diff --git a/MANIFEST.in b/MANIFEST.in
index ac2af8ff93547e9a0b30bc6bc4b3c0294c613cf2..2f44dbf82157a6e81e14acf9fd7c3ae8998a3ca4 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,5 @@
include README.md
include setup.cfg
include jass/swagger/swagger.yaml
-recursive-include jass/static *
-recursive-include celery_files *
+recursive-include jass/models/data/ *.tsv
+recursive-include jass/static *
\ No newline at end of file
diff --git a/data/coef_mean_model.tsv b/data/coef_mean_model.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..ca07eb89886c598f96afb4e4f998f870e57d30f1
--- /dev/null
+++ b/data/coef_mean_model.tsv
@@ -0,0 +1,7 @@
+ 0
+k_coef_mv 0.07740334977380119
+log10_avg_distance_cor_coef_mv -0.6999110771883902
+log10_mean_gencov_coef_mv 0.746794584985343
+avg_Neff_coef_mv 0.07289261717080556
+avg_h2_coef_mv -0.516496395500929
+avg_perc_h2_diff_region_coef_mv 0.15727591593399
diff --git a/data/combi_sample.tsv b/data/combi_sample.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..7264b25dc3fd8cb6baad069e87b3e281a9404d3f
--- /dev/null
+++ b/data/combi_sample.tsv
@@ -0,0 +1,201 @@
+,trait,k,avg_distance_cor,mean_gencov,avg_Neff,avg_h2_mixer,avg_perc_h2_diff_region
+1590,z_VAN-HEEL_CELIAC z_GLG_HDL z_GLG_LDL z_WILLER_ATRIALFIBRI z_PGC_BIP z_SPIRO-UKB_FEV1-FVC z_UKIBD_CD,7,0.0590830304651648,0.015347619047619,127387.05229530609,0.2194681225668153,0.5603096008205554
+563,z_GEFOS_BMD-FOREARM z_GLG_HDL z_MAGIC_HOMA-B z_GLG_LDL z_DIAGRAM_T2D z_IMSGC-WTCC2_MULTSCLE z_TAGC_ASTHMA z_NEIGHBORHOOD_POAG z_WILLER_ATRIALFIBRI z_GLG_TC,10,0.0976903717379686,0.0123144082,72409.04275154244,0.0960473769919533,0.4744109853496899
+1176,z_MAGIC_HBA1C z_NEIGHBORHOOD_POAG z_DIAGRAM_T2D z_UKBIOBANK_POAG z_GEFOS_BMD-FOREARM z_SPIRO-UKB_FEV1-FVC z_MAGIC_2HGLU-ADJBMI z_WILLER_ATRIALFIBRI z_GLG_HDL,9,0.1259168550911433,0.0127416666666666,101526.3699107523,0.1035261413153112,0.532680760371381
+647,z_VGHRV_SDNN z_MAGIC_HBA1C z_MAGIC_2HGLU-ADJBMI z_GLG_LDL z_UKIBD_CD z_C4D_CHD,6,0.1497129719310509,0.01508,42349.44331194103,0.1336805621997407,0.5317645663141873
+706,z_GEFOS_BMD-FOREARM z_VAN-HEEL_CELIAC z_MAGIC_HBA1C z_GLG_TG z_MAGIC_HOMA-B z_IMSGC-WTCC2_MULTSCLE,6,0.1279517984531755,0.0141858306,39131.43430378047,0.1027698883137745,0.4699712597742396
+487,z_SPIRO-UKB_FEV1 z_GLG_LDL z_VAN-HEEL_CELIAC z_GLG_TG,4,0.0845412610596257,0.018,152338.49375122687,0.1474322876775918,0.5349461523209061
+1134,z_MAGIC_HBA1C z_BCAC_BREAST-CANCER-ERPOS z_TAGC_ASTHMA z_SSGAC_COLLEGE z_MAGIC_HOMA-B z_CARDIOGRAMPLUSC4D_CAD,6,0.1420672595836319,0.00838,112852.50999980255,0.0445240218365629,0.5854264271884885
+1375,z_VGHRV_SDNN z_MAGIC_HOMA-B z_UKIBD_UC z_GLG_LDL z_MAGIC_HBA1C z_IGAP_AZ z_DIAGRAM_T2D,7,0.1203259304226253,0.0097523809523809,49070.67952593441,0.0942467793088732,0.4743856536919613
+1167,z_WILLER_ATRIALFIBRI z_NEIGHBORHOOD_POAG z_ICBP_DBP z_GLG_TG z_IGAP_AZ z_VAN-HEEL_CELIAC z_TAGC_ASTHMA z_SPIRO-UKB_PEF,8,0.0580211015914355,0.0082642857142857,145566.2038757884,0.140015833098121,0.5704705953116378
+336,z_ISGEC_SLEEP-SD z_C4D_CHD z_MAGIC_HOMA-B z_TAGC_ASTHMA z_BCAC_BREAST-CANCER-ERPOS z_DIAGRAM_T2D,6,0.1337544044789961,0.00758,82705.81943479212,0.0407266907992723,0.5956343242037365
+41,z_GLG_TG z_GLG_LDL z_SPIRO-UKB_FEV1-FVC z_GLG_HDL,4,0.1283763678145305,0.02655,174196.0,0.1236007456024044,0.4317283091016877
+298,z_TAGC_ASTHMA z_MAGIC_HBA1C z_ISGEC_SLEEP-SD z_SSGAC_COLLEGE z_BCAC_BREAST-CANCER-ERPOS z_IGAP_AZ z_DIAGRAM_T2D z_CARDIOGRAMPLUSC4D_CAD z_MAGIC_HOMA-B z_NEIGHBORHOOD_POAG z_IMSGC-WTCC2_MULTSCLE,11,0.1472338050632623,0.0086706810727272,80626.02514303278,0.0646078587377984,0.617459161574024
+1647,z_IMSGC-WTCC2_MULTSCLE z_SSGAC_COLLEGE,2,0.0134755833219838,0.0013,75649.31540888772,0.1323774813348991,0.7223225759517726
+315,z_SPIRO-UKB_FEV1 z_SPIRO-UKB_PEF z_WILLER_ATRIALFIBRI z_IMSGC-WTCC2_MULTSCLE z_GLG_TC z_PGC_BIP,6,0.0532904011707912,0.0144466666666666,194193.69290476877,0.1775083022574499,0.61681560971493
+1887,z_MAGIC_HBA1C z_GLG_LDL z_VGHRV_SDNN z_MAGIC_HOMA-B,4,0.1252416298957048,0.0103333333333333,55141.5,0.0571012630925966,0.4261467763873102
+1644,z_VAN-HEEL_CELIAC z_MAGIC_2HGLU-ADJBMI z_GLG_TC z_SPIRO-UKB_FVC,4,0.0960418188010153,0.0156833333333333,130885.99375122684,0.1511967311641081,0.6432719530887009
+1723,z_GLG_HDL z_WILLER_ATRIALFIBRI z_C4D_CHD z_MAGIC_HOMA-B z_NEIGHBORHOOD_POAG z_GLG_LDL z_TAGC_ASTHMA z_MAGIC_HBA1C z_MAGIC_2HGLU-ADJBMI z_VAN-HEEL_CELIAC z_GLG_TC,11,0.1228551517980456,0.0144281521636363,68721.8996648356,0.0909802678793049,0.5132459717744748
+1785,z_VGHRV_SDNN z_GIANT_HIP z_GLG_TG z_GLG_LDL z_VAN-HEEL_CELIAC z_SSGAC_COLLEGE z_IMSGC-WTCC2_MULTSCLE z_PGC_BIP z_ICBP_DBP z_GLG_TC,10,0.0855612012893199,0.0182488888888888,94514.0566135874,0.1749872756491363,0.6058872516205108
+1042,z_ISGEC_SLEEP-SD z_ICBP_DBP z_ICBP_SBP z_BCAC_BREAST-CANCER-ERPOS z_CARDIOGRAMPLUSC4D_CAD z_SPIRO-UKB_FEV1-FVC z_UKBIOBANK_ALAT z_PGC_BIP z_GIANT_HIP z_SSGAC_COLLEGE z_ISGEC_SLEEP-MEAN,11,0.0906698729760977,0.0171521947818181,207684.2586868554,0.1684968807560016,0.7340270038141383
+1230,z_WILLER_ATRIALFIBRI z_VGHRV_SDNN z_GLG_TC z_UKBIOBANK_POAG,4,0.0217326319938325,0.0044166666666666,103337.56838834115,0.1183176857517645,0.537311156472734
+559,z_ISGEC_SLEEP-MEAN z_SPIRO-UKB_FEV1-FVC z_ICBP_SBP z_BCAC_BREAST-CANCER-ERPOS z_SPIRO-UKB_PEF z_SPIRO-UKB_FEV1 z_ISGEC_DIURNAL-IN z_SSGAC_COLLEGE z_ICBP_DBP,9,0.0784367531306572,0.0245916666666666,258089.6972913661,0.1722573777134419,0.7216792330462357
+465,z_UKIBD_UC z_PGC_BIP z_ICBP_DBP z_GLG_TC z_GIANT_HIP z_UKIBD_CD z_SPIRO-UKB_PEF z_WILLER_ATRIALFIBRI z_VGHRV_SDNN z_ICBP_SBP z_GLG_HDL,11,0.0626871605470878,0.0222490909090909,152934.99516552722,0.2313358725913795,0.5733177064733321
+182,z_ISGEC_DIURNAL-IN z_GLG_HDL z_UKIBD_UC z_SPIRO-UKB_FEV1-FVC z_SPIRO-UKB_FVC z_SPIRO-UKB_FEV1 z_ICBP_SBP z_GLG_TG z_SPIRO-UKB_PEF,9,0.0646731128297258,0.0236194444444444,246347.8395287294,0.1797318914674525,0.5950392415887262
+1445,z_GIANT_HIP z_TAGC_ASTHMA z_SSGAC_COLLEGE z_MAGIC_HOMA-B z_ISGEC_DIURNAL-IN z_NEIGHBORHOOD_POAG z_SPIRO-UKB_FVC z_GLG_HDL z_UKBIOBANK_ALAT,9,0.1219434243714489,0.0142957880277777,157963.7288433767,0.097487736094446,0.6351440382469806
