Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants

The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single-nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi-step method (GBLUP) and single-step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

PSI-B-21 Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants

This study aimed to evaluate the accuracy of genomic prediction with simulated data, using SNP markers, causal quantitative trait nucleotide (QTN), and the combination of both. The methods used were the best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP), with alternative SNP weights. Data were simulated using the package AlphasimR. Trait heritability of 0.3 was assumed, and genetic variance was fully accounted for by 100 or 1000 QTNs. A population with an effective size of 200 was selected, and 20 generations were simulated. The genomic information mimicked the 29 bovine chromosomes and included 50k SNP markers evenly distributed across the genome. Approximately 16800 genotypes were available from selected sires and dams in generations 16–19, and 2000 animals in generation 20. Phenotypes for young animals were not included in the analysis, as they were used in the validation. For GBLUP, three pseudo-phenotypes were considered: the raw phenotype, the true breeding value, and the true breeding value with noise added. The genomic relationship matrix was weighted using quadratic weights, calculated based on the SNP variance, and non-linear A, following different equation parameters. The scenario with exclusively causal variants presented accuracies close to 1 for 100 QTL, and slightly lower in the 1000 QTL. For the SNP + QTN scenario, quadratic weights promoted higher accuracy gains than the SNPs alone, especially in the 100 QTN trait. Accuracies converged at higher values for both quadratic and non-linear A weights in the 100 QTN scenario. For the 1000 QTN trait, quadratic weights diverged and reduced accuracy, while non-linear A maintained accuracy at their peaks, depending on the equation parameters. Parameters of non-linear A for highest accuracy were different in each scenario and type of analysis. Proportionally, gains in accuracy were more prominent with GBLUP than with ssGBLUP.