Research article - Peer-reviewed, 2013
Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in white Leghorn crossesAmuzu-Aweh, E. Nancy; Bijma, P.; Kinghorn, B.P.; Vereijken, A.; Visscher, J.; Van Arendonk, J.A.M.; Bovenhuis, H.
AbstractPrediction of heterosis has a long history with mixed success, partly due to low numbers of genetic markers and/or small data sets. We investigated the prediction of heterosis for egg number, egg weight and survival days in domestic white Leghorns, using B400 000 individuals from 47 crosses and allele frequencies on B53 000 genome-wide single nucleotide polymorphisms (SNPs). When heterosis is due to dominance, and dominance effects are independent of allele frequencies, heterosis is proportional to the squared difference in allele frequency (SDAF) between parental pure lines (not necessarily homozygous). Under these assumptions, a linear model including regression on SDAF partitions crossbred phenotypes into pure-line values and heterosis, even without pure-line phenotypes. We therefore used models where phenotypes of crossbreds were regressed on the SDAF between parental lines. Accuracy of prediction was determined using leave-one-out crossvalidation. SDAF predicted heterosis for egg number and weight with an accuracy of B0.5, but did not predict heterosis for survival days. Heterosis predictions allowed preselection of pure lines before field-testing, saving B50% of field-testing cost with only 4% loss in heterosis. Accuracies from cross-validation were lower than from the model-fit, suggesting that accuracies previously reported in literature are overestimated. Cross-validation also indicated that dominance cannot fully explain heterosis. Nevertheless, the dominance model had considerable accuracy, clearly greater than that of a general/specific combining ability model. This work also showed that heterosis can be modelled even when pure-line phenotypes are unavailable. We concluded that SDAF is a useful predictor of heterosis in commercial layer breeding.
2013, volume: 111, number: 6, pages: 530-538
Amuzu-Aweh, E. Nancy (Amuzu-Aweh, E. Nancy)
Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics
University of New England
Van Arendonk, J.A.M.
UKÄ Subject classification
Genetics and Breeding
URI (permanent link to this page)