjEFVCpq4z/t+uA79etf35/DKUhIiKiODCIK6Cla7ajt6/f9+uYYoSIiCg/Eg3iRGS4iNwrIjtF5A0R6RKRj5rr2kVEReSg5XGry77GicijInLI3N+1yf0l2banpzfwa0td5QhLQkRERHFJuiauCcBuAB8EcCKAWwE8JCLtlm1aVfUE8/F1l33dBeAogFMAzAHwPRGZEk+x82VCa0vg1y5dsz3CkhAREVFcmpJ8M1U9BGChZdHjIvIHAOcB2OR1PyIyCsAsAO9R1YMAfiUiqwB8BsC86EqcT3NnTsZNKzYHem05RC0eURwWlLqxfMNu9KuiUQSzp5+KRZ1T0y4WEVHqUu0TJyKnAHgngK2WxTtF5I8i8gMROdnhpe8E0K+qz1qWbQHAmjgAnR1tgV/LDHOUJQtK3Xhg/a6B/pr9qnhg/S5ccufadAtGRJQBqQVxItIMYBmA+1V1G4BXAbwPwGkwauZGm+vtnACgevjlAfM1du91g4hsFJGNe/fujaL4hcWhDZQlyzfstl3+3CuHsKDUnXBpiIiyJZUgTkQaAPwIRp+2GwFAVQ+q6kZVPaaqL5vLLxWRMTa7OAigevkYAG/YvZ+q3q2q01R12vjx4yP7O4goXm4jpp0CPCKiepF4ECciAuBeGAMSZqlqn8OmlbO3XQvfswCaRORMy7JzMLhZlogKjClxiKjepVET9z0A7wJwhaoO9KIXkekiMllEGkTkJAD/BGCtqg7JWmsOkFgJ4GsiMkpEZgD4OIzaPQqBM28RERHlQ9J54k4D8DkA5wJ4yZIPbg6AMwD8DEaT6L8DOAJgtuW1t4jITy27+wKAFgCvAFgO4POqypo409iRzYFed+x4xAUhIiKiWCSdYmQn3AdALnd57R1Vz/cB6IymZMVz8E2nVmp3bKAiIiLKBzaeFVQfa9SIiIgKjUEcERERUQ4xiCMiIiLKIQZxRERERDnEIK6AmMmeiIio+BjEFRAz2RMRERUfg7gCYiZ7IiKi4mMQR0RERJRDDOKIiIiIcohBHBEREVEOMYgrIB5UIiKi4uP1voA44xYREVHxNaVdACKiIil1lbF0zXbs6enFhNYWzJ05GZ0dbWkXi4gKiEEcEVFESl1lzF/Zjd6+fgBAuacX81caybcZyBFR1NicWkCNImkXgSh2rS3NaRdhiKVrtg8EcBW9ff1YumZ7SiUioiJjEFdAs6efGvi1DAApL7L4Vd3T0+trORFRGAziCmhR59TAB5azPVBe7D/cl3YRhmgdaV876LSciCgMBnEFde0FE9MuAlGsslhr7HQPxHsjIooDg7gCKnWV8eCGXWkXgyhWWaw1PtBrXzvotJyIKAwGcQW0cNVWHM/e9Y0oUm2tLWkXYYgmhzOq03IiojB4aimgHt71Ux246KzxaRdhiD6HTNtOy4mIwmAQR0S59NS2vWkXgYgoVQziiCiXmLaDiOodg7gCGst0BlQHsjc2lYgoWQziCui2K6aguZGXOCq2LHYzGzWs0ddyIqIwGMQVUGdHG5Z+8py0i0FUd77xialobBh8A9XYIPjGJ6amVCIiKjIGcQXV2dEWOAXDnHuejrg0RPWhs6MNs88/dSARcaMIZp9/Kjo72lIuGREVEYO4Aps7czJamv0346zbsS+G0mRPqauMGUuexOnzVmPGkidR6iqnXSTKuVJXGSt+s3sgEXG/Klb8Zje/W0QUCwZxBdbZ0YbFV00NVCO3oNQdQ4myo9RVxvyV3Sj39EIBlHt6MX9lNy+2FMrCVVvRV5Vpu++4YuGqrSmViIiKjEFcwXV2tGHdvIvxwpLL8Z1rzkVba4unUX3LN+yOvWxpWrpmO3r7+gct6+3rx9I121MqERWBU6JtJuAmojg0pV0Ailepq4yla7ZjT08vJrS2YO7MyQCAm1Zsdn1dFueljJJTjjHmHiMiorxgEFdglSbDSo1TuacXcx/ewgRbACa0tqBsE7BNyOB8nERERHbYnFpgdk2GfccVff3FrmXzYu7MyUO+/A3mcsqHygjQLGltsU+07bSciCgMBnEFFqZpMGh6krzYuHPfkGSxx83llA+zp5+adhGGWHjlFFSliUODGMuJiKLGIK7AwjQNFr1Gatn6Xb6WU/ZMO21c2kWwVV1DmMUaQyIqBgZxBbSg1I1J85+w7fPVXF1N4KDoyUmdGpTZ0JwfWRxJPH/lM7YpRrJYViLKv0SDOBEZLiL3ishOEXlDRLpE5KOW9R8SkW0iclhEnhKR01z2NU5EHhWRQ+b+rk3mr8i2BaVuPLB+l+3o0kYRXHP+qbjugok198N8aZR1WRtJvKDUjd4++xld7W6oiIjCSromrgnAbgAfBHAigFsBPCQi7SJyMoCV5rJxADYCWOGyr7sAHAVwCoA5AL4nInXf8eQBl+bAflU8sqmMaaeNwymjh7nuhzUHlHUnZmywQNFzKxJR9iSaYkRVDwFYaFn0uIj8AcB5AE4CsFVVHwYAEVkI4FUROUtVt1n3IyKjAMwC8B5VPQjgVyKyCsBnAMyL/Q/JsUpC25ffOOq6HWsOKOuylkC36LkViSh7Uu0TJyKnAHgngK0ApgDYUllnBnw7zOXV3gmgX1WftSzb4rAtVfHaDFX0qbeIiIjyLLUgTkSaASwDcL9Z03YCgANVmx0AMNrm5X62hYjcICIbRWTj3r17wxW8ALw2Qz2wfhf7xhEREWVUKkGciDQA+BGMPm03mosPAhhTtekYAG/Y7MLPtlDVu1V1mqpOGz9+fOByF0V1AmA37BtHRESUTYkHcSIiAO6FMSBhlqpWOrZsBXCOZbtRACaZy6s9C6BJRM60LDvHYVuqcuSY/Qg6O1kbAUhERESGNGrivgfgXQCuUFVrhPAogPeIyCwRGQHg7wA8Uz2oARjoL7cSwNdEZJSIzADwcRi1exSh1pHZGgFIREREhqTzxJ0G4HMAzgXwkogcNB9zVHUvjBGn3wCwH8B0AJ+2vPYWEfmpZXdfANAC4BUAywF8XlVZExcxDrgjIiLKpqRTjOwE4DhlgKr+C4CzHNbdUfV8H4DOKMtHQx3IWBoHIiIiMnDaLXKVtYSqVD/m3PN02kUgIso0BnHkinN3U1rW7diXdhEiw98REcUh0ebUrCl1lbF0zXbs6enFhNYWzJ05OfcTv8+YNC7Si1/PYTanEoXV0sT7ZSKKXt0GcaWuMr708Bb0Hzd67pd7evGlh40JI/IcyG3dY5sqLzA2pxKF19vnPa0PEZFXdXt7+NVHuwcCuIr+44qvPprvqaaink+SzUBE4U1obUm7CERUQHUbxB06aj9rgdPyesXmVMqqPN1fzJ05Oe0iEFEB1W0QR96wBoGyauSwxrSLMMhw9nsjooTxrEOuWINAWZW1WvOjLtPZLVzFPOREFL26DeIaHdpinJbnRdR92PI8yMNNS7P9V99pOSVvWM5+jG5T1EXdV5WICKjjIK7fYTopp+V5wWmyvFl81dlDvvwN5nLKhve1j027CL682ZetmkEiKr66DeKKqjHiqrhL7lwb6f6yorOjDddeMHHg82oUwbUXTCxszWMerX9+f9pF8IVpRIgoaQziCqY/4qq45145FOn+sqLUVcYjm8oDn1e/Kh7ZVEapq5xyyagi6u9ymkaymZ6IYlCXZ5YiX6jbOJrUk6VrtqO3qvmrt68fS9dsT6lEVC3qWuW4jXXpEzesKVsjaYmoGOoyiCvyhZqjSb3Z09Prazklb/b0U9Mugi+3XTHFcd0BDmwgohjUZRDndqEelbHcU351drS51giQwSn/HfPiZceizqlpFyEyI9icSjlR6ipjxpIncfq81Zix5MlCt1wVQV2eWdzmA81a7qkg3GoEgijij7j9JPtgzWk5US1uNfwc9EB5UOoq4+aHNqPc0wuFMaf4zQ9tLuQ1oChcgzgRuUxEHhORbhH5hYh8XiRnHVVs5P8vcBf1CMsiNj87jXzM24jIosvTTzWtpnjWnFBUbln5DKqmFMdxNZZTNjkGcSJyNYDHAZwJYCuA0QC+C+CbyRQtPvUwH2iUncLLBewn5jTysUgjIvOu1FVGno6GW7LfuJS6ypi/sntQzcn8ld0M5CiQww41xk7LKX1uNXFfBrAcwLtU9dOqegGA+QD+m4g0JVK6mKRxsk1alJ3CG/JUHUKFkbca4DTif46yJqpvbkHcZAA/UB10aroHwDAAp8daqpjVQ2XLtNPGOa5rdekTaKe6ep0oCbWaJ7PWoTeNqbWcasmLWHtOREO5nQdPAPB61bLK89HxFCcZ9TCPodud+Otv+v/7F5S6wxSHyLdaI4Wzdm+RRl47t1ryOfc8nVxBqPDYRJ9NtZpFPyAiJ1ueN8A4d84QkbdbN1TVJ6IuXFwaRRz7Pvmtpcoqt1qMIDVryzfsLlTKB8q+9pNaXGuUshbEpdGf0u23vG7HvuQKQoW3dM12TkuYQbWCuDsdlv9j1XMFkJsEa24n24VXRpueIy0TWt0vgH6xwz8l7dc5C0JaW5rropaf6hOb6LPJLYjLdb+3oIpypzF35mR8ccXmyGorOLiBkpa324aipy6i+sbvdzY5BnGqujPJgmRFqatciECus6MNG3fuwwPrd0WyPw5uIHJXD6mLqH6xMSabsjbAK3VFGpo/7bRxaGYVGhVU1qbIcxuIEddUeDMmOY9CJ6LiYxBXpQjt/pUM7jet2Iw+VqFRQR3O2BR5F5013nHd5We/I5b3vHraRMd1LZyvlajw+CsvGGsGd6Iiq5WCJGlPbdvruO7xLS/G8p5uLQeLrzo7lvckouxgEFcwC1dtHZLBnaiI5s6cnHYRBnFL6xPXqFW39yxC315KFjvf5A+DuAIpdZWZ4sAjpy8+fxDZUSt5btaClBNTyDHpVBvZlrFaSsoHdr7Jn5pzoIrIJAAfAXAWgLEwjnMPgG0AfqaqO+IsIHl3+2Nb0y5CbrQMa8Qhmz5VLRnrLF8kpa4ylq7Zjj09vZjQ2oK5Mye7BmKzp58a2ejqJKSRgmHuzMn40sNb0G/p+9rYIJmrpSSieDhWPIhIi4j8EMB2AN8GcBGA02Dkj7sIwFIA20XkfhEZkURhoxLXSLG07WeKA8/sAji35RSOta+mwhhANH9lt+tUPnmbIcQtxUhcM8Fs3LlvUAAHAP3HFRt35itRMhEF49Z69C0AlwKYDWCMqr5HVf9cVf9MVd8D4EQAnza3+Vb8RY1OXCPFiMje0jXbh/TV7O3rL1RKH7eBFh87J55zjlNNZZ5qMIkoOLcg7tMAvqiqD6vqkFtMVe1T1Z8AuBlGoJcbq5+JZ6QY5YdTzUhR5s7NGqcO+G4d8/PGrQkzjXMOJywnKj63IG4EgP0e9tEDYHgkpUkImx3JqWYkrhqTeudUSxUmTUjWghS3/n1pnHPmPrw58fckomS5BXH/AmCRiDhmkxSRUwF83dyWKDec8nbFlc+r3jklwnVLkFtLkZpi49B3PO0SEFHc3Ean/hcAPwewQ0R+C2M0ag+M0amtMEarvs9cfmOspYyYSDHngWtpbkAvz9yeOKViYYqWeDglwnVLkFtLkZpiiYiCcKyJU9U9AM4B8JcAngMwBcDHAFwB4D0Afm+ue6+5rScicqOIbBSRIyJyn2V5u4ioiBy0PG512c84EXlURA6JyE4RudZrGdwCuDznCRvRHG96jKw1X1F+BO0T55YrLmszNhARJc01T5yq9gNYbj6isgfAIgAzAdidhVtV9ZiH/dwF4CiAUwCcC2C1iGxR1ZrJ0tpaWxynpcpzPZZbioMoLF2zPdIEqwtK3Vi+YTf6VdEogtnTT81EWolSVzlziWTzboLDb65WINbvcscVpimWiPxpauB8DlmUeMWTqq5U1RKA14LuQ0RGAZgF4FZVPaiqvwKwCsBnvLy+qIkw466ZiLL5akGpGw+s3zVwke5XxQPrd2FBqTuy9wiKfa2iN3fmZLRU1RS3NDfW/C26TeIepimWiPw5dryAfZAKIHQQJyJ/ISJPRlEY004R+aOI/EBETnbY5p0A+lX1WcuyLTCafGsqai1L3MFplEHisg32eayclieJfa2i19nRhlnntQ00jzaKYNZ5ba6/xVJX2bWPJ48TFcWCUjcmzX8C7fNWY9L8JzJxM0v5EEVN3HgAH4xgP6/CGChxGoDzAIwGsMxh2xMAHKhadsB8zRAicoPZD2/j3r28ew9CEG2Q6NRKloUBJ60FndEjrFJXGTOWPInT563GjCVP+uojWeoq45FN5UE1r49sKrvuo9Y0cuwTR0WQ5VYJyj7HPnEi8pce9/G+KAqiqgcBbDSfviwiNwJ4UUTGqOrrVZsfBDCmatkYAG847PtuAHcDwLRp0zIQJsRj4ar45k79wKRxha3BrJaFQDJrKtNmVWZdqEybBXir2XabscHp9bVyqxW1WwTVl+Ubdjsuz0IfYco2t4EN98FIJ+KlN2Mcl73KPu3e/1kATSJypqo+Zy47B0BdzwAfZ3qMzburKz7DGdncgMM2TWUjXfpARaVWDdIBphkZIkgQZhX1jA0tzQ11c1MRVBtrKnPBafCO26Aeogq3K+ZLAP4PjCZKt4fXGjsAgIg0icgIAI0AGkVkhLlsuohMFpEGETkJwD8BWKuqQ6IHVT0EYCWAr4nIKBGZAeDjAH7kpQyspvYv6onh77jqbF/Lo1Rr4AKb6YYKG4RFPWPD4gS+J3nXfhK/x3nglEbHLb0OUYVbEPc0gPNU9ZDbA4DfW+kF5mvmAbjO/P8CAGcA+BmMJtF/B3AEljlZReQWEfmpZT9fgJGi5BUYKVA+7yW9CAAsc5kcOomaoLiM9dGX6zvXnDtktGDSqk9RSZ2yagUebKYbKmwQFmR0qtM1TJDNwUlZy6O4bsc+3rCmaM49T6N93uqBx5x7nrbdbvb0U30tJ7Jyi1hWAHjewz7+A8DXvL6hqi5UVal6LFTV5ap6uqqOUtV3qOpfqupLltfdoaoftTzfp6qd5vYTVfVBz2VwWWfXxJcXt13haXAuAKM2atZ56V0Ib1n5zJDjoObyuNUKPLIYIKQtaIqQis6ONrx34omDlr134omun/UHzhhnu/xto4d5es+kZTE1jdsNK8Vnzj1PY92OfYOWrduxzzaQW9Q5FdddMHHQyO3rLpjI/nDkiduMDQ+p6qdq7UBVf6eqt0dbrPTkuQrbT/BR7unFI5vSqzlwCpaTCKJrBR5Zq1HJgs6ONiy+airaWlsgMPpbLb5qqufv3IJSt+1Fza2m6IXX7GtMX37jaCZrmLKY8oS9qtJR/V2vtXxR51TsWHwZXlhyOXYsviy1AI75fPMnv22HIYwa5tyMWE+dSas7qrsp0o+7VuCRxRqVLOjsaMO6eRfjD0sux7p5F/u6aQiSF9AtKHoggzVM7EtJecd8vvnjKYgzE/o6Pf5MRM4WkeFxFzYq3/gEq6n9Gt5UP/F+FmtU8i5IXsC8BUXsS0lESfN6ZV4L4CnLw/r8lwC6AOwTkX8UkXR7y3vAPk/+vRlxM6fTdEpu0ywlJW/BQ1HlbW5UnleIKGlueeKsPgzgXgBPwJijdC+MmRo+DuAyAF8C8C4At8BIxPvVyEtKqTqxJdpZDBZfdTZuXrEZ1tCwAdlIHcEalWzg3KjJKXWVsXTNduzp6cWE1hbMnTmZQWmC+PlTUF6DuBsB3K+qC6uWrxGRhQCuV9UrRKQJwPVgEBdKFn/Qh44eQ6mrHFk5OjvasHHnPizfsBv9qmgUwezppwbef5SfWdqfNRnYrJ2MsLNxUDj8/CkMr21XlwL4lcO6dQAuMv//fwG8I2yh6lnlB13u6YXirR+01xGTcY0/6OvXSDv8B5lL021ffj4zjj7Nh7Bz2IaZ67WeuM3GQfHj509heA3i9gG40mHdleZ6ABiJoRPTZ04W0xNUhP1Bxzm4KMqakShPXH73xZNjPoQZKF7qKmPuw1sGBfY3rdic6d9+WqKeEo384edPYXgN4r4F4EYRWSUinxWRTvPfxwH8FwDfNLe