publications

2024

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

   LaPorte, Mary-Francis, Suwarno, Willy Bayuardi, Hannok, Pattama, Koide, Akiyoshi, Bradbury, Peter, Crossa, José, Palacios-Rojas, Natalia, and Diepenbrock, Christine Helen

   G3: Genes, Genomes, Genetics 2024

   Abs

   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. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability 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 genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

2022

1. Simultaneous dissection of grain carotenoid levels and kernel color in biparental maize populations with yellow-to-orange grain

   LaPorte, Mary-Francis, Vachev, Mishi, Fenn, Matthew, and Diepenbrock, Christine

   G3 2022

   Abs

   Abstract Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the US maize nested association mapping panel that have yellow to orange grain. Quantitative trait loci were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color quantitative trait loci ranging from 2.4% to 17.5%. These quantitative trait loci were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized ∼27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the α- or β-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with β-branch and total carotenoids and negligible correlations with α-branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids.

2021

1. Overcoming the challenges to enhancing experimental plant biology with computational modeling

   Dale, Renee, Oswald, Scott, Jalihal, Amogh,  LaPorte, Mary-Francis, Fletcher, Daniel M, Hubbard, Allen, Shiu, Shin-Han, Nelson, Andrew David Lyle, and Bucksch, Alexander

   Frontiers in Plant Science 2021