Genomic Selection Tutorial
Eli Gacasan
Overview
- What is genomic selection?
- History
- Tutorial
What is genomic selection?
The general process
André Eggen, The development and application of genomic selection as a new breeding paradigm, Animal Frontiers, Volume 2, Issue 1, January 2012, Pages 10–15, https://doi.org/10.2527/af.2011-0027
History
History
History
History
Implementation of genomic selection
Step 1. Getting Data 
Getting Data
Loading in the data
Viewing the data (Genotypes)
1_1952 1_16151 1_36102 1_37028 1_37519 1_37667 1_37980 1_43021 1_43024
Si_102 1 0 0 0 0 0 0 0 0
Si_105 0 0 0 0 0 0 0 0 0
Si_106 NA 0 0 0 0 0 0 0 0
Si_108 0 0 0 0 0 0 0 0 0
Si_109 0 0 0 0 0 0 0 0 0
Si_113 0 0 0 0 0 0 0 0 0
Si_127 0 0 0 0 0 0 0 0 0
Si_129 0 0 0 0 0 0 0 0 0
Si_132 0 0 0 0 0 0 0 2 2
Si_134 0 0 0 1 0 0 0 0 0
Si_137 0 0 0 0 0 0 0 0 0
Si_139 0 0 0 0 0 0 0 0 0
Si_141 0 0 0 0 0 0 0 0 0
Si_143 0 0 0 0 0 0 0 0 0
Si_150 0 0 0 2 0 0 0 0 0
Si_151 0 0 0 0 0 0 0 0 0
Si_153 0 0 0 0 0 0 0 0 0
Si_156 0 0 0 0 0 0 0 0 0
Si_157 0 0 0 0 0 0 0 0 0
Si_162 0 0 0 0 0 0 0 NA NA
Si_163 0 0 0 1 0 0 0 0 0
Si_166 0 0 0 2 0 0 0 0 0
Si_180 0 0 0 0 0 0 0 0 0
Si_181 0 0 0 0 0 0 0 0 0
Si_182 0 0 0 0 0 0 2 0 0
Si_183 0 0 0 0 0 0 0 0 0
Si_186 0 0 0 0 0 0 0 0 0
Si_189 0 0 0 0 0 0 0 0 0
Si_195 0 0 0 0 0 0 0 0 0
Si_197 0 0 0 0 0 0 0 0 0
Si_198 0 0 0 0 0 0 0 0 0
Si_200 0 0 0 2 0 0 0 0 0
Si_203 0 0 0 0 0 0 0 0 0
Si_204 0 0 0 0 0 0 0 0 0
Si_205 0 0 0 0 0 0 0 0 0
Si_206 0 0 0 0 0 0 2 0 0
Si_207 0 0 0 NA 0 0 0 0 0
Si_208 0 0 0 0 0 0 0 0 0
Si_209 0 0 0 0 0 0 0 0 0
Si_210 0 0 0 2 0 0 0 0 0
Si_211 0 0 0 0 0 0 1 0 0
Si_212 0 1 0 0 0 0 0 0 0
Si_213 0 0 0 0 0 0 0 0 0
Si_214 0 0 0 0 0 0 0 0 0
Si_215 0 0 0 0 0 0 2 0 0
Si_217 0 0 0 0 0 0 0 0 0
Si_218 0 1 0 0 0 0 0 0 0
Si_219 0 0 0 0 0 0 0 0 0
Si_220 0 0 0 0 0 0 0 0 0
Si_223 0 1 0 0 0 0 0 0 0
Si_225 0 0 0 0 0 0 0 0 0
Si_226 0 0 0 0 0 0 0 0 0
Si_228 0 0 0 0 0 0 0 0 0
Si_231 0 0 0 0 NA NA 0 0 0
Si_234 0 0 0 0 0 0 0 1 1
Si_236 0 1 0 0 0 0 0 0 0
Si_239 0 0 0 0 0 0 0 NA NA
Si_240 0 0 0 0 0 0 0 0 0
Si_242 0 0 0 1 0 0 0 0 0
Si_245 0 0 0 0 0 0 0 0 0
Si_246 0 0 0 2 0 0 0 0 0
Si_248 0 0 0 0 0 0 0 0 0
Si_249 0 0 0 1 0 0 0 0 0
Si_251 0 0 0 0 0 0 0 0 0
Si_252 0 0 0 0 0 0 0 0 0
Si_253 0 0 0 0 0 0 0 0 0
Si_254 0 0 0 1 0 0 0 0 0
Si_255 0 0 0 0 0 0 0 0 0
Si_256 0 0 0 0 0 0 1 0 0
Si_257 0 0 0 0 0 0 0 0 0
Si_259 0 0 0 2 0 0 0 0 0
Si_260 0 0 0 0 0 0 0 0 0
Si_262 0 0 0 1 0 0 0 0 0
Si_263 0 0 0 0 0 0 0 0 0
Si_264 0 0 0 0 0 0 0 0 0
Si_269 0 0 0 0 0 0 0 0 0
YL_10 0 0 0 0 0 0 0 0 0
YL_100 0 1 0 0 0 0 0 0 0
YL_1004 0 0 0 0 0 0 0 0 0
YL_1005 0 0 0 0 0 0 0 0 0
YL_1009 0 0 0 0 0 0 0 0 0
YL_1013 0 0 0 0 0 0 0 0 0
YL_1016 0 0 0 0 0 0 0 0 0
YL_1018 0 0 0 0 0 0 0 0 0
YL_1019 0 0 0 0 0 0 0 0 0
YL_102 0 0 0 0 0 0 0 0 0
YL_1020 0 0 0 0 0 0 0 0 0
YL_1021 1 0 0 0 0 0 0 0 0
YL_1034 0 1 0 0 0 0 0 0 0
YL_1035 0 0 0 0 0 0 0 0 0
YL_1036 0 0 0 0 0 0 0 0 0
YL_1037 0 0 0 0 0 0 0 0 0
YL_1038 0 0 0 0 0 0 0 0 0
YL_104 0 1 0 0 0 0 0 0 0
YL_1044 0 0 0 0 0 0 0 0 0
YL_1045 0 0 0 0 0 0 0 2 2
YL_1046 0 1 0 0 0 0 0 0 0
YL_1047 0 0 0 0 0 0 0 0 0
YL_105 0 0 0 0 0 0 0 0 0
YL_1050 0 0 0 0 0 0 0 0 0
YL_1052 0 0 0 0 0 0 0 0 0
YL_1056 0 0 0 0 0 0 0 0 0
YL_1057 1 0 0 0 0 0 0 0 0
YL_106 0 0 0 0 0 0 0 0 0
YL_1062 1 0 0 0 0 0 0 0 0
YL_1070 0 0 0 0 0 0 0 0 0
YL_1071 0 0 0 0 0 0 0 0 0
YL_1072 0 1 0 0 0 0 0 0 0
YL_1073 0 0 0 0 0 0 0 0 0
YL_1075 0 0 0 0 0 0 0 0 0
YL_1076 0 1 0 0 0 0 0 0 0
YL_1077 0 0 0 0 0 0 0 0 0
YL_1078 0 0 0 0 0 0 0 0 0
YL_108 0 0 0 0 0 0 0 0 0
YL_1080 0 0 0 0 0 0 0 0 0
YL_1082 0 0 0 0 NA NA 0 0 0
YL_1084 0 1 0 0 0 0 0 0 0
YL_1086 0 1 0 0 0 0 0 0 0
YL_1088 0 0 0 0 0 0 0 0 0
YL_1090 0 0 0 NA 0 0 0 0 0
YL_1096 0 0 0 0 0 0 0 0 0
YL_1098 0 0 0 0 0 0 0 0 0
YL_11 0 0 0 0 0 0 0 0 0
YL_1100 0 0 0 0 0 0 0 0 0
YL_1101 0 0 0 0 0 0 0 0 0
YL_1104 0 0 0 0 0 0 0 0 0
YL_1106 0 0 0 0 0 0 0 0 0
YL_1107 0 0 0 0 0 0 0 0 0
YL_1108 1 0 0 0 0 0 0 0 0
YL_1109 0 0 0 0 0 0 0 0 0
YL_111 0 2 0 0 0 0 0 0 0
YL_1112 0 0 0 0 0 0 0 0 0
YL_1114 0 0 0 0 0 0 0 0 0
YL_1115 0 0 0 NA 0 0 0 0 0
YL_1116 0 0 0 0 0 0 0 0 0
YL_1117 0 0 0 0 0 0 0 0 0
YL_1119 0 2 0 0 0 0 0 0 0
YL_1120 0 0 0 0 0 0 0 0 0
YL_1122 0 0 0 0 0 0 0 0 0
YL_1123 0 0 0 0 0 0 0 0 0
YL_1127 0 0 0 0 0 0 0 0 0
YL_1128 0 0 0 0 0 0 0 0 0
YL_1129 0 0 0 0 0 0 0 0 0
YL_1130 0 0 0 0 0 0 0 0 0
YL_1131 0 0 0 0 0 0 0 1 1
YL_1132 0 0 0 0 0 0 0 0 0
YL_1133 0 0 0 0 0 0 0 0 0
YL_1135 0 0 0 0 0 0 0 0 0
YL_1136 0 0 0 0 0 0 0 0 0
YL_1137 0 0 0 0 0 0 0 0 0
YL_1140 0 1 0 0 0 0 0 0 0
YL_1141 0 0 0 0 0 0 0 0 0
YL_1143 0 0 0 0 0 0 0 NA NA
YL_1146 0 0 0 0 0 0 0 0 0
YL_1147 0 0 0 0 0 0 0 0 0
YL_1149 0 0 0 0 0 0 0 0 0
YL_1150 0 0 0 0 0 0 0 0 0
YL_1153 0 0 0 0 0 0 0 0 0
YL_1154 0 0 0 0 0 0 0 0 0
YL_1155 0 0 0 0 0 0 0 0 0
YL_1156 0 0 0 0 0 0 0 0 0
YL_1157 0 0 0 0 0 0 0 0 0
YL_116 0 0 0 0 0 0 0 0 0
YL_1160 0 0 0 0 0 0 0 0 0
YL_1163 0 0 0 0 0 0 0 0 0
YL_1164 0 0 0 0 0 0 0 0 0
YL_1166 0 0 0 0 0 0 0 0 0
YL_1167 0 0 0 1 0 0 0 0 0
YL_1169 0 0 0 0 0 0 0 0 0
YL_117 0 0 0 0 0 0 0 0 0
YL_1170 0 0 0 1 0 0 0 0 0
YL_1171 0 0 0 0 0 0 0 0 0
YL_1173 0 0 0 0 0 0 0 0 0
YL_1174 0 0 0 0 0 0 0 0 0
YL_1175 0 0 0 0 0 0 0 0 0
YL_1176 0 0 0 0 0 0 0 0 0
YL_1177 0 0 0 0 0 0 0 0 0
YL_1179 0 0 0 0 0 0 0 0 0
YL_1181 0 0 0 0 0 0 0 0 0
YL_1183 0 0 0 0 0 0 0 0 0
YL_1185 0 0 0 0 0 0 0 0 0
YL_1186 0 0 0 0 0 0 0 0 0
YL_1187 0 0 0 0 0 0 0 0 0
YL_1188 0 0 0 0 0 0 0 0 0
YL_1192 0 0 0 0 0 0 0 0 0
YL_1194 0 0 0 0 0 0 0 0 0
YL_1197 0 0 0 0 1 1 0 0 0
YL_12 0 0 0 0 0 0 0 0 0
YL_1200 0 1 0 0 0 0 0 0 0
YL_1201 0 0 0 0 0 0 0 0 0
YL_1203 0 0 0 0 0 0 0 0 0
YL_1204 0 0 0 0 0 0 0 0 0
YL_1205 0 0 0 0 0 0 0 0 0
YL_1206 0 0 0 0 0 0 0 0 0
YL_1207 0 0 0 0 0 0 0 0 0
YL_1208 0 0 0 0 0 0 0 0 0
YL_1209 0 0 0 0 0 0 0 0 0
YL_121 0 0 0 0 0 0 0 0 0
YL_1211 0 0 0 0 0 0 0 0 0
YL_1212 0 0 0 0 0 0 0 NA NA
YL_1214 0 0 0 0 0 0 0 0 0
YL_1215 0 0 0 0 0 0 0 0 0
YL_1216 0 0 0 2 0 0 0 0 0
YL_1217 0 0 0 0 0 0 0 0 0
YL_1218 0 0 0 0 0 0 0 0 0
YL_1219 0 0 0 0 0 0 0 0 0
YL_1220 0 0 0 0 0 0 0 0 0
YL_1221 0 0 0 0 0 0 0 0 0
YL_1224 0 0 0 0 0 0 0 1 1
YL_1228 0 0 0 0 0 0 0 0 0
YL_1230 0 0 0 0 0 0 0 0 0
YL_1234 0 0 0 0 0 0 0 0 0
YL_1235 0 0 0 0 0 0 0 0 0
YL_1237 0 0 0 0 0 0 0 0 0
YL_1238 0 0 0 0 0 0 0 0 0
YL_1239 0 0 0 0 0 0 0 0 0
YL_124 0 0 0 0 0 0 0 0 0
YL_1240 0 0 0 0 0 0 0 0 0
YL_1241 0 0 0 0 0 0 0 0 0
YL_1243 0 0 0 0 0 0 0 0 0
YL_1245 0 0 0 0 0 0 0 0 0
YL_1246 0 0 0 0 0 0 0 0 0
YL_1247 0 0 0 0 0 0 0 0 0
YL_125 0 0 0 0 0 0 0 0 0
YL_1254 0 0 0 0 0 0 0 0 0
YL_1257 0 0 0 0 0 0 0 0 0
YL_1262 0 0 0 0 0 0 0 0 0
YL_1264 0 0 0 0 0 0 0 0 0
YL_1265 0 0 0 0 0 0 0 0 0
YL_127 0 0 0 0 0 0 0 0 0
YL_1275 0 0 0 0 0 0 0 0 0
YL_1276 0 0 0 0 0 0 0 0 0
YL_1277 0 0 0 0 0 0 0 0 0
YL_1278 0 0 0 0 0 0 0 0 0
YL_1279 0 0 0 0 0 0 0 0 0
YL_1280 0 0 0 0 0 0 0 0 0
YL_1281 0 0 0 0 1 1 0 0 0
YL_1282 0 0 0 0 0 0 0 0 0
YL_1283 0 0 0 0 0 0 0 0 0
YL_1284 0 0 0 0 0 0 0 0 0
YL_1287 0 0 0 0 0 0 0 0 0
YL_1288 0 0 0 0 0 0 0 0 0
YL_1289 0 0 0 0 0 0 0 0 0
YL_129 0 NA 0 0 0 0 0 0 0
YL_1292 1 0 0 0 0 0 0 0 0
YL_1293 0 0 0 0 0 0 0 0 0
YL_1294 0 0 0 0 0 0 0 0 0
YL_1295 0 0 0 0 0 0 0 0 0
YL_1296 0 1 0 0 0 0 0 0 0
YL_1298 0 0 0 0 0 0 0 0 0
YL_1299 0 0 0 0 0 0 0 0 0
YL_1300 0 0 0 0 0 0 0 0 0
YL_1301 1 NA 0 0 0 0 0 0 0
YL_1302 0 0 0 0 0 0 0 0 0
YL_1303 0 0 0 0 0 0 0 0 0
YL_1304 0 0 0 0 0 0 0 0 0
YL_1306 0 0 0 0 0 0 0 0 0
YL_1309 0 0 0 0 0 0 0 0 0
YL_131 0 0 0 0 0 0 0 0 0
YL_1311 0 0 0 0 0 0 0 0 0
YL_1312 0 0 0 0 0 0 0 0 0
YL_1314 0 0 0 0 0 0 0 0 0
YL_1315 0 0 0 0 0 0 0 0 0
YL_1316 0 0 0 0 0 0 0 0 0
YL_1318 0 0 0 0 0 0 0 0 0
YL_1319 0 0 0 0 0 0 0 0 0
YL_1321 0 0 0 0 0 0 0 0 0
YL_1323 0 1 0 0 0 0 0 0 0
YL_1325 0 0 0 0 0 0 0 0 0