7CMBv4yholJwmHM6CssMP12l5kqLs8B/licvvvnhyzIcwcwEvXLUVfTb5Eh5Yv4s1clWc+rtG3Q+WDNXfv6inpKP64imIU9XvApgF4O0A7oIxb+ldAN4G4CpVvcvcdDEsU2VlVZZzwWV1Hj0/2fm9iPLE5Xdftd6jyBf5UlcZ7771pwNTAZ0+f3Vma6fCfOfdAsAi1cRGcVZwnN6sQLkhs6T6+xd2NhSqb57zOajqo6p6PoARMPq9jVDV81W1ZNnmVVV9PfpiRivL5yanANNL4HnJnWsjLs1bZp3XFmkn2yhPXH73Ves9inSRt7rkzrW4acXmQbNiqBq1U1kM5OK62SpSTWwUn1DPYfuA12k5hVP9/Qs7GwrVN6+jU63GARgD4BiA16ItTjJGDmvEoaPeZytIUltri23TaZuHGqrnXjkUR5EAAI9sKmPaaeMiHZ0KIJIRpZXXLFy1daAGZoRLvrnOjjbMX/kMeh1y3xXpIl8x556nXb8fyzfsztxcjU6/hbDCDpgomgkOn3MWm/OyOHLfL7vPtbMj2ptkqh+ea+JE5BoR+R2AlwFsA/CKiPxORK6OrXQxOZzRAA4A2k+yP3E6LU+K20CBoKMAN+7ch5cOvAkF8NKBN7Fxp/28gl4dOfZWULb/cJ/jCNVSV9k1eXEWL15hOc3ZWJHFLgZuNaZhZhDJ4J8aWBStCnNnTkZz1bx6zQ2Suea8sCP3syJrnyvlm9dpt2YDWA7geQB/DSPB71+bz38sIp+OrYQxyPJF+unn7S+2TsuTZFdDFfTEuqDUjQfW7xqUYiRMs56fEaq3P7bVtRmqHk+yWe5iYOfoMfcZRNz+ngMhBkxkTWTxaPUHlsEvRFFScWS5xi2Dh51q8Ho7+1UAd6vq5ar6Q1VdY/57OYB7ACyIr4jRy/JF2mkC4ixMTGwX/AY9sTqNEA46ctjPSNT9Nfr6ZPkkG5eRw7I3W96XHtrsuK7Wz8FtfZZv4tKwdM129PUP/sSizgsZhaKMNs9yzWEGLjPkk9cg7k8BPOKw7hFzfW7U40U6LKeBAkFPrGEGcNjhMP1wstbFoNRVRr/LV6HWyFW39XHNyZrFwSFe5CU4KkoqlKwFx5RvXoO4lwFMc1g3zVxPKQvTT6gWp9FSQYOnqFOpXHTW+CFNARym712WOvtXmujdnDF+pOt6t5uB1c+8GKhctSxbvyuW/YbR6OHnlJfgqCipULIWHFO+eb3q/wDAQhFZICJnichYEZksIgsA3Abg+/EVkbyq1U8oDkFThcyefqqv5W5KXWU8sH7XkKaAqNOiFFmW0knYNdFX+32Nkdhuo7lrNacHlcWmKLfazIq8BEdFSYXidIPLaeIoCK9B3NcAfBvAPABbAbwK4D/M59821+dGUX8ccV5EvvyTLbbLg+Y4WtQ5FdddMHGg5q1RBNddMDFQmou5D2+2Xb7iN9mrGcmqLAUgXmoqapU3ribTIspLcFSULhN2N7hZGXl73QUTE30/Cs/rjA3HVfWrAE4FcCGMWRkuBHCqqi5Qzdeg/YWrtqZdhNw52q+OJ5SgqUIWdU7FjsWXDZw4Hli/C5PmP+G7b5FTthCXLCKukurbxDtve1E04z21bW8EJcm2qL6neQmOspp+yS+7G9yFq7ZmYuRt1nJFUm2+kv2q6n4A/xZTWRITZk7GerZ0zfYhJ6BKqpCKSqoQwNsJIezr45BE4tvKnXflxF258wacB94UIdGpF7WaUp1YP59c3VUGtGxDNDXNc2dOHvRdBLLZn3T98/t9Lc+LUlfZ8ZoUZ/85p/OJwL6mO+2pH8meYxAnIpf52ZGqPhG+OPHLWhNBVJKoxbE7oTh15l62fpenQMgppciyDd5eH4ckEt+6pWaxC8yCBH15dSRA387qz6eo/AaqMyaNq7lNlLOnxCnqEe1Z4VbbFldtqNP5ZOPOfWhoEPTb5LQK0l+Z4udWE/c4jIDcS/itALKXaMrGS6+/iZPTLoQLp7ugWgchiWp3uxOK0+nT62nV6QSsCrTPW43WlmYsvHJK4AtKqavs+7VJ3HH6TevgN+irN14GQ+RdkED19PEneNqO0z4lp/qc5FbbFlffTqfzyfINu23PycObGtjUmlFufeJOB3CG+W+txxnxFjM6ff3Jj+D0Y45Dx1Kn5RVJDFuPunnFS+1hT28fbl6x2XXbFpd5UoP0f0zijtNvPySnOUTjmFs0bUFC6HpI2xAkUH0woiZXik71DbdbbVtcfTudfi9ON9VHjh1nn92Mcrz6qepOP48kCx1Gc2N8udSiMO20cUMOSoO53E0SnZCjvFP3kgus4jjcgzGniewBIwj0e/Kp9VlHwW9qFrfawaINiqh1w2Ina53w4xAkUM3CTC80WPVxdLs5juvmxOn34nae4YDAbPId0YhIg4g8KSJnxlGguL19zIjQ+4hzVOHSNdtRHZIcR+3m0qx1Qq7Fb62C22CUWs2fTilInFQ+6ziPs9/ULG79fvI6EbiTSvoZP+ohpUjWku9SMNXHsbOjDa0Oxzaum5O5MyejuSoTdHOj4IIzxjq+hgMCsylItZTASC8yOtqiJKN1ZDNGujS/1RJ3Pp+gzWaVUUV5EeUdZq2OzX5TjZR7etE+bzVuWrF50HGe+/CW1AIlp5N8Ra10BGe+bVTURYqV3/43Kzf9MaaSZEecXTWZ7iY5dl16Fl45JVDS9FCqTpt9/Ypf7/CWHooMC0rdmDT/CbTPWx0oPVYUst22GBO35rdagk747lXQ6ajm3PN0rtIqRHmH2ZBQ9Np3XCNrUvB7M+Cl1tItMP7FzRcGLGk+HA7xm86LIDNN1Ar+gewkmq0Xh2zmKQ6aND2opWu2o8+mrT1P15C0VdJjVSoRKumxkg7k6i6IK3scmu/2ej/L/Qo6jH5dAndQUZ7U7fqEBZVkv5+omhT83gx4SbvhFhgX7YIcojK9bjTAqOGpJe4bU/Kms6MN6+ZdjD8suRzr5l0c62jhehgIFDe39FpJ8pXsFwBUtV9ELgKQy1/4vkNH8Q6X9WNrTATeKGIbUEWVlqKttcU2IHSbCzIpUeYls+amChMA5zU48ZtipJbmBnFteinaBfma87M3PVBzg3PTfRpdHU4c2ezptxr1d5HcOV0qFpS6B1J8NIpg9vRTY0vrMcHhOkPehU2vFZVA97Oq+ktVdZ+BOqfe/Q73rn5xJ5x06qCdhY7bUd+dV+48wwSoeQ1OnDqpB+68XiNKKNoFecVvd2cugBeXG7kgI27D8prYPC/TbhWF3aUi6aa5vE1VRs7YKFGlVrOkU8ARVU3Z41te9LU8aUGCgVqdpu1GSkVZnjADWeLilK8waB7Dvn4Nnfm9VlCUpc7vff2K2x/LVsqDo/3Z6lHkNQjzm+6GwrG7VjjNXOO0PCwOYCiO7F3dMsDt4jR35mQ0V/Wkr9WU5YdTn6usDO/2e3fupdN0Z0fbkM+0mlMH7REeArQ7rjp70PMsjOK169zsttwLt4DWy/fTrTN7qauMuT/ZMni07k/SG60LBOvoX5F0MBrXxdiN19r7zo42zDqvbaBLSKMIZp3HGRziYvdbTHpKsWzdbiQrSzejUWAQZ6NWE131DysLc/e5zVoQpcqFofJDqMVrp+laows/ds5bPRmtP8IgI43TP1rxCJtHzK25/PbHtqKvqqYpi7VhXiUdjKZxjnhkU9nT31XqKuORTeVBTXleX0v+2QXHQbMSkD9FHInNIM6GW4fPrz7aPWQ05HE1lqepIaEf++pnXhz0Q6glqk7TlTxg1T9CL25+aPC0XWFOjBlsmR1w9JhzLZ7X1ChOx8Wp1itMbVhYXtJneJFEMJrGtdhrH1aOTk2f01R/cU0BOGpYLqY6j1wRv+sZviRlUxzNYFFI6v33H+7zNdtCVJ2mKzV1QeaPPK6Da1fD1Iq0n5zdpLlutZlem+Pz1JndS/oMr2IPRjWdkdRebpbqaV5eqywkaq2ozFJibdK+7oKJsY1O/cR767OpvIjfdd8pRoj8fOHnzpyM+Su7BwVeQTtNl7rKgX9s1ouZUxoXL557pZCDsgdEPQq61FXO9V1uVBTRpujxKk9BeZIqo0ErKqNBAf+zhfhV6irbfgcWdU6N/b0rHqmDGU7qReI1cSJyo4hsFJEjInJf1boPicg2ETksIk+JyGku+xknIo+KyCER2Ski18Ze+AyLqmnJy/t4bY48fd5qLF2zHbPOa3PNRO61hqJyEQyi1ZL/r6ij7mrlOPTiqW17bZc7NSO7NS/7aXYPIm/BYRrNNpXvepjO3EXrCA5gUADnZXmUsvC9DTNrESXD+rtrHt/uGN2n0Zy6B8AiAN+3LhSRkwGsBHArgHEANgJY4bKfuwAcBXAKgDkAvici0bWv5MzCK6fUHOEZ1ft4bY6sdBx9ZFMZc2dOdsxE7rU/od9m1EFlsRS5iKPuGhsEt13h/PX3GuA5Nb85Zc9wy6oRpOnbjyhz30Xx0/GyiySbbcaayX5LXWXMfbhqZLHHeYCL2BG8lribVYuWs5GiV/27k8amYU7bJh7EqepKVS0BeK1q1VUAtqrqw6r6JoCFAM4RkbOq9yEiowDMAnCrqh5U1V8BWAXgM7EWvganvhVJ9L3o7GjD0qvPiXy/VjMmjQsUALnVQCwodSfSn+9ARlK0BFUrQNAagbVbgGfl1PzmNLWZ25RnYS5WXoIEa+3qKaMdz3GeXDs9fDJeL7c2SY42rBzzhau2Dpkn0+s8wEXsCF6Ll9q4MLWTbOKmWvzcAGdpYMMUAFsqT8wZIXaYy6u9E0C/qj5rWbbFYdtEVWfaTjITd9w1TFdPC36hc6qBSKL5Aoj/xBl3k1OtFDLHFa4jLL18N6JO8Ool5cl1NjMZVO5Ca7HGrXsPHvVVtoq4O5BXCzOoJmhtYZjck5ySa6iwtZNF7c5B0fHz+8pSEHcCgANVyw4AsJsHy8+2EJEbzH54G/sPV78sHg9uMIKTpDNxx+mmFZsDvzbtbEdxnjiTaHKqlUcPcB9h6aUs1X0Vw/JS6bTh+eoKee93oZUgpNRVdq0RtNPa0owXllyOHYsvSyyAA8LN7OK3tjCK2jJOyTVU2NrJLHTnmDFpXNpFyD27G1C35RVebvj95PzMUhB3EMCYqmVjALwRcluo6t2qOk1VpzWOPDF0Qb2oXFSSzsQdt6CBid1fm1S/GkG8J848NDl5KYvbZxQkGamXtB12o3293oVW3jnI55xG83rSU1lFUVvGKbmGKkKaihdey09Zs2pR51SMGT74tzFmeKPrTaHXG/7DR495LkeWgritAAY6dZn93iaZy6s9C6BJRM60LDvHYdtUJZ2JO+4+N1EFJl6bzKIgEk/AWLmjcjp5B72IxlFWv2WpvltM8mbEay2PwihnkM+5NYKRvH6Frelc5rPrQRS1ZWGm5CriqFagGLMr5CngzKpL7lyL148Mvnl//Ug/LrlzreNrvN7w+5mHOY0UI00iMgJAI4BGERkhIk0AHgXwHhGZZa7/OwDPqOq26n2Y/eVWAviaiIwSkRkAPg7gR1GVM6qTT1KZuCsnzLhr+IIGJk1VHXriHrloVZ3sN4zKX1HqKuPmhza7ngydLqK1vk92d2ZhLw9+quft7hadODUPhvnNzJ052fPfu3TN9kDByus5HOji55ctiKYLQdApuYo8qjWOG5qkA978hJvZ5ZQz1C2XaBx9TNOoiVsAoBfAPADXmf9foKp7YYw4/QaA/QCmA/h05UUicouI/NSyny8AaAHwCoDlAD6vqpHVxFlPPtXTNvnhJRO39QccRNz5uKyC3t0fq+q0lHTH6Mr7VUYKB1WpwfnKI8+49sNyanLyUgNpd2c2p0Y/i1r8BMx+Amy75MBha1k7O9o8Byx7enoDBSv9aowmb5+32vXO2Y9auRq9pvWIQlMD8MUVmz3Nb+wmaFeBrHcxSCqvpp3q70AaAW8+O/PkXxx9TNNIMbJQVaXqsdBc9y+qepaqtqjqhar6guV1d6jqRy3P96lqp6qOUtWJqvpgXGU+rsAtK58J/PpFnVOxY/Flth2pg8wFWi3JWq0wd/fWO8xaoy2jNqG1ZchI4SAq/byOHHMeaGCX0LjC67GqDnKnnRauI7JbeWu9txu75MBJfh8ntLaE7u/43CuHIgnkjh93/4y9pvWIQt9x1KxF9SJozUHWR7V6nYYuKLeW1eq8mFkPeCk6cfQx5bRbHnkZHRhEFBe8JPs3dHa0BR6lWrnDBIBeH0FFFC46azyWbQifzqRRpOYd8rp5Fzuu83oRq74zS+LiX5kOaIKPacnstkvyQh1VB/soplOr7h9jJ+7gIWqtI5ttB6hY+xQuKHVj+Ybd6FdFowhmTz/V8TtUL6Na3e4Tq/NiFmGgRF4Mb2qwvaEd3pRMpULlhnPpmu3Y09OLCa0tmDtzcqgbUQZxMTp93uqaBymKC16jSG5Gu1buMJMu7rINuyJ5z37VUHfIXgOk6uAkiYv/3J9sGXhvr4G6XWduP0GgHT/NSJUZCQT5biJaUOrGgxt2DTTRtzQ3YPFVZ6eejsLpN1NZ7jQH6ZlvG2X7ujBz886YNA7rduyzXZ4VQeYKzvt3N08aHWpInZbHobPD28Agr7I0OrVwvPRviOLONC8BXMWent7EO9ZG9REJwt0hv9HrLSltGhfvvn7F7Y9txcadQy+UTuy+e3ZNBl4F6U+3dM32XF8EK4GQtY9lb99x3LwieF/cqNRKFOyUrNupZtNpbl4vln32/UMCthmTxmHZZ98feJ9R89I3ufrc5/bdjXsKsHrj1KIWV0tbElgT59HIEH24evv6cdOKzQO1G9YTz9yZkzH34S1DpsXxo7WlObFmmiguKiOaG3I7AbOXozTnnqcdLyxemtzStP9wn69UFnajUysBaJBmd7/dC0pd5dw3Ozkl/j4O4EsPGbWjadfIRSVsy0OWAjY7Xr67fgYpLduwK9Fk1JQ/rInz6I6rzo5sX+t27MOce55+a0HIaqkk0xO5Te3kVV4DOK/W7dgX2YjHqIwa5r1mzOvthFuH3KBBh9+AbOma7ZnIzxXm5satJr1ftTCpOYDwLQ95zj0ngO0Ub26/zZw1smSe05ki/TNIcHUXxDU3ZuNPrvTtWLpmO/p8JPaz0+MhM35UvGThJ6M5KUsXmOrO1E68pF4QuI/ATdKent7UuxOETalSKwjN60jF5qrckM0NEmogijU3Y9j0T2FaVoJOndbUKLajzLNyTaoHTmeKPMfKdfftefuYEYFeF0UNlJ0oBjaM9FHLQsnJ0oXXy+TpzQ2ChVdOqbndH5ZcjnXzLk49gAOMmp0w85FGIewIcy+Jv7OSmsOP6vr2sPXvt6wcmpsxaPqn0SOC9yQKOjij0ue0Wt5GLFO21F0QF1SQGigvVbRhpv+pNC14rWWhcBq9REIWYS68UdbieZkgvrWlGUuvPsdTYNY+bzUmzX+iZqfrJHIBzp05OfV5PMMGWJWE4G7frjym5uiv+tL1H7cPYryq1Sm9ksjby/fz5TecBxjV+pWv3PRHx3W1zhF21xGfpxWiQeouiNtzILk72j8subzmNm8GvINPcpYGMlRflGoJc+GNshZv7sOba24zaniTr5q1SioJtwulnwTDQUU9XN+JW1+suAOsIk04H1d3jOpE3l6+n05qDTxwG8no9xwBoOYNll+X3Ll2YDaSKGckoWyquyAuyI8sTkE7+SeZFZ+CqTS7BJlWLcrmMy9fsXJP70BgMtZH7bBTigkg2MUp4Yk8PKk1LVL7SeE76y9bv8uxX857J54YW9O8W41vFo+FE6fvodv300mco0H9Tvfl57cIGAFcdXqXqGYkoWxiihGP/P6Y4rCg1D1wgsljH5l689S2vQMBgN+AO0wze1A3rdiMjTv3+R4RZ5exP+g0YcOaGtGXse4BbtMidXa02Sag9bt/t4887P7dzHfpT1bwQeSJ89rn1Orys9/ha/sgk7LXkwbY983M0f3KEHkue6Juu8Lfj6/CqdN1ZfkwH6miH1i/a+DOOY99ZPLOb9eVPT29gWtMrYFUmP5xfl/7wPpdvjta2zVj3Rxwaja//TuTGAEc97RIad6QJZ3uJw/dv+JKY9J3XH0l0gaA1c+8GMl7k8Hp257kryDq7xeDOI8q/W78noRqTXjrN8nvTSs24/R5q3H46DEevIR9wOf0PhNaWwJfoA+YgVSpqzwwHZZfYVNfhJHUSTHIyES/nFKARJWfrp5uyPwkuq3W5DACwGm5G6dpwUY0SmRpTOz47afHlE7FUqtrRhCMA3zy282ns6MNi6+airbWFtv8WkFSXCmMHzdbO5L1wmveA7IGMQL4oM2ilQv77Y9tDZxHMI/9Jv2OaE1iuhynPHRR5aebO3NyLmqoohCmv9m3rz5nyOck5nK/nt972Hb5m/1qm8bkK49Ed7PgNEMHxc/pvivI/ViQxMFuXTOCYhDnUZhIubOjDevmXWybXysL2ebJGz+1aqrGcQ86+rhSUxvmTjyP/SaP9Wfv1sSpP+zYkc2R1NB0drTlOtmoH9O/8YvAr+3saMM/XHPuoBvif7jm3ECjk/0G4H5GWtc6p6ednLqefeAM+9YUp+VugiQOdjonW5f7PacwiPMoTKTs1AZe6iqjQfiDzosTfYwsqxzVMH2Owk5+ncbgiLCy2JneKZDef7gvsubqtBMWOxneZH+JcFpey8tvHB085aAPC0rd+KKlqXP/oSOB9hOU14srg7TscmpN8dPKEoZT1wnr8oWr/OVSrLsgLujJJ2gnZqc28AWlbsxf2Z3JixbZ89PhP2wN61cf7Q7d7HLwTfan8couiKrcfLmJqrnaru9sFnxz1tlDktE2iLE8qCFzR3tQyQNnjY8O9x331F8tqvQaRZrDtl55qQmLU60+8oD/GTzqLogLmoA06EXZqQ18+YbdueuvRN5VplIKmprm0NH+QHf01otMHm8Q0sheb5dMN+lk2pW+s2l0r3DKXdba0ozOjjbc+anBTZh3fipYE6aV37QpTjc0x7V2K4ldeo0gNZ9RzWGb1VrXeuClJixOtfrIB1F3QVxQQavInSL8oPsbNayxbjpB59V1F0wc6MB92xVT0OwjjYxVkAt6luZr9aJ6lOC104OPXvTL7SSaxqCQzo42HE+hKW7hlVNsJ6uv5DRz69ObFLfzZZBalLkzJwf6XUZRY1OU2TfyyEtNWNxq/Z783vgziPMo6N2TU4Qf5AI9rFGw9Wsf8TSdF6XHOgKvs6MNSz/pf/ScwNvE6NWsF5msz8l45ttG4Rc3XzhoWWUeUa9m+Ez7UjF2ZLNrUBJX80qtn30a6UY6O9qw9OpzBtUOeJ1HNylu58sgn1nld2m9YLa2NNe8gEZxfLLwuX7nmnPTLkIq4qgJi5rfnLR1F8SNGzUs0OsqkbrXi8YI8y7PKfKfPf1U331gvhUgGKDogpmgP5bOjjbfNwFzzNq86y6Y6Cvgt15kgtRqtTQnU9Pb0twwJICrWNQ51TGPl9WIRsGyz74/0PvXOlHGFUzNqXFMvPaNu+6CiZE0y1UC5qRr2/wG3043NJVUPn4C/4rOjjZ0/d2leGHJ5XhhyeXYfNuluO2KKY6ff6XGJsznbtd0nca9VpDjm6dp2NxkoWbZTWdHG04Z7T1OKchh8a6ttcXXD15g3LVUDvSyz76/5gloRKNg2zcuA+Ac+S/qnDqwHHC/02xpbhhUBsD9TirOk8J1F0wc0vQSpAzXXTDR04U6rFNGDxvo0xNGW2sL7rzm3JrfHafj4vXi3CCDm2MXdU7FjsWX4TvXnFvzc69uFvASBM6YNG7Id9MtIWtU088tvsq9Y/wvbr7Q9UR25ttGDfzGAHj+TTc3YMhvyY6X4zVj0jjPN2KNIoOOqxO780UlYKs8/84152JR51Ssm3cxXlhyec0TvtPRHzO8MfQ8oUFqQmdMGuc7+K58l61f5ZHNDQP98xZ1TnX8HPyU0fr5A2+dl601NnbfDUHt74PTtFtuvze/NWZO29st93v+XXq1v7IUndM5J8gNRbUNX73EcyAnWmfDoadNm6YbN24EYIx4WrbhrRFPI5sbcMdVZ2cuMndS6ipj6Zrt2NPTiwmtLZg7c/JA2a3zWUahzbL/6ve96KzxeGrbXttyVJelMrdm9cXDqbynjB6GDV+9ZNDfvHDV1oERPGNHNuO2K6Zg4859gya7FrxVm+X0mZ3Y0gwRoOdw30C5ATh+ptX7uf2xrQPpJ1pbmrHwyimu3x234+VF5fV2ne3bAuzPjdsxq/47Rg5rGNR5fMakcTh9/Am2x9PL52Tl5zPz8j3zw/reI5obcOTYcRxXDNp32GMaJbeyVE+MbteUHdSce54eNFBhWKPgfe1jsf75/ZEdi6BlCRIweuH0Wdc6v7h9d6O6Fvn5TlZ/L6yaG4Bjx5H69zrLoj7nVKscy43/+DkcefE52/uxug7iiIiIiLJMRDap6jS7dXXXnEpERERUBAziiIiIiHKIQRwRERFRDjGIIyIiIsohBnFEREREOcQgjoiIiCiHGMQRERER5RCDOCIiIqIcYhBHRERElEMM4oiIiIhyiEEcERERUQ4xiCMiIiLKocwFcSKyVkTeFJGD5mO7y7ZfFJGXROSAiHxfRIYnWVYiIiKitGQuiDPdqKonmI/JdhuIyEwA8wB8CEA7gDMA3J5cEYmIiIjSk9Ugzou/AnCvqm5V1f0Avg7g+nSLRERERJSMrAZxi0XkVRFZJyIXOmwzBcAWy/MtAE4RkZPiLhwRERFR2rIYxH0FRtNoG4C7ATwmIpNstjsBwAHL88r/R1dvKCI3iMhGEdm4d+/eqMtLRERElLjMBXGqukFV31DVI6p6P4B1AC6z2fQggDGW55X/v2Gzz7tVdZqqThs/fnz0hSYiIiJKWOaCOBsKQGyWbwVwjuX5OQBeVtXXEikVERERUYoyFcSJSKuIzBSRESLSJCJzAPwFgDU2m/8QwN+IyLtFZCyABQDuS7C4RERERKnJVBAHoBnAIgB7AbwK4L8C6FTV7SIy0cwbNxEAVPVnAL4F4CkAO83HbekUm4iIiChZTWkXwEpV9wJ4n8O6XTAGM1iX3QngzgSKRkRERJQpWauJIyIiIiIPGMQRERER5RCDOCIiIqIcYhBHRERElEMM4oiIiIhyiEEcERERUQ4xiCMiIiLKIQZxRERERDnEII6IiIgohxjEEREREeUQgzgiIiKiHGIQR0RERJRDDOKIiIiIcohBHBEREVEOMYgjIiIiyiEGcUREREQ5xCCOiIiIKIcYxBERERHlEIM4IiIiohxiEEdERESUQwziiIiIiHKIQRwRERFRDjGIIyIiIsohBnFEREREOcQgjoiIiCiHGMQRERER5RCDOCIiIqIcYhBHRERElEMM4oiIiIhyiEEcERERUQ4xiCMiIiLKIQZxRERERDnEII6IiIgohxjEEREREeUQgzgiIiKiHGIQR0RERJRDDOKIiIiIcihzQZyIjBORR0XkkIjsFJFrXbb9ooi8JCIHROT7IjI8ybISERERpSVzQRyAuwAcBXAKgDkAviciU6o3EpGZAOYB+BCAdgBnALg9uWISERERpSdTQZyIjAIwC8CtqnpQVX8FYBWAz9hs/lcA7lXVraq6H8DXAVyfWGGJiIiIUpSpIA7AOwH0q+qzlmVbAAypiTOXbana7hQROSnG8hERERFlQlPaBahyAoADVcsOABjtYdvK/0cDeM26oYjcAOAG8+kREfn38EWllJ0M4NW0C0Gh8TgWB49lMfA4Zs9pTiuyFsQdBDCmatkYAG942Lby/yHbqurdAO4GABHZqKrTwheV0sTjWAw8jsXBY1kMPI75krXm1GcBNInImZZl5wDYarPtVnOddbuXVfU1m22JiIiICiVTQZyqHgKwEsDXRGSUiMwA8HEAP7LZ/IcA/kZE3i0iYwEsAHBfYoUlIiIiSlGmgjjTFwC0AHgFwHIAn1fVrSIyUUQOishEAFDVnwH4FoCnAOw0H7d52P/d8RSbEsbjWAw8jsXBY1kMPI45IqqadhmIiIiIyKcs1sQRERERUQ0M4oiIiIhyqHBBHOdejZeIrBWRN83+iQdFZLtl3YdEZJuIHBaRp0TkNMs6EZFvishr5uNbIiKW9e3maw6b+/hw1fteax7PQyJSEpFxlnXDzeP3unk8b6567bkissnc9yYRObdqvefvQa19ZZWI3CgiG0XkiIjcV7WucMfNz3mg1r6yxOk4msdBLb/LgyJyq2U9j2OGmJ/Zvebf9IaIdInIRy3r+ZvMybFMnaoW6gFjMMQKGMmA/wxGEuApNtvNBPAyjJkfxgJYC2BJ2uXP+sP8nP6zzfKTzc/6agAjACwFsN6y/nMAtgP4EwBtAP4DwN9a1j8N4E4Yg1pmAegBMN5cNwVG/r+/MI/rgwB+bHntYgD/Zh7HdwF4CcBHzHXDYAx6+SKA4QD+m/l8mN/vQa19ZfkB4CoAnQC+B+C+oh83eDwP+P0OpP1wOY7tABRAk8PreBwz9AAwCsBC87g1APiY+Rm3g7/JXB3LtB+pFyDSP8b4YRwF8E7Lsh/ZHXzzy3uH5fmHALyU9t+Q9Qecg7gbAPy66lj0AjjLfP5rADdY1v9N5cQEY7q1IwBGW9b/W+XEBOAOAA9a1k0yj/No83kZwKWW9V+vnJgAXGquF8v6XZYTk+fvQa195eEBYBEGX/wLd9z8nAf8fgey8rA5ju1wD+J4HDP+APAMjKCLv8mcH8skH0VrTuXcq8lYLCKvisg6EbnQXDbo81Qj598OvPXZ233e1nXPq+obLuut+94B84QgRo7ACTX2/YyaZwLTMzXK5fQ9qLWvPCricfNzHqi1r7zZKSJ/FJEfiMjJluU8jhkmIqfA+Hu3gr/JWvsii6IFcVHNvUrOvgLgDBjV+HcDeExEJqH2Z2/3eZ9g9uXw+1rr+hMsz/2+1qlcgLfvTPW+8qiIx83vcSrCueBVAO+DMcfieTDKvsyynscxo0SkGcaxul9Vt4G/yVr7IouiBXGxzL1Kb1HVDar6hqoeUdX7AawDcBlqf/Z2n/dB867O72ut6w9anvt9rVO5AG/fmep95VERj5vf45T7c4GqHlTVjap6TFVfBnAjgEtFpPK38DhmkIg0wGhWPArjmAH8TdbaF1kULYjj3KvJUwCCqs9TREbB6G9R+eztPm/rujNEZLTLeuu+z4DRsfZZVd0P4MUa+z7bOnoLwNk1yuX0Pai1rzwq4nHzcx6ota+8qjR5VT4/HseMMT+TewGcAmCWqvaZq/ibzNmxTFXanfKifgD4MYxRMKMAzIDz6NSPwBh5824Yo1+eBEe/1PpsW2GMGhoBoAnAHACHAEwGMN78rGeZ67+JwSOq/hbA72A0w06A8SO1jqhaD+Db5ms/gaEjql4H8OfmcX0Ag0dULQHwS/M4ngXjRFQ9ouq/wzhZ3YjBI6o8fw9q7SvLD/N4jYAx+uxHlmNYyOMGj+cBv9+BtB8ux3E6jN9hA4CTYIwCfIrHMZvH0Szv/zI/9xOqlvM3mbNjmer3KO0CRP4HAeMAlGAEF7sAXGsunwijinaiZdubYQxjfh3ADwAMT7v8WX6YJ5ffwqjS7jFPFpdY1n8YwDYYI6nWAmi3rBMYc93uMx/fwuBRTu3ma3phDJ//cNV7X2sez0MA/hnAOMu64QC+bx7HlwHcXPXaDgCbzH3/PwAdVesdvwcAfgrgFq/7yuoDRjoDrXosLPBxsz0PmOtyey5wOo4AZgP4g/n3vgjghwDezuOY2eN4mnns3jT/hspjDn+T+TqWaT84dyoRERFRDhWtTxwRERFRXWAQR0RERJRDDOKIiIiIcohBHBEREVEOMYgjIiIiyiEGcUREREQ5xCCOqABEZKGIqOWxR0QeMee1jfJ9VERutDy/QUQ6bbZ7QUS+HeV7p01EzhWRFSLykogcNT/j+0Tk3ZZtCvd3E1F2NaVdACKKzAEYmc4B4AwAXwfwryIyRVUPRfQe74eRVLbiBgD/DiOJp9UnABRmihwRuQpGxvn/C+CLAMoA/gRG4tR1MLLKExElikEcUXEcU9X15v/Xi8guAP8G4DIAD0fxBpb919quK4r3ywIRmQDgfhhTBl2vgzOkPygiH4vgPVpUtTfsfoiovrA5lai4Npn/tgOAiJwsIveLyGsiclhE1orINOsLRORKEdkkIodEZL+IbBCRD1rWDzSnishaAOcB+CtLM+715rohzYoi8ikR6RaRIyKyW0S+ISJNlvXXm/uYKiK/MMuwzawFcyUiI0Xkn8ymzjdF5LcicmnVNmtF5Ccicq2I/F5EXheRn4rIn9TY/X+GMW/kl9RmihtVfdymPF8UkT+an+GPRaTVsu5C8++cKSKrROQggO+a684VkX81j89+EVkmIqdYXttuvvbTIvID82/4o4hcZ67/stnMu1dEvikiDVXlutg8pm+KyMsi8v+JyAmW9c0i8m0R2WUepz0i8qiIDLNsE3cZ3yMiq0XkDfPxsIi83U8ZieoFgzii4mo3/33J/LcEYCaA/wHgGhi//6dE5E8BQIz+cz+BMdn0FQDmAHgcxpyHdr4AY37HJ2A0s74fwGq7Dc2AagWM+RY/DuB/muX4rs3mDwJYBaNJ9jkAP/YQaN0D4K8BfMN83W4Aq0Xkz6q2mw5j4u4vwWgKfi+Au2vs+4MANqrqqzW2q/gUgA+Z+/8KgI8BuMNmu3sBbAFwJYB7RWQ8jDkvR8Jopv2v5nv/wiZA+SaMOVJnwahtvV9E/h7A+QD+E4DvAPiyWRYAgBh9934G4FXzdbeZ7/MTy37nwzjutwK4BMBNMJrpG819xF3GP4XRPD0CwGcAXA9j0vbHRES8lJGorqQ9eSsffPAR/gFjEvRXYXSRaALwTgBPwZg8+h0w+sopgA9aXjMKwF4A/9t8/kkAr9V4HwVwo+X5RgD32Wz3AoBvW56vB/BU1TZfBtAP4E/M59eb+/9Plm1OAnAMwN+6lOldAI4D+CvLsgYYffXWWJathXGxH2tZdpP5ni0u+98GYLnH4/ACgB0AmizLvgPgJcvzC833/Ieq1y4B0ANgjGXZ+ea2s83n7ebzH1i2GQOgD0bA22hZ/hsAKyzPf2yzzafM/b3ffP44gL93+fviLuOPYEzaPsyy7Ezze3K5lzLywUc9PVgTR1QcJ8G4UPbBuBCeAeAaVX0RxoV2r6r+srKxGoMdHgdQqa3qBnCiGE2ul4rIqCgKJSKNMGq8qvvlrYARbL2/avnPLWV8DcArMAYROHkfALHuX1WPm8+ra+J+q6r7Lc//w/y3zf2vwJBmVBdPqeqxqvd4m01NVXWt5fkAfq6qrw+8qepvYASG1X/Hv1q2eR1GMP5LVe23bPN7DP67zgfwaNU2j8AIkiv73wzgerPJ82xL7VdSZfwwgEcBHBeRJrO5/Q/m/itN/7XKSFQ3GMQRFccBGAHNNBhBT7uq/tRc9w4AL9u85mWYzaWquh1GU+cZMJpIXxWRB80mtDBOBtBs8/6V59XNtT1Vz4/CaF5z8g4AB1X1sM3+R4rI8Br7Ro39lwFMdFlfze49BEa/uuryWdU8RjXew26Z9e8asn8zoHrNsv9FAO6C0VS+BcBuEfnvCZbxZBhN0H1VjzMAnOqxjER1g0EcUXEcU9WNqrpJVcuqaq09ehHA22xecwqAfZUnqrpaVf8cRq3e38CoGfmfIcv1KowLcfX7VzrD70M4LwI4QURG2uz/sKoeCbn/tQCmiYhT38Cgqmv3PB2jEIbs36wlPamyf1V9U1X/TlXbYTTJrwDwHRH5iNM+Ii7jPgD/G8bNSPVjkccyEtUNBnFE9WEDjCa9v6gsMIOeywH8qnpjVT2gqg/CaNp6d/V6i1q1ZJXank0Arq5a9SkYfdme9vIHuPgtjIDok5UFZhPbJ2HztwVwL4wg1DaJr4hcHsF7AMYxmikioy37fh+MPmZR/B0bAHzCDNwqroLRh9LuO/AcjMEnR/DWdyDuMv4rgPcA2GTekFgfL3gsI1HdYJ44ojqgqmtEZB2AFSIyD0YT2v8A0AJgKQCIyOdg9E/7GYA9MDqUXw3ghy673gbjoj7T3OcfzH5s1W4DsEZEfgCjg/1UGMmI71HVP4b8234nIssBfFdExsDoZ/VZAGcB+HyYfZv73yNG6pTl5ijZ78NoYm2DMcr3g3AewevHnTDKu0ZEvgngBBgDCbph9F0LaxGALgAlEfkejCb3b8IY/PE0AIjIozAC7i4AvTAC4SYYSY6TKONCGIMdVovI92HU4rbBGIV6n6qu9VBGorrBmjii+vEJAL+AMVryYRj9tC5W1d+b658BMB7GhfrnABbASN3xFZd9LgLwOwAPwagRu8JuI1X9OYBPw+iv9xiMUaF/DyPdRxQ+CyMh760A/hnAaQA+pqpR1A5BVR+BkZ6kB8A/wkjDcieAQzCanKN4j70ALgLwJozEwnfBSM1xiaoedXutx/1vBfBRGM2hK2Ecu+Ww1GAC+DWAThhpXv4ZRh7AWaq6MaEyPgvgAgCHYaR++SmA22HUtFW+p65lJKonMrjbDBERERHlAWviiIiIiHKIQRwRERFRDjGIIyIiIsohBnFEREREOcQgjoiIiCiHGMQRERER5RCDOCIiIqIcYhBHRERElEMM4oiIiIhy6P8HE9+gmBX2JB4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "# Manhattan Plot\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches((10, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#bp_values = bp_values.to_numpy()\n",
