Genomic selection in egg-laying chickens

Heidaritabar, M. (2016). Genomic selection in egg-laying chickens. PhD thesis, Wageningen University, the Netherlands In recent years, prediction of genetic values with DNA markers, or genomic selection (GS), has become a very intense field of research. Many initial studies on GS have focused on the accuracy of predicting the genetic values with different genomic prediction methods. In this thesis, I assessed several aspects of GS. I started with evaluating results of GS against results of traditional pedigree-based selection (BLUP) in data from a selection experiment that applied both methods side by side. The impact of traditional selection and GS on the overall genome variation as well as the overlap between regions selected by GS and the genomic regions predicted to affect the traits were assessed. The impact of selection on genome variation was assessed by measuring changes in allele frequencies that allowed the identification of regions in the genome where changes must be due to selection. These frequency changes were shown to be larger than what could be expected from random fluctuations, indicating that selection is really affecting the allele frequencies and that this effect is stronger in GS compared with BLUP. Next, concordance was tested between the selected regions and regions that affect the traits, as detected by a genome-wide association study. Results showed a low concordance overall between the associated regions and the selected regions. However, markers in associated regions did show larger changes in allele frequencies compared with the average changes across the genome. The selection experiment was performed using a medium density of DNA markers (60K). I subsequently explored the potential benefits of whole-genome sequence data for GS by comparing prediction accuracy from imputed sequence data with the accuracy obtained from the 60K genotypes. Before sequencing, the selection of key animals that should be sequenced to maximize imputation accuracy was assessed with the original 60K genotypes. The accuracy of genotype imputation from lower density panels using a small number of selected key animals as reference was compared with a scenario where random animals were used as the reference population. Even with a very small number of animals as reference, reasonable imputation accuracy could be obtained. Moreover, selecting key animals as reference considerably improved imputation accuracy of rare alleles compared with a set of random reference animals. While imputation from a small reference set was successful, imputation to whole-genome sequence data hardly improved genomic prediction accuracy compared with the predictions based on 60K genotypes. Using only those markers from the whole-genome sequence that are more likely to affect the phenotype was expected to remove noise from the data, but resulted in slightly lower prediction accuracy compared with the complete genome sequence. Finally, I evaluated the inclusion of dominance effects besides additive effects in GS models. The proportion of variance due to additive and dominance effects were estimated for egg production and egg quality traits of a purebred line of layers. The proportion of dominance variance to the total phenotypic variance ranged from 0 to 0.05 across traits. Also, the impact of fitting dominance besides additive effects on prediction accuracy was investigated, but was not found to improve accuracy of genomic prediction of breeding values.