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Cheng Soon Ong committed a60f95d Merge

Merged nicta-bio/chillo into master

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  • Parent commits 89bb76d, d10a32a

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Files changed (16)

 This requires the package 'nose'
 
 
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 External code
 =============
 link_clustering.py from Jim Bagrow, Yong-Yeol Ahn
 
 
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 Notes from Cristovao
 ====================
 
 gss2graph.py
 -------------
 reads in results as multicolumn spreadsheet with multiple tests
-initialise a SnpGraph
-outputs JSONs
+initialise a SnpGraph outputs JSONs
 
 process_WTCCC.py
 -------------------------
 
 reads in results from all GWIS tests and produces a JSON file.
 
+
+Produce JSON file from plink files 
+==================================
+
+script/process_opensnp.sh
+
+This script produces JSON file from plink files like epi.cc that has the SNP pairs.
+
+
+
+
+
+
+

File chillo/io_gwis.py

 import numpy
 from numpy import genfromtxt, unique, zeros, empty
 
+def read_gss_file(filename):
+    """Parse the file output from matlab GSS code, and return an object"""
+    raw_data = open(filename, 'r')
+    raw_data.next() # Ignore first line
+    second_line = raw_data.next()
+    raw_data.close()
+    (info_start, format_row, header_row, data_start, dummy) = map(int, second_line.split())
+    format_list, header_list = get_header(filename, format_row-1, header_row-1)
+    data = genfromtxt(filename, delimiter='\t', skip_header=data_start-1,
+                      dtype={'names': header_list, 'formats': format_list})
+    return data
+
+
 def read_gwis_file(filename, score_col=2, min_score=1.0, max_pairs=1000000, line_start=4, delim='\t'):
     """Load the pairs and scores from the GWIS file format,
     returns a structured numpy array

File chillo/snp_graph.py

         self.set_adj_mat(input_data.dtype.names[2])
         self.init_graph_labels()
 
+
+    def init_from_sarray_plink(self, input_data, stat_tests):
+        """Initialize class from numpy structured array provided by input_data.
+        stat_tests lists the names of the columns that we parse for each pair.
+        For snps, we expect columns (two of them):
+        - rs
+        - prb
+        - browser
+        - prbCode
+        """
+        self._init_snp2idx(input_data['prb_1'], input_data['prb_2'])
+        self.snp = zeros(self.num_vertex, #initialaze with zeros
+            dtype={'names':['chrom','rs','bp_position','prbCode','prb'],
+                   'formats':[int,'S16',int,'S16','S16']})
+
+        for idx,elem in enumerate(input_data['prb_1']):
+            snp_idx = self.snp2idx[elem]
+
+            self.snp['rs'][snp_idx] = str(input_data['SNP1'][idx])
+            self.snp['chrom'][snp_idx] = int(input_data['CHR1'][idx])
+            self.snp['bp_position'][snp_idx] = int(input_data['position1'][idx])
+            self.snp['prbCode'][snp_idx] = str(input_data['SNP1'][idx])
+            self.snp['prb'][snp_idx] = str(input_data['prb_1'][idx])
+
+ 
+        for idx,elem in enumerate(input_data['prb_2']):
+            snp_idx = self.snp2idx[elem]
+
+            self.snp['rs'][snp_idx] = str(input_data['SNP2'][idx])
+            self.snp['chrom'][snp_idx] = int(input_data['CHR2'][idx])
+            self.snp['bp_position'][snp_idx] = int(input_data['position2'][idx])
+            self.snp['prbCode'][snp_idx] = str(input_data['SNP2'][idx])
+            self.snp['prb'][snp_idx] = str(input_data['prb_2'][idx])
+
+        # Create members for code to work. To remove later.
+        self.rs = self.snp['rs']
+        self._init_chroff()
+        self._init_bedline()
+        
+        #for assoc_name in ['fltGSS_prtv', 'fltGSS', 'fltGSS_cntr', 'fltSS', 'fltDSS', 'fltChi2']:
+        for assoc_name in stat_tests:
+            self._init_graph_sparse(assoc_name, input_data['prb_1'], input_data['prb_2'], input_data[assoc_name])
+
+        # Create members for code to work. To remove later.
+        self.set_adj_mat(stat_tests[0])
+        self.init_graph_labels()
+
+
+    def init_from_sarray_gwis_data(self, input_data, stat_tests):
+        """Initialize class from numpy structured array provided by input_data.
+        stat_tests lists the names of the columns that we parse for each pair.
+        For snps, we expect columns (two of them):
+        - rs
+        - prb
+        - browser  
+        - prbCode
+        """
+        self._init_snp2idx(input_data['prb1'], input_data['prb2'])
+        self.snp = zeros(self.num_vertex, #initialaze with zeros 
+            dtype={'names':['chrom','rs','bp_position','prbCode','prb'],
+                   'formats':[int,'S16',int,'S16','S16']})
+
+        for idx,elem in enumerate(input_data['prb1']):
+            snp_idx = self.snp2idx[elem]
+
+            self.snp['rs'][snp_idx] = str(input_data['rs_1'][idx])
+            self.snp['chrom'][snp_idx] = int(input_data['chr_1'][idx])
+            self.snp['bp_position'][snp_idx] = int(input_data['bp_1'][idx])
+            self.snp['prbCode'][snp_idx] = str(input_data['rs_1'][idx])
+            self.snp['prb'][snp_idx] = str(input_data['prb1'][idx])
+
+ 
+        for idx,elem in enumerate(input_data['prb2']):
+            snp_idx = self.snp2idx[elem]
+
+            self.snp['rs'][snp_idx] = str(input_data['rs_2'][idx])
+            self.snp['chrom'][snp_idx] = int(input_data['chr_2'][idx])
+            self.snp['bp_position'][snp_idx] = int(input_data['bp_2'][idx])
+            self.snp['prbCode'][snp_idx] = str(input_data['rs_2'][idx])
+            self.snp['prb'][snp_idx] = str(input_data['prb2'][idx])
+
+        # Create members for code to work. To remove later.
+        self.rs = self.snp['rs']
+        self._init_chroff()
+        self._init_bedline()
+        
+        #for assoc_name in ['fltGSS_prtv', 'fltGSS', 'fltGSS_cntr', 'fltSS', 'fltDSS', 'fltChi2']:
+        for assoc_name in stat_tests:
+            self._init_graph_sparse(assoc_name, input_data['prb1'], input_data['prb2'], input_data[assoc_name])
+
+        # Create members for code to work. To remove later.
+        self.set_adj_mat(stat_tests[0])
+        self.init_graph_labels()
+
+
+
     def _init_graph_sparse(self, assoc_name, snp1, snp2, val):
         """Construct the weighted graph from the snp pairs.
 