    "pvals = -np.log10(p_values)\n",
    "print(pvals)\n",
    "ax.scatter(bp_values, pvals)\n",
    "\n",
    "\n",
    "#raise NotImplementedError()\n",
    "\n",
    "ax.set_xlabel(\"Position on Chromosomes\", size=15)\n",
    "ax.set_ylabel(\"-log10 P\", size=15)\n",
    "ax.set_xlim(left=0, right=max(bp_values))\n",
    "ax.set_ylim(bottom=0, top=max(pvals))\n",
    "ax.set_xticklabels(ax.get_xticks(), size=12)\n",
    "ax.set_yticklabels(ax.get_yticks(), size=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "attached-intranet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "9      9.059583\n",
      "12    15.854182\n",
      "0     17.540005\n",
      "6     11.162475\n",
      "8      8.765736\n",
      "4      8.224171\n",
      "5     16.588044\n",
      "1      7.614572\n",
      "3     13.315963\n",
      "7     19.437826\n",
      "10     9.825359\n",
      "2     19.669383\n",
      "13    18.733298\n",
      "11     7.531062\n",
      "Name: P, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-41-e7c6c82936dd>:23: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticks(), size=12)\n",
      "<ipython-input-41-e7c6c82936dd>:24: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), size=12);\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "# Manhattan Plot\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches((10, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "p_values = schizo_df_final['P']\n",
    "bp_values = schizo_df_final['BP']\n",
    "\n",
    "#bp_values = bp_values.to_numpy()\n",
    "pvals = -np.log10(p_values)\n",
    "print(pvals)\n",
    "ax.scatter(bp_values, pvals)\n",
    "\n",
    "\n",
    "#raise NotImplementedError()\n",
    "\n",
    "ax.set_xlabel(\"Position on Chromosomes\", size=15)\n",
    "ax.set_ylabel(\"-log10 P\", size=15)\n",
    "ax.set_xlim(left=0, right=max(bp_values))\n",
    "ax.set_ylim(bottom=0, top=max(pvals))\n",
    "ax.set_xticklabels(ax.get_xticks(), size=12)\n",
    "ax.set_yticklabels(ax.get_yticks(), size=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "configured-fiber",
   "metadata": {},
   "source": [
    "### QQ Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "requested-asbestos",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-42-6f7c838276fe>:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticks(), size=12)\n",
      "<ipython-input-42-6f7c838276fe>:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), size=12);\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "## QQ Plots\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "p_values = schizophrenia_df['P']\n",
    "\n",
    "obs = (-np.log10(p_values)).sort_values(ascending=True)\n",
    "\n",
    "# Generate some random data from uniform distribution\n",
    "exp = -np.log10(np.random.uniform(0, 1, size=len(obs)))\n",
    "exp.sort()\n",
    "\n",
    "# Make a QQ plot\n",
    "ax.scatter(exp, obs, s=5, color=\"blue\")\n",
    "\n",
    "#raise NotImplementedError()\n",
    "\n",
    "ax.plot([0,10],[0,10], color=\"gray\")\n",
    "ax.set_xlabel(\"Expected -log10 P\", size=15)\n",
    "ax.set_ylabel(\"Observed -log10 P\", size=15)\n",
    "#ax.set_xlim(left=0, right=max(exp))\n",
    "#ax.set_ylim(bottom=0, top=max(obs))\n",
    "ax.set_xticklabels(ax.get_xticks(), size=12)\n",
    "ax.set_yticklabels(ax.get_yticks(), size=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "prompt-battery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/IPython/core/magics/pylab.py:159: UserWarning: pylab import has clobbered these variables: ['exp']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  warn(\"pylab import has clobbered these variables: %s\"  % clobbered +\n",
      "<ipython-input-43-c48f09804502>:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticks(), size=12)\n",
      "<ipython-input-43-c48f09804502>:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), size=12);\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "## QQ Plots\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "p_values = schizo_df_filt['P']\n",
    "\n",
    "obs = (-np.log10(p_values)).sort_values(ascending=True)\n",
    "\n",
    "# Generate some random data from uniform distribution\n",
    "exp = -np.log10(np.random.uniform(0, 1, size=len(obs)))\n",
    "exp.sort()\n",
    "\n",
    "# Make a QQ plot\n",
    "ax.scatter(exp, obs, s=5, color=\"blue\")\n",
    "\n",
    "#raise NotImplementedError()\n",
    "\n",
    "ax.plot([0,10],[0,10], color=\"gray\")\n",
    "ax.set_xlabel(\"Expected -log10 P\", size=15)\n",
    "ax.set_ylabel(\"Observed -log10 P\", size=15)\n",
    "#ax.set_xlim(left=0, right=max(exp))\n",
    "#ax.set_ylim(bottom=0, top=max(obs))\n",
    "ax.set_xticklabels(ax.get_xticks(), size=12)\n",
    "ax.set_yticklabels(ax.get_yticks(), size=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moved-manchester",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "sufficient-listing",
   "metadata": {},
   "source": [
    "### Exploration for PTSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "foster-entity",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHR</th>\n",
       "      <th>SNP</th>\n",
       "      <th>BP</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>FRQ_A_29529</th>\n",
       "      <th>FRQ_U_166145</th>\n",
       "      <th>INFO</th>\n",
       "      <th>OR</th>\n",
       "      <th>SE</th>\n",
       "      <th>P</th>\n",
       "      <th>ngt</th>\n",
       "      <th>Direction</th>\n",
       "      <th>HetISqt</th>\n",
       "      <th>HetDf</th>\n",
       "      <th>HetPVa</th>\n",
       "      <th>Nca</th>\n",
       "      <th>Nco</th>\n",
       "      <th>Neff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>rs9459288m</td>\n",
       "      <td>157790612</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.7144</td>\n",
       "      <td>0.7312</td>\n",
       "      <td>0.862271</td>\n",
       "      <td>0.915212</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>1.804000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>--+---?-+---++-------+?+-----++-----?++++++---...</td>\n",
       "      <td>15.8</td>\n",
       "      <td>66</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>18424</td>\n",
       "      <td>48144</td>\n",
       "      <td>46094.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>rs57753395</td>\n",
       "      <td>99412436</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.905427</td>\n",
       "      <td>1.371220</td>\n",
       "      <td>0.0611</td>\n",
       "      <td>2.412000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>-?+?+-++??++++++++++++?+-+++??++-++?+--+-++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7344</td>\n",
       "      <td>25970</td>\n",
       "      <td>157806</td>\n",
       "      <td>77802.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>rs73553233</td>\n",
       "      <td>99413222</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.905832</td>\n",
