37 - Single-step Genomic BLUP Fitting Snps or Haplotypes in Genetically-diverse Populations: A Simulation Study

The successful implementation of genomic selection in more genetically diverse populations (e.g., sheep and goats) require larger training populations. Haplotype-based genomic predictions are hypothesized to perform better in comparison to single-SNP methods mainly due to the possibility of better capturing QTL effects in linkage disequilibrium (LD) with the markers. However, most genomic-prediction studies based on haplotypes were performed in populations with low effective population size (Ne < 150). We aimed to investigate alternative approaches for fitting haplotypes using the single-step GBLUP method (ssGBLUP) in a genetically diverse population (Ne = 400). We simulated a composite sheep population, mimicking real populations based on literature parameters, using the QMSim software, with five replicates. We simulated a HD panel (600K) and two traits with different heritabilites (0.10 and 0.30). Pseudo-SNPs from unique haplotype alleles derived from LD blocks with thresholds of 0.1, 0.3, and 0.6 (LD01, LD03, and LD06, respectively) were used in the analyses. The LD-blocks were constructed using a 50K panel designed from the simulated HD. The training population was composed of 60,000 individuals with phenotypes, 8,000 of them also had genotypes, and 2,000 young genotyped individuals were used as the validation set. The genomic relationship (G) in the ssGBLUP was constructed using both independent markers and pseudo-SNPs (haplotypes). A linear mixed effects model was used to test the effect of the G on the accuracies of prediction, followed by the Tukey test with 5% of significance. No blocks were created with LD06. The accuracies with the 50K panel, LD01, and LD03 for the moderate heritability were 0.41(0.00), 0.40(0.01) and 0.41(0.00), respectively, and 0.24(0.01) 0.23(0.01), and 0.24(0.01) for the low heritability scenario. No statistical differences were observed. Based on our findings, haplotype-based predictions did not improve the accuracy of genomic breeding values in genetically diverse populations.