File scripts/gss2graph.py

 #from networkx.readwrite import json_graph
 from chillo.snp_graph import SnpGraph
 from chillo.io_gwis import read_gss_file
+from numpy import genfromtxt
+from optparse import OptionParser
+
 
 def process(filename):
     """
         print('Writing to %s' % json_file)
         data.export_json(json_file,'/home/cristovao/Desktop/imp_files/'+disease+'WTC')
 
+
+def process_plink(infilename1,infilename2,outfilename):
+    """
+    Read the data contained in infilename1 (.txt) and infilename2 (.h5), which should be the outputs
+    of the programs plink2hdf5.py and plinkcc2txt.py.
+    outfilename will be the name of .json.
+    Construct the SnpGraph, and find connected subgraphs and communities.
+    Export the result to a JSON file.
+    """
+    #CHR1         SNP1 CHR2         SNP2       OR_INT         STAT            P position1 position2
+  
+    filters = ['OR_INT', 'STAT','P']
+    
+    datafile = genfromtxt(infilename1,
+                     dtype={'names':['CHR1', 'SNP1', 'CHR2','SNP2','OR_INT', 'STAT','P', 'position1', 'position2','prb_1','prb_2'],
+                   'formats':['S16','S16','S16','S16',float,float,float,int,int,int,int]})
+
+
+
+    data = SnpGraph("expt_name")
+
+
+    data.init_from_sarray_plink(datafile, filters)
+
+    data.set_adj_mat('OR_INT')
+    print('Finding connected subgraphs')
+    data.colour_connected()
+    data.sort_colours()
+#    print('Finding communities')     #comment here to not use find communitie
+#    data.find_community()            #comment here to not use find communitie
+    #I replace this method data.count_edges for data.count_edges_subgraphs() and data.count_edges_communities()
+    data.count_edges_subgraphs()
+#    data.count_edges_communities()   #comment here to not use find communitie
+
+
+    json_file = outfilename 
+    print('Writing to %s' % json_file)
+    data.export_json(json_file,infilename2)
+
+
 if __name__ == '__main__':
 #     process('../test/snp_sig_sel2w.txt')  
-     process('/home/cristovao/epistasis/sel2w4Cris2.txt')
+#     process('/home/cristovao/epistasis/sel2w4Cris2.txt')
+
+
+#     process('../test/snp_sig_sel2w.txt')  
+#     process('/home/cristovao/epistasis/sel2w4Cris2.txt')
+
+    # build option parser:
+    class MyParser(OptionParser):
+        def format_epilog(self, formatter):
+            return self.epilog
+    
+    usage = "usage: python %prog [options] filename\n"    
+
+    description = """ 
+write some explanation here
+"""
+
+    epilog = """ """
+
+    parser = MyParser(usage, description=description,epilog=epilog)
+    parser.add_option("--inputtxt",  dest="inputtxt", action="store",
+                      help='file .txt - with snp pairs')
+    parser.add_option("--h5name", dest="h5name",   action="store",
+                      help='file .h5')
+    parser.add_option("--outjson", dest="outjson",   action="store",
+                      help='output json file')
+ 
+
+    (options, args) = parser.parse_args()   
+    inputtxt = options.inputtxt
+    h5name = options.h5name
+    outjson = options.outjson
+
+
+    process_plink(inputtxt,h5name,outjson)
+
+#    process_plink('/home/cristovao/Desktop/AUS_project/opengwas/public_datas/Strabismus/strabismus-plink-epi-01.epi.txt',
+#              '/home/cristovao/Desktop/AUS_project/opengwas/public_datas/Strabismus/strabismus',
+#               'strabismus-plink-epi-01.json')
+
+
     

File scripts/gwispaperdataset.py

+from numpy import genfromtxt, hstack, vstack, zeros, ones, empty
+import sn
+import sys,string
+import numpy as np
+import math
+import csv
+import os.path
+from collections import namedtuple
+import os
+import vcf
+import fnmatch
+from optparse import OptionParser
+import time
+from chillo.snp_graph import SnpGraph
+from chillo.io_gwis import read_gss_file
+
+
+
+
+def get_data(filename):
+    """
+    This function read and return the information inside the file that has the fellow columns:
+
+    'rs_1', 'rs_2', 'chr_1', 'bp_1', 'chr_2', 'bp_2', 'fltChi2_1', 'fltChi2_2', 'fltGSS'
+    """
+    data = genfromtxt(filename,skip_header=1,
+                     dtype={'names':['rs_1', 'rs_2', 'chr_1', 'bp_1', 'chr_2', 'bp_2', 'fltChi2_1', 'fltChi2_2', 'fltGSS'],
+                          'formats':['S16',  'S16',   'S16',   int ,   'S16',   int,      float,       float,       float]})
+    return data
+
+
+
+def add_prb(infile):
+    """
+    This function return a numpy array with more two columns from the information in a file that has the SNPs pairs 
+    in regarding to GWIS paper dataset.
+    The numpy array has 11 columns and the last two columns are ID (prb1 and prb2) for each snp.
+
+    columns:
+    'rs_1', 'rs_2', 'chr_1', 'bp_1', 'chr_2', 'bp_2', 'fltChi2_1', 'fltChi2_2', 'fltGSS', 'prb1' ,'prb2'
+    """
+
+    print "Reading ..."
+    data_input = get_data(infile)
+
+    rs =    np.unique(np.append( data_input['rs_1'],data_input['rs_2'] ))
+
+    shape_data=np.shape(data_input)
+
+    full_data = zeros((shape_data[0],), dtype={'names':['rs_1', 'rs_2', 'chr_1', 'bp_1', 'chr_2', 'bp_2', 'fltChi2_1', 'fltChi2_2', 'fltGSS', 'prb1' ,'prb2'], 'formats':['S16',  'S16',   'S16',   int ,   'S16',   int,      float,       float,     float,    int,    int]})
+
+    #ofile = open(outfile,'w')    # open file for writing  'rs_1', 'rs_2', 'chr_1', 'bp_1', 'chr_2', 'bp_2', 'fltChi2_1', 'fltChi2_2', 'fltGSS'
+    print "Writing ..."
+    for i in range(shape_data[0]): 
+     
+        full_data[i]['rs_1'] = data_input[i]['rs_1']
+        full_data[i]['rs_2'] =data_input[i]['rs_2']
+        full_data[i]['chr_1'] =data_input[i]['chr_1']
+        full_data[i]['bp_1'] =data_input[i]['bp_1']
+        full_data[i]['chr_2'] =data_input[i]['chr_2']
+        full_data[i]['bp_2'] =data_input[i]['bp_2']
+        full_data[i]['fltChi2_1'] =data_input[i]['fltChi2_1']  
+        full_data[i]['fltChi2_2'] =data_input[i]['fltChi2_2']
+        full_data[i]['fltGSS'] =data_input[i]['fltGSS']
+        full_data[i]['prb1']=np.flatnonzero(rs==data_input[i]['rs_1'])[0]
+        full_data[i]['prb2']=np.flatnonzero(rs==data_input[i]['rs_2'])[0]
+    
+    return full_data
+
+
+
+
+def process_data(infilename1,outfilename):
+    """
+    Read the data contained in infilename1 (.txt) and infilename2 (.h5), which should be the outputs
+    of the programs plink2hdf5.py and plinkcc2txt.py.
+    outfilename will be the name of .json.
+    Construct the SnpGraph, and find connected subgraphs and communities.
+    Export the result to a JSON file.
+    """
+    # 'rs_1', 'rs_2', 'chr_1', 'bp_1', 'chr_2', 'bp_2', 'fltChi2_1', 'fltChi2_2', 'fltGSS', 'prb1' ,'prb2'
+  
+    filters = ['fltChi2_1', 'fltChi2_2', 'fltGSS']
+    
+    #datafile = genfromtxt(infilename1,
+    #                 dtype={'names':['CHR1', 'SNP1', 'CHR2','SNP2','OR_INT', 'STAT','P', 'position1', 'position2','prb_1','prb_2'],
+    #               'formats':['S16','S16','S16','S16',float,float,float,int,int,int,int]})
+
+    datafile = add_prb(infilename1)
+
+    data = SnpGraph("expt_name")
+
+
+    data.init_from_sarray_gwis_data(datafile, filters)
+
+    data.set_adj_mat('fltChi2_1')
+    print('Finding connected subgraphs')
+    data.colour_connected()
+    data.sort_colours()
+#    print('Finding communities')     #comment here to not use find communitie
+#    data.find_community()            #comment here to not use find communitie
+    #I replace this method data.count_edges for data.count_edges_subgraphs() and data.count_edges_communities()
+    data.count_edges_subgraphs()
+#    data.count_edges_communities()   #comment here to not use find communitie
+
+
+    json_file = outfilename 
+    print('Writing to %s' % json_file)
+    data.export_json(json_file,False)
+
+
+
+
+if __name__ == '__main__':
+
+    # build option parser:
+    class MyParser(OptionParser):
+        def format_epilog(self, formatter):
+            return self.epilog
+    
+    usage = "usage: python %prog [options] filename\n"    
+
+    description = """ 
+This script write a output file (JSON file) from information txt file with one disease getting from the GWIS paper dataset.
+The output file has 11 columns and the last two columns are ID (prb1 and prb2) for each snp.\n
+The 11 columns:\t
+'rs_1', 'rs_2', 'chr_1', 'bp_1', 'chr_2', 'bp_2', 'fltChi2_1', 'fltChi2_2', 'fltGSS', 'prb1' ,'prb2'
+"""
+
+    epilog = """ """
+
+    parser = MyParser(usage, description=description,epilog=epilog)
+    parser.add_option("-i",  dest="i", action="store",
+                      help='txt file with one disease getting from the GWIS paper dataset')
+    parser.add_option("-o", dest="o",   action="store",
+                      help='output JSON file')
+ 
+
+    (options, args) = parser.parse_args()   
+    i = options.i
+    o = options.o
+    
+    process_data(i,o)
+
+
+
+
+
+
+
+
+