       "      <td>1.363430</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>3.666000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>-?+?+-++??++++++++++++?+++++??++-++?+--+-++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>25970</td>\n",
       "      <td>157806</td>\n",
       "      <td>77802.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>rs4078035</td>\n",
       "      <td>131887167</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>0.947899</td>\n",
       "      <td>1.191720</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>4.597000e-07</td>\n",
       "      <td>4</td>\n",
       "      <td>+-+++-+-+---+++++-+++++-++-+--+-+++++-???????+...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.9893</td>\n",
       "      <td>24335</td>\n",
       "      <td>155025</td>\n",
       "      <td>73349.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>rs114981056</td>\n",
       "      <td>35761341</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0.9641</td>\n",
       "      <td>0.9702</td>\n",
       "      <td>0.953328</td>\n",
       "      <td>0.814729</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>5.079000e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>-+------+---++---+-----+++-++---+-+--?????????...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.9216</td>\n",
       "      <td>25314</td>\n",
       "      <td>155596</td>\n",
       "      <td>75163.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9788616</th>\n",
       "      <td>9</td>\n",
       "      <td>rs4740906</td>\n",
       "      <td>7698716</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.6942</td>\n",
       "      <td>0.6953</td>\n",
       "      <td>0.986972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>--++-+-+-+-++++-+-++-+--+---+++--+-+++--++-+-+...</td>\n",
       "      <td>17.4</td>\n",
       "      <td>69</td>\n",
       "      <td>0.1124</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9788617</th>\n",
       "      <td>9</td>\n",
       "      <td>rs55880338</td>\n",
       "      <td>130629693</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.9767</td>\n",
       "      <td>0.9765</td>\n",
       "      <td>0.929439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>-+-??++??-+--++-+++-+?++-???-+-?-+-+-++-+++--+...</td>\n",
       "      <td>3.4</td>\n",
       "      <td>57</td>\n",
       "      <td>0.4027</td>\n",
       "      <td>26247</td>\n",
       "      <td>160199</td>\n",
       "      <td>78937.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9788618</th>\n",
       "      <td>9</td>\n",
       "      <td>rs62548019</td>\n",
       "      <td>30521517</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.3924</td>\n",
       "      <td>0.3929</td>\n",
       "      <td>1.005880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>+---+-+-+-++-+++----+++-++++++----++?+--+-+---...</td>\n",
       "      <td>13.3</td>\n",
       "      <td>68</td>\n",
       "      <td>0.1816</td>\n",
       "      <td>19167</td>\n",
       "      <td>50346</td>\n",
       "      <td>48018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9788619</th>\n",
       "      <td>9</td>\n",
       "      <td>rs7856891</td>\n",
       "      <td>83932855</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.3421</td>\n",
       "      <td>0.3432</td>\n",
       "      <td>0.993334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>--++-----+-++++--+-+--+-+--+-+--+-+-+-+--+-++-...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.7062</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9788620</th>\n",
       "      <td>9</td>\n",
       "      <td>rs7871600</td>\n",
       "      <td>28940111</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.7810</td>\n",
       "      <td>0.7822</td>\n",
       "      <td>0.996311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>28</td>\n",
       "      <td>-+++---+--+++-++--+--++++-++++-----+++++-+----...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.5795</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9788621 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CHR          SNP         BP A1 A2  FRQ_A_29529  FRQ_U_166145  \\\n",
       "0          6   rs9459288m  157790612  T  C       0.7144        0.7312   \n",
       "1         13   rs57753395   99412436  A  G       0.0178        0.0133   \n",
       "2         13   rs73553233   99413222  A  G       0.0178        0.0134   \n",
       "3         12    rs4078035  131887167  T  G       0.0519        0.0448   \n",
       "4          4  rs114981056   35761341  A  T       0.9641        0.9702   \n",
       "...      ...          ...        ... .. ..          ...           ...   \n",
       "9788616    9    rs4740906    7698716  T  C       0.6942        0.6953   \n",
       "9788617    9   rs55880338  130629693  A  G       0.9767        0.9765   \n",
       "9788618    9   rs62548019   30521517  T  C       0.3924        0.3929   \n",
       "9788619    9    rs7856891   83932855  A  G       0.3421        0.3432   \n",
       "9788620    9    rs7871600   28940111  T  C       0.7810        0.7822   \n",
       "\n",
       "             INFO        OR      SE             P  ngt  \\\n",
       "0        0.862271  0.915212  0.0170  1.804000e-07    0   \n",
       "1        0.905427  1.371220  0.0611  2.412000e-07    0   \n",
       "2        0.905832  1.363430  0.0609  3.666000e-07    0   \n",
       "3        0.947899  1.191720  0.0348  4.597000e-07    4   \n",
       "4        0.953328  0.814729  0.0408  5.079000e-07    1   \n",
       "...           ...       ...     ...           ...  ...   \n",
       "9788616  0.986972  1.000000  0.0140  1.000000e+00    1   \n",
       "9788617  0.929439  1.000000  0.0544  1.000000e+00    4   \n",
       "9788618  1.005880  1.000000  0.0149  1.000000e+00    1   \n",
       "9788619  0.993334  1.000000  0.0138  1.000000e+00    1   \n",
       "9788620  0.996311  1.000000  0.0157  1.000000e+00   28   \n",
       "\n",
       "                                                 Direction  HetISqt  HetDf  \\\n",
       "0        --+---?-+---++-------+?+-----++-----?++++++---...     15.8     66   \n",
       "1        -?+?+-++??++++++++++++?+-+++??++-++?+--+-++-++...      0.0     53   \n",
       "2        -?+?+-++??++++++++++++?+++++??++-++?+--+-++-++...      0.0     53   \n",
       "3        +-+++-+-+---+++++-+++++-++-+--+-+++++-???????+...      0.0     53   \n",
       "4        -+------+---++---+-----+++-++---+-+--?????????...      0.0     50   \n",
       "...                                                    ...      ...    ...   \n",
       "9788616  --++-+-+-+-++++-+-++-+--+---+++--+-+++--++-+-+...     17.4     69   \n",
       "9788617  -+-??++??-+--++-+++-+?++-???-+-?-+-+-++-+++--+...      3.4     57   \n",
       "9788618  +---+-+-+-++-+++----+++-++++++----++?+--+-+---...     13.3     68   \n",
       "9788619  --++-----+-++++--+-+--+-+--+-+--+-+-+-+--+-++-...      0.0     69   \n",
       "9788620  -+++---+--+++-++--+--++++-++++-----+++++-+----...      0.0     69   \n",
       "\n",
       "         HetPVa    Nca     Nco     Neff  \n",
       "0        0.1415  18424   48144  46094.3  \n",
       "1        0.7344  25970  157806  77802.5  \n",
       "2        0.7393  25970  157806  77802.5  \n",
       "3        0.9893  24335  155025  73349.8  \n",
       "4        0.9216  25314  155596  75163.0  \n",
       "...         ...    ...     ...      ...  \n",
       "9788616  0.1124  29529  166007  86058.0  \n",
       "9788617  0.4027  26247  160199  78937.8  \n",
       "9788618  0.1816  19167   50346  48018.0  \n",
       "9788619  0.7062  29529  166007  86058.0  \n",
       "9788620  0.5795  29529  166007  86058.0  \n",
       "\n",
       "[9788621 rows x 19 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptsd_df_2 = pd.read_csv(\"pts_all_freeze2_overall.results\", sep='\\t')\n",
