Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain
Ryokei Tanaka | Di Wu | Xiaowei Li | Laura E. Tibbs‐Cortes | Joshua C. Wood | Maria Magallanes‐Lundback | Nolan Bornowski | John P. Hamilton | Brieanne Vaillancourt | Xianran Li | Nicholas T. Deason | Gregory R. Schoenbaum | C. Robin Buell | Dean DellaPenna | Jianming Yu | Michael A. Gore
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.
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