File scripts/plink2graph.py

+
+#from networkx.readwrite import json_graph
+from chillo.snp_graph import SnpGraph
+from chillo.io_gwis import read_gss_file
+from numpy import genfromtxt
+from optparse import OptionParser
+
+
+def process_plink(infilename1,infilename2,outfilename):
+    """
+    Read the data contained in infilename1 (.txt) and infilename2 (.h5), which should be the outputs
+    of the programs plink2hdf5.py and plinkcc2txt.py.
+    The outfilename will be the name of json file.
+    Construct the SnpGraph, and find connected subgraphs and communities.
+    Export the plink result to a JSON file.
+    """
+    #CHR1         SNP1 CHR2         SNP2       OR_INT         STAT            P position1 position2
+  
+    filters = ['OR_INT', 'STAT','P']
+    
+    datafile = genfromtxt(infilename1,
+                     dtype={'names':['CHR1', 'SNP1', 'CHR2','SNP2','OR_INT', 'STAT','P', 'position1', 'position2','prb_1','prb_2'],
+                   'formats':['S16','S16','S16','S16',float,float,float,int,int,int,int]})
+
+
+
+    data = SnpGraph("expt_name")
+
+
+    data.init_from_sarray_plink(datafile, filters)
+
+    data.set_adj_mat('OR_INT')
+    print('Finding connected subgraphs')
+    data.colour_connected()
+    data.sort_colours()
+#    print('Finding communities')     #comment here to not use find communitie
+#    data.find_community()            #comment here to not use find communitie
+    #I replace this method data.count_edges for data.count_edges_subgraphs() and data.count_edges_communities()
+    data.count_edges_subgraphs()
+#    data.count_edges_communities()   #comment here to not use find communitie
+
+
+    json_file = outfilename 
+    print('Writing to %s' % json_file)
+    data.export_json(json_file,infilename2)
+
+
+if __name__ == '__main__':
+#     process('../test/snp_sig_sel2w.txt')  
+#     process('/home/cristovao/epistasis/sel2w4Cris2.txt')
+
+
+#     process('../test/snp_sig_sel2w.txt')  
+#     process('/home/cristovao/epistasis/sel2w4Cris2.txt')
+
+    # build option parser:
+    class MyParser(OptionParser):
+        def format_epilog(self, formatter):
+            return self.epilog
+    
+    usage = "usage: python %prog [options] filename\n"    
+
+    description = """ 
+write some explanation here
+"""
+
+    epilog = """ """
+
+    parser = MyParser(usage, description=description,epilog=epilog)
+    parser.add_option("--inputtxt",  dest="inputtxt", action="store",
+                      help='file .txt - with snp pairs')
+    parser.add_option("--h5name", dest="h5name",   action="store",
+                      help='file .h5')
+    parser.add_option("--outjson", dest="outjson",   action="store",
+                      help='output json file')
+ 
+
+    (options, args) = parser.parse_args()   
+    inputtxt = options.inputtxt
+    h5name = options.h5name
+    outjson = options.outjson
+
+
+    process_plink(inputtxt,h5name,outjson)
+
+#    process_plink('/home/cristovao/Desktop/AUS_project/opengwas/public_datas/Strabismus/strabismus-plink-epi-01.epi.txt',
+#              '/home/cristovao/Desktop/AUS_project/opengwas/public_datas/Strabismus/strabismus',
+#               'strabismus-plink-epi-01.json')
+
+
+    

File scripts/process_dataset_from_gwis_paper.sh

+
+# This script produces JSON files from the dataset used in GWIS paper
+# As cited in the GWIS platform paper. http://www.biomedcentral.com/content/supplementary/1471-2164-14-s3-s10-s2.xls
+
+# to create the input file .txt open the 1471-2164-14-s3-s10-s2.xls
+# and get copy and past the information of each columns below:
+# rs_1	rs_2	chr_1	bp_1	chr_2	bp_2	fltChi2_1	fltChi2_2	fltGSS
+# To do this for each disease and give this files like input to gwispaperdataset.py
+
+
+
+ python gwispaperdataset.py  -i ../test/BD.txt -o BD_gwis_paper.json
+
+
+ python gwispaperdataset.py  -i ../test/CAD.txt -o CAD_gwis_paper.json
+
+
+ python gwispaperdataset.py  -i ../test/HT.txt -o HT_gwis_paper.json
+
+
+ python gwispaperdataset.py  -i ../test/RA.txt -o RA_gwis_paper.json
+
+
+ python gwispaperdataset.py  -i ../test/T1D.txt -o T1D_gwis_paper.json
+
+
+ python gwispaperdataset.py  -i ../test/T2D.txt -o T2D_gwis_paper.json
+
+
+ python gwispaperdataset.py  -i ../test/CD.txt -o CD_gwis_paper.json

File scripts/process_opensnp.sh

+
+# Bellow produces the JSON file from plink results
+# ================================================
+
+
+# 6 - Run the plink2graph.py that is inside the chillo/tmp-scripts with the small implementation to create the json file from openSNP
+
+# Lactose intolerance
+python plink2graph.py --inputtxt lactose_int-plink-epi-0001.epi.txt  --h5name lactose_int --outjson lactose_int-plink-epi-0001.epi.json
+# Asthma
+python plink2graph.py --inputtxt asthma-plink-epi-0001.epi.txt  --h5name asthma --outjson asthma-plink-epi-0001.epi.json
+# Dyslexia
+python plink2graph.py --inputtxt dyslexia-plink-epi.epi.txt  --h5name dyslexia --outjson dyslexia-plink-epi.epi.json
+
+