    "ptsd_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "meaning-young",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHR</th>\n",
       "      <th>SNP</th>\n",
       "      <th>BP</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>FRQ_A_29529</th>\n",
       "      <th>FRQ_U_166145</th>\n",
       "      <th>INFO</th>\n",
       "      <th>OR</th>\n",
       "      <th>SE</th>\n",
       "      <th>P</th>\n",
       "      <th>ngt</th>\n",
       "      <th>Direction</th>\n",
       "      <th>HetISqt</th>\n",
       "      <th>HetDf</th>\n",
       "      <th>HetPVa</th>\n",
       "      <th>Nca</th>\n",
       "      <th>Nco</th>\n",
       "      <th>Neff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>rs9459288m</td>\n",
       "      <td>157790612</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.7144</td>\n",
       "      <td>0.7312</td>\n",
       "      <td>0.862271</td>\n",
       "      <td>0.915212</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>1.804000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>--+---?-+---++-------+?+-----++-----?++++++---...</td>\n",
       "      <td>15.8</td>\n",
       "      <td>66</td>\n",
       "      <td>0.14150</td>\n",
       "      <td>18424</td>\n",
       "      <td>48144</td>\n",
       "      <td>46094.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>rs57753395</td>\n",
       "      <td>99412436</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.905427</td>\n",
       "      <td>1.371220</td>\n",
       "      <td>0.0611</td>\n",
       "      <td>2.412000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>-?+?+-++??++++++++++++?+-+++??++-++?+--+-++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.73440</td>\n",
       "      <td>25970</td>\n",
       "      <td>157806</td>\n",
       "      <td>77802.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>rs73553233</td>\n",
       "      <td>99413222</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.905832</td>\n",
       "      <td>1.363430</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>3.666000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>-?+?+-++??++++++++++++?+++++??++-++?+--+-++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.73930</td>\n",
       "      <td>25970</td>\n",
       "      <td>157806</td>\n",
       "      <td>77802.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>rs4078035</td>\n",
       "      <td>131887167</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>0.947899</td>\n",
       "      <td>1.191720</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>4.597000e-07</td>\n",
       "      <td>4</td>\n",
       "      <td>+-+++-+-+---+++++-+++++-++-+--+-+++++-???????+...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.98930</td>\n",
       "      <td>24335</td>\n",
       "      <td>155025</td>\n",
       "      <td>73349.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>rs114981056</td>\n",
       "      <td>35761341</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0.9641</td>\n",
       "      <td>0.9702</td>\n",
       "      <td>0.953328</td>\n",
       "      <td>0.814729</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>5.079000e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>-+------+---++---+-----+++-++---+-+--?????????...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.92160</td>\n",
       "      <td>25314</td>\n",
       "      <td>155596</td>\n",
       "      <td>75163.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68802</th>\n",
       "      <td>3</td>\n",
       "      <td>rs62248671</td>\n",
       "      <td>73646103</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>0.4168</td>\n",
       "      <td>0.4098</td>\n",
       "      <td>0.997458</td>\n",
       "      <td>1.038730</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>+++------+-++++-+++++-+-++-+-+-+++-++-++-++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.73850</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68803</th>\n",
       "      <td>3</td>\n",
       "      <td>rs9863450</td>\n",
       "      <td>82084702</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0832</td>\n",
       "      <td>0.0885</td>\n",
       "      <td>1.001810</td>\n",
       "      <td>0.933980</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>++---+-+-+--++--+-----+-+--++-+--++---+-+-++--...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.62160</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68804</th>\n",
       "      <td>6</td>\n",
       "      <td>rs66532036</td>\n",
       "      <td>32519319</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0.4327</td>\n",
       "      <td>0.4409</td>\n",
       "      <td>0.899690</td>\n",
       "      <td>0.960789</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>+++--+--?--+-+--------?+-++--+-+----?-++------...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>0.73990</td>\n",
       "      <td>18664</td>\n",
       "      <td>48460</td>\n",
       "      <td>46701.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68805</th>\n",
       "      <td>7</td>\n",
       "      <td>rs1579224</td>\n",
       "      <td>125256395</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0.5395</td>\n",
       "      <td>0.5471</td>\n",
       "      <td>1.000290</td>\n",
       "      <td>0.965412</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>---+-+--++-+++----+--+-------+-----+-+-+-+-+-+...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.92330</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68806</th>\n",
       "      <td>9</td>\n",
       "      <td>rs883815</td>\n",
       "      <td>80938410</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.1036</td>\n",
       "      <td>0.1107</td>\n",
       "      <td>0.981519</td>\n",
       "      <td>0.933513</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>---+-----+-++-+-+++-+++----+++--+------++-++-+...</td>\n",
       "      <td>19.2</td>\n",
       "      <td>69</td>\n",
       "      <td>0.08752</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68807 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CHR          SNP         BP A1 A2  FRQ_A_29529  FRQ_U_166145      INFO  \\\n",
       "0        6   rs9459288m  157790612  T  C       0.7144        0.7312  0.862271   \n",
       "1       13   rs57753395   99412436  A  G       0.0178        0.0133  0.905427   \n",
       "2       13   rs73553233   99413222  A  G       0.0178        0.0134  0.905832   \n",
       "3       12    rs4078035  131887167  T  G       0.0519        0.0448  0.947899   \n",
       "4        4  rs114981056   35761341  A  T       0.9641        0.9702  0.953328   \n",
       "...    ...          ...        ... .. ..          ...           ...       ...   \n",
       "68802    3   rs62248671   73646103  C  G       0.4168        0.4098  0.997458   \n",
       "68803    3    rs9863450   82084702  T  C       0.0832        0.0885  1.001810   \n",
       "68804    6   rs66532036   32519319  A  T       0.4327        0.4409  0.899690   \n",
       "68805    7    rs1579224  125256395  A  C       0.5395        0.5471  1.000290   \n",
       "68806    9     rs883815   80938410  T  C       0.1036        0.1107  0.981519   \n",
       "\n",
       "             OR      SE             P  ngt  \\\n",
       "0      0.915212  0.0170  1.804000e-07    0   \n",
       "1      1.371220  0.0611  2.412000e-07    0   \n",
       "2      1.363430  0.0609  3.666000e-07    0   \n",
       "3      1.191720  0.0348  4.597000e-07    4   \n",
       "4      0.814729  0.0408  5.079000e-07    1   \n",
       "...         ...     ...           ...  ...   \n",
       "68802  1.038730  0.0135  5.000000e-03    1   \n",
       "68803  0.933980  0.0243  5.000000e-03    1   \n",
       "68804  0.960789  0.0143  5.000000e-03    0   \n",
       "68805  0.965412  0.0125  5.000000e-03    1   \n",
       "68806  0.933513  0.0245  5.000000e-03    1   \n",
       "\n",
