Accurate prediction of maize grain yield using its contributing genes for gene-based breeding
2020
Zhang, Meiping | Cui, Yanru | Liu, Yun-Hua | Xu, Wenwei | Sze, Sing-Hoi | Murray, Seth C. | Xu, Shizhong | Zhang, Hong-Bin
Accurately predicting the phenotypes of complex traits is crucial to enhanced breeding in plants and livestock, and to enhanced medicine in humans. Here we reports the first study accurately predicting complex traits using their contributing genes, especially their number of favorable alleles (NFAs), genotypes and transcript expressions, with the grain yield of maize, Zea mays L. When the NFAs or genotypes of only 27 SNP/InDel-containing grain yield genes were used, a prediction accuracy of r = 0.52 or 0.49 was obtained. When the expressions of grain yield gene transcripts were used, a plateaued prediction accuracy of r = 0.84 was achieved. When the phenotypes predicted with two or three of the genic datasets were used for progeny selection, the selected lines were completely consistent with those selected by phenotypic selection. Therefore, the genes controlling complex traits enable accurately predicting their phenotypes, thus desirable for gene-based breeding in crop plants.
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Эту запись предоставил National Agricultural Library