File test/1471-2164-14-s3-s10-s2.xls

Binary file added.
+rs_1	rs_2	chr_1	bp_1	chr_2	bp_2	fltChi2_1	fltChi2_2	fltGSS
+rs7844299	rs11984645	8	50430790	8	55069305	0.94	50.38	14.38
+rs6983650	rs11984645	8	50406226	8	55069305	0.36	50.38	14.37
+rs99080	rs11984645	8	50357376	8	55069305	0.83	50.38	13.69
+rs6473901	rs11984645	8	50386264	8	55069305	1.45	50.38	13.51
+rs6473903	rs11984645	8	50388147	8	55069305	0.04	50.38	12.57
+rs4654792	rs909812	1	22672	1	22603366	3.2	2.46	12.69
+rs199698	rs41515647	1	75636696	1	75647488	0.84	19.7	11.87
+rs625045	rs668860	1	85434613	1	85436462	2.09	4.49	16.53
+rs668860	rs10873672	1	85436462	1	85450726	4.49	2.28	16.58
+rs668860	rs6691970	1	85436462	1	85450920	4.49	2.26	16.32
+rs10922726	rs1782127	1	90265279	1	90280342	3.58	17.54	13.2
+rs7417737	rs3811480	1	230468063	1	230469202	0.52	8.28	14.96
+rs7414802	rs3811480	1	230468275	1	230469202	0.5	8.28	14.3
+rs7597849	rs1595734	2	35744997	2	35813424	1.08	5.11	17.8
+rs1371427	rs1595734	2	35779358	2	35813424	1.35	5.11	19
+rs2719093	rs1595734	2	35795	2	35813424	3.73	5.11	15.35
+rs2378	rs1595734	2	35801114	2	35813424	2.98	5.11	14.17
+rs2379	rs1595734	2	35802886	2	35813424	0.73	5.11	18.4
+rs1595734	rs1439684	2	35813424	2	35826687	5.11	1.08	16.75
+rs1595734	rs2043914	2	35813424	2	35838	5.11	1.26	14.46
+rs1595734	rs13011599	2	35813424	2	35848903	5.11	0.93	14.74
+rs13028177	rs6543990	2	36236450	2	36274	1.78	0.52	17.53
+rs7570865	rs6544738	2	44305057	2	44305240	9.75	0.99	13.8
+rs350747	rs350753	2	52868656	2	52873788	6.12	1.26	16.17
+rs17046061	rs17046067	2	54596679	2	54145	0.63	11.38	14.16
+rs16849921	rs10197379	2	214061022	2	205912603	18.57	0.01	30.42
+rs16849921	rs12694298	2	214061022	2	205913268	18.57	0.16	28.58
+rs1983218	rs2738290	2	217336645	2	217352253	1.14	9.37	14.8
+rs2243	rs2738290	2	217342396	2	217352253	0.88	9.37	15.47
+rs1110998	rs2738290	2	217343952	2	217352253	0.71	9.37	15.72
+rs2738287	rs2738290	2	217349836	2	217352253	0.82	9.37	16.57
+rs17193526	rs17786145	3	1657	3	1445217	0.32	14.22	16.65
+rs17786145	rs17039822	3	1445217	3	1453429	14.22	0.07	15.94
+rs17786145	rs17786151	3	1445217	3	1455381	14.22	0.49	15.95
+rs939298	rs1550	3	20477076	3	20104	2.05	13.29	13.38
+rs9859908	rs1550	3	20489086	3	20104	2.79	13.29	12.73
+rs6809441	rs33916626	3	41494605	3	41539388	2.7	0.52	19.24
+rs506996	rs382441	4	258	4	9095	8.06	1.62	14.68
+rs4860	rs4688938	4	5508488	4	5516030	0.72	10.94	13
+rs4688938	rs4586871	4	5516030	4	5516378	10.94	0.67	14.82
+rs4688938	rs6446371	4	5516030	4	5526209	10.94	0.05	11.38
+rs4688938	rs2267654	4	5516030	4	5527249	10.94	0.14	12.08
+rs4688938	rs16837389	4	5516030	4	5536310	10.94	0.6	12.33
+rs4695062	rs32741	4	44909419	4	44960444	3.17	15.69	11.26
+rs1948584	rs32741	4	44909853	4	44960444	1.81	15.69	14.29
+rs7690764	rs32741	4	44924382	4	44960444	0.86	15.69	13.35
+rs7690944	rs32741	4	44924464	4	44960444	1.75	15.69	11.23
+rs1390911	rs32741	4	44253487	4	44960444	2.95	15.69	13.79
+rs1845945	rs32741	4	44933047	4	44960444	0.19	15.69	14
+rs9998147	rs32741	4	44934205	4	44960444	0.28	15.69	13.94
+rs32741	rs1908806	4	44960444	4	44964953	15.69	0.51	15.07
+rs2545308	rs2613097	4	181637915	4	181639323	6.23	4.79	13.68
+rs34691926	rs6945822	4	183829027	7	129698576	7.56	3.9	11.81
+rs34691926	rs11624794	4	183829027	14	24260146	7.56	11.24	11.19
+rs7735940	rs12515142	5	36423931	5	36426411	0.63	0.9	14.16
+rs2935260	rs2992406	5	54444880	5	54444983	0.02	6.28	19.06
+rs60331	rs12188163	5	93050	5	93250608	6.18	13.69	12.86
+rs13154650	rs12188163	5	93052205	5	93250608	4.49	13.69	13.16
+rs1470150	rs12188163	5	93138654	5	93250608	7.18	13.69	12.4
+rs13155452	rs12188163	5	93164449	5	93250608	3.32	13.69	13.26
+rs17083377	rs12188163	5	93164870	5	93250608	6.3	13.69	12.53
+rs75217	rs12188163	5	93176774	5	93250608	3.32	13.69	13
+rs6874766	rs12188163	5	93213807	5	93250608	3.13	13.69	13
+rs7715562	rs12188163	5	93222034	5	93250608	3.49	13.69	12.91
+rs896729	rs12188163	5	93222178	5	93250608	6.32	13.69	12.34
+rs17314825	rs12188163	5	93234937	5	93250608	2.75	13.69	13.3
+rs10214278	rs12188163	5	93237679	5	93250608	2.84	13.69	13.3
+rs12188163	rs53405	5	93250608	5	93275403	13.69	1.9	12.95
+rs11744199	rs3749820	5	132527098	5	132533063	1.19	7.11	11.94
+rs17287085	rs7706	5	141284815	5	141294362	2.72	17.92	13.37
+rs7706	rs6580205	5	141294362	5	141294691	17.92	4.08	13.37
+rs7706	rs758462	5	141294362	5	141297178	17.92	5.03	12
+rs12515561	rs1552835	5	152955611	5	152959769	1.12	4.83	13.03
+rs12515563	rs1552835	5	152955632	5	152959769	2.48	4.83	13.2
+rs12515520	rs1552835	5	152955663	5	152959769	1.23	4.83	12.7
+rs1552837	rs1552835	5	152959669	5	152959769	2.53	4.83	14.02
+rs1552835	rs17519558	5	152959769	5	152960319	4.83	1.41	14.7
+rs1552835	rs17519656	5	152959769	5	152960528	4.83	3.24	13.37
+rs1552835	rs17591636	5	152959769	5	152960615	4.83	2.13	14.52
+rs2438077	rs2438083	6	1272236	6	1277371	3.41	0.11	14.64
+rs2496292	rs2438083	6	1274617	6	1277371	6.82	0.11	16.78
+rs2438083	rs977674	6	1277371	6	1277702	0.11	7.47	21.14
+rs2438083	rs977673	6	1277371	6	1277715	0.11	7.69	24.34
+rs9357438	rs9357440	6	9492158	6	9492393	1.05	11.34	18
+rs1886330	rs365237	6	18162229	6	18186697	2.24	23.17	11.21
+rs365237	rs214614	6	18186697	6	18201306	23.17	4.27	11.32
+rs365237	rs214610	6	18186697	6	18203094	23.17	4.08	11.32
+rs365237	rs214599	6	18186697	6	18207443	23.17	2.44	11.56
+rs10499047	rs9320174	6	106983128	6	106985408	4.82	10.02	16.41