+1944,z_TAGC_ASTHMA z_GLG_TC z_MAGIC_2HGLU-ADJBMI z_MAGIC_FAST-INSULIN z_WILLER_ATRIALFIBRI z_SSGAC_COLLEGE z_ISGEC_DIURNAL-IN z_MAGIC_HOMA-B z_SPIRO-UKB_FEV1-FVC,9,0.0645102163671844,0.0090194444444444,123337.749006027,0.0873275459892302,0.6416114601273238
+1428,z_IMSGC-WTCC2_MULTSCLE z_PGC_BIP z_MAGIC_FAST-INSULIN z_CARDIOGRAMPLUSC4D_CAD z_GIANT_HIP z_UKBIOBANK_POAG,6,0.1068441352448858,0.0126733333333333,76243.65133443482,0.159147851665268,0.7281644888393856
+150,z_VAN-HEEL_CELIAC z_MAGIC_HBA1C z_MAGIC_2HGLU-ADJBMI,3,0.1294340950690383,0.0174999999999999,24785.325001635807,0.1155362667696048,0.6002841362666933
+274,z_PGC_BIP z_ICBP_SBP z_GIANT_HIP z_GLG_LDL z_SPIRO-UKB_FEV1 z_ISGEC_SLEEP-MEAN z_UKIBD_UC z_ICBP_DBP z_UKIBD_CD z_VAN-HEEL_CELIAC z_MAGIC_2HGLU-ADJBMI z_SPIRO-UKB_FEV1-FVC,12,0.0695153298236532,0.0221272727272727,153646.11296067174,0.2421499659078964,0.6462169458043557
+133,z_VGHRV_SDNN z_SPIRO-UKB_FEV1 z_SPIRO-UKB_FEV1-FVC z_GLG_HDL z_VAN-HEEL_CELIAC z_PGC_BIP z_SPIRO-UKB_PEF z_GEFOS_BMD-FOREARM z_GLG_TG z_UKBIOBANK_ALAT,10,0.0781496526754806,0.0185533333333333,189990.79353180985,0.14834882668588,0.5723473049474492
+625,z_GLG_HDL z_SPIRO-UKB_FEV1-FVC z_GLG_LDL z_WILLER_ATRIALFIBRI z_CARDIOGRAMPLUSC4D_CAD z_MAGIC_2HGLU-ADJBMI z_ICBP_DBP z_VAN-HEEL_CELIAC z_UKBIOBANK_POAG z_DIAGRAM_T2D z_ISGEC_SLEEP-MEAN,11,0.0946034984804523,0.0122963636363636,136864.04808980078,0.1509498682208043,0.6035377645402723
+1334,z_UKBIOBANK_POAG z_IMSGC-WTCC2_MULTSCLE z_MAGIC_2HGLU-ADJBMI z_GLG_LDL z_VGHRV_SDNN z_ISGEC_SLEEP-SD z_NEIGHBORHOOD_POAG z_IGAP_AZ z_ICBP_DBP z_ISGEC_DIURNAL-IN z_MAGIC_FAST-INSULIN z_TAGC_ASTHMA,12,0.0945538220831093,0.0077681818181818,72649.03564810402,0.1167604478703124,0.6666392704974057
+1278,z_ISGEC_SLEEP-MEAN z_SSGAC_COLLEGE z_SPIRO-UKB_PEF z_CARDIOGRAMPLUSC4D_CAD z_ISGEC_SLEEP-SD z_SPIRO-UKB_FEV1 z_BCAC_BREAST-CANCER-ERPOS z_ICBP_SBP z_SPIRO-UKB_FVC z_SPIRO-UKB_FEV1-FVC z_PGC_BIP,11,0.0855037656709843,0.01762,236644.1677777645,0.1507616301147009,0.7410848932841055
+641,z_MAGIC_2HGLU-ADJBMI z_ICBP_DBP z_GLG_HDL z_MAGIC_FAST-INSULIN z_TAGC_ASTHMA z_MAGIC_HBA1C z_GEFOS_BMD-FOREARM z_C4D_CHD z_ISGEC_SLEEP-SD z_IGAP_AZ z_GLG_TG z_UKBIOBANK_ALAT,12,0.1550634761335628,0.0158355813484848,106494.37502418408,0.094676737091809,0.5862564612773997
+1471,z_VAN-HEEL_CELIAC z_UKIBD_UC z_TAGC_ASTHMA,3,0.0716413235817607,0.0147,38751.56850668967,0.1895962040425665,0.5360869999645724
+36,z_MAGIC_HBA1C z_DIAGRAM_T2D z_TAGC_ASTHMA z_SSGAC_COLLEGE z_BCAC_BREAST-CANCER-ERPOS z_C4D_CHD,6,0.2121136291435215,0.0136733333333333,89755.81943479214,0.0517372922116864,0.5840143317337964
+1476,z_TAGC_ASTHMA z_GIANT_HIP z_MAGIC_FAST-INSULIN z_ISGEC_DIURNAL-IN z_UKIBD_CD z_SPIRO-UKB_PEF,6,0.087021521727048,0.0120599999999999,129641.17320133257,0.1523996983463591,0.686046589714835
+1697,z_GLG_TC z_TAGC_ASTHMA z_MAGIC_HBA1C z_DIAGRAM_T2D z_IGAP_AZ z_IMSGC-WTCC2_MULTSCLE z_GLG_LDL,7,0.1141526306042843,0.0130755932857142,60081.715213907,0.0982385576043977,0.4831980138232068
+1144,z_GLG_TC z_GLG_HDL z_IMSGC-WTCC2_MULTSCLE z_GLG_TG,4,0.0884884070994064,0.0291666666666666,79243.90770444386,0.1346552715905047,0.4454331496965424
+1601,z_SPIRO-UKB_FEV1 z_PGC_BIP,2,0.0208041117370982,0.0035,208322.98015659556,0.263873901967294,0.7649789686500086
+192,z_GLG_LDL z_WILLER_ATRIALFIBRI z_NEIGHBORHOOD_POAG z_TAGC_ASTHMA z_C4D_CHD z_GLG_TG z_GLG_TC z_GEFOS_BMD-FOREARM z_UKIBD_UC z_UKIBD_CD z_IMSGC-WTCC2_MULTSCLE,11,0.1154003610074032,0.022290909090909,66805.45203054749,0.1469200872830987,0.4959194871515098
+1316,z_SSGAC_COLLEGE z_C4D_CHD z_TAGC_ASTHMA z_IGAP_AZ z_MAGIC_FAST-INSULIN z_MAGIC_HBA1C,6,0.1663614753168403,0.0110066666666666,60600.083381701465,0.0494602447159337,0.6354365108954337
+845,z_UKIBD_CD z_SPIRO-UKB_PEF,2,0.0551897688832407,0.0136,217053.4322256991,0.29602996223388,0.5594205759183581
+1268,z_ISGEC_SLEEP-SD z_SPIRO-UKB_PEF,2,0.041210212574476,0.0018,242271.5,0.0721643471173446,0.7657436043057917
+383,z_VAN-HEEL_CELIAC z_ICBP_SBP z_VGHRV_SDNN,3,0.0833788109386555,0.0155999999999999,113296.65833496914,0.2199112384468608,0.6114702939860062
+1307,z_MAGIC_HBA1C z_GLG_TC z_NEIGHBORHOOD_POAG,3,0.1924394827501134,0.015,51881.12827793105,0.0853806379083743,0.4763850235201798
+1377,z_SPIRO-UKB_FVC z_GIANT_HIP z_UKBIOBANK_ALAT z_ISGEC_SLEEP-MEAN,4,0.0866251282826679,0.0135666666666666,267068.5,0.1345575915622025,0.7408854716352948
+168,z_WILLER_ATRIALFIBRI z_GIANT_HIP z_ICBP_DBP,3,0.1593487546287759,0.0441999999999999,224233.52209921528,0.203761331485528,0.6278651256075946
+594,z_BCAC_BREAST-CANCER-ERPOS z_SPIRO-UKB_FVC z_ICBP_SBP z_SPIRO-UKB_FEV1,4,0.051818316940652,0.0308333333333333,331729.0689055737,0.1830704849428388,0.6886005152031707
+958,z_CARDIOGRAMPLUSC4D_CAD z_C4D_CHD z_BCAC_BREAST-CANCER-ERPOS,3,0.2826585241361947,0.0257666666666666,140379.5602208221,0.0318530947963322,0.6825843118926381
+1408,z_DIAGRAM_T2D z_TAGC_ASTHMA z_GLG_HDL z_MAGIC_HBA1C z_MAGIC_2HGLU-ADJBMI z_C4D_CHD z_GLG_TC z_GLG_LDL z_UKIBD_CD z_UKBIOBANK_POAG,10,0.1366673607281095,0.0153210748666666,59062.8212693575,0.1348361504523458,0.5018382143227557
+592,z_SPIRO-UKB_FEV1-FVC z_SPIRO-UKB_FVC,2,0.0278200239750478,0.0135,400102.0,0.1688233005882726,0.6327641933858442
+1182,z_UKBIOBANK_POAG z_SSGAC_COLLEGE z_IGAP_AZ z_BCAC_BREAST-CANCER-ERPOS z_IMSGC-WTCC2_MULTSCLE z_MAGIC_FAST-INSULIN,6,0.1137541479724746,0.00562,88900.85730152547,0.0913860901707053,0.7237872912979717
+1801,z_DIAGRAM_T2D z_GEFOS_BMD-FOREARM z_NEIGHBORHOOD_POAG z_IGAP_AZ z_MAGIC_HOMA-B z_GLG_TG z_WILLER_ATRIALFIBRI z_UKIBD_CD z_TAGC_ASTHMA z_MAGIC_HBA1C z_IMSGC-WTCC2_MULTSCLE,11,0.1218458315016612,0.0128106810727272,59290.165643653294,0.1146575041657396,0.4939800534284094
+1400,z_PGC_BIP z_ISGEC_DIURNAL-IN z_CARDIOGRAMPLUSC4D_CAD z_GIANT_HIP z_ISGEC_SLEEP-MEAN z_SPIRO-UKB_FEV1-FVC z_ISGEC_SLEEP-SD z_ICBP_DBP z_BCAC_BREAST-CANCER-ERPOS,9,0.0694501616857693,0.0109269642499999,167381.53839504547,0.1511892195903571,0.7710492852189827
