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Core Ideas Augmenting RR‐BLUP models with peak GWAS markers can hypothetically boost prediction accuracy We conducted a simulation study in maize and sorghum to test the performance of such models For most of the simulated traits, we observed a decrease in prediction accuracy These augmented models tended to yield greater variability in prediction accuracy An increase of bias in predicted breeding values from these models was noted Certain agronomic crop traits are complex and thus governed by many small‐effect loci. Statistical models typically used in a genome‐wide association study (GWAS) and genomic selection (GS) quantify these signals by assessing genomic marker contributions in linkage disequilibrium (LD) with these loci to trait variation. These models have been used in separate quantitative genetics contexts until recently, when, in published studies, the predictive ability of GS models that include peak associated markers from a GWAS as fixed‐effect covariates was assessed. Previous work suggests that such models could be useful for predicting traits controlled by several large‐effect and many small‐effect genes. We expand this work by evaluating simulated traits from diversity panels in maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] using ridge‐regression best linear unbiased prediction (RR‐BLUP) models that include fixed‐effect covariates tagging peak GWAS signals. The ability of such covariates to increase GS prediction accuracy in the RR‐BLUP model under a wide variety of genetic architectures and genomic backgrounds was quantified. Of the 216 genetic architectures that we simulated, we identified 60 where the addition of fixed‐effect covariates boosted prediction accuracy. However, for the majority of the simulated data, no increase or a decrease in prediction accuracy was observed. We also noted several instances where the inclusion of fixed‐effect covariates increased both the variability of prediction accuracies and the bias of the genomic estimated breeding values. We therefore recommend that the performance of such a GS model be explored on a trait‐by‐trait basis prior to its implementation into a breeding program.

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Abstract Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.