+rs9486383	rs9320174	6	106983359	6	106985408	4.52	10.02	15.73
+rs9480682	rs9320174	6	106983406	6	106985408	4.13	10.02	16.17
+rs13218960	rs9320174	6	106983465	6	106985408	4.61	10.02	16.17
+rs9320173	rs9320174	6	106984669	6	106985408	4.47	10.02	16.47
+rs9320174	rs783397	6	106985408	6	106987161	10.02	1.54	14.2
+rs1729549	rs1190806	6	129087423	6	129106943	4.57	0.46	14.21
+rs985307	rs985882	7	19573763	7	19575505	4.09	0.47	15.7
+rs985306	rs985882	7	19574066	7	19575505	4.91	0.47	15.12
+rs2192481	rs985882	7	19574677	7	19575505	7.25	0.47	14.73
+rs985882	rs985881	7	19575505	7	19575555	0.47	4.38	14.88
+rs7781714	rs6949019	7	23593479	7	23594050	1.25	6.61	16.85
+rs10253608	rs10266	7	158469060	7	158474325	2.34	2	14.12
+rs12113120	rs10266	7	158480	7	158474325	2.21	2	14.45
+rs10949739	rs10266	7	158472082	7	158474325	2.54	2	14.3
+rs4909259	rs10266	7	158473	7	158474325	2.21	2	14.45
+rs10266	rs10237585	7	158474325	7	158474373	2	2.04	13.02
+rs10266	rs3793181	7	158474325	7	158481827	2	2.95	14.38
+rs10266	rs6459895	7	158474325	7	158482870	2	2.87	13.85
+rs10266	rs12698265	7	158474325	7	158489297	2	2.21	14.45
+rs16919784	rs11984645	8	55063538	8	55069305	2.96	50.38	13.1
+rs11984645	rs4737503	8	55069305	8	55071319	50.38	2.65	15.27
+rs2915	rs4437686	8	114310388	8	114357413	0.93	2.87	13.49
+rs7012271	rs4437686	8	114317672	8	114357413	0.82	2.87	15.71
+rs2447183	rs2469997	8	120352978	8	120353267	0.49	1.39	18.07
+rs2469996	rs2469997	8	120353011	8	120353267	0.24	1.39	17.71
+rs2469997	rs6469823	8	120353267	8	120353984	1.39	0.31	17.54
+rs2469997	rs2447179	8	120353267	8	120355775	1.39	0.43	18.07
+rs2469997	rs2447178	8	120353267	8	120356188	1.39	0.32	17.9
+rs2469997	rs2402	8	120353267	8	120357424	1.39	0.24	17.71
+rs2469997	rs2425	8	120353267	8	120364727	1.39	0.29	17.9
+rs2469997	rs2447169	8	120353267	8	120365063	1.39	0.31	17.99
+rs2469997	rs2426	8	120353267	8	120365112	1.39	0.24	16.82
+rs2469997	rs2447168	8	120353267	8	120365613	1.39	0.34	18.54
+rs2469997	rs2440	8	120353267	8	120385452	1.39	0.42	16.68
+rs7013124	rs7014384	8	130232771	8	130236557	13.83	0.27	13.15
+rs17459521	rs892542	10	80496476	10	80498057	3.23	0.69	13.36
+rs12805895	rs1461902	11	37707868	11	37708266	4.68	0.75	17.48
+rs12805895	rs7929645	11	37707868	11	37708514	4.68	0.71	17.82
+rs12805895	rs4756413	11	37707868	11	37708626	4.68	0.33	16.59
+rs12805895	rs1381428	11	37707868	11	37708653	4.68	1.21	16.96
+rs11237746	rs11237747	11	75175059	11	75180559	8.66	0.18	14.73
+rs1102478	rs858719	11	189380	11	130160456	1.32	27.88	16.6
+rs7943478	rs858719	11	189402	11	130160456	0.41	27.88	16.56
+rs2892410	rs11168985	12	38990282	12	39045983	0.82	2.35	14.53
+rs11168985	rs826886	12	39045983	12	39095797	2.35	1.62	16.67
+rs11168985	rs826838	12	39045983	12	39106731	2.35	1.77	15.01
+rs11168985	rs1164971	12	39045983	12	39110284	2.35	1.62	16.15
+rs869877	rs869878	13	75282465	13	75282895	8.54	1.45	12.63
+rs9561326	rs9561329	13	97107	13	94011169	5.11	8.16	14.2
+rs9561329	rs9561336	13	94011169	13	94034140	8.16	7.14	13.92
+rs11624794	rs1958305	14	24260146	14	24273124	11.24	33.35	18.45
+rs1958305	rs17184408	14	24273124	14	24282020	33.35	0.49	17.92
+rs1958305	rs12601	14	24273124	14	24284173	33.35	1.95	17.37
+rs1439170	rs7152370	14	46638877	14	46735956	1.39	2.8	18.02
+rs858870	rs7152370	14	46666812	14	46735956	6.08	2.8	14.81
+rs17737767	rs7152370	14	46718257	14	46735956	1.45	2.8	17.82
+rs7152370	rs10483596	14	46735956	14	46740909	2.8	5.84	17
+rs7152370	rs8017858	14	46735956	14	46741726	2.8	1.31	16.8
+rs4899113	rs12323479	14	63916159	14	63924082	0.99	1.19	16.39
+rs10152067	rs12323479	14	63916963	14	63924082	1.71	1.19	15.13
+rs12323479	rs8684	14	63924082	14	63944791	1.19	1.12	17.42
+rs12323479	rs17101239	14	63924082	14	63949994	1.19	1.59	14.68
+rs6574039	rs17105918	14	72491015	14	72494808	2.28	16	11.19
+rs17105918	rs4902976	14	72494808	14	72505876	16	1.7	13.17
+rs746655	rs921535	15	74107677	15	74111343	4.53	9.63	21.28
+rs921535	rs999742	15	74111343	15	74115517	9.63	14.76	13.93
+rs12593542	rs7166105	15	75282147	15	75343942	7.56	1.47	11.18
+rs17139	rs17139608	16	6279094	16	6281175	1.16	11.17	12.45
+rs886889	rs10521202	17	12804201	17	12814564	1.21	9.99	14.74
+rs10521202	rs5017214	17	12814564	17	12817783	9.99	0.85	12.53
+rs12941	rs9906443	17	30185022	17	30185565	0.94	1.17	15.12
+rs12941	rs9899093	17	30185022	17	30191954	0.94	1.85	14.41
+rs8085631	rs7233258	18	57654043	18	57655248	2.07	2.49	14.9
+rs17066458	rs7233258	18	57654067	18	57655248	1.25	2.49	16.18
+rs1944328	rs1944327	18	61838457	18	61838947	21.76	4.41	24.97
+rs1944328	rs8096	18	61838457	18	61842934	21.76	0.27	23
+rs1944328	rs9675798	18	61838457	18	61856967	21.76	1	21.91
+rs1944328	rs9676116	18	61838457	18	61857065	21.76	2.16	21
+rs16999569	rs12980129	19	22902710	19	22908911	4.47	23.39	11.71
+rs12980129	rs2194111	19	22908911	19	22922549	23.39	4.18	11.71
+rs6095722	rs6020395	20	33045515	20	33074440	0	9.45	11.56
+rs16986890	rs6020395	20	33061637	20	33074440	0.01	9.45	12.53
+rs6020395	rs6020846	20	33074440	20	33140988	9.45	0.02	12.24
+rs2011703	rs6014572	20	54556388	20	54574544	16.69	1.58	16.7
+rs8130402	rs999789	21	40420409	21	40426451	2.7	16.34	18.62
+rs2836860	rs999789	21	40423964	21	40426451	1.55	16.34	12.35
+rs4277	rs999789	21	40424954	21	40426451	1.6	16.34	13.06
+rs999789	rs428424	21	40426451	21	40445319	16.34	1.09	14.41
+rs999789	rs445593	21	40426451	21	40448382	16.34	0.97	14.41
+rs2837630	rs2837632	21	41818543	21	41819281	0.16	3.48	14.57