YL_1326 0 2 0 0 0 0 0 0 0
YL_1327 0 0 0 0 0 0 0 0 0
YL_1328 0 0 0 0 0 0 0 0 0
YL_133 0 0 0 0 0 0 0 0 0
YL_1331 0 0 0 0 0 0 0 0 0
YL_1332 0 0 0 0 0 0 0 0 0
YL_1334 1 2 0 0 0 0 0 0 0
YL_1335 0 0 0 0 0 0 0 0 0
YL_1336 0 0 0 0 0 0 0 0 0
YL_1337 0 0 0 0 0 0 0 0 0
YL_1339 0 0 0 0 0 0 0 0 0
YL_134 0 0 0 0 0 0 0 0 0
YL_1340 0 0 0 0 0 0 0 0 0
YL_1343 0 1 0 0 0 0 0 0 0
YL_1344 0 0 0 0 0 0 0 0 0
YL_1345 0 0 0 0 0 0 0 0 0
YL_1346 0 0 0 0 0 0 0 0 0
YL_1347 0 0 0 0 0 0 0 0 0
YL_1348 0 1 0 0 0 0 0 0 0
YL_1349 0 0 0 0 0 0 0 0 0
YL_1352 0 1 0 0 0 0 0 0 0
YL_1353 0 0 0 0 0 0 0 0 0
YL_1355 0 2 0 0 0 0 0 0 0
YL_1356 0 0 0 0 0 0 0 0 0
YL_1358 0 1 0 0 0 0 0 0 0
YL_1359 0 0 0 0 0 0 0 0 0
YL_136 0 0 0 0 0 0 0 0 0
YL_1360 0 0 0 0 0 0 0 0 0
YL_1361 0 1 0 0 0 0 0 0 0
YL_1362 0 0 0 0 0 0 0 0 0
YL_1363 0 0 0 0 0 0 0 0 0
YL_1364 0 0 0 0 0 0 0 0 0
YL_1365 0 1 0 0 0 0 0 0 0
YL_1367 1 0 0 0 0 0 0 0 0
YL_1369 0 0 0 0 0 0 0 0 0
YL_1370 0 0 0 0 0 0 0 0 0
YL_1371 0 0 0 0 0 0 0 0 0
YL_1373 0 0 0 0 0 0 0 0 0
YL_1374 0 0 0 0 1 1 0 0 0
YL_1376 0 0 0 0 0 0 0 0 0
YL_1377 0 1 0 0 0 0 0 0 0
YL_1378 0 1 0 0 0 0 0 0 0
YL_1379 0 0 0 0 0 0 0 0 0
YL_138 0 0 0 0 0 0 2 0 0
YL_1381 0 0 0 0 0 0 0 0 0
YL_1382 0 0 0 0 0 0 0 0 0
YL_1383 0 1 0 0 0 0 0 0 0
YL_1384 0 0 0 0 0 0 0 0 0
YL_1386 0 0 0 0 0 0 0 0 0
YL_1387 0 1 0 0 0 0 0 0 0
YL_1388 0 1 0 0 0 0 0 0 0
YL_1389 0 0 0 0 0 0 0 0 0
YL_1390 0 0 0 0 0 0 0 0 0
YL_1391 0 0 0 0 0 0 0 0 0
YL_1392 0 0 0 0 0 0 0 0 0
YL_1393 0 1 0 0 0 0 0 0 0
YL_1394 0 0 0 0 0 0 0 0 0
YL_1397 0 0 0 0 0 0 0 0 0
YL_1398 0 0 0 0 0 0 0 0 0
YL_1399 0 0 0 0 0 0 0 0 0
YL_140 0 0 0 0 0 0 0 0 0
YL_1400 0 1 0 0 0 0 0 0 0
YL_1404 2 0 0 0 0 0 0 0 0
YL_1405 0 0 0 0 0 0 0 0 0
YL_1406 0 0 0 0 0 0 0 0 0
YL_1407 0 0 0 0 0 0 0 0 0
YL_1411 0 0 0 0 0 0 0 0 0
YL_1415 0 0 0 0 0 0 0 0 0
YL_1416 0 1 0 0 0 0 0 0 0
YL_1417 0 0 0 0 0 0 0 0 0
YL_1418 0 0 0 0 0 0 0 0 0
YL_1419 0 1 0 0 0 0 0 0 0
YL_1420 0 0 0 0 0 0 0 0 0
YL_1422 0 0 0 0 0 0 0 0 0
YL_1423 0 0 0 0 0 0 0 0 0
YL_1424 0 0 0 0 0 0 0 0 0
YL_1426 0 0 0 0 0 0 0 0 0
YL_1427 0 0 0 0 0 0 0 0 0
YL_1430 0 0 0 0 2 2 0 0 0
YL_1431 0 0 1 0 0 0 0 0 0
YL_1432 0 0 0 0 0 0 0 0 0
YL_1433 1 0 0 0 0 0 0 0 0
YL_1434 0 0 0 0 0 0 0 0 0
YL_1435 0 0 0 0 0 0 0 0 0
YL_1436 1 0 0 0 0 0 0 0 0
YL_1437 0 0 0 0 0 0 0 0 0
YL_1438 0 0 0 0 0 0 0 0 0
YL_144 0 0 0 0 0 0 0 0 0
YL_1440 0 0 1 0 0 0 0 0 0
YL_1442 0 0 0 0 0 0 0 0 0
YL_1444 0 0 0 0 0 0 0 0 0
YL_1445 0 0 0 0 2 2 0 0 0
YL_1446 0 0 0 0 0 0 0 0 0
YL_1447 0 0 0 0 0 0 0 0 0
YL_1449 0 0 0 0 0 0 0 0 0
YL_145 0 2 0 0 0 0 0 0 0
YL_1450 0 0 0 0 0 0 0 0 0
YL_1451 0 0 0 0 0 0 0 0 0
YL_1452 0 0 0 0 0 0 0 0 0
YL_1453 0 0 0 0 0 0 0 0 0
YL_1455 0 0 0 0 0 0 0 0 0
YL_1456 0 0 0 0 0 0 0 0 0
YL_1457 0 1 0 0 0 0 0 0 0
YL_1459 0 0 0 0 0 0 0 0 0
YL_1460 0 0 0 0 0 0 0 0 0
YL_1461 0 0 0 0 0 0 0 0 0
YL_1462 0 0 0 0 0 0 0 0 0
YL_1463 0 0 0 0 0 0 0 0 0
YL_1464 0 0 0 0 0 0 0 0 0
YL_1465 0 0 0 0 0 0 0 0 0
YL_1466 0 0 0 0 0 0 0 0 0
YL_1467 0 0 0 0 0 0 0 0 0
YL_1468 0 0 0 0 0 0 0 0 0
YL_1469 0 0 0 0 0 0 0 0 0
YL_147 0 0 0 0 0 0 0 0 0
YL_1470 0 0 0 0 0 0 0 0 0
YL_1471 0 0 0 0 0 0 0 0 0
YL_1473 0 0 0 0 0 0 0 0 0
YL_1474 0 0 0 0 0 0 0 0 0
YL_1475 0 0 0 0 0 0 0 0 0
YL_1476 0 0 0 0 0 0 0 0 0
YL_1477 0 0 0 0 0 0 0 0 0
YL_1478 0 0 0 0 0 0 0 0 0
YL_1479 0 0 0 0 0 0 0 0 0
YL_1480 0 0 0 0 0 0 0 0 0
YL_1483 0 0 0 0 0 0 0 0 0
YL_1486 0 0 0 0 0 0 0 0 0
YL_1489 0 0 0 0 0 0 0 0 0
YL_1490 2 0 0 0 0 0 0 0 0
YL_1491 0 0 0 0 0 0 0 0 0
YL_1492 0 1 0 0 0 0 0 0 0
YL_1493 0 NA 0 0 0 0 0 0 0
YL_1495 0 0 0 0 0 0 0 0 0
YL_15 0 0 0 0 0 0 0 0 0
YL_151 0 0 0 0 0 0 0 0 0
YL_152 0 0 0 0 0 0 0 0 0
YL_153 0 0 0 0 0 0 2 0 0
YL_156 0 0 0 0 0 0 0 0 0
YL_1575 0 0 0 0 0 0 0 0 0
YL_1578 0 0 0 0 0 0 0 0 0
YL_1580 0 0 0 0 0 0 0 0 0
YL_1581 0 0 0 0 0 0 0 0 0
YL_1582 0 0 0 0 0 0 0 0 0
YL_1584 1 0 0 0 0 0 0 0 0
YL_1587 0 NA 0 0 0 0 0 0 0
