Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals

Abstract

As crossbreeding is extensively used in some livestock species, we aimed to evaluate the performance of single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) methods to predict Genomic Estimated Breeding Values (GEBVs) of crossbred animals. Different training population scenarios were evaluated: (SC1) ssGBLUP based on a single-trait model considering purebred and crossbred animals in a joint training population; (SC2) ssGBLUP based on a multiple-trait model to enable considering phenotypes recorded in purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model considering purebred and crossbred animals jointly in the training population (both populations were used for SNP weights' estimation); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population (crossbred population only used for SNP weights' estimation); (SC5) WssGBLUP based on a single-trait model and the training population characterized by purebred animals (purebred population used for SNP weights' estimation). A complex trait was simulated assuming alternative genetic architectures. Different scaling factors to blend the inverse of the genomic (G −1 ) and pedigree (A −1 22 ) relationship matrices were also tested. The predictive performance of each scenario was evaluated based on the validation accuracy and regression coefficient. The genetic correlations across simulated populations in the different scenarios ranged from moderate to high (0.71-0.99). The scenario mimicking a completely polygenic trait (h 2 QTL = 0) yielded the lowest validation accuracy (0.12; for SC3 and SC4). The simulated scenarios assuming 4,500 QTLs affecting the trait and h 2 QTL = h 2 resulted in the greatest GEBV accuracies (0.47; for SC1 and SC2). The regression coefficients ranged from 0.28 (for SC3 assuming polygenic effect) to 1.27 (for SC2 considering 4,500 QTLs). In general, SC3 and SC5 resulted in inflated GEBVs, whereas other scenarios yielded deflated GEBVs. The scaling factors used to combine G −1 and A −1 22 had a small influence on the validation accuracies, but a greater effect on the regression coefficients. Due to the complexity of multiple-trait models Alvarenga et al. ssGBLUP Approaches for Crossbred Evaluation and WssGBLUP analyses, and a similar predictive performance across the methods evaluated, SC1 is recommended for genomic evaluation in crossbred populations with similar genetic structures [moderate-to-high (0.71-0.99) genetic correlations between purebred and crossbred populations].

Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations

“…A reason for these underestimated values in Duroc-ssGBLUP scenarios might be due to the used scaling factors (τ = 1 and ω = 1) to combine G −1 and matrices. Alvarenga et al (2020) indicated that optimization of scaling factors might be helpful for obtaining more unbiased values using ssGBLUP method ( Alvarenga et al, 2020 ). Another possible reason for these underestimated values in Duroc-ssGBLUP scenarios can be due to the preferential treatment to select elite pigs for genotyping in breeding companies.…”

Applying an association weight matrix in weighted genomic prediction of boar taint compounds

“…Using tuning parameters to construct H matrix may be an alternative for bias reduction. However, the tuning parameters ( τ and ω ) do not have a specific ideal value; in other words, many parameters combination should be tested to find the best combination for the database (Alvarenga et al., 2020; Oliveira et al., 2019).…”

Recent genetic advances on boar taint reduction as an alternative to castration: a review

“…As for other difficult to measure traits, using genome-assisted breeding value prediction would be an interesting option (de Campos et al 2015). Different types of genomic evaluation models could be qualified, but most likely multitrait single-step models allowing the inclusion of information obtained in GWAS studies, adapted for crossbreeding, should be considered and studied (Alvarenga et al 2020;Misztal et al 2020). Therefore, the genetic and genomic evaluation of boar taint will need careful choices of traits, models, and methods, taking into account reported issues in order to allow performing a successful selection against boar taint.…”