Bias and accuracy of body weight trait evaluations of an F2 chicken using single-step genomic best linear unbiased prediction (ssGBLUP)

The objectives of this study were (i) to compare the accuracy and bias of estimates of breeding values for body weight (BW) at 2-7 weeks of age using pedigree-based best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) methods, and (ii) to determine the best level of minor allele frequencies (MAFs) for pre-selection of SNPs for genomic prediction (GP). Records of 488 F2 broiler chickens obtained from crossbreeding of fast-growing Arian chickens and slow-growing Iranian native chickens at 2-7 weeks of age were used. Samples were genotyped using Illumina Chicken 60K BeadChip. To investigate the effect of MAFs on the accuracy of prediction, 48 379 quality-controlled SNPs were grouped into five subgroups with MAF bins 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, and 0.4-0.5. Our results confirmed the superiority of ssGBLUP compared to traditional BLUP methodology. The average accuracy of GP improved by 59.03%, 220.34%, 0.46%, 5.61%, 0.45%, and 2.73% using ssGBLUP compared to BLUP for BW at 2-7 weeks of age, respectively. Depending on the age group, using a subset of SNPs with a specific MAF bin compared to all SNPs resulted in a remarkable improvement of GP accuracy for the observed traits.