PSVIII-21 Bivariate genomic prediction with different heritability, QTL and SNP panel scenarios using artificial neural network

Abstract

Abstract A bivariate simulation study was carried out to compare the accuracies of genomic predictions from bivariate artificial neural network model with 1 to 10 neurons (ANN-1–10) using the SNPs on the first chromosome of Brangus beef cattle for 50% genetically correlated two traits with heritabilities of 25% and 50% (T1h2=0.25 and T2h2=0.5) determined either by 50, 100, 250 or 500 QTL. After QTL were created by randomly selecting 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals, their effects were sampled from a bivariate normal distribution. The breeding value of animal i in each QTL scenario was generated as Σgijβ j where gij is the genotype of animal i at QTL j and the vector of β j has the effects of QTL j from a bivariate normal distribution for T1h2=0.25 and T2h2=0.5. Phenotypic values (Σgijβ j+ei) of animal i for traits were generated by adding residuals (ei) from a bivariate normal distribution to the Σgijβ j of animal i. Genomic predictions for T1h2=0.25 and T2h2=0.5 were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons with three sets of SNP panels, only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). The correlations between phenotypes and predicted phenotypes from 10-fold cross validation for bivariate analysis of T1h2=0.25 and T2h2=0.5 were used to assess predictive ability of bivariate ANN-1–10 neurons based on 4 QTL scenarios with 3 Panels of SNP panels. Correlations for genomic predictions of T2h2=0.5 were higher than those from T2h2=0.25 for all QTL and Panel scenarios (Table 1). Panle2 including QTL and SNP performs better prediction than Panel1 and Panel3 in QTL100, QTL250 and QTL500 scenarios and the correlation from Panel3 including only SNP, which is more realistic, are similar to or higher than those from Panel1.