YL_1589 0 0 0 0 0 0 0 0 0
YL_16 0 0 0 0 0 0 0 0 0
YL_160 0 0 0 0 0 0 0 0 0
YL_1600 0 0 0 0 0 0 0 0 0
YL_1603 0 1 0 0 0 0 0 0 0
YL_1604 0 0 0 0 0 0 0 0 0
YL_1605 0 0 0 0 0 0 0 0 0
YL_1606 0 0 0 0 0 0 0 0 0
YL_1607 0 0 0 0 0 0 0 0 0
YL_161 0 0 0 0 0 0 0 0 0
YL_1612 0 0 0 0 0 0 0 0 0
YL_1615 0 0 0 0 0 0 0 0 0
YL_1616 0 0 0 0 0 0 0 0 0
YL_1618 0 0 0 0 0 0 0 0 0
YL_162 0 0 0 0 0 0 0 0 0
YL_1620 0 0 0 0 0 0 0 0 0
YL_1621 0 0 0 0 0 0 0 0 0
YL_1624 0 0 0 0 0 0 0 0 0
YL_1631 0 0 0 0 0 0 0 0 0
YL_1633 0 0 0 0 0 0 0 0 0
YL_1634 0 0 0 0 NA NA 0 0 0
YL_164 0 0 0 0 0 0 0 0 0
YL_165 1 0 0 0 0 0 0 0 0
YL_166 0 0 0 0 0 0 0 0 0
YL_167 0 0 0 0 0 0 0 0 0
YL_168 0 1 0 0 0 0 0 0 0
YL_169 0 0 0 0 0 0 0 0 0
YL_17 0 0 0 0 0 0 0 NA NA
YL_170 0 0 0 0 0 0 0 0 0
YL_172 0 1 0 0 0 0 0 0 0
YL_173 0 1 0 0 0 0 0 0 0
YL_175 0 0 0 0 0 0 0 0 0
YL_176 0 0 0 0 0 0 0 0 0
YL_178 0 NA 0 0 0 0 0 0 0
YL_18 0 0 0 0 0 0 0 2 2
YL_180 0 NA 0 0 0 0 0 0 0
YL_181 0 0 0 0 0 0 0 0 0
YL_185 0 0 0 0 0 0 0 0 0
YL_19 0 0 0 0 0 0 0 0 0
YL_193 0 0 0 0 0 0 0 0 0
YL_195 0 0 0 0 0 0 0 0 0
YL_196 0 0 0 0 1 1 0 0 0
YL_197 0 0 0 0 0 0 0 0 0
YL_199 0 0 0 0 NA NA 0 0 0
YL_20 0 0 0 0 0 0 0 0 0
YL_202 0 0 0 0 0 0 0 0 0
YL_208 0 0 0 0 0 0 0 0 0
YL_21 0 1 0 0 0 0 0 0 0
YL_210 0 0 0 0 0 0 0 NA NA
YL_211 0 0 0 0 0 0 0 0 0
YL_214 0 0 0 0 0 0 0 0 0
YL_215 0 0 0 0 0 0 0 0 0
YL_217 0 0 0 0 0 0 0 0 0
YL_219 0 0 0 0 0 0 0 0 0
YL_223 0 0 0 0 0 0 0 0 0
YL_225 0 0 0 0 0 0 0 0 0
YL_23 0 0 0 0 0 0 0 0 0
YL_230 0 0 0 0 0 0 0 0 0
YL_231 0 0 0 0 0 0 0 0 0
YL_234 0 1 0 0 0 0 0 0 0
YL_236 0 0 0 0 0 0 0 0 0
YL_238 0 0 0 0 0 0 0 0 0
YL_239 0 0 0 0 0 0 0 0 0
YL_24 0 0 0 0 0 0 0 0 0
YL_241 0 0 0 0 0 0 0 0 0
YL_242 0 1 0 0 0 0 0 0 0
YL_245 0 0 0 0 0 0 0 0 0
YL_247 0 0 0 0 0 0 0 0 0
YL_249 0 0 0 0 0 0 0 0 0
YL_25 0 0 0 0 0 0 0 0 0
YL_250 0 0 0 0 0 0 0 0 0
YL_251 0 0 0 0 0 0 0 0 0
YL_252 0 0 0 0 0 0 0 0 0
YL_253 0 0 0 0 0 0 0 0 0
YL_254 0 0 0 0 0 0 0 0 0
YL_255 0 0 0 0 0 0 0 0 0
YL_258 0 0 0 0 0 0 0 0 0
YL_259 0 0 0 0 NA NA 0 0 0
YL_26 0 0 0 0 0 0 0 0 0
YL_260 0 0 0 0 0 0 0 0 0
YL_262 0 0 0 0 0 0 0 0 0
YL_263 0 0 0 0 0 0 0 0 0
YL_265 0 0 0 0 0 0 0 0 0
YL_266 0 0 0 0 0 0 0 0 0
YL_268 0 0 0 0 0 0 0 0 0
YL_273 0 0 0 0 0 0 2 0 0
YL_274 0 0 0 0 0 0 0 0 0
YL_275 0 0 0 0 0 0 0 0 0
YL_276 0 0 0 0 0 0 0 0 0
YL_28 0 0 0 0 0 0 0 0 0
YL_280 0 0 0 0 0 0 0 0 0
YL_281 0 0 0 0 NA NA 0 0 0
YL_283 0 2 0 0 0 0 0 0 0
YL_287 0 0 0 0 0 0 1 0 0
YL_289 0 1 0 0 0 0 0 0 0
YL_29 0 0 0 0 0 0 0 0 0
YL_290 0 0 0 0 0 0 0 0 0
YL_291 0 0 0 0 0 0 0 0 0
YL_293 0 0 0 0 0 0 0 0 0
YL_294 0 0 0 0 0 0 0 0 0
YL_299 0 0 0 0 0 0 0 0 0
YL_3 0 0 0 0 0 0 0 0 0
YL_30 0 0 0 0 0 0 0 0 0
YL_305 0 0 0 0 0 0 0 0 0
YL_306 0 0 0 0 0 0 0 0 0
YL_307 0 0 0 0 0 0 0 0 0
YL_31 0 0 0 0 0 0 0 0 0
YL_310 0 0 0 0 0 0 1 0 0
YL_315 0 0 0 0 0 0 0 0 0
YL_316 0 0 0 0 0 0 0 0 0
YL_317 0 0 0 0 0 0 0 0 0
YL_318 0 0 0 0 0 0 0 0 0
YL_319 0 0 0 0 0 0 0 1 1
YL_32 0 0 0 0 0 0 0 0 0
YL_320 0 0 0 0 0 0 0 0 0
YL_321 0 0 0 0 0 0 0 0 0
YL_323 0 1 0 0 0 0 0 0 0
YL_324 0 0 0 0 0 0 0 0 0
YL_325 0 0 0 0 0 0 0 0 0
YL_33 0 0 0 0 0 0 0 1 1
YL_330 0 0 0 0 0 0 0 0 0
YL_34 0 0 0 0 0 0 0 0 0
YL_342 0 0 0 0 0 0 0 0 0
YL_345 0 0 0 0 0 0 0 0 0
YL_346 0 0 0 0 0 0 0 0 0
YL_347 0 0 0 0 0 0 0 0 0
YL_348 0 0 0 0 0 0 0 0 0
YL_349 0 0 0 0 0 0 0 0 0
YL_35 0 0 0 0 0 0 0 0 0
YL_350 0 0 0 0 0 0 0 0 0
YL_351 0 0 0 0 0 0 0 0 0
YL_352 0 1 0 0 0 0 0 0 0
YL_353 0 0 0 0 0 0 0 0 0
YL_354 0 0 0 0 0 0 0 0 0
YL_355 0 0 0 0 0 0 0 0 0
YL_356 0 0 0 0 0 0 0 0 0
YL_357 0 0 0 0 0 0 0 0 0
YL_358 0 0 0 0 0 0 0 0 0
YL_359 0 0 0 1 0 0 0 0 0
YL_36 0 0 0 0 0 0 0 0 0
YL_366 0 0 0 0 0 0 0 0 0
YL_367 0 0 0 0 0 0 0 0 0
YL_37 0 0 0 0 2 2 0 0 0
YL_370 0 0 0 0 0 0 0 0 0
YL_371 0 0 0 0 0 0 0 0 0
YL_373 0 0 0 0 0 0 0 0 0
YL_377 0 0 0 0 0 0 0 0 0
YL_380 0 0 0 0 0 0 0 0 0
YL_384 0 0 0 0 2 2 0 0 0
