Some of the most important agronomic crop traits of interest are complex and thus governed by many genes of small effect. The statistical models typically used in a genome-wide association study (GWAS) and genomic selection (GS) quantify the contributions of genomic markers in linkage disequilibrium with these genes to trait variation. In general, the GWAS has been successful at identifying genomic regions containing markers with moderate to strong marker-trait associations. It is possible to incorporate markers tagging such GWAS signals into breeding programs through marker-assisted selection, where plants with favorable alleles at the peak GWAS signals are selected for the next cycle of breeding. In the absence of such signals, GS is typically effective at accurately predicting trait values. These two strategies have been used separately until recently, when the predictive ability of GS models that include peak associated markers from GWAS as fixed effect covariates was assessed. Theoretically, these models should be optimal for predicting traits that have several genes of large effect and many genes of smaller effect.This work is expanded upon by evaluating simulated traits from a diversity panel in maize and one in sorghum using a Ridge Regression Best Linear Unbiased prediction (RR-BLUP) model that included 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 is quantified. Expansion of this work will have implications as breeders navigate how to utilize the various types and substantial amount of data becoming readily available.
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Evaluation of genomic prediction models that incorporates peak GWAS signals in maize and sorghum diversity panels
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