## 4 Discussion
### Genomic Selection Predictive Performance  
Based on our study population, our findings indicate that compared to the traditional BLUP method, genomic prediction using SNP information significantly improves prediction accuracy while maintaining an unbiasedness close to 1. Specifically, for egg quality traits, the prediction accuracy doubled with the use of the GBLUP model. Other studies have similarly reported varying degrees of improvement in genomic selection prediction accuracy with the GBLUP model compared to the BLUP model [6, 49, 50]. The higher accuracy of breeding value predictions using SNP information may be due to the more precise estimation of genetic relationships compared to traditional pedigree methods [6]. In this study, the heritability estimates for growth traits decreased after conventional selection, resulting in lower prediction accuracy. In contrast, the heritability estimates for reproductive traits and egg quality traits were moderate, leading to relatively higher prediction accuracy. This suggests a positive correlation between heritability estimates and prediction accuracy [7]. Compared to the GBLUP method, the ssGBLUP method allows for a more comprehensive integration of genomic data and pedigree data, thereby improving the prediction accuracy of genomic selection [25]. Additionally, the ssGBLUP method is more efficient in handling large-scale data, particularly in the prediction of complex traits in poultry and livestock [24]. In our study, the prediction accuracy for various traits showed almost no improvement from the GBLUP to the ssGBLUP model. This may be because all 6045 individuals in the study population were genotyped, a finding that has also been corroborated by similar study [6].  
Nevertheless, unlike BLUP, which constructs the relationship matrix exclusively using pedigree information, ssGBLUP integrates both pedigree and genomic data to develop the relationship matrix H. This integration more accurately reflects individual differences and significantly enhances the accuracy of genetic assessments. Consequently, ssGBLUP serves as an excellent method for combining traditional breeding data with genotyping information. Furthermore, utilizing traditional pedigree records in conjunction with partial genotyping can effectively reduce breeding costs while improving prediction accuracy. The data source population in this study comprises a specialized line selected for egg laying traits, with a breeding program aimed at maximizing egg laying performance while ensuring appropriate growth rates and feed conversion efficiency. Reproductive traits are critical for breeding efficiency; however, traditional selection methods have been ineffective in improving these traits. The results of this study demonstrated significant improvements in egg laying ability after more than three consecutive generations of selection using a genomic selection program. Compared to traditional selection methods, genomic selection offers superior accuracy for selecting egg laying traits in Pekin ducks. Additionally, the one-step method effectively integrates genealogical and genotypic information, thereby reducing breeding costs. These findings suggest that incorporating genomic selection and partial genotyping into breeding strategies can enhance trait improvement efficiency and economic viability in future breeding programs.