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

Abstract

Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)--an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. This project assesses the accuracy of several genomic prediction (GP) strategies for these traits ({beta}-carotene, {beta}-cryptoxanthin, provitamin A, lutein, and zeaxanthin) within and between four environments in Mexico. Methods employing Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, or Reproducing Kernel Hilbert Spaces had high accuracy in predicting all tested provitamin A carotenoid traits and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, prediction accuracies were higher when using genome-wide markers rather than only the markers proximal to two previously identified carotenoid-related genes that have been used in marker-assisted selection, suggesting that GP is worthwhile for these traits, even though key genes have already been identified. Prediction accuracy was maintained for all traits (except lutein) in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of GP methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.