Tester and environment affecting genomic prediction in exotic maize germplasm and derivation penal in China

Abstract

Abstract Exotic maize germplasm and derivation have formulate new heterotic groups in China. The breeding value are urgent to evaluate for better application. Genomic prediction(GP) could predict breeding values using all the genomic markers jointly rather than testing the significance of each of them. A panel of 636 exotic maize lines derivated from national project were genotyped and crossed to two testers Jing2416 and Z58. The testcrosses were evaluated in 2017 and 2018 in two sites. The mean performance of two testcrosses for each line were used to train a whole GP model. Fivefold cross validation was performed to assess the prediction accuracies of the GP models for all traits in the same population. Meanwhile the tester GP model of each type testcrosses for one tester was also constructed. The result indicated that the accuracy of prediction for all the traits ranged from 0.36 to 0.56 in whole GP model. The accuracy of ear width was highest 0.56, plant height second 0.53. The forecast of grain yield was 0.49 lower than ear width and plant height. The prediction accuracy of Jing2416 model were always above that of the Z58 model, the whole model in the middle for most traits. The major reason was that the genetic relationship of Jing 2416 with training population was more far. Thus more consanguinity ties of tester should be chosen with training-validation population. The prediction accuracy of whole model were always more than that of Jinan model and Xinxiang model for all the traits. It underlines that the prediction model basing multi-environments had better forecast result. Single environment phenotypic value had lower prediction effect.