File test/CAD.txt

+rs_1	rs_2	chr_1	bp_1	chr_2	bp_2	fltChi2_1	fltChi2_2	fltGSS
+rs4471699	rs11863150	16	30320307	16	30385503	3.97	0.75	32.59
+rs2146340	rs1782127	1	90185106	1	90280342	4.84	17.54	12.09
+rs12135351	rs1782127	1	90254276	1	90280342	3.14	17.54	14.31
+rs10922719	rs1782127	1	90254411	1	90280342	3.92	17.54	15.03
+rs7538578	rs1782127	1	90257041	1	90280342	4.27	17.54	14.43
+rs10922725	rs1782127	1	90265153	1	90280342	3.81	17.54	14.09
+rs10922726	rs1782127	1	90265279	1	90280342	3.58	17.54	17.4
+rs6663717	rs1782127	1	90267116	1	90280342	3.63	17.54	14.66
+rs13069584	rs6809441	3	41516619	3	41494605	0.94	2.7	14.9
+rs6599155	rs6809441	3	41489604	3	41494605	1.96	2.7	13.42
+rs12054014	rs6809441	3	41464	3	41494605	0.22	2.7	14.22
+rs12054016	rs6809441	3	41490294	3	41494605	0.35	2.7	13.96
+rs6809441	rs33916626	3	41494605	3	41539388	2.7	0.52	16.68
+rs959880	rs2314349	3	183088613	3	183091098	5.23	7.85	25.67
+rs2314349	rs906719	3	183091098	3	183091144	7.85	4.56	24.37
+rs2314349	rs2314348	3	183091098	3	183091330	7.85	5.44	25.76
+rs2314349	rs2089588	3	183091098	3	183091474	7.85	5.17	26.15
+rs65019	rs417769	5	71794569	5	71811478	2.48	7.14	12.25
+rs4703937	rs417769	5	71797851	5	71811478	2.59	7.14	12.47
+rs246571	rs417769	5	71805150	5	71811478	2.69	7.14	12.47
+rs246562	rs417769	5	71810246	5	71811478	1.88	7.14	12.89
+rs17287085	rs7706	5	141284815	5	141294362	2.72	17.92	13.79
+rs7706	rs758462	5	141294362	5	141297178	17.92	5.03	13.79
+rs11526287	rs2250603	7	135297419	7	135315708	0.21	2.73	15.26
+rs1535624	rs7028357	9	7195	9	7132694	1.25	1.35	11.38
+rs17152197	rs17152205	10	12792541	10	12794336	0.99	0.37	13.28
+rs17152205	rs41380844	10	12794336	10	12797828	0.37	1.17	21.82
+rs11027910	rs10219185	11	24458713	11	24504903	2.27	0.18	14.57
+rs10978	rs10219185	11	24493480	11	24504903	2.39	0.18	15.68
+rs10979	rs10219185	11	24493876	11	24504903	0.3	0.18	14.57
+rs10980	rs10219185	11	24494465	11	24504903	1.16	0.18	14.75
+rs11849674	rs7154773	14	60688023	14	40912999	4.68	2.49	30.61
+rs10148587	rs7154773	14	60689160	14	40912999	4.22	2.49	28.02
+rs188620	rs7154773	14	40853319	14	40912999	0.81	2.49	24.71
+rs10137732	rs7154773	14	60689209	14	40912999	4.38	2.49	34.4
+rs6573298	rs7154773	14	40869612	14	40912999	2.07	2.49	24.53
+rs7145505	rs7154773	14	40869718	14	40912999	1.56	2.49	24.26
+rs8019531	rs7154773	14	40871554	14	40912999	3.07	2.49	29.2
+rs11628587	rs7154773	14	40871574	14	40912999	1.8	2.49	25.08
+rs11628628	rs7154773	14	40871689	14	40912999	1.99	2.49	24.8
+rs8011227	rs7154773	14	40882151	14	40912999	1.9	2.49	41.73
+rs7158657	rs7154773	14	40888940	14	40912999	2.91	2.49	27.71
+rs10142834	rs7154773	14	40898846	14	40912999	3.04	2.49	27.71
+rs17097262	rs7154773	14	40902242	14	40912999	1.03	2.49	41.3
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+rs7154773	rs8012816	14	40912999	14	40914246	2.49	1.05	33.93
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+rs7896275	rs4256909	10	114081995	10	114093585	1.12	0.06	32.47
+rs4256909	rs7898941	10	114093585	10	114094931	0.06	9.09	16.63
+rs4256909	rs11195939	10	114093585	10	114142104	0.06	5.95	11.2
+rs3825075	rs6598060	11	217140	11	243987	4.07	3.39	15.43
+rs6421986	rs6598060	11	221659	11	243987	5.81	3.39	16.67
+rs6598060	rs7116130	11	243987	11	244129	3.39	3.7	17.52
+rs6598060	rs1128322	11	243987	11	244197	3.39	3.29	21.85
+rs2334501	rs10768666	11	1620784	11	1632411	0.91	1.1	12.76
+rs11027910	rs10219185	11	24458713	11	24504903	2.27	0.18	14.5
+rs10978	rs10219185	11	24493480	11	24504903	2.39	0.18	14.5
+rs10979	rs10219185	11	24493876	11	24504903	0.3	0.18	14.14
+rs10980	rs10219185	11	24494465	11	24504903	1.16	0.18	14.68
+rs11604421	rs4988327	11	64363232	11	64413928	0.06	4.52	13.54
+rs11606508	rs4988327	11	64392274	11	64413928	0.19	4.52	19.65
+rs4376999	rs10842357	12	24620969	12	24628433	2.08	2.44	13.47
+rs4963762	rs10842357	12	24622160	12	24628433	2.44	2.44	17.02
+rs10785426	rs7486175	12	43703858	12	43716240	1.23	0.82	19.92
+rs7295668	rs7486175	12	43713372	12	43716240	1.12	0.82	21.84
+rs6582453	rs7486175	12	43714825	12	43716240	1.13	0.82	22.57
+rs6582454	rs7486175	12	43714906	12	43716240	1.09	0.82	22.57
+rs7486175	rs1995339	12	43716240	12	43718844	0.82	0.97	23.97
+rs6490403	rs7322082	13	29954105	13	29955805	0.04	1.6	14.24
+rs6490403	rs7997274	13	29954105	13	29957085	0.04	1.81	14.13
+rs6490403	rs7332561	13	29954105	13	29962516	0.04	2.03	15.05
+rs6490403	rs7319196	13	29954105	13	29967550	0.04	1.78	14.37
+rs9548796	rs9315704	13	94490	13	40140215	2.74	1.05	15.19
+rs1022808	rs9315704	13	94679	13	40140215	1.28	1.05	14.76
+rs4545673	rs9315704	13	40118499	13	40140215	7.11	1.05	14.35
+rs6563726	rs9315704	13	40131922	13	40140215	1.71	1.05	20.42
+rs9315704	rs1885758	13	40140215	13	40145536	1.05	1.12	21.52
+rs9315704	rs2324344	13	40140215	13	40151443	1.05	1.68	15.04