       "                                               Direction  HetISqt  HetDf  \\\n",
       "0      --+---?-+---++-------+?+-----++-----?++++++---...     15.8     66   \n",
       "1      -?+?+-++??++++++++++++?+-+++??++-++?+--+-++-++...      0.0     53   \n",
       "2      -?+?+-++??++++++++++++?+++++??++-++?+--+-++-++...      0.0     53   \n",
       "3      +-+++-+-+---+++++-+++++-++-+--+-+++++-???????+...      0.0     53   \n",
       "4      -+------+---++---+-----+++-++---+-+--?????????...      0.0     50   \n",
       "...                                                  ...      ...    ...   \n",
       "68802  +++------+-++++-+++++-+-++-+-+-+++-++-++-++-++...      0.0     69   \n",
       "68803  ++---+-+-+--++--+-----+-+--++-+--++---+-+-++--...      0.0     69   \n",
       "68804  +++--+--?--+-+--------?+-++--+-+----?-++------...      0.0     65   \n",
       "68805  ---+-+--++-+++----+--+-------+-----+-+-+-+-+-+...      0.0     69   \n",
       "68806  ---+-----+-++-+-+++-+++----+++--+------++-++-+...     19.2     69   \n",
       "\n",
       "        HetPVa    Nca     Nco     Neff  \n",
       "0      0.14150  18424   48144  46094.3  \n",
       "1      0.73440  25970  157806  77802.5  \n",
       "2      0.73930  25970  157806  77802.5  \n",
       "3      0.98930  24335  155025  73349.8  \n",
       "4      0.92160  25314  155596  75163.0  \n",
       "...        ...    ...     ...      ...  \n",
       "68802  0.73850  29529  166007  86058.0  \n",
       "68803  0.62160  29529  166007  86058.0  \n",
       "68804  0.73990  18664   48460  46701.3  \n",
       "68805  0.92330  29529  166007  86058.0  \n",
       "68806  0.08752  29529  166007  86058.0  \n",
       "\n",
       "[68807 rows x 19 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptsd_df_filt = ptsd_df_2.loc[ptsd_df_2['P']<=5e-3]\n",
    "ptsd_df_filt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "entitled-forward",
   "metadata": {},
   "source": [
    "### Generating Manhattan and QQ plots for visual analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "documentary-quality",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHR</th>\n",
       "      <th>SNP</th>\n",
       "      <th>BP</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>FRQ_A_29529</th>\n",
       "      <th>FRQ_U_166145</th>\n",
       "      <th>INFO</th>\n",
       "      <th>OR</th>\n",
       "      <th>SE</th>\n",
       "      <th>P</th>\n",
       "      <th>ngt</th>\n",
       "      <th>Direction</th>\n",
       "      <th>HetISqt</th>\n",
       "      <th>HetDf</th>\n",
       "      <th>HetPVa</th>\n",
       "      <th>Nca</th>\n",
       "      <th>Nco</th>\n",
       "      <th>Neff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>rs9459288m</td>\n",
       "      <td>157790612</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.7144</td>\n",
       "      <td>0.7312</td>\n",
       "      <td>0.862271</td>\n",
       "      <td>0.915212</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>1.804000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>--+---?-+---++-------+?+-----++-----?++++++---...</td>\n",
       "      <td>15.8</td>\n",
       "      <td>66</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>18424</td>\n",
       "      <td>48144</td>\n",
       "      <td>46094.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>rs57753395</td>\n",
       "      <td>99412436</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.905427</td>\n",
       "      <td>1.371220</td>\n",
       "      <td>0.0611</td>\n",
       "      <td>2.412000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>-?+?+-++??++++++++++++?+-+++??++-++?+--+-++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7344</td>\n",
       "      <td>25970</td>\n",
       "      <td>157806</td>\n",
       "      <td>77802.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>rs73553233</td>\n",
       "      <td>99413222</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.905832</td>\n",
       "      <td>1.363430</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>3.666000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>-?+?+-++??++++++++++++?+++++??++-++?+--+-++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>25970</td>\n",
       "      <td>157806</td>\n",
       "      <td>77802.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>rs4078035</td>\n",
       "      <td>131887167</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>0.947899</td>\n",
       "      <td>1.191720</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>4.597000e-07</td>\n",
       "      <td>4</td>\n",
       "      <td>+-+++-+-+---+++++-+++++-++-+--+-+++++-???????+...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.9893</td>\n",
       "      <td>24335</td>\n",
       "      <td>155025</td>\n",
       "      <td>73349.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>rs114981056</td>\n",
       "      <td>35761341</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>0.9641</td>\n",
       "      <td>0.9702</td>\n",
       "      <td>0.953328</td>\n",
       "      <td>0.814729</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>5.079000e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>-+------+---++---+-----+++-++---+-+--?????????...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.9216</td>\n",
       "      <td>25314</td>\n",
       "      <td>155596</td>\n",
       "      <td>75163.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578184</th>\n",
       "      <td>9</td>\n",
       "      <td>rs10983363</td>\n",
       "      <td>119588994</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>0.9821</td>\n",
       "      <td>0.9834</td>\n",
       "      <td>0.992586</td>\n",
       "      <td>0.887275</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>+??--?--+-+??--?+??????-?-??+-??-+?+-+--+?-?-+...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43</td>\n",
       "      <td>0.9513</td>\n",
       "      <td>23019</td>\n",
       "      <td>148540</td>\n",
       "      <td>69648.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578185</th>\n",
       "      <td>9</td>\n",
       "      <td>rs111238454</td>\n",
       "      <td>5042599</td>\n",
       "      <td>A</td>\n",
       "      <td>AGGCCCAGCCCC</td>\n",
       "      <td>0.6116</td>\n",
       "      <td>0.6169</td>\n",
       "      <td>0.995040</td>\n",
       "      <td>0.971902</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>+----+----++-++--+-+++?--+-+-+--+---?-+-----+-...</td>\n",
       "      <td>12.6</td>\n",
       "      <td>67</td>\n",
       "      <td>0.1962</td>\n",
       "      <td>18842</td>\n",
       "      <td>48549</td>\n",
       "      <td>46917.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578186</th>\n",
       "      <td>9</td>\n",
       "      <td>rs112986973</td>\n",
       "      <td>1236496</td>\n",
       "      <td>A</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.9690</td>\n",
       "      <td>0.9718</td>\n",
       "      <td>0.980910</td>\n",
       "      <td>0.921180</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>+-+-+-+?-+---++--++-+-?--+-+-+-++---?-+--++?--...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>63</td>\n",
       "      <td>0.3460</td>\n",
       "      <td>16945</td>\n",
       "      <td>46782</td>\n",
       "      <td>43294.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578187</th>\n",
       "      <td>9</td>\n",
       "      <td>rs148378963</td>\n",
       "      <td>84034681</td>\n",
       "      <td>CA</td>\n",
       "      <td>C</td>\n",
       "      <td>0.9236</td>\n",
       "      <td>0.9275</td>\n",
       "      <td>0.969191</td>\n",
       "      <td>0.926168</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>+---+++--+??++-+-???--?--++-+-+?--+-?----+-+-+...</td>\n",
       "      <td>11.9</td>\n",
       "      <td>58</td>\n",