YL_385 0 0 0 2 0 0 0 0 0
YL_387 0 0 0 0 0 0 0 0 0
YL_39 1 0 0 0 0 0 0 0 0
YL_4 0 0 0 0 0 0 0 0 0
YL_403 0 0 0 0 2 2 0 0 0
YL_405 0 0 0 NA 0 0 0 0 0
YL_408 0 0 0 0 0 0 0 0 0
YL_411 0 0 0 0 0 0 0 0 0
YL_412 0 0 0 0 0 0 0 0 0
YL_413 0 0 0 0 0 0 0 0 0
YL_415 0 0 0 0 0 0 0 0 0
YL_416 0 NA 0 0 0 0 0 0 0
YL_417 0 0 0 0 0 0 0 0 0
YL_418 0 0 0 0 0 0 0 0 0
YL_419 0 0 0 1 0 0 0 0 0
YL_420 0 0 0 0 0 0 0 0 0
YL_422 0 0 0 0 0 0 0 0 0
YL_424 0 0 0 0 0 0 0 0 0
YL_426 0 0 0 0 0 0 0 0 0
YL_427 0 0 0 0 0 0 0 0 0
YL_428 0 0 0 0 NA NA 0 0 0
YL_43 0 0 0 0 0 0 0 0 0
YL_430 0 0 0 0 0 0 0 0 0
YL_432 0 0 0 0 0 0 2 0 0
YL_435 0 0 0 0 0 0 0 0 0
YL_437 0 0 0 0 0 0 0 0 0
YL_443 0 0 0 0 0 0 0 0 0
YL_444 0 NA 0 0 0 0 0 0 0
YL_445 0 0 0 0 0 0 0 0 0
YL_446 0 0 0 0 0 0 0 0 0
YL_447 0 0 0 0 0 0 0 0 0
YL_448 0 0 0 0 0 0 0 0 0
YL_449 0 0 0 0 0 0 0 0 0
YL_450 0 1 0 0 0 0 0 0 0
YL_451 0 0 0 0 0 0 0 0 0
YL_452 0 0 0 0 0 0 0 0 0
YL_454 0 0 0 0 0 0 0 0 0
YL_455 0 0 0 0 0 0 0 0 0
YL_456 0 0 0 0 0 0 0 0 0
YL_459 0 0 0 0 1 1 0 0 0
YL_46 0 0 0 0 0 0 0 0 0
YL_460 0 0 0 0 0 0 0 0 0
YL_461 0 0 0 0 0 0 0 0 0
YL_463 0 0 0 0 0 0 0 0 0
YL_464 0 0 0 0 0 0 0 0 0
YL_467 0 0 0 0 0 0 0 0 0
YL_47 0 2 0 0 0 0 0 0 0
YL_474 0 0 0 0 0 0 0 0 0
YL_475 0 0 0 0 0 0 0 0 0
YL_481 0 0 0 0 0 0 0 0 0
YL_485 0 1 0 0 0 0 0 0 0
YL_487 0 0 0 NA 0 0 0 0 0
YL_488 0 0 0 0 0 0 0 0 0
YL_489 0 0 0 0 0 0 0 0 0
YL_494 0 0 0 0 0 0 0 0 0
YL_495 0 0 0 0 0 0 0 0 0
YL_497 0 0 0 0 0 0 0 NA NA
YL_498 0 0 0 0 0 0 0 0 0
YL_500 0 0 0 0 0 0 0 1 1
YL_501 0 0 0 0 NA NA 0 0 0
YL_505 0 0 0 2 0 0 0 0 0
YL_506 0 0 0 0 0 0 0 0 0
YL_507 0 0 0 2 0 0 0 0 0
YL_512 0 0 0 0 0 0 0 0 0
YL_520 0 0 0 0 0 0 0 0 0
YL_521 0 1 0 0 0 0 0 0 0
YL_523 0 0 0 2 0 0 0 0 0
YL_524 0 0 0 0 NA NA 0 0 0
YL_530 0 0 0 0 0 0 0 0 0
YL_531 0 0 0 0 0 0 0 0 0
YL_538 0 0 0 0 0 0 0 0 0
YL_54 0 0 0 0 0 0 0 0 0
YL_540 0 0 0 0 0 0 0 0 0
YL_542 0 0 0 1 0 0 0 0 0
YL_545 0 0 0 0 0 0 0 0 0
YL_547 0 0 0 0 0 0 0 0 0
YL_548 0 0 0 0 0 0 0 0 0
YL_550 0 0 0 0 0 0 0 0 0
YL_551 0 0 0 NA 0 0 0 0 0
YL_552 0 0 0 0 0 0 0 0 0
YL_554 0 0 0 1 0 0 0 0 0
YL_558 0 0 0 0 0 0 0 0 0
YL_56 0 0 0 0 0 0 0 0 0
YL_560 0 0 0 0 0 0 0 0 0
YL_561 0 0 0 0 0 0 0 0 0
YL_563 0 0 0 0 0 0 0 0 0
YL_568 0 0 0 0 0 0 0 0 0
YL_569 0 0 0 0 0 0 0 0 0
YL_57 0 0 0 0 0 0 0 0 0
YL_570 0 0 0 0 0 0 0 0 0
YL_571 0 0 0 0 0 0 0 0 0
YL_572 0 0 0 0 0 0 0 0 0
YL_573 0 0 0 0 0 0 0 0 0
YL_575 0 0 0 0 0 0 0 0 0
YL_58 0 0 0 0 0 0 0 0 0
YL_580 0 0 0 0 0 0 0 0 0
YL_582 0 0 0 0 0 0 0 0 0
YL_587 0 0 0 0 0 0 0 0 0
YL_588 0 0 0 0 0 0 0 0 0
YL_59 0 0 0 0 0 0 0 0 0
YL_593 0 0 0 0 0 0 0 0 0
YL_597 0 0 0 0 0 0 0 0 0
YL_60 0 0 0 0 0 0 0 0 0
YL_601 0 0 0 NA 0 0 0 0 0
YL_604 0 0 0 0 0 0 0 0 0
YL_605 0 0 0 0 0 0 0 0 0
YL_606 0 0 0 0 0 0 0 0 0
YL_61 0 0 0 0 0 0 0 0 0
YL_616 0 0 0 0 0 0 0 0 0
YL_62 0 0 0 0 0 0 0 0 0
YL_625 0 0 0 0 0 0 0 NA NA
YL_627 0 0 0 0 0 0 0 0 0
YL_628 0 0 0 0 0 0 0 0 0
YL_630 0 0 0 0 0 0 0 0 0
YL_633 0 0 0 0 0 0 0 0 0
YL_634 0 0 0 0 0 0 0 0 0
YL_639 0 0 0 0 0 0 0 0 0
YL_64 0 1 0 0 0 0 0 0 0
YL_640 0 0 0 0 0 0 0 0 0
YL_641 0 0 0 0 0 0 0 0 0
YL_642 0 0 0 0 0 0 0 0 0
YL_645 0 0 0 0 0 0 0 0 0
YL_650 0 0 0 0 0 0 0 0 0
YL_651 0 0 0 0 0 0 0 NA NA
YL_653 0 0 0 0 0 0 0 NA NA
YL_656 0 0 0 0 0 0 0 0 0
YL_657 0 0 0 0 0 0 0 NA NA
YL_659 0 0 0 0 0 0 0 1 1
YL_66 0 0 0 0 0 0 0 0 0
YL_660 0 0 0 0 0 0 0 0 0
YL_664 0 0 0 0 NA NA 0 0 0
YL_67 0 0 0 0 0 0 0 0 0
YL_670 0 0 0 1 0 0 0 0 0
YL_671 0 0 0 0 0 0 0 0 0
YL_673 0 0 0 0 0 0 0 0 0
YL_674 0 0 0 0 0 0 0 0 0
YL_678 0 0 0 0 0 0 0 0 0
YL_679 0 0 0 0 0 0 0 0 0
YL_68 0 0 0 0 0 0 0 0 0
YL_680 0 0 0 0 0 0 0 0 0
YL_682 0 0 0 0 0 0 0 0 0
YL_683 0 0 0 0 0 0 0 0 0
YL_684 0 0 0 0 0 0 0 NA NA
YL_685 0 0 0 1 0 0 0 0 0
YL_686 0 0 0 0 0 0 0 1 1
YL_687 0 0 0 2 0 0 0 0 0
YL_688 0 0 0 NA 0 0 0 0 0
YL_69 0 0 0 0 0 0 0 0 0
YL_690 0 0 0 0 0 0 0 0 0
YL_691 0 0 0 0 0 0 0 0 0
