Evaluation of RR‐BLUP Genomic Selection Models that Incorporate Peak Genome‐Wide Association Study Signals in Maize and Sorghum

Abstract

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.

Gwas and Genomic Selection For Increased Anthocyanin Content in Purple Corn

“…We also added all of these to the model simultaneously which significantly reduced accuracy. These findings are similar to those recently published in a study simulating the addition of fixed effects in this manner 57 .…”

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

“…Bernardo [3] introduced the concept of combining MAS and GP. Ever since, reports have validated the methodology [9][10][11]. Our results showed no advantage in combining MAS and GP for predicting LNTI, a low-heritability highly polygenic trait [18], of tropical maize hybrids.…”

“…Simulations have shown which scenarios are favorable for utilizing GWAS-significant markers/major genes in GP [3,11]. Our work aggregates new insights as putative QTL are identified in parents (inbred lines) and GP is applied to hybrids.…”

“…Additionally, further adaptations have been proposed to principal methods such as compressed BLUP, SUPER BLUP [8], and GP with higher-effect markers differentially modeled (e.g., as fixed effects; [3,9,10]). Simulations and empirical studies have shown that the ability of GP to associate phenotypic patterns to genomic variations is intrinsically related to the genetic architecture of traits [4,11,12]. Hence, the aforementioned GP methods were idealized to match genetic architectures by adjusting a priori assumptions regarding the variance and effect of genomic markers.…”

Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx

“…In a Bayesian framework, the authors evaluated several GS models and verified the slight superiority of the LALDA models in comparison to traditional LDA models, concluding that the best performance evidenced by the LALDA approach can be due mainly to the small number of markers used, since it enabled to exploit relevant genomic regions that were not directly considered in the LDA, in which this extra information may have contributed to the improvement of the model performance. Similarly, other studies have explored the benefits of including fixed-effect covariates tagging peak genome-wide association study (GWAS) signals [20]. Based on their results, the authors conclude that the universal implementation of GS + GWAS for predicting the breeding values of all possible traits should be investigated on a trait-by-trait basis.…”