+rs9315704	rs1160321	13	40140215	13	40155143	1.05	0.78	15.86
+rs9315704	rs3858867	13	40140215	13	40155612	1.05	1.41	16.82
+rs9315704	rs927515	13	40140215	13	40157114	1.05	0.64	14.41
+rs9315704	rs2324346	13	40140215	13	40158827	1.05	1.12	13.04
+rs9315704	rs4567589	13	40140215	13	40160541	1.05	1.16	13.64
+rs9315704	rs9566462	13	40140215	13	40169423	1.05	2.21	13.06
+rs7327105	rs9591470	13	34144716	13	34146991	1.38	1.13	14.68
+rs9591470	rs1407760	13	34146991	13	34149445	1.13	1.06	13.52
+rs7336478	rs2038825	13	46675360	13	46683136	0.07	0.05	14.98
+rs12430163	rs17710571	13	99538377	13	99552723	0.02	0.12	21.11
+rs2296999	rs17710571	13	99550351	13	99552723	0.14	0.12	16.77
+rs279937	rs279936	13	103704077	13	103704608	3.98	2.85	23.77
+rs279937	rs188096	13	103704077	13	103705044	3.98	2.28	23.77
+rs279937	rs157266	13	103704077	13	103712499	3.98	1.94	19.27
+rs12427557	rs7328544	13	104369267	13	104369448	5.86	5.7	21.53
+rs17731911	rs12873360	13	89294665	13	89298845	0.22	0.02	14.52
+rs7152370	rs10483596	14	46735956	14	46740909	2.8	5.84	16.45
+rs10498441	rs2144977	14	52544224	14	52561350	0.4	2.25	28.28
+rs1956286	rs2144977	14	52553691	14	52561350	0.92	2.25	23.8
+rs17125074	rs2144977	14	52559041	14	52561350	1.82	2.25	40.68
+rs2144977	rs1998093	14	52561350	14	52563297	2.25	1.32	41.04
+rs2144977	rs17125124	14	52561350	14	52572995	2.25	0.46	41.56
+rs11849674	rs7154773	14	60688023	14	40912999	4.68	2.49	25.1
+rs10148587	rs7154773	14	60689160	14	40912999	4.22	2.49	22.43
+rs188620	rs7154773	14	40853319	14	40912999	0.81	2.49	23.65
+rs10137732	rs7154773	14	60689209	14	40912999	4.38	2.49	28.84
+rs6573298	rs7154773	14	40869612	14	40912999	2.07	2.49	22.15
+rs7145505	rs7154773	14	40869718	14	40912999	1.56	2.49	21.79
+rs8019531	rs7154773	14	40871554	14	40912999	3.07	2.49	33.08
+rs11628587	rs7154773	14	40871574	14	40912999	1.8	2.49	22.94
+rs11628628	rs7154773	14	40871689	14	40912999	1.99	2.49	23.22
+rs8011227	rs7154773	14	40882151	14	40912999	1.9	2.49	39.5
+rs7158657	rs7154773	14	40888940	14	40912999	2.91	2.49	33.54
+rs10142834	rs7154773	14	40898846	14	40912999	3.04	2.49	34.88
+rs17097262	rs7154773	14	40902242	14	40912999	1.03	2.49	36.77
+rs1887103	rs7154773	14	40907104	14	40912999	3.1	2.49	25.32
+rs7154773	rs8012816	14	40912999	14	40914246	2.49	1.05	31.25
+rs7154773	rs10130695	14	40912999	14	40919869	2.49	1.3	37.33
+rs7154773	rs1998225	14	40912999	14	40941373	2.49	1.9	18.06
+rs7154773	rs4280	14	40912999	14	60780429	2.49	2.2	17.85
+rs7154773	rs1951116	14	40912999	14	40946079	2.49	1.87	14.98
+rs7154773	rs1951117	14	40912999	14	60782214	2.49	2.03	18.16
+rs8035210	rs8034355	15	73230766	15	73231011	2.68	1.34	14.34
+rs8034178	rs8034355	15	73230849	15	73231011	1.39	1.34	19.81
+rs8034355	rs4777568	15	73231011	15	73232225	1.34	4.29	14.34
+rs8034355	rs1899	15	73231011	15	73234080	1.34	2.97	14.56
+rs8034355	rs7172629	15	73231011	15	50112864	1.34	0.14	12.33
+rs8034355	rs11858307	15	73231011	15	50115951	1.34	0.22	11.95
+rs8034355	rs7174	15	73231011	15	50121893	1.34	1.48	13.14
+rs2881439	rs7205378	16	7283490	16	7283842	1.01	0.86	15.41
+rs4238755	rs1420247	16	52746089	16	52748342	8.73	9.91	40.11
+rs4784244	rs1420247	16	52748247	16	52748342	0.08	9.91	26.1
+rs1420247	rs1420248	16	52748342	16	52748361	9.91	6.67	35.08
+rs1420247	rs12446384	16	52748342	16	52768754	9.91	12.35	22.42
+rs1420247	rs1362413	16	52748342	16	52776657	9.91	1.19	15.19
+rs1420247	rs7499961	16	52748342	16	52778697	9.91	11.88	23.44
+rs9302946	rs11077601	17	70346698	17	70350140	4.15	5.72	14.81
+rs12452792	rs11077601	17	70349624	17	70350140	5.66	5.72	24.24
+rs11077601	rs8065637	17	70350140	17	70355387	5.72	5.09	15.81
+rs11077601	rs9916746	17	70350140	17	70355568	5.72	5.28	15.87
+rs11077601	rs3744313	17	70350140	17	70358058	5.72	6.32	19.67
+rs3923514	rs901064	17	78515210	17	78596040	0.76	1.75	14.22
+rs11150863	rs901064	17	78532321	17	78596040	0.9	1.75	13.45
+rs4889863	rs901064	17	78550468	17	78596040	0.74	1.75	20.27
+rs7503807	rs901064	17	78591111	17	78596040	1.13	1.75	72.27
+rs4856	rs901064	17	78591211	17	78596040	1.26	1.75	65.25
+rs901064	rs8080265	17	78596040	17	78604814	1.75	0.25	22.66
+rs901064	rs12939549	17	78596040	17	78611724	1.75	1.65	73.1
+rs901064	rs12946972	17	78596040	17	78618922	1.75	1.34	24.52
+rs901064	rs9897453	17	78596040	17	78634	1.75	1.03	24.09
+rs901064	rs884204	17	78596040	17	78669248	1.75	2.59	32.03
+rs901064	rs9913162	17	78596040	17	78695546	1.75	3.62	32.83
+rs901064	rs9896771	17	78596040	17	78703899	1.75	0.87	22.52
+rs901064	rs9915378	17	78596040	17	78708418	1.75	2.34	32.41
+rs1435188	rs1527436	18	41082592	18	37971767	3.64	3.64	14.16
+rs13370227	rs7407082	18	75595652	18	75114	1.42	1.49	16.1
+rs7279935	rs1893382	21	17382916	21	17389011	3.47	0.59	14.94
+rs7279935	rs2823549	21	17382916	21	17389741	3.47	0.75	13.87