YL_694 0 0 0 0 0 0 0 0 0
YL_699 0 0 0 0 0 0 0 0 0
YL_70 0 0 0 0 0 0 0 0 0
YL_700 0 0 0 NA 0 0 0 0 0
YL_701 0 0 0 2 0 0 0 0 0
YL_702 0 0 0 1 0 0 0 0 0
YL_703 0 0 0 1 0 0 0 1 1
YL_704 0 0 0 0 2 2 0 1 1
YL_706 0 0 0 0 0 0 0 NA NA
YL_707 0 0 0 0 0 0 0 2 2
YL_709 0 0 0 0 0 0 0 0 0
YL_710 0 0 0 0 0 0 0 0 0
YL_711 0 0 0 0 0 0 0 0 0
YL_712 0 0 0 0 0 0 0 0 0
YL_713 0 0 0 0 0 0 0 2 2
YL_719 0 0 0 0 0 0 0 0 0
YL_72 0 0 0 0 0 0 0 0 0
YL_720 0 0 0 0 0 0 0 0 0
YL_732 0 1 0 0 0 0 0 0 0
YL_733 0 0 0 0 0 0 0 0 0
YL_734 0 0 0 0 0 0 0 0 0
YL_735 0 0 0 0 0 0 0 0 0
YL_737 0 0 0 0 0 0 0 0 0
YL_738 0 0 0 0 0 0 0 0 0
YL_739 0 0 0 0 0 0 0 0 0
YL_74 0 0 0 0 0 0 0 1 1
YL_740 0 0 0 0 0 0 0 0 0
YL_741 0 1 0 0 0 0 0 0 0
YL_743 0 0 0 0 0 0 0 0 0
YL_744 0 0 0 0 0 0 0 0 0
YL_746 0 0 0 0 0 0 0 0 0
YL_748 0 0 0 0 0 0 0 0 0
YL_749 0 0 0 0 0 0 0 0 0
YL_75 0 0 0 0 0 0 0 0 0
YL_753 0 0 0 0 0 0 0 0 0
YL_793 0 0 0 0 0 0 0 NA NA
YL_80 0 0 0 0 0 0 0 0 0
YL_81 0 0 0 0 0 0 0 0 0
YL_813 0 0 0 0 0 0 0 0 0
YL_814 0 0 0 NA 0 0 0 0 0
YL_819 0 0 0 0 0 0 0 0 0
YL_82 0 0 0 0 0 0 0 0 0
YL_821 0 0 0 0 0 0 0 NA NA
YL_823 0 0 0 0 0 0 0 0 0
YL_83 0 0 0 1 0 0 0 0 0
YL_836 0 0 0 0 0 0 0 0 0
YL_837 0 0 0 0 0 0 0 0 0
YL_838 0 0 0 0 0 0 0 0 0
YL_849 0 0 0 0 0 0 0 0 0
YL_85 0 0 0 NA 0 0 0 0 0
YL_86 0 0 0 0 0 0 0 0 0
YL_862 0 0 0 2 0 0 0 0 0
YL_864 0 0 0 0 0 0 0 0 0
YL_865 0 0 0 0 NA NA 0 0 0
YL_866 0 0 2 0 0 0 0 0 0
YL_868 0 0 2 0 0 0 0 0 0
YL_87 0 0 0 0 0 0 0 0 0
YL_871 0 0 1 0 0 0 0 0 0
YL_874 0 0 2 0 0 0 0 0 0
YL_876 0 0 0 0 NA NA 0 1 1
YL_877 0 0 0 0 NA NA 0 0 0
YL_88 0 0 0 0 0 0 0 0 0
YL_886 0 0 0 0 0 0 0 0 0
YL_887 0 0 0 0 0 0 0 0 0
YL_888 0 0 0 0 0 0 0 0 0
YL_889 0 0 0 0 0 0 0 0 0
YL_89 0 0 0 0 0 0 0 0 0
YL_891 0 0 0 0 0 0 0 0 0
YL_892 0 0 0 0 0 0 0 0 0
YL_894 0 0 0 0 0 0 0 0 0
YL_895 0 0 2 0 0 0 0 0 0
YL_896 0 0 2 0 0 0 0 0 0
YL_897 0 0 2 0 0 0 0 0 0
YL_90 0 0 0 0 0 0 0 0 0
YL_903 0 0 0 0 0 0 0 0 0
YL_905 0 0 0 0 0 0 0 0 0
YL_906 0 0 0 0 0 0 1 0 0
YL_91 0 0 0 NA 0 0 0 0 0
YL_911 0 0 0 0 0 0 0 0 0
YL_912 0 0 0 0 0 0 0 0 0
YL_913 0 0 0 0 0 0 0 0 0
YL_914 0 0 0 0 0 0 NA 0 0
YL_916 0 0 0 0 0 0 0 0 0
YL_92 0 0 0 0 0 0 0 0 0
YL_923 0 0 0 0 0 0 0 0 0
YL_93 0 0 0 0 0 0 0 0 0
YL_931 0 0 0 0 0 0 1 0 0
YL_935 0 0 0 0 0 0 0 0 0
YL_936 0 0 0 NA 0 0 0 0 0
YL_94 0 0 0 0 0 0 0 0 0
YL_943 0 0 0 0 0 0 0 0 0
YL_945 0 0 0 0 0 0 0 0 0
YL_946 0 0 0 0 0 0 0 0 0
YL_947 0 0 0 0 0 0 0 0 0
YL_95 0 0 0 0 0 0 0 0 0
YL_953 0 0 0 0 0 0 0 0 0
YL_96 0 0 0 0 0 0 0 0 0
YL_965 0 0 2 0 0 0 0 0 0
YL_967 0 0 0 0 0 0 0 0 0
YL_968 0 0 0 0 0 0 0 0 0
YL_969 0 0 0 0 0 0 0 0 0
YL_97 0 0 0 0 0 0 0 NA NA
YL_975 0 0 0 0 0 0 0 0 0
YL_977 0 0 0 0 0 0 0 0 0
YL_978 0 0 0 0 0 0 0 0 0
YL_982 0 0 0 0 0 0 0 0 0
YL_983 0 0 0 0 0 0 0 0 0
YL_986 0 0 0 0 0 0 0 0 0
YL_988 1 0 0 0 0 0 0 0 0
YL_989 0 0 0 0 0 0 0 0 0
YL_990 0 0 0 0 0 0 0 0 0
YL_991 0 0 0 0 0 0 0 0 0
YL_992 0 0 0 0 0 0 0 0 0
YL_995 0 0 0 0 0 0 0 0 0| LINE | Top second leaf length | Top second leaf width | Main stem height | Main stem width | Panicle length of the main stem | Panicle diameter of the main stem | Fringe neck length | Main stem panicle weight | Per plant grain weight | Hundred kernel weight | Spikelet number of the main stem | Grain number per spike |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Si_102 | 37.77 | 3.03 | 115.8 | 6.07 | 24.57 | 33.4 | 29.77 | 28.81 | 23.34 | 0.32 | 90.33 | 140.67 |
| Si_105 | 64.6 | 4.07 | 155.5 | 6.43 | 29.33 | 31.3 | 33.57 | 38.97 | 29.01 | 0.29 | 153.33 | 99.33 |
| Si_106 | 34.37 | 3.43 | 93.87 | 5.43 | 20.83 | 33.43 | 24.87 | 24.49 | 20.17 | 0.32 | 111 | 93 |
| Si_108 | 45.63 | 3.23 | 151.8 | 8.5 | 29.53 | 31.57 | 42.7 | 23.65 | 20.27 | 0.32 | 95 | 93.33 |
| Si_109 | 43.33 | 3.53 | 125.53 | 6.67 | 27.27 | 33.4 | 32.97 | 30.93 | 26.39 | 0.33 | 131.33 | 133 |