+294,z_SPIRO-UKB_FEV1-FVC z_SPIRO-UKB_FEV1 z_SPIRO-UKB_FVC z_GLG_TG z_GLG_HDL z_GEFOS_BMD-FOREARM,6,0.0826064408550955,0.0289933333333333,234205.16666666663,0.1286495461550943,0.5262108653001604
+791,z_SSGAC_COLLEGE z_SPIRO-UKB_FVC z_IMSGC-WTCC2_MULTSCLE z_ICBP_DBP z_GLG_LDL z_MAGIC_FAST-INSULIN z_UKBIOBANK_POAG z_IGAP_AZ z_GEFOS_BMD-FOREARM z_GIANT_HIP z_GLG_TC z_SPIRO-UKB_FEV1-FVC,12,0.0864742810515255,0.0111378787878787,146158.15568223817,0.1410714520107773,0.6223449420906045
+58,z_ISGEC_SLEEP-SD z_IGAP_AZ z_BCAC_BREAST-CANCER-ERPOS z_NEIGHBORHOOD_POAG z_MAGIC_HOMA-B z_IMSGC-WTCC2_MULTSCLE z_CARDIOGRAMPLUSC4D_CAD z_TAGC_ASTHMA z_MAGIC_FAST-INSULIN z_C4D_CHD,10,0.1275981228024508,0.0087244444444444,75052.24011839958,0.0569925123866621,0.6553178811045668
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+1335,z_GLG_TG z_VGHRV_SDNN z_SPIRO-UKB_FEV1 z_GIANT_HIP z_GLG_HDL z_MAGIC_2HGLU-ADJBMI z_UKBIOBANK_ALAT z_ICBP_SBP z_UKIBD_CD,9,0.0955452129622085,0.0181916666666666,172886.87382793313,0.1826763020618484,0.5802105538041229
+759,z_GLG_TC z_C4D_CHD z_DIAGRAM_T2D z_UKIBD_UC z_GLG_TG z_GEFOS_BMD-FOREARM z_MAGIC_2HGLU-ADJBMI z_IGAP_AZ,8,0.159396788753913,0.0194607142857142,46104.444012723616,0.1042583013096612,0.5266574898619478
+1937,z_WILLER_ATRIALFIBRI z_UKIBD_CD z_MAGIC_2HGLU-ADJBMI z_PGC_BIP,4,0.0540024681989385,0.0185166666666666,73500.84776555875,0.2538079260462135,0.6648391873208915
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+1518,z_UKIBD_CD z_VGHRV_SDNN z_GLG_TG z_GEFOS_BMD-FOREARM,4,0.0905571280731117,0.0182666666666666,41714.46611284955,0.1690639491037382,0.467698950646491
+1106,z_UKBIOBANK_POAG z_ISGEC_SLEEP-SD z_IGAP_AZ z_MAGIC_HBA1C z_TAGC_ASTHMA z_C4D_CHD,6,0.1024978474031893,0.0058666666666666,56143.36792432128,0.067789736501491,0.6056649612409757
+1229,z_CARDIOGRAMPLUSC4D_CAD z_MAGIC_HBA1C z_UKBIOBANK_POAG z_C4D_CHD z_IGAP_AZ z_BCAC_BREAST-CANCER-ERPOS z_DIAGRAM_T2D z_TAGC_ASTHMA,8,0.1634373290803187,0.0111785714285714,85397.59544971985,0.0673893449574684,0.5799729817275958
+1708,z_UKIBD_CD z_MAGIC_HOMA-B z_GLG_TC z_IMSGC-WTCC2_MULTSCLE z_NEIGHBORHOOD_POAG z_DIAGRAM_T2D z_VGHRV_SDNN z_UKIBD_UC z_GLG_LDL z_VAN-HEEL_CELIAC z_C4D_CHD,11,0.1261221842377835,0.0200236363636363,41973.62519057268,0.1539435956798403,0.5193811119015223
+470,z_ICBP_SBP z_DIAGRAM_T2D z_GLG_HDL z_GLG_TG z_ICBP_DBP z_IGAP_AZ z_PGC_BIP z_MAGIC_HOMA-B z_SPIRO-UKB_FEV1-FVC z_ISGEC_SLEEP-SD z_WILLER_ATRIALFIBRI,11,0.0932666770274273,0.0183799999999999,150644.7934121648,0.164998835004596,0.5778542362236005
+1153,z_SPIRO-UKB_FEV1-FVC z_SPIRO-UKB_PEF z_ICBP_DBP z_ICBP_SBP z_SPIRO-UKB_FEV1 z_UKIBD_CD z_UKBIOBANK_ALAT z_MAGIC_2HGLU-ADJBMI z_SPIRO-UKB_FVC,9,0.0481348822782422,0.0303777777777777,298329.09605015535,0.2346116646253,0.6258846959094124
+1597,z_GEFOS_BMD-FOREARM z_SSGAC_COLLEGE z_GLG_LDL z_ISGEC_DIURNAL-IN z_ICBP_SBP z_ISGEC_SLEEP-MEAN z_GLG_TG z_ISGEC_SLEEP-SD z_VAN-HEEL_CELIAC z_MAGIC_FAST-INSULIN z_MAGIC_2HGLU-ADJBMI,11,0.1147501593653129,0.0155636363636363,87185.90681862795,0.1188710492794739,0.7041953884707207
+1009,z_UKBIOBANK_ALAT z_ICBP_DBP z_GLG_TC z_GLG_HDL z_ISGEC_SLEEP-SD z_SPIRO-UKB_FVC z_GLG_LDL z_GIANT_HIP z_IMSGC-WTCC2_MULTSCLE z_IGAP_AZ,10,0.1009167352542022,0.0131533333333333,173323.6160931139,0.1444744979103856,0.6044475028778752
+1732,z_ICBP_DBP z_GEFOS_BMD-FOREARM,2,0.1084535271625758,0.024,153583.5,0.2252484404358153,0.5161628672992529
+116,z_MAGIC_HOMA-B z_IMSGC-WTCC2_MULTSCLE z_C4D_CHD z_GEFOS_BMD-FOREARM z_GLG_TC z_NEIGHBORHOOD_POAG,6,0.1318655607001345,0.0119533333333333,36470.30184530277,0.0852337972089061,0.5102007861999914
+60,z_BCAC_BREAST-CANCER-ERPOS z_ISGEC_SLEEP-SD z_NEIGHBORHOOD_POAG z_UKBIOBANK_POAG z_MAGIC_FAST-INSULIN z_TAGC_ASTHMA,6,0.1085654124442258,0.0069066666666666,83506.92374473398,0.0678803617400797,0.707973853804313
diff --git a/data/combination_example.tsv b/data/combination_example.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..b42f3a73d6e4e8d2ef72070c2b7174f804ce0ed9
--- /dev/null
+++ b/data/combination_example.tsv
@@ -0,0 +1,2 @@
+GRP1,z_GIANT_HIP z_GLG_HDL z_GLG_LDL z_MAGIC_2HGLU-ADJBMI
+GRP2,z_SPIRO-UKB_FVC z_SPIRO-UKB_FEV1 z_TAGC_ASTHMA
diff --git a/data/gain_results.tsv b/data/gain_results.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..cd44d997b8fe28075d6dd53ef1374fc77cf4e6af
--- /dev/null
+++ b/data/gain_results.tsv
@@ -0,0 +1,3 @@
+ traits k avg_distance_cor mean_gencov avg_Neff avg_h2 avg_perc_h2_diff_region log10_mean_gencov log10_avg_distance_cor gain
+1 ['z_SPIRO-UKB_FVC', 'z_SPIRO-UKB_FEV1', 'z_TAGC_ASTHMA'] 0.1 0.1731946683845993 0.0637 0.3843393026739591 0.2785193310634847 0.7976315890930669 0.8139196701681637 0.8013809378674498 0.06428524764535551
+0 ['z_GIANT_HIP', 'z_GLG_HDL', 'z_GLG_LDL', 'z_MAGIC_2HGLU-ADJBMI'] 0.2 0.14899001074867035 0.01535 0.12076877719858631 0.22628198390356655 0.9055326131023057 0.6573854616675169 0.7879956172999502 -0.010766494024690904
diff --git a/data/range_feature_gain_prediction.tsv b/data/range_feature_gain_prediction.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..30c63afb46258403eeb27bf17cb6a9095afbd972
--- /dev/null
+++ b/data/range_feature_gain_prediction.tsv
@@ -0,0 +1,7 @@
+ minimum_value maximum_value
+k 2.0 12.0
+log10_avg_distance_cor -4.675617219570908 0.20864138105896807
+log10_mean_gencov -4.4093921991254446 -0.46117501106209624
+avg_Neff 6730.5 697828.0
+avg_h2 0.014033707225812 0.4361454950334251
+avg_perc_h2_diff_region 0.0906544694784672 0.9831222899777692
diff --git a/doc/source/get_predicted_gain.rst b/doc/source/get_predicted_gain.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9dea792471e07a2846adb7a73d92f8f0c14ea103
--- /dev/null
+++ b/doc/source/get_predicted_gain.rst
@@ -0,0 +1,39 @@
+Compute JASS power gain from the genetic architecture of traits
+===============================================================
+
+In a recent study :cite:`suzuki2023trait`, we explore how the genetic architecture of the set of traits (heritability, genetic covariance, heritability undetected by the univariate test, ...) can be predictive of statistical power gain of the multi-trait test.
