Location: Wheat Health, Genetics, and Quality Research
Title: Leveraging prior biological knowledge improves prediction of tocochromanols in maize grainAuthor
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TANAKA, RYOKEI - Cornell University |
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WU, DI - Cornell University |
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LI, XIAOWEI - Cornell University |
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TIBBS-CORTES, LAURA - Iowa State University |
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WOOD, JOSHUA - University Of Georgia |
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MAGALLANES-LUNDBACK, MARIA - Michigan State University |
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BORNOWSKI, NOLAN - Michigan State University |
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HAMILTON, JOHN - University Of Georgia |
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VAILLANCOURT, BRIEANNE - University Of Georgia |
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Li, Xianran |
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DEASON, NICHOLAS - Michigan State University |
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SCHOENBAUM, GREGORY - Iowa State University |
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BUELL, C - University Of Georgia |
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DELLAPENNA, DEAN - Michigan State University |
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YU, JIANMING - Iowa State University |
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GORE, MICHAEL - Cornell University |
Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 9/21/2022 Publication Date: 11/2/2022 Citation: Tanaka, R., Wu, D., Li, X., Tibbs-Cortes, L.E., Wood, J., Magallanes-Lundback, M., Bornowski, N., Hamilton, J.P., Vaillancourt, B., Li, X., Deason, N.T., Schoenbaum, G.R., Buell, C.R., DellaPenna, D., Yu, J., Gore, M.A. 2022. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. The Plant Genome. Article e20276. https://doi.org/10.1002/tpg2.20276. DOI: https://doi.org/10.1002/tpg2.20276 Interpretive Summary: Tocochromanols, i.e., Vitamin E, are essential human nutrients acquired from diet. Cereal grains have large variation in the content of tocochromanols, therefore, biofortication breeding for high level of tocochromanols has a great potential. As the current breeding paradigm is shifting towards genomic-assited approaches, tespecially genomic selection, his study tested the various strategies of incorporated transcriptome information to enhance the prediction model and found a combination of genomic relationship matrix and expression level of large-effect genes had the best performance. Technical Abstract: With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils, but the vitamin E activity of the most abundant tocochromanol isomers in grain of maize and other cereals is often lower. Several large-effect genes with cis-acting variants affecting mRNA expression are mostly responsible for tocochromanol variation in maize grain, but other relevant associated quantitative trait loci (QTL) have yet to be fully resolved. Leveraging this existing genomic and transcriptomic information could improve prediction when selecting for higher vitamin E content. Here, we evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines, finding MK-GBLUP to have higher average predictive abilities (+7.2% to +11.9%) relative to GBLUP. Illustrating the importance of expression variation in prediction of the same nine phenotypes within a panel of 545 subsetted lines, we found the best average predictive ability (+15.4%) for a model that included genome-wide markers and transcript abundances (developing grain) for only a few large-effect genes likely to be causal (1-3 genes per trait) when compared to GBLUP. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes. |