| Si_113 | 47.27 | 2.97 | 159.97 | 6.67 | 20.23 | 28.63 | 24.7 | 16.2 | 7.88 | 0.31 | 65 | 94 |
Splitting Data into testing and training dataset
### Creating Testing and Training Dataset by randomization ###
set.seed(123)
#
training_indices <- sample(1:827, size = 600, replace = FALSE)
# Training data
training.genotypes <- genotypes[training_indices, ]
training.phenotypes <- phenotypes$MSPD[training_indices]
# Testing data (select all rows NOT in training_indices)
testing_indices <- (1:827)[-training_indices] # Selects all indices *not* in training_indices
testing.genotypes <- genotypes[testing_indices, ]
testing.phenotypes <- phenotypes$MSPD[testing_indices]
### End Creating Testing and Training Dataset ###
# We pick MSPD (Main Stem Panicle Diameter) as the traitThe general process
André Eggen, The development and application of genomic selection as a new breeding paradigm, Animal Frontiers, Volume 2, Issue 1, January 2012, Pages 10–15, https://doi.org/10.2527/af.2011-0027
Step 2. Define the model
\[ y = X\beta + Zg +\epsilon, \] \[ g \sim N(0,G\sigma_g^2), \] \[ \epsilon \sim N(0,I\sigma_\epsilon^2) \]
Example: 2 Different Environments 2 Replications
\[ \small{\begin{pmatrix}y_1\\y_2\\y_3\\y_4\\y_5\\y_6 \end{pmatrix}}= \small{\begin{pmatrix}1 & 0 \\1 & 0 \\ 1 & 0 \\ 0 & 1 \\ 0 & 1 \\ 0 & 1 \end{pmatrix}} \begin{pmatrix} \beta_1 \\ \beta_2 \end{pmatrix} + \begin{pmatrix}1&0&0\\ 0&1&0\\ 0&0&1 \\ 1&0&0 \\0&1&0 \\0&0&1 \end{pmatrix} \begin{pmatrix}g_1 \\g_2 \\ g_3 \end{pmatrix} + \begin{pmatrix}\epsilon_1 \\ \epsilon_2 \\ \epsilon_3 \\ \epsilon_4 \\ \epsilon_5 \\ \epsilon_6 \end{pmatrix} \]
Example: 2 Different Environments 2 Replications
\[ \begin{pmatrix}y_1\\y_2\\y_3\\y_4\\y_5\\y_6\end{pmatrix}= \begin{pmatrix}\beta_1 \\ \beta_1 \\ \beta_1 \\ \beta_2 \\ \beta_2 \\ \beta_2 \end{pmatrix}+\begin{pmatrix}g_1 \\g_2 \\ g_3 \\g_1 \\ g_2 \\ g_3 \end{pmatrix} + \begin{pmatrix}\epsilon_1 \\ \epsilon_2 \\ \epsilon_3 \\ \epsilon_4 \\ \epsilon_5 \\ \epsilon_6 \end{pmatrix} \]
Example: 1 Environment
\[ \small{ \begin{pmatrix}y_1\\y_2\\y_3\\y_4\\y_5\\y_6 \end{pmatrix}= \begin{pmatrix}1 \\1 \\ 1 \\ 1 \\ 1 \\ 1 \end{pmatrix} \begin{pmatrix} \beta_1 \\ \end{pmatrix} + \begin{pmatrix}1&0&0&0&0&0 \\ 0&1&0&0&0&0\\ 0&0&1&0&0&0 \\ 0&0&0&1&0&0 \\0&0&0&0&1&0 \\0&0&0&0&0&1 \end{pmatrix} \begin{pmatrix}g_1 \\g_2 \\ g_3 \\g_4 \\ g_5 \\ g_6 \\ \end{pmatrix} + \begin{pmatrix}\epsilon_1 \\ \epsilon_2 \\ \epsilon_3 \\ \epsilon_4 \\ \epsilon_5 \\ \epsilon_6 \end{pmatrix}} \]
Example: 1 Environment
\[ \begin{pmatrix}y_1\\y_2\\y_3\\y_4\\y_5\\y_6\ \end{pmatrix}= \begin{pmatrix}\beta_1 \\ \beta_1 \\ \beta_1\\ \beta_1 \\ \beta_{1} \\ \beta_{1} \end{pmatrix}+\begin{pmatrix}g_1 \\g_2 \\ g_3 \\g_4 \\ g_5 \\ g_6 \end{pmatrix} + \begin{pmatrix}\epsilon_1 \\ \epsilon_2 \\ \epsilon_3 \\ \epsilon_4 \\ \epsilon_5 \\ \epsilon_6 \end{pmatrix} \]
Creating the genomic relationship matrix G
library(ASRgenomics)
### End Creating Genomic Relationship Matrix from Training Genotypes ###
genomic.relationship.matrix <- ASRgenomics::G.matrix(training.genotypes)Initial data:
Number of Individuals: 600
Number of Markers: 161562
Missing data check:
Total SNPs: 161562
0 SNPs dropped due to missing data threshold of 0.5
Total of: 161562 SNPs
MAF check:
No SNPs with MAF below 0
Heterozigosity data check:
No SNPs with heterozygosity, missing threshold of = 0
Summary check:
Initial: 161562 SNPs
Final: 161562 SNPs ( 0 SNPs removed)
Completed! Time = 102.27 seconds Fitting the model
Summary of the model
Linear mixed model fit by REML ['lmerUvcov']
Formula: y ~ (1 | G_ind)
Data: data
REML criterion at convergence: 4201.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.68395 -0.42267 0.00754 0.40694 2.17887
Random effects:
Groups Name Variance Std.Dev.