+
+
+
+We implement an additional command line tool to give access our predictive model (the **jass predict-gain** command).
+This command allows the to score swiftly a large number of traits combinations and to focus on set of traits the most promising for multi-trait testing.
+
+To work the inittable provided to the **jass predict-gain** command must contain the genetic covariance between traits.
+
+
+.. code-block:: shell
+
+ jass predict-gain --inittable-path inittable_curated_111_traits_20-03-2024.hdf5 --combination_path ./combination_example.tsv --gain-path predicted_gain.tsv
+
+
+The second argument (--combination_path) is a path to a file containing the set of traits to be scored.
+
+.. csv-table:: Set of traits
+ :widths: 20, 70
+ :header-rows: 1
+
+ GRP1,z_GIANT_HIP z_GLG_HDL z_GLG_LDL z_MAGIC_2HGLU-ADJBMI
+ GRP2,z_SPIRO-UKB_FVC z_SPIRO-UKB_FEV1 z_TAGC_ASTHMA
+
+When executed the command will created a report at --gain-path
+
+.. csv-table:: Predicted gain
+ :header-rows: 1
+
+ traits,k,avg_distance_cor,mean_gencov,avg_Neff,avg_h2,avg_perc_h2_diff_region,log10_mean_gencov,log10_avg_distance_cor,gain
+ ['z_SPIRO-UKB_FVC'; 'z_SPIRO-UKB_FEV1'; 'z_TAGC_ASTHMA'],0.1,0.1731946683845993,0.0637,0.3843393026739591,0.2785193310634847,0.7976315890930669,0.8139196701681637,0.8013809378674498,0.06428524764535551
+ ['z_GIANT_HIP'; 'z_GLG_HDL'; 'z_GLG_LDL'; 'z_MAGIC_2HGLU-ADJBMI'],0.2,0.14899001074867035,0.01535,0.12076877719858631,0.22628198390356655,0.9055326131023057,0.6573854616675169,0.7879956172999502,-0.010766494024690904
+
+The last column provide the predicted gain ("the higher the more promising"). Note that extrapoling on new data might give lesser performances than reported in :cite:`suzuki2023trait`.
+
+.. bibliography:: reference.bib
\ No newline at end of file
diff --git a/doc/source/index.rst b/doc/source/index.rst
index 270f8c072ce9a715e642250b4daf5aa1b29772b3..87967f4a2da31ea34bc97b1035c41e2f138b0896 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -14,6 +14,7 @@ JASS documentation
install
data_import
generating_joint_analysis
+ get_predicted_gain
command_line_usage
web_usage
web_admin
diff --git a/doc/source/reference.bib b/doc/source/reference.bib
index 469a6903a53fa58d8e729b5e0d0e3b16c820968f..901b14a9691c75bac3e660837b9f99777a24a58a 100644
--- a/doc/source/reference.bib
+++ b/doc/source/reference.bib
@@ -20,6 +20,13 @@
publisher={Oxford University Press}
}
+@article{suzuki2023trait,
+ title={Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability},
+ author={Suzuki, Yuka and M{\'e}nager, Herv{\'e} and Brancotte, Bryan and Vernet, Rapha{\"e}l and Nerin, Cyril and Boetto, Christophe and Auvergne, Antoine and Linhard, Christophe and Torchet, Rachel and Lechat, Pierre and others},
+ journal={bioRxiv},
+ year={2023},
+ publisher={Cold Spring Harbor Laboratory Preprints}
+}
@article{Pasaniuc2014,
diff --git a/jass/__main__.py b/jass/__main__.py
index 586b489bc19b43b64b7d1ad111983e4e04613f5f..4a7cd1e8c133f485a1875caa841970c91a5cbafc 100644
--- a/jass/__main__.py
+++ b/jass/__main__.py
@@ -21,6 +21,8 @@ from jass.models.plots import (
create_local_plot,
create_qq_plot,
)
+from jass.models.gain import create_features, compute_gain
+
from pandas import read_hdf
def absolute_path_of_the_file(fileName, output_file=False):
@@ -279,6 +281,15 @@ def w_gene_annotation(args):
gene_data_path, initTable_path, df_gene_csv_path, df_exon_csv_path
)
+def w_compute_gain(args):
+ inittable_path = absolute_path_of_the_file(args.inittable_path)
+ combi_path = absolute_path_of_the_file(args.combination_path)
+ combi_path_with_gain = absolute_path_of_the_file(args.gain_path, True)
+
+ features = create_features(inittable_path, combi_path)
+ compute_gain(features, combi_path_with_gain)
+
+
def get_parser():
parser = argparse.ArgumentParser(prog="jass")
@@ -619,6 +630,27 @@ def get_parser():
help="Existing key are 'SumStatTab' : The results of the joint analysis by SNPs - 'PhenoList' : the meta data of analysed GWAS - 'COV' : The H0 covariance used to perform joint analysis - 'GENCOV' (If present in the initTable): The genetic covariance as computed by the LDscore. Uniquely for the worktable: 'Regions' : Results of the joint analysis summarised by LD regions (Notably Lead SNPs by regions) - 'summaryTable': a double entry table summarizing the number of significant regions by test (univariate vs joint test)",
)
parser_create_mp.set_defaults(func=w_extract_tsv)
+
+ # ------- compute predicted gain -------#
+ parser_create_mp = subparsers.add_parser(
+ "predict-gain", help="Predict gain based on the genetic architecture of the set of multi-trait. To function, this command need the inittable to contain genetic covariance store under the key 'GEN_COV in the inittable'"
+ )
+ parser_create_mp.add_argument(
+ "--inittable-path",
+ required=True,
+ help="Path to the inittable",
+ )
+ parser_create_mp.add_argument(
+ "--combination-path",
+ required=True,
+ help="Path to the file storing combination to be scored",
+ )
+ parser_create_mp.add_argument(
+ "--gain-path", required=True, help="path to save predicted gain"
+ )
+
+ parser_create_mp.set_defaults(func=w_compute_gain)
+
return parser
diff --git a/jass/models/data/coef_mean_model.tsv b/jass/models/data/coef_mean_model.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..ca07eb89886c598f96afb4e4f998f870e57d30f1
--- /dev/null
+++ b/jass/models/data/coef_mean_model.tsv
@@ -0,0 +1,7 @@
+ 0
+k_coef_mv 0.07740334977380119
+log10_avg_distance_cor_coef_mv -0.6999110771883902
+log10_mean_gencov_coef_mv 0.746794584985343
+avg_Neff_coef_mv 0.07289261717080556
+avg_h2_coef_mv -0.516496395500929
+avg_perc_h2_diff_region_coef_mv 0.15727591593399
diff --git a/jass/models/data/range_feature_gain_prediction.tsv b/jass/models/data/range_feature_gain_prediction.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..30c63afb46258403eeb27bf17cb6a9095afbd972
--- /dev/null
+++ b/jass/models/data/range_feature_gain_prediction.tsv
@@ -0,0 +1,7 @@
+ minimum_value maximum_value
+k 2.0 12.0
+log10_avg_distance_cor -4.675617219570908 0.20864138105896807
+log10_mean_gencov -4.4093921991254446 -0.46117501106209624
+avg_Neff 6730.5 697828.0
+avg_h2 0.014033707225812 0.4361454950334251
+avg_perc_h2_diff_region 0.0906544694784672 0.9831222899777692
diff --git a/jass/models/gain.py b/jass/models/gain.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0cef418e986c6a041b520dac976a061f1c32f5a
--- /dev/null
+++ b/jass/models/gain.py
@@ -0,0 +1,145 @@
+import pkg_resources
+import pandas as pd
+import numpy as np
+import os
+
+# data issued from https://doi.org/10.1101/2023.10.27.564319
+stream = pkg_resources.resource_stream(__name__, 'data/range_feature_gain_prediction.tsv')
+X_range = pd.read_csv(stream, sep="\t", index_col=0)
+
+stream = pkg_resources.resource_stream(__name__, 'data/coef_mean_model.tsv')
+model_coefficients = pd.read_csv(stream, sep="\t", index_col=0)
+
+# Scale according to observed
+def scale_feature(X, feature_name):
+ X_std = (X - X_range.loc[feature_name, "minimum_value"]) / ( X_range.loc[feature_name, "maximum_value"] - X_range.loc[feature_name, "minimum_value"])
+ return X_std
+
+def preprocess_feature(df_combinations):
+ # transformation of features
+
+ df_combinations['log10_mean_gencov'] = np.log10(df_combinations.mean_gencov)
+ df_combinations['log10_avg_distance_cor'] = np.log10(df_combinations.avg_distance_cor)
+ for f in ["k", "log10_avg_distance_cor", "log10_mean_gencov", "avg_Neff", "avg_h2", "avg_perc_h2_diff_region"]:
+ df_combinations[f] = scale_feature(df_combinations[f], f)
+ return df_combinations
+
+def compute_gain(df_combinations, path_output):
+
+ preprocess_feature(df_combinations)
+ df_combinations["gain"] = df_combinations[["k", "log10_avg_distance_cor", "log10_mean_gencov", "avg_Neff", "avg_h2", "avg_perc_h2_diff_region"]].dot(model_coefficients["0"].values)
+ df_combinations.sort_values(by="gain", ascending=False).to_csv(path_output, sep="\t")
+
+# cov to cor
+def cov2cor(c):
+ """
+ Return a correlation matrix given a covariance matrix.
