Accuracy and bias of genomic prediction with different de-regression methods

Comparison of different response variables in genomic prediction using GBLUP and ssGBLUP methods in Iranian Holstein cattle

We compared the reliability and bias of genomic evaluation of Holstein bulls for milk, fat, and protein yield with two methods of genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP). Four response variables of estimated breeding value (EBV), daughter yield deviation (DYD), de-regressed proofs based on Garrick (DRPGR) and VanRaden (DRPVR) were used as dependent variables. The effects of three weighting methods for diagonal elements of the incidence matrix associated with residuals were also explored. The reliability and the absolute deviation from 1 of the regression coefficient of the response variable on genomic prediction (Dev) using GBLUP and ssGBLUP methods were estimated in the validation population. In the ssGBLUP method, the genomic prediction reliability and Dev from un-weighted DRPGR method for milk yield were 0.44 and 0.002, respectively. In the GBLUP method, the corresponding measurements from un-weighted EBV for fat were 0.52 and 0.008, respectively. Moreover, the un-weighted DRPGR performed well in ssGBLUP with fat yield values for reliability and Dev of 0.49 and 0.001, respectively, compared to equivalent protein yield values of 0.38 and 0.056, respectively. In general, the results from ssGBLUP of the un-weighted DRPGR for milk and fat yield and weighted DRPGR for protein yield outperformed other models. The average reliability of genomic predictions for three traits from ssGBLUP was 0.39 which was 0.98% higher than the average reliability from GBLUP. Likewise, the Dev of genomic predictions was lower in ssGBLUP than GBLUP. The average Dev of predictions for three traits from ssGBLUP and GBLUP were 0.110 and 0.144, respectively. In conclusion, genomic prediction using ssGBLUP outperformed GBLUP both in terms of reliability and bias.

Estimation of direct and maternal genetic effects and annotation of potential candidate genes for weight and meat quality traits in a genotyped outdoor dual-purpose cattle breed

With regard to potential applications of genomic selection in small numbered breeds, we evaluated genomic models and focused on potential candidate gene annotations for weight and meat quality traits in the local Rotes Höhenvieh (RHV) breed. Traits included 6,003 birth weights (BWT), 5,719 200 d-weights (200dw), 4,594 365 d-weights (365dw), and 547 records for intramuscular fat content (IMF). A total of 581,304 SNP from 370 genotyped cattle with phenotypic records were included in genomic analyses. Model evaluations focused on single- and multiple-trait models with direct and with direct and maternal genetic effects. Genetic relationship matrices were based on pedigree (A-matrix), SNP markers (G-matrix), or both (H-matrix). Genome-wide association studies (GWASs) were carried out using linear mixed models to identify potential candidate genes for the traits of interest. De-regressed proofs (DRP) for direct and maternal genetic components were used as pseudo-phenotypes in the GWAS. Accuracies of direct breeding values were higher from models based on G or on H compared to A. Highest accuracies (> 0.89) were obtained for IMF with multiple-trait models using the G-matrix. Direct heritabilities with maternal genetic effects ranged from 0.62 to 0.66 for BWT, from 0.45 to 0.55 for 200dW, from 0.40 to 0.44 for 365dW, and from 0.48 to 0.75 for IMF. Maternal heritabilities for BWT, 200dW, and 365dW were in a narrow range from 0.21 to 0.24, 0.24 to 0.27, and 0.21 to 0.25, respectively, and from 0.25 to 0.65 for IMF. Direct genetic correlations among body weight traits were positive and favorable, and very similar from different models but showed a stronger variation with 0.31 (A), −0.13 (G), and 0.45 (H) between BWT and IMF. In gene annotations, we identified 6, 3, 1, and 6 potential candidate genes for direct genetic effect on BWT, 200dW, 365dW, and IMF traits, respectively. Regarding maternal genetic effects, four (SHROOM3, ZNF609, PECAM1, and TEX2) and two (TMEM182 and SEC11A) genes were detected as potential candidate genes for BWT and 365dW, respectively. Potential candidate genes for maternal effect on IMF were GRHL2, FGA, FGB, and CTNNA3. As the most important finding from a practical breeding perspective, a small number of genotyped RHV cattle enabled accurate breeding values for high heritability IMF.

Genome-wide association study and genomic predictions for exterior traits in Yorkshire pigs1

The objectives of this study were to identify informative genomic regions that affect the exterior traits of purebred Korean Yorkshire pigs and to investigate and compare the accuracy of genomic prediction for response variables. Phenotypic data on body height (BH), body length (BL), and total teat number (TTN) from 2,432 Yorkshire pigs were used to obtain breeding values including as response variable the estimated breeding value (EBV) and 2 types of deregressed EBVs-one including the parent average (DEBVincPA) and the other excluding it (DEBVexcPA). A final genotype panel comprising 46,199 SNP markers was retained for analysis after quality control for common SNPs. The BayesB and BayesC methods-with various π and weighted response variables (EBV, DEBVincPA, or DEBVexcPA)-were used to estimate SNP effects, through the genome-wide association study. The significance of genomic windows (1 Mb) was obtained at 1.0% additive genetic variance and was subsequently used to identify informative genomic regions. Furthermore, SNPs with a high model frequency (≥0.90) were considered informative. The accuracy of genomic prediction was estimated using a 5-fold cross-validation with the K-means clustering method. Genomic accuracy was measured as the genomic correlation between the molecular breeding value and the individual weighted response variables (EBV, DEBVincPA, or DEBVexcPA). The number of identified informative windows (1 Mb) for BH, BL, and TTN was 4, 3, and 4, respectively. The number of significant SNPs for BH, BL, and TTN was 6, 4, and 5, respectively. Diversity π did not influence the accuracy of genomic prediction. The BayesB method showed slightly higher genomic accuracy for exterior traits than BayesC method in this study. In addition, the genomic accuracy using DEBVincPA as response variable was higher than that using other response variables. Therefore, the genomic accuracy using BayesB (π = 0.90) with DEBVinPA as a response variable was the most effective in this study. The genomic accuracy values for BH, BL, and TTN were calculated to be 0.52, 0.60, and 0.51, respectively.

Impact of including information from bulls and their daughters in the training population of multiple-step genomic evaluations in dairy cattle: A simulation study

The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters' average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo-phenotypes in multiple-step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple-step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.