File test/T1D.txt

+rs_1	rs_2	chr_1	bp_1	chr_2	bp_2	fltChi2_1	fltChi2_2	fltGSS
+rs9271850	rs9272346	6	0	6	0	4.42	5.26	11.09
+rs199698	rs41515647	1	75636696	1	75647488	0.84	19.7	12
+rs640874	rs1627391	1	108650358	1	108652144	1.88	4.59	12.72
+rs1627391	rs499535	1	108652144	1	108654479	4.59	1.51	12.43
+rs1627391	rs601063	1	108652144	1	108655473	4.59	1.73	12.43
+rs7525703	rs2077749	1	146649064	1	146652637	4.29	12.4	12.92
+rs621793	rs16827732	1	187889323	1	187889812	9.61	2.86	13.71
+rs350747	rs350753	2	52868656	2	52873788	6.12	1.26	14.32
+rs16849921	rs10197379	2	214061022	2	205912603	18.57	0.01	32.1
+rs16849921	rs12694298	2	214061022	2	205913268	18.57	0.16	30.55
+rs9811898	rs3773090	3	29834	3	29609837	2.74	0.05	16.89
+rs3773090	rs2168765	3	29609837	3	29663099	0.05	1.38	23.43
+rs3773090	rs9818762	3	29609837	3	29618	0.05	1.3	19.37
+rs3773090	rs9832480	3	29609837	3	29695	0.05	2.52	18.8
+rs3773090	rs9832974	3	29609837	3	29680561	0.05	1.18	18.83
+rs3773090	rs9833015	3	29609837	3	29680632	0.05	1.89	19.45
+rs3773090	rs9838725	3	29609837	3	29681858	0.05	1.25	20.82
+rs3773090	rs9858796	3	29609837	3	29691014	0.05	2.26	20.56
+rs2420412	rs2420407	4	134348127	4	130103904	7.05	7.07	13.49
+rs2438074	rs2438083	6	1269690	6	1277371	3.71	0.11	14.33
+rs2438077	rs2438083	6	1272236	6	1277371	3.41	0.11	17.34
+rs2496292	rs2438083	6	1274617	6	1277371	6.82	0.11	19.2
+rs2438083	rs977674	6	1277371	6	1277702	0.11	7.47	25.17
+rs2438083	rs977673	6	1277371	6	1277715	0.11	7.69	25.36
+rs3129768	rs9272346	6	0	6	0	1.1	5.26	14.97
+rs3129768	rs9272723	6	0	6	0	1.1	7.5	15.24
+rs7774418	rs2655693	6	80234010	6	80248326	0.64	0.3	11.75
+rs2655694	rs2655693	6	80248295	6	80248326	0.24	0.3	16.63
+rs2655693	rs7775536	6	80248326	6	80264812	0.3	0.3	11.75
+rs1639044	rs1724932	7	2714335	7	2714357	1.88	1.14	15.43
+rs6592988	rs12673016	7	52281433	7	52286230	1.1	1.06	13.63
+rs6592988	rs4523204	7	52281433	7	52287973	1.1	0.92	13.63
+rs691184	rs12706898	7	129197408	7	129254997	0.68	17.51	13.21
+rs11526287	rs2250603	7	135297419	7	135315708	0.21	2.73	11.42
+rs1806	rs4840393	8	8961017	8	8962666	2.46	0.36	13.22
+rs99080	rs11984645	8	50357376	8	55069305	0.83	50.38	18.65
+rs6473901	rs11984645	8	50386264	8	55069305	1.45	50.38	18.44
+rs6473903	rs11984645	8	50388147	8	55069305	0.04	50.38	18.04
+rs6983650	rs11984645	8	50406226	8	55069305	0.36	50.38	22.74
+rs7844299	rs11984645	8	50430790	8	55069305	0.94	50.38	22.72
+rs16919782	rs11984645	8	55063437	8	55069305	3.91	50.38	11.43
+rs16919784	rs11984645	8	55063538	8	55069305	2.96	50.38	14.51
+rs11991952	rs11984645	8	55069061	8	55069305	4.01	50.38	11.58
+rs11984645	rs4737503	8	55069305	8	55071319	50.38	2.65	14.59
+rs2919408	rs11782342	8	73739252	8	69281738	5.09	14.04	15.03
+rs4571768	rs11782342	8	73743348	8	69281738	2.21	14.04	12.45
+rs4307385	rs11782342	8	73743688	8	69281738	2.41	14.04	11.74
+rs4307386	rs11782342	8	73743751	8	69281738	2.06	14.04	11.74
+rs11780806	rs11782342	8	73771329	8	69281738	0.4	14.04	15.19
+rs11782342	rs96807	8	69281738	8	69283209	14.04	2.04	18.27
+rs11782342	rs6994225	8	69281738	8	69299056	14.04	0.01	12.03
+rs1833226	rs1420	8	132701504	8	132701563	0.82	0.67	13.06
+rs1420	rs529894	8	132701563	8	132716	0.67	5.3	13.06
+rs3852458	rs10764038	10	19767401	10	19769651	4.49	2.42	23.19
+rs3904887	rs10764038	10	19768895	10	19769651	4.51	2.42	22.1
+rs6481942	rs10764038	10	19769118	10	19769651	4.6	2.42	23.19
+rs10764038	rs3852459	10	19769651	10	19770716	2.42	4.37	23.63
+rs12765184	rs12573160	10	89510229	10	89540888	2.76	1.93	14.27
+rs12775041	rs12573160	10	89515818	10	89540888	2.83	1.93	15.54
+rs9664653	rs12573160	10	89525860	10	89540888	2.66	1.93	14.61
+rs12573160	rs11202557	10	89540888	10	89552283	1.93	2.72	14.61
+rs12573160	rs11816798	10	89540888	10	89555714	1.93	2.61	14.3
+rs12573160	rs12781171	10	89540888	10	89565	1.93	2.66	14.61
+rs12573160	rs12762731	10	89540888	10	89579646	1.93	2.65	14.65
+rs7395011	rs10791991	11	68471479	11	68484983	0.51	1.27	17.03
+rs4628676	rs10791991	11	68482230	11	68484983	0.26	1.27	17.03
+rs10791991	rs7101673	11	68484983	11	68489	1.27	0.47	17.03
+rs2196519	rs12790605	11	91012	11	91024468	7.48	5	12.68
+rs2196519	rs12791202	11	91012	11	91024722	7.48	5.73	12.01
+rs2196519	rs1436623	11	91012	11	91024970	7.48	4.63	13.3
+rs7152833	rs4144189	14	21861211	14	21918876	3.59	2.28	11.44
+rs11624794	rs1958305	14	24260146	14	24273124	11.24	33.35	14.15
+rs1958305	rs17184408	14	24273124	14	24282020	33.35	0.49	20.68
+rs1958305	rs12601	14	24273124	14	24284173	33.35	1.95	20.02
+rs11849674	rs7154773	14	60688023	14	40912999	4.68	2.49	24.19
+rs10148587	rs7154773	14	60689160	14	40912999	4.22	2.49	21.24
+rs188620	rs7154773	14	40853319	14	40912999	0.81	2.49	20.18
+rs10137732	rs7154773	14	60689209	14	40912999	4.38	2.49	27.19
+rs6573298	rs7154773	14	40869612	14	40912999	2.07	2.49	18.02
+rs7145505	rs7154773	14	40869718	14	40912999	1.56	2.49	18.3
+rs8019531	rs7154773	14	40871554	14	40912999	3.07	2.49	35.42
+rs11628587	rs7154773	14	40871574	14	40912999	1.8	2.49	19.26
+rs11628628	rs7154773	14	40871689	14	40912999	1.99	2.49	19
+rs8011227	rs7154773	14	40882151	14	40912999	1.9	2.49	34.59
+rs7158657	rs7154773	14	40888940	14	40912999	2.91	2.49	33.68
+rs10142834	rs7154773	14	40898846	14	40912999	3.04	2.49	35.2
+rs17097262	rs7154773	14	40902242	14	40912999	1.03	2.49	34.26
+rs1887103	rs7154773	14	40907104	14	40912999	3.1	2.49	29.24
+rs7154773	rs8012816	14	40912999	14	40914246	2.49	1.05	27.56
+rs7154773	rs10130695	14	40912999	14	40919869	2.49	1.3	34.26
+rs7154773	rs1998225	14	40912999	14	40941373	2.49	1.9	17.21
+rs7154773	rs4280	14	40912999	14	60780429	2.49	2.2	16.97
+rs7154773	rs1951116	14	40912999	14	40946079	2.49	1.87	14.63
+rs7154773	rs1951117	14	40912999	14	60782214	2.49	2.03	17.19
+rs2757527	rs2757528	14	660933	14	661077	1.18	0.01	13.44
+rs2757527	rs2766696	14	660933	14	661320	1.18	0.11	14.22
+rs17478618	rs17473	15	26568722	15	26577328	0.73	1.66	13.12
+rs17478618	rs17706	15	26568722	15	26579253	0.73	1.44	13.12
+rs6016703	rs7262414	20	37529966	20	37549074	0.84	5.91	14.11
+rs6019	rs7262414	20	37526	20	37549074	0.38	5.91	14.17
+rs2223424	rs7262414	20	37539550	20	37549074	3.09	5.91	14.96
+rs7262414	rs6072650	20	37549074	20	37566	5.91	3.08	12.88
+rs7262414	rs6102728	20	37549074	20	37570311	5.91	2.86	14.66
+rs7262414	rs2867064	20	37549074	20	37582450	5.91	3.34	14.7
+rs7262414	rs6072667	20	37549074	20	37606093	5.91	3.17	14.97
+rs7262414	rs10485689	20	37549074	20	40879061	5.91	3.71	14.67
+rs2823543	rs2404032	21	17375633	21	17376332	1.9	1.19	17

File test/T2D.txt

+rs_1	rs_2	chr_1	bp_1	chr_2	bp_2	fltChi2_1	fltChi2_2	fltGSS
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