Genomic prediction of tocochromanols in exotic-derived maize

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dc.creatorTibbs-Cortes, Laura E.
dc.creatorGuo, Tingting
dc.creatorLi, Xianran
dc.creatorTanaka, Ryokei
dc.creatorVanous, Adam E.
dc.creatorPeters, David
dc.creatorGardner, Candice
dc.creatorMagallanes-Lundback, Maria
dc.creatorDeason, Nicholas T.
dc.creatorDellaPenna, Dean
dc.creatorGore, Michael A.
dc.creatorYu, Jianming
dc.date2023-12
dc.date2025-07-17T17:01:50Z
dc.date2025-07-17T17:01:50Z
dc.date.accessioned2026-06-27T15:18:03Z
dc.descriptionTocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.
dc.identifierhttps://hdl.handle.net/10568/175678
dc.identifier.urihttp://hdl.handle.net/123456789/100434
dc.languageen
dc.publisherWiley
dc.rightsOpen Access
dc.sourceTibbs-Cortes, Laura E.; Guo, Tingting; Li, Xianran; Tanaka, Ryokei; Vanous, Adam E.; Peters, David; et al. 2023. Genomic prediction of tocochromanols in exotic-derived maize. Plant Genome 16(4): e20286. https://doi.org/10.1002/tpg2.20286
dc.subjectvitamin e
dc.subjectmaize
dc.subjectnutrition
dc.subjectgermplasm
dc.subjectbiofortification
dc.subjectfat soluble vitamins
dc.titleGenomic prediction of tocochromanols in exotic-derived maize
dc.typeJournal Article

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)