+ : c = covariance matrix
+ """
+ D = 1 / np.sqrt(np.diag(c)) # takes the inverse of sqrt of diag.
+ return D * c * D
+
+def compute_detected_undected_h2(inittable_path):
+
+ phenoL = pd.read_hdf(inittable_path, "PhenoList")
+ gen_cov = pd.read_hdf(inittable_path, "GEN_COV")
+ region = pd.read_hdf(inittable_path, "Regions")
+
+ combi_c = list(phenoL.index)
+ reg_start=0
+ reg_end=50
+
+ chunk_size=50
+ Nchunk = region.shape[0] // chunk_size + 1
+ start_value = 0
+
+ zscore_threshold = 5.452
+ h2_GW = np.zeros(len(combi_c))
+
+ for chunk in range(start_value, Nchunk):
+ print(chunk)
+ binf = chunk * chunk_size
+ bsup = (chunk + 1) * chunk_size
+
+ init_extract = pd.read_hdf(inittable_path, "SumStatTab", where= "Region >= {0} and Region < {1}".format(reg_start, reg_end))
+
+ init_extract[combi_c] = init_extract[combi_c].abs()
+ max_zscore = init_extract[["Region"] + combi_c].groupby("Region").max()
+
+ Neff_term = np.ones(max_zscore.shape)
+ Neff_term = Neff_term* (1/phenoL.loc[combi_c, "Effective_sample_size"].values)
+
+ beta_2 = max_zscore.mask((max_zscore < zscore_threshold))
+ beta_2 = beta_2 * np.sqrt(Neff_term)
+ beta_2 = beta_2.mask( (beta_2 > 0.019))
+
+ h2_GW += (beta_2**2).sum()
+
+ h2 = np.diag(gen_cov.loc[combi_c, combi_c])
+ undetected_h2 = ((h2 - h2_GW) / h2)
+
+ phenoL["h2"] = h2
+ phenoL["h2_GW"] = h2_GW
+ phenoL["undetected_h2_perc"] = undetected_h2
+
+ return phenoL
+
+def add_h2_to_pheno_description(inittable_path):
+ phenoL_before = pd.read_hdf(inittable_path, "PhenoList")
+ if "avg_perc_h2_diff_region" in phenoL_before.columns:
+ phenoL = compute_detected_undected_h2(inittable_path)
+ phenoL.to_hdf(inittable_path, key="table")
+
+
+def compute_mean_cov(cov, combi_c):
+ rows, cols = np.indices(cov.loc[combi_c, combi_c].shape)
+ mean_gencov = cov.loc[combi_c, combi_c].where(rows != cols).stack().abs().mean()
+ return mean_gencov
+
+def compute_diff_cor(res_cov, gen_cov, combi_c):
+ res_cor = cov2cor(res_cov.loc[combi_c, combi_c])
+ gen_cor = cov2cor(gen_cov.loc[combi_c, combi_c])
+ rows, cols = np.indices(res_cor.loc[combi_c, combi_c].shape)
+ off_gencor = res_cor.where(rows != cols).stack()
+ off_rescor = gen_cor.where(rows != cols).stack()
+ return (off_gencor - off_rescor).abs().mean()
+
+def compute_mean_undetected_h2(phenoL, combi_c):
+ mean_h2 = np.mean(phenoL.loc[combi_c, "undetected_h2_perc"])
+ return mean_h2
+
+def compute_mean_h2(phenoL, combi_c):
+ mean_h2 = np.mean(phenoL.loc[combi_c, "h2"])
+ return mean_h2
+
+def compute_mean_Neff(phenoL, combi_c):
+ mean_neff = np.mean(phenoL.loc[combi_c, "Effective_sample_size"])
+ return mean_neff
+
+
+#beta_2_GW = beta_2_GW * (1/phenoL.loc[combi_c, "Effective_sample_size"].values)
+def create_features(inittable_path, combi_file):
+ add_h2_to_pheno_description(inittable_path)
+
+ phenoL = pd.read_hdf(inittable_path, "PhenoList")
+ gen_cov = pd.read_hdf(inittable_path, "GEN_COV")
+ res_cov = pd.read_hdf(inittable_path, "COV")
+
+ combi = pd.read_csv(combi_file, sep=",", index_col=0, names=["combi"])
+ combi = list(combi.combi.str.split(" "))
+
+ D = {'traits':[] ,'k':[], 'avg_distance_cor': [], 'mean_gencov': [],
+ 'avg_Neff':[], 'avg_h2':[], 'avg_perc_h2_diff_region':[]}
+
+ for c in combi:
+ D['traits'].append(str(c))
+ D["k"].append(len(c))
+ D["avg_distance_cor"].append(compute_diff_cor(res_cov, gen_cov, c))
+ D["mean_gencov"].append(compute_mean_cov(gen_cov, c))
+ D["avg_Neff"].append(compute_mean_Neff(phenoL, c))
+
+ D["avg_h2"].append(compute_mean_h2(phenoL, c))
+ D["avg_perc_h2_diff_region"].append(compute_mean_undetected_h2(phenoL, c))
+
+ return pd.DataFrame.from_dict(D)
+
diff --git a/jass/test/data_real/initTable.hdf5 b/jass/test/data_real/initTable.hdf5
index 821bdcc09b2740e389cec2d5049da4fb1b2d3523..bbeb66410fbcbb9dcbfaf5dae49355d0b9018cf9 100644
Binary files a/jass/test/data_real/initTable.hdf5 and b/jass/test/data_real/initTable.hdf5 differ
diff --git a/jass/test/data_real/worktable.hdf5 b/jass/test/data_real/worktable.hdf5
index 6c0162e61d22b93316dfe293a05b5b47d6c68fe4..809f790a23140377941366ffc081c41dfd0e4e97 100644
Binary files a/jass/test/data_real/worktable.hdf5 and b/jass/test/data_real/worktable.hdf5 differ
diff --git a/jass/test/data_test1/COV.csv b/jass/test/data_test1/COV.csv
deleted file mode 100644
index 2c45ac17818b90efa5afbc3d58060bf9c65263a8..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/COV.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ID z_DISNEY_RATATOUY z_DISNEY_POCAHONT
-z_DISNEY_RATATOUY 2.05403006060606 0.394332909090909
-z_DISNEY_POCAHONT 0.394332909090909 1.17729254545455
diff --git a/jass/test/data_test1/SumStatTab.txt b/jass/test/data_test1/SumStatTab.txt
deleted file mode 100644
index c35ae04ecf7404997a7401dfd84c29a2ef2dfe40..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/SumStatTab.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-rsid position chr region Z_ratatouy Z_pocahont
-rs1 1 1 1 0.812 -1.06
-rs2 2 1 1 2.197 0.937
-rs3 3 1 2 2.049 0.854
-rs4 1 2 3 1.632 1.461
-rs5 2 2 3 0.254 -1.413
-rs6 3 2 4 0.491 0.567
-rs7 4 2 4 -0.324 0.583
-rs8 5 2 4 -1.662 -1.307
-rs9 1 3 5 1.768 -0.54
-rs10 2 3 5 0.026 1.948
-rs11 3 3 6 1.129 0.054
-rs12 4 3 7 -2.38 0.352
diff --git a/jass/test/data_test1/chr.txt b/jass/test/data_test1/chr.txt
deleted file mode 100644
index 337d7e8e0b79701321e9bffec5fd47093cd0a61b..0000000000000000000000000000000000000000
Binary files a/jass/test/data_test1/chr.txt and /dev/null differ
diff --git a/jass/test/data_test1/initTable.hdf5 b/jass/test/data_test1/initTable.hdf5
deleted file mode 100644
index 43518edff657babee693ad6a63b453d2fbe434cb..0000000000000000000000000000000000000000
Binary files a/jass/test/data_test1/initTable.hdf5 and /dev/null differ
diff --git a/jass/test/data_test1/metadata.txt b/jass/test/data_test1/metadata.txt
deleted file mode 100644
index 8eb75e7afde9d6be08b89e2cf8892384a384af5a..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/metadata.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-information content
-title Mock dataset with disney
-description "lorem ipsum"
-ancestry DIS
-assembly dSNY
diff --git a/jass/test/data_test1/regions.txt b/jass/test/data_test1/regions.txt
deleted file mode 100644
