Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

Abstract

ABSTRACTWith 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 grain is low. Several large-effect genes with cis-acting variants affecting 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 to 13.6% when compared to 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.Core IdeasWith varying levels of vitamin E activity, tocochromanols found in maize grain are essential for human healthSelecting for higher vitamin E content in maize grain can be enhanced with genomic predictionPrediction models leveraging existing biological knowledge were evaluated in two panels of maize inbred linesMultikernel prediction models based on previously identified QTL improved predictive abilityA multi-trait prediction model that had transcript abundances of a few large-effect causal genes performed the best