Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

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 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 Ideas ### Competing Interest Statement The authors have declared no competing interest. * BLUE : best linear unbiased estimator BLUP : best linear unbiased predictor ceeQTL : correlated expression and effect QTL CV : cross-validation eQTL : expression QTL GBLUP : genomic best linear unbiased prediction GBS : genotyping-by-sequencing GP : genomic prediction GRM : genomic relationship matrix GWAS : genome-wide association studies HPLC : high-performance liquid chromatography IBS : identity-by-state JL : joint linkage LD : linkage disequilibrium MCMC : Markov chain Monte Carlo MK-GBLUP : multikernel genomic best linear unbiased prediction NAM : nested association mapping PVE : phenotypic variance explained PCA : principal component analysis QTL : quantitative trait loci SNP : single-nucleotide polymorphism TRM : transcriptomic relationship matrix TWAS : transcriptome-wide association studies SI : support interval αT : α-tocopherol αT3 : α-tocotrienol γT : γ-tocopherol γT3 : γ-tocotrienol δT : δ-tocopherol δT3 : δ-tocotrienol ΣT : total tocopherols ΣT3 : total tocotrienols ΣTT3 : total tocochromanols