Bias and accuracy 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.