       "      <td>0.2243</td>\n",
       "      <td>15380</td>\n",
       "      <td>38990</td>\n",
       "      <td>37968.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578188</th>\n",
       "      <td>9</td>\n",
       "      <td>rs1932170</td>\n",
       "      <td>122944186</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.2868</td>\n",
       "      <td>0.2816</td>\n",
       "      <td>0.992311</td>\n",
       "      <td>1.027780</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>5.000000e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>--+++--+---++--+--+++-+-+++--+-+-+++-+--+++-++...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.6216</td>\n",
       "      <td>29529</td>\n",
       "      <td>166007</td>\n",
       "      <td>86058.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>578189 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CHR          SNP         BP  A1            A2  FRQ_A_29529  \\\n",
       "0         6   rs9459288m  157790612   T             C       0.7144   \n",
       "1        13   rs57753395   99412436   A             G       0.0178   \n",
       "2        13   rs73553233   99413222   A             G       0.0178   \n",
       "3        12    rs4078035  131887167   T             G       0.0519   \n",
       "4         4  rs114981056   35761341   A             T       0.9641   \n",
       "...     ...          ...        ...  ..           ...          ...   \n",
       "578184    9   rs10983363  119588994   C             G       0.9821   \n",
       "578185    9  rs111238454    5042599   A  AGGCCCAGCCCC       0.6116   \n",
       "578186    9  rs112986973    1236496   A            AT       0.9690   \n",
       "578187    9  rs148378963   84034681  CA             C       0.9236   \n",
       "578188    9    rs1932170  122944186   A             G       0.2868   \n",
       "\n",
       "        FRQ_U_166145      INFO        OR      SE             P  ngt  \\\n",
       "0             0.7312  0.862271  0.915212  0.0170  1.804000e-07    0   \n",
       "1             0.0133  0.905427  1.371220  0.0611  2.412000e-07    0   \n",
       "2             0.0134  0.905832  1.363430  0.0609  3.666000e-07    0   \n",
       "3             0.0448  0.947899  1.191720  0.0348  4.597000e-07    4   \n",
       "4             0.9702  0.953328  0.814729  0.0408  5.079000e-07    1   \n",
       "...              ...       ...       ...     ...           ...  ...   \n",
       "578184        0.9834  0.992586  0.887275  0.0610  5.000000e-02    0   \n",
       "578185        0.6169  0.995040  0.971902  0.0145  5.000000e-02    0   \n",
       "578186        0.9718  0.980910  0.921180  0.0419  5.000000e-02    0   \n",
       "578187        0.9275  0.969191  0.926168  0.0391  5.000000e-02    0   \n",
       "578188        0.2816  0.992311  1.027780  0.0140  5.000000e-02    1   \n",
       "\n",
       "                                                Direction  HetISqt  HetDf  \\\n",
       "0       --+---?-+---++-------+?+-----++-----?++++++---...     15.8     66   \n",
       "1       -?+?+-++??++++++++++++?+-+++??++-++?+--+-++-++...      0.0     53   \n",
       "2       -?+?+-++??++++++++++++?+++++??++-++?+--+-++-++...      0.0     53   \n",
       "3       +-+++-+-+---+++++-+++++-++-+--+-+++++-???????+...      0.0     53   \n",
       "4       -+------+---++---+-----+++-++---+-+--?????????...      0.0     50   \n",
       "...                                                   ...      ...    ...   \n",
       "578184  +??--?--+-+??--?+??????-?-??+-??-+?+-+--+?-?-+...      0.0     43   \n",
       "578185  +----+----++-++--+-+++?--+-+-+--+---?-+-----+-...     12.6     67   \n",
       "578186  +-+-+-+?-+---++--++-+-?--+-+-+-++---?-+--++?--...      5.8     63   \n",
       "578187  +---+++--+??++-+-???--?--++-+-+?--+-?----+-+-+...     11.9     58   \n",
       "578188  --+++--+---++--+--+++-+-+++--+-+-+++-+--+++-++...      0.0     69   \n",
       "\n",
       "        HetPVa    Nca     Nco     Neff  \n",
       "0       0.1415  18424   48144  46094.3  \n",
       "1       0.7344  25970  157806  77802.5  \n",
       "2       0.7393  25970  157806  77802.5  \n",
       "3       0.9893  24335  155025  73349.8  \n",
       "4       0.9216  25314  155596  75163.0  \n",
       "...        ...    ...     ...      ...  \n",
       "578184  0.9513  23019  148540  69648.1  \n",
       "578185  0.1962  18842   48549  46917.1  \n",
       "578186  0.3460  16945   46782  43294.9  \n",
       "578187  0.2243  15380   38990  37968.9  \n",
       "578188  0.6216  29529  166007  86058.0  \n",
       "\n",
       "[578189 rows x 19 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptsd_df_filt = ptsd_df_2.loc[ptsd_df_2['P']<=5e-2]\n",
    "ptsd_df_filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "muslim-wallace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "0        6.743763\n",
      "1        6.617623\n",
      "2        6.435808\n",
      "3        6.337525\n",
      "4        6.294222\n",
      "           ...   \n",
      "68802    2.301030\n",
      "68803    2.301030\n",
      "68804    2.301030\n",
      "68805    2.301030\n",
      "68806    2.301030\n",
      "Name: P, Length: 68807, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-41681837a6dd>:23: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticks(), size=12)\n",
      "<ipython-input-12-41681837a6dd>:24: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), size=12);\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "# Manhattan Plot\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches((10, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "p_values = ptsd_df_filt['P']\n",
    "bp_values = ptsd_df_filt['BP']\n",
    "\n",
    "#bp_values = bp_values.to_numpy()\n",
    "pvals = -np.log10(p_values)\n",
    "print(pvals)\n",
    "ax.scatter(bp_values, pvals)\n",
    "\n",
    "\n",
    "#raise NotImplementedError()\n",
    "\n",
    "ax.set_xlabel(\"Position on Chromosomes\", size=15)\n",
    "ax.set_ylabel(\"-log10 P\", size=15)\n",
    "ax.set_xlim(left=0, right=max(bp_values))\n",
    "ax.set_ylim(bottom=2.0, top=max(pvals))\n",
    "ax.set_xticklabels(ax.get_xticks(), size=12)\n",
    "ax.set_yticklabels(ax.get_yticks(), size=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "differential-train",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/IPython/core/magics/pylab.py:159: UserWarning: pylab import has clobbered these variables: ['exp']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  warn(\"pylab import has clobbered these variables: %s\"  % clobbered +\n",
      "<ipython-input-19-c4e6ae4a95bc>:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticks(), size=12)\n",
      "<ipython-input-19-c4e6ae4a95bc>:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), size=12);\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "## QQ Plots\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "p_values = ptsd_df_2['P']\n",
    "\n",
    "obs = (-np.log10(p_values)).sort_values(ascending=True)\n",
    "\n",
    "# Generate some random data from uniform distribution\n",
    "exp = -np.log10(np.random.uniform(0, 1, size=len(obs)))\n",
    "exp.sort()\n",
    "\n",
    "# Make a QQ plot\n",
    "ax.scatter(exp, obs, s=5, color=\"blue\")\n",
    "\n",
    "#raise NotImplementedError()\n",
    "\n",
    "ax.plot([0,10],[0,10], color=\"gray\")\n",
    "ax.set_xlabel(\"Expected -log10 P\", size=15)\n",
    "ax.set_ylabel(\"Observed -log10 P\", size=15)\n",
    "#ax.set_xlim(left=0, right=max(exp))\n",
    "#ax.set_ylim(bottom=0, top=max(obs))\n",
    "ax.set_xticklabels(ax.get_xticks(), size=12)\n",
    "ax.set_yticklabels(ax.get_yticks(), size=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "latest-emergency",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (clean)",
   "language": "python",
   "name": "python3_clean"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}