G_ind (Intercept) 36.25 6.021
Residual 22.79 4.774
Number of obs: 600, groups: G_ind, 600
Fixed effects:
Estimate Std. Error t value
(Intercept) 19.6438 0.2277 86.29\[ y = 1\beta+g+\epsilon,\text{ } g \sim N(0,G\sigma_g^2), \epsilon \sim N(0,I\sigma_\epsilon^2) \\ \hat{\beta}=20.6037, \hat{\sigma}^2_g=37.48,\hat{\sigma}^2_\epsilon=23.28 \]
The general process
Using the testing set
# Calculating Genomic Relationships for the entire dataset (between testing individuals and training individuals)
full.genomic.relationship.matrix <- ASRgenomics::G.matrix(genotypes)Initial data:
Number of Individuals: 827
Number of Markers: 161562
Missing data check:
Total SNPs: 161562
0 SNPs dropped due to missing data threshold of 0.5
Total of: 161562 SNPs
MAF check:
No SNPs with MAF below 0
Heterozigosity data check:
No SNPs with heterozygosity, missing threshold of = 0
Summary check:
Initial: 161562 SNPs
Final: 161562 SNPs ( 0 SNPs removed)
Completed! Time = 148.56 seconds Using the testing set
\[ y = 1\beta+g+\epsilon\\ g \sim N(0,G\sigma_g^2)\\ \epsilon \sim N(0,I\sigma_\epsilon^2) \]
Genomic Estimated Breeding Values
[,1]
Si_102 3.63721413
Si_106 3.80270060
Si_108 1.20609519
Si_127 -1.35969562
Si_132 -1.25698249
Si_139 5.75076470
Si_150 2.47713127
Si_153 -0.21276646
Si_156 4.38594092
Si_166 4.78286876
Si_182 4.71500115
Si_186 3.80326294
Si_189 -0.86524323
Si_200 11.09486721
Si_212 0.63524725
Si_213 6.40476915
Si_214 1.27558909
Si_223 9.16416755
Si_228 0.53999192
Si_236 10.82119041
Si_240 2.73474888
Si_245 -1.11774780
Si_248 1.60105634
Si_249 7.25275246
Si_253 -0.11201061
Si_257 7.86791331
YL_10 -9.93715945
YL_1013 -3.52587310
YL_102 -5.10055678
YL_1037 -3.09649381
YL_1038 -2.69194562
YL_1046 2.04425710
YL_105 -12.88642059
YL_1052 2.77484466
YL_1056 1.53824312
YL_1071 7.91115393
YL_1073 5.96198114
YL_11 -10.34467925
YL_1104 5.35423790
YL_1123 3.14904354
YL_1128 -0.23842924
YL_1130 0.54769526
YL_1131 4.50665431
YL_1132 4.72681966
YL_1133 5.92404066
YL_1136 7.02008220
YL_1137 3.78298538
YL_1146 0.99826979
YL_1150 5.78295883
YL_1171 -0.87551823
YL_1173 7.03041881
YL_1176 8.42727805
YL_1186 5.18692417
YL_1187 -0.17695821
YL_1204 5.73769581
YL_1206 0.62912844
YL_121 -5.80488994
YL_1221 1.79704433
YL_1235 1.81244711
YL_1237 -3.04814823
YL_1238 3.64821715
YL_1239 5.82398206
YL_127 -13.17188387
YL_1277 7.53992800
YL_1292 7.46667427
YL_1294 5.49956980
YL_1296 1.28628769
YL_1301 2.52259630
YL_1302 7.31913945
YL_1304 3.47338039
YL_1306 4.07558327
YL_1319 4.50865834
YL_1325 2.42824815
YL_1331 14.15175401
YL_1332 2.64645002
YL_1343 11.36994929
YL_1345 3.86617498
YL_1356 10.80424545
YL_136 -8.55085104
YL_1363 4.09294138
YL_1373 -1.13975507
YL_138 -10.60547430
YL_1381 -3.58224598
YL_1386 8.99289068
YL_1388 1.17382516
YL_1389 2.81181635
YL_1392 8.16005177
YL_1393 -0.12544605
YL_1397 9.71916002
YL_1398 7.95331780
YL_1404 0.06870488
YL_1407 5.38027183
YL_1419 7.15253107
YL_1420 10.35789924
YL_1442 -7.04739297
YL_1444 2.34566111
YL_1450 3.87447089
YL_1452 5.27918149
YL_1455 7.42698938
YL_1457 -3.89903509
YL_1466 7.14042263
YL_1469 2.27420741
YL_1470 10.79409793
YL_1473 -1.04540017
YL_1480 2.88416482
YL_152 -4.20232232
YL_153 -12.12750351
YL_156 3.91303796
YL_1575 -2.43027442
YL_1580 1.37968678
YL_1581 4.12568099
YL_1587 8.04943895
YL_16 -7.02157203
YL_1603 0.57360341
YL_1607 -8.86476605
YL_1612 2.36105967
YL_1634 2.24671715
YL_166 -13.26017604
YL_167 -3.50491159
YL_168 -14.23397799
YL_172 -12.05227752
YL_175 -8.29825059
YL_18 9.52703984
YL_19 -18.58577568
YL_202 -6.10456688
YL_21 -15.13787032
YL_217 -7.26605875
YL_223 0.66310595
YL_23 -7.85504558
YL_234 -10.37714803
YL_238 -6.11817681
YL_24 -1.81854708
YL_245 -9.86653723
YL_253 -14.65366590
YL_259 1.96984360
YL_26 -11.85300063
YL_275 -15.65466006
YL_28 -10.15316684
YL_283 -14.45182554
YL_290 -11.09618003
YL_294 -8.74356982
YL_30 -12.27356959
YL_307 1.64940362
YL_321 1.91379208
YL_324 -5.74334861
YL_325 -3.10126640
YL_330 1.67683297
YL_347 -3.50931761
YL_349 -1.04153184
YL_352 1.89740491
YL_355 2.83303818
YL_358 -1.14576547
YL_367 -9.71553781
YL_380 -0.99668506
YL_384 3.79287470
YL_39 -5.42037067
YL_405 2.11402825
YL_408 -11.76954185
YL_415 2.64411892
YL_417 5.85069502
YL_427 3.94421210
YL_428 9.40408945
YL_448 0.37563446
YL_449 -2.33387719
YL_452 0.40154272
YL_46 -7.39428653
YL_463 1.33724819
YL_464 -10.41716360
YL_467 -2.34883602
YL_474 6.65985007
YL_487 1.29174765
YL_501 3.32218584
YL_505 7.38045172
YL_506 1.31704446
YL_521 6.22948131
YL_524 -1.68286183
YL_530 7.18288405
YL_538 3.98340603
YL_545 5.67473287
YL_547 -2.27874299
YL_550 -0.58831889
YL_551 0.37510475
YL_554 2.71473409
YL_572 -2.06160537
YL_573 -0.29030787
YL_575 0.68718243
YL_582 4.75765625
YL_587 -0.83009656
YL_605 5.31196773
YL_606 2.67365220
YL_616 2.90182106
YL_642 2.47972724
YL_651 -0.81169106
YL_664 5.95484146
YL_67 -4.30604249
YL_673 4.17090233
YL_68 -2.49441737
YL_683 7.70477662
YL_688 5.42055235
YL_70 -15.44205979
YL_703 6.48497783
YL_709 3.59155785
YL_713 6.04072267
YL_719 5.76981860
YL_732 3.66518687
YL_737 7.09949527
YL_74 3.85212114
YL_748 -5.01416846
YL_749 2.90942350
YL_81 -7.45086233
YL_821 1.56121910
YL_83 -6.23194272
YL_87 -11.21262876
YL_876 2.89741059
YL_887 -2.31211885
YL_891 -8.48651185
YL_895 -0.10059342
YL_896 -9.02267074
YL_90 -5.27228602
YL_905 -1.87462487
YL_914 -6.52855392
YL_916 -3.74814938
YL_947 -6.50863270
YL_97 -4.19788962
YL_977 3.86040810
YL_990 4.37994040
YL_991 -3.91031741Calculating expected phenotypic value
\[ \hat{y}=\hat{\beta}+\hat{g} \]
Observed vs Predicted phenotypes of testing dataset
Histogram of the observed phenotypes
The general process
Summary
- Reference Population
- Model
- From the model and reference population you can train the model, predict breeding values, get a measure of predictive accuracy
- You can use the model to get breeding values for new candidates and select candidates based on predicted breeding values.
References
Symposium review: How to implement genomic selection
Linkage disequilibrium — understanding the evolutionary past and mapping the medical future
Genomic selection: A paradigm shift in animal breeding
Genomic selection M.E. Goddard & B.J. Hayes
Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction
Course Notes ‘QTL Mapping, MAS, and Genomic Selection’ (Hayes, 2007)
Genomic selection in plant breeding: from theory to practice
Genome-wide association and genomic selection in animal breeding (Ben Hayes and Mike Goddard)
Will genomic selection be a practical method for plant breeding?
Progress and perspectives on genomic selection models for crop breeding
Genomic selection in plant breeding: Key factors shaping two decades of progress
The development and application of genomic selection as a new breeding paradigm (nice illustration)