Accuracy of genomic prediction using whole genome sequence data in White egg layer chickens

Heidaritabar, M.; Calus, M.P.L.; Megens, H.J.W.C.; Groenen, M.A.M.; Vereijken, A.; Bastiaansen, J.W.M.


There is an increasing interest in using whole genome sequence data in genomic selection breeding programs. Prediction of breeding values is expected to be more accurate when whole genome sequence is used, since the causal mutations are assumed to be in the data. We report genomic prediction for number of eggs in White egg layer chickens using whole genome re-sequence data including more than 4.5 million SNPs. We compared the prediction accuracies based on sequence data with accuracies from the 60k SNP chip data. We applied Genomic Best Linear Unbiased Prediction (GBLUP) and BayesC prediction. Moreover, we evaluated the prediction accuracy using different types of variants (Synonymous, non-synonymous and non-coding SNPs). Genomic prediction using 60k, resulted in prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60k genotypes. With both 60k and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non-synonymous SNPs) did not improve accuracy of genomic prediction. The small number of sequenced animals in this study, which affects the accuracy of imputed sequence data, may have limited the potential to improve the prediction accuracy. We expect, however, that the limited improvement is because the 60k SNPs are sufficient to accurately determine the relationships between animals.