Genomic prediction of heterosis for egg production traits in White Leghorn crosses

E.N. Amuzu-Aweh^1,2, P. Bijma¹, B.P. Kinghorn³, A. Vereijken⁴, J. Visscher⁴, J.A.M. van Arendonk¹, H. Bovenhuis¹

¹Animal Breeding and Genomics Centre, Wageningen University, Wageningen, The Netherlands, ²Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden, ³School of Environmental and Rural Science, University of New England, Armidale, Australia, ⁴Institut de Sélection Animale B.V., Hendrix Genetics, Boxmeer, The Netherlands; esinam.amuzu@wur.nl

The genetic basis of heterosis has puzzled geneticists for decades. Accurate prediction of heterosis would benefit animal and plant breeding by identifying parental lines suitable for crossbreeding. Prediction of heterosis has a long history with mixed success, partly due to low numbers of genetic markers and/or small data sets. We investigated prediction of heterosis for egg number, egg weight and survival time in domestic White leghorns, using ~400,000 individuals from 47 crosses and allele frequencies on ~53,000 genome-wide SNPs. For a single locus, heterosis is solely due to dominance and proportional to the squared difference in allele frequency between parental lines (SDAF). We, therefore, used linear mixed models where phenotypes of crossbreds were regressed on the SDAF between parental lines. Accuracy of prediction was determined using leave-one-out cross-validation. SDAF predicted heterosis for egg number and weight with an accuracy of ~0.5, but not for survival time. Heterosis predictions allowed pre-selection of pure lines prior to field-testing, saving ~50% of field-testing costs with only 4% loss in heterosis. Accuracies from cross-validation were lower than those from the model-fit, indicating that values in the literature may be overestimated. Cross-validation also indicated dominance cannot fully explain heterosis. Nevertheless, the dominance model yielded a considerable accuracy, clearly greater than that of a general-specific combining-ability model. Our results show that SDAF can be used to predict heterosis in commercial layer breeding.