Comparison of Bayesian Methods in the Genomic Evaluation with Different Genetic Architecture

The aim of this study was to compare different methods of Bayesian (parameteric) approaches for predicting genomic breeding values of traits with different genetic architecture in different distribution of gene effects, number of quantitative traits loci, heritability and the number of reference population using simulated data. A genome contained 3 chromosomes, with the length of 100 cM and 1000 evenly spaced single nucleotide polymorphisms (SNP) on each chromosome was simulated. For hypotheses simulation attributes, different distribution of gene effects (uniform, normal and gamma), 3 levels of QTL (50, 200 and 400), 2 levels of heritability (0.16 and 0.5) and 2 levels of the reference population (1000 and 2000) was considered. In order to predict breeding values of individuals in the population of reference and verification, 5 Bayesian methods including Bayes Ridge regression (BRR), A, LASSO (L), Cπ and B were used. As the distance between reference population and selection candidates increased, due to disruption of linkage phase, the accuracy of genomic breeding values in all methods decreased. As heritability increased from 0.16 to 0.5, all methods showed an increase of about 0.2 in accuracy of genomic breeding values. When the number of reference population had increased from 1000 to 2000, all methods showed increased accuracy by nearly 0.18. When the distribution of gene effects was gamma, the Bayes A method showed a clear preference along with both levels of heritability compared with other methods. When the distribution of gene effects was uniform, all methods showed similar accuracy. For high heritability traits, the accuracy of prediction reduced as the number of QTL increased from 50 to 200. Conversly, in low heritability traits, there was no visible influence in accuracy of genomic breeding values.