Accuracy of genomic evaluation considering the interaction effect between estimation method of marker effects, population structure, and genetic architecture of the trait

This study aimed to investigate the interaction effects between marker effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations. A reference population with two different effective population sizes (100 and 500) was simulated using the QMSim software. 500 markers and two different numbers of quantitative trait loci or QTLs (50 and 200) were distributed randomly through the genome including a 100 cM chromosome. In this study, three traits with different heritabilities (0.1, 0.3, and 0.5) were simulated. The genomic breeding values were predicted using Bayesian ridge regression, Bayes A, Bayes B, Bayes C, Bayesian LASSO, Reproducing kernel Hilbert space, and neural networks methods. Through the three heritabilities, as the effective population size increased, the accuracy of genomic evaluation decreased with different trends. As the number of QTLs increased, the accuracy of low heritability trait increased, but the accuracy of medium and high heritability traits decreased. Similarly, as the number of QTLs increased, the accuracy of the trait with normal distributed QTLs increased, but the accuracy of traits with gamma and univariate distributed QTLs decreased. For all types of QTL distributions, the increment of effective population size decreased the accuracy of genomic evaluations. The results of this study clearly showed the interaction effects between markers effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations.