diff --git a/peak_finder.py b/peak_finder.py
index 85be79e..f853a75 100755
--- a/peak_finder.py
+++ b/peak_finder.py
@@ -20,15 +20,17 @@ def get_log(pv):
         return(math.log10(float(pv)))
 
 # Function to find and annotate top association:
-def find_top_association(chrom):
+def find_top_association(chrom):    
     global input_df
     global window
     global threshold
 
+    # The dataframe is filtered by chromosome:
     test_df = input_df.loc[input_df.chromosome == chrom]
-    # test_df.loc[:,'pvalue'] = test_df['pvalue'].astype(float) # changing type of p-value column <- too dangerous. Underflow hazard.
+
+    # Calculate the -log(pv) and update column type of the position:
     test_df['mLogPv'] = test_df.pvalue.apply(get_log)
-    test_df.loc[:,'bp_location'] = test_df['bp_location'].astype(float) # changing type of position column
+    test_df.loc[:,'bp_location'] = test_df['bp_location'].astype(float)
 
     # sorting rows by p-value:
     for index in test_df.sort_values(['mLogPv']).index:
@@ -43,17 +45,20 @@ def find_top_association(chrom):
             test_df.loc[index, 'isTopAssociation'] = 'false'
             continue
 
-        # We have found a top snp!
+        # We have found a top snp!  
         else:
             pos = test_df.loc[index]['bp_location']
+            pval = test_df.loc[index]['mLogPv']
+
             # Setting false flag for ALL variants within the window.
             test_df.loc[test_df[abs( test_df.bp_location - pos ) <= window].index,'isTopAssociation'] = 'false'
             
-            # If there are multiple variants with the same p-value, request review, othervise it's a true peak.
-            if len(test_df.loc[test_df['mLogPv'] == test_df.loc[index]['mLogPv']].chromosome) > 1:
-                test_df.loc[ test_df['mLogPv'] == test_df.loc[index]['mLogPv'], 'isTopAssociation'] = 'REQUIRES REVIEW'
+            # If there are multiple variants with the same p-value within the window they require review:
+            if len(test_df.loc[(test_df.mLogPv == pval) & (abs( test_df.bp_location - pos ) <= window)]) > 1:
+                test_df.loc[ (test_df.mLogPv == pval) & (abs( test_df.bp_location - pos ) <= window), 'isTopAssociation'] = 'REQUIRES REVIEW'
+            # Otherwise it's a true peak:
             else:
-                test_df.loc[ test_df['mLogPv'] == test_df.loc[index]['mLogPv'], 'isTopAssociation'] = 'true'
+                test_df.loc[ index, 'isTopAssociation'] = 'true'
 
     # Modifying the original dataframe: 
     input_df.loc[ test_df.index, 'isTopAssociation'] = test_df.isTopAssociation