Genomic prediction of tocochromanols in exotic‐derived maize

Abstract

Tocochromanols (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.

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

“…Therefore, large-effect causal variants at both por loci in the Goodman panel may be at a markedly lower frequency rel-ative to the Ames panel, which could result in the observed negative improvements when predicting δT and γT in the Goodman panel (Heslot et al, 2012;Scutari et al, 2016). To improve the low predictive ability for these two phenotypes in the Goodman panel, it could be worthwhile to apply trainingset optimization approaches that were recently tested by Tibbs-Cortes et al (2022) for prediction of grain tocochromanols in exotic-derived maize.…”

“…To improve the low predictive ability for these two phenotypes in the Goodman panel, it could be worthwhile to apply training‐set optimization approaches that were recently tested by Tibbs‐Cortes et al. (2022) for prediction of grain tocochromanols in exotic‐derived maize.…”

“…However, for phenotypes having less complex genetic architectures that include large-effect QTL like tocochromanol levels in maize grain, Bayesian regression models (e.g., BayesA and BayesB) that assume the existence of loci with major effects could achieve higher predictive performance than GBLUP (de Los Campos et al, 2013;Gianola, 2013). Illustrative of the potential for genomic selection, a range of low to moderately high predictive abilities were obtained via GP of tocochromanol levels in fresh kernels of a sweet corn diversity panel (GBLUP model) (Baseggio et al, 2019;Hershberger et al, 2022) and in mature maize grain samples of both the Ames panel and an exotic derived population (GBLUP and Bayesian models) (Tibbs-Cortes et al, 2022).…”

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

“…The high heritabilities of the carotenoid traits in the CAM panel in tested tropical/subtropical environments (as reported in Suwarno et al 2015 ) make them good candidates for GP given that heritability represents the proportion of phenotypic variance that is explained by genetic factors. Implementation of GP strategies has been recently explored to support the biofortification of vitamin E ( Tibbs-Cortes et al 2022 ) and zinc-related ( Guo et al 2020 ) traits in maize, iron and zinc (among other health-beneficial mineral nutrients) in wheat ( Velu et al 2016 , Manickavelu et al 2017 , Joukhadar et al 2021 ), and zinc in rice ( Rakotondramanana et al 2022 ).…”

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

“…The high heritabilities of the carotenoid traits in this panel (as reported in Suwarno et al 2015) make them good candidates for GP given that heritability represents the proportion of phenotypic variance that is explained by genetic factors. Implementation of GP strategies has been recently explored to support the biofortification of vitamin E (Tibbs-Cortes et al 2022) and zinc-related (Guo et al 2020) traits in maize, iron and zinc (among other health-beneficial mineral nutrients) in wheat, and zinc in rice (Rakotondramanana et al 2022).…”