Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

Abstract

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large‐effect genes with cis‐acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK‐GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12–21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK‐GBLUP models improved predictive abilities by 7.0–13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi‐trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large‐effect candidate causal genes (1–3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.

The Plant Genome special section: Grain quality and nutritional genomics for breeding next‐generation crops

“…This study provided strength to the hypothesis for optimal designing of training population to efficiently incorporate new exotic germplasm into a plant breeding program. Tanaka et al (2022) worked on the same trait and provided sound data support for the hypothesis that prior major QTL contributing regions and candidate causal genes conferring biological knowledge to elevate vitamin E content improves prediction based on analysis deploying multi-trait prediction model in two panels of maize inbred lines. Next, research by Brzozowski et al (2023) on genomic selection was in oat targeting seed nutritional traits in biparental populations by testing multiple genomic prediction methods for 12 seed fatty acid content traits.…”