diff --git a/jass_preprocessing/jass_preprocessing/__init__.py b/jass_preprocessing/jass_preprocessing/__init__.py
index 8669fc771e2bee31c822c9d69beb0f3e14bba93a..fcd7f03a80ca7191f10bb338edd3eac51a21fcd2 100644
--- a/jass_preprocessing/jass_preprocessing/__init__.py
+++ b/jass_preprocessing/jass_preprocessing/__init__.py
@@ -1,2 +1,2 @@
-import jass_preprocessing.map_gwas.map_gwas
-import jass_preprocessing.dna_utils.dna_utils
+import jass_preprocessing.map_gwas
+import jass_preprocessing.dna_utils
diff --git a/jass_preprocessing/jass_preprocessing/dna_utils/__init__.py b/jass_preprocessing/jass_preprocessing/dna_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/jass_preprocessing/jass_preprocessing/map_gwas/__init__.py b/jass_preprocessing/jass_preprocessing/map_gwas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/jass_preprocessing/jass_preprocessing/map_gwas/__pycache__/map_gwas.cpython-35.pyc b/jass_preprocessing/jass_preprocessing/map_gwas/__pycache__/map_gwas.cpython-35.pyc
deleted file mode 100644
index a080559f1ba2ed9e2ae37219fc43f4397bf6141d..0000000000000000000000000000000000000000
Binary files a/jass_preprocessing/jass_preprocessing/map_gwas/__pycache__/map_gwas.cpython-35.pyc and /dev/null differ
diff --git a/jass_preprocessing/jass_preprocessing/map_gwas/map_gwas.py b/jass_preprocessing/jass_preprocessing/map_gwas/map_gwas.py
index aef00bb1ea9bec4f6e56f0bf280751e31f49c6d1..2becf225ce9c74cb4e01603e06cd6ec9cc7001d6 100644
--- a/jass_preprocessing/jass_preprocessing/map_gwas/map_gwas.py
+++ b/jass_preprocessing/jass_preprocessing/map_gwas/map_gwas.py
@@ -1,6 +1,6 @@
 import os
 import sys
-
+import pandas as pd
 
 
 
@@ -29,3 +29,16 @@ def gwas_internal_link(GWAS_table, GWAS_path):
                   'internalDataLink': walkfs(GWAS_path, GWAS_filename)[2]})
     Glink = pd.DataFrame(Glink, columns=('filename', 'internalDataLink'))
     return Glink
+
+
+def convert_missing_values(df):
+    """
+    Convert all missing value strings to a standart np.nan value
+    """
+    def_missing = ['', '#N/A', '#N/A', 'N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN',
+                   '-nan', '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'nan', 'na', '.']
+
+    nmissing = len(def_missing)
+    nan_vec = [np.nan] * nmissing
+
+    return df.replace(def_missing, nan_vec)