index c7241b86a4541d8c7d7f5c5ba9e6317e339a64e2..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/regions.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-chr start stop
-chr1 1 2
-chr1 3 4
-chr2 1 2
-chr2 3 5
-chr3 1 2
-chr3 3 3
-chr3 4 4
diff --git a/jass/test/data_test1/summary.csv b/jass/test/data_test1/summary.csv
deleted file mode 100644
index 149b6ca13274a17c43fced1f210ca452e0d7f733..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/summary.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Outcome FullName Consortium Type Reference ReferenceLink dataLink internalDataLink Nsample Ncase Ncontrol
-RATATOUY Ratatouille ou la mort de l'hygiène en cuisine DISNEY BrainWashing Courgette et al., 1754 http://www.marmiton.org/recettes/recette_ratatouille_23223.asp pouet pouet 1000000
-POCAHONT Pocahontas mange des tapas DISNEY BrainWashing Rolfe et al., 1614 https://fr.wikipedia.org/wiki/Pocahontas Gargar Gargar 1000000
diff --git a/jass/test/data_test1/worktable.hdf5 b/jass/test/data_test1/worktable.hdf5
deleted file mode 100644
index 69f0978b16d217d89fc994c26c296cc397846bb5..0000000000000000000000000000000000000000
Binary files a/jass/test/data_test1/worktable.hdf5 and /dev/null differ
diff --git a/jass/test/data_test1/z_DISNEY_POCAHONT_chr1.txt b/jass/test/data_test1/z_DISNEY_POCAHONT_chr1.txt
deleted file mode 100644
index ded3476cd998c42133d1ca9c4d5d27b3f4b60c22..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/z_DISNEY_POCAHONT_chr1.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs1 1 C T -1.06
-rs2 2 G T 0.937
-rs3 3 C T 0.854
diff --git a/jass/test/data_test1/z_DISNEY_POCAHONT_chr2.txt b/jass/test/data_test1/z_DISNEY_POCAHONT_chr2.txt
deleted file mode 100644
index d4eec567807c2eec9e6932c02faec9daa46fcf22..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/z_DISNEY_POCAHONT_chr2.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-rsid position refAllele altAllele Z
-rs4 1 G T 1.461
-rs5 2 A T -1.413
-rs6 3 A C 0.567
-rs7 4 A G 0.583
-rs8 5 C G -1.307
diff --git a/jass/test/data_test1/z_DISNEY_POCAHONT_chr3.txt b/jass/test/data_test1/z_DISNEY_POCAHONT_chr3.txt
deleted file mode 100644
index 18264b859cb897c27cc558548820a1d5b2abd83a..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/z_DISNEY_POCAHONT_chr3.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-rsid position refAllele altAllele Z
-rs9 1 C T -0.54
-rs10 2 G T 1.948
-rs11 3 A C 0.054
-rs12 4 A C 0.352
diff --git a/jass/test/data_test1/z_DISNEY_RATATOUY_chr1.txt b/jass/test/data_test1/z_DISNEY_RATATOUY_chr1.txt
deleted file mode 100644
index a4e8fa9cd4ec1ba8dac092aab3230362196e20c3..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/z_DISNEY_RATATOUY_chr1.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs1 1 C T 0.812
-rs2 2 G T 2.197
-rs3 3 C T 2.049
diff --git a/jass/test/data_test1/z_DISNEY_RATATOUY_chr2.txt b/jass/test/data_test1/z_DISNEY_RATATOUY_chr2.txt
deleted file mode 100644
index 86f61ecbd7d6aab1dc8b7655cebfed8fbf272ee6..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/z_DISNEY_RATATOUY_chr2.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-rsid position refAllele altAllele Z
-rs4 1 G T 1.632
-rs5 2 A T 0.254
-rs6 3 A C 0.491
-rs7 4 A G -0.324
-rs8 5 C G -1.662
diff --git a/jass/test/data_test1/z_DISNEY_RATATOUY_chr3.txt b/jass/test/data_test1/z_DISNEY_RATATOUY_chr3.txt
deleted file mode 100644
index c1959185fdc4e2157ede61fc170790e6c863d8bc..0000000000000000000000000000000000000000
--- a/jass/test/data_test1/z_DISNEY_RATATOUY_chr3.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-rsid position refAllele altAllele Z
-rs9 1 C T 1.768
-rs10 2 G T 0.026
-rs11 3 A C 1.129
-rs12 4 A C -2.38
diff --git a/jass/test/data_test2/COV.csv b/jass/test/data_test2/COV.csv
deleted file mode 100644
index 46119fe1b9a6a3eb86a4666fb07f93d318018fa0..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/COV.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-ID z_BMW_ISETTA z_BMW_MINI z_FIAT_CINQCENT z_FIAT_CINQUECENTO z_MERCO_SMART z_TATA_TATANANO
-z_BMW_ISETTA 2 1 1 1 1 1
-z_BMW_MINI 1 2 1 1 1 1
-z_FIAT_CINQCENT 1 1 2 1 1 1
-z_FIAT_CINQUECENTO 1 1 1 2 1 1
-z_MERCO_SMART 1 1 1 1 2 1
-z_TATA_TATANANO 1 1 1 1 1 2
diff --git a/jass/test/data_test2/chr.txt b/jass/test/data_test2/chr.txt
deleted file mode 100644
index 9c5544b333bb58576eb22e68abfca405ed445acb..0000000000000000000000000000000000000000
Binary files a/jass/test/data_test2/chr.txt and /dev/null differ
diff --git a/jass/test/data_test2/create_unit_test_smart.R b/jass/test/data_test2/create_unit_test_smart.R
deleted file mode 100644
index 71a0e7631b1c5ea8ab8804b94ff984665164ea91..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/create_unit_test_smart.R
+++ /dev/null
@@ -1,87 +0,0 @@
-## There 6 phenotypes
-sumtab <- read.table("summary.txt", sep="\t", header=TRUE, stringsAsFactors = FALSE)
-## Zscore ID: z_CONSORITUM_PHENOTYPE_chr#chr.txt
-ids <- sprintf("z_%s_%s",sumtab$Consortium, sumtab$Outcome)
-## The covariance is set to 1 and the variance to 2
-COV <- toeplitz(c(2,1,1,1,1,1))
-rownames(COV) <- colnames(COV) <- ids
-
-
-## Structure:
-# - 5 chromosomes, 2 regions per chromosomes
-# - 10 regions, 2 regions per chromosome
-# - 30 SNPs, 3 SNPs per region
-
-## Structure of missing values region per region ;
-## ".." means no missing values
-## "XX" means the whole region is missing
-#
-# Z1 Z2 Z3 Z4 Z5 Z6
-# R1 .. .. .. .. .. ..
-# R2 .. .. .. .. .. ..
-# R3 XX .. .. .. .. ..
-# R4 .. XX .. .. .. ..
-# R5 .. .. XX .. .. ..
-# R6 .. .. .. XX .. ..
-# R7 .. .. .. .. XX ..
-# R8 .. .. .. .. .. XX
-# R9 XX XX XX .. .. ..
-# R10 .. .. .. XX XX XX
-filenames <- paste0(rep(ids,e=5), "_chr", rep(1:5, 6), ".txt")
-
-## rsid : rs_#chr_#region_#snp
-rsid <- paste0("rs", "_chr", rep(1:5, e=6), # chr
- "_reg", sprintf("%02i", rep(1:10, e=3)), # region
- "_", sprintf("%02i", 1:30)) # snp
-pos <- rep(1:6, 5)
-chr <- rep(1:5, e=6)
-reg <- rep(1:10, e=3)
-ref <- "A"
-alt <- "G"
-BIGZ <- matrix(1:(30*6), 30, 6)
-BIGZ[grep("reg03", rsid),1] <- NA
-BIGZ[grep("reg04", rsid),2] <- NA
-BIGZ[grep("reg05", rsid),3] <- NA
-BIGZ[grep("reg06", rsid),4] <- NA
-BIGZ[grep("reg07", rsid),5] <- NA
-BIGZ[grep("reg08", rsid),6] <- NA
-BIGZ[grep("reg09", rsid),1:3] <- NA
-BIGZ[grep("reg10", rsid),4:6] <- NA
-rownames(BIGZ) <- rsid
-
-# What does it look like ?
