Assessment of Genomic Prediction Strategies after Animal Genome-Wide Association Study

Abstract

Background The detection of candidate variants with interesting traits is a major goal of a genome-wide association study (GWAS). GWAS-associated markers are considered candidate functional loci regarding animal and plant breeding and can serve to predict and treat human genetic diseases. Significant selected markers are functionally validated via molecular biology experiments or statistically validated by genomic prediction (GP) in an individual population. GWAS in a whole population used for GP causes an overprediction regarding accuracy. However, whether this overprediction exists in any traits with different genetic architectures remains unknown, while the extent of the difference between overprediction and actual prediction is also undetermined. The lack of whole key genetic information and linear dependence ubiquity can make perfect prediction of traits of interest impossible. A stable and adaptable prediction method for multiple genetic architectures is thus essential. Results We used a public dataset to present the accuracy bias in a cross-validation population with different genetic architectures and developed an approach termed “marker-assisted best linear unbiased prediction (MABLUP),” with removed linear dependence to improve the prediction accuracy for complex traits with genetic architectures. The MABLUP showed better prediction accuracy than other methods for traits under the control of few quantitative trait nucleotides (QTNs) and similar prediction accuracy to the best-known methods for traits under many QTNs. Conclusions The reasonable design of GP in the cross-validation after animal GWAS can be used to present actual potential breeding ability of detected significant markers. The MABLUP is a more stable and accurate GP method for more complex genetic traits.