Comparison of genomic evaluation methods in complex traits with different genetic architecture

The objective of this study was to compare six statistical methods for prediction of genomic breeding values for traits with different genetic architecture in term of gene effects distributions and number of Quantitative Traits Loci (QTLs). A genome consisted of 500 bi-allelic single nucleotide polymorphism (SNP) markers distributed over a chromosomes with 100 cm length was simulated. Three different gene effects distributions (uniform، normal and gamma) were considered. Number of QTLs varied from 50 to 200. Finally، nine quantitative traits with different genetic architecture were generated. The performance of six statistical methods of genomic prediction that differ with respect to assumptions regarding distribution of marker effects، including i) Genomic Best Linear Unbiased Prediction (GBLUP)، ii) Ridge Regression Best Linear Unbiased Prediction (RRBLUP)، iii) Bayes A، iv) Bayes B، v) Bayes C، and vi) Bayesian least absolute shrinkage and selection operator (Bayes L) are presented. The accuracy of prediction declined significantly over generations (P< 0. 05) but Bayesian methods outperformed GBLUP and RRBLUP in persistence of accuracy of genomic estimated breeding values over generations. Bayesian methods were superior to GBLUP and RRBLUP when the gene effects distribution generated from gamma distribution. The highest accuracy of genomic breeding values was observed when the gene effects come from normal distribution. In all statistical evaluation methods with increasing the number of QTLs towards 200، the accuracy of predicted genomic values has been decreased. In general، Bayesian and GBLUP methods performed better in prediction than RRBLUP method. These results gave some evidences that when the genetic architecture of quantitative traits deviated from infinitesimal model assumptions، Bayesian methods usually perform better than GBLUP and RR-BLUP.