|TIBBS-CORTES, LAURA - Iowa State University|
|GUO, TINGTING - Huazhong Agricultural University|
|TANAKA, ROYKEI - Cornell University|
|MAGALLANES-LUNDBACK, MARIA - Michigan State University|
|DEASON, NICHOLAS - Michigan State University|
|DELLAPENNA, DEAN - Michigan State University|
|GORE, MICHAEL - Cornell University|
|YU, JIANMING - Iowa State University|
Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/16/2022
Publication Date: 12/27/2022
Citation: Tibbs-Cortes, L.E., Guo, T., Li, X., Tanaka, R., Vanous, A.E., Peters, D.W., Gardner, C.A., Magallanes-Lundback, M., Deason, N.T., DellaPenna, D., Gore, M.A., Yu, J. 2022. Genomic prediction of tocochromanols in exotic-derived maize. The Plant Genome. Article e20286. https://doi.org/10.1002/tpg2.20286.
Interpretive Summary: Vitamin E is a critical nutrient in human diet. Favorable genes present in exotic germplasm pools, but utilization of exotic germplasm to increase Vitamin E content of elite varieties remains challenging. Attributed to the affordable sequencing technologies, genomic prediction is a powerful technology to speed up the breeding process. This study explored the genomic prediction strategy for exploiting exotic germplasm by evaluation two unique populations, Ames Diverse Panel, representing adapted elite gene pool, and BGEM, representing exotic gene pool. The results suggested that genomic prediction with an optimally designed training set could be leveraged to efficiently utilize exotic germplasm pools.
Technical Abstract: Tocochromanols, commonly known as vitamin E, are an essential part of the human diet. Plant products including maize grain are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Exotic germplasm has previously been used in maize breeding to improve ß-carotene and pro-vitamin A levels, demonstrating its utility in biofortification. However, genomic prediction of exotic-derived lines using available training data from adapted germplasm needs additional research. In this study, genomic prediction was systematically investigated for nine tocochromanol traits (a-tocopherol, ß-tocopherol, '-tocopherol, a-tocotrienol, ß-tocotrienol, '-tocotrienol, total tocopherols, total tocotrienols, and total tocopherols) within both an adapted (Ames Diversity Panel) and an exotic-derived (BGEM) maize population. Mean prediction accuracies up to 0.79 were achieved using gBLUP when predicting and validation were conducted within each population. In addition, optimal training population (OTP) design methods FURS, MaxCD, and PAM were adapted for inbreds and, along with the methods CDmean and PEVmean, often improved prediction accuracies compared to random training sets of the same size. Genomic prediction across populations was more challenging, resulting in low prediction accuracies. However, OTP design using only 2.5% of the combined population enabled successful prediction of the rest of the exotic-derived population. Our findings highlighted the significance of obtaining genotyping data and designing the training set to leverage genomic prediction to incorporate new exotic germplasm into plant breeding.