-require(pheatmap)
-png("zscores.png", res = 100)
-pheatmap(BIGZ, cluster_rows = FALSE, cluster_cols = FALSE, cellwidth = 10, cellheight = 10)
-dev.off()
-# Create regions
-
-# Create Z scores
-
-
-# Write covariance matrix
-write.table(data.frame(ID=ids, COV), file="COV.csv", row.names = F, quote=F, sep="\t")
-
-# Write region file
-regions <- data.frame(chr=sprintf("chr%i", rep(1:5,e=2)),
- start=rep(c(1,4),5),
- stop=rep(c(3,6),5))
-write.table(regions, file="regions.txt", row.names = F, quote=F, sep="\t")
-# Write all the Z files
-k <- 1
-for (j in 1:6) {
- for (chrnum in 1:5) {
- ind <- grep(sprintf("chr%i", chrnum), rsid)
- tmp <- data.frame(rsid = rsid[ind],
- pos = pos[ind],
- ref=ref,
- alt=alt,
- Zscore=unname(BIGZ[ind, j]))
- write.table(na.omit(tmp), file = filenames[k], quote=FALSE)
- k <- k+1
- }
-}
-
-# Compute the summary statistic
-
-
diff --git a/jass/test/data_test2/initTable.hdf5 b/jass/test/data_test2/initTable.hdf5
deleted file mode 100644
index b7dc5d7db470bf3819f4617cd3b9e92e1a2c1f0d..0000000000000000000000000000000000000000
Binary files a/jass/test/data_test2/initTable.hdf5 and /dev/null differ
diff --git a/jass/test/data_test2/metadata.txt b/jass/test/data_test2/metadata.txt
deleted file mode 100644
index e70e8c5f23fb4ce9142e833a6da916d228e9f001..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/metadata.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-information content
-title Mock dataset with car
-description "lorem ipsum"
-ancestry CAR
-assembly car1
diff --git a/jass/test/data_test2/regions.txt b/jass/test/data_test2/regions.txt
deleted file mode 100644
index 020c4d931922cf1e0b267f44c89dea30e9b73385..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/regions.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-chr start stop
-chr1 1 3
-chr1 4 6
-chr2 1 3
-chr2 4 6
-chr3 1 3
-chr3 4 6
-chr4 1 3
-chr4 4 6
-chr5 1 3
-chr5 4 6
diff --git a/jass/test/data_test2/summary.csv b/jass/test/data_test2/summary.csv
deleted file mode 100644
index 6aef4f3a55b773fa1a7faf0ec0488aba59e3373d..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/summary.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-Outcome FullName Consortium Type Reference ReferenceLink dataLink internalDataLink Nsample Ncase Ncontrol
-ISETTA Wer an seine Frau denkt, fahrt Isetta. BMW Pascher Ghirlanda, S., Jansson, L., & Enquist, M. (2002). Chickens prefer beautiful humans. Human Nature, 13(3), 383_389. https://fr.wikipedia.org/wiki/Isetta /data_test2/isetta /data_test2/isetta 1000000
-MINI Matte hi BMW Cher Fardin, M. A. (2014). On the rheology of cats. Rheology Bulletin, 83(2). https://fr.wikipedia.org/wiki/Mini_(1959-2000) /data_test2/mini /data_test2/mini 1000000
-CINQCENT TE baby come on, uh-huh Trackmasters uh-huh FIAT Pascher Liu, J., Li, J., Feng, L., Li, L., Tian, J., & Lee, K. (2014). Seeing Jesus in toast: Neural and behavioral correlates of face pareidolia. Cortex, 53, 60_77. https://www.fiat500nelmondo.it/fr/tag/cinquecento/ /data_test2/cinqcent /data_test2/cinqcent 1000000
-CINQUECENTO Elle a tout d une grande FIAT Cher Royet, J.-P., Meunier, D., Torquet, N., Mouly, A.-M., & Jiang, T. (2016). The Neural Bases of Disgust for Cheese: An fMRI Study. Frontiers in Human Neuroscience, 10, 511. https://doi.org/10.3389/fnhum.2016.00511 https://fr.wikipedia.org/wiki/Cinquecento /data_test2/cinquecento /data_test2/cinquecento 1000000
-SMART Pas assez cher mon fils MERCO Cher Barss, P. (1984). Injuries due to falling coconuts. The Journal of Trauma, 24(11), 990_1. https://fr.wikipedia.org/wiki/Smart /data_test2/smart /data_test2/smart 1000000
-TATANANO Qu est-ce que t as sous ton grand chapeau? TATA Pascher Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: how difficulties in recognizing ones own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121_34. https://fr.wikipedia.org/wiki/Tata_Nano /data_test2/tatanano /data_test2/tatanano 1000000
diff --git a/jass/test/data_test2/worktable.hdf5 b/jass/test/data_test2/worktable.hdf5
deleted file mode 100644
index 3f450db54e44deaaebf5df421e71aeb66a69e8ea..0000000000000000000000000000000000000000
Binary files a/jass/test/data_test2/worktable.hdf5 and /dev/null differ
diff --git a/jass/test/data_test2/z_BMW_ISETTA_chr1.txt b/jass/test/data_test2/z_BMW_ISETTA_chr1.txt
deleted file mode 100644
index ba168c87cb4a0d7a71bb9e345e8e52058f81c6c6..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_ISETTA_chr1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr1_reg01_01 1 A G 1.001
-rs_chr1_reg01_02 2 A G 2.001
-rs_chr1_reg01_03 3 A G 3.001
-rs_chr1_reg02_04 4 A G 4.001
-rs_chr1_reg02_05 5 A G 5.001
-rs_chr1_reg02_06 6 A G 6.001
diff --git a/jass/test/data_test2/z_BMW_ISETTA_chr2.txt b/jass/test/data_test2/z_BMW_ISETTA_chr2.txt
deleted file mode 100644
index f654684b8edbad4b0c8570e6e673a898bf3968e8..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_ISETTA_chr2.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr2_reg04_10 4 A G 10.001
-rs_chr2_reg04_11 5 A G 11.001
-rs_chr2_reg04_12 6 A G 12.001
diff --git a/jass/test/data_test2/z_BMW_ISETTA_chr3.txt b/jass/test/data_test2/z_BMW_ISETTA_chr3.txt
deleted file mode 100644
index 30b5fefe1978c0e8d5b8002adcebc6f674bab97b..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_ISETTA_chr3.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr3_reg05_13 1 A G 13.001
-rs_chr3_reg05_14 2 A G 14.001
-rs_chr3_reg05_15 3 A G 15.001
-rs_chr3_reg06_16 4 A G 16.001
-rs_chr3_reg06_17 5 A G 17.001
-rs_chr3_reg06_18 6 A G 18.001
diff --git a/jass/test/data_test2/z_BMW_ISETTA_chr4.txt b/jass/test/data_test2/z_BMW_ISETTA_chr4.txt
deleted file mode 100644
index f5269355934349a3552ac1421a1761b3e57c9af4..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_ISETTA_chr4.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr4_reg07_19 1 A G 19.001
-rs_chr4_reg07_20 2 A G 20.001
-rs_chr4_reg07_21 3 A G 21.001
-rs_chr4_reg08_22 4 A G 22.001
-rs_chr4_reg08_23 5 A G 23.001
-rs_chr4_reg08_24 6 A G 24.001
diff --git a/jass/test/data_test2/z_BMW_ISETTA_chr5.txt b/jass/test/data_test2/z_BMW_ISETTA_chr5.txt
deleted file mode 100644
index be6510ece435217aad150b535b63a6c7a3b36843..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_ISETTA_chr5.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr5_reg10_28 4 A G 28.001
-rs_chr5_reg10_29 5 A G 29.001
-rs_chr5_reg10_30 6 A G 30.001
diff --git a/jass/test/data_test2/z_BMW_MINI_chr1.txt b/jass/test/data_test2/z_BMW_MINI_chr1.txt
deleted file mode 100644
index fe30e814732cf650b50b165aecfe015eabb061bc..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_MINI_chr1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr1_reg01_01 1 A G 31.001
-rs_chr1_reg01_02 2 A G 32.001
-rs_chr1_reg01_03 3 A G 33.001
-rs_chr1_reg02_04 4 A G 34.001
-rs_chr1_reg02_05 5 A G 35.001
-rs_chr1_reg02_06 6 A G 36.001
diff --git a/jass/test/data_test2/z_BMW_MINI_chr2.txt b/jass/test/data_test2/z_BMW_MINI_chr2.txt
deleted file mode 100644
index 8d43408a9306776e8d1d8f5888e5a734ac8b163b..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_MINI_chr2.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr2_reg03_07 1 A G 37.001
-rs_chr2_reg03_08 2 A G 38.001
-rs_chr2_reg03_09 3 A G 39.001
diff --git a/jass/test/data_test2/z_BMW_MINI_chr3.txt b/jass/test/data_test2/z_BMW_MINI_chr3.txt
deleted file mode 100644
index 01931f84bbf48153933ae21b4590f8e367157f4a..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_MINI_chr3.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr3_reg05_13 1 A G 43.001
-rs_chr3_reg05_14 2 A G 44.001
-rs_chr3_reg05_15 3 A G 45.001
-rs_chr3_reg06_16 4 A G 46.001
-rs_chr3_reg06_17 5 A G 47.001
-rs_chr3_reg06_18 6 A G 48.001
diff --git a/jass/test/data_test2/z_BMW_MINI_chr4.txt b/jass/test/data_test2/z_BMW_MINI_chr4.txt
deleted file mode 100644
index 7cbd68f13544cfe0da4b81cf38b0a1853e63ec0a..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_MINI_chr4.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr4_reg07_19 1 A G 49.001
-rs_chr4_reg07_20 2 A G 50.001
-rs_chr4_reg07_21 3 A G 51.001
-rs_chr4_reg08_22 4 A G 52.001
-rs_chr4_reg08_23 5 A G 53.001
-rs_chr4_reg08_24 6 A G 54.001
diff --git a/jass/test/data_test2/z_BMW_MINI_chr5.txt b/jass/test/data_test2/z_BMW_MINI_chr5.txt
deleted file mode 100644
index ee8a72149b041dba76cde9e38347aac4fc8dc185..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_BMW_MINI_chr5.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr5_reg10_28 4 A G 58.001
-rs_chr5_reg10_29 5 A G 59.001
-rs_chr5_reg10_30 6 A G 60.001
diff --git a/jass/test/data_test2/z_FIAT_CINQCENT_chr1.txt b/jass/test/data_test2/z_FIAT_CINQCENT_chr1.txt
deleted file mode 100644
index 90c8298ac67ab48c1521708e82dd2cc8aadc08bd..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQCENT_chr1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr1_reg01_01 1 A G 61.001
-rs_chr1_reg01_02 2 A G 62.001
-rs_chr1_reg01_03 3 A G 63.001
-rs_chr1_reg02_04 4 A G 64.001
-rs_chr1_reg02_05 5 A G 65.001
-rs_chr1_reg02_06 6 A G 66.001
diff --git a/jass/test/data_test2/z_FIAT_CINQCENT_chr2.txt b/jass/test/data_test2/z_FIAT_CINQCENT_chr2.txt
deleted file mode 100644
