Genomic selection accuracy and bias using imputed genotypes on growth, welfare and fitness traits in two Pekin duck lines
The current study investigated the genomic selection accuracies and biases estimates from two commercial Pekin duck lines reared under commercial breeding practices. A large dataset of 26K duck records comprising both phenotype and imputed genotype information (60K chip) were analysed for growth, welfare and primary feather length traits. First, we employed mixed linear models with relationship matrices computed from the pedigree (BLUP) or markers (GBLUP) to estimate the variance components and breeding values. Then, we estimated the selection accuracies and selection biases to assess the more appropriate models. Our results showed moderately high imputation accuracies of 0.93 and 0.92 for lines A and D respectively. In both lines, the heritability estimates obtained using the pedigree were generally higher than using genomic markers in all traits considered. These ranged for juvenile weight (JW) from 0.22{+/-}0.01 vs 0.25{+/-}0.01 in line A vs line D using marker information to 0.39{+/-}0.02 to 0.50{+/-}0.02 using the pedigree in line A vs line D for slaughter body weight (BW). We observed very low estimates of heritability for gait 0.07{+/-}0.01 using markers in both lines. Breast muscle depth (BD) also had lower estimates of 0.15-0.16 using markers. For line A, the genomic predictions were generally higher when using the G-matrix than the A-matrix with the highest prediction was for BW (r2=0.68-0.70) and JW with r2 of 0.49. The estimates for gait and foot pad dermatitis (FPD) were greatly improved by using the G-Matrix at 0.58 vs 0.24 and 0.68 vs 0.44 respectively for markers vs pedigree information. For line D, the same improvements for G-Matrix vs A-Matrix were observed with estimates for BD being similar in the two lines. However, for BD the G-Matrix greatly improved the estimates from 0.50 to 0.71 unlike in line A where they remained at 0.50. The bias in line A were minimal (0.01- 0.19) using the G-Matrix compared to 0.02- 0.41 when using A-Matrix. The highest observed bias was for JW followed by BD for the G-matrix whereas when using the A-matrix we observed higher biases in many traits (JW, BW, BD and gait). The biases for line D were generally lower for the G-matrix (0.02 - 0.17 vs 0.00 - 0.19) than those observed in line A using markers whereas higher biases were observed using the pedigree (0.01 - 0.37). Current findings pinpointed that all traits were heritable with higher prediction accuracies and lower biases when using GBLUP as opposed to traditional BLUP. The present study demonstrates the effectiveness of GBLUP for improving prediction accuracy and reducing bias in selection traits of Pekin ducks, particularly for traits with low heritability. Author SummaryThe study explored genomic selection in two commercial Pekin duck lines. Using a large dataset of 26,000 records, including phenotype and genotype data, researchers analyzed growth, welfare, and feather length traits. They applied statistical models to assess variance components and breeding values, comparing traditional pedigree-based methods (BLUP) with genomic marker-based methods (GBLUP). Results showed high imputation accuracies (93% for line A and 92% for line D). Heritability estimates varied, with genomic markers generally producing lower estimates than pedigrees, except for traits like gait and breast muscle depth where genomic predictions were superior. For example, line A showed higher accuracy using genomic data for body weight and juvenile weight. Overall, genomic predictions (GBLUP) provided higher accuracy and lower bias compared to traditional methods, especially for traits with low heritability. This highlights the effectiveness of GBLUP in improving selection processes in Pekin ducks.