index eaeaedff3e765943efabe07bc592cafafe2e2153..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQCENT_chr2.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr2_reg03_07 1 A G 67.001
-rs_chr2_reg03_08 2 A G 68.001
-rs_chr2_reg03_09 3 A G 69.001
-rs_chr2_reg04_10 4 A G 70.001
-rs_chr2_reg04_11 5 A G 71.001
-rs_chr2_reg04_12 6 A G 72.001
diff --git a/jass/test/data_test2/z_FIAT_CINQCENT_chr3.txt b/jass/test/data_test2/z_FIAT_CINQCENT_chr3.txt
deleted file mode 100644
index 79f6654e10d70614c5df7b159e4632649943f512..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQCENT_chr3.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr3_reg06_16 4 A G 76.001
-rs_chr3_reg06_17 5 A G 77.001
-rs_chr3_reg06_18 6 A G 78.001
diff --git a/jass/test/data_test2/z_FIAT_CINQCENT_chr4.txt b/jass/test/data_test2/z_FIAT_CINQCENT_chr4.txt
deleted file mode 100644
index e3ee62385639908a2e5b4ac9a6fe25701391101b..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQCENT_chr4.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr4_reg07_19 1 A G 79.001
-rs_chr4_reg07_20 2 A G 80.001
-rs_chr4_reg07_21 3 A G 81.001
-rs_chr4_reg08_22 4 A G 82.001
-rs_chr4_reg08_23 5 A G 83.001
-rs_chr4_reg08_24 6 A G 84.001
diff --git a/jass/test/data_test2/z_FIAT_CINQCENT_chr5.txt b/jass/test/data_test2/z_FIAT_CINQCENT_chr5.txt
deleted file mode 100644
index ff4a6d54b2699b83c398267413910ea680412861..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQCENT_chr5.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr5_reg10_28 4 A G 88.001
-rs_chr5_reg10_29 5 A G 89.001
-rs_chr5_reg10_30 6 A G 90.001
diff --git a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr1.txt b/jass/test/data_test2/z_FIAT_CINQUECENTO_chr1.txt
deleted file mode 100644
index ab76c899a0c8cacc10549e329126c162467efe95..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr1_reg01_01 1 A G 91.001
-rs_chr1_reg01_02 2 A G 92.001
-rs_chr1_reg01_03 3 A G 93.001
-rs_chr1_reg02_04 4 A G 94.001
-rs_chr1_reg02_05 5 A G 95.001
-rs_chr1_reg02_06 6 A G 96.001
diff --git a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr2.txt b/jass/test/data_test2/z_FIAT_CINQUECENTO_chr2.txt
deleted file mode 100644
index 41efda8549747b5aa27865a3e3bf1e4c7eb6589b..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr2.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr2_reg03_07 1 A G 97.001
-rs_chr2_reg03_08 2 A G 98.001
-rs_chr2_reg03_09 3 A G 99.001
-rs_chr2_reg04_10 4 A G 100.001
-rs_chr2_reg04_11 5 A G 101.001
-rs_chr2_reg04_12 6 A G 102.001
diff --git a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr3.txt b/jass/test/data_test2/z_FIAT_CINQUECENTO_chr3.txt
deleted file mode 100644
index bb75d15270802fdd2ce684f50b84cb10da58aa8e..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr3.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr3_reg05_13 1 A G 103.001
-rs_chr3_reg05_14 2 A G 104.001
-rs_chr3_reg05_15 3 A G 105.001
diff --git a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr4.txt b/jass/test/data_test2/z_FIAT_CINQUECENTO_chr4.txt
deleted file mode 100644
index f39337397edcfd212ec5f5a0d84ad2b08cd518ce..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr4.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr4_reg07_19 1 A G 109.001
-rs_chr4_reg07_20 2 A G 110.001
-rs_chr4_reg07_21 3 A G 111.001
-rs_chr4_reg08_22 4 A G 112.001
-rs_chr4_reg08_23 5 A G 113.001
-rs_chr4_reg08_24 6 A G 114.001
diff --git a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr5.txt b/jass/test/data_test2/z_FIAT_CINQUECENTO_chr5.txt
deleted file mode 100644
index ce0f5748472d5cd7e27f945ed87aecef2840603a..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_FIAT_CINQUECENTO_chr5.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr5_reg09_25 1 A G 115.001
-rs_chr5_reg09_26 2 A G 116.001
-rs_chr5_reg09_27 3 A G 117.001
diff --git a/jass/test/data_test2/z_MERCO_SMART_chr1.txt b/jass/test/data_test2/z_MERCO_SMART_chr1.txt
deleted file mode 100644
index c1690edd2238a345d6579cb796abe8d21817eaa7..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_MERCO_SMART_chr1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr1_reg01_01 1 A G 121.001
-rs_chr1_reg01_02 2 A G 122.001
-rs_chr1_reg01_03 3 A G 123.001
-rs_chr1_reg02_04 4 A G 124.001
-rs_chr1_reg02_05 5 A G 125.001
-rs_chr1_reg02_06 6 A G 126.001
diff --git a/jass/test/data_test2/z_MERCO_SMART_chr2.txt b/jass/test/data_test2/z_MERCO_SMART_chr2.txt
deleted file mode 100644
index b550771006bb7919a0199d3a3d2f7e0429eb5b80..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_MERCO_SMART_chr2.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr2_reg03_07 1 A G 127.001
-rs_chr2_reg03_08 2 A G 128.001
-rs_chr2_reg03_09 3 A G 129.001
-rs_chr2_reg04_10 4 A G 130.001
-rs_chr2_reg04_11 5 A G 131.001
-rs_chr2_reg04_12 6 A G 132.001
diff --git a/jass/test/data_test2/z_MERCO_SMART_chr3.txt b/jass/test/data_test2/z_MERCO_SMART_chr3.txt
deleted file mode 100644
index e81f2ac70279ecaa6fc7f626e35952af4a47e49a..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_MERCO_SMART_chr3.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr3_reg05_13 1 A G 133.001
-rs_chr3_reg05_14 2 A G 134.001
-rs_chr3_reg05_15 3 A G 135.001
-rs_chr3_reg06_16 4 A G 136.001
-rs_chr3_reg06_17 5 A G 137.001
-rs_chr3_reg06_18 6 A G 138.001
diff --git a/jass/test/data_test2/z_MERCO_SMART_chr4.txt b/jass/test/data_test2/z_MERCO_SMART_chr4.txt
deleted file mode 100644
index bb4267422d8903207c9f986377365925fede7c97..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_MERCO_SMART_chr4.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr4_reg08_22 4 A G 142.001
-rs_chr4_reg08_23 5 A G 143.001
-rs_chr4_reg08_24 6 A G 144.001
diff --git a/jass/test/data_test2/z_MERCO_SMART_chr5.txt b/jass/test/data_test2/z_MERCO_SMART_chr5.txt
deleted file mode 100644
index 84a041f4660f4e7927c65caa88997efb7b2f9505..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_MERCO_SMART_chr5.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr5_reg09_25 1 A G 145.001
-rs_chr5_reg09_26 2 A G 146.001
-rs_chr5_reg09_27 3 A G 147.001
diff --git a/jass/test/data_test2/z_TATA_TATANANO_chr1.txt b/jass/test/data_test2/z_TATA_TATANANO_chr1.txt
deleted file mode 100644
index 909ae54449de02a93e2988cdcbcc12205d913dff..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_TATA_TATANANO_chr1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr1_reg01_01 1 A G 151.001
-rs_chr1_reg01_02 2 A G 152.001
-rs_chr1_reg01_03 3 A G 153.001
-rs_chr1_reg02_04 4 A G 154.001
-rs_chr1_reg02_05 5 A G 155.001
-rs_chr1_reg02_06 6 A G 156.001
diff --git a/jass/test/data_test2/z_TATA_TATANANO_chr2.txt b/jass/test/data_test2/z_TATA_TATANANO_chr2.txt
deleted file mode 100644
index 7b83919ad2352ca41bb726672d95f5795939cb6a..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_TATA_TATANANO_chr2.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr2_reg03_07 1 A G 157.001
-rs_chr2_reg03_08 2 A G 158.001
-rs_chr2_reg03_09 3 A G 159.001
-rs_chr2_reg04_10 4 A G 160.001
-rs_chr2_reg04_11 5 A G 161.001
-rs_chr2_reg04_12 6 A G 162.001
diff --git a/jass/test/data_test2/z_TATA_TATANANO_chr3.txt b/jass/test/data_test2/z_TATA_TATANANO_chr3.txt
deleted file mode 100644
index 2e18b72c75b85e8b36de115ad275c31e2ee23431..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_TATA_TATANANO_chr3.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr3_reg05_13 1 A G 163.001
-rs_chr3_reg05_14 2 A G 164.001
-rs_chr3_reg05_15 3 A G 165.001
-rs_chr3_reg06_16 4 A G 166.001
-rs_chr3_reg06_17 5 A G 167.001
-rs_chr3_reg06_18 6 A G 168.001
diff --git a/jass/test/data_test2/z_TATA_TATANANO_chr4.txt b/jass/test/data_test2/z_TATA_TATANANO_chr4.txt
deleted file mode 100644
index c10363107c3e979971a3e60e8ff1efef7fd0f3a3..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_TATA_TATANANO_chr4.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr4_reg07_19 1 A G 169.001
-rs_chr4_reg07_20 2 A G 170.001
-rs_chr4_reg07_21 3 A G 171.001
diff --git a/jass/test/data_test2/z_TATA_TATANANO_chr5.txt b/jass/test/data_test2/z_TATA_TATANANO_chr5.txt
deleted file mode 100644
index 765fd14e4bc6d5742677609829a04b54c1ec48c9..0000000000000000000000000000000000000000
--- a/jass/test/data_test2/z_TATA_TATANANO_chr5.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-rsid position refAllele altAllele Z
-rs_chr5_reg09_25 1 A G 175.001
-rs_chr5_reg09_26 2 A G 176.001
-rs_chr5_reg09_27 3 A G 177.001
diff --git a/jass/test/data_test2/zscores.png b/jass/test/data_test2/zscores.png
deleted file mode 100644
index d311c401bcdb3f586728a01a5314a19be8b9d6dd..0000000000000000000000000000000000000000
Binary files a/jass/test/data_test2/zscores.png and /dev/null differ
diff --git a/setup.py b/setup.py
index f219c359653ae00d87b9dfacea28330db078ba1f..61bba8e85879dc9f5f6e8efca8c09667813475b9 100644
--- a/setup.py
+++ b/setup.py
@@ -31,7 +31,7 @@ setup(
install_requires=REQUIRES,
license="MIT",
keywords=["GWAS", "Data analysis", "summary statistics"],
- package_data={'jass': ['swagger/swagger.yaml']},
+ package_data={'jass': ['swagger/swagger.yaml', "data/*.tsv"]},
include_package_data=True,
long_description=readme,